esa-sceva/satcom-chunk-collection huggingface dataset:
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 3 The Proposed Method
### Cross-Stage Semantic Enhancement Module  
The multi-stage approach produces increasingly refined estimation results by extending the depth of the network and the number of paths for information flow. However,there are semantic gaps in the cross-stage features, and simply adding them together not only fails to improve the performance but also introduces background noise to perturb the segmentation results. To bridge the semantic gap between the different stages, we designed the cross-stage semantic enhancement (CSE) module [33] to efficiently model the long-range dependencies and improve the model performance, which is shown in Figure 2.  
The CSE module consists of three main branches, \(\mathcal{B}_{1}\), \(\mathcal{B}_{2}\), and \(\mathcal{B}_{3}\), and each branch is implemented by the corresponding module for cross-stage semantic enhancement. Specifically, SSAM is used to guide the deeper stage to aggregate high-resolution features, and CAM is applied to reweight channels. In addition, \(\mathcal{B}_{3}\) utilizes the LRM to generate refined features, which are provided to guide SSAM and CAM to fuse the different scale features from \(\mathcal{B}_{1}\) and \(\mathcal{B}_{2}\).  
Formality, the coarse feature maps from branch \(\mathcal{B}_{1}\) are defined as \(G_{1}\), and \(G_{2}\) is the low-level feature maps from branch \(\mathcal{B}_{2}\). We first use \(1\times 1\) convolution \(f^{1}(\cdot)\) on \(G_{1}\) to obtain \(\hat{G_{1}}\), and \(\hat{G_{2}}\) is obtained by downsampling \(G_{2}\). By concatenating \(\hat{G_{1}}\) with \(\hat{G_{2}}\), \(G_{3}\) is obtained. We send \(G_{3}\) into LRM and obtain the refined features \(\hat{G_{3}}\). Then, \(\hat{G_{1}}\) is convolved by \(3\times 3\) convolution \(f^{3}(\cdot)\) and CAM employs global average pooling to extract global context from \(\hat{G_{3}}\). By multiplying their results, we obtain \(G_{5}\), which is filtered for irrelevant information. \({G_{1}}^{\prime}\) is calculated as follows:  
\[{G_{1}}^{\prime}=\xi_{C}\big{(}\xi_{L}(\,\hat{G_{3}}\,)\big{)}\odot\ f^{1} \Big{(}f^{3}(G_{1})\Big{)} \tag{3}\]  
where \(\xi_{C}\) and \(\xi_{L}\) represent results generated by CAM and LRM, respectively. Parallely, we use \(3\times 3\) convolution on \(\hat{G_{2}}\), and SSAM is applied to aggregate discriminative contextual information. Based on the results, we obtain \(G_{6}\) via element-wise multiplication. \({G_{2}}^{\prime}\) is given in Equation (4).  
\[{G_{2}}^{\prime}=\xi_{S}\big{(}\xi_{L}(\hat{G_{3}})\big{)}\odot f^{1}\Big{(}f ^{3}(G_{2})\Big{)} \tag{4}\]  
where \(\xi_{S}\) is the feature map generated by SSAM. Finally, the output \(G_{out}\) is obtained by fusing \(G_{1}^{\prime}\) and \({G_{2}}^{\prime}\).  
