esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### In Vivo Studies
[54] demonstrated that strontium incorporation into scaffolds significantly enhances bone regeneration in osteopororotic defects by modulating both phenomena. Similarly, [PERSON] et al. [31] demonstrated that hydroxyapatite biocearamics with micro-nano-hybrid surfaces and different strontium doping contents significantly promoted bone and blood vessel regeneration in rat models, finding a synergistic effect of surface modification and strontium doping on enhancing the osteogenic and angiogenic capacity of biocearamics. [PERSON] et al. [37] also identified an enhancement of angiogenesis while investigating strontium-substituted hydroxyapatite coatings for polyethylene terephthalate (PET) artificial ligaments used in ACL reconstruction, in a rabbit model of anterior cruciate ligament reconstruction. The study revealed that the incorporation of strontium not only enhanced the mechanical properties of the implants but also led to improved ligament-bone integration by modulating angiogenesis and osteoclastogenesis. The role of strontium in improving mechanical and physicochemical properties of CaPs was demonstrated by [PERSON] et al. [42], who showed that Sr-doped hydroxyapatite derived from clam shells exhibited improved mechanical properties, making it a promising candidate for bone tissue engineering and in vivo applications. [PERSON] et al. [43] further supported that Sr-substituted calcium phosphates improve mechanical strength and enhance osteogenic differentiation, showing significant improvements in mineral density and bone formation in rat femur defect models. [PERSON] et al. [46] indicated that their newly developed Sr-containing hydroxyapatite bone cement exhibited improved compressive strength along with a favorable biocompatibility. [PERSON] et al. [25] demonstrated that strontium-substituted hydroxyapatite coatings on magnesium alloys significantly controlled the degradation rate and enhanced bone regeneration in rabbit models. The study indicated that optimizing the Sr content in the coatings is crucial for balancing degradation behavior and osteogenic response in biomedical applications. Overall, the reviewed studies provide robust in vivo evidence from animal models that the incorporation of strontium into various calcium phosphate-based biomaterials enhances bone regeneration and mechanical properties while maintaining favorable biocompatibility. These findings underscore the potential of Sr-doped biomaterials for clinical applications in bone repair and regeneration.
## 7 Open Issues and Opportunities for Future Research
### Heterogeneity in the Managed Systems for Network-Cloud/Edge Convergence  
The converged network-cloud/edge systems in future networks comprise various network-compute-storage infrastructures that are highly diverse in management capabilities; for example, NFV MANO functions for network slices, host-/system-level management for MEC infrastructures, and VM/container orchestrators for cloud data centers. In addition, various infrastructure systems may employ different models for resource/service abstraction; for example, the YANG model is often used in SDN networks, while the TOSCA model is typically adopted for cloud data centers. Therefore, how to design a unified management architecture for the heterogeneous infrastructure systems in future networks is a challenging problem.  
Cloud-native network design based on service-oriented network virtualization certainly offers a promising direction for future research for addressing the heterogeneity issue. How to fully exploit the cloud-native principle to achieve optimal management architecture design still needs more thorough investigation. A possible research topic for the ENI architecture is service-based design for the API broker, which allows encapsulation of the diverse APIs of the managed systems in well-defined service interfaces that can be communicated via standard messaging protocols. For the ITU-T ML architecture, leveraging the latest progress in (micro)service composition and container orchestration for realizing the ML pipeline and MLFO offers interesting topics for future research.
# Numerical Study of Coolant Flow Phenomena and Heat Transfer at the Cutting-Edge of Twist Drill
## 3 CFD Model
### Research Methodology  
Figure 2 represents the geometry and the fluid model of one of the analysed twist drills. To focus on the coolant flow near the cutting-edge area, only a small section of the drill (10 mm of length) is modelled. The governing equations are solved in a given three-dimensional geometry using the finite volume method and commercial CFD software AVLFire 2017 is used for the simulations. The pressure-velocity coupling and correction is adopted in the CFD model using the SIMPLE algorithm. The second-order upwind scheme is used for the discretization of the convective terms.  
