esa-sceva/satcom-chunk-collection huggingface dataset:
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 3 Experimental Evaluation
### Detection Performance in Brick Laying  
The detection performance of YOLOv8 and SAMURAI was also evaluated for the brick laying task. Unlike the brick transportation task, which used 60 evaluation time-steps, the brick laying task was analyzed over 240 time-steps. As in the previous analysis, three occlusion conditions were considered: non-occlusion, partial occlusion, and severe occlusion. Figure 8 presents representative detection cases for each occlusion level of YOLOv8 and SAMURAI, including four cases per algorithm and per occlusion condition, totaling 24 detection examples.  
Table 3 provides a structured comparison of detection performance across different occlusion conditions, summarizing the number of correctly detected time-steps for each algorithm. As in Section 3.1, detection accuracy was calculated using the previously introduced formula.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **YOLOv8** & **SAMURAI** & **YOLOv8** & **SAMURAI** & **YOLOv8** & **SAMURAI** \\  & **Case 1** & **Case 1** & **Case 2** & **Case 2** & **Case 3** & **Case 3** \\ \hline T\({}_{1}\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ T\({}_{2}\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\  
Table 3 provides a detailed breakdown of detection success at each recorded time-step for the brick laying task. YOLOv8 detected 223 out of 240 time-steps in the non-occlusion case, achieving an accuracy of 92.92%. In the partial occlusion case, it detected 185 out of 240 time-steps, achieving an accuracy of 77.08%. In the severe occlusion case, it detected 117 out of 240 time-steps, resulting in an accuracy of 48.75%. Similarly, SAMURAI detected 236 out of 240 time-steps in the non-occlusion case, achieving an accuracy of 98.33%. In the partial occlusion case, it detected 226 out of 240 time-steps, achieving an accuracy of 94.17%. In the severe occlusion case, it detected 222 out of 240 time-steps, resulting in an accuracy of 92.50%.
The total number of time-steps used for evaluation was 300, derived from 60 time-steps in the brick transportation task and 240 time-steps in the brick laying task. The final accuracy comparison was computed using the following equation:  
\[\frac{Total\ Correct\ Detections}{Total\ Time\ Steps(300)} \tag{3}\]  
Table 4 summarizes the comparative detection accuracy of YOLOv8 and SAMURAI for each occlusion condition across both tasks.  
The results in Table 4 indicate that detection accuracy was highest in Case 1 (non-occlusion), followed by Case 2 (partial occlusion), and lowest in Case 3 (severe occlusion). Additionally, SAMURAI consistently achieved higher detection accuracy than YOLOv8 across all occlusion conditions. These findings highlight variations in detection robustness depending on the occlusion level and the nature of the construction task. Further analysis of these results will be discussed in subsequent sections.
## 4 Results and Discussion
### Emerging Digital Payment Technologies
#### 4.1.3 Mobile Payments  
Mobile payments refer to payments for goods and services using mobile devices, including wireless handsets, PDAs, radio frequency devices and NFC-based devices [86]. Through the usage of mobile devices, mobile banking allows customers to perform various banking activities. Mobile banking activities are conducted by using mobile internet technologies ([PERSON], 2013). Out of 58 studies reviewed, 28 studies (Table 5 and Figures 2 and 3) investigated emerging mobile payment technologies. These studies were conducted in various countries, such as India, Sweden, USA, Indonesia, 6 GCC (Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Bahrain, and Oman), Denmark and Kyrgyz Republic. The prominent mobile payment technologies mentioned are: Mobile Wallets, P2P fund transfer, NFC-based mobile payment, mobile app (Venmo), MWR app, OPERA (Google Wallet, Apple Pay, Samsung Pay), GoPay & OVO (Indonesia), SWIPP and UPI. For example, India is emerging as a global competitor in innovative payment systems, and various mobile payment technologies have been introduced nationwide, such as UPI (Unified Payment Interface), mobile banking and mobile wallets. Subsequently, challenges in terms of acceptance of digital payment by users belonging to different age groups were also identified [65].  
Mobile payment technologies are increasingly popular for transactions as they offer relatively high security online as well as in physical shops [37]. For example, Swipp, a common bank-sector mobile payment app that was launched in the Danish market in 2013, allows for secure P2P payments. Swipp is used by customers of some of the largest banks in Denmark--Nordea, Nykredit, Jyske Bank, Sydbank, Arbejdernes landsbank and Spar Nord--and some local banks. Eighty-one banks in Denmark are in partnership with the Swipp network and it is integrated with the mobile banking app of each of the participating banks. This app makes transactions easy as a user could send money to anyone who also has Swipp, regardless of which mobile banking app they were using. Swipp is an account-based mobile payment technology that is used for transferring money through a mobile phone number [71].  