\[G_{out}={G_{1}}^{\prime}+{G_{2}}^{\prime} \tag{5}\]
## 1 Introduction
[20], the image matching method based on hierarchical SGM yielded better results than the traditional image matching method based on area-based matching and the cross-correlation threshold. [PERSON] et al. [21] tested five commercial and open-source software packages (ERDAS IMAGINE, PCI Geomatica, RSP, MicMac and FORSAT) using a WorldView-1 stereo pair with 0.5 m GSD taken on Terrassa, Spain, in the framework of the ISPRS satellite stereo matching benchmark [22]. In this experiment, RSP (RPC stereo processor by Qin [23]) and PCI Geomatica (PCI Geomatica) were the software packages that best matched the LiDAR DSM benchmark, closely followed by ERDAS IMAGINE and MicMac, while the worst results were attained using FORSAT. It is important to note that RSP, PCI Geomatica, MicMac and ERDAS IMAGINE adopt a SGM implementation [17], while FORSAT [24] applies a multi-feature matching method. There are several works about DSM generation from VHR satellite imagery over different types of land covers, including urban areas [18; 25], agricultural greenhouse areas [20], active tectonics [26], forest [24], glaciers [27] and even small isolated objects [11]. DSM quality assessment is usually carried out by studying its completeness and vertical accuracy [18; 19; 20; 21; 25; 28], defining completeness as the percentage of correctly matched points over the area of interest [28]. Most of the published works have used an accurate airborne LiDAR-derived DSM as reference [18; 19; 20; 21; 29; 30; 31; 32]. To the best of the authors' knowledge, there are no published works about the extraction of DSM from Deimos-2 L1B stereo imagery. The aim of this paper is to assess the geopositioning accuracy capabilities of Deimos-2 L1B panchromatic (PAN) stereo imagery for extracting DSM over three different land covers such as plastic covered agricultural greenhouse areas, bare soil, and urban areas. As a previous step, the accuracy of the sensor orientation phase is also statistically analysed based on two sources of variation: (i) sensor orientation model and (ii) number of well-distributed GCPs used in the triangulation process. Once the best combination of sensor orientation model and number of GCPs are selected, the completeness and vertical accuracy of two DSMs photogrammetrically derived from two Deimos-2 stereo pairs over different land covers are computed. Another stereo pair from the well-known WorldView-2 optical sensor taken over the same study area was used as a benchmark for testing the capabilities of Deimos-2 satellite imagery. The rest of this paper is organized as follows. The study area is described in Section 2. In Section 3, the datasets are described, while Section 4 outlines a detailed explanation of the methodological approach devised to carry out sensor orientation, DSM extraction and accuracy assessment. The results are presented in Section 5 and they are discussed in Section 6. Finally, conclusions are provided in Section 7.
## 3 Results
### Comparing with Existing Tools for Ngly Site Prediction  
The prediction performance of PTG-PLM is compared with the existing tools for Ngly site prediction on the Ngly independent dataset which is obtained from a previous study, PUStack-NGly. Besides PUStack-NGly, six other tools including SPRINT-GLY, GlycoMine_PU, GlycoMine, GlycoEP, GlycoPP, and GPP are compared with PTG-PLM on the same testing dataset. These tools are described briefly in Section 1.2 On the Ngly independent dataset, PTG-PLM based on embedding extracted from ESM-1b PLM has the best performance as was shown in Table 7. So, we compare the performance of PTG-PLM based on ESM-1b embedding against the seven Ngly existing tools in terms of accuracy, recall, precision, F1, AUC, and MCC as shown in Table 9 and Figure 7. It is shown that PTG-PLM outperforms the existing tools for Ngly site prediction with a 0.965 accuracy, 1 recall, 0.852 precision, 0.92 F1, 0.978 AUC, and 0.902 MCC.  
Figure 6: Comparison of performance between the best PTG-PLM model (based on ProtBert-BFD) and the best-supervised learning model (SVM based on ProtBert-BFD) on the Kgly independent dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**PLM \textgreater{}** & **ProtBert-BFD** & \multicolumn{3}{c}{**ProtBert**} & \multicolumn{3}{c}{**ProtAlbert**} & \multicolumn{3}{c}{**ProtXLNet**} & \multicolumn{3}{c}{**ESM-1b**} & \multicolumn{3}{c}{**TAPE**} \\ \hline
