esa-sceva/satcom-chunk-collection huggingface dataset:
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 1 Introduction
In a typical GNSS-INS system, an IMU with three orthogonally mounted accelerometers and three gyroscopes is used. For land based vehicular applications, to reduce the cost associated with a full INS, a reduced number of sensors can be used, also known as a reduced inertial Sensor system (RISS) [27; 28; 29]. Even though integration of GNSS and INS provides robust navigation, performance will be degraded under spoofing attacks. Under such attacks, GNSS measurements will be erroneous which in turn makes the integrated solution unreliable. In an integrated GNSS-INS system, since only GNSS measurements are potentially erroneous due to spoofing, INS measurements can be used in an integrity monitoring role to detect an attack. Authors in [19] proposed an integrated GPS/INS navigation system to detect a spoofing attack based on the receiver autonomous integrity monitoring (RAIM) concept. The integrity risk has been evaluated in the presence of high-end and low-end INS systems and it is shown that the proposed approach is able to successfully detect spoofing attacks that do not have previous knowledge of the receiver's trajectory. A shipboard IMU measurements in [18] was used to detect the presence of spoofing signals; their approach compares the relative platform motion estimates provided by a shipboard receiver to the ones provided by the onboard IMU. It was shown that high frequency pitch/roll motion of the ship caused by mild sea conditions can lead to successful spoofing detection. In [17] a tightly coupled GNSS/INS approach to detect spoofing signals was proposed. The method detects spoofing attacks by monitoring the residuals and sets the spoofing detection based on minimum detectable blunder test statistics. It assumes that a subset of visible PRNs is spoofed at a given time which limits its practicality. Herein, a spoofing detection approach based on a consistency check of GNSS and INS/odometer (odo) mechanization is proposed. To detect a spoofing attack, the proposed method analyzes GNSS and IMU/odo measurements during an observation window and compares the two solutions (trajectories). The INS-vehicle odometer integrated solution is self-contained and therefore not vulnerable to external signal jamming and spoofing. However, like all dead reckoning devices, it is susceptible to sensor induced errors, especially drift. The spoofing detection observation window is defined based on the INScharacteristics and the specific application. GNSS signal authenticity is verified if its navigation solution is consistent with the corresponding solution provided by INS/odo. If GNSS signal authenticity is verified, the GNSS/INS/odometer loose coupling solution is performed to estimate and remove IMU errors.
###### Abstract  
An autonomous vessel uses multiple different radio technologies such as satellites, mobile networks and dedicated narrowband systems, to connect to other ships, services, and the remote operations center (ROC). In-ship communication is mainly implemented with wired technologies but also wireless links can be used. In this survey paper, we provide a short overview of autonomous and remote-controlled systems. This paper reviews 5G-related standardization in the maritime domain, covering main use cases and both the role of autonomous ships and that of people onboard. We discuss the concept of a connectivity manager, an intelligent entity that manages complex set of technologies, integrating satellite and terrestrial technologies together, ensuring robust in-ship connections and ship-to-outside connections in any environment. This survey paper describes the architecture and functionalities of connectivity management required for an autonomous ship to be able to operate globally. As a specific case example, we have implemented a research environment consisting of ship simulators with connectivity components. Our simulation results on the effects of delays to collision avoidance confirm the role of reliable connectivity for safety. Finally, we outline future research directions for autonomous ship connectivity research, providing ideas for further work.  
Keywords:5G; 6G; satellites; unmanned autonomous vehicles +
Footnote †: journal: Remote Sensing, 2020, 12, 2507; doi:10.3390/rs12152507
## 3 Results and Discussion
### Long-Term Cost Projection  
A 25-year cost projection has been generated to compare the financial impact of using solar power versus relying solely on grid electricity. The future energy projections indicate sustained cost reductions and an increasing reliance on solar power. The cumulative cash flow analysis and cost projection over 25 years demonstrate long-term financial benefits,  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Solar Projections**} & **Value (kWh)** & **Cost from Grid (S)** \\ \hline System Suggested & 33 kW & - \\ \hline
33 kW solar system annual energy production (CEC Average) & 31,132 & - \\ \hline Conservative annual estimate at site due to PV system orientation & 54,240 & - \\ \hline Usage estimates based on a 5.5-day week with some standing power usage & & \\ \hline Peak Usage substituted by Solar & 27,373 & 7765.87 \\ \hline Off-Peak Usage substituted by Solar & 3041 & 862.86 \\ \hline Potential excess solar production feed in & 23,825 & - \\ \hline Total Savings & 54,240 & 8627.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Future savings estimations.  
