esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 1 Introduction
Considering the telecom service as the use case, the telecom operators replace the proprietary hardware with commodity servers for hosting VNFs for the SFC. Before deploying telecom services on the cloud, the operator has to verify and validate if the cloud system is adequate for the carrier-grade services. According to the ETSI testing specification [27], both functionality and performance tests are the basis of the verification process on the NFV cloud. The functional tests aim to check if the system works correctly. After verifying that the system is operational, evaluating the performance of the system is also necessary. In many use cases, network performance is a critical concern in deploying and orchestrating cloud services. However, the performance is degraded due to the virtualization overhead. Telecom operators need a systematic approach to verify the network performance with different enhanced technologies of the cloud system. In this regard, this paper investigates the enhancement of network techniques to mitigate the deterioration in a practical cloud system. A comprehensive testing framework is proposed to evaluate the cloud system with different EPA technologies in three areas, which are functionality, performance, and application. The functionality tests focus on instantiating VMs, building a tenant network, and the connectivity of VMs. In performance tests, both PVP and PVVP scenarios are considered to estimate the network throughputs. As for the application test, this paper not only deploys a virtual IP multimedia subsystem (vIMS) on the cloud, but also evaluates the performance of the vIMS via a session initiation protocol (SIP) stress benchmark. The performance metric of successful call rate is measured for the vIMS test. Empirical results show that the throughput of the PVP test performs near the line rate when applying the SR-IOV acceleration, while OVS-DPDK approaches 90% of the line rate when packet size is 1518. Increasing the number of lcores and pmd-cpus further enhances the performance by about 50% in OVS-DPDK. In addition, the successful call rates in vIMS validations with different techniques are similar in lower call rates. Whenthe call rate exceeds 100, the improvement brought by OVS-DPDK is up to 14%. To sum up, the contributions of this paper are:  
* Proposing a comprehensive testing framework to investigate a practical cloud system on different areas of performance effects. * Conducting performance comparisons between the generic and the enhanced cloud system for NFV. * Considering both PVP and PVVP packet paths for different SFC scenarios. * Deploying the vIMS application on the enhanced cloud system to show effective performance. The rest of this paper is organized as follows. Section 2 presents the background and discusses related works to support our research. The comprehensive testing framework and the test methodologies are proposed in Section 3. Section 4 introduces the experiments and results. Finally, Section 5 concludes this paper with summarizing remarks.
## 3 Materials and Methodology
### Incorporation of Conditional GAN (cGAN) in DKPS  
In the dynamic knowledge prediction system (DKPS), conditional generative adversarial networks (cGANs) are used to support both personalized content generation and feedback-driven refinement. The system generates learner-specific materials that adapt to each student's current skill level, learning goals, and progress. This approach helps improve engagement, supports diverse learning preferences, and creates a dynamic and responsive learning experience.  
The cGAN model consists of two main components: a generator (G) and a discriminator (D). The generator creates learning content--such as quizzes, hints, and problem-solving tasks--based on the learner's target module and predicted skill level. The discriminator then evaluates this content to determine whether it aligns with the learner's proficiency and the module's objectives. If the content is too advanced or not relevant, the discriminator rejects it and prompts the generator to produce simpler or more suitable materials.
## 3 Framework
### Online Phase
#### 3.3.2 Learning-Guided Branching  
To fully leverage the GNN's stability predictions, every integer variable in the model is assigned a customized branch priority. The stability scores generated by our GNN not only guide variable fixing but also serve as a critical indicator for divide-and-conquer strategies in B&B. Variables with low stability (close to 0) exhibit inconsistent values across nodes in the B&B tree, implying that their unresolved status contributes significantly to the solution space fragmentation. Prioritizing these variables for early branching helps accelerate bound tightening, and resolving unstable variables reduces feasible region ambiguity, enabling faster pruning of suboptimal branches.  
