esa-sceva/satcom-chunk-collection huggingface dataset:
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Fuzzy-Logic-Based Method  
Fuzzy logic (FL) is an effective conceptual framework for addressing the issue of representing the data in a context of uncertainty and ambiguity [69; 91]. It is a logic that shows that methods of thinking are not accurate but estimated [70]. The Fuzzy combinations offer effective concepts for handling complex modeling in a new and better way [92]. Several methods based on the FL have been used for fraud detection. To detect anomalous behaviors in credit card transactions, the FUZ-ZGY hybrid model, based on the fuzzy and Fogg behavioral models, was introduced in [69]. A system based on fuzzy logic was employed to track the historical activities of the merchant, and the Fogg behavioral method was employed to characterize the customer's behavior along two different but related dimensions: the ability to commit fraud and motivation. Another fuzzy-based method was proposed in [68] to detect fraud in credit cards by categorizing the fraud transactions and non-fraud transactions with decreased false positives. The method used fuzzy c-means clustering and the ANN model. The model was evaluated on synthetic data and the results showed that the combination of clustering techniques and learning mechanisms helps in reducing false positives.  
A study in [69] provides a fuzzy logic-based fraud-detection method in the banking system. This work was able to improve detection accuracies in classifying fraudulent and non-fraudulent activities in banking transactions. The authors defined some rules for fuzzy logic based on the experience of the experts to improve detection accuracies. This approach was improved in [52] by constructing fuzzy rules using fuzzy logic for the improved detection of fraud transactions. [PERSON] et al. [91] introduced rule-based technique using a firefly algorithm and the threshold-accepting method to differentiate between fraudulent and non-fraudulent transactions based on their financial activities. [PERSON] [92] designed a fuzzy-rule-based approach for detecting financial fraud by integrating a rule-based approach with genetic feature selections. The approach was capable in achieving good performances by using the feature selection method to remove irrelevant attributes and performing a fuzzy unordered rule induction.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 1 Introduction
### Research Aspects of the Unmanned Aerial Vehicles (UAVs)  
Research into UAVs can be grouped into three main areas, as outlined below.  
* Group 1 includes publications describing drone parts and operating conditions of drones [3; 4; 5].  
The focus of these publications is the evaluation of the energy consumed by the different drone parts, based on which energy weights can be assigned to the different path segments. The created drone \"energy map\" revealed that most of the energy was consumed by the drive rotors. The power of these rotors is highly dependent on velocity, ascending, descending, and the air resistance of the drone. The velocity can be controlled by an Electronic Speed Controller (ESC) or Flight Controller (FC).
* Group 2 incorporates literature on 3D path planning and environment modelling methods. Space partitioning is studied, e.g., in [6; 7; 8; 9; 10], while 3D path planning methods are presented in [11; 12; 13; 14; 15; 16; 17].  
These publications provide a good basis for designing a novel space partitioning and path planning algorithm. One of the shortcomings of the surveyed documentation is that almost none of them deal with partitioning of the WS, which may be due to two reasons, as follows: (a). Drones are equipped with environmental sensing equipment (RADAR or LIDAR sensors, cameras, vision systems, US sensors, etc.). (b). Drones fly a visible distance and are controlled via joystick or remote controllers. GPS is standard equipment in drones.  
None of the examined works delved into the topic of energy used in aviation. In order to take this condition into account, several routes between the START and GOAL  
Figure 1: The process of the creation of point representation. (**a**) The drone and the sphere around the drone by “R” radius; (**b**) the drone represented as a point by its coordinates and orientation; (**c**) the drone represented as a point by its orientation (this will figure in the environment model).  
positions must be established, reviewed, and the optimal route requiring the least energy for execution selected. Unfortunately, this will only work in static environments, because recalculating and re-weighting the path segments takes some time, depending on the complexity of the flight control (FC) processor.
* Last but not least, Group 3 discusses legal regulations of drone flight and civil aviation, with information that should be considered during the study (see [18]).  
