esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 3 Existing Fraud Detection Techniques
### Feature Selection and Dimensionality Reduction in CCFD  
In the IL process, pre-processing is performed on the basis of the target variable to avoid overlapping problems. FS studies are then carried out for feature reduction  
\begin{table}
\begin{tabular}{c c c} \hline
**Resampling Type** & **Resampling Method** & **Abbreviation** \\ \hline \multirow{8}{*}{Undersampling} & Random Undersampling & RUS \\  & One-Sided Selection & OSS \\  & Condensed Nearest Neighbor & CDNN \\  & Edited Nearest Neighbors & ENN \\  & Repeated Edited Nearest Neighbors & RENN \\  & All k-Nearest Neighbors & AllKNN \\  & Instance Hardness Threshold & IHT \\  & Near Miss Sampling & NMS \\  & Neighborhood Cleaning Rule & NCR \\  & TomeKlinks & TMLK \\  & Cluster Centroid Sampling & CCS \\  & Sequence Aware Undersampling & SAUS \\ \hline \multirow{8}{*}{Oversampling} & Random Oversampling & ROS \\  & Synthetic Minority Oversampling Technique & SMOTE \\ \cline{1-1}  & Borderline Synthetic Minority Oversampling Technique & Borderline SMOTE \\ \cline{1-1}  & k-Means Synthetic Minority Oversampling Technique & k-Means SMOTE \\ \cline{1-1}  & Support Vector Machine Synthetic Minority Oversampling Technique & SVM SMOTE \\ \cline{1-1}  & Adaptive Synthetic Sampling & ADASYN \\ \hline \multirow{2}{*}{Oversampling followed by Undersampling} & Synthetic Minority Oversampling Technique + Edited Nearest Neighbors & SMOTEENN \\ \cline{1-1}  & Synthetic Minority Oversampling Technique + TomeKlinks & SMOTETomek \\ \hline \end{tabular}
\end{table}
Table 1: Imbalance learning approaches used in credit card fraud studies.  
by identifying important features in accordance with the data and directing important features into the analysis and modelling process [21; 22]. Since the training model cannot be executed with raw data, depending on the data set and variables, different FS approaches are applied. Although the FS process is quite costly, it is very important in terms of reducing the dimensionality and identifying important features. In dimensionality reduction (DR), there is a transition from a high-dimensional solution space to a low-dimensional solution space on the data with linear and non-linear processors to eliminate the complexity from dimensionality [23; 24]. Both FS and DR studies enable the identification of important features and the discovery of patterns for respective fraud models.
## 4 Results
### Notification Experience Comparison
#### 4.2.2 Frequency Versus Experience  
Our second comparison was regarding the notification experience versus perceived notification frequency (see Figure 6). Here we notice a distinction between overwhelming and calm characteristics. Calm--Reactive is situated in the lowest plane of the frequency spectrum, indicating that notifications are deemed infrequent but have a moderately positive experience suggesting a desired volume in notifications. Following with Calm--Proactive,  
Figure 5: Comparison of notification importance versus notification experience.  
which shows similar traits and evokes similar positivity in experience but is noted to have slightly more frequently transmitted notifications.  
Overwhelming--Reactive characteristics elicit a neutral stance in notification frequency but demonstrate a slight to moderate positive attitude which is likewise to Expected--Proactive.  
Out of all the characteristics, Overwhelming--Reactive is regarded as the one that has the highest perceived volume of notifications. Despite the notification experience still being on a positive scale, it is situated at a lower extent, implying that such high volumes of exposure should be managed to mitigate user displeasure. Last but not least, the Unexpected--Reactive showcases the lowest average in terms of notification experience with a slight to moderate notification frequency.  
