esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Examples  
In Table 1 six examples are shown, in the first column the initial contour and the algorithm results are provided, while in the other two columns the polygon phase and the offset values applied are shown.  
As is clear from the results, the proposed algorithm is able to handle both internal and external offset of complex initial contours and offset arrays. The first example shows how the algorithm works in an internal simple geometry with a triangle-shape offset array (same example in Figure 1) and how self-intersection is intrinsically managed by the proposed approach. In the second, a sinusoidal offset array with high frequency is imposed to the same initial contour. The fluctuations generate a sinusoidal allowance on straight lines and asymmetrical intersections at the vertices, accurately represented by using the proposed algorithm. A sinusoidal offset array is provided to a more complex shape featuring convex and concave angles. Applying this offset externally (example 3) leads to variable thicknesses of the straight lines and edge rotation with different radii on the four convex vertices, while the intersection is computed in the concave angle. Example 4 shows the same complex shape with an internal sinusoidal offset and similar considerations can be drawn. The algorithm correctly computes both circular edge rotation at the convex vertex and the four intersections at the concave angles. In example 5, an offset array causing the creation of two different polylines is applied to the same initial contour of the previous two examples, while in example 6 an external offset to a curve (i.e., airfoil-shape) using a complex offset array is shown.
## 1 Introduction
To improve the accuracy of estimating soil nutrient levels of the area, the major objectives of the study are (1) to estimate accurately the organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK); and (2) to predict soil nutrients using different optimal bands such as particle swarm optimization (PSO) bands, cuckoo search optimization (CSO) bands, grey wolf optimization (GWO) bands, moth flame optimization (MFO), and hybrid optimization selection bands from PSO, CSO, GWO, and MFO. This study uses PRISMA hyperspectral data and a SAM classification method to better map the AOC of soil content. This study assesses soil nutrient parameters and predicts soil nutrient levels using advanced models. The aim of this study is to examine and analyse the research as follows:  
* This study aims to critically analyse the application of PRISMA hyperspectral spectroscopy in the evaluation of soil nutrient levels within the tropical region of Tamil Nadu, India. * To generate precise and high-quality soil nutrient maps utilizing PRISMA hyperspectral data, with the aim of offering significant insights for agricultural practices, land administration, and environmental research within the specified area. * To explore the capabilities of hyperspectral remote sensing technology in enhancing the evaluation of soil nutrient levels. This advancement has the potential to contribute to sustainable land use planning and effective crop management, particularly in tropical regions. * In this study, we want to establish a systematic approach for utilizing hyperspectral data in order to accurately estimate essential soil nutrient parameters, specifically nitrogen, phosphorus, and potassium. Our primary emphasis will be on achieving high levels of precision and dependability in the estimation process. * This study aims to enhance the comprehension of remote sensing techniques' application in treating soil nutrient deficiencies and variability in tropical soils. The findings of this research can have substantial consequences for improving agricultural production and promoting sustainable land use practices in Tamil Nadu, India.
## 1 Introduction  
UAVs are revolutionizing engineering practices thanks to the recent developments in electronics, communication systems, robotics, and intelligent signal processing. UAVs have proved their usefulness in areas such as surveillance, transportation, and remote sensing [1, 2, 3, 4]. However, the next generation of UAVs requires self-controlling systems to estimate their position and perform autonomous missions [5, 6]. The current relevance of this technology is evident, but one challenge is the limited time of the UAV battery, which often lasts between twenty and thirty minutes [7, 8]. For missions that require constant monitoring or covering large areas, this becomes a serious problem. A solution for overcoming this problem is the design of ground landing stations (GLS) which allow the landing of UAVs and the replacement or recharging of their batteries. One particular design problem is to correctly estimate the UAV's position with respect to the GLS, to enable a safe landing of the UAV. In the past, it has been proposed to use embeddedsystems in combinations with the received signal strength indicator (RSSI) technique to accomplish this feature [9]. However, the RSSI signal has variations due to the propagation environment, such as signal degradation, reflection, and absorption [10].  
