esa-sceva/satcom-chunk-collection huggingface dataset:
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Synthesis Results
Table 5 show the different types of financial fraud. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fraud Type** & **Description** & **Technique Used** & **References** & **No. of** \\ \hline \multirow{6}{*}{Financial Statement Fraud} & & Support Vector Machine & [33; 54; 55; 56] & \\ \cline{2-5}  & This is a corporate fraud such that the financial statements are illegtimately modified to allow the organizations to look more beneficial. & Clustering based method & [37; 56] & \\ \cline{1-1} \cline{3-5}  & This is a corporate fraud such that the financial statements are illegtimately modified to allow the organizations to look more beneficial. & Decision Tree & [33; 57; 58; 59; 60] & \\ \cline{1-1} \cline{3-5}  & This is a corporate fraud such that the financial statements are illegtimately modified to allow the organizations to look more beneficial. & Logistic Regression & [35] & \\ \cline{1-1} \cline{3-5}  & & Naive Bayes & [33; 61] & \\ \cline{1-1} \cline{3-5}  & & Artificial Neural Network & [33; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 9  
.2.2 RQ2: What Are the ML-Based Techniques for Financial Fraud Detection Employed in the Literature? Machine learning (ML) is referred to as analytic techniques that find specified patterns without the manual guidance of an expert [87, 88]. Financial fraud detection has been widely studied using ML methods by many researchers [17, 89, 90]. This includes SVM, ANN, HMM, KNN, Decision Tree, etc. Therefore, to answer the above research question (RQ2), this section presents different popular ML methods that were used for financial fraud detection based on the selected articles in the review. A detail explanation of the ML techniques used for detecting financial fraudulent activities is provided in the following sub-section. Support Vector Machine (SVM)  
SVM is a supervised ML method that seeks a maximum margin hyperplane for classifying input training data into two categories [41, 66]. SVM is capable of classifying new data points based on a labeled training set for each class [68]. Based on the reviewed literature, several researchers investigated SVM techniques for fraud detection [65, 66, 80]. For example, [PERSON] and [PERSON] [65] introduced a hybrid technique based on SVM and the fusion Danger theory for the fraudulent detection approach. Based on the experimental results, the authors showed that this study outperformed the existing approaches in terms of time complexity and F-measure. [PERSON] et al. [80] used the SVM technique to propose fraud detection by investigating an automated medical bill architecture. This research method aims provide a quick response for detecting medical fraud in real time. Experimental results showed that the model performed better compared to the previous approaches. [PERSON] and [PERSON] [66] applied optimized SVM to detect fraudulent activities in an online credit card.
# Aerospace
## 2 Mathematical Models
### Approach Trajectory, Entry State, and Deflection Maneuvers  
Given an interplanetary arrival \(\vec{V}_{\infty}\), the approach trajectory is completely defined by two parameters: (1) the trajectory periapsis magnitude \(r_{p}\), and (2) the angular position \(\psi\) of the aim point on the B-plane measured clockwise from the +ve B.R axis. By choosing various values of \(\psi\) from \(\frac{3\pi}{2}\) to \(\pi\), it becomes possible target inclinations ranging from the arrival declination up to polar orbits. The hyperbolic approach trajectory of the aerocapture vehicle until the entry interface and the entry state vector [\(r_{e}\), \(\theta_{e}\), \(\phi_{e}\), \(V_{e}\), \(\gamma_{e}\), \(\chi_{e}\)] is computed using the analytic method developed by [PERSON] [32]. Once an entry state is obtained, the aerocapture corridor can be computed, and the approach trajectory can be recomputed iteratively by adjusting \(r_{p}\) to obtain an approach trajectory that delivers the vehicle at the desired \(\gamma_{\rm e}\) within the aerocapture corridor defined by [\(\gamma_{\rm min}\), \(\gamma_{\rm max}\)].  
Given two approach trajectories defined by (\(r_{p1}\), \(\psi_{1}\)) and (\(r_{p2}\), \(\psi_{2}\)), which correspond to two aim points on the B-plane, and the distance from the planet at which the maneuver is performed, the analytic solution developed by [PERSON] [32] is used to compute the magnitude of the deflection maneuver required to change the approach trajectory. The calculation of approach trajectory and entry state vector from an arrival \(\vec{V}_{\infty}\) using a given (\(r_{p}\), \(\psi\)) and the deflection maneuvers is implemented in AMAT.
