esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
## 2 Materials and Methods
### Data Preparation and Processing  
Class selection and data collection are important to increase the accuracy of object search by training the model. The F11 4K PRO was used as the drone for filming. It has an adjustment distance of 10 m and a Wi-Fi image distance of 100 m. It is also suitable for object detection because it supports 4k camera image quality. According to the purpose of the study, the classes were designated as objects that are confusing to distinguish. Therefore, person, car, and notice were set as Classes, and the distance from the object was divided by less than 10 m: Low altitude and more than 10 m: High altitude. In addition, we took photos in various environments by changing the altitude of the drone, surrounding background, and weather. The shooting was conducted in the mountain and a downtown area, at low light: Evening and Night. In addition, it was filmed while changing the altitude of the drone. This is caused to create an environment where it is confused to identify objects.  
Additionally, drone photographs were added from VisDrone ([[http://aiskyeye.com](http://aiskyeye.com)]([http://aiskyeye.com](http://aiskyeye.com)), accessed on 5 June 2021) [38] to collect more diverse data. VisDrone is a dataset used annually for object detection using drones and is very reliable [39]. This is to increase the accuracy of the experiment through reliable data combinations. In the VisDrone dataset, only data photographed above 10m: High altitude were added to meet the existing data and standard. Figure 8 shows the samples used in the experiment. In the final dataset used in the experiment were 2080 images: Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, High altitude in training, 960 images: Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, High altitude in validation, and 320 images: Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, and High altitude in testing, prepared a total of 3360 images: Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, and High altitude. Details are summarized in Table 1.  
The collected data were then labeled from the online platform makesense ([[http://www.makesense.ai/](http://www.makesense.ai/)]([http://www.makesense.ai/](http://www.makesense.ai/)), accessed on 14 July 2019) [40]. As shown in Figure 9, the label was created as three objects: person, car, and notice and annotated, and the annotated image was converted to a txt format according to the YOLO format.  
Figure 8: Images of various experimental conditions captured by drone.
## 4 Integrated Signal Performance Metrics from Simulation Experiments
We can see that the quality of transmitted data can already be improved significantly at an SNR of 15 dB. Figure 12: The target echo after pulse compression. (**a**) Radar echoes of five targets; (**b**) radar echo from a single target. Figure 13: Comparison of communication BER performance under different methods [19]. However, the system proposed in this paper still has some limitations. First, the integrated signal uses a cyclic prefix and a virtual carrier. Since they are not used for transmitting data, the spectral efficiency will be reduced. Second, when facing high-speed moving mobile devices, the OFDM system has certain limitations in Doppler resistance, potentially leading to performance degradation due to the frequency offset phenomena. Third, although chirp signals have good autocorrelation performance and can suppress  
Figure 14: Comparison of error vector magnitude (EVM) values for different scenarios. Figure 15: The comparison of the transmitted image quality at different SNR. (**a**) Image to be transferred; (**b**) image received at SNR = 5 dB; (**c**) image received at SNR = 5 dB; (**d**) image received at SNR = 15 dB. a certain degree of interference, such as non-target scattering and clutter, multi-target interference, time bias, and speed processing, they still need to be paid attention to in practical applications. Therefore, in order to meet different application scenarios and needs, more advanced processing techniques and a more flexible LDPC coding structure [29] are needed. Finally, we will continue to consider the trade-offs [30] between communication and sensing performance in different operating environments, such as urban and rural areas, in future research.
## 2 Materials and Methods
### Study on an Anthropomorphic Phantom for Electromagnetic Experiment  
The anthropomorphic phantom plays an important role in the electromagnetic field exposure measurement. An ideal phantom is supposed to accurately simulate the anatomy of the body part it represents in detail, though it is impractical to achieve this goal. Therefore, it is necessary to define and standardize properties, dimension, and material characteristics which can affect SAR measurement.  
The following two rules for standardization were adopted: (1) Since SAR is mainly associated with permittivity, conductivity, and density of tissue, tissues with consistent or similar electromagnetic parameters are combined; (2) considering the fluidity and heat conductivity of body fluids, tissues in the same circulatory system are combined. In view of the basic rules above, the human tissues were classified into skin (fat), muscle, and organ, among other types. The human body shape, as well as the shape, size, and location of each tissue, were described with voxels of different colors. Take practicability into consideration, an electromagnetic experimental model consisting of an eccentric cylinder of three or four layers and plug-in components was adopted to simulate normal tissues of the human body. The simplified procedure is shown in Figure 1. To be specific, the anthropomorphic phantom had three parts, i.e., head, chest and abdomen, legs, and six kinds of organs were simulated. Main dimensional parameters of the phantom were: Height 170 cm, head diameter 22 cm, shoulder width 48.5 cm, arm length 55 cm, leg length 75 cm. Main organs and tissues simulated were: Brain, muscle, heart, lungs, stomach, kidneys. The internal volume of the brain was 1500 cm\({}^{3}\); internal volume of the stomach was 350 cm\({}^{3}\); internal volume of the heart was 400 cm\({}^{3}\); internal volume of the lungs was 4000 cm\({}^{3}\); and the internal volume of the kidney (single) was 150 cm\({}^{3}\).