The distribution of bubbles within the chart demonstrates a preference for proactive notifications that are perceived in moderation and associated with positive experiences. Overwhelming--Reactive notifications, while not as common, highlight a challenge in notification design: they are sometimes perceived in higher volumes and can lead to negative user experiences. This suggests that the frequency and nature of notifications should be carefully managed to prevent user discomfort. However, Overwhelming--Proactive does not have as high of an attitude in terms of notification volume but is situated on a relatively more positive part of the chart. Calm-Reactive and Calm-Proactive show positive attributes and are deemed to be less in notification volume suggesting a desired outcome and quadrant to be in.  
Figure 6: Comparison of perceived notification frequency versus notification experience.
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### Overall Architecture and Measurement Setup
#### 3.4.3 Setup 3: Redundant Transmissions via 5G URLLC Test System and 5G Mid-Band System  
In Setup 2, the goal is to investigate what benefit FRER offers when different 5G systems are used. Therefore, the replicated data sent from Switch 1 will be sent parallel via the Release 15 mid-band system and the URLLC testbed to Switch 2 for elimination, as shown in Figure 7. The timestamps of the data were measured after replication and before elimination to determine the increase in the reliability of the 5G transmission. Please note that the URLLC test system natively supports Ethernet PDU sessions, and therefore, support for tunnel is not needed.  
Figure 6: Setup 2: Redundant transmissions using FRER via different 5G mid-band systems.  
4.4 Setup 4: Redundant Transmission via 5G Mid-Band and 5G mmW Systems Sharing the Same Core Network  
We investigated the use of two 5G systems operating in different frequencies. In particular, we use FRER-based redundancy for 5G systems operating in the 3.7-3.8 GHz (5G n78 band) and the 26 GHz (5G n258 band), as shown in Setup 4. Please note that in this setup, the two non-standalone 5G systems share the same core network. The replicated data sent from Switch 1 are sent in parallel via the Release 15 mid-band and 5G mmW systems, as shown in Figure 8. The timestamps of the data were measured after replication and before elimination to determine the reliability and packet delay variations in the 5G transmissions.  
Figure 8: Setup 4: Using FRER for transmission via 5G mid-band and 5G mmW systems sharing the same core network.  
Figure 7: Setup 3: Using FRER for redundant transmission in the 5G mid-band and URLLC testbed deployment.
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods
### Methods for Adapting Medical Imaging
#### 2.2.2 Personal Information Protection Methods within Medical Imaging  
In order to protect personal information within medical imaging, this study referred to the privacy regulations related to the Protected Health Information (PHI) section of the Health Insurance Portability and Accountability Act (HIPAA). In the relevant section, there are the expert determination and the safe harbor methods [17].  
The expert determination method requires experts to take part in an 11-step process in which de-identification is processed with personal information existing within medical data. The first step requires the determination of identifiers that relate to personal information and, in the next steps, to prepare a model for potential threats, select the necessary information, and conduct an analysis of minimized personal information damage. Afterwards, a review of the actual threats is conducted with sample data and provided to external sources in the final step. This is a high level de-identifier method but it requires a significant amount of time for data to be collected and processed and has the downside of requiring an expert.  
On the other hand, the safe harbor method defines 18 different elements that can be classified as personal information and makes sure that such data are not included with medical data. Personal information such as one's name, date of birth, phone number, email address, date of hospitalization, and leave are accepted as protected personal information without the involvement of an expert as long as they are removed. This method allows for a quick and easier application compared to the expert determination method.  
Figure 1: Medical Imaging Related Resource Diagram (ImagingStudy, Patient, Endpoint).  
DICOM also offers a de-identifying method. Information regarding the security and management of medical imaging can be found in DICOM PS 3.15. Moreover, the Attribute Confidentiality Profile in Annex E offers a method for a de-identifier process for DICOM medical imaging. There are a total of seven profiles with the most basic profile being the \"Basic Profile.\" The other six profiles can be applied according to different situations. The de-identifier subjects classified by DICOM's basic profile satisfy the requirements of HIPAA's safe harbor [18]. As such, the study used DICOM's basic profile as it enabled the use of medical imaging for medical data and as it desired to use the study data of the quickly developing artificial intelligence technology.  