## 9 The MID Instrument
### MID Instrumentation and Capabilities
The PP laser beam is delivered to EH via a transfer pipe to a laser table next to the MPC allowing additional tailoring of the beam before it is directed towards the sample position. Temporal and spatial overlaps of the optical laser beam and the X-rays can be controlled through imaging and timing diagnostics [49] and tuned by adjusting optical components in the laser beam path located in the ILH. The radiation scattered from the sample is measured using the AGIPD detector. In SAXS configuration the distance from sample to detector can be varied from \(\sim\)200 to 8000 mm. This provides an angular detection resolution between 1 mrad and 25 \(\upmu\)rad and a field-of-view between 1 rad and 25 mrad. With the direct beam in the center of the detector at 10 keV, it translates into a q-resolution and q-range of 5.0 \(\times\) 10\({}^{-3}\) and 2.3 A\({}^{-1}\) for 200 mm, and 1.3 \(\times\) 10\({}^{-4}\) and 6.3 \(\times\) 10\({}^{-2}\) A\({}^{-1}\) for 8000 mm, respectively. Special configurations with even shorter sample-detector distance and exploitation of the full energy range of the instrument (5-25 keV) allow tuning these values. In the SAXS case, a hole in the center of the detector (adjustable by movable quadrants) permits unhindered passage of the  
Figure 8: Schematic outline of the MID scientific instrument at the SASE2 FEL. Shown are major instrumentation items installed in the photon beam transport tunnel, optics hutch and experiment hutch comprising the beam transport, focusing and diagnostic devices. direct beam, i.e., without destroying the sensor. The exit port of AGIPD is connected to a diagnostics end-station where intensity, size and spectrum of the transmitted beam can be quantified with high resolution. In particular, a semi-transparent bent diamond spectral analyzer has been developed allowing to quantify the SASE spectrum down to a resolution of \(\sim\)0.1 eV [118]. This spectrometer will operate in parallel with AGIPD and the spectral information together with scattering data enable a better data analysis as well as easy tuning of the self-seeded mode. A similar transparent diamond spectrometer can be inserted upstream of the MPC to measure the spectrum before interaction with the sample [118]. In this manner absorption spectroscopy [119] can be combined with, e.g., pump-probe and coherent scattering techniques providing unique new possibilities of investigating interactions of ultra-bright fs X-ray pulses with matter. The MID instrument also features the option of measuring in a horizontal WAXS geometry (scattering angle up to \(\sim\)55\({}^{\circ}\) with the sample-detector distance varying between 2000 and 8000 mm. This will enable high resolution detection at large q (beyond 10 A\({}^{-1}\)) investigating (coherent) diffraction originating from, e.g., structural, charge, or magnetic ordering in combination with the pulsed magnetic field or the fs pump laser to access ultrafast dynamics processes.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 2 Problem Statement
### Cost Function  
The cost function is generally used to represent the smoothness and comfort of the vehicle motion. Assuming the trajectory planning time domain is \(t\in[0,t_{t}]\), the cost function \(J\) is constructed as follows:  
\[J=\int_{\tau=0}^{T}\left({\rm w}_{\rm a}\cdot a^{2}(\tau)+{\rm w}_{\omega} \cdot\omega^{2}(\tau)\right)\cdot d\tau, \tag{5}\]  
where \({\rm w}_{\rm a},{\rm w}_{\omega}({\rm w}_{\omega},{\rm w}_{\omega}>0)\) are user-defined weights, \(a\) represents the vehicle's acceleration, and \(\omega\) represents the vehicle's angular velocity.
## 4 Performance Evaluation
### Flight Test Data Analysis
At a flight altitude of 1 km, 99% of \(\Delta p_{k}\) were within 5 m. At a flight altitude of 3 km, 85.6% of \(\Delta p_{k}\) were less than 5 m. The resolution of the DEM used in the TRN simulation was about 10 m, but \(\Delta p_{k}\) of less than 5 m is too small to confirm the performance difference between the two formulas. At a flight altitude of 5 km, 53.4% of \(\Delta p_{k}\) were less than 10 m.  