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 5 Comparative Analysis of Different Algorithm Experiments
### Experimental Dataset Analysis
#### 5.1.3 ShanghaiTech  
The ShanghaiTech dataset [90] is a dataset for video abnormal detection and crowd counting. This dataset has 13 complex scenes, recording the pedestrian flow on the campus. It also contains 130 abnormal events (such as fighting, falling, running, etc.) and more than 270,000 training frames, with a duration ranging from about 30 s to 90 s.
# Mdp1 _sciences_
###### Abstract
[17] presented an approach for wind farm modeling based on coherence functions and tested their model on the Danish offshore wind farm Horns Rev. The authors were able to show that their model provided a fair prediction of the spectral characteristics and the 10-min ramp distributions. While the papers discussed above deal with the mitigation or management of wind power fluctuations, other authors have discussed approaches through which wind farms and turbines can provide frequency support to the grid through adjustments of their active power in response to variations in grid frequency. The main mechanisms include turbine de-loading or de-rating by operating wind turbines above their MPP shaft speed, de-loading through pitching, and energy storage using the DC link of turbines with a full-scale converter [18]; also see Reference [19] for an interesting combination of these mechanisms. As mentioned above, frequency support deals with the ways wind turbines and farms respond to grid fluctuations, rather than with the mitigation of their fluctuations, but the two topics obviously bear a natural relationship. As discussed above, the variability in wind power output, and particularly negative ramps, can be minimized at the planning stage (by appropriate siting), managed during grid operations (through risk-oriented probabilistic unit commitment), modeled and fore cast, mitigated by storage devices and optimal strategies, and reduced by curtailment measures, such as flat curtailment or ramp-rate restrictions. While storage-based solutions require substantial add-on investments (and also imply a certain loss of energy because of finite round-trip efficiencies), curtailment-based options lead to quite substantial energy losses and should be considered as the last resort, restricted to systems with substantial lags in grid upgrades and expansion. An overview of the recent investigation into the management of wind power variability and grid support is shown in Table 1.  
A way out of the dilemma described above was proposed by [PERSON] [20] by introducing and developing a new strategy called _Forecast-Based Curtailment_ (FBC). The basic idea is to slightly curtail (if possible) the power output of a wind turbine or farm before a large expected fall in power, i.e., a large negative ramp, based on an accurate forecast of the power output for two consecutive time intervals after the last measurement. Analytical expressions for the increased compliance with a given critical ramp rate and the mitigation efficiency were provided for typical power change distributions. The analytical framework included both perfect and non-ideal forecasts, and the results were validated against numerical simulations using both simulated power change data and data from an operating wind farm. The actual capabilities of realistic approaches to short-term forecasting were, however, not subject of the work mentioned. Exploring the suitability of state-of-the-art forecasting methods for the FBC method is the main objective of the present work.
###### Abstract  
This study analyzes the phenomenon of performance deterioration in fully grouted rock bolts in tunnels with a dry, hot environment and high geothermal activity with a focus on temperature effects on interfacial bond performance. Three groups of fully grouted rock bolt specimens were designed based on similar mechanical principles. They were produced and maintained at 20 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C. Through the indoor gradual loading tensile test of specimens, variations of axial force and shear stress between the rock bolt and mortar adhesive interface were obtained under different environmental temperatures. Distribution of the axial force and shear stress on the anchorage section were found under different tensile forces. Results showed that, with an increase in specimen environmental temperature, maximum shear stress of the rock bolt section became smaller, while shear stress distribution along the rock bolt segment became more uniform. In addition, the axial force value at the same position along the pull end was greater, while axial stress along the anchorage's length decayed faster. With an increase in tensile force under different temperatures, the axial force and maximum shear stress of rock bolt specimens along the anchorage section has a corresponding increase.  
