esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 2 Materials and Methods
Through this structured comparison, software teams and IT decision-makers will gain actionable insights into how these platforms perform under various real-world conditions, ultimately helping them make an informed decision about their CI/CD adoption strategy [16; 17; 18]. The selection of CI/CD platforms was guided by their market share, unique features, and relevance to a broad range of software development teams, as visually summarized in Figure 1. The analysis focuses on Azure DevOps and GitHub, as well as the choice of a large ecosystem that includes various tools such as Jenkins, GitHub CI/CD, Atlassian Bitbucket, AWS CodePipeline, and JetBrains TeamCity. Each platform offers distinct use cases, organizational needs, and infrastructure preferences, resulting in a diverse and dynamic CI/CD landscape. According to recent Microsoft market statistics, GitHub leads with a 33% share, followed by Azure DevOps at 24%. These platforms account for over half of the market, reflecting their widespread use and significant influence. Jenkins holds a 14% share and remains a popular choice due to its open-source nature, extensibility, and robust plugin ecosystem. Jenkins' ability to integrate with any tool or platform makes it highly customizable, though this flexibility often comes at the cost of increased maintenance and operational complexity. As a result, Jenkins is particularly valuable for organizations that need technical expertise to manage and optimize their infrastructure. GitLab CI/CD, with its integrated platform, offers a compelling alternative by combining version control and project management in a single solution. Its growing adoption, reflected in a 9% market share, demonstrates its appeal to teams seeking an all-in-one platform. GitLab's emphasis on collaboration and support makes it a strong contender for modern software development teams. Atlassian's Bitbucket and JetBrains TeamCity integrate well with other tools, such as Jira and Confluence, making them a natural fit for teams already using the Atlassian suite. Its support for both Git and Mercurial version control systems, along with its built-in CI/CD capabilities, makes it an excellent choice for small to medium-sized teams. TeamCity, known for its ease of use and powerful build configurations, is another option for enterprises seeking a reliable and scalable solution. Its support for a wide range of build and deployment scenarios and user-friendly interface makes it particularly appealing to organizations with complex build requirements. Other tools, such as TravisCI and CircleCI, collectively represent a minor but significant part of the market. This distribution highlights the dominance of GitHub and Azure DevOps while underscoring the importance of alternative tools in addressing specific development and operational requirements. Figure 1: CI/CD platforms’ worldwide usage.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 4 Scheduling Schemes
### Suboptimal Schemes from Previous Works  
As indicated in Section 1, some scheduling schemes for MIMO systems have been proposed in the literature. Below, two suboptimal schemes are detailed, which have a good trade-off between performance and complexity, in order to make a comparison in terms of performance with the scheme proposed in this work.
# Coupling Efficiency of a Partially Coherent Radially Polarized Vortex Beam into a Single-Mode Fiber
## 4 Conclusions  
In this paper, we derived a general expression for the coupling efficiency of PCRP vortex beams into a single-mode optical fiber in terms of its CSD matrix. We were able to derive an analytical expression for the coupling efficiency, and the field distribution of the fundamental mode of an optical fiber is approximated. We studied the effects of the coherence, topological charge and wavelength of the PCRP vortex beams, and NA on the coupling efficiency through some numerical simulation. The results showed that the coupling efficiency was significantly affected by the numerical aperture and parameters of the incident beam, such as topological charge, initial spatial coherence, and wavelength, and we also determined the best choice for the PCRP vortex beams with different topological charge. Our results will help users to determine for themselves which initial beam parameters and NA provides optimal results for their applications. It should be useful for any application requiring coupling of a partially coherent radially polarized vortex beam into optical fibers.  
X.Z. (Data curation, Writing-original draft, Methodology); K.W. (Formal analysis, Writing-review and editing); F.W. (Supervision, Writing-review and editing); C.Z. (Supervision, Project administration, Writing-review and editing). Y.C. (Supervision, Project administration, Writing-review and editing). This research was funded by the National Natural Science Foundation of China [grant number 11774250 and 91750201]; National Natural Science Fund for Distinguished Young Scholar [grant number 11525418]. The authors declare no conflict of interest.
