esa-sceva/satcom-chunk-collection huggingface dataset:
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 6 GitHub Actions CI/CD Pipelines  
Automation is the cornerstone of modern DevOps practices, providing the repeatability, reliability, and auditability required for deploying cloud infrastructure at scale. GitHub Actions has been chosen as the CI/CD platform for orchestrating the entire deployment lifecycle of the application and its Azure infrastructure. To further illustrate the operational benefits of infrastructure as code and automation, it is helpful to compare performance metrics between traditional manual deployments and the automated deployment pipeline implemented in this project. The section extends beyond operational description by offering empirical data for evaluating automation efficiency, comparing deployment time, reliability, and manual error rates, thus contributing to reproducible cloud engineering research.  
Manual deployment, typically performed through the Azure portal or CLI tools, requires a series of repetitive and error-prone tasks such as manual configuration, environment setup, dependency resolution, and verification. In practice, this process can take up to a few full working days to complete, depending on the complexity of the infrastructure and the availability of skilled personnel. In contrast, the automated pipeline developed using Azure Bicep and GitHub Actions executes the same end-to-end deployment in approximately 15 min, with no human intervention required. This time reduction to just 15 min demonstrates the dramatic efficiency gains enabled by IaC and CI/CD practices.  
In terms of reliability and consistency, manual processes are also susceptible to configuration drift and deployment failures. Across several real-world scenarios, it was observed that manual deployments often result in misconfigured environments or incomplete resource provisioning, with an estimated failure or deviation rate of approximately 30%. These issues typically arise due to skipped steps, inconsistent parameter values, or differences between development and production environments. On the other hand, automated deployments executed through declarative templates and version-controlled workflows demonstrated a 1% failure rate during repeated execution. The use of Bicep ensured that infrastructure definitions were deterministic and idempotent, reducing the possibility of human error and enabling predictable outcomes across all environments.  
This comparison highlights the tangible advantages of infrastructure automation, not only in terms of deployment speed but also in reducing operational risk, improving reproducibility, and supporting continuous delivery on a large scale.  
This section provides a detailed description of the design, implementation, and operational principles of the two primary workflows used in the solution: deployment and deletion. It also examines the prerequisites for configuring secure access to Azure and details how secrets are managed within GitHub to enforce security best practices [28].
## 3 Computational Methodology  
Figure 4 presents the simulation process to identify the merge of passive and active technologies to improve the building's energy performance. This methodological approach is comprised of six stages. The first phase consists of developing a 3D model based on the construction properties of the medical center and the information gathered from the energy audits. Subsequently, a calibration stage is executed where the model's electric consumption simulations are contrasted with the grid analyzer measurements. Once medical center calibration was carried out, passive building technologies were evaluated to improve the building's energy performance. In the same way, active systems are integrated with high-efficiency air conditioning to maximize energy savings. At this stage, various configurations were analyzed, looking for the optimal combination for the location. Finally, in the fifth and last stage, the energy performance index (according to the ISO 50001:2018 standard) pollution mitigation, and the techno-economic feasibility of investment are contrasted through a decision-making tool to quantitatively identify the trade-off where the three indices converge. A more detailed description of the six phases is provided in the following subsections.  
Figure 3: Summary of the results of the energy audit for the ambulatory medical center: (**a**) monthly baseline of energy consumption and equivalent CO\({}_{2}\) emissions, (**b**) electricity consumption associated with air conditioning equipment, (**c**) electricity consumption associated with lighting equipment.  
Figure 4: Computational methodology implemented to improve thermal performance in a medical center under hot-humid climate conditions.