The horizontal position difference between the two formulas can be roughly calculated by the following equation. \[\Delta p_{k}\rightleftharpoons R\tan((\theta+\gamma)-\ \xi) \tag{40}\]  
where \(R\) is slant range of IRA, \(\theta\) is look angle of IRA, \(\gamma\) is roll angle of INS and \(\xi\) is effective look angle. Since the range (\(R\)) and angle difference (\((\theta+\gamma)-\ \xi\)) were largest when the flight altitude was 5 km, the maximum value of \(\Delta p_{k}\) was also largest for the Equation (40). Therefore, we performed the TRN simulation using the data at the flight altitude of 5 km and compared the performance of each formula.
# Multi-Modal X-ray Imaging and Analysis for Characterization of Urinary Stones
## 3 Results  
### X-ray Diffraction  
The 2D diffraction images from the XRD measurements from the STOE-IPDS instrument are shown in Figure 2a-c) for the three investigated samples. They allow for differentiating between the exciting structural features with respect to the phases, crystallinity, and orientation. The dog sample shows the highest crystallinity based on the observed single crystal diffraction behavior, which is clearly seen through the diffraction spots on the 2D detector. The cat sample reveals a random orientation of crystallites. This is monitored through sharp rings on the 2D images, also indicating a high crystallinity and exceeding the detector resolution. The human sample is clearly nano- and polycrystalline, which is seen by broader diffraction rings compared to the cat sample. The 1D diffraction patterns for these three samples were produced from a powder, and are presented in Figure 2d. The results of the phase analysis are presented, respectively, in Table 1. It should be recalled that the analysis was conducted for a piece of the stone samples, so the information regarding the \"main\" and \"minor\" assignment does not necessarily represent the whole stone, but the outer part of the sample.  
From Figure 2, it can be seen that very different crystallographic phases are found in the extracted mineralizations. Detailed phase analyses are illustrated in Figure A1 of the Appendix. While, from the cat and human samples, mostly calcium oxalate and phosphate compounds are identified, the dog sample shows a single crystalline magnesium phosphate in its struvie form (see Table 1 and Figure A1). Struvie is a normal component of dog's urine and will remain dissolved as long as the struvie concentration is low and the urine is acidic. Higher concentrations and alkaline urine result in stone formation. The cat sample reveals mainly hydroxyapatite and whewellite as a minor phase. In the human sample, only calcium phosphate components were identified--hydroxyapatite, as the main phase, and whitelockite as the minor phase. Details on phase formations are given in Table 1. Figure 2d reveals a strong increase of crystallinity from the human to the cat, then to the dog sample. Crystallinity evaluates the crystalline (well-resolved reflections) relative to the amorphous (background) part of the sample. The dog sample shows single crystalline behavior, which would be an indication of the slow growth of the entire stone.  
We see a clear difference in the type of phase formation and the crystalline state, both of which will affect the disease treatment.  
Figure 2: The 2D diffraction patterns of the stone samples from (**a**) dog, (**b**) cat, and (**c**) human urinary stone samples. (**d**) The 1D diffraction profiles of the three stones.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 3 System Applications
### Imaging through Scatterers
The spacing between the two USAF objects was modified to 3 mm and the experiment was repeated for this case. The \(I_{OBJ}\) was recorded again and reconstructed using the \(I_{PSH}\) recorded at the two planes of the object. The reconstruction results using the two \(I_{PSH}\)s are shown in Figure 29a,b, respectively. Figure 27: Optical configuration for imaging through a scatterer [69]. The SNR of the reconstructed images can be improved by recording multiple intensity patterns using different scatterers and averaging over the entire reconstructions as in the case of COACH systems [52]. Alternatively, improvement of the SNR can be achieved using a nonlinear correlator in which both spectral amplitudes of \(I_{OBJ}\) and of \(I_{PSH}\) are raised to a power of two independent parameters, which are optimized for maximum SNR [70; 71]. The I-COACH techniques are simple and useful for imaging through scatterers. However, the need for recording the point spread hologram makes this technique invasive for seeing through biomedical tissues. In another way, this technique can be used to convert any 2D imager into a 3D imager by attaching a scatterer to the lens. Once the system is calibrated by the recording of the point spread holograms for some longitudinal range, a 3D image of the object can be reconstructed by cross-correlation with the PSH library.