t  
tunneling engineering; fully grouted rock bolt; interfacial bond strength; shear stress; axial force distribution
# Critical Factors behind Construction Delay in Petrochemical Projects in Saudi Arabia
## 7 Discussion of the Results
### Contribution of the Experts to the Results  
After a review of past literature, a list of 23 delay factors was produced and presented in a questionnaire survey. The survey was distributed to various experts in the field of the construction industry. Then, 90 respondents evaluated the twenty-three (23) delay factors based on importance (the delay impact on construction project) and frequency (how often the factor is implemented or considered). The gathered data of 90 complete responses were analyzed using different ranking methods such as the Relative Importance Index (RII), Frequency Index (FI), and Frequency adjusted Importance Index (FAIL). In terms of importance, we used Equation (1) to find the RII and found the top important factors to be the delay in material or equipment delivery, Poor site management and supervision by contractors, Poor planning and scheduling of projects by the contractor, Long time for approval and decision making by owner and Delay in getting work permits. On the other hand, using Equation (2) to find the top 5 frequent factors (FI), which found to be Delay in getting work permits, Delay of material or equipment delivery, Poor site management and supervision by contractors, Long time for approval and decision making by owner, Poor Planning and Scheduling of Projects by the Contractor and Shortage of skilled laborers. Ultimately, the best way is to find the top 5 in terms of importance and frequency, is to use Equation (3) which is Frequency Adjusted Importance Index (FAIL) since there is agreement between owner and contractor for those factors. Therefore, the top five as per FAIL found to be Delay of material or equipment delivery, Delay in getting work permits, Poor site management, and supervision by contractors, Poor Planning and Scheduling of Projects by the Contractor, and Long time for approval and decision-making by owner. From Table 6, it can be concluded that the factor which is considered the most significant is the Delays in material or equipment delivery (53.4%). In fact, this a fixed risk for all projects regardless of the type and magnitude. If the required material is related to an activity within the critical path, then this will affect the whole project schedule and cost as the assigned resources will be idly waiting in standby for the materials. The second critical factor based on the survey is Delays in getting work permits (51.0%). This factor was not highlighted during the literature review but found to be the second critical in the list. In fact, this is more applicable for petrochemical companies, where the permits are issued
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 3 Results
### Evaluation Metric  
For the binary classification problem, A is referred to as a positive example (Positive), B is referred to as a negative example (Negative), and the classifier predicts correctly is noted as True and predicts incorrectly as False. From these four basic combinations, the four base elements of the confusion matrix are formed as TP (True Positive), FN (False Negative), TN (True Negative) and FP (False Positive), as shown in Table 1.  
In semantic segmentation experiments, the commonly used experimental metrics are mIoU [25] and pixel accuracy (PA). Where IoU represents the ratio of the intersection to the merge between the predicted outcome of each category and the True label, as shown in Equation (5). For the binary classification problem, mIoU is the sum and then the average of IoU, which can be expressed as Equation (6). Pixel accuracy represents the ratio of the number of pixels correctly predicted for a category to the total number of pixels, as shown in Equation (7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Truth**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-3}  & **T** & **F** \\ \hline P & TP & FN \\ N & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
Figure 7: Results of data augmentation.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{5}\]  
\[mIoU=[\frac{TP}{TP+FP+FN}+\frac{TN}{TN+FP+FN}]/2 \tag{6}\]  
\[PixelAccuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 6 Conclusions  
This study meticulously selected and compared metrics, to identify the most suitable ones for lung nodule detection, emphasizing the need for metrics that accurately reflect the true performance of models, especially in unbalanced datasets. This research determined that the \(F_{5}\) score, which gives greater weight to recall, is the most effective metric for evaluating medical image classification models. The appropriate metrics led to the identification of the most potent combinations of augmentations for nodule classification. The results of these experiments were rigorously documented and analyzed, with comprehensive tables comparing the effects of different augmentations. This analysis not only underscored the benefits of specific techniques but also revealed the synergistic impact of combining them, significantly boosting the model's precision in detecting lung nodules.  
Conceptualization, D.L. and J.B.; methodology, D.L. and I.Y.; software, Y.W., I.Y., and D.L.; writing--original draft preparation, D.L.; writing--review and editing, D.L. and J.B.; visualization, D.L.; supervision, J.B.; project administration, J.B. All authors have read and agreed to the published version of the manuscript.  
This study has been supported by MOTIE funding program \"Advanced Graduate Education for Management of Convergence Technology\".  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MCC Matthew's Correlation Coefficients CT Computed Tomography CNN Convolutional Neural Network CAD Computer-Aided Diagnosis FPR False Positive Rate TPR True Positive Rate SOTA State-of-the-Art TPs True Positives
# Evaluation of ISO 31010 Techniques for Supply Chain Risk Management in Automotive Suppliers
## 4 Results  
### Delphi Method Results  
After the first round of the Delphi method, the experts selected techniques applicable to the supplier risk management process, as shown in Table 4.  