De-identifier methods through CTP were applied according to DICOM PS 3.15 Annex E. Attribute Confidentiality Profile's basic profile and all authentic medical imaging within the CTP module were processed with the de-identifier method according to the basic profile. Figure 2 below displays the de-identifier items according to the DICOM basic profile within the CTP module.
## 4 Classification of Routing Protocols
### Postural-Movement-Based Routing Protocols
#### 4.4.2 Probabilistic Routing (PRPLC)  
In [22] [PERSON] and [PERSON] proposed a store-and-forward packet routing protocol based on postural partitioning to minimize end-to-end packets delay by dynamically choosing routes having low storage/buffering delays. As shown in Figure 12(b), redrawn from [22], the network architecture used in this scheme consists of seven bio-medical sensor nodes: two on the upper arms, two on the thighs, two on the ankles and one on the waist area, forming a mesh topology divided into one or multiple simultaneous network partitions. The sensor node attached on the right ankle is assigned as a sink node (base station). The sink node is responsible for collecting the raw data from the other bio-medical sensor nodes and forward the processed results to the server located outside of the body, resulting in multi-point-to-point routing.  
The impact of human posture mobility on the network partitioning was observed by asking the human test subjects to follow a predefined sequence of postures. In this protocol, the likelihood of any link '\(L_{i,j}\)' between any two nodes '\(i\)' and '\(j\)' to be connected for a discrete time slot '\(t\)' is defined as Link Likelihood Factor (LLF) denoted by \(P_{i,j}^{t}\left(0\leq P_{i,j}^{t}\leq 1\right).\) The LLF is supposed to be updated dynamically at regular interval '\(t\)' as:  
Figure 13: Network Architecture of **(a)** OBSFR [21] **(b)** PRPLC [22].  
time of interval, _i.e._, the sink node is in NLoS with the sensor node, then the sensor node will send a wakeup packet to relay node. Once the relay node is ready for communication, it will inform both the sensor node and sink node to start communication. After successfully receiving all the data packets, the sink node will send a Receive Acknowledge (RAck) packet to the sensor node. If the sensor node does not receive the RAck packet, then the aforementioned procedure will be repeated until successful communication.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 7 Evaluations  
The primary objective of our experimentation was to evaluate the effectiveness and security of using true random frequency hopping in a simulated drone network with a ring topology. The setup included the following key components:  
* QRNG Device or RPU: A Quside QRNG device [9; 31] installed in the internal network of the BARCELONA Supercomputing Centre Facility was utilized to generate true random numbers, which were used to create the frequency hopping sequences. The device is a QRNG Module ETH based on the FMC 400 Randomness Module by Quside.
* Drones: Each drone in the network was a simulated entity capable of implementing frequency hopping based on the generated sequences.
* Secure Communication Protocol: A secure communication protocol was developed, incorporating AES encryption, Hash-based message authentication code (HMAC) for data integrity, and secure key exchange mechanisms.
* Synchronization Mechanism: Synchronization between drones was achieved using a preset time, that in practice would correspond to GPS, ensuring a common start time for frequency hopping across all nodes.  
The QRNG Module ETH, see Figure 4, is based on the FMC 400 Randomness Module by Quside. It includes an FPGA baseboard to provide high-quality and fast random bit rates through a 1 Gb-Ethernet connection.  
The QRNG Module ETH employs a digital quantum entropy source based on the accelerated phase diffusion process in semiconductor lasers. The system comprises:  
* Laser: Produces a stream of phase-randomized optical pulses.
* Unbalanced MACH-ZEHNDER Interferometer: Converts random phases into random amplitudes.
* Photodetector: Translates the optical signal into an electrical signal.
* Digitizer: Converts the analog signal into a digital bit stream.
* Control and Clocks: Manages the digital control, health monitoring, and randomness extraction.
* RJ-45 Connection (Front Panel): Connects the QRNG Module ETH to the PC for control, monitoring, and data transfer.
* ON/OFF Switch (Front Panel): Powers the device on or off. The system restarts with default settings when switched on.
