esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Failure Mode Analysis of Frame Test  
#### 3.1.1 Specim KJ-1  
Load control phase  
After applying the vertical load to KJ-1, the vertical load was kept stable. When the positive load was 20 kN, the crack began to appear from above the left end of L1, with a crack width of about 0.01 mm and a crack length of 7 cm. and the crack was closed after unloading. As shown in Figure 9a, some new cracks were generated at both ends of L1, L2, and L4 during continuous loading, and the cracks continued to develop with the increase of load. When the load was \(-40\) kN, the cracks above the right end of L1 and L2 and below the left end of L4 were penetrated. With the increase of load, some new cracks continued to appear. The generation position moved to the themed-span direction, and the original crack continued to develop into a through the crack. When the load was \(+80\) kN, cracks began to appear at the bottom of the left side of columns Z2 and Z3 (Figure 9b).  
When the load was \(-130\) kN, the existing cracks at the column bottom began to penetrate, and new cracks at the column bottom were generated upward. When loaded to \(+150\) kN, micro cracks began to appear at L3 and Z2 nodes and L4 and Z3 nodes. When loaded to \(165\) kN, the crack width at the upper ends of Z1 and Z2 reached 0.2 mm, and the crack at the end of L4 reached 0.5 mm. When the load was \(-190\) kN, it can be seen from the measured value of reinforcement strain that the plastic hinges at both ends of L1 and L2 were basically formed. All cracks in the beam and column occurred within 30 cm from the node and are relatively concentrated.  
Figure 8: Loading pattern of the test. (**a**) Vertical loading programs. (**b**) Horizontal loading programs.  
Displacement control phase  
When the reverse load was \(-\)31 mm, the Z2 second-floor column produced transverse through cracks and the cracks at the column bottom were widened. It can be seen from the measured value of reinforcement strain that the plastic hinge at the bottom of the column was basically formed. When the positive load was 62 mm, the crack width of the beam column exceeded 2 mm, and a large number of vertical cracks appear in the column. The concrete at the bottom of Z1, Z2, and Z3 columns was crushed, and the damage at the bottom of Z3 was the most serious (Figure 9c). The concrete was crushed, bulging, and peeling. When the reverse load was \(-\)62 mm, the concrete in the plastic hinge area of the beam was crushed. Among them, L1 and L2 ends are most seriously damaged, the concrete was crushed and peeled, and the longitudinal reinforcement of the stirrup was exposed. When the positive load was 93 mm, the concrete at the column bottom was seriously crushed and separated, and the cracks at the beam column joints continued to develop. When the reverse load was \(-\)93 mm, the concrete at the bottom of the column fell off. Z1, Z2, and Z3 stirrups were exposed, the longitudinal bars on both sides were also exposed, and the specimen was damaged.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 4 The Experiments of Liver Tumor Segmentation
### Evaluation Metrics
#### 4.2.2 Model Accuracy Metric  
This metric assesses the accurate classification of positive and negative observations. The parameter (_TP_) is used for \"true positive\", (_FP_) is used for \"false positive\", and (_FN_) is used for \"negative\" results.  
\[\text{Accuracy }=(TP+TN)/(TP+TN+FP+FN) \tag{4}\]  
We use the Adam optimizer to optimize our model on the training dataset in liver segmentation with Monai. This is one of the optimization algorithms available that is well-suited for refining the segmentation model. With its adaptive nature, Adam adjusts the learning rate for each weight of the neural network based on the gradient of the loss function. This makes it particularly effective for training deep neural networks, where the gradient loss function can vary greatly across different network layers. In liver segmentation, the goal is to identify the various regions of the liver in medical imaging tests, such as CT or MRI. This is typically performed using a convolutional neural network (CNN) that has been trained on a large dataset of annotated images. The Adam optimizer can be used to fine-tune this pre-trained CNN by reducing the contrast between the predicted and true segmentation masks of new liver images [32]. One of the advantages of using Adam for liver segmentation is that it can help improve the accuracy of the segmented image by optimizing the weights of the neural network to fit the given data better.