## 3 Results and Discussion
### Neutralisation Method
#### 3.1.1 Laboratory Tests
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Element**} & \multicolumn{8}{c}{**Neutralizer**} \\ \cline{2-9}
**Neutralization from pH 3** & **Fe** & **Mn** & **Cu** & **Zn** & **Cd** & **Co** & **Ni** & **2.5 Mass \% Lime milk** \\ \cline{2-9}  & \multicolumn{8}{c}{**Concentration, mg L\({}^{-1}\)**} & \multicolumn{8}{c}{**Consumption, mL L\({}^{-1}\)**} \\ \hline to pH 7 & 0.02 & 43.0 & 0.06 & 1.04 & 0.022 & 0.41 & 0.24 & 37 \\ to pH 7.5 & 0.02 & 37.2 & 0.03 & 0.27 & 0.013 & 0.20 & 0.12 & 40 \\ to pH 8 & 0.02 & 31.9 & 0.03 & 0.07 & 0.007 & 0.09 & 0.07 & 43 \\ \hline \hline \multicolumn{9}{c}{**Element**} & \multicolumn{8}{c}{**Neutralizer**} \\ \cline{2-9}
**Neutralization from pH 4** & **Fe** & **Mn** & **Cu** & **Zn** & **Cd** & **Co** & **Ni** & **2.5 mass \% Lime milk** \\ \cline{2-9}  & \multicolumn{8}{c}{**Concentration, mg L\({}^{-1}\)**} & \multicolumn{8}{c}{**Consumption, mL L\({}^{-1}\)**} \\ \hline to pH 7 & 0.01 & 42.2 & 0.04 & 0.65 & 0.019 & 0.33 & 0.21 & 31.8 \\ to pH 7.5 & 0.01 & 39.8 & 0.05 & 0.22 & 0.015 & 0.23 & 0.14 & 33.8 \\ to pH 8 & 0.01 & 36.8 & 0.03 & 0.11 & 0.010 & 0.16 & 0.10 & 40.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Concentration of some elements after the second neutralization step of real AMD. Figure 3: Dependence of Zn, Cd, and Co removal degree of pH value. Copper concentration was drastically reduced during the neutralization on different pH value (7, 7.5, and 8) below the value of maximum allowed concentration of 1 mg L\({}^{-1}\)[24]. At each pH, Mn concentration was much higher than the maximum allowed value (1 mg L\({}^{-1}\)) (Table 4). Figure 4 shows the total lime consumption at two different pH value during two neutralization steps of real AMD from the Robule Lake. The total lime milk consumption had the minimum value for the first neutralization step on pH 4 and second neutralization step on pH 7 (Figure 4). Based on the above discussion about the concentration of some elements after two neutralization steps and the value of the total lime consumption at different pH values, a test on a semi industrial plant was carried out as the following: neutralization up to pH 4 as the first neutralization step and neutralization up to pH 7 as the second neutralization step.
## 1 Introduction
It is found that when the random noise generated on sensors increases to a large one (relative to the modeled one by particle filter), the estimated result cannot reflect the true state. More extreme, when the random noise increases to a certain extent, the particle filter algorithm cannot continue normally, which will then cause the system involving the particle filter to collapse. Thus, the absolute particle weight is substituted by the exponential weight, as an adjustment of the particle weight in the update stage when facing large sensor noise. Specifically, first, the residual of each particle is judged if \(3\sigma\) rule is violated. If the number of particles that violate the \(3\sigma\) rule reaches a certain proportion, the exponential weights for particles falling outside the \(3\sigma\) range are calculated, while the absolute weights are preserved for particles within the \(3\sigma\) range, as an adjustment for particle weights. Together, the state is estimated via these two kinds of particles with associated weights calculated in different ways. Then, the observation (output) is predicted based on the estimated state values. Through such an adjustment, the particle filter algorithm can continue suffering large noise so that the entire control system will not be interrupted and the reliability of the system can be guaranteed. As for the time delay, the residuals between the delayed output and the undelayed output particles contribute to the calculation of the particle weight [14]. The overall robust nonlinear uncertain heat exchange control system is designed and the bounded input bounded output (BIBO) stability and tracking performance are guaranteed based on the operator theory [26; 27]. In summary, the contributions of this paper are as follows. * The particle filter algorithm is integrated into a robust nonlinear control system to simultaneously deal with the effects of sensor noise and time delay, and the control performance is guaranteed. * Particle weight adjustment is used when the noise is so large that it affects the particle filter algorithm and the entire control system. In such a method, the \(3\sigma\) rule is first used to evaluate the impact of the noise, and then the original particle weight is replaced by the exponential weight for particles that violate the \(3\sigma\) rule when the number of such particles reaches a certain proportion. This kind of weight adjustment ensures the normal operation of the particle filter and control system. * The simulation results and the corresponding analysis of the results verify the effectiveness of the proposed method. In what follows, the preliminaries and problem statement are outlined in Section 2. Particle filter with weight adjustment design for the compensation of sensor noise and time delay is formulated in Section 3. Then, in Section 4, the simulation results are shown to demonstrate the effectiveness of the method, and Section 5 is the conclusions.
