esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cost-Benefit Analysis  
The cost-benefit analysis covering the study duration is presented in Table 7. The AM/PM treatment had a significantly lower feed cost to egg mass ratio compared with the control treatment (\(p\) < 0.001), indicating that the return on investment was higher for AM/PM feeding compared with the conventional feeding regimen.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameter 1**} & \multicolumn{3}{c}{**Treatment**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{2-2} \cline{4-4}  & **AM/PM** & & & \\ \hline IDE & 2305 b  & 2808 a  & 81.20 & \textless{}0.001 \\ IDEC & 0.61 b  & 0.70 a  & 0.02 & 0.008 \\ IDNC & 0.70 b  & 0.01 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of AM/PM feeding on nutrient digestibility at 53 weeks of age.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Feed Cost (AUD)/Egg Mass (kg)**} \\ \hline AM/PM & 0.047 b  \\ Control & 0.059 a  \\ SEM & 0.002 \\ \(p\)-value & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cost–benefit analysis of the dietary treatments over 20 weeks of this study.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameter 2**} & \multicolumn{2}{c}{**Treatment**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{2-2} \cline{4-4}  & **AM/PM** & & & \\ \hline Eggshell reflectivity (\%) & 24.60 & 25.20 & 0.27 & 0.142 \\ Egg shape index & 76.70 & 77.00 & 0.24 & 0.392 \\ Eggshell breaking strength (Kgf) & 4.27 & 4.23 & 0.06 & 0.783 \\ Eggshell weight (g) & 6.07 & 6.01 & 0.04 & 0.289 \\ Eggshell thickness (mm) & 0.42 & 0.43 & 0.002 & 0.491 \\ Albumen height (mm) & 8.31 & 8.29 & 0.14 & 0.937 \\ Yolk height (mm) & 21.50 & 21.50 & 0.08 & 0.966 \\ Yolk weight (g) & 16.07 & 15.76 & 0.13 & 0.097 \\ Yolk diameter (mm) & 40.50 & 40.80 & 0.23 & 0.473 \\ Yolk index & 0.53 & 0.53 & 0.004 & 0.502 \\ Yolk colour & 12.30 a  & 11.6 b  & 0.13 & 0.002 \\ Haugh unit & 89.6 & 89.7 & 0.80 & 0.915 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of AM/PM feeding on egg quality at 53 WOA.
# Influence of the Tip Clearance on the Aeroelastic Characteristics of a Last Stage Steam Turbine
## 2 Research Methodology
### Fluid Dynamics  
The commercial software ANSYS CFX 18.2 (ANSYS, Inc., Canonsburg, PA, USA) was applied in the CFD simulations. The \(k\)-\(\epsilon\) turbulence model with automatic wall functions was applied, and the turbulence intensity at the inlet boundary was set to 10%. Steady-state simulations with both the stator and rotor stage were analyzed for each tip gap height value as the initial results. A stageaveraged velocity mixing plane on the stator/rotor interface was applied in the steady state simulations. Only the rotor domain was considered in the aeroelastic analyses. Circumferential-averaged total pressure and velocity at the stator/rotor interface extracted from the corresponding initial results were applied as the inlet boundary condition of unsteady simulations. The time-transformation method was applied to solve the unsteady flow field with different traveling wave modes. Standard periodic boundary conditions were applied at the periodic boundaries, and therefore, the passage number was determined by the analyzed inter-blade phase angle (IBPA). For example, eight passages were used when IBPA equaled \(\pm 45^{\circ}\), and two passages were used for IBPA equal to \(180^{\circ}\). Sixty-five time steps were included in a blade vibration period, and a maximum of 20 inner iterations was calculated per time step. The result of unsteady simulations was integrated until a time-periodic flow solution was achieved, generally after seven blade vibration periods.  
The acoustic wave reflection from the outlet of the computational domain can influence the predicted aeroelastic stability of turbomachinery [13; 14]. Fortunately, the acoustics wave was cut-off for the least unstable IBPA analyzed in this paper, and thus, the impact of acoustic reflection on the aeroelastic characteristic would not be critical with the extended fluid domain in the flutter test case.
# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 4 Experiments and Analysis
### Detection Results in Application Scenarios  
To evaluate the identification accuracy and resilience of the revised method in complicated building sites, we demonstrate the detection effect in a subset of complex natural situations. As seen in Figure 5, the detection effects are shown for four states: easily misidentified, tiny target, severe occlusion, and thick picture. Because there are several portable helmets on the actual construction site shown in Figure 5a, the general model incorrectly determines whether or not workers are wearing helmets. According to Figure 5a, the better model is distinguishable, and the helmet's location and the person's state are shown in red and blue boxes, respectively. In Figure 5b, the vast distance in the picture makes it difficult to identify tiny things, and it is possible to experience the issue of missed detection. In this study, we apply the HorNet module to improve feature extraction, enabling the detection of tiny targets at great distances. Figure 5c,d demonstrates that the issue of dense people and severe obstruction may be resolved using an algorithm with an enhanced SIOU loss function. Based on the results of various scenario tests, the improved algorithm has an excellent detection impact.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **mAP** & **FPS** \\ \hline SSD & 82.6 & 81.3 & 81.9 & 82.6 & 25.8 \\ YOLOv3 & 83.4 & 82.1 & 82.7 & 84.2 & 31.6 \\ YOLOv4 & 87.5 & 86.4 & 86.9 & 87.6 & 33.4 \\ YOLOv5-s & 89.6 & 84.6 & 87.0 & 89.3 & 32.2 \\ YOLOX-s & 90.8 & 91.2 & 91.2 & 90.1 & 37.5 \\ OURS & 95.6 & 92.4 & 94.1 & 95.5 & 41.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison under different models.  
Figure 5: (**a**) Hand-held helmet prone to wrong inspection, (**b**) long-range small target phenomenon, (**c**) severe obstruction problem, (**d**) crowding phenomenon.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 2 Background and Related Work
### 5G Networks  
The 5G architecture is structured around four key components: the user equipment (UE), the radio access network (RAN), the transport network, and the core network (CN) [6]. The UE encompasses end-user devices, including mobile phones, IoT sensor devices, etc., whereas the transport network links the two critical modules at the heart of the 5G network: the CN and the RAN. The 5G CN, or 5 GC, is the central part of the 5G mobile network architecture, responsible for managing mobile voice, data, and Internet services.  
The Third-Generation Partnership Project (3 GPP) has delineated the 5 GC architecture as a service-based architecture (SBA) composed of several modular entities known as network functions (NFs) [5]. The NFs communicate with each other using standardized interfaces, which allows for interoperability and flexibility in network deployment [4]. The 5 GC NFs are exclusively software-based and are designed for cloud deployment, obviating the need for dedicated hardware for each function and enhancing deployment agility and flexibility.  
The main NFs as described by 3 GPP are [5] as follows: access and mobility management function (AMF), responsible for registration, authentication, and tracking of the UE; session management function (SMF), which establishes and manages sessions for user data, supports customized mobility management schemes together with AMF, coordinates data routing and forwarding with the UPF, and is responsible for policy enforcement and QoS; and user plane function (UPF), which connects the mobile infrastructure and the data network (DN), being responsible for packet routing and forwarding, packet inspection, and QoS handling for the user plane. Other relevant functions that are used in this work are as follows: network repository function (NRF), which supports NF services management including registration, deregistration, authorization, and discovery; policy control function (PCF), which supports unified policy framework to govern network behavior and providespolicy rules to control plane functions; and security edge protection proxy (SEPP), which supports message filtering and policing between service consumers on public land mobile network (PLMN) control plane interfaces.  
The RAN is responsible for managing the radio spectrum and ensuring the quality of service (QoS) for the end-user through implementing the 5G base stations, known as gNodeB (gNB), which provide wireless connectivity to the end-user devices. The gNB tasks relevant to this work are the selection of an AMF at UE attachment when no routing to an AMF can be determined from the information provided by the UE, the routing of user plane data towards the UPF(s), the routing of control plane information towards the AMF, and the support of network slicing (NS) [5].