Mobile payment technologies are mostly adopted by millennials. A study conducted by [PERSON] and [PERSON] [69] in India shows that mobile phones are particularly popular amongst Generation Y and that they are not merely used as a music player, a navigation device or a camera device but largely for their payment services [69]. India has been the world's second-largest smartphone consumer since 2016 and has launched several mobile wallet options, such as _Paytm_, _Mobikwik_, _m-Pesa_, _Airtel Money_, etc. One of the latest trends in short-range wireless technology that is widely used in the payment industry is the NFC chip. The use of NFC in mobile payment technology has gained popularity mainly because it is fast, efficient and reliable. NFC-based mobile payments are made through \"touch and go\" apps, which are relatively easy to use [38].
# Study on the Extraction Method for Ecological Corridors under the Cumulative Effect of Road Traffic
## 3 Case Study and Analysis
### Ecological Corridor Extraction and Analysis  
First, the GlobeLand30, Landsat8 L2, and OpenStreetMap road network data are analyzed and calculated using the method in Section 2.2 to obtain information on the landscape, habitat quality, remote sensing ecology, and road traffic resistance in Wuhan. Then, the combined resistance is calculated using the weights and resistance values in Table 1 and the method in Section 2.2.3. The calculation results of the combined resistance are detailed in Figure 4, where the resistance values gradually increase from blue to red. In the central city of Wuhan, the combined resistance is larger, and the further the distance away from the city center, the smaller the resistance value becomes. However, there is a general abrupt change in the resistance values, mainly due to the abrupt change from the artificial surface to the natural surface, and the ecological resistance values of these two surfaces differ greatly.  
Using the method in Section 2.1 to analyze and calculate the GlobeLand30 data, we extracted the ecological source areas of Wuhan, as shown in the green areas in Figure 5. We can see that the ecological source areas are mainly distributed in the peripheral areas of Wuhan, especially in the northern and northeastern mountainous areas, and the ecological source areas consist of multiple plots with large areas and concentrated distribution. The ecological source areas in the central and southern areas are smaller in area and sporadically distributed.  
According to Table 1, the weights of landscape, habitat quality, and remote sensing ecology were calculated as 0.538, 0.231, and 0.231, respectively, by using hierarchical analysis without considering the influence of road traffic, and then the combined resistance was calculated by using the method in Section 2.2. Finally, the ecological corridors were extracted by using the method in Section 2.3 without considering the influence of road traffic, as shown by the blue path in Figure 5. Each ecological source area is connected to at least one other ecological source area through one or more ecological corridors.  
Figure 4: Spatial distribution of combined resistance considering the impact of road traffic.  
Figure 5: Spatial distribution of ecological corridors without considering the impact of road traffic.  
According to Table 1, the ecological corridors under the influence of road traffic were extracted using the method in Section 2.3, as detailed by the red paths in Figure 6. It can be seen that ecological corridors remain basically the same before and after the improvement of the extraction method, but there are still some obvious improvements.
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 5 Evaluation
### Comparison against DB-VDG  
Comparing CS-VDH to DB-VDG as it was originally designed would be unfair because, in DB-VDG, every node at receiving a packet inserts its sensed information without taking into account the area in the map where the measurement was taken. That is, if different nodes receive a packet in the same area, they will append their information dramatically, increasing the packet size as intermediate nodes forward it to its destination. For this reason, we have adapted it to perform as our proposal does. That is, the urban RoI is divided into areas, and a node using DB-VDG only inserts the sensed measurements of its current area into the received packets, but only if this information is not already inserted in the packet.  
After making these modifications to DB-VDG, we have compared both proposals as Table 7 presents. As we can observe, the overhead (in terms of number of messages) of DB-VDG in its two variants, SBSS and DBSS, is enormous. This tremendous overhead jeopardizes the performance of the network in terms of packet delivery complicating both query dissemination and harvesting tasks. The cause for such a behavior is heterogeneity and realism of the scenario, which makes DB-VDG generate a considerable number of messages.  
On the other hand, our approach, in all its variants, obtains a better performance in both the query dissemination and the harvesting process overhead, which is about 200 times better. Besides, regarding the number of messages generated during the simulation, our compression proposal outperforms the one without compression, reducing the number of messages that are required. So, in light of these results, we can state that, although in reduced and controlled VANET environments, DB-VDG has provided promising results, in heterogeneous and realistic environments it encounter scalability problems causing a very high overhead in the network.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 4 Cooperation Scheme  
In this section, a cooperative method is designed to maximize the total network throughput with an efficient relay scheme. An initialization phase is required, during which sensors evaluate the optimal transmission powers based on the wireless channel state. Sensors that can harvest sufficient energy to support data transmission using the optimized powers are expected to work as relays. Then, the coordinator schedules the collaboration plan to facilitate cooperation among sensors during the cooperation phase.  