* Power Plug (Rear Panel): Connects to a 110/240 VAC power source.
* Status LED (Front Panel): Indicates the system status.  
Figure 4: Reference front panel for the QRNG Module ETH powered by the FMC 400 quantum entropy source by Quside.  
This device leverages the inherent quantum phase noise of lasers to generate true random numbers, providing a raw bit rate of up to 1 Gbit/s with strong entropy characteristics. The phase diffusion technique involves modulating a semiconductor laser to produce a stream of phase-randomized optical pulses, which are then converted into amplitude fluctuations using an interferometer [32; 33; 34]. These fluctuations are detected and digitized to produce high-quality random bit streams suitable for cryptographic applications and secure communications.
# Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
## 3 Observations and Results
### Influence of Quantities on Column Separation Events  
#### 3.3.1 Effect of Initial Flow Velocity  
The pressure measurements at PT6# (BV2# closure) and PT3# (BV1# closure) for two initial flow velocities and three static heads at Tank 2# are presented in Figures 7 and 8, respectively. With a constant downstream reservoir static head, both the duration of column separation and the instantaneous pressure rise following cavity collapse increased as the initial flow velocity increased. However, the location of maximum pipeline pressure may not be near the valve. For the test conditions of \(H_{T2}\) = 2.55 m and \(V_{0}\) = 0.638 m/s presented in Figures 7b and 5a, for instance, \(H_{\text{max}}\) occurred at PT7# at a proportion 30.9% higher than the maximum value at PT6#. The type of pressure oscillation pattern evolved from cases where the duration of column separation decreased non-sequentially with time (i.e., Type 3 or 4) to cases in which the duration of column separation decreased sequentially with time (i.e., Type 1 or 2).
## 1 Introduction
Researchers are exploring ways to optimize energy consumption in UWOC systems, (IV) Experimental Deployments and Field Trials [19; 20; 21; 22; 23]: Field trials and experimental deployments have been conducted to test the performance of UWOC systems in real-world environments, such as underwater environments. These trials help validate theoretical models and identify practical challenges, and (V) Integration with Other Sensor Networks [24]: Integration of UWOC with other underwater sensor networks, such as acoustic or radio-frequency systems, has been explored to create more robust and versatile communication networks for underwater applications. It is helpful to state that the optical signal (bit stream) is converted into the electrical domain at the photodetector output, which represents the current due to the shot noise and Additive Gaussian Thermal Noise (AGTN) originating from pre-amplifier and amplifier at the detector backend. These electronic components are added to form the input to the electronic filter \(h_{f}(t)\) (Figure 3), which acts as a pulse-shaping unit whose optimum bandwidth needs to be concluded [25]. The last task is the main objective of this paper. This work presents the required steps to develop a new mathematical optical filter design optimization technique using a UWOC optical receiver as a case study. Section 1 briefly surveys the optical design efforts in UWOC and optical fiber systems to render this possible. Also, it identifies the gaps related to UWOC filter design efforts. Section 2 shows the necessary tooling of the mathematical steps to formulate the framework of the optical receiver design model. Section 3 presents the formulation of the optical receiver filter equation (ORFE). This section uses three use cases with ascending noise complexity to demonstrate the evolution of the ORFE from a simple scenario to a complex one. Section 4, titled \"Results and discussions\" covers the following topics: (A) MGF model validation, (B) software implementation notes, and (C) computation data visualization and optimum filter characteristics. Finally, Section 5 presents the main conclusion and future work. It is essential to mention here that this work covers the temporal profile of the optical received signal and the resultant electronic signal at the input of the decision unit. Figure 3: Typical Direct Detection Optical Receiver Model.