## 6 FWA Transport Network Solutions
### Wired Solution
#### 6.2.2 Ethernet Solution  
The Ethernet-based approach employs packet technologies that support statistical multiplexing. With this feature, the line bandwidth usage can be significantly improved and traffic convergence can be realized. Moreover, owing to its support for PtMP transmission, a substantial amount of fiber resources can be conserved. Furthermore, when the existing Ethernet infrastructure is employed for encapsulating and transporting CPRI between the network elements, a considerable amount of expenses can be saved. However, a few issues like low-latency services identification and respective fast-forwarding deserve significant research attention in the Ethernet-based solution. In addition, more research efforts are needed to make the option backward compatibility with CPRI serial constant bit rate (CBR) and the related high-precision synchronization. To address the latency and jitter requirements, the Institute of Electrical and Electronics Engineers (IEEE) has created the Time-Sensitive Networking (TSN) task group. The TSN task group is a branch of the IEEE 802.1 working group that has been investigating the latency-sensitive Ethernet forwarding technology. In this context also, there has been the establishment of the IEEE 1914 NGFI working group. Apart from the development of the NGFI transport architectures, the group is also defining the radio signal encapsulation specification into Ethernet packets [387, 47].
## 5 Application Scenario
### Writing Paper
#### 5.2.4. All Paths Criterion  
As mentioned above, for counting test cases, two different criteria will be used. First, using the criterion _all paths_, where it is necessary to count the net paths directly on _CPN4M_ shown in Figure 15. Here, applying the path counting algorithm directly on the net, we have four different paths representing a failure and one path representing a successful execution, totaling five possible execution paths. Below are the five paths taking into account the places that represent the goals:  
1. \(Start\to wtitle\to fail\).
2. \(Start\to wtitle\to wabs\to fail\).
3. \(Start\to wtitle\to wabs\to wsectitle\to fail\).
4. \(Start\to wtitle\to wabs\to wsectitle\to fds\to wsec\to wref||wcon\to fail\).
5. \(Start\to wtitle\to wabs\to wsectitle\to fds\to wsec\to wref||wcon\to finish\to sv\to wp\).
## 8 Discussion  
This paper proposes an output feedback control method based on the MTN that is appropriate for non-linear strict systems so that the system output can automatically track the desired signal. The original strict-feedback system is first transformed in the proposed method, and the state variables are redefined to obtain the new standard form. A state observer is then designed to complete the identification process of the adaptive system under the good approximation characteristics of the MTN. Based on this, the adaptive control law is designed to complete the system tracking process. Numerical simulations on a servo-hydraulic system model as the control object verify the effectiveness of the suggested method.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Error Comparison** & **RMSE** & **MAE** & **MAPE** \\ \hline MTN & 0.01997 & 0.04468 & 0.65589 \\ BPNN controller & 0.03958 & 0.10296 & 0.7236 \\ RBF controller & 0.02232 & 0.06112 & 0.69082 \\ \hline \end{tabular}
\end{table}
Table 2: The tracking response comparison among three metrics obtained using different control methods.  
Figure 4: System output.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Error Comparison** & **RMSE** & **MAE** & **MAPE** \\ \hline MTN & 0.03360 & 0.04857 & 1.0844 \\ BPNN controller & 0.06321 & 0.11697 & 1.18105 \\ RBF controller & 0.03682 & 0.06465 & 1.15638 \\ \hline \end{tabular}
\end{table}
Table 1: The unit step response comparison among three metrics obtained using different control methods.  
**Author Contributions:** Methodology, Q.S. and Y.Z.; Software, Q.S.; Validation, S.W.; Formal analysis, X.J.; Investigation, Q.S. and C.Z.; Writing--original draft, Q.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (Grant No. 22 KJB510026), as a subject of Educational Informatization in Colleges and Universities in Jiangsu Province (Grant No. 2021)SETKT062), as a research topic of Modern Educational Technology in Jiangsu Province (Grant No. 2022-R-101321), as part of research on Quality Assurance and Evaluation of Higher Education in Jiangsu Province (Grant No. 2021)SETKT062), as part of the Entrepreneurship Training Program for College students in Jiangsu Province (Grant No. 202210298001T), from the State Scholarship Fund under Grant 202108410235, by the Special Research and Promotion Program of Henan Province under Grant 212102210015, and as an Education and Teaching Reform Research and Practice Project of Henan Institute of Technology under Grant DQXY-2021005.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 2 Modelling and Methods
### Heat Transfer Analysis, Fire Load, Assumptions, and Conventions
#### 2.2.2 Boundary Conditions and Fire Load  
A definition of the applicable boundary conditions is necessary before the solution of the differential equations can be attempted. The analysis initiates assuming an ambient room temperature of 20 \({}^{\circ}\)C on both faces of the wall assembly. This condition is adhered to throughout the analysis for the non-exposed wall face.  