Figure 13: Future Cost Consumption Projection (**a**) Cash purchase cumulative cashflow (**b**) finance cumulative cashflow.  
reinforcing the project's economic viability. The ability to fulfill 57% of the site's electricity needs through solar energy highlights the potential for further expansion. With technological advancements in battery storage and solar efficiency, future projects may achieve even higher levels of grid independence and cost savings. The results are summarized in Figure 14. In a finance term, approximately $23,470 can be saved, with projections to reach USD $241,600 over 25 years.
# Combination of Permanent and Variable Load Is Dependent
## 2 Materials and Methods  
Equations are given which can be used to calculate load combinations of single loads and load groups using various assumptions [5; 6; 16].  
The uncertainty is omitted.  
The target reliability index is \(\beta_{50}=3.83\), i.e., the 50-year failure probability is P\({}_{\rm E0}=1/15000\). The design point is fixed to unity, where the characteristic values of the distributions are fixed, i.e., to the mean of the permanent load distribution, the 50-year return load of the variable load distribution and the 0.05 fractile of the material property distribution.  
The permanent load distribution G(x; \(\mu_{\rm G}\), \(\sigma_{\rm G}\)) is normal G(x; 1, 0.091), V\({}_{\rm G}=0.091\), \(\gamma_{\rm G}=1.35\).  
The variable load distribution Q(x; \(\mu_{\rm Q}\), \(\sigma_{\rm Q}\)) is the 50-year return load given by Gumbel distribution G(x; 0.491, 0.196), V\({}_{\rm Q}=0.4\), \(\gamma_{\rm Q}=1.5\).  
The loads are combined with load proportion \(\alpha\), the variable load in the total load, \(\alpha=\mu_{\rm Q}/\mu_{\rm G}+\mu_{\rm Q}\).  
Safety factors are calculated for timber material [4] using various load combination options. The distribution is assumed log-normal, 0.05 fractile fixed to the design point, M(x; 1.412, 0.282), V\({}_{\rm M}=0.2\).  
When one load L with the cumulative distribution \(\mathrm{F_{L}}(x;\ \mu_{\mathrm{L}},\ \sigma_{\mathrm{L}})\) and the safety factor \(\gamma_{\mathrm{L}}\) strikes a material with the density distribution of \(\mathrm{f_{M}}(x;\ \mu_{\mathrm{M}},\ \sigma_{\mathrm{M}})\), the equation to calculate the safety factors \(\gamma_{\mathrm{L}}\) or \(\gamma_{\mathrm{M}}\) or the failure probability \(\mathrm{P_{f}}\) is  
\[\int_{0}^{\infty}\mathrm{F_{L}}\bigg{(}x;\ \frac{\mu_{\mathrm{L}}}{\gamma_{ \mathrm{L}}},\frac{\sigma_{\mathrm{L}}}{\gamma_{\mathrm{L}}}\bigg{)}\ f_{\mathrm{M}}(x;\ \mu_{\mathrm{M}}\gamma_{\mathrm{M}},\ \sigma_{\mathrm{M}}\gamma_{\mathrm{M}})\ \mathrm{d}x=1- \mathrm{P_{f}}. \tag{1}\]  
Both distributions are fixed with equal characteristic values.  
The cumulative distribution of the ILC is calculated by using the convolution equation [5; 6; 16],  
\[\mathrm{F_{i}}=\int_{-\infty}^{\infty}\mathrm{G}\bigg{(}r;\ \frac{\mu_{\mathrm{G}}(1-\alpha)}{\gamma_{\mathrm{G}}},\frac{\sigma_{\mathrm{G} }(1-\alpha)}{\gamma_{\mathrm{G}}}\bigg{)}^{n}\frac{\mathrm{d}}{  
structure with the same occurrence probability. We know from physics that the sum of these halves is the same as each full load. This can be proved in the reality: One half of the structure is constructed and loaded by the permanent load. The other half is added and loaded with the variable load. The resistance of the total structure is the arithmetic sum of the halves [5]. The ILC does not fulfil this condition and is in contradiction with the physics and deterministic mechanic. The contradiction applies to all load proportions \(\alpha\).