Figure 2 is an example of the superframe structure and data upload process in a cooperative mechanism of a body area network (BAN) with five sensors. Figure 2b shows a superframe and data upload process in the initialization phase, where the coordinator assigns time slots to sensors without considering cooperative transmission among nodes.  
Figure 1: Network model.  
Each sensor uploads its own data packet within the assigned time slot, and when one sensor is uploading a packet, other sensors enter a low-power state to save energy. At the same time, each sensor assesses the wireless channel and evaluates the threshold transmission power according to its own energy harvesting efficiency, to determine whether it can serve as a potential cooperative relay node. The potential relay nodes package their energy and channel state information in data packets and send them to the coordinator. In the cooperative transmission phase, the coordinator formulates and optimizes the cooperative transmission strategy based on the energy and channel state of the sensors. Figure 2c shows a superframe and data upload process in the cooperation phase, where sensors \(n_{2}\) and \(n_{5}\) enter a listening state while sensors \(n_{3}\) and \(n_{4}\) transmit their packets, and each uploads its own packet and received packets to the coordinator within its assigned time slot. By effectively utilizing the surplus energy of potential relay nodes, network throughput and energy efficiency can be improved.
## 5 Optimization Algorithm--Genetic Algorithm Based on Double-Elite Strategy
### Genetic Algorithm Based on Double-Elite Strategy
#### 5.2.3 Selection of Genetic Operators  
In genetic algorithm optimization, it is crucial to determine the appropriate genetic operators in advance. The selection, crossover, and mutation operators are the critical genetic operators that must be chosen carefully. In this paper, the authors utilized roulette wheel selection, partially matched crossover, and displacement mutation to optimize the integer sequencing problem.  
Roulette wheel selection is a widely used strategy that calculates the probability of each individual appearing in the next generation and selects individuals randomly based on this probability. To mitigate the randomness of roulette wheel selection and ensure that the optimal individual in the current generation is not overlooked, an elite reservation strategy is also implemented. And we choose partially matched crossover (PMC) and displacement mutation to address the integer sequencing optimization problem. They enable us to ensure that the new individuals generated are still feasible solutions, where all integers are within the specified range and cannot be repeated.  
In practice, four key factors still need to be determined for the genetic algorithm: population size, iteration times, crossover rate, and mutation rate.
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 4 Trigger Condition
### Integration of Metrics  
As discussed before, none of these metrics like RSSI or SNR could make an accurate decision to trigger handoff on their own due to the varied and harsh environments. However, Fuzzy Logic theory is an approach where integrating three metrics. As the decision-making schemes is developing on the electronic area, Fuzzy Logic theory can be used to obtain the more accurate decision based on linguistic variables and inference rules. The gap to combine multiple conflicting or independent objectives by classical logic can be bridged. The degree can be replaced with a linguistic variable whose values are words or sentences in natural or artificial language. Formally, a fuzzy set \(A\) in \(X=\{x\}\) is characterized by its membership function \(\mu_{A}:X\rightarrow[0,1]\) and \(\mu_{A}(x)\) is interpreted as the degree, where \(x\) is the membership of element. It is clear that when \(A\) is completely determined by the set of tuple \(A=\{(x,\mu_{A}(x))|x\in X\}\). If \(\mu_{A}(x)=1\) or \(0\), then the fuzzy set \(A\) becomes an ordinary set. Each metric provides partial information and finally the trigger is determined by predefined fuzzy rules. However, the rules are extreme for practical multiple objective problems. Therefore, extensive studies have been carried out regarding the solution of multiple objective problems by using compensatory operators such as [33; 34]. The Ordered Weighted Averaging (OWA) operator introduced by [35] is one of the operators in our mechanism which allows easy adjustment of the degree. Accordingly, \"andlike\" and \"orlike\" operators with \(n\) fuzzy sets are given by  
\[\mu_{and}=\beta\min_{i}(\mu_{i}(x))+\frac{1-\beta}{n}\sum_{i=1}^{n}\mu_{i}(x) \tag{5}\]  
\[\mu_{or}=\beta\max_{i}(\mu_{i}(x))+\frac{1-\beta}{n}\sum_{i=1}^{n}\mu_{i}(x) \tag{6}\]  
where \(\beta\in[0,1]\) is a compensatory coefficient. It is recommended in the range \([0.5...0.8]\) according to [36]. Since data has strict requirements in industrial production, the \"andlike\" operator is more suitable for the result in [37]. Therefore, the calculation of the trigger degree in the following handoff process results from (5).