At time \(t=0\) sec, the \"exposed\" wall face becomes subject to the increasing fire load represented by the standard fire curve ISO 834 as present in Eurocode EN1991-1-2 [28]. Although scientific research is being carried out to identify and compile new, more accurate fire curves and loading regimes [29], it was considered that the well-established methodology proposed by the Eurocodes currently fulfils the needs of this study. Equation (5)mathematically describes the relationship of temperature increase due to the applied fire load over time, while Figure 4 is the corresponding visual representation:  
\[\text{Qg}=20+345\text{ log}10\text{ (8 t+1)} \tag{5}\]  
where t is the time in seconds and Qg is the developed temperature in \({}^{\circ}\)C.  
The boundaries between the various layers of the wall assembly follow the fundamental heat transfer equations, as described in the previous paragraph.
## 3 The Proposed Exact Scheme
### Modeling of Physical Network  
The physical network is formulated as undirected graph denoted by \(G=(N,L)\), where \(N\) is the physical nodes set and \(L\) is the physical links (edge) set. Each node is denoted as \(N_{u}\in N,u\in[1,m]\), and \(m\) is the number of nodes in \(N\). Each node has a set of resources \(K\), such as CPU, memory, and storage. Given that NFVI should contain no single point of failure, ETSI NFV framework recommends different types of hypervisors that provide different types of virtualization techniques [22]. Thus, each \(N_{u}\) supports one virtualization technique \(\{t\in T\}\), where \(T\) is the set of virtualization technique types (hypervisors) in NFVI. Each node \(N_{u}\) has a set of resources \(\{R_{N_{u}}^{t,k}|t\in T\text{ and }k\in K\}\). The embedding cost of a resource unit of type \(k\) and \(N_{u}\) with a virtualization type \(t\) is denoted by \(C_{N_{u}}^{t,k}\). Each physical link between \(N_{u}\) and \(N_{v}\) is denoted by \(L_{uv}\in L\), where \(u,v\in[1,m]\). \(L_{uv}\) has a bandwidth \(B_{L_{uv}}\) and a propagation delay \(D_{L_{uv}}\). The embedding cost of a bandwidth unit is denoted as \(C_{L_{uv}}\).
## 4 Numerical Method
### Interaction System and Earthquake Records
The final calibrated earthquake IMs are used as the predictive variables of the ANN model to reduce the aleatory uncertainty, as shown in Table 1. Meanwhile, the 90 GM records are adjusted from 0.1 g to 1.2 g to generate an appropriate structural response. Figure 8: The earthquake magnitude and fault distance of the selected GM records. Figure 9: The spectral acceleration of the selected GM records. On the other hand, the dynamic boundary effect induced by seismic waves may be mitigated by utilizing a virtual artificial FE boundary, which involves extracting the calculation region from the semi-infinite elastic medium. In the recent literature, the VSAB is frequently employed as it is capable of imitating the elastic recovery capabilities of the medium and absorbing the dispersed wave during dynamic analysis. More specifically, the VSAB can connect the FE model's truncated boundary through three continuous springs and dampers in different directions \(i=x,y,z\), as demonstrated in the work of [PERSON] et al. [47]. Then, the nodal boundary forces in the direction \(i=x,y,z\) of strong GMs are applied at the FE boundary, which can be expressed as  
\[f_{li}=K_{li}d^{f}_{li}+C_{li}v^{f}_{li}+A_{l}v^{f}_{li} \tag{14}\]  
where \(K_{li}\) and \(C_{li}\) indicate the stiffness coefficients and damping coefficients, respectively; \(A_{l}\) is the average area of all elements around truncated boundary node 1; \(d^{f}_{li}\), \(v^{f}_{li}\), and \(\sigma^{f}_{li}\) indicate the time history of the displacement, velocity, and nodal stress of the FE node in the free-field seismic wave, respectively.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Iso 27017:2015
#### 3.3.1 Security Controls for Cloud
\begin{table}
\begin{tabular}{c c} \hline
**Controls** & **Description** \\ \hline \hline \multirow{4}{*}{\begin{tabular}{c} Asset Nanagement \\ Controls: \\ \end{tabular} } & Asset inventory: Keep track of all cloud-based assets, including data, apps, systems, and infrastructure components. \\  & Classification of assets: Classify cloud assets based on their criticality and sensitivity to ensure appropriate security measures are applied. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Access Controls \\ \end{tabular} } & Identity and access management: Implement controls to manage user identities, authentication, and authorization for accessing cloud resources. \\  & Control and monitoring of privileged access to cloud environments to reduce the danger of \\  & unauthorised acts. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Cryptographic Controls \\ \end{tabular} } & Encryption: Use encryption technologies to safeguard data at rest and in transit in the cloud. \\  & Key management: Establish proper key management processes to ensure the secure generation, storage, and destruction of encryption keys. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Incident Management \\ Controls \\ \end{tabular} } & Incident response planning: Create and implement a cloud incident response plan that outlines roles, responsibilities, and processes for dealing with security events. \\  & Logging and monitoring: Implement logging and monitoring mechanisms to detect and respond to security events and potential breaches within the cloud environment. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Supplier Management \\ Controls \\ \end{tabular} } & Supplier evaluation: Evaluate and choose cloud service providers (CSPs) based on their security \\  & capabilities and compliance with recognised security standards. \\  & Contractual agreements: In contracts with CSPs, define specific security standards and duties, such \\  & as data protection, incident reporting, and compliance obligations. \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Compliance Controls \\ \end{tabular} } & Legal and regulatory compliance: Ensure that all applicable laws, regulations, and contractual duties \\  & for information security and data protection in the cloud are met. \\  & Auditing and evaluation: Conduct frequent audits and evaluations to ensure compliance with \\  & security measures and standards. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Data Protection Controls \\ \end{tabular} } & Implement procedures to ensure the logical and physical separation of client data within the \\  & cloud environment. \\ \cline{1-1}  & Establish data backup and recovery mechanisms to prevent data loss or destruction within the \\ \cline{1-1}  & cloud environment. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Controls of ISO/IEC. Figure 4: Standards of ISO/IEC 27017.
## 4 Designed Schemes
### Destination Nodes Selection and Information Proceeding  
The DSN is purely selected by considering the certain function parameters (highest residual energy, shortest distance, and lowest BER). The final DSN (sink node) receives the packet directly from a neighbor SRN. Otherwise, the SRN sends the packet by considering the multi-hopping. In such a scenario, the SRN first accomplishes the selection of the DSN. The above-mentioned criteria are given below for the selection of DSN, where it is the most probable node for data forwarding.  
\[f=\frac{Residual\quad energy}{BER\times Distance} \tag{3}\]  
Equation (3) shows that a node closest to the sink node that holds the highest residual energy and lowest BER is specified as the first DSN. The BER should be lower than a threshold value else an optimal relay node is chosen. Upon the DSN selection, the source leads the information packet towards the DSN. The selection criteria for next DSN is same as defined for the first DSN. The process of data forwarding is continued until the packet reaches to the final DSN as shown in Figure 3.  
The picture of the data forwarding node with the neighbors is shown in Figure 4.  
Figure 5 shows that after the network initialization phase, the SRN finds an optimal DSN for the information forwarding. For this purpose, the SRN calculates the function and decides the DSN based on highest function values. The SRN sends the information packet towards the DSN, it checks its BER, in case of low BER it leads the information packet in the direction of the sink node. However, if BER exceeds the threshold value, the DSN discards the packet. This process is continued until the packets get to the sink node.  
Figure 3: Data forwarding in EERD scheme.  
Figure 4: Source and destination with optimal relay table.  
Figure 5: Flow chart of EERD scheme.
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 1 Introduction  
The objective of this work is the design of an IoT (Internet of Things) network architecture for the collection, processing and distribution of environmental information. The proposed architecture is intended to be flexible and powerful enough to integrate, in the future, the different solutions developed by the research group in this field.  
The network is made up of several parts. On the one hand, a radio access network based on an LPWAN (Low Power Wide Area Network) suitable for mass sensor communications. More specifically, a LoRaWAN (Long Range Wide Area Network) [1] network has been chosen due to its adequate characteristics of low power consumption, high coverage, easy scalability, and its license free operation, which facilitates its development and reduces costs.  