## 4 Evaluation
### Reconstruction the Rate of Voice Data Using an Autoencoder  
Figure 5 shows the original data and the restored data using the autoencoder. \"Original\" represents the original data, \"encoded\" represents the features extracted by the encoder, and \"decoded\" represents the original data reconstructed using a decoder based on those features. From this, it can be seen that there was a voice pattern for each data, and the input data was successfully restored through the autoencoder with an MSE loss value of about 0.00076. Therefore, it was found that the features extracted through the encoder of the autoencoder extracted an appropriate latent vector for data restoration, and the latent vector was used to perform user authentication and deep voice classification.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design
### Fuzzy PID Acceleration Controller Design
#### 3.1.1 Controller Structure  
A two-dimensional fuzzy controller [17] was chosen for this paper; that is, the speed deviation (\(e_{v}\)) and the variation (\(\dot{e}_{v}\)) of the speed deviation are taken as double inputs, and the expected acceleration of the ferry vehicle (\(a_{des}\)) is taken as the single output control quantity. The changes in velocity deviation and velocity deviation are shown in Formulas (9) and (10), respectively.  
\[e_{v}=v_{d}-v \tag{9}\]  
\[\dot{e}_{v}=d/dt(e_{v}) \tag{10}\]  
where \(v\) represents the ferry vehicle's actual speed, and \(v_{d}\) is its intended speed.
## 2 Materials and Methods
### Statistical Analysis  
Data are shown as mean with standard deviation (SD). Linear regression analysis was performed to examine the linear relationship between the VGRF and the resultant acceleration (shank, thigh, and lumbar sensors). A linear relationship was verified for the LSI, the value on the involved limb, and the value on the uninvolved limb. A 2 \(\times\) 2 table was created to examine the relationship between the LSI of the VGRF and resultant acceleration. To determine the increased load on the involved limb during single-leg drop landing, the criterion for the 2 \(\times\) 2 table was set at an LSI of 110%. For the assessment of LSI, a deficit of more than 10% in the involved limb compared to the uninvolved limb in both strength and single-leg hop testing after ACLR has been considered a dysfunction in the involved limb [25; 35; 36; 37]. Therefore, in the present study, an LSI of 110% was defined as an overload on the involved limb. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were calculated using a 2 \(\times\) 2 table. The statistical significance level was set at \(p\) < 0.05. Statistical analyses were performed using JMP Pro Version 16 (SAS Institute Inc., Cary, NC, USA).
###### Abstract  
Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT's foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN-transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture's high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount.  
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## 4 Aircraft Tracking and Activity Recognition
### Aircraft Entry and Exit Detection  
For satellites operating in staring mode, the airport background changes slightly in the acquired video images. Therefore, we use the background subtraction method [31] to  
Figure 9: Aircraft activity differentiated according to its speed of movement.  
detect new incoming aircraft. To further reduce the amount of computation, we only detect new incoming aircraft around the edges of the image. The size of the edge range for the detection operation is equal to the size of the largest aircraft in the current field of view. Aircraft entry detection includes the following five operations:  
1. Build a KNN-based background subtractor for background detection.
2. Obtain the foreground mask image and perform image binarization processing.
3. Morphologically process candidates in the foreground region based on the rectangular kernel.
4. Eliminate obviously wrong results within dimensional constraints. A target whose size is smaller than 90% of the smallest aircraft size or larger than 110% of the largest aircraft size in the current field of view is considered to be obviously wrong.
5. Judge whether the candidate result is the tracked aircraft that has moved into the edge area. This can be easily achieved by judging whether their bounding boxes overlap.  
Aircraft exit detection is simpler. If an aircraft fails to be tracked for 25 consecutive frames, and its latest tracked position was in the image boundary area, the aircraft is considered to have exited.
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 2 Related Works  
During the past two decades, many research works have been published for automatic SL recognition in different languages and countries [13; 14; 15; 16], most of them used device-based sensors such as gloves and wearable devices [17], and Leap Motion Controller (LMC) [7; 15; 18; 19; 20; 21; 22]. As device-based methods are expensive and need the person to be equipped with sensors and due to the recent advances in AI, the current trend and efforts in SL research has been directed to the use of vision-based techniques combined with machine learning and deep learning [23; 24; 25; 26]. In this section, extensive literature review studies in SL classification field are discussed from 2020 until present. Prior to these years, review papers by [18; 27; 28] have investigated SL research works in the gap between 1998 to 2021, which provide a systematic analysis of research employed in SL recognition-related studies. Spanning over a period of two decades, these reviews cover various stages involved in the process, such as image acquisition, image segmentation, feature extraction, and classification algorithms. They also highlight the accomplishments of different researchers in achieving high recognition accuracy. Additionally, the reviews discuss several limitations and obstacles faced by vision-based approaches, including a lack of available datasets, the complexity of certain techniques, the nature of signs themselves, the complexity of backgrounds, and illumination conditions in images and videos.