**Metric** & **Model** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** & **ProtProt** \\ \hline SVM & 0.631 & 0.631 & 0.261 & 0.596 & 0.595 & 0.191 & 0.585 & 0.586 & 0.171 & 0.573 & 0.573 & 0.146 & 0.608 & 0.608 & 0.219 & 0.583 & 0.583 & 0.166 \\ LR & 0.613 & 0.613 & 0.226 & 0.558 & 0.558 & 0.116 & 0.606 & 0.606 & 0.211 & 0.565 & 0.565 & 0.131 & 0.573 & 0.573 & 0.147 & 0.54 & 0.541 & 0.082 \\ RF & 0.563 & 0.563 & 0.127 & 0.55 & 0.55 & 0.101 & 0.533 & 0.533 & 0.066 & 0.52 & 0.52 & 0.041 & 0.523 & 0.523 & 0.046 & 0.578 & 0.578 & 0.156 \\ XGB & 0.591 & 0.59 & 0.181 & 0.623 & 0.623 & 0.246 & 0.543 & 0.541 & 0.099 & 0.553 & 0.553 & 0.106 & 0.563 & 0.563 & 0.126 & 0.601 & 0.601 & 0.201 \\ PTG-PLM & **0.641** & **0.641** & **0.282** & **0.633** & **0.633** & **0.268** & **0.628** & **0.628** & **0.258** & **0.621** & **0.621** & **0.241** & **0.636** & **0.636** & **0.272** & **0.626** & **0.626** & **0.251** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of PTG-PLM compared with four supervised learning methods on the Kgly independent dataset based on the six PLMs.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 9 RE-AEDG: Optimization Models
### Energy Consumption Minimization Model  
In order to improve PAR at the destination, our proposed model incorporates cooperative communication technique, however; at the cost of excessive energy consumption. Thus, our objective to minimize the energy consumption cost is formulated as follows:  
\[Min\sum_{i=1}^{n}E_{i}\qquad\forall\quad i\epsilon N \tag{20}\]  
Subject to:  
\[0\leq\rho_{h_{i}}\leq\rho_{th} \tag{20a}\] \[h_{i}=\begin{cases}z\quad if\quad d_{i,z}\leq T_{xR}&\qquad \forall\quad i\epsilon MNs\quad\&zeGWs\\ j\quad if\quad d_{i,z}\geq T_{xR}&\qquad\forall\quad i,j\epsilon MNs\quad \&zeGWs\end{cases}\] (20b) \[R_{e}^{h_{i}}\geq\ R_{e}^{\widehat{N_{i}}}\quad|\quad h_{i} \epsilon widelhatN_{i}\qquad\forall\quad i\,\epsilon\ MNs\] (20c) \[P_{e}^{min}\leq P_{e}\quad\leq P_{e}^{max}\] (20d) \[h_{z}\ =\ AUV\qquad\qquad\forall\,z\,\epsilon\ GWs \tag{20e}\]  
Our objective in Equation (20) is to minimize the overall network energy consumption. Equation (20a) indicates that the number of predecessor nodes of the selected hop of \(i\) should be less than or equal to the predefined threshold predecessors. Therefore, it causes equal data flow burden among nodes. Violation of this rule can cause an extensive data flow at a node and may result in enhanced energy depletion and a shorter network stability period. Equation (20b) illustrates that if \(z\) is a GW and it lies inside transmission range of a sender node \(i\) then \(z\) would be a preferred node. Hence, number of hops a packet takes to reach to the destination are lowered resulting is lesser energy consumption is single packet transmission process. Equation (20c) provides the the balanced energy consumption among nodes. It depicts that next hop of \(i\) should have maximum residual energy out of the Eligible Neighborhood set of a node. Equation (20d) defines that per packet energy consumption \(P_{e}\) should be an optimum value, provided the upper and lower bounds \(P_{e}^{min}\) and \(P_{e}^{max}\) of per packet energies. This energy bound limits transmission distance among communicating nodes. Equation (20e) states that for \(z\) being a GW the next hop is always an AUV and no inter-hop communication exists between the two GWs. Failing to implement this clause can enhance the number of retransmissions of data packets and as a result an increased energy consumption.
## 3 Results
### Plasma Treatment Modulated Inflammatory Milieu of Melanoma and Immune Cells  
In response to 120 s plasma treatment, melanoma cell supernatants (analyzed after 24 h) showed significantly increased levels of TNF\(\alpha\), IL-10, CCL4, and IL-1\(\beta\) compared to untreated control cells (Figure 1g). An increase of damage-associated molecules, such as ATP (Figure 1h) and CXCL1 (Figure 1i), was also observed 6 h after a 120 s plasma-treatment in B16F10 cells. Before investigating the immunological consequences of murine splenocytes co-cultured with plasma-treated melanoma  
Figure 1: Effects of plasma treatment in liquids and treatment of B16F10 melanoma cells with physical plasma. (**a**) Plasma deposited hydrogen peroxide in PBS and (**b**) cell culture medium at nearly similar levels; (**c**) growth behavior of melanoma cells under different concentration of FCS; (**d**) reduction of metabolic activity in melanoma cells after plasma treatment in cell culture medium with or without FCS, normalization was done with respect to a control in each condition; (**e**) representative images of scratches in control or 180 s plasma-treated melanoma cells after 0 h or 24 h, respectively, and (**f**) their normalized regrowth quantified; (**g**) cytokine/chemokine profile of plasma-treated melanoma cells; (**h**) release of the danger molecules ATP and (**i**) CXCL1 in plasma-treated melanoma cells. (**a–d,f**) Data are mean +S.E. from several replicates; statistical analysis was performed using t-test; PBS: phosphate-buffered saline; FCS: fetal calf serum.  