The fluid flow is analysed for four types of drills. These drills are the same in diameter (6 mm); however, they are different regarding the shape of the coolant channels (standard and profile) and their exits. For every simulation, the inlet boundary condition is set to the volume flow rate and the outlet is set to the pressure boundary condition. The rotation of the fluid is introduced in the CFD model by applying the moving reference frames (MRFs) approach and the rotational speed is set to 2000 rpm. The applied boundary conditions and number of elements of each model are listed in Tables 1 and 2, respectively. For analysing heat transfer, an approximate temperature of 450 \({}^{\circ}\)C, as usually occurs in practice, found from the literature, and suggested by industry (Sutton Tools), is set for the cutting-edge area and the cooling agent is supplied at a temperature of 27 \({}^{\circ}\)C.  
Figure 2: Model geometry (interpretation of CAD and fluid model).
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
###### Abstract  
The issue of unintentional islanding in grid interconnection still remains a challenge in grid-connected, Distributed Generation System (DGS). This study discusses the general overview of popular islanding detection methods. Because of the various Distributed Generation (DG) types, their sizes connected to the distribution networks, and, due to the concern associated with out-of-phase reclosing, anti-islanding continues to be an issue, where no clear solution exists. The passive islanding detection technique is the simplest method to detect the islanding condition which compares the existing parameters of the system having some threshold values. This study first presents an auto-ground approach, which is based on the application of three-phase, short-circuit to the islanded distribution system just to reclose and re-energize the system. After that, the data mining-decision tree algorithm is implemented on a typical distribution system with multiple DGs. The results from both of the techniques have been accomplished and verified by determining the Non-Detection Zone (NDZ), which satisfies the IEEE standards of 2 s execution time. From the analysis, it is concluded that the decision tree approach is effective and highly accurate to detect the islanding state in DGs. These simulations in detail compare the old and new methods, clearly highlighting the progress in the field of islanding detection.  
Keywords: islanding detection; distributed generation system; auto-grounding; data mining +
Footnote †: journal:
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 4 Case Study
### Base Learners  
The ensemble methods described in the \"Methodology\" section, including bagging, boosting, and stacking methods, were employed to predict flight delay. In addition to the RF algorithm and the AdaBoost algorithm, other well-known methods were also selected as candidates for base learners in the stacking model, and are briefly introduced here:  
* Linear regression (LR): LR is a classical regression method that uses a linear model to fit the relationship between features and target values.
* K-nearest neighbor (KNN): KNN uses the average of the closest K samples of a sample to represent its value. Manhattan and Euclidean distances are usually used.
* Support vector regression (SVR): SVR involves the application of a support vector machine (SVM) for regression problems. It works by locating a hyperplane so that the distance from the hyperplane to the farthest sample point is minimized.
* Light gradient boosting machine (LGBM): LGBM is a framework for implementing the gradient boosting decision tree (GBDT) algorithm, which supports efficient parallel training and has faster training speed, lower memory consumption, and better accuracy.
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 3 Results and Discussion
### Text Detection  
The evaluation metrics employed in text detection are recall, precision, and F-measure [11]. We compare the EAST model pre-trained on ICDAR 2013 and ICDAR 2015 with a fine-tuned version, trained on the SINATRA augmented data. The detailed results can be observed in Table 4.  