## 3 Experiments and Results  
### Experimental Design  
In this section, we describe the experimental design for k-means clustering and decision trees as core methodologies for the proposed PPRE analysis system. First, we briefly explain the results of data preprocessing and statistical methodologies. The total number of records (frames) is 4035 frames (spot A: 2635 frames and spot B: 1400 frames). Through preprocessing, we removed outlier frames based on extreme feature values, yielding 2291 and 987 frames, respectively. We then conducted [0, 1] normalization on the features as follows:  
\[\hat{d}_{i}=\frac{d_{i}-min(d)}{max(d)-min(d)} \tag{13}\]  
Prior to the main analyses, we conducted statistical analyses such as histogram and correlation analysis. Figure 6(a)-f illustrated histograms of all features and each feature, respectively. VV and PV features are skewed low since almost all cars and pedestrians stopped or moved slowly in these areas. Averages of VV and PV are at about 24.37 and 2.5 km/h, respectively. When considering speed limits are 30 km/h in these spots, and the average person's speed is approximately 4 km/h, these are reasonable values. DVP (distance from vehicle to pedestrian) shows two local maxima: One for pedestrians ahead of the car, and one for behind.  
Next, we can study the relationships between each feature by performing correlation analysis. Figure 8a,b represents correlation matrices in spots A and B. In spot A, we can observe negative correlation between VV and DVP. This indicates that vehicles tended to move quickly near pedestrians, indicating a dangerous situation for the pedestrian. In spot B, there is negative correlation between PV and DVP, which could be interpreted in two ways: Pedestrians moved quickly to avoid a near-miss by an approaching car, or pedestrians slowed down or stopped altogether to wait for the car to pass nearby. Since we extracted only video clips containing scenes where at least one pedestrian and one car were in the camera view at the same time, DVP and DVC have positive correlation.  
Figure 8: Correlation matrices in (a) spot A; and (b) spot B.  
Figure 7: Histograms of (a) all features; (b) VV; (c) VA; (d) PV; (e) DVP; and (f) DVC.  
For our experiment, we conducted both quantitative and qualitative analyses. In the quantitative analysis, we performed k-means clustering to obtain the optimal number of clusters (_K_). Each clustering experiment was evaluated by the SSE depending on \(K\) between 2 to 10, and through the elbow method, we chose the optimal \(K\). Then, we used the decision tree method to validate the classified dataset with a chosen \(K\). The proportion of training and test data were at about 70% (2287 frames) and 30% (991 frames), respectively. In the qualitative analysis, we analyzed each feature and its relationship with multiple other features by clustering them. Then, we analyzed the rules derived from the decision tree and their implications for the behavior and safety at the two sites.
## 4 Results and Discussion  
### Comparative Evaluation with Relevant UAVs  
As demonstrated in Table 2, the proposed novel amphibious inspection UAV exhibits significant advantages over comparable small-scale UAV systems in both maximum payload capacity and endurance at maximum takeoff weight (MTOW). Moreover, the designed UAV achieves a commendable maximum demonstrated speed, with its high maneuverability proving sufficient for rapid deployment in emergency scenarios requiring expedited flight operations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UAV Model** & \begin{tabular}{c} **Novel Amphibious** \\ **Inspection UAV** \\ \end{tabular} & \
\begin{tabular}{c} **Small Electric** \\ **UAV** [38] \\ \end{tabular} &
\begin{tabular}{c} **Black Widow Micro** \\ **Air Vehicle** [39] \\ \end{tabular} \\ \hline Max Payload (g) & 1000 & 800 & 80 \\ Endurance Speed (m/s) & 10 & 15 & 5 \\ Endurance at MTOW (min) & 40 & 10 & 30 \\ Maximum Demonstrated Speed (m/s) & 18 & 20 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative study with analogous small unmanned aerial vehicles.  
Figure 12: Network structure of YOLOv5s-Ghost.  
As detailed in Table 3, the comparative analysis of cage-equipped UAVs reveals distinct operational characteristics. References [18; 19] present systems designed for central HVAC ducts inspection, capable of both terrestrial locomotion (sliding/rolling) and aerial flight, incorporating thermal cameras. Reference [20] introduces a UAV with a compliant-circuit hybrid 3D-printed safety cage, exclusively flight-capable and requiring manual control, equipped with a 2D camera. Similarly, reference [22] describes a safety-focused cage-equipped UAV limited to flight operations under human supervision, also utilizing a 2D camera. Reference [23] deploys a cage-equipped UAV for an antenna reliability assessment, restricted to flight with manual control, and employing printed antennas. Critically, all the aforementioned cage configurations induce sensor obstruction. In contrast, our mechanically optimized design structurally circumvents this limitation through a depth camera enabling dual-mode infrared and visible-light detection, supporting terrestrial-aerial multimodal operation.