## 4 Performance Evaluation in Edge Scenarios
### Experimental Setup  
The hardware specifications, system parameters, and software versions used for the tests are shown in Table 3. The virtual environment utilizes the docker environment provided by Occlum, version 0.26.4. For both Gramine and Occlum, default values are used for the relevant parameters, and the tests are conducted on the SGX version 1. We disable CPU frequency boost features and fix the CPU frequency at 4.7 GHz to reduce performance data fluctuation. In the network tests, we disconnect external network connections and use local addresses to conduct the tests, minimizing external interference. To avoid the impact of other processes on the experimental results, we disable unnecessary background processes and services.
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 4 Design of SDDA MAC Protocol
### The Data Transmitting Phase  
This phase mainly concerns data transferring and lost packet retransmission or not. In this phase, each terminal node extracts its own waiting duration information (\(wd_{tx\_DATA\_i}\)) from \(TS\) by using node ID after receiving \(TS\). When the waiting duration is due, the terminal node commences pouring data packets onto the acoustic channel. The layout of the data packet is detailed in Figure 3, where \(pkt\_seq\_num\) is the sequence number of data packets. After finishing receiving data packets from all terminal nodes, BS broadcasts an \(ACK\) packet to acknowledge all data packets successfully transferred by all terminal nodes. The \(ACK\) packet contains the sequence number of each data packet received from each terminal node, which is \(pkt\_seq\_num\_recv\) listed in Figure 3, where _node_num_recv_ is the number of those terminal nodes delivering at least one data packet successfully, and \(pkt\_num\_recv\) is the number of data packets received from some terminal node. Data packets that fail to be transferred will be retransferred in the next round. This acknowledge mechanism reduces the number of \(ACK\) packets, thereby decreasing energy consumption and improving channel utilization, while ensuring data reliability to a certain extent. For non-critical data, two retransmissions are allowed. For important command data, multiple retransmissions are executed to ensure successful delivery. For data with high real-time requirement such as AUV status, the latest data are transmitted every time without retransmission. The pseudo code of this phase is described in Algorithm 3. Before the start of the next cycle, BS can relay data or commands to a designated terminal node at the end of this phase.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 5 Context-Aware Pending Information Table Entry Management (CPITEM) Scheme
### Non-Safety PITE Priority
#### 5.2.1 Eminence  
\(Eminence_{PITE_{i}}\) is computed using Equation (1) considering the information components: (i) remaining lifetime of \(PITE_{i}\) (\(PEL_{PITE_{i}}\)), (ii) size of the \(PITE_{i}\) (\(Size_{PITE_{i}}\)), (iii) remaining lifetime and size of all entries in the PIT. \(Size_{PITE_{i}}\) is the number of InRecords associated with a \(PITE_{i}\), representing the current demand for the content. Eminence represents the popularity of \(PITE\) by considering both \(PITE\) size and PEL. To compute the popularity of the current PITE, we have to divide it by the summation of the product of the size and PEL of all PIT entries (\(\sum_{j=1}^{k}(Size_{PITE_{i}}*PEL_{PITE_{j}})\)). If two PITEs have equal PEL (\(PEL_{PITE_{i}}==PEL_{PITE_{i}}\)), lower \(Eminence\) value is calculated for the \(PITE\) with smaller size (less number of InRecords), whereas higher \(Eminence\) value is calculated for the \(PITE\) with larger size. Some of the PITEs may have a large size and a large PEL, resulting in a higher Eminence value, which creates a load on the PIT. Therefore, \(PITE\) utility (Equation (2)) is computed to prioritise those PITEs that have a large size but have spent more time in PIT. If two PIT entries \(PITE_{i}\), and \(PITE_{j}\) have nearly equal Eminence values, then a higher Utility value will be computed for \(PITE\) which rests in PIT for a longer duration. This is due to the fact that the entries that spend a longer time in PIT might be resolved sooner. Consequently, the PIT storage will be available for the upcoming PIT entries. This in turn will improve PIT utilization. The Eminence of the \(PITE\) is computed as follows.  