Our research goal consists of developing a robotic ground sensing station system for UAVs, integrated with electronic embedded devices for target position estimation. The presented approach incorporates a robotic mechanism design and the development of sensing, low-power data-processing, and communication devices. The localization and tracking system uses a network of Bluetooth low energy (BLE) devices that estimates the distance between the UAV and the sensing nodes using an RSSI-range model. A maximum likelihood estimator (MLE) is next applied to determine the position of the UAV from the GLS. The system performance ensures a stable target position estimation with average errors of less than 0.04 m when the UAV is inside a radius of 0.5 m. The proposed system has also done away with the need for costly infrastructures, such as in [11; 12], while reducing the ongoing expenses associated with power-hungry devices. The system is also easily scalable to other GLS systems, which means a larger coverage area for UAV missions.  
This paper is structured as follows. Section 2 presents an article revision related to state-of-art UAV landing systems and the optimum estimation of BLE-RSSI target positions. The GLS platform design is presented in Section 3. The hardware electronic circuits and the MLE algorithm for estimating the target position based on BLE-RSSI are described in Section 4. Experimental results of the target position estimation accuracy based on the MLE algorithm are presented in Section 5. The discussion and concluding remarks are presented in Sections 6 and 7, respectively.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 6 Experimental Setup
### Experiments  
Different experiments regarding the speaker adaptation of end-to-end VSR systems were carried out as part of this work. The results are reported and discussed in Section 7.  
**Face Recognition.** First, experiments were conducted on the automatic face recognition system that a real application would need, as depicted in Figure 1. Specifically, we explored a traditional approach based on Principal Component Analysis for feature extraction (representation known in the literature as _eigenfaces_[60]) and Support Vector Machine for classification. Results of around 99% accuracy were obtained using this approach, possibly due to the limited number of speakers considered in our case study (twenty speakers). Henceforth, we assume a perfect face recognition system throughout the rest of experiments presented in this paper. In addition, it should be noted that LIP-RTVE includes different head poses and occluded faces, as well as profile views, due to its in-the-wild nature. These type of situations were not controlled, i.e., any restrictions were considered during estimation using the face recogniser.  
**Independent Speaker-Adaptation Studies.** First, each adaptation method proposed in this work was independently analysed. Section 7.1 focuses on comparing the three different fine-tuning strategies described in Section 5.1. Section 7.2 discusses the Adapters-based method's effectiveness, comparing different Adapter sizes in terms of performance. In both cases, it is studied how all of these methods behave depending on the amount of data available for their estimation, using either the training or development set of the corresponding data partition. Using the development set, we study the robustness of each proposed adaptation method against data-scarcity scenarios, as dealing with this dataset means, in average terms, using an amount of data 4.5 times less than when using the training set [49].  
**Overall Analysis.** In this case, the best approaches of each proposed adaptation method are compared only in terms of performance and in terms of different deployment aspects, such as training time and storage costs.
## 8 Conclusions  
The success of any reconstructive operation depends on the reconstruction technique used in the reenactment of custom-design implants. The mirroring technique is the most commonly used method to recover mandibular defects, but it does have some anomalies and inaccuracies. In this research, a new technique known as the cavity-filled approach was explored as opposed to mirroring reconstruction. To estimate their performance, a 3D comparison was carried out between the reconstructed models of the two modeling techniques, analyzing the deviation. The statistical analysis utilizing hypothesis testing displayed statistically meaningful differences in the two reconstruction techniques. It was discovered on the basis of the 3D deviation study that the new or rather infrequently used cavity-filled technique provided a superior fitting as compared to the mirroring technique. The mirror reconstruction method in 3D-printed models led to a relatively higher RMS error of 1.2683 mm compared to 1.1019 mm in the cavity-filled technique due to increased user involvement and often inconsistent symmetrical plane approximation. By using the cavity-filled method, the RMS error decreased by 16.48% when the STL comparison was implemented. Moreover, the evaluation of 3D-printed models disclosed a notable 13.12% drop in RMS error with the cavity-filled in contrast to the mirror reconstruction technique. This new design approach demonstrated an integrated framework for the reconstruction of personalized mandibular implants, which provides greater facial symmetry and reduces operating time by minimizing revisions. However, the cavity-filled technique is a great and promising technique which might be limited to expanding lesions that have intact or minimal cortical wall destruction. Conversely, in the case of severe destructive lesions with distorted anatomy and loss of cortical plates or in the case of pathological mandibular fractures with loss of pre-tumor dimensions, applying such a technique might be challenging.  