cells, the response of splenocytesotes alone treated with plasma was investigated. For this, splenocytes were harvested from mice (Figure 2a) and left unstimulated or were pulsed with PMA. Lymphocyte activation is typically triggered by the interaction of the cell surface receptor to its specific ligand, resulting in an activation cascade. In vitro, lymphocyte activation can be induced by chemicals, such as PMA, leading to an increased cellular proliferation. Following this, splenocytes were exposed to plasma, and metabolic activity was assessed 24 h later. Reduced metabolic activity was observed in both naive and PMA-stimulated splenocytes, but in the latter to a lesser extent (Figure 2b). Studying the splenocytes' supernatants, increased levels of cytokines were observed after plasma treatment, including IL-10, CCL4, IL-4, IL-12, and IL-1\(\beta\). Altogether, plasma treatment not only affected cell viability but also the inflammatory profile of tumor and immune cells (Figure 2c).
###### Abstract
In contrast, Fe(OH)\({}_{3}\) precipitate is typically amorphous, and it simply depends on the van der Waals force of the precipitated colloid to adsorb onto the fracture surface and consolidate. Once the water temperature is high, the van der Waals force can be reduced, resulting in a loose structure for the consolidated body. In this case, the consolidated body can hardly resist erosion from high water pressure, and the PR curve can easily show a \"rebound\" phenomenon in the repair process. When the test water temperature is lowered, the two precipitates can produce similar repair and PR effects. At the same time, experimental research on repair and PR for different fractured rock specimens was carried out using CaCO\({}_{3}\) precipitate [24]. It was found that the PR rate and effect in fractured rock specimens are closely related to the scaling process of the precipitate's adsorption, growth, and consolidation on the fracture surface. The greater the roughness/uneveness of the fracture surface, the smaller the macroscopic openness of the fracture, and the faster the precipitate grows on its surface. Meanwhile, the consolidated structure that is formed is more stable. Thus, the PR achieves higher efficiency and a better effect. When the concentration of key ions (such as CO\({}_{3}\)\({}^{2-}\)) involved in the precipitate reaction exceeds a certain critical value, the repair and PR efficiency of CaCO\({}_{3}\) on WCFs will no longer change obviously. The above experiments mainly focused on studying the PR law of a single chemical precipitate to repair WCFs. However, if there are multiple chemical precipitates in the environment, can they produce similar or better PR effects? Or, will certain chemical precipitates affect the adsorption-consolidation process of others? These questions are highly worthy of further study. Therefore, a typical sandstone sample with a single fracture was selected in this study to conduct a PR experiment using a mixed Fe(OH)\({}_{3}\) and CaCO\({}_{3}\) precipitate. While simulating water conduction and seepage, repair reagents were injected into the fracture to generate the mixed precipitate. Then, the precipitate remediation and PR law under different mixing ratio conditions were explored to provide support and reference to future field applications and practices involving the chemical precipitate repair of mining-induced WCFs and the restoration of aquifers.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Accuracy and Speed
#### 5.3.2 Precision-Recall Curve  
In Figure 4a-c, Adaptive-DecayRank demonstrates higher precision and recall compared to AnomRank for all three datasets. It consistently achieves higher precision on various anomalous snapshots (\(top50,100,250,\ldots,800\)). This indicates that Adaptive-DecayRank effectively identifies anomalies while maintaining a low false positive rate. This is crucial in real-world scenarios, such as the power grid, fraud detection, and cyberthreats, considering that anomalous instances are associated with a significant influx of unusual patterns.