On the other hand, a backbone network that will integrate the different entities necessary for this type of networks and for the processing and distribution of data. Since a LoRaWAN network is being used for the radio part, the backbone network will include the required network and application servers. These types of servers usually utilizes the MQTT (Message Queuing Telemetry Transport) [2] protocol to exchange information with both internal and external entities. Many of these entities receive the same information, which makes multicast suitable and more efficient for this type of networks. For this reason, as will be explained in the following sections, we will introduced a multicast-based solution that will reduce both MQTT generated traffic and its latency. An Artificial Intelligence (AI) platform will also be included for data processing. All entities will be implemented as Cloud-Native Network Functions (CNFs) to facilitate their orchestration, deployment and execution in local clouds.  
A Software Defined Network (SDN) will be used for the communication between the radio access network and the backbone network. SDN provides great flexibility and ease of development to include, e.g., new protocols or optimizations over existing ones.
## 3 Materials, Methods, and Equipment
### Pile Material Properties
#### 3.2.1 Case Study I- Hybrid Steel-Concrete Composite Pile  
A circular hybrid steel-concrete composite pile was examined in Kano State, Nigeria. The pile's features include 0.75 m diameter (D), 11 m total length (L), 5 m steel lower section (L\({}_{\rm s}\)), 6 m concrete upper section (L\({}_{\rm c}\)), and groundwater level at 6 m depth, as shown in Figure 3. The circular configuration provides uniform stress distribution, enhanced lateral load resistance, and reduced soil displacement during installation. In Kano's loose to medium-density sandy soils with clay lenses, these piles were implemented for the Kano River irrigation project to support bridge abutments in areas with fluctuating groundwater. The steel section penetrated dense sandy layers while the concrete section resisted lateral  
\begin{table}
\begin{tabular}{l l} \hline
**Sand Geotechnical Properties** & **Values** \\ \hline Maximum void ratio (\(e_{\rm max}\)) & 0.920 \\ \hline Minimum dry unit weight (\(\gamma_{d,\,\rm min}\)) & 13.7 kN/m\({}^{3}\) \\ \hline Maximum dry unit weight (\(\gamma_{d,\,\rm max}\)) & 18.2 kN/m\({}^{3}\) \\ \hline Minimum optimum moisture content (OMC\({}_{\rm min}\)) & 7.0\% \\ \hline Minimum optimum moisture content (OMC\({}_{\rm max}\)) & 10.0\% \\ \hline \end{tabular} Notes: Dry unit weights rounded to one decimal place for clarity (original ASTM precision: \(\pm\)0.01 kN/m\({}^{3}\)). Specific gravity determined using ASTM D84-23.  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Steel Pile** & **Concrete Pile** & **Timber Pile** \\ \hline Material Model & Linear Elastic & Linear Elastic & Linear Elastic \\ \hline Poisson’s Ratio, \(\
u\) & 0.20 \(\pm\) 0.02 & 0.20 \(\pm\) 0.02 & 0.20 \(\pm\) 0.02 \\ \hline Modulus of Elasticity, E (kN/m\({}^{2}\)) & 2.01 \(\times\) 10\({}^{8}\)\(\pm\) 5\% & 2.35 \(\times\) 10\({}^{7}\)\(\pm\) 5\% & 1.22 \(\times\) 10\({}^{7}\)\(\pm\) 5\% \\ \hline Dry Unit Weight, \(\gamma_{\rm d}\) (kN/m\({}^{3}\)) & 76.5 \(\pm\) 1.5 & 25.0 \(\pm\) 0.5 & 5.10 \(\pm\) 0.10 \\ \hline \end{tabular} Notes: 1. Material properties are nominal values derived from ASTM A36 (steel), ACI 318 (concrete), and ANSI/AWC NDS (timber). 2. Tolerances reflect typical manufacturing variability (\(\pm\)5% for E, \(\pm\)0.02 for \(\
u\), \(\pm\)2% for \(\gamma_{\rm d}\)).  
\end{table}
Table 3: Parameters of the piles model.  
loads from seasonal river flow. Load testing demonstrated a 22% increase in ultimate bearing capacity compared to conventional bored piles [27].