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology
### Spatial-Temporal-Semantic Feature Extraction  
A trajectory is composed of a series of track points, expressed as \(T=\{p_{i}|i=1,2,3,\ldots,N\}\), where \(N\) is the number of track points. Each track point is composed of spatial information such as longitude, latitude and time stamp, expressed as \(p_{i}=(lon_{i},lat_{i},t_{i})\), which is continuous in space and time. To discretize the trajectory, both spatial and temporal factors should be added into the model. The feature extraction method introduced in this section aims to transform the trajectory to fixed, discrete location sequences without losing much information.
## 4 Simulation Experiment and Results Analysis
### Experimental Analysis and Summary
#### 4.3.3 Experiment on the Effect of Different Tasks on Speedup  
The analysis of experiment (2) shows that the scheduling length increases as the number of tasks increases. It is known that this phenomenon is related to the task scheduling attribute. But it is unknown whether it is associated with the decrease in algorithm performance caused by the increasing number of tasks. In order to observe the effect of task number changes on the algorithm performance, this experiment compares and analyzes the speedup of the algorithm by setting different task numbers (number of tasks: 20, 40, 60, 80). Other parameters are uniformly set: the number of DAGs is 1, and the number of processors is 4.  
The experimental results are shown in Figure 11. When CCR is small (CCR = 0.1) or large (CCR = 4), the speedup of each algorithm does not decrease with the number of tasks, indicating that the increase in the number of tasks does not lead to the decrease in the algorithm performance and that the increase in scheduling length with the number of tasks in the experiment (2) is caused by the task scheduling attribute. In addition, the speedup tends to increase as the number of tasks increases. This proves the suitability of the HSP-QGA algorithm for computation-intensive tasks and its ability to search the solution space compared to other algorithms. It is observed that the speedup fluctuation of the same algorithm is slight for different numbers of tasks, and the trend of the line graph is similar  
Figure 10: Comparison of scheduling lengths for different numbers of tasks: (**a**) CCR = 0.1; (**b**) CCR = 4.  
across the algorithms. Considering that parallel factors (number of tasks per layer in the DAG) and heterogeneous factors (difference in processing ability between heterogeneous processors) are selected at random in experimental parameter settings, indicating that speedup fluctuations in the number of tasks are due to randomness generated by the DAG graphs. In addition, the speedup of HSP-QGA is more significant than other algorithms for each number of tasks, which proves that HSP-QGA has better performance.
# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
## 2 Materials and Methods
### Object Detection Model  
YOLOv8 is a state-of-the-art object detection model that detects objects in real time. It is based on the You Only Look Once (YOLO) algorithm, which is a single-stage object detection algorithm. YOLOv8 is faster and more accurate than previous versions of YOLO, and can detect a wide variety of objects. YOLOv8 is a one-stage object detection model proposed in 2023. YOLOv8 architecture can be seen in Figure 5[45]. The network architecture of YOLOv8 includes three primary elements: the backbone, neck, and head. The backbone, based on CSPDarknet53, is responsible for the extraction of features from input images. It employs a series of convolutional layers and cross-stage partial (CSP) connections to efficiently learn hierarchical features [46]. The neck, which utilizes a Path Aggregation Network (PANet), facilitates information flow between different scales by fusing features from various backbone levels [47]. These features enhance the model's ability to detect objects at multiple scales. The head is responsible for final predictions, including bounding box coordinates and class probabilities [46].  
Throughout the network, YOLOv8 incorporates several key components. The C2f (CSP Concatenation and Feature Fusion) module enhances feature fusion and reduces computational complexity [48; 49]. Convolutional (Conv) layers are fundamental building blocks that perform feature extraction and transformation. The Spatial Pyramid Pooling-Fast (SPPF) layer, an optimized version of SPP, aggregates features at multiple scales, improving the model's ability to handle objects of various sizes [50; 51]. This architecture allows YOLOv8 to achieve a balance between accuracy and computational efficiency, making it suitable for real-time object detection tasks across diverse applications.  
Figure 4: Screenshot of image labeling with LabelIMG.
## 4 Federated Learning with Differential Privacy  
### Role of DP in FL  
As previously stated, DP is a powerful technique that gives strong mathematical guarantees for privacy protection. Differential privacy in the context of FL was explored at the early stages of FL by [PERSON] et al. in 2017 [PERSON] et al. (2017). DP offers several benefits, including:  
1. Protecting individual participant's data: DP achieves this by adding noise to the shared updates, thereby hiding the contributions of each individual in the FL process.
2. Protecting data against membership inference and reconstructions attacks: DP is known to be robust to this type of attacks.
3. Encouraging the user to participate in the learning process: DP provides strong privacy guarantees to the user by offering plausible deniability for them.
4. Facilitating compliance with regulations: DP offers a way for companies to comply with the requirements of various data protection regulations, such as the General Data Protection Regulation (GDPR).  
In summary, DP in FL provides multiple advantages, including individual data protection, defense against privacy attacks, enhanced user privacy guarantees, and regulatory compliance support.