We map stability scores \(\mathbb{X}^{x}_{g,t}\in[0,1]\) to integer priorities \(\mathbb{P}^{x}_{g,t}\in\left\{\mathbb{P}_{min},\ldots,\mathbb{P}_{max}\right\}\) via  
\[\mathbb{P}^{x}_{g,t}=\left\lfloor\mathbb{P}_{min}+\left(\mathbb{P}_{max}- \mathbb{P}_{min}\right)\left(1-\mathbb{X}^{x}_{g,t}\right)\right\rfloor \tag{20}\]  
where \(\left\lfloor\cdot\right\rfloor\) is the rounding operator to the nearest integer; \(\mathbb{P}_{max}\) is the maximum priority; and \(\mathbb{P}_{min}\) is the minimum priority.  
In practice, this mapping can be integrated right before invoking the solver. After constructing the MIP model, we traverse each integer variable \(x_{g,t}\), retrieve its predicted \(\mathbb{X}^{x}_{g,t}\) from the offline-trained model, compute \(\mathbb{P}^{x}_{g,t}\) according to the above formula, and set the corresponding branch priority in the solver. Once the branch priorities are updated, the solver will systematically prioritize branching on high-priority (low-stability) variables, the solver can more quickly prune large swathes of the search tree, leading to faster convergence in practice.  
\(\tau^{stable},\tau^{v},\Delta\) are set to 0.5, 0.99, and 1, respectively, in the experiments.
## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution
#### 5.2.1 Fuzzification  
For real-time applications, the triangular membership function stands out as one of the most frequently employed, effective, and recommended functions. To refine our fuzzy logic system's membership functions, we employed a trial-and-error approach based on the specific application requirements. Figures 5-9 show the membership functions for the SINR, CBT, PDR, the number of neighbors, and the mobility.  
Each vehicle calculates the CBT degree using Equation (1) and applies the CBT membership function to classify the computed value as either small, medium, or large. Similarly, the mobility factor is computed using Equations (3) and (4), with the mobility membership function then determining the degree of mobility as either low, medium, or high. Furthermore, each vehicle utilizes the PDR membership function to determine the degree of packet delivery ratio (PDR) as either small, medium, or large. Likewise, the number of neighbors' membership function is employed to determine the degree of the number of neighbors as either small, medium, or big. Subsequently, the SINR factor is computed using Equations (5) and (6), and the SINR membership function is applied to establish the degree to which it belongs, categorized as either small, medium, or large. Prior to utilizing any of the aforementioned metrics, they are normalized using the min-max normalization method.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 3 Results and Discussions
### Surface Plots
#### 3.2.1 Optimum Tensile Strength  
The experimental results of the understudy CFRP tensile strength (Table 2) were transformed into regression Equation (2) and surface plots (Figure 9). Equation (2) was used to predict _TS_ within the upper and lower limit of curing parameters (_T\({}_{c}\)_, _t\({}_{c}\)_, and _P\({}_{au}\)_).  
The two surface plots of tensile strength at constant _P\({}_{au}\)_ of 3 and 7 bar are shown in Figure 9. It was observed from the surface plots of Figure 9b that the MRCC resulted in tensile strength of 1274.8 MPa at CC7 (120 \({}^{\circ}\)C, 120 min, 7 bar), whereas the enhanced tensile strength of 1420 MPa was achieved at CC8 (140 \({}^{\circ}\)C, 120 min, 7 bar) in our study. With increasing _T\({}_{c}\)_, more epoxide rings of the CFRP matrix open and larger molecules are formed [48], giving rise to a higher degree of cross-linking [49]. During cross-linking, the unordered amorphous state of the CFRP matrix transforms into an ordered glassy state which results in a higher tensile strength [49; 50] of the epoxy matrix. On the contrary, for insufficiently high values of _T\({}_{c}\)_, matrix resin would not cure fully, resulting in reduced mechanical properties [51]. As we used a high _T\({}_{c}\)_ of 140 \({}^{\circ}\)C (equal to T\({}_{\text{geo}}\)), an increase of 11.4% in strength was observed as compared to the tensile strength at MRCC. The result of optimal curing parameters along with optimum tensile strength is summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Non-Optimal Curing** & **Non-Optimum Properties** & **Optimal Curing Parameters** & **Optimum Properties** & **Improvement (\%)** \\ \hline \hline \multirow{3}{*}{**Tensile Strength**} & & & 140 \({}^{\circ}\)C & & \\  & & & 120 min & & \\  & 120 \({}^{\circ}\)C & & 7 bar & & \\  & 120 min & & (CC8) & & \\ \hline \multirow{3}{*}{**Impact Toughness**} & & & 140 \({}^{\circ}\)C & & \\  & (MRCC/CC7) & & 10.9 & 120 min & \\ \cline{1-1}  & & & 3 bar & & \\ \cline{1-1}  & & & (CC4) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparative summary of non-optimum and optimum properties. This table compares MRCC (non-optimal) curing parameters and non-optimum properties with optimal curing parameters and optimum properties.  