Drone flight rules (policies) in the EU, or indeed the world, have not yet been fully developed. There are certain local provisions in place, but they are not completed. There are some drone categories (A, B, C), some flight altitudes, and required permissions for drone flights (drone driving licenses). For this study, the most significant is taken into account, namely, to maintain 120 m altitude from the nearest ground point.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 5 Discussion of the Results
### Multi-Hop Communications with Bluetooth Sniff Low Power Mode
The communication from master to slave is defined with the same model that defines the communication from slave to master, but the coefficients change, as shown next. The achievement of the models followed the same steps followed for the case of park mode. Taking into account the dependences shown in Figures 6 and 7, the end-to-end latency when gateways nodes are in the sniff mode can be defined as in Equation (2), which considers the time contributions described in [48] and includes the delay time of the first hop (\(t_{\text{1s\it{inf}}}\)), the amount of hops (\(N\)), the latency contribution per hop (\(\delta_{\text{a}}\)), the inactivity period of the mode (\(T_{\text{sniff}}\)), and the ratio of inactivity period affecting the latency (\(\alpha\)):  
\[\delta_{\text{sniff}}=t_{\text{1s\it{inf}}}+\sum_{i=2}^{N}\left(\delta_{a}+ \alpha\cdot T_{\text{sniff}}\right) \tag{2}\]  
Then, the values for the coefficients in Equation (2) were calculated through linear regression to define the curves that best fit to the results of the measurements summarized in Figure 6 for the sniff mode. The values are summarized in Table 2 for both the multi-hop communication from master to slave and from slave to master when links are in the sniff mode. Table 2 also includes the results of the validation with R\({}^{2}\) statistical datum and the results for the MSE. As a summary, the upper limit of the latency in asynchronous links of Bluetooth multi-hop networks can be defined with just one equation for each one of the directions, i.e., master-to-slave and slave-to-master communication in sniff low power mode. Furthermore, the models were validated with the R\({}^{2}\) statistical parameter and the MSE.
## 3 Design and Analysis  
### Loss Budget of 25.78-Gbit/s Multimode Optical Link  
To define the target specification of the proposed AOC, a loss budget of a 25.78-Gbit/s multi mode fiber (MMF) optical link was designed. Figure 2 shows the designed loss budget. Transmitter (Tx) optical power was assumed as that of a VCSEL with minimum optical modulation amplitude (OMA) set to 0 dBm under high-temperature operation at VCSEL temperature of 80 \({}^{\circ}\)C. In consideration of optical coupling loss caused by beam spreading from the 25-Gbit/s VCSEL, Tx optical-coupling efficiency between the VCSEL and the MMF was set to \(-\)2.0 dB [7]. Receiver (Rx) optical efficiency between the MMF and the 25-Gbit/s PD was set to \(-\)1.0 dB [7]. Optical transmission loss of the MMF up to 100 m (including effects of modal and chromatic dispersions on bit-error-rate (BER) power penalty) was set to 1.5 dB. Moreover, considering the performance of the 25-Gbit/s TIA, optical sensitivity of the Rx side was set to \(-\)6.0 dBm [7]. The link margin was estimated to be 1.0 dB as shown  
Figure 1: Structure of the proposed active optical cable (AOC) (**a**) whole view and (**b**) cross sectional view. MMF, multi mode fiber; IC, integrated circuit; VCSEL, vertical cavity surface emitting laser; PD, photodiode; MCU, micro control unit; EEPROM, electrically erasable programmable read-only memory; MT, mechanical transfer; TIA, transimpedance amplifier.  
in Figure 2. This margin is assigned to characteristic variations of components and crosstalk penalty under multi-channel operation.