\[Eminence_{PITE_{i}}=\frac{(Size_{PITE_{i}}*PEL_{PITE_{i}})}{\sum_{j=1}^{k}( Size_{PITE_{j}}*PEL_{PITE_{j}})} \tag{1}\]
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 3 Proposed Fuzzy VIKOR Reputation System  
This section presents a fuzzy reputation-based trust management system for federated testbeds and a credibility mechanism for the experimenters' ratings. The fuzzy VIKOR reputation framework has a horizontal structure, which can easily scale up and is actually a multi-criteria decision technique. It can process simultaneously various types of data, e.g., binary, numeric and linguistic values. This allows us to use numeric QoS and SLA data combined with linguistic QoE data that are appropriate to express the vague and subjective user preferences. Each testbed provides a set of services. The services of a testbed depend on the type of available resources and refer to computing or network Key Performance Indicators (KPIs), e.g., node/link/server availability or network delay, bandwidth and packet loss ratio. The proposed framework considers various QoS and QoE criteria from SMICloud [24]. In a federated environment, several experimenters can use the same testbed with different goals. Thus, in our approach, each experimenter is able to adjust the weight of criteria according to his/her needs. The consistency of the weights is checked in order to provide meaningful rates and discourage malicious evaluators.  
Our framework is based on fuzzy VIKOR (Visekriterijumsko Kompromisno Rangiranje), which is a multi-objective decision-making approach. This technique is applicable to cases where the best provider must be selected among different alternatives. For instance, the fuzzy VIKOR approach is used for renewable energy planning [25]. The fuzzy VIKOR method handles only fuzzy inputs. Thus, we propose a modification of fuzzy VIKOR that considers both numeric and linguistic values. Furthermore, the user can assign the weight of each criterion according to his/her requirements. The reputation system modifies the reputation score of each testbed after conducting an experiment. Furthermore, a credibility mechanism compares the experimenter's opinion with SLA and monitoring measurements in order to protect the reputation of testbeds against malicious users. We choose this multi-objective methodology to investigate the effectiveness of using both numerical and fuzzy inputs on the reputation score of the federated testbeds. The evaluation of our framework in Section 4 showcases the importance of using fuzzy criteria for the QoE metrics. A presents the basic information on fuzzy numbers and sets.
## 2 Results and Discussion
### Results
#### 2.1.2 Pain Assessment  
The VAS results were calculated as % change from baseline at all treatment time points (Figure 2). Results of pain assessment (VAS scores) measured at baseline and outcomes for the entire population are summarized in Table 2.  
Figure 1: Enrollment and outcomes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **aMSCs** & **PRGF** & \(p\)**-Value** \\ \hline Age (months) & \(53\pm 43\) (8–135) & \(93\pm 35.5\) (18–66) & 0.007 \\ Weight & \(34.9\pm 12.8\) (18.3–66.2) & \(36.5\pm 10.6\) (20–62.8) & 0.805 \\ Gender (male-female) & 10-8 & 14-3 & 0.201 \\ Radiographic Osteoarthritis degree Bioarth Score * & \(15.47\pm 6.02\) (13.37–17.57) & \(17.36\pm 3.8\) (16.07–18.65) & 0.259 \\ Functional limitation & \(5.88\pm 2.83\) (2–11) & \(8.22\pm 3.39\) (1–14) & 0.003 \\ Joint mobility & \(4.18\pm 0.63\) (3–5) & \(3.92\pm 0.84\) (2–6) & 0.131 \\ Muscle atrophy & \(1.05\pm 0.55572\) & \(1.0526\pm 0.62126\) & 0.890 \\ Owner Vas Score & \(28.29\pm 14.49\) (6.57–72.37) & \(42.55\pm 20.52\) (9.21–72.37) & 0.023 \\ Patients & 18 & 17 & \\ \hline \hline \end{tabular} Quantitative variables are expressed as mean \(\pm\) standard deviation (range). \(p<0.05\) is considered statistically significant. * The Bioarth score is an index of severity for hip mobility that includes 4 subscales (radiographic findings (0–21 points), functional limitation (0–23), joint mobility (0–7 points) and muscle atrophy (0–2 points).  