###### Abstract  
Background/Objectives: The anti-_Leishmania_ potential of _Curcuma longa_ and its derivatives, such as curcuminoids, is well-established, yet their mechanisms of action remain underexplored. This study investigates the inhibitory effects of _C. longa_ extracts and curcumin on _Leishmania infantum_ arginine, a key enzyme in polyamine and trypanothione biosynthesis, and evaluates their antiparasitic activity. **Methods**: Extracts were prepared via rhizome successive maceration with hexane (HEXCURC), dichloromethane (DCCURC), and ethanol (ETOHCURC) and chemically characterized by a combination of chromatographic and spectrometric methods. The inhibition of recombinant _L. infantum_ arginase (_Li_ARG) was assessed by urea quantification, while molecular docking explored interactions between the main compounds annotated in the extracts and the enzyme's active site. Biological activity was tested against _L. infantum_ promastigotes, intracellular amastigotes, and mammalian cells. **Results**: LC-MS and GC-MS revealed curcuminoids and turmerones as main compounds annotated in the extracts. DCCURC, HEXCURC, and curcumin showed the strongest _Li_ARG inhibition (IC\({}_{50}\) = 10.04, 14.4, and 17.55 \(\upmu\)g/mL, respectively). Docking analysis revealed that curcumin, demethyloxycurcumin, and bisdemethoxycurcumin bind near the active site, with binding energies of -3.43, -4.14, and -3.99 kcal/mol, respectively. Curcumin demonstrated superior anti-promastigote activity (IC\({}_{50}\) = 15.01 \(\upmu\)g/mL) and selectivity (SI = 12.7) compared to the extracts. It also significantly reduced amastigote burden in infected macrophages (IC\({}_{50}\) = 13.6 \(\upmu\)g/mL). **Conclusions**: This is the first report demonstrating that _C. longa_ extracts and curcumin inhibit _Li_ARG. These findings support curcumin's potential as a lead compound for developing multi-target therapies against leishmaniasis, combining enzyme inhibition with direct antiparasitic effects.  
Keywords:antileishman; curcumin; turmerone; ADMET; molecular docking; enzymatic inhibition
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 7 Experimental Results
### Convergence Analysis  
The evaluation of accuracy in the final solution cannot completely assess the abilities of an optimization algorithm. On the other hand, the convergence of a metaheuristic scheme represents an important property to assess its performance. This analysis determined the velocity, which determined metaheuristic scheme reaches the optimum solution. In this subsection, a convergence study has been carried out. In the comparisons, for the sake of space, the performance of the best four metaheuristic schemes is considered adopting a representative set of six functions (two multimodal, two unimodal and two hybrids), operated in 30 dimensions. To generate the convergence graphs, the raw simulation data produced in the different experiments was processed. Since each simulation is executed 30 times for each metaheuristic method, the convergence data of the execution corresponds to the median result. Figures 6-8 show the convergence graphs for the four best-performing metaheuristic methods. A close inspection of Figure 6 demonstrates that the proposed SOA scheme presents a better convergence than the other algorithms for all functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & **SOA** & **SOA** & **SOA** & **SOA** & **SOA** & **SOA** \\  & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** \\  & **ABC** & **CMAES** & **CSA** & **DE** & **MFO** & **PSO** \\ \hline \(f_{17}(\mathbf{x})\) & \(4.35\times 10^
# Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
###### Abstract  
In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing--Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.  
5G; compressed sensing; DCS-SOMP; parameter estimation [1]  
Our proposal has two novelties: first, our approach entails the dynamic adaptation of the sensing matrix, obtaining rapid and accurate parameter estimation through the DCS-SOMP method and second, we perform all parameter estimation within a three-dimensional (3D) environment. Our proposal relies on accurate channel modeling using two-dimensional AoD and AoA parameters coupled with Antennas's Uniform Circular Arrays (UCA).  
The remainder of this article is organized as follows: in Section 2, a literature review is presented. Section 3 outlines the system model, focusing on the channel and received signal modeling. Section 4 elaborates on constructing the sensing matrix and applying the DCS-SOMP method, including the proposed modification for adaptive search in the sensing matrix. In Section 5, simulation results are presented and discussed. Finally, Section 6 concludes the article.