## 4 Experimental Results and Discussion
### Results  
#### 4.4.1 Invisibility of Watermarked Image  
When \(s=1\), the watermarked image's average PSNR to the original host image from the training result showed 43.23 dB. We also applied the trained weight set to the evaluation dataset, the result of which showed the PSNR range of [37.46 dB, 42.13 dB]. The average was 40.58 dB. Figure 3 shows three example pairs of the host image (a), watermarked image (b), and the 100 times magnified difference image (c) from the test dataset. They are the ones showing the lowest (1 st row), middle (2 nd row), and the highest (3 rd row) invisibility, respectively. As you can see, it is not easy to distinguish the original image and the watermarked image with the naked eyes, even for one with the lowest invisibility. Therefore, it can be said that the invisibility of our scheme is very high.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Hyper-parameter & Value \\ Epoch & 4000 \\ Mini-batch & 100 \\ Optimization & Adam \\ \(\lambda_{1}\) & 45 \\ \(\lambda_{2}\) & 0.2 \\ \(\lambda_{3}\) & 20 \\ Embedding network & 0.0001 \\ learning rate & 0.00001 \\ Extraction network & 0.00001 \\ learning rate & 1 \\ \(s\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper-parameters used in training.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms
### Simulator Results  
To create a realistic distribution of fish ponds, an algorithm was devised using some basic assumptions of how a typical fish farm is organized. First, a randomly generated convex polygon is created to replicate the farm's available land and a grid of points is filled inside it. Then, 20% of the points are removed in order to replicate empty ponds and add additional variation. This creates a pattern of points that are not completely regular yet not  
Figure 2: Plot of HPP clusters and their associated route plans. The blue dot signifies the depot location. The circled section is shown on the right with the resulting path for that drone.  
random, and is a reasonably accurate generalization of the spatial distribution of typical fish farms. Examples of a commercial aquaculture farm and our simulated farm are shown in Figure 3.  
These three methods are tested on a set of these generated aquaculture farm locations, varying in size from 50 ponds to 700 ponds. The results for these three methods differed depending on the scale of the simulated farm, as shown in Tables 1-3. All results were run in Ubuntu 21.04 with an AMD 5600x CPU, NVIDIA RTX 3080 GPU, and 16 GB of 2133 MHz DDR4 RAM. GAM is implemented in Python PyTorch using CUDA to leverage GPU parallelization, GLOP is primarily C++ and Python, and HPP is purely MATLAB. For small farms, which consist of 50 to 200 ponds, GLOP was the most efficient router with the lowest average maximum route length. For larger farms, which were simulated as 200 ponds to 700 ponds, HPP provided the best routing. GLOP is not able to compute routes for farms that large due to the high computational complexity of the method, so the optimal solutions cannot be found in a reasonable amount of time. While GAM was unable to outperform GLOP in small farms or HPP in large farms, it achieved comparable quality of results for large and small farms in a fraction of the time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ponds** & **HPP** & **GAM50** & **GAM100** & **GLOP** \\ \hline
50 & 7.62 & 7.19 & 7.6 & **6.37** \\ \hline
100 & 9.94 & 9.45 & 9.85 & **7.72** \\ \hline
200 & 12.66 & 13.06 & 12.95 & **9.12** \\ \hline
300 & **15.37** & 17.22 & 16.47 & - \\ \hline
500 & **19.02** & 25.46 & 22.79 & - \\ \hline
700 & **21.72** & 35.35 & 29.74 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average Total Cost.  
Figure 3: (**left**) America’s Catch Catfish Farm with approximately 700 ponds. (**right**) Example of 300 pond simulated aquaculture distribution. Adapted from [32].
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 2 System Model  
In this section, this study first proposes a 3D dynamic UAV communication network model based on NOMA. Then the network model, propagation model, and communication model of the system are proposed.
## 3 System and Methodology
### Object Detection  
The system was applied to UAVs, which have high real-time requirements. In addition, for safety reasons, in an actual scenario, the UAVs need to maintain a certain distance from the target, resulting in a limited region of interest obtained by the detector. To deal with these challenges, we proposed a strategy: cropping the image from 960 \(\times\) 1280 to a small size during the object detection stage to improve the inference efficiency of the network.  