## 2 Materials and Methods
### Materials
#### 2.1.3 GLASS LAI Data  
For the MODIS GPP algorithm, the FPAR is an essential input. To examine the potential error contributions from LAI/FPAR, the Global Land Surface Satellite Leaf Area Index (GLASS LAI) products, which are an improved LAI dataset, were introduced into our study [41]. The GLASS LAI algorithm uses general regression neural networks (GRNNs) to retrieve LAI data. In addition, the GRNNs are trained by the fused LAI values from time-series multi-sensor remote sensing data, including MODIS and CYCLOPES LAI products and the corresponding reprocessed MODIS reflectance values [42]. It has been demonstrated that the GLASS LAI algorithm is able to produce temporally continuous LAI datasets with significantly improved accuracies compared with current MODIS and CYCLOPES LAI products [43]. In this paper, the GLASS LAI subsets (0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) resolution, eight-day interval) during the 2003 to 2005 period centered over each of the flux tower locations were obtained from the Generation and Applications of Global Products of Essential Land Variables website [44].  
Using a simple Beer's Law approach [45], the GLASS LAI data can be converted to FPAR data by:  
\[\rm FPAR\;=\;1-e^{(-K)\;\times\;LAI} \tag{4}\]  
where K is the canopy light extinction coefficient, which is set to 0.5. To explore the influence of FPAR on GPP, we replaced the MOD15A2 FPAR with the above FPAR, which originated from GLASS LAI, to recalculate MODIS GPP in this study.
## 3 Transformer Tank Cover Disassembly Algorithm
### Segmentation and Disassembly of Welded Covers
#### 3.4.2 Automated Cutting Line Generation for Welded-Type Cover Plates  
To achieve the effective separation of the transformer cover from the housing, a graphic-based processing method is proposed. This approach aims to accurately extract edges and generate safe, reliable cut lines by considering the geometric features of the transformer cover (e.g., bolt holes, piping interfaces) and the contamination present in the industrial environment (e.g., oil stains, light reflections). Additionally, this method incorporates mask expansion for each component to ensure that the cut line remains at a safe distance from the edge of the transformer cover, preventing cuts into other components.  
1. Semantic Segmentation of Images: First, the input image of the transformer cover \(I_{\text{input}}\) is processed by a semantic segmentation model to generate a mask \(M_{\text{mask}}\) that identifies the location of key components such as bolt holes and pipe connections. \[M_{\text{mask}}=\text{SemanticSegmentation}(I_{\text{input}})\] (27) where \(M_{\text{mask}}\) is the binary image containing each component area.
2. Surface Part Mask Expansion Treatment: To maintain a safe distance for cutting, the mask is expanded using morphological operations, resulting in an inflated mask \(M_{\text{dilated}}\). \[M_{\text{dilated}}=\text{Dilation}(M_{\text{mask}},k_{i})\] (28) where \(k_{i}\) is the size of the dilation kernel. The expansion treatment ensures that the cut line remains clear of the actual boundaries of the part, avoiding cuts into critical components [28].
3. Canny Edge Detection: Canny edge detection is performed on the inflated mask \(M_{\text{dilated}}\) to extract the initial edges of the transformer cover \(E_{\text{initial}}\): \[E_{\text{initial}}=\text{Canny}(M_{\text{dilated}})\] (29) where \(E_{\text{initial}}\) denotes the edge information extracted by the Canny operator, including the edges of each component and the outer contour of the transformer cover plate [28].
4. Generation of Closed-loop Contours: Discontinuous edges are connected by a shortest path algorithm and a minimum spanning tree algorithm to create a complete closed-loop profile \(E_{\text{closed}}\). \[E_{\text{closed}}=\text{Close}(E_{\text{initial}})\] (30) where the closed-loop operation connects disconnected edges, ensuring that the generated edges form complete closed contours, suitable for subsequent cut line generation [30].
5. Selection of Suitable Contours and Smoothing: A closed-loop profile matching the transformer cover is selected and smoothed to create the optimized profile \(E_{\text{smooth}}\): \[E_{\text{smooth}}=\text{Smooth}(E_{\text{closed}}).\] (31) Smoothing is accomplished by spline interpolation, which reduces noise and irregularities in the edges, ensuring the continuity and smoothness of the cut line in practical applications [29].