Figure 9: Surface plots of the tensile strength (_TS_) at a constant pressure of (**a**) 3 bar; (**b**) 7 bar. _TS_ was observed to increase with temperature, time, and pressure. The optimum tensile strength was observed at CC8 (140 \({}^{\circ}\)C, 120 min, and 7 bar), represented by a black dot.
## 3 Recognition Methods and Their Implementation
### Region Growing Algorithm  
The process of the region growth algorithm includes the selection of seed points and growth. The first step of regional growth is to select a group of seed points, which is based on the need to identify cracks. Because cracks are often dark areas in the image, seed points are selected based on grayscale in this study. Firstly, search all pixels to find the lowest gray point of the whole picture, and then find the pixels whose gray level is close to the lowest point in each image block, and put them into the seed point set.  
After obtaining the seed point set, compare the gray values of eight adjacent pixels from each seed point. If a value is lower than the threshold, it will be added to the tail of the seed point set. The region growth pseudo code is shown in Pseudo Code.  
The region growing algorithm has the advantages of fast image segmentation, clear edges, and a simple theory. However, it is sensitive to noise and the threshold needs to be provided manually. The noise problem can be solved by image preprocessing, and the threshold problem will be solved by regression analysis in the next part.  
The Region Growth method could be surmised by pseudo code of Algorithm 1 below.  
```
1. Input: image A
2. Output: image with growing result
3. For each pixel P\(\in\)A
4. If P\(\in\)Grayscale-origin\(\_\)threshold
5. Add P\(\sim\)Seeds
6. For each pixel K\(\in\)Seeds
7. If K\(\_\)surrounding.grayscale-K\(\_\)grayscale \(<\) growing\(\_\)threshold and K\(\_\)surrounding \(\
otin\) Seeds
8. Add K\(\_\)surrounding\(\sim\)Seeds.fail
```  
**Algorithm 1** Region Growth  
For the detailed procedure of the algorithm, refer to the steps below.  
1. Load the image dataset containing concrete structure crack images.
2. Preprocess the images: Perform image equalization; Calculate the central pixel average; Apply the median filter.
3. Extract the image features: Calculate the grayscale mean and variance for each image; Implement the feature recognition algorithm BRIEF.
4. Train the regression function using the extracted image features: Use the regression method to learn the image features and determine the region growth threshold; Train the regression function on the open-source dataset.  
* Implement the region growth algorithm:  
* Implement the seed points for crack recognition; Expand the threshold from seed points using the region growth algorithm.
* Evaluate the crack recognition results: Compare the results with the traditional region growth algorithm; Measure the accuracy improvement and computational cost.
* Experiment and analyze the algorithm: Use the OpenCV toolkit for image processing and feature extraction; Utilize the RegionGrow toolkit for the region growth algorithm; Apply the sklearn toolkit for regression analysis.
* Display the experimental results: Present the experimental environment details (processor, memory, GPU); Show the crack detection dataset and subsets used.
* Repeat steps 2-8 for each image in the dataset.
* Output the final crack recognition results and performance metrics.