We selected the classic SSD algorithm as the object detector because of its high efficiency in detecting objects of different sizes and shapes and its robustness because of the multi-scale feature maps used for prediction. As an excellent single-stage object detection algorithm with high accuracy, compared with the previous two-stage method [47], SSD significantly improves the detection speed by canceling the bounding box proposals and consequent feature resampling. We split the original image of size 960 \(\times\) 1280 into sub-images of size 300 \(\times\) 300. The size of the sub-images is an empirical choice, considering the inference speed of each sub-image and the number of sub-images that need to be detected by SSD. Hereafter, multi-scale convolutional feature maps generated at different scales in SSD can predict the bounding boxes of objects in these sub-images with gratifying accuracy. Finally, the bounding boxes predicted by SSD were mapped to the original image, as shown in Figure 3.
## 2 Theoretical Background
### Log-Distance-Based Path Loss Prediction Models  
Generally, path loss models are a set of mathematical models, expressions, resources and algorithms used for signal attenuation loss prediction between the paths of a base transmitter and the mobile station receiver. These models are helpful planning tools that assist the radio network designers of cellular telecommunication systems in optimally positioning base station transmitters to meet the desired signal coverage level and service quality requirements of the networks.  
The predictive performance of any path loss model is determined by the resultant prediction accuracy with actual field measured loss data.  
The log-distance-based path loss models are models whose average power loss logarithmically depends on distance (transmission path length) intertwined with a propagation exponent modelling parameter. The propagation exponent is usually employed to account for a specific radio propagation environment. They can also be described as simplified models that attempt to model variations, fluctuations and attenuations in the received signal power. Examples of log-distance-based models include the Walfisch-Ikegami, Walfisch Bertoni, cluster factor, COST 234 Hata, Hata Okumura, SUI model, Lee model, Egli Model and others [41]. Though these models have varying frequency validity thresholds, different correction factors have been applied to ease their applicability at the tested frequency band. Detailed descriptions of these models are contained in [14; 17; 42].
## 4 Impact of the Constellation Mesh Area on Localization Accuracy
### Experimental Results
The collected data leads to a straightforward conclusion: we can assess a tradeoff between network complexity and estimation accuracy; in fact, the smallest cell leads to the better local accuracy, while the larger leads to better mean error across the entire room. From this consideration, we foresee that considering smallest mesh sized repeated side-by-side lead to a better accuracy across larger areas, in place of one larger mesh. For example, following the data reported in Table 3 and considering the three mesh sizes, cf. Table 1, we see that the accuracy could be improved by a factor 2 (from 0.66 m to 0.32 m) moving from a single \"large\" mesh to a combination of about 20 \"tiny\" meshes, which would be necessary to cover the same area. Obviously, the significant increase of anchor nodes may cause a dramatic increase of costs, thus this trading-off drives the optimization of the positioning system between accuracy and complexity. Nevertheless, this assessment requires an experimental validation, which is at presently beyond the scope of this work. Finally, from the comparison between the results provided in Tables 2 and 3, we can observe that for the cases of study 'A' and 'B' there are significant matches for both the 'full area' and 'within the mesh area'. The differences between the two accuracies are between 1 cm and 14 cm, and indicate that the measured results are consistent with the simulations of Section 4.2. Contrastingly, the comparison for the case of study 'C', i.e., the tiny mesh, reveals the worst agreement for both the data related to the 'entire room' and 'within the mesh area'. This is due to the effective difference between the mesh size and the room size, which amplifies the model inaccuracy given the fact that the small number of points within the mesh causes large relative fluctuation in the results.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 5 Performance Evaluation
### The Impact of CTP, RPL, and LIBP on CPS Dependability  
As suggested earlier, the existing CTPs may have a varied impact on CPS dependability of sensitive or non-sensitive applications. While non-sensitive applications can support an extended operation with nodes in the attacked status, sensitive applications will rather favour an extended operation with nodes in the susceptible states and tolerate only a minimised operation with nodes in the attacked state to avoid putting the CPS safety at risk. The impact of such protocols on the dependability of the CPS has been evaluated in this section by conducting experiments to evaluate the epidemic status levels reached by IoT nodes when data collection is guided by three CTPs (CTP, RPL, and LIBP) under the similar threshold values \(T_{1}\) and \(T_{2}\). The experiments were conducted on an emulated Tmote sky mote on the Contiki platform running on Cooja. UDGM (Distance Loss) was considered as the radio medium of choice. The default implementations of CTP [5] and RPL [6] protocols on Contiki were used, while LIBP was forked from the Contiki implementation of the CTP protocol. This was done by disabling the trickle algorithm and modifying the code to meet the requirements expressed by the compartmental routing problem formulation.  