###### Abstract  
In this paper, a model based on constraint convex optimization is proposed for the radiation pattern synthesis in cylindrical and spherical microstrip conformal array antennas. The optimization algorithm is modeled to decrease the Euclidean distance between the obtained radiation pattern of the deformed array and the desired radiation pattern of the planar array by calculating the suitable amplitude and phase values for the individual array patches. Four prototypes of \(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\) microstrip patch array antenna are used as the radiating elements which are deformed from planar arrangement and placed on the faces of prescribed spherical and cylindrical shapes with various radii of curvatures. The results reveal that the performance of optimization algorithm performs better in \(5\times 5\) array, followed by \(4\times 4\) array, \(3\times 3\) array, and \(2\times 2\) array. However, \(5\times 5\) array provides more degrees of freedom for tuning the radiation pattern in desired form. The \(5\times 5\) array computes suitable amplitude and phase values for the radiating elements of the array, which restore the radiation pattern in terms of mainlobe reconstruction, null locations, sidelobe levels, up to the deformation of 20 cm in both spherical and cylindrical configurations of conformal array. The proposed optimization technique when applied to conformal antenna array offers fast computational speed and good convergence accuracy for radiation pattern synthesis, which can be useful for various engineering applications.  
osratin convex optimization; conformal array antenna; radiation pattern correction; mainlobe; sidelobe levels Article
## 2 Results and Discussion
### Optimization of SPME-Arrow Conditions for Analysis of Bound VOCs
Thus, the extraction temperature of 60 \({}^{\circ}\)C and exposure time of 60 min were chosen. Incubation time again did not have significant influence on the extraction efficiency. The analytical signals tend to increase with a decrease in both extraction temperature and incubation time. In Figure 5 are presented 3D surface plots for the effect of extraction temperature and exposure time for all classes of VOCs. This interaction is statistically significant for alcohols, C\({}_{13}\)-norisoprenoids, acids and monoterpenes. Alcohols have vapor pressure with the average value of 93 Pa. Based on these values, these compounds are quite volatile even at room temperature, thus the observation that the rising temperature up to 60 \({}^{\circ}\)C in the short exposure time gave maximal extraction efficiency is not a surprise. The analytical signals in case of C\({}_{13}\)-norisoprenoids, acids and monoterpenes tend to increase with an increase in extraction temperature and with an increase in exposure time, with the maximal extraction efficiency achieved at  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Terms**} & \multicolumn{2}{c}{**Alcohols**} & \multicolumn{2}{c}{**Acids**} & \multicolumn{2}{c}{**Carbonysis**} & \multicolumn{2}{c}{**Nonisoprenoids**} & \multicolumn{2}{c}{**Monoteprenes**} \\  & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** \\ \hline Model & & 0.0064 & & \(<\)0.0001 & & 0.0364 & & 0.0008 & & 0.0012 \\ Lack of Fit & & 0.6491 & & 0.6201 & & 0.7224 & & 0.0524 & & 0.6034 \\ Intercept & \(2.29\times 10^{11}\) & & \(2.30\times 10^{10}\) & & \(3.10\times 10^{10}\) & & \(4.28\times 10^{10}\) & & \(8.37\times 10^{9}\) & \\ A-Temperature & \(-2.92\times 10^{10}\) & & 0.0027 & \(1.40\times 10^{10}\) & \(<\)0.0001 & \(3.83\times 10^{9}\) & 0.0436 & \(6.14\times 10^{9}\) & \(<\)0.0001 & \(4.02\times 10^{9}\) & \(<\)0.0001 \\ B-Incubation & \(-9.16\times 10^{9}\) & & 0.1450 & \(-4.94\times 10^{9}\) & 0.4751 & \(3.89\times 10^{9}\) & 0.0415 & \(15.25\times 10^{9}\) & 0.7835 & \(-2.35\times 10^{8}\) & 0.4811 \\ C-Exposure & \(-1.60\times 10^{9}\) & & 0.0296 & \(8.24\times 10^{9}\) & \(<\)0.0001 & \(6.86\times 10^{9}\) & 0.0048 & \(2.73\times 10^{9}\) & \(0.0015\) & \(2.15\times 10^{9}\) & 0.0009 \\ AB & \(2.03\times 10^{9}\) & & 0.7981 & \(-1.13\times 10^{9}\) &around 60 min of exposure. Furthermore, the extraction temperature of 60 \({}^{\circ}\)C was chosen as an optimum, so a compromise had to be made. The incubation time did not show as a significant factor. The optimal conditions obtained by applying Derringer function or desirability function for SPME-Arrow extraction of bound VOCs are presented in Table 4. The estimated and experimentally obtained values presented are similar, indicating a good performance of the method developed for the extraction of bound VOCs from grape skins.