## 4 Kinematic Control of a CDHRM  
In a CDHRM, each joint is controlled by three cables, but with the motions of two DOFs (yaw and pitch in the axial direction). Besides, the number of the manipulator's DOFs is higher than end-effector's DOFs with the increasing of joint numbers according to specific task requirements. Therefore, a CDHRM has the characteristic of hyper-redundancy, which makes it difficult to model and control. Moreover, there is an error between the practical kinematics and the theoretical kinematics of the CDHRM when working in typical confined underwater scenarios (e.g., pipe networks, narrow deep cavities, etc.).  
To achieve good tracking performance of the desired task trajectory in some typical underwater scenarios, a control scheme of a CDHRM is designed in this section, which is shown in Figure 5. In details, the desired angles \(\alpha_{di}\) and \(\beta_{di}\) are calculated by the joint-end kinematics (Figure 4) with regard to the desired end-effector's pose \(\hat{X}_{d}\), which can be planned with the desired trajectory \(X_{d}\) in the practical task. The desired cable length \(l_{dr}\) is calculated by the cable-joint kinematics (1)-(6) with \(\alpha_{di}\), \(\beta_{di}\). The practical cable length \(l_{r}\) is measured by the linear magnetic encoder mounted on the driving subsystem. Thus, a closed-loop controller \(u_{r}\), where the actuators are working in the speed-loop, can be designed as follows:  
\[u_{r}=K_{p}\left(l_{dr}-l_{r}\right)+K_{c}\int_{0}^{t}\left(l_{dr}-l_{r} \right)dt+K_{d}\left(l_{dr}-l_{r}\right) \tag{8}\]  
Figure 4: Inverse kinematics of CDHRM.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Industrial Automation and Smart Manufacturing  
The integration of edge-cloud computing in industrial automation enhances predictive maintenance, robotic coordination, and process optimization, significantly improving production efficiency. Edge nodes facilitate localized decision-making by continuously monitoring machine performance, detecting anomalies, and initiating preventive measures to reduce downtime. AI-enhanced fault detection systems ensure that deviations in operational parameters trigger immediate corrective actions, minimizing financial losses [122; 123].  
Real-time quality control benefits from edge-based vision systems, which identify product defects through high-speed image analysis and AI-assisted pattern recognition. Cloud integration enhances process optimization by aggregating quality metrics across multiple production lines, refining models for defect prediction and performance improvement. The ability to balance real-time processing at the edge with comprehensive cloud analytics ensures optimal manufacturing workflows [124; 125; 126].  
Collaborative robotics, or cobots, rely on edge intelligence for distributed control, ensuring synchronized operations in automated assembly lines. Real-time data exchange between robotic agents enables adaptive task execution and improves efficiency in dynamic production environments. The integration of industrial AI, cloud-driven analytics, and distributed edge computing creates highly flexible and autonomous manufacturing ecosystems [127; 128].
## 1 Introduction
### Contributions of This Study  
This study aims to provide a comprehensive survey of the existing drone routing algorithms for drone-based delivery by covering the dominant aspects, design issues, and research challenges. The major contributions of this study are summarized as follows:  
* First, we discuss several crucial design issues of drone routing in drone-based delivery systems. This discussion includes the important issues for implementing such systems.
* Next, we provide a novel taxonomy where we emphasize three major aspects: trajectory planning, charging, and security algorithms for designing drone routing algorithms in drone-based delivery systems.
* To provide the readers with an overview of the current progress of drone routing in drone-based delivery systems, the existing drone routing algorithms are extensively reviewed in terms of their design model, optimization objective, assumptions, and operational strategy.
* Further, we compare the drone routing schemes in terms of their main idea, optimization objective, constraints, advantages, limitations, and performance.
* Finally, important research and development challenges are discussed to motivate further research in this domain.