## 3 Methodology
### Data Pre-Processing  
Data pre-processing plays a fundamental role in the data mining process, as it transforms raw data into a format suitable for analysis and knowledge discovery. This step typically consists of three main stages: data filtration, numeralization, and normalization.
## 5 MTMD Applications on Taizhou Bridge  
### Layout of MTMD on the Main Girder  
To exert the effect of MTMD to the maximum extent, the MTMD system should be deposited near the maximum amplitude of the dominant mode. As the lateral and vertical buffeting displacements are both dominated by the mode whose maximum amplitude appears in the middle of the span, the MTMD systems are attached to the center region of Taizhou Bridge in each span, as shown in Figure 13.  
Figure 12: Time-frequency spectrum of the buffeting displacements: (**a**) Lateral; (**b**) Vertical.  
Figure 13: Distribution of the MTMD system in the main girder: (**a**) Top view; (**b**) in the main girder.  
In this study, there are two independent MTMD systems, a lateral MTMD system and a vertical MTMD system, for the buffeting control of Taizhou Bridge. The layouts of TMDs in the two main spans are entirely the same considering the structural symmetry. In the longitudinal direction of each span, the TMDs of two systems are both symmetric about the central line of the main girder. For the lateral MTMD system in each span, there is only one MTMD included. As shown in Figure 13a, the TMDs are distributed on the axial line of the main girder with equivalent distance between each other. The two TMDs symmetrical about the central line are equipped with the same mechanical parameters. For the vertical MTMD system in each span, there are two MTMDs with the same configuration included. Identical to the TMDs in the lateral MTMD system, the TMDs in one vertical MTMD are distributed in parallel to the axial line with equivalent distance between each other. The two TMDs symmetrical about the central line are also equipped with the same mechanical parameters. For the two corresponding TMDs from the two vertical MTMD, they are symmetric about the axial line with the same mechanical parameter. As there is a distance between the vertical MTMD and the axial line, the MTMD can also to some extent participate in the mitigation of torsional buffeting responses. The section view of TMDs of lateral and vertical MTMD systems is shown in Figure 13b.  
For the simulation of TMDs, there is no element directly available in ANSYS. It is simulated with the combination of two basic elements, the MASS21 element and COMBIN14 element. The MASS21 element is utilized to simulate the mass block of TMD, and the COMBIN14 element is applied to simulate the spring and the damper with the stiffness coefficient and the damping coefficient included. Hence, TMD can be represented via the connection of the MASS21 element and COMBIN14 element.
## 4 Discussion
### Three-Dimensional Karst Structure Modeling in Baishazhou  
Baishazhou in Wuhan City is located on the edge of the Yangtze River and is also the central area of Wuhan's MDA (Figure 1d). Due to the widespread distribution of carbonate rocks in this area, which are fragmented by faults (Figure 2), and the frequent interaction between the water of the Yangtze River and groundwater, this region has become the most severely affected by karst collapses in Wuhan. Therefore, accurately understanding the distribution characteristics of karst development in Baishazhou is of great significance for the prevention and control of karst collapses in Wuhan.  
The karst development in this area is mainly sourced from carbonate rocks and transported through faults. Under the intense interaction between surface water and groundwater, dissolved carbonate-rich groundwater is continuously transported away, forming a series of karst erosion features such as karst pores, fractures, and caves of varying sizes. Ultimately, these features evolve into karst collapses.  
In this study, by utilizing the 3D geological model of Wuhan's MDA, we accurately revealed the spatial structural characteristics of carbonate rocks and faults in Baishazhou. Using these geological constraints, combined with relevant karst collapse monitoring data, we constructed a 3D karst structural model of the Baishazhou area. The accuracy of the model reached 91.6%, as shown specifically in Figure 15.  
Figure 15: Three-dimensional karst structure model of Baishazhou. Shadows show the carbonate strata after transparency treatment. (**a**) Three-dimensional karst structure model of Baishazhou in Wuchang area (west of Yangtze River); (**b**) three-dimensional karst structure model of Baishazhou in the Hankou region (east of the Yangtze River) (vertical magnification 5\(\times\)).