# Sewage Sludge Derived Materials for CO2 Adsorption
## 4 Conclusions  
The char resulted after the pyrolysis process of sewage sludge was used to obtain CO\({}_{2}\) adsorption materials. Four types of material were obtained with different surface areas and similar pore volumes and diameters. The material treated with HCl presented good properties, such as higher surface area and pore volume, in comparison with the raw materials and treated only with KOH; therefore, it was used further for CO\({}_{2}\) adsorption test. The material was pelletized by using starch powder as a binder. For adsorption tests, a handmade installation equipped with a sandwich support in order to avoid the material spread was used. Saturation was achieved after seven injections with gas rich in CO\({}_{2}\) (74 vol.%). When the material was tested with the CO\({}_{2}\)-depleted gas (only 0.1 vol.%), the saturation was not achieved even up to 100 injections. Additionally, in the case of the tests with the gas rich in CO\({}_{2}\), the material presented an adsorption capacity of 11.87 cm\({}^{3}\)/g and a great separation efficiency and CO\({}_{2}\) recovery of 99.68% and 98.11%, respectively. Therefore, the obtained material could be a potential adsorbent for CO\({}_{2}\) capture and storage even at high concentrations.  
Conceptualization, M.G.M. and V.C.N.; Data curation, M.G.M., M.S.R. and R.E.I.; Formal analysis, M.G.M., A.Z., F.B. and M.C.; Funding acquisition, C.F.; Investigation, A.Z., F.B. and M.C.; Methodology, M.G.M., F.B., M.C. and V.C.N.; Project administration, S.O.; Resources, M.S.R.; Supervision, M.S.R. and C.F.; Validation, S.O., M.S.R., R.E.I. and V.C.N.; Visualization, C.F., R.E.I., M.C. and V.C.N.; Writing--original draft, M.G.M. and V.C.N.; Writing--review and editing, M.G.M. and V.C.N. All authors have read and agreed to the published version of the manuscript.  
Part of this research was financially supported by the Romanian Ministry of Research, Innovation and Digitization, under NUCLEU Program-Financing Contract No. 9N/2019, under Project PN 19 11 03 01 and the other part of the research was financed by the Romanian Ministry Education and Research, UEFISCDI Authority, under project PN-III-P1-1.2-PCCDI-2017-0776/No. 36 PCCDI/15.03.2018.  
**Acknowledgments:** The authors are grateful to [PERSON], [PERSON] and Chem. Eng. [PERSON], from National R&D Institute for Cryogenic and Isotopic Technologies--ICSI Ramnicu Valcea, Romania, for scanning electron microscopy, specific area and pore distribution measurements and metal content determinations.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Turbo Coded OFDM with MIMO Antennas in Cooperation
The source-S node transmits \(M1\) code word bits (RSC-1 encoded OFDM code word) during first time slot to decoder 1 (as it corresponds to the RSC-1 encoder at the source-S) and relay-R node transmits \(M2\) code word bits (RSC-2 encoded OFDM code word) during the second time slot to decoder 2 (as it corresponds to the RSC-2 encoder at the relay-R). The total code word bits reach at the destination during one complete transmission is \(M=M1+M2\). Thus, the code rate for the proposed scheme is given as follows:  
\[R_{c}^{o}=\frac{i_{1}}{M1+M2}=\frac{i_{1}}{M} \tag{29}\]  
where \(R_{c}^{o}\) is \(1/3\) for the proposed coded cooperative scheme.
## 2 Method
### First Step Selection  
(1) Standardized operation  
The purpose of standardized operation is to eliminate the influence of the different dimensions on the threshold setting. Assume that the maximum and minimum values in the feature vector \(X_{i}\) are \(a_{j}^{(q)}|_{max}\) and \(a_{j}^{(q)}|_{min}\), where \(h_{max}\) and \(h_{min}\) are the maximum and minimum values of the normalized interval, respectively. The elements in \(X_{i}\) are standardized by Equation (1).  