## 4 Requirements
In such cases, the millisecond-order latency should be ensured to allow the collection of near-RT location data samples (for 160 km/h, \(\sim\)20 ms is needed to provide 1 m accuracy). In addition, the reliability of transmission has to be high in order to deliver RT accurate positioning. The creation of orthophotomaps (UC3) requires a very high degree of image overlap: 30% inter-row and 60-80% intra-row [88]; for a 42.4-megapixel photogrammetric camera with 14-bit coding, 1 cm per pixel, and 80% linear overlap, the uncompressed data volume \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Parameters** & & \\ \cline{3-6}
**Use Case** & **Exchange** & **Exchange** & **Data** & **Max.** & **Reliability** \\  & **Intensity** & **Type** & **Rate** & **Delay** & & \\ \hline UC1 & high & stream & 2.4 kbps & — & high \\ \hline UC2 * & low & burst & \(\leq\)1 kbps & — & low \\ \hline \multirow{3}{*}{UC3} & S1: photogrammetry * & high & burst & \(\sim\)Gbps & 200 ms & low \\  & S2: local sensing & low & burst & \(\leq\)1 kbps & — & low \\ \hline UC4 & high & burst & \(\sim\)Gbps & — & low \\ \hline UC5 * & low & burst & \(\leq\)1 kbps & — & low \\ \hline UC6 & low & burst & \(\leq\)1 kbps & — & low \\ \hline UC7 & high & stream & 120 Mbps & \(\sim\)ms & high \\ \hline UC8 ** & high & stream & 120 Mbps & 20 ms & 99.99\% \\ \hline UC9 * & low & burst & \(\sim\)Mbps & — & low \\ \hline UC10 & high & burst & \(\sim\)Mbps & \(\sim\)ms & very high \\ \hline \multirow{3}{*}{UC11 **} & S1: C2 & high & stream & 28 kbps & 40 ms & 99.9\% \\  & S2: UTM & low & stream & \(\sim\)kbps & 500 ms & 99.9\% \\  & S3: FPV, video streaming & high & stream & 120 Mbps & 20 ms & 99.99\% \\ \hline UC12 ** & high & stream & 65 Mbps & 20 ms & 99.99\% \\ \hline UC13 ** & high & burst & 1.1 Gbps & 2 ms *** & 99.9\% \\ \hline UC14 ** & high & stream & 0.1–1 Gbps & 10 ms & 99.99\% \\ \hline \hline \end{tabular}  
* Requirements calculated using sector-specific data-source characteristics [58]. ** Requirements derived from the 3 GPP standardization documents [83, 84, 85]. *** Achievable in low-area campus networks only.  
\end{table} 
Table 2: Peak service requirements for the analyzed use cases. is about 9.5 gigabytes per hectare per band. In the case of UC4, the volume of data is on the order of hundreds of megabytes per hectare [89]. The required data rate, however, will be largely dependent on the imaging configuration, such as the image resolution or a number of frequency bands in which the LiDAR image is taken (aggregate volume on the order of multiple gigabytes per hectare). Finally, it has to be noted that the AFBW use cases will be generally implemented in distant rural areas or span very wide areas. To this end, the provision of coverage and connectivity extension to the areas of concern can result in immense costs (building regular infrastructure, networking, provision of computation power, energy supply, etc.) and could be a major obstacle to the implementation of the given use cases.