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
## 1 Introduction
In addition, a cerium hydrogen phosphate Ce(PO4)(HPO4)0.5(H2O)0.5 with a two-dimensional structure can remove Sr2+; however, it was concluded that the Sr2+ ions were only adsorbed on the surface of the material without entering the structure [40]. Our research group has been focusing on the development of new inorganic ion exchangers for the removal of radioactive metal ions and has obtained a variety of anionic framework materials with various counter-cations as exchangeable ions for the removal of Sr2+, Cs+, and UO2+ [3; 21; 41; 42; 43]. In this work, we screened an anionic layered neodymium phosphate with K+ as the counter-cation, namely K3 Nd(PO4)2 [44; 45; 46], which can be synthesized in gram-scale. K3 Nd(PO4)2 exhibits the ion exchange performance for Sr2+, and it has a wide range of pH applicability (pH = 3.07-12.17). In both the neutral and alkaline environment, the removal efficiency (\(R^{\rm Sr}\)) was greater than 99.7%. Notably, the distribution coefficient \(K_{\rm d}\) was as high as 1.46 \(\times\) 10\({}^{6}\) mL/g at pH = 8.07, indicating a strong affinity for Sr2+. In addition, through batch adsorption experiments and various characterization (XPS, EDS, PXRD) verifications, the Sr2+ adsorption mechanism was identified clearly as the ion exchange between Sr2+ and K+ ions in K3 Nd(PO4)2. As far as we know, K3 Nd(PO4)2 is the first rare earth phosphate with a two-dimensional structure that exhibits as an ion exchange material for the efficient removal of Sr2+ ions. This work paves the way for the development of layered rare earth metal phosphates as ion exchange materials for radionuclide remediation.
## 2 Background
### Multi-Channel Operation
#### 2.2.1 The European MCO Standard
They are followed by low-priority DENMS with TC 1; CAMs with TC2; and, finally, SPATEMs, MAPEMs, and IVIMs with TC 3. As we distinguish DENMs with different priorities, this may mean that we can distinguish the service or the application that creates the messages [17]. Although the authors of [17] do not discuss this further, it can be crucial in calculating channel access priority in the future. ETSI has introduced the DCC (Decentralized Congestion Control) entity used in vehicular communications, which can control the transmission parameters of individual vehicles based on the saturation state of the channel in a way that the communication suffers as little damage as possible. DCC decisions are optimized based on the feedback of channel information. It continuously detects channel saturation metrics such as the CBR. Based on this, algorithms with different strategies can modify the transmission parameters. Each channel is assigned a CTH (Congestion Threshold) value, and the DCC is responsible for keeping the CBR of the channel below this threshold [22]. One of the reactive DCC strategies is that the DCC samples the channel state every 100 ms and decides the frequency of message generation based on the CBR value. The optimal decision is assisted by a state machine containing each state's transmission parameters. The highest message delivery rate is achieved in the relaxed state when the channel saturation is less than 19% and the lowest when the channel CBR exceeds 59%. The T\({}_{\text{off}}\) field also indicates this but in milliseconds. In other words, this is the time that should elapse between two messages being sent. Messages are first placed in a priority queue, and then after the T\({}_{\text{off}}\) time expires, the DCC takes the first message from the highest-priority queue and sends it out [22]. In [23] five states are described. The first one is the \"relaxed\" state. It stays there if the CBR remains below 0.3, with a maximum transmission rate of 10 Hz. As the CBR rises and exceeds 0.3, the DCC switches to the next \"active1\" state, with a 5 Hz transmission rate. If the DCC goes beyond all active states (active1-2-3), it reaches the final \"restrictive\" state. This state is active when the CBR is greater than 0.6 and limits the transmission rate to 1 Hz.