In the first round, the experts partially agreed about which techniques were applicable or not, classifying only four techniques as 0% or 100% applicable, and differing about the remaining ten techniques. In none of the completed reports did we note lack of knowledge of any of the 14 techniques and/or the suggestion of including a technique previously discarded.  
In the second round of Delphi, twelve techniques were covered, since the two techniques that had zero applicability in the first round were deleted. Table 5 shows the results of this round.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Management Items** & **Quantity** & **Percentage** \\ \hline Failure mode and effect analysis (FMEA) & 20 & 100\% \\ Business impact analysis (BIA) & 20 & 100\% \\ Cause and consequence analysis & 18 & 90\% \\ Structured what if technique (SWIFT) & 17 & 85\% \\ Consequence/probability matrix & 17 & 85\% \\ Hazard and operability studies & 11 & 55\% \\ (HAZOP) & 8 & 40\% \\ Cost/benefit analysis & 4 & 20\% \\ Scenario analysis (MCDA) & 4 & 20\% \\ Multi-criteria decision analysis (MCDA) & 3 & 15\% \\ Risk index & 2 & 10\% \\ FN curves & 1 & 5\% \\ Environmental risk assessment & 1 & 5\% \\ Reliability centered maintenance & 0 & 0\% \\ Human reliability analysis (HRA) & 0 & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Delphi first round for the fourteen applicable/highly applicable tools of ISO 31010.  
Figure 2: Hierarchical level defined for AHP.  
The lowest possible score would be 20 points if all specialists assigned a score of 1 to a particular technique, while the highest possible score would be 100 points if all participants awarded a score of 5. In this regard, Table 5 shows that five techniques were classified with a final score above 90 points (high applicability). On the other hand, the remaining techniques received scores equal to or less than 32 points (low applicability).  
Thus, there was \"stability\" in the group's responses, as there was a division between the most and the least recommended techniques. According to [PERSON] [88], \"stability\" is defined as the consistency of responses between successive rounds of a study. Although it was dispensable [89], given the stability of the responses, we opted to conduct a third round to avoid potential doubts. Therefore, we used the Delphi method, and the experts received a report with the results of the second round so they could express agreement or disagreement with the five techniques that obtained the highest scores. All 20 experts agreed that the following techniques were the most suitable for supply chain risk management of suppliers (Tier 2) in the automotive industry:  
1. Cause and consequence analysis.
2. Business impact analysis.
3. Failure mode and effect analysis (FMEA).
4. Structured what if technique (SWIFT).
5. Consequence/probability matrix.
## 4 Evaluation
### Results
#### 4.3.2 Results of Experiment 2  
Experiment 2 was carried out on the topology labelled with purple and \"2\" in Figure 2. The source router was R7, and the destination router was R10. The path selected by OSPF was R7-8-10, and the backup one was R7-9-11-10. The evaluation results are shown in Figure 4a,b and Tables 5, 8 and 9. In Figure 4a, the real-life latencies of paths (R7-8-10 and R7-9-11-10) are presented with curves in orange (R7-8-10) and blue (R7-9-11-10) colors. The latency of the current path chosen by OSPF R7-8-10 is denoted by the orange curve in Figure 4a, and the blue curve is the latency of the backup path R7-9-11-10. Same as in Experiment 1, the indicator \(I_{\Delta t}\) at each time point is shown as a gray spot in Figure 4a. When the PSI is on (or has a value of 1), it indicates that the route should be switched to the backup path, because a steady increase of traffic load (or an elephant flow) in the current path is expected, and vice versa, when the PSI is off, it indicates the route should be switched back to the current path, because the current path has better conditions and will continue to stay better in a foreseeable future. The x-axis in Figure 4a is the step of each measurement, and each step is 5 s. The parameters chosen in the experiment are shown in Table 3.  
\[\begin{array}{l}T_{PSI}=\frac{Total\ Time}{Total\ Num.of\ I_{\Delta sungs}} \times\Delta t\\ =\frac{3338}{19}\times\Delta t=175.736\times 5=715s\end{array} \tag{12}\]  
\(T_{PSI}\) in Experiment 2 is calculated in Equation (12), which means that on average, after a PSI change, it can stay consistent for 761.15 s. This is quite stable compared to the route change time expense, which is at the scale of milliseconds.