In general, the fine-tuned model performs better than the pre-trained model, as denoted by an improvement in the F-measure of, approximately 21% and 3% for UC and FM, respectively. Recall greatly increases for both equipment after the model is introduced to images of this domain, meaning that this model is more capable of retrieving the correct regions. This improvement in performance was expected as the domains of these datasets present different characteristics: ICDAR datasets contain natural scene images, whereas SINATRA is composed of images of digital screens, which are luminous and hence more susceptible to glare. The results presented are on par with the capabilities shown by the EAST model on the benchmark ICDAR 2015, where the proposed algorithm achieves an F-measure of 78.20% [11]. However, our implementation uses ResNet-50 rather than PVANET [22].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training** & **Equipment** & **Recall** & **Precision** & **F-Measure** \\ \hline \multirow{4}{*}{EAST} & Pre-trained on ICDAR & UC & 41.68 & 63.11 & 50.20 \\  & 2013 and 2015 & FM & 70.82 & 79.08 & 74.73 \\ \cline{2-5}  & Fine-tuned on & UC & 73.85 & 68.74 & 71.21 \\ \cline{1-1}  & SINATRA & FM & 86.57 & 70.54 & 77.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for text detection module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Model** & **[EMAIL_ADDRESS]** & **mAP@[0.5, 0.95]** \\ \hline \multirow{2}{*}{UC} & UC-specific model & 17.15 & 9.78 \\  & Equipment-agnostic model & 73.95 & 59.61 \\ \hline \multirow{2}{*}{FM} & FM-specific model & 46.52 & 31.16 \\  & Equipment-agnostic model & 62.39 & 38.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for screen bounding box detection module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **IoU** & **ED** \\ \hline UC & \(94.77\pm 3.39\) & \(7.63\pm 6.38\) \\ FM & \(92.58\pm 3.55\) & \(6.79\pm 4.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for screen keypoint detection module.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 4 Testbed
### Configuration  
From a performance point of view, FIWARE recommendations were taken into account to refine the following testbed components: (1) MongoDB; (2) Orion and (3) Cygnus-NGSI. The most important aspects of configuration are as follows:  
Figure 4: A testbed to evaluate the performance of the FIWARE platform.  
First, we used version 3.0.12 of MongoDB, recommended for intensive update scenarios. In addition, the adjustments limiting use to deployed VM resources were checked and _Transparent Huge Pages_ were disabled (THP) [59].  
Second, Orion parameters were adjusted for scenarios with high update/notification rates (_-reqPoolSize_, _-dbPoolSize_ and _-notificationModelHeadPool:q:n_) [59]. These parameters were adjusted for each test.  
Third, Cygnus-NGSI components were adjusted to increase performance and avoid potential bottlenecks in order to properly assess Orion. To achieve this we opted for an HTTP source where the _OrionRestHandler_ was configured to convert NGSI events from Orion into Flume events. Concerning channel design, we chose _Memory Channel_ because of its performance when implemented directly in memory. This type of channel is ideal for flows that require increased performance and are willing to lose data in the case of Flume agent errors. Finally, we chose an _Orion HDFS Sink_ whose performance could be enhanced by using the _batching_ mechanism to reduce the number of writes in the HDFS storage configured in Cosmos. This mechanism is configured in each simulation through batch size and batch_timeout parameters [60].
# Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing
## 3 Results and Discussion
### Static Experiments
#### 3.1.3 Comparisons of Experimental and Simulation Results  
For the experimental results, Figure 7 shows a maximum size of 1.2 mm for the melt pool width. Comparing Figures 7 and 9, it is observed that the simulation that best fits this value was obtained with a constant absorption coefficient equal to 0.35 (\(a_{s}=a_{l}=0.35\)). However, we will see below that this curve is probably not the one to consider for the comparison.  
In the experimental solution, the melt pool width seems to reach a constant value, which is not the case for the simulation results. However, this effect is less visible when using a smaller absorption coefficient. In Figure 12, a comparison between experimental and simulation results (two different absorptions) for the time evolution of the melt pool during the heating part and during the cooling part are given. From this figure, it is obvious that after 70 ms, the width predicted is significantly lower than the measured one (between 20 and 50% lower). If a comparison is made for the cooling part, it seems evident that the simulation curve that best fits the experimental results corresponds to an absorption of \(\approx\)25% in the liquid. An explanation could be that we chose a weakly coupled model and so neglected the influence of the convection in the liquid in the thermal model. This explanation is supported by the fact that effects of \"wetting\" are observed during the experiment, which prove that convection movements are important. Finally, the precision of the experimental measurement is \(\pm\)0.1 mm.  