## 4 Probabilistic Analyses
### Non-Linear Finite Element Analyses
#### 4.8.3 Probabilistic NLFEM-Modeling
_U-MINHO:_ University of Minho, Institute for Sustainability and Innovation in Structural Engineering (ISISE). It can be seen from the trend of the mean values (Figure 12b-d) that the maximum values of the normal force from STUBA, UNIZG-FCE and U-MINHO are above the experimental values. The STUBA analyses (PDF) with regard to the moments associated with the normal forces show, as for BOKU, a failure well before the \(I\)-\(D_{m}\) on the mean level, see Figure 12b on the right. Such an underestimation of moment capacity was not seen in the simulations of the partners UNIZG-FCE and U-MINHO, see Figure 12c,d. Therefore, we can conclude that BOKU, UNIZG-FCE and U-MINHO came to completely different results for moment distribution. The probabilistic analyses of most of the partners showed that the calculated normal force mean values are significantly higher than the experimentally determined ones, as can be seen from the box whisker plots (P-NLFEM results in pink; experimental results in blue) and the histograms of Figure 12. In these histograms of the normal forces it can also be seen that these differences are smaller in the lower fractile ranges. By considering a model uncertainty of 1.10, an agreement between the normal force mean values of the P-NLFEM and the experiments can be found, see the graphs in the middle column of Figure 12. Consequently, the lower P-NLFEM fractile values shift below the experimental ones, which is on the safe side. Since these deviations appeared in all simulations of the partners and errors can be excluded in the experiments due to the high quality controls, it is important to clarify which effects in the NLFEM lead to this overestimation of the column performance. Considering the normal force maxima of the P-NLFEM and the experiments, see Figure 12a-d, it can be seen that the distance in the corresponding moments (at half column height) to moments of the EN1992-1 interaction diagram (_I-D_) at the mean value level are significant. These experimental and P-NLFEM detected column collapses in front of or to the left of the _I-D\({}_{\text{mean}}\)_ according to EN1992-1 indicate a possible defect and possibly safety problems in the _I-D_ formulations too, in particular for the examined columns geometry. Figure 12: Cont.
###### Abstract  
The outage performance is a significant problem to implement the Cognitive Radio (CR) paradigm in the Vehicle to Everything (V2X) networks. Recently, more interest has focused on Non-Orthogonal Multiple Access (NOMA) in wireless-powered communication. In the conventional CR-enabled V2X-NOMA network, spectrum sensing and limited battery capacity at the Roadside Unit (RSU) may cause serious outage performance. In this study, RSU selection scheme is adopted. This paper presents an interesting model of a system with Simultaneous Wireless Information and Power Transfer (SWIPT) and a CR-enabled V2X-NOMA network. In the downlink, the RSU harvests wireless energy from Radio Frequency (RF) signals and senses the spectrum state at the same time. A CR-enabled V2X-NOMA system performance is presented by deriving exact expressions of outage probability of distant vehicles. In the overlay CR-enabled V2X-NOMA network, the constraints are transmit power and the number of designed RSU that make significant impacts on system performance. Simulation results show that the CR-enabled V2X-NOMA get benefits from energy harvesting and RSU selection scheme.  
cognitive radio; NOMA; energy harvesting; vehicle to everything +
Footnote †: journal: Sensors
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 1 Introduction
### Contribution  
To overcome the aforementioned challenge, a MIMO-based energy-efficient unequal hybrid clustering (MIMO-HC) routing protocol for IoT communication systems is proposed. In this method, data from the sensing area can be collected methodically, in which each sensor node must send the collected data to the central station in each round. Specifically, this article has the following major contributions.  
1. We propose three algorithms for Cluster Head selection, hybrid cluster formation, and inter-cluster multi-hop routing. In particular, in the centralized-based MIMO-HC protocol, the central station determines each cluster's head to reduce message transmission overhead. Furthermore, the proposed MIMO-HC divides the network hierarchically into unequal hybrid clusters to share the energy consumption among the cluster heads.
2. Clusters closer to the central station have small radius and their member nodes communicate with each other using chain topology to minimize the issue of overcrowding and collisions at cluster head. However, clusters that are farther away from the central station have a larger radius and their member nodes communicate with their respective cluster head using a single-hop topology to reduce delay when delivering data from source to destination.
3. A probability sub-optimal multi-hop routing mechanism among cluster heads is proposed to reduce the load on the cluster heads and to increase the lifespan of the sensor network. Finally, a ranking method is applied for choosing the proper communication interface.
4. We evaluate with existing techniques (namely UN-LEACH [11]), and the proposed protocol has a much better network lifespan. For example, in the proposed protocol, the first node and the final node die about 3 times as long as compared with the existing protocol. Moreover, samples of energy consumption by cluster heads have a standard deviation of \(1.07\times 10^{-2}\) in the proposed protocol and those in the UN-LEACH have \(3.16\times 10^{-2}\). In other words, the proposed protocol has a better load balancing capability than the UN-LEACH protocol.