K.M., conceptualization and experiments; K.M., methodology and draft preparation; S.H.M., analysis and investigation; K.M. and S.H.M., data curation, resources, and investigation; M.A., project administration and funding acquisition; S.R., validation; W.A. and O.A., visualization and supervision; K.M., writing; M.A. and S.R., review and editing. All authors read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Saud University through Research Group no. RG-1440-075.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group no. RG-1440-075.  
The authors declare no conflict of interest.  
Ethical Committee at the College of Dentistry Research Center, King Saud University, Riyadh, Saudi Arabia (Registration # FR 0467).
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 3 Network and Jamming Models  
### Smart Grid Wireless Mesh Network Model  
We model a general wireless mesh network in the Smart Grid consisting of \(n\) wireless device nodes in refs. [1; 17]. Each device node has the constant wireless communication range \(r\). Thus, the wireless mesh network can be considered as a _Unit Disk Graph_ (UDG) \(G=(V,E)\), where \(V\) denotes the group of devices and \(E\) represents the group of wireless communication connections. Where \(d(u,v)\) implies the Euclidean distance from \(u\) to \(v\) and vice versa, there exists a link \((u,v)\in E\) iff \(d(u,v)\leq r\) between any two Smart Grid device nodes \(u,v\in V\).  
We further consider a set of jammers \(J\) existing at unknown locations in the network having a transmission range at most \(R=\alpha r\) where \(\alpha>1\). Note that if two jammers have a distance less and equal to \(R\), they may consider the signals from each other as some activity in the network and keep injecting _Noise_ until their energy is depleted. Thus, we may consider the distance between any two jamming devices to be larger than \(R\).  
Any Smart Grid wireless communication node \(u\in V\) is said to be a trigger node if there is a jammer \(j\in J\), such that \(d(u,j)\leq r\). That is, when \(u\) transmits, it inevitably invokes the jammer \(j\). A device node \(v\) is said to be a victim node, iff \(d(v,j)\leq R\). (Note that by definition, a trigger node is always a victim node). If a jammer is invoked, then all the victim nodes within its transmission range are jammed from receiving any messages.
## 3 Results and Discussion
### LC-MS Method Development
#### 3.1.3 Optimizing MS Detector Parameters  
To determine the optimal parameters of the mass spectral detector, ethanolic solutions containing 10 \(\upmu\)g/mL of arachidonic acid and 10 \(\upmu\)g/mL of d8-AA were directly injected into the mass spectrometer by a deviation valve with a 100 \(\upmu\)L loop. Since AA contains a carboxylic group, which is easily deprotonated, the mass detector was set to negative electrospray mode (ESI-) [31]. Negatively formed ions [M-H]\(-\) were monitored in a full-scan mode from \(m/z\) 100 to \(m/z\) 350 Da. Different cone voltages were tested: 15, 20, 25, 30, and 35 V. For arachidonic acid, we obtained better results for \(m/z\) 303.3, CV 15 (used as quantifier ion) and \(m/z\) 304.3, CV30 and 259.30, CV 35 (used as qualifier ions) and for the IS at \(m/z\) 311.32, CV 15. These optimized LC-MS conditions were used for further AA analysis.  
Figures 1 and 2 represent LC-MS chromatograms of AA and IS standard solutions, and a real serum sample. Total ion MS spectrum of the real serum sample is shown in Figure 3.  