\end{table}
Table 1: Baseline characteristics of patients assessed.  
In the owner's pain assessment, significant differences were observed in the aMSCs and PRGF groups between baseline and 1, 3 and 6 months post-treatment, with no statistically significant differences between them.  
In the investigator assessment, significant differences were observed in both groups between baseline and all other follow-up time points. Comparing both treatments, there were only differences at 6 months post-infiltration, where patients treated with aMSCs showed more pain relief than those treated with PRGF.  
Figure 2: Changes in VAS from baseline value assessed by the owner (**A**) and the investigator (**B**). The changes in VAS which differed significantly with baseline (\(p<0.05\)) are marked as *. The circles, both green and blue, correspond to anomaly/outlier data included in the statistical study. The blue asterisk corresponds to an outlier, to which the Dixon Q test was applied in order to confirm deletion of the value from the database.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase II (Rescue): Signal Routing and Victim Extraction  
In a Search and Rescue, once the trapped person (or the target) is located, the MANet nodes reorganize themselves into a relay network to inform the base station of the target coordinates and call for help. In this phase, for computation purposes, the scattered MANet nodes can be treated like a graph \(G\): a data structure consisting of two sets \((V,E)\), where \(V\) is the set of vertices and \(E\) is the set of edges connecting any two vertices in the graph.  
A graph is usually represented as a diagram where vertices are symbolized as points and edges as lines connecting its end vertices [106].  
Graphs have two main traversal approaches: Depth First Search (DFS) and Breadth First Search (BFS). DFS approach starts graph traversing at an initial node, which is called a root, and then goes down one path (branch) till it reaches the end of it. Then, it backtracks until it reaches the root and chooses another path to explore it. This is repeated until the graph is fully traversed. On the other hand, BFS starts at an initial root node then moves layer by layer in the graph, where a node is explored first with its neighbors, then the node is explored in the next layer. These layers are called depth levels [106].
## 3 Results and Discussion
### Range Analysis and Analysis of Variance
#### 3.3.2 Range Analysis and ANOVA of Elastic Modulus  
In terms of the elastic modulus of the LPCs, the mean S/N ratio for each level of the experimental control factor is listed in Table 13, and its S/N response diagram is shown in Figure 11. From Table 13 and Figure 11, it is easy to see that the aggregate-to-binder ratio was the significant factor in controlling the elastic modulus. In this study, there were three levels of the aggregate-to-binder ratio. The first level was 6.2, the second level was 5.9, and the third level was 5.6. As the aggregate to binder ratio decreased, the elastic modulus decreased and then increased. In other words, the maximum value of the elastic modulus occurred when the aggregate-to-binder ratio was at the lowest level. This was because the smaller aggregate-to-binder ratio meant that the amount of cement paste required to adequately cover the granular skeleton could be obtained. Therefore, it helped to improve the elastic modulus of the LPCs. However, it can be observed from Figure 12 that the relationship between the elastic modulus and void ratio was highly dispersed. This showed that the trend between the elastic modulus and void ratio was quite different from other measurement results. The reason was mainly due to the dispersion, irregularity, and fluctuations that occurred with the collapse of voids and the rearrangement of aggregates during the test. Thus, the elastic modulus had great variability. Furthermore, the results of the ANOVA and \(F\) test for the elastic modulus are given in Table 14. From this point of view, the ratio of aggregate to binder was the most significant factor contributing to the elastic modulus.  
Figure 10: Relationship between compressive strength and void ratio.  
Figure 9: The S/N response graph for the compressive strength of the LPC.  
modulus of the LPC, with a contribution percentage of 67.84%. By examining the above analysis results, it can be seen that the optimal combination level of experimental control factors to achieve the maximum elastic modulus was A\({}_{2}\)B\({}_{3}\)C\({}_{2}\)D\({}_{3}\), i.e., the coarse aggregate size at level 2, fine aggregate content at level 3, matrix type at level 2, and aggregate to binder ratio at level 3.