In our experiments, the RPL protocol was run as two experimental instances using various Objective Functions (OF) referred to as RPL-0 or RPL-ETX. For this work, Cooja parameters were set similar to those of LIBP [4; 38]. The experiment runtime was based on the following key features:  
* Upon starting up, 2 min were given to allow each network \"settle\", thereafter the network ran for 8 min, giving a total simulation runtime of 10 min;
* Each node periodically sent a packet containing the string \"Hello from node\" as its packet data (payload). The payloads were sent at a 30 s time interval over the 8 min duration. This resulted in each node sending 16 packets data to be collected by the sink;
* For the various experiments, Cooja's existing profiling tools were used. For time sensitive experiments, both the simulation timers and node real-time timers were used to ensure accuracy;
* To distinguish between control and data plane traffic, packets were tagged. Counters were updated when specific packets types were received;
* All experimental results are presented with epidemic colours--blue, red, and green, linked to the amount of nodes in a specific epidemic state (susceptible, attacked, or removed). This was applied to all three routing protocols (LIBP, CTP, and RPL) tested to reveal their suitability for the safety of the CPS. This helps putting these results into the context of the CPS dependability under study.
## 7 Application to an Industrial Case  
This work was carried out in collaboration with the hospital center of Troyes (HCT). To effectively meet the needs of patients and to improve working conditions and employees' well-being, the hospital center of Troyes implements important measures to improve its daily efficiency. It is in this context that this study of hospital catering activity optimization takes place.  
The HCT is carrying out a project to revise its supply chain, which must, in particular, consider the management of food flows. The contribution of the present work is to determine the best plan for meeting customers' demands in terms of food flows and to propose ways to improve the well-being and working conditions of employees of the catering service of the hospital center of Troyes. The aim is to provide methods and tools for scheduling meal-making processes throughout the day.  
The hospital center of Troyes is the main member of the South Champagne Hospitals group. Its logistic network (Figure 10) is composed of a set of customers whose meal needs are met from a warehouse, which is the central kitchen and a set of suppliers. These customers are hospitals, nursing homes, and psychiatric clinics. The central food production kitchen of the HCT produces, on average, 4800 meals per day. Its maximum daily production capacity is 5000 meals per day. The HCT catering service includes a production unit divided into several sectors, from the reception of raw materials to the dispatch of finished products and a coordination unit for menu management. In this study, we are particularly interested in the sectors from the pre-treatment of raw materials to the stock of finished products (Figure 11). This figure represents the production areas of the studied system and the different machines available for carrying out the operations of the meal-production process.  
Figure 10: Logistics network of the hospital center of Troyes.  
Table 10 represents the results of the genetic algorithm for some examples of real production days with a comparison between the real solutions, as these production days were organized and the solutions were proposed by the genetic algorithm. In this table, for each instance, the number of dishes, the number of sub-lots of dishes, the total number of operations, the number of machines available, and the average number of meals produced per day are given. The performance indicators between solutions are based on the total flow time and the gaps between them. From these results, we remark that the gaps between the real solutions and those of the genetic algorithm are very important and significant. The performance of the genetic algorithm for these instances depends on their types and sizes. For example, for the instance with 62 dishes, 68 sub-lots, 218 operations, and 29 machines, we brought about a considerable improvement of 18.72% over the real solution, which shows the quality and performance of the developed algorithms.