\[a_{j}^{(q)}|_{nor}=h_{min}+(a_{j}^{(q)}-a_{j}^{(q)}|_{min})*\frac{h_{max}-h_{ min}}{a_{j}^{(q)}|_{max}-a_{j}^{(q)}|_{min}}\,. \tag{2}\]  
where \(q\in[1,k],j\in[1,m]\) and the normalized feature matrix is expressed as follows:  
\[X_{i}|_{nor}=\left[\begin{array}{cccc}a_{1}^{(1)}|_{nor}&a_{2}^{(1)}|_{nor}& \ldots&a_{m}^{(1)}|_{nor}\\ a_{1}^{(2)}|_{nor}&a_{2}^{(2)}|_{nor}&\ldots&a_{m}^{(2)}|_{nor}\\ \ldots&\ldots&\ldots\\ a_{1}^{(k)}|_{nor}&a_{2}^{(k)}|_{nor}&\ldots&a_{m}^{(k)}|_{nor}\end{array}\right] \tag{3}\]  
(2) Mean operation  
Suppose the feature vectors after mean operation is \(\left.X_{i}^{\prime}\right|_{nor}\), then:  
\[X_{i}^{\prime}|_{nor}=(a_{1},a_{2}\ldots a_{m})=(\frac{1}{k}\sum_{q=1}^{k}a_{ 1}^{(q)}|_{nor},\frac{1}{k}\sum_{q=1}^{k}a_{2}^{(q)}|_{nor}\ldots\frac{1}{k} \sum_{q=1}^{k}a_{m}^{(q)}|_{nor}) \tag{4}\]  
(3) Tolerance calculation operation  
Calculate and obtain the tolerance vector between groups \(P=(p_{1},p_{2}\ldots p_{m-1})\), where \(p_{1}=\mid a_{2}-a_{1}\mid\), \(p_{2}=\mid a_{3}-a_{2}\mid\), \(\ldots\), \(p_{m-1}=\mid a_{m}-a_{m-1}\mid\). Prescribe the threshold as \(p_{thre}\); then, count the number of elements less than \(p_{thre}\) in the vector \(P\), which is denoted as \(r\). Then, prescribe the threshold as \(r_{thre}\), and judge the size relationship between \(r\) and \(r_{thre}\). Retain this feature if \(r\) is less than \(r_{thre}\), and discard this feature if \(r_{thre}\) is less than \(r\). The theoretical range of \(p_{thre}\) is \((0,+\infty)\), and the value is related to the \(h_{max}\) and \(h_{min}\) in standardized operation. The theoretical range of \(r_{thre}\) is \([0,\mathrm{m}-1]\). With the difference of research area, the fluctuation of parameters is diverse. Hence, the idea of machine learning is referred to during the definitions of \(p_{thre}\) and \(r_{thre}\). The initial thresholds can be determined manually. Then, the moisture identification model, whose input vector is the feature parameters selected by the two-step method, can be trained using mass data. The thresholds can be adjusted to obtain the optimal effect.
## 4 The Proposed AAC-IoT System Model
### Registration and Authentication
#### 4.2.2 DU Registration  
Step 1: Consider a \(DU\), \(u_{1}\) from \(\mathcal{U}_{n}\) that registers the same as a \(DO\). We perform step 1 to step 4 as in the \(DO\) registration.  
Step 2: CA checks \((T_{2}-T_{1})<\Delta T\) and submits a request for the PUF of the corresponding, and the message is \(R_{3}\)\(\rightarrow\)\(\{Id_{1},SU_{1},T_{3}\}\).  
Step 3: The \(u_{1}\) checks the timestamp validity as \((T_{3}-T_{2})<\Delta T\), then gives a PUF that consists of challenges \((C_{1},C_{2},...,C_{k})\) and the corresponding responses \((Re_{1},Re_{2},....,Re_{k})\), where \(k\) denotes the total number of challenges and the responses in the PUF of the \(u_{1}\). The \(u_{1}\) sends \(R_{4}\)\(\rightarrow\)\(\{Id_{1},SU_{1},(C_{k},Re_{k})\),\(T_{4}\}\) to CA.  
Step 4: The CA receiving \(R_{4}\) from \(u_{1}\), extracts the message and checks timestamp \((T_{4}-T_{3})<\Delta T\), and forwards the \((C_{k},Re_{k})\) pairs to the HLF blockchain.  
Step 5: After submission of all the credentials from \(u_{1}\) to CA, the process of registration is successfully completed, and the user can access data from the cloud server based on the access policy. The complete workflow of this registration is shown in Figure 4.  
Figure 3: Workflow of the DO registration.