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Mining Slope Stability Analysis Lower Extension Mining  
#### 4.2.1 Analysis of Slope Energy Evolution  
As shown in Figure 8, during the open-pit mining process, energy dissipation experiences significant changes as excavation progresses downward. Following the initial excavation, the starting energy dissipation value is 0.69 MJ/m\({}^{3}\), and it steadily increases with each subsequent excavation stage. By the fourth excavation, the energy dissipation reaches 0.728 MJ/m\({}^{3}\), and after the tenth excavation, it further increases to 0.77 MJ/m\({}^{3}\), indicating a gradual accumulation of energy dissipation with increasing excavation depth. Notably, when excavation reaches the thirteenth stage, near the pit bottom, energy dissipation sharply spikes to 1.26 MJ/m\({}^{3}\). In the slope area between elevations of 1195 m and 1240 m, energy dissipation undergoes a dramatic change. As shown in Figure 8e, the significant increase in energy dissipation after the thirteenth excavation suggests that the energy dissipation zone in the slope rock mass may have fully connected, forming a critical region for energy release and transformation. This pattern of change underscores the profound impact of excavation activities on the geological environment and highlights the potential safety risks associated with such operations.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Iterative MLFMA-MADBT Method
#### 3.4.1 Field Calculation from the MLFMA Region to the BT Region  
After applying the MADBT method to the BT region, the field of the BT region can be gained as follows:  
1. The incident field of each initial beam is approximately equal to the field of the MLFMA region at the beam's center and can be calculated by (1).  
Figure 6: The couple intersection between the MLFMA region and the BT region.  
For each hit beam, the incident field of its reflected beam can be calculated by geometrical optics (GO). Then the current reflected beam can be treated as a new incident beam, which can be recursively used to generate new reflected beam for the next reflection until it exits the platform or the number of reflections reaches the threshold. Thus, the incident field of all beams can be calculated recursively by traversing the beam tree of each initial beam.
3. After the calculation of the incident field of each hit beam, the near and far field of the BT region can be calculated by PO integral [22].  
Please note that the mesh size for PO integral should be less than 0.3\(\lambda\) (\(\lambda\) indicates the wavelength) for accuracy. Thus, if the size of a hit beam is too large for the PO integral, it is necessary to split the hit beam into several new small beams of 0.3\(\lambda\) during the integral process.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 3 Proposed Framework--AMAN
### Mammogram Classification Using Deep Learning  
In this dataset, the mammogram classification was incredibly challenging due to the high variation in breast shapes, density, and tumor size. Inevitably, extracting mammogram features is commonly done in two ways: using data annotation if the dataset is small, and using pre-trained models for feature extraction targeting larger datasets, which has proven to achieve good results in this field. As a result, due to the size of our dataset, manual data annotation was not possible. Thus, several pre-trained deep learning models on ImageNet have been compared to find the most suitable model for correctly classifying mammograms into three classes: benign, malignant, and normal. The deep learning model was implemented on the cloud using _Google Collab Pro_ for GPU utilization. Multiple _Python_ libraries have been used to implement the model, such as TensorFlow and Keras V2.8, NumPy V1.21.6, and Pandas ver. 1.3.5, Matplotlib V3.2.2, Sklearn V1.0.2, and OpenCV V4.1.2.
## 6 Discussion  
### The Position of the Toppling-Bending Zone  
Through the analysis of the above test phenomena and the test data, the development position of the toppling-bending zone of the slope is further discussed below.  