Figure 1: LC-MS Chromatogram of: (**a**) arachidonic acid standard solution 1 \(\upmu\)g/mL in ethanol: Linear Formula CH3(CH2)4(CH=CHCHCH2)4 CH2 CH2 CO2H; Molecular formula C20H32 OZ; RT 4.72 min; Molecular weight 304.47; Monoisotopic mass 304.2 g/mol; Detected mass [M-H]\(-\) 303.30 \(m/z\); AA—arachidonic acid; (**b**) d8-arachidonic acid standard solution 1 \(\upmu\)g/mL in ethanol (IS): Linear Formula CH3(CH2)4(CD=CDCH2)4 CH2 CH2 CO2H; Molecular Formula C20H32 OZ; RT 4.69 min. Molecular weight 312.52; Monoisotopic mass 312.3 g/mol Detected mass [M-H]\(-\) 311.32 \(m/z\); d8-AA—d8-arachidonic acid.  
Figure 3: Total ion MS spectrum of real serum sample (**a**) endogenous arachidonic acid RT: 4.72 min, \(m/z\) 303.17; (**b**) d8-arachidonic acid RT: 4.69 min, \(m/z\) 311.05.  
Figure 2: LC-MS Chromatogram of (**a**) endogenous arachidonic acid in real serum sample, \(m/z\) 303.30, RT: 4.72 min; AA—arachidonic acid; (**b**) d8-arachidonic acid in real serum sample, \(m/z\) 311.32, RT: 4.69 min; d8-AA—d8-arachidonic acid.
# Personalization in Digital Ecomuseums: The Case of Pros-Eleusis
## 6 Recommender System
### Recommender System Graph Schema  
Personalization and recommendation for the Pros-Eleusis app were modeled as a graph traversal problem. As such, the Pros-Eleusis digital ecomuseum personalized map view utilizes a graph-based recommender system that uses a graph with different types of nodes and edges in its core (Figure 3).  
The **nodes** are divided into three categories, (a) _users_, (b) _POIs_, and (c) _features_ (characteristics). (POI and feature nodes can be collectively referenced as _object_ nodes). The nodes can be connected through edges:  
* _User_ nodes can be connected to POI nodes via HasInterest edges.
* _POI_ nodes can be connected to feature nodes via HasFeature edges.
* _POI_ nodes can be connected with other POI nodes via SimilarWith edges.  
The **edges** are determined by a source node, a target node, and some weights. They are divided into three categories: (a) _HasInterest_, i.e., edges denoting the user's interest in a POI; (b) _SimilarWith_, i.e., edges denoting the degree of similarity between two POIs; and (c) _HasFeature_, i.e., indicating that the POI has the characteristic described by the respective feature.  
Each edge can be associated with different weight values:  
* For the _SimilarWith_ edges, there is a weight that corresponds to the degree of similarity between two POIs;
* For the _HasFeature_ edges, there is a weight that corresponds to the degree to which the feature characterizes the POI;
* For the _HasInterest_ edges, there are two weight values, namely strength and certainty:
* The _strength_ value is associated with the system's prediction of the intensity of the user's interest in an object and takes values in the continuous interval \([-1,\,1]\);
* The _certainty_ value relates to the reliability of that prediction, i.e., how confident the system is in the validity of this relation. It takes values in the continuous interval \([0,\,1]\).  
Figure 3: Top level graph schema: finding the interest of a user in POI\({}_{3}\).  
An edge between a user and a POI indicates the system knowledge of the user's interest in that POI.  
Figure 3 presents an example of this process. To find the interest of a user in POI\({}_{3}\), firstly, we calculate the similarity of POI\({}_{1}\) and POI\({}_{4}\) to POI\({}_{3}\), accounting for the common features Feature\({}_{1}\) and Feature\({}_{2}\). We then aggregate the edges User--HasInterest\(\rightarrow\)POI\({}_{1}\)--SimilarWith\(\rightarrow\)POI\({}_{3}\) and--HasInterest\(\rightarrow\)POI\({}_{4}\)--SimilarWith\(\rightarrow\)POI\({}_{3}\) to predict an indirect HasInterest edge between the user and POI\({}_{3}\).