## 2 Materials and Methods
### Volatile Composition by Gas Chromatography  
Solid phase microextraction (SPME) was used for the analysis of volatile compounds from fresh and dried plants. Briefly, the fresh _S. ramossissima_ was crushed with a mortar and pestle until a paste was formed, after which 1.5 g of paste was transferred to a GC-MS vial. For the dried plants, 0.5 g was transferred directly to a GC-MS vial. A 20 mL headspace vial (La-Pha-Pack(r), Langerwehe, Germany) capped with a white PTFE silicone septum (Specanalitica, Carcavelos, Portugal) was used in both situations. The SPME operating conditions consisted of an extraction temperature of 40 \({}^{\circ}\)C for 40 min, a rotating speed of 250 rpm, an agitation of 10 s, and desorption time of 3 min at 250 \({}^{\circ}\)C. Analyses were performed in a GCMS QP2010 Plus (Shimadzu, Kyoto, Japan) equipped with an AOC-5000 autosampler (Shimadzu(r)). For headspace, SPME sampling used a divinylbenzene/carboxen/polydimethylsiloxane (DVB/Car/PDMS) fiber (Supelco Analytical, Bellefonte, PA, USA). For the separation of volatile compounds, a capillary column Sapiens-5-MS (Teknokroma, Barcelona, Spain) with dimensions of 30 m, 0.25 mm (IS), and 0.25 \(\upmu\)m (film thickness) was used. The working conditions were as follows. The injector and detector temperature were maintained at 250 \({}^{\circ}\)C. The injection mode was carried out in the splitless mode during 1.5 min and high-purity helium (\(\geq\)99.999%) was used as the carrier gas. The column oven temperature was kept at an initial temperature of 40 \({}^{\circ}\)C for 5 min, increased to 170 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\), and then was increased to 230 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\) and maintained for 4 min; carrier gas (He) was maintained with a flow of 2.00 mL min\({}^{-1}\). In the MS interface, the temperature was 250 \({}^{\circ}\)C and the ion source temperature was 250 \({}^{\circ}\)C. Mass spectra were acquired in the electron ionization (EI) mode at 70 eV in a \(m/z\) range between 29 and 300 with a scan speed of 588 scans s\({}^{-1}\). Identification of compounds was performed using the mass spectra library (NIST 2005 mass spectra database, Boulder, CO, USA) and Linear Retention Index (RI) [27; 28]. Each sample was analyzed in duplicates.
## 3 Background  
### Intrusion Detection Systems  
An IDS is a widely used network security technology employed across various enterprise and organizational networks. As the name suggests, an IDS is a system or software established in a network system that has the role of monitoring the traffic and immediately sending alerts to administrators if malicious activities or policy violations are detected. The most optimal and common position for an IDS to be placed is \"behind-the-firewall\" (strategic points) because this placement enables the IDS to have high visibility into incoming network traffic while ensuring that it does not intercept traffic between users and the network.  
The NIDS, which is shown in Figure 2 is one of the two main types of IDS and it is strategically positioned within the network to analyze the traffic originating from all devices on the network. Traditional NIDS systems use two main attack detection methods: signature-based and anomaly-based methods. The signature-based method uses pre-defined attack signatures through rule sets to effectively identify known threats and malicious patterns. This method helps the system respond accurately and promptly to known attacks, but it is largely ineffective at detecting new attacks or other variations of known attacks. Additionally, the anomaly-based method establishes the baselines of normal network behavior, allowing it to detect anomalous activities that do not match regular patterns, thereby identifying them as suspicious behaviors and sending alerts to the administrator. This method still does not guarantee reliability, as it still faces limitations such as high rates of false positives and false alarms. To overcome the limitations of traditional NIDS systems, many deep learning techniques, such as CNN [29; 30], RNN [31], and GNN [32] were applied to NIDS to improve the accuracy and performance of detecting cyber-attacks.