Static experiments with relatively long laser illuminating times are more difficult to simulate than regular polishing experiments (moving laser) (see Sections 3.2 and 3.3). The reason is that the convection effects which are not taken into account in the model become more important in the static context.  
The heating and cooling rates are well captured by the model. In other words, the development of the melt pool follows a similar growth as the measured values. The solidification predicted by the model is a little slower (see the slope of curves in the inset of Figure 12), but this is also consistent with a narrower melt pool. Indeed, if the convection is the reason for a larger melt pool, it will also mean that the real melt pool is not as deep as the model. Ex situ observations of the cross section of the workpiece have shown that the depth of the melt pool measured is only 90 \(\upmu\)m whereas the modelled depth is \(\approx\)117 \(\upmu\)m (a difference of \(\approx\)25%) for the results with \(a_{l}=25\%\). It is also known that a flatter melt pool will cool faster than a narrower but deeper melt pool due to an increased surface area.  
Figure 11: Melting pool diameter and depth evolution for various absorption coefficients (zoom in the cooling part).
# Siamese Tracking with Adaptive Template-Updating Strategy
## 4 Experiments and Results
### DARPA Dataset Results
#### 4.2.2 Quantitative Evaluation  
We calibrated and recorded the result deviations of SiamFC, our Siamese tracking network, and our Siamese tracking network with a template-updating strategy, respectively. In this set of experiments, we only compared the centering error of the different methods in each video sequence because, in our application, center precision was the most significant and could have had serious consequences. We calculated the centering error between the tracking results and the calibrated ground-truth centers every 10 frames for the three different methods, outlined in Figure 12. It is worth noting that the target size in the first four video sequences was about 20 to 40 pixels, and in the video sequence, 5 was about 100 pixels. In most frames, the central error of our method was less than 10 pixels. This deviation could be tolerated as long as the detected target center was still in the actual target location. When faced with challenges such as occlusion or deformation, our Siamese tracking with adaptive template-updating strategy had the least mistakes during the tracking process. The'success of updating templates in our latest method generated some spikes that can be seen in the results of video sequences 3 and 5. Overall, the average centering error of our latest method was much lower than that of the others.  
Figure 11: Results on images in datasets taken by the Defense Advanced Research Projects Agency (DARPA): (**a–f**) are six groups of comparison results on the DARPA dataset between the Siamese tracking methods without or with adaptive template-updating strategy. Bottom image in each group, tracking result in origin frame; top left, cropped image showing partial enlarged detail of tracking result; top right, corresponding heat map denoting similarity. Green boxes, ground truth; red, tracking results.  
Figure 12: _Cont._
# Safety-Oriented System Hardware Architecture Exploration in Compliance with ISO 26262
## 4 Safety-Oriented System Hardware Architecture Exploration Framework  
In this study, we propose a safety-oriented system hardware architecture exploration framework whose goal is to achieve a system hardware architecture that complies with the ISO-2626 safety metrics and the hardware overhead constraint simultaneously. Figure 2 exhibits the overall flow of the proposed framework.  
Figure 1: ISO 26262 fault classification and failure rate calculation process.  
points of the system, by comparing their failure probabilities to the target failure probability for the required ASIL. The details for this analysis are illustrated in Section 4.2.
2. ASIL-oriented hardware architecture exploration algorithm.  
In last phase, the hardware elements identified as weak points have been listed. Thus, the safety mechanisms need to be deployed to those hardware elements to reduce their failure rates. The issue for this phase is proposing an effective measure to evaluate the extent of failure rates reductions that are required to achieve the target ASIL goal and then recognize which safety mechanisms are sufficient for the identified hardware elements. The proposed ASIL-oriented hardware architecture exploration, which is introduced in Section 4.3.2, can aptly address the safety issue to be solved.