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 3 Existing Fraud Detection Techniques
### Supervised Learning Models in CCFD
In recent years (2022 and 2024), it seems that the use of oversampling followed by undersampling approaches such as SMOTEENN and SMOTETomek known as hybrid resampling approaches have increased. Note that hybrid approaches reduce the overfitting problem of the minority class from the oversampling process [34]. In addition, hybrid approaches minimize the loss of information resulting from the undersampling process in all data [38]. It is thought that the use of a hybrid approach will increase in the resampling process and different hybrid approaches could be developed. With the progress in data science, many different approaches such as heuristic and meta-heuristic algorithms for FS are also used and some of those are listed in Table 3. As can also be seen in Table 3, singular value decomposition (SVD), correlation analysis (CRA), mutual information (MI), nearest neighborhood (relief feature elimination (RFE) and t-distributed stochastic neighbor embedding (t-SNE)) approaches, latent vector-based approaches (auto encoder (AE) and variational auto encoder (VAE)), one class support vector machine (OCSVM) with USL for fraudsters and minimum redundancy maximum relevance (MRMR) have been used among the papers (years 2020-2024) in CCFD. Moreover, it can be seen that the genetic algorithm (GA) is very effective in determining crucial features in card fraud data [36]. Due to the limitations of linearity assumptions, t-SNE, one of the manifold learning approaches, has been used for pattern discovery in fraud FS processing [49]. The basic-level SL techniques such as LR, DT, SVM, and MLP have frequently been used in CCFD. Additionally, ensemble SL methods such as RF, XGBoost, and AdaBoost have frequently been used, and they have outperformed other SL methods employed for CCFD. Similarly, the training performances of deep learning algorithms are very good in CCFD. In CCFD, SL models have been widely used to discover patterns in predictive studies. However, SL models create offline structure in CCFD studies, and these models may be insufficient to capture the changes in the fraudulent behavior. In this context, the usability of existing approaches such as SL models have been examined as well as different approaches supported by agent technology.
## 1 Introduction  
The electromagnetic rail launcher uses two or more pairs of rails to form the body of the rail launcher, where the main rail carries the armature movement and the other pair or pairs of secondary rails act as an enhancement to the magnetic field in the gun chamber. The inductance gradient, namely the inductance per unit length, is one of the most important parameters in determining the performance of the electromagnetic launcher system and is closely related to the armature speed as well as the energy conversion efficiency of the system, which is reflected in the literature [1, 2]. Therefore, it is important to calculate the dynamic inductance gradient parameters of the electromagnetic rail launcher considering the high-speed motion of the U-shaped armature for predicting the armature velocity.  
The literature [3] used the finite element method to obtain the inductance gradient of the enhanced four-rail electromagnetic launcher. The literature [4] obtained an inductance gradient by analyzing the average speed of the armature. The literature [5] studied the effect of rail radius on inductance gradient. The literature [6] researched the effect of rail size on the inductance gradient. However, the above methods were all based on finite element method research and could not directly obtain the inductance gradient.  
The literature [7] mentioned that the high-speed motion of the armature caused the velocity skin effect and affected the magnetic field diffusion between the rails. The literature [8] calculated the inductance gradient, taking into account the dynamic skin depth, but it did not consider the speed of the armature. The literature [9] also took into account the dynamic skin depth, but it did not consider the height of the armature. The literature [10] presented an analytical method for calculating the inductance gradient of electromagnetic rail launchers with complex structures.  
The above studies of rail inductance gradients were all about conventional rail launchers, not electromagnetic rail launchers, and the obtained inductance gradient did not take into account the effects of launcher geometry and rail dynamic skin depth. This paper starts by establishing a mathematical model of the electromagnetic launcher considering the high-speed motion of the armature and deriving the equivalent rail dynamic skin depth under the U-shaped armature. The analytical calculation of the inductance gradient considering the launcher size and velocity skin effect is obtained by applying the Biot-Savard law. Finally, the theoretical, calculated values are compared with the experimental data to achieve an accurate prediction of the armature speed.