## 2 Materials and Methods
### Scientific Background of Data Requirement and Modules of AGEM-S
#### 2.2.2 Nutrition Module
Feed additives: Since NH\({}_{3}\) is formed from manure under the influence of bacterial fermentation, the pH of the urine and feces excreted affects the process. For this reason,any feed additive that reduces the pH of urine and feces will result in lower ammonia emissions. For instance, reducing the pH of urine can be achieved by partially replacing limestone with calcium sulphate or calcium chloride or by supplementing the feed with benzoic acid. According to the literature results, additional Ca benzoate reduces the pH of manure from 6.8 to 5.3 and that of urine from 8 to 6.4, in comparison with a control diet [40]. At a concentration of 0.5%, it reduces the ammonia produced in the stable by 3.6-5%, and at a concentration of 1%, by 7.5-14% [44]. The additional use of benzoic acid in the AGEM-S system was taken into account as an NH\({}_{3}\) emission decreasing factor in the pig house. A 4% ammonia emission reduction was considered in line with the 0.5% mixing rate, as employed in practice [44]. In the nutrition module the number of feeding phases for each age category must be defined first, followed by the feed intake for each phase, the initial and final weight of piglets and fattening pigs, and the number of days spent in that phase in the case of other age groups. After this, the crude protein content and digestible energy content of the feed, and also the amino acid and benzoic acid supplementation, must be indicated (Table 2). In the background, the model calculates the TAN and N excretion for the age categories and feeding phases, taking into account the genotype-specific N retention factors as well as the protein reduction and phase feeding correction factors. The cumulative daily TAN excretion of the farm is calculated by adding the daily TAN excretion of all existing livestock in each feeding phase.
## 1 Introduction
In addition, the BIM cloud-based radio frequency identification (RFID) location system has tremendous potential for practical applications, such as site protection control, safety management, asset management, and productivity tracking [24]. Several studies have been performed to determine accidental safety factors [25; 26]; however, high fatalities among construction workers remain a major global concern for policy makers [27]. A recent case study or prototype was conducted to integrate certain digital technologies for construction safety monitoring and inspection [28]. However, very limited empirical studies have integrated BIM with other digital technologies for construction safety management, where the current approaches or studies have mainly focused on BIM-based AR and VR applications for data exchange and improved safety performance through case studies and prototypes [29]. In addition, BIM-based 4-D modeling has been used to establish the rule checking platform for construction safety [30]. Likewise, the machine learning-based object detection technique offers a platform for construction safety and surveillance through cloud fuse point, semantic data, and computer vision [31]. Furthermore, numerous researchers have made major contributions to the field of high-rise building construction safety, but there are some limits. For example, [PERSON] et al. [26] identified the safety factors that cause accidents in high-rise building projects but were unable to suggest mitigating solutions. A recent study conducted by [PERSON] et al. [32] identified the safety elements affecting the safety performance of high-rise building projects but did not suggest mitigation measures. Therefore, the aim of this study was to mitigate accidental safety factors in high-rise building projects by integrating BIM with other emerging digital technologies for construction safety improvement. Qualitative research was conducted via interviews with related stakeholders to investigate the in-depth knowledge of this relative new integration of BIM and other emerging digital technologies for improving the construction safety in high-rise building projects. The research thus explored and uncovered new knowledge gaps and the practical needs of this integration for construction safety management in high-rise building projects. It also serves as a theoretical foundation for improving safety performance in future digitalized construction projects. The remainder of the paper is organized as follow. Section 2 presents a literature review and the rationale for this research. Section 3 describes the research methodology. Section 4 provides the results. Subsequently, Section 5 elaborates on the discussion and research framework followed by Section 6, which contains the conclusion, limitations, and future directions.
###### Abstract  
The issue of Electrical Impedance Tomography (EIT) is a well-known inverse problem that presents challenging characteristics. In order to address the difficulties associated with ill-conditioned inverses, regularization methods are typically employed. One commonly used approach is total variation (TV) regularization, which has shown effectiveness in EIT. In order to meet the requirements of real-time tracking, it is essential to acquire fast and reliable algorithms for image reconstruction. Therefore, we present a modified second-order generalized regularization algorithm that enables more-accurate reconstruction of organ boundaries and internal structures, to reduce EIT artifacts, and to overcome the inability of the conventional Tikhonov regularization method in solving the step effect of the medium boundary. The proposed algorithm uses the improved alternating direction method of multipliers (ADMM) to tackle this optimization issue and adopts the second-order generalized total variation (SOGTV) function with strong boundary-preserving features as the regularization generalization function. The experiments are based on simulation data and the physical model of the circular water tank that we developed. The results showed that SOGTV regularization can improve image realism compared with some classic regularization.  
electrical impedance tomography; generalized total variation; TV regularization; inverse problems