3. HO-oriented hardware architecture exploration algorithm.  
In phase ii, the safety mechanisms are used to reach the target ASIL goal without considering the cost of hardware overhead induced from the deployed safety mechanisms. Thus, the additional hardware overhead could cause the overall system hardware cost to exceed the acceptable upper bound. To address this issue, we propose a hardware overhead (HO)-oriented hardware architecture exploration methodology. Through this methodology, the system hardware architecture with safety mechanism deployment derived from the previous phase is analyzed to recognize the bottleneck when the additional hardware overhead exceeds the constraint specified by the system engineers. Accordingly, the system hardware is adjusted to meet the safety metrics and hardware overhead constraint simultaneously. More details can be found in Section 4.3.3.  
After the safety-oriented system architecture exploration is accomplished and both the safety metrics and hardware overhead constraint are conformed, the final system hardware architecture can be obtained with additional hardware costs invested. Besides, the corresponding FMEDA report is also generated to provide more detailed information for the designers. The effectiveness of the proposed framework is demonstrated with an autonomous emergency braking system design as described in Section 5.  
In the following subsections, we will introduce the proposed safety-oriented system hardware architecture exploration framework and illustrate the details of the exploration process through a simple example.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Genie Channel  
We added an optional genie channel subsystem to WISCANet in [73]. A genie channel, or perfect (near) instantaneous feedback channel, provides a mechanism to pass around information without concerns for constraints or the feasibility of a communications link. The control node provides a Network File System (NFS) directory share that is mounted on all edge nodes. This directory is accessible to the running baseband software in real time. This enables baseband software to share data through files placed in that directory. Baseband software can also wait for the existence of a file to proceed with the next cycle. This enables the researcher to simplify the design of feedback channels for experiments, as well as accurately impose constraints on the feedback, such as delay, error rate and more, while still allowing the system under test to operate over the air.
# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 3 Experiments and Discussions
### Experiments and Results Analysis
#### 3.2.2 Quantitative Evaluation
\begin{table}
\begin{tabular}{c|c|c c c|c c c|c c c|c} \hline \multirow{3}{*}{**Approach**} & \multirow{3}{*}{**Behaviors 1**} & \multicolumn{8}{c|}{**Measures**} & \multirow{3}{*}{**Average**} \\ \cline{3-11} \cline{3-11}  & & & \multicolumn{2}{c|}{**0.5 s**} & & \multicolumn{2}{c|}{**L0 s**} & & \multicolumn{2}{c|}{**1.5 s**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-11}  & & **prc** & **F1** & **G-Mean** & **prc** & **F1** & **G-Mean** & **prc** & **F1** & **G-Mean** & **Time (s)** \\ \hline \multirow{3}{*}{A} & LK & 0.99 & 0.97 & 0.97 & 0.78 & 0.86 & 0.84 & 0.68 & 0.79 & 0.73 & – \\  & LCR & 0.98 & 0.98 & 0.98 & 0.97 & 0.85 & 0.86 & 0.97 & 0.76 & 0.78 & 2.10 \\  & LCL & 0.97 & 0.98 & 0.96 & 0.96 & 0.82 & 0.83 & 0.94 & 0.60 & 0.66 & 1.85 \\ \hline \multirow{3}{*}{B} & LK & 0.94 & 0.88 & 0.87 & 0.75 & 0.79 & 0.77 & 0.54 & 0.65 & 0.49 & – \\  & LCR & 0.88 & 0.93 & 0.92 & 0.84 & 0.79 & 0.80 & 0.75 & 0.53 & 0.59 & 1.34 \\ \cline{1-1}  & LCL & 0.96 & 0.93 & 0.93 & 0.95 & 0.81 & 0.83 & 0.75 & 0.20 & 0.33 & 1.16 \\ \hline \end{tabular}  
* \({}^{1}\) LK: Lane Keeping; LCR: Lane Change to Right; LCL: Lane Change to Left.  
\end{table} 
Table 4: Performance of the two approaches for time horizons of 0.5 s, 1.0 s, and 1.5 s.