The test results of the three slope models showed that the toppling-bending zone, caused by the toppling deformation of the slope, started at the slope foot and extended to the crest of the slope in the form of a ladder. From the profile view, the toppling-bending zone tended to be a straight line and could be represented by a straight line k0 crossing the slope foot, which was called the base line for toppling-breaking (Figure 12). The plane passing through the straight line k0 with the same direction as the slope surface was defined as the base plane. The upper part of this plane was the toppling zone of the slope, and the lower part of this plane was the un-toppling zone. The angle between k0 and the normal of the rock strata was expressed by \(\theta\), and we found that the \(\theta\) values obtained by the three test models were almost the same: that of the model with a slope angle of 55\({}^{\circ}\) was 16\({}^{\circ}\); that of 65\({}^{\circ}\) was 12\({}^{\circ}\); that of 75\({}^{\circ}\) was 14\({}^{\circ}\). In other words, the angle \(\theta\) did not change with changing slope angle. This understanding was consistent with the conclusion that the angle between the base plane of the toppling deformation and the normal of the rock strata lies between 12 and 20\({}^{\circ}\) obtained by [PERSON] et al. (2022, 2023) through mechanical analysis. The angle was designed \(\theta\) to be between 12 and 16\({}^{\circ}\). Assuming the dip angle of the rock strata is \(\beta\), the angle \(\delta\) between the base plane and the horizontal plane e can be calculated by the following formula (Figure 12).  
\[\delta=\theta+90^{\circ}-\beta \tag{1}\]  
Conditions are required for the toppling deformation of the anti-dip rock slope; only the rock strata above the base plane of the toppling-breaking could be unstable and fail due to toppling. Therefore, the following could be inferred: Provided that the dip angle of the rock strata is \(\beta\) for the anti-dip layered rock slopes, we obtained the value of \(\delta\) through Formula (1). If the slope angle \(\alpha<\delta\), the whole slope was located below the base plane of the toppling-breaking; thus, it could be inferred that toppling deformation occurred with difficulty on this slope.
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods
#### 3.2.3 Latent Variable Methods  
The latent variable methods estimate an underlying latent structure where the influence of the unobserved sources of variability are estimated. Although hundreds or thousands of predictors can be collected, many predictors are correlated and constitute manifestations of a few unmeasured sources of variability. Thus, these methods estimate the underlying sources of variability and, in turn, compress the dimensionality of the dataset. Three methods were considered from this class: principal component regression (PCR), partial least squares (PLS), and interval PLS (iPLS).
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Components
#### 4.1.3 Cryptographic Service Provider  
The Cryptographic Service Provider (CSP) is responsible for executing all cryptographic operations and managing the associated cryptographic secrets. Specifically, it manages the generation of secret keys, the production of salts required for cryptographic processes, the encryption of facial regions in images, the implementation of key splitting for shared access, the decryption of facial regions for authorized users, and the secure storage of sensitive data.  
The storage system within the CSP is designed to mitigate the risk of unauthorized access. This component utilizes authentication mechanisms to allow the server to perform various actions within the CSP, such as generating, storing, and retrieving keys, as well as salt management. Additionally, the CSP ensures that the server can access stored data only through authenticated and secure internal communication channels, thereby preventing unauthorized access. To further enhance security, the CSP encrypts sensitive secrets using the user's password, ensuring that only the correct password holder can decrypt the key. The implementation details of secure data handling in the CSP's storage system are discussed in Section 4.3.1.
## 2 Background
### IoT Networks  
An IoT system comprises two types of networks: constrained network and public network. The smart devices operate in the constrained network. As shown in Figure 3, in a constrained network, the devices can use multiple protocols, such as 6 LoWPAN [68], Zigbee [69], ZWave [38], and BLE [70], for communications. The communication links of a constrained network are low power and lossy. Therefore, smart devices exchange information between them at a rate which is significantly lower than conventional digital networks, such as Ethernet and WiFi. In the constrained network, smart devices use IPv6 for addressing and CoAP [71] as the application protocol. In constrained networks, the RPL routing protocol [72] is used to route network packets over the lossy links.  
The Internet (Wide Area Network) is considered to be the public network in an IoT system. A public network provides connections between multiple IoT systems, such as smart home, building, medical, and industry, located in the edge of the networks. The 3G, 4G, or 5G technologies can be used for communication in the public networks. The data rates of the public networks are significantly higher than those of the constrained networks. The public networks use IPv4 and HTTP for message delivery and conventional routing protocols [73], such as RIP and OSPF, for routing packets.