esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 3 Proposed Fusion Model  
In machine learning, a feature refers to a specific, measurable attribute of an image that helps in distinguishing patterns. This study focuses on the integration of two types of features (local descriptors) obtained from the LBP and HOG with KAZE features before the classification. To reduce computing costs and meaningful results, the detection of important frames and the elimination of insufficient frames are necessary. In the first step, the keyframes are extracted from videos within an interval of 0.5 s. The first and last 10 frames are overlooked (only if necessary) because they usually have information about the introduction or credits that are not directly related to deepfake detection preprocessing. Additionally, to identify the important keyframes, a similarity check between frames is used as the criterion. Various threshold values are used to determine different approaches. This part of the algorithm results in a pool of distinctive images of frames, which are available for feature extraction. The images were resized to a \(28\times 28\) single-channel format, ensuring a standardized input for processing. The research explores the use of frames extracted from video footage or standalone images, treating keyframes as textured representations. In the second step of feature extraction, the LBP, HOG, and KASE techniques are applied separately, which typically generates histograms. Furthermore, the fusion of LBP and KAZE and that of HOG and KAZE are used to input futures for the Extra Trees, Random Forest, Support Vector, and XGB classifiers. The proposed fusion model aims to improve detection accuracy while maintaining efficiency.  
Figure 1 illustrates a general abstract of the proposed fusion model with important key concepts involved as data preprocessing (extracting keyframes), extracting features from keyframes using LBP and HOG feature extraction methods, feature-level fusion with KAZE features, classifier selection of the combined features, and subsequent classification on the basis of the chosen classifiers. The keyframes are converted to a logarithmic scale and divided into multiple bands to capture localized information about the texture patterns. We analyze each band of these frame divisions by calculating the normalized histogram of HOG or LBP features to pinpoint characteristics using our classifiers. After these histograms are combined to form an LBP feature vector, they are combined with KAZE features to create supporting feature sets. To handle the dimensionality caused by merging features at the level of characteristics, various classifiers are utilized to identify features, with importance scores that are then fed into the ultimate classifier.  
The importance and understanding of feature extraction, along with its configurations, are explained in the following section.
## 1 Introduction
Unlike the visual localization which is done using a single marker reported in [29], we have used ArUco markers map (AMM) that resulted in the improved measurement accuracy. The measurement performance of this approach is compared using the standard industrial robot KUKA KR6 R900 robot (KUKA, Augsburg, Germany) [30]. Being cognizant of the above facts, we set the following objectives: \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**System** & **Locomotion** & \(\mathbf{n_{L}},\mathbf{n_{W}}\) & \(\mathbf{n_{B}}\) & **R** & **M** & **Environment** \\ \hline PCIRs [4] & 2-Tracking wheels & \(-,2\) & \(3\) & N & N & SP (C) \\ KARO [5] & 4-WID & \(-,4\) & \(2\) & N & N & SP (C) \\ KANTARO [6] & Passively adapted wheels & \(-,4\) & \(2\) & N & Y & SP (C) \\ KURT [7] & Wheeled & \(-,3\) & \(3\) & N & Y & SP (C) \\ MAKRO [8] & Wheeled & \(-,2\)n & \(3\) & Y & Y & SP (C) \\ BISAM [13] & Legged & \(4,-\) & \(5\) & N & Y & RT \\ Warp1 [14] & Legged & \(3,-\) & \(5\) & N & Y & RT \\ TITAN VIII [17] & Legged & \(1,-\) & \(5\) & N & Y & RT \\ IPR [20] & Legged & \(1,-\) & \(3\) & Y & N & SP (C) \\ _Tarantula_ & Wheeled & \(4,4\) & \(4\) & Y & Y & D \\ \hline \hline \end{tabular}  
* R: Reconfigurable, M: Modularity in mechanism, hardware and software, \(n_{L}\) Active degrees of freedom (DOF) in each leg. \(n_{W}\): number of wheels, \(n_{B}\): DOF of the moving platform, (C): Circular cross-section. SP: Sewer pipes, RT: Rough Terrain.  
\end{table} 
Table 1: Wheeled and legged robot discussed in this work. * Design of the robotic platform that can change its height and is holonomic,
* Formulation for kinematics of the wheeled locomotion coupled with the leg kinematics,
* Identification of kinematic parameters after the assembly of the robot, using monocular vision and ArUco markers,
* Trajectory tracking of the robot using the same set-up of monocular vision and ArUco markers. This paper is divided into five sections. Section 2 lists the design requirements and the mechanical layout, i.e., system architecture of the _Tarantula_ is discussed in detail. Section 3 introduces the workspace analysis of the Tarantula along with the kinematics of wheeled locomotion coupled with the leg kinematics. Experiments for identification of the kinematic parameters of the assembled _Tarantula_ along with the trajectory tracking in Section 4. Finally, Section 5 concludes the paper.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Digital Twin
#### 2.2.2 Signal Estimation: Second Step to Design the Digital Twin
The artificial intelligence-based technique, (ANF), to compensate the performance of the bearing's digital twin output, \((Z_{ANF}(k))\), is introduced using the following definition. \[\left\{ \begin{array}{c}Z_{ANF}(k)=\frac{\sum_{r}Z_{r}\times\theta_{r}}{\sum _{r}\theta_{r}}\\ \hat{\sigma}_{r}=\sum_{r}e^{(-0.5\sum_{r}(\frac{(\beta_{r})-\theta_{r}}{\sigma })^{2})}\end{array} \right. \tag{35}\]  
Here, \(Z_{r}(k)\), \(\mu_{r}\), and \(\sigma\) are the reference point of uncertainty estimation using the ANF technique, the membership function of the fuzzy set to design the proposed ANF, and variance, respectively. To improve the performance of \(Z_{ANF}(k)\), the adaptive membership function and the adaptive variance of the fuzzy set to design the proposed ANF, \((\mu_{r}{}^{*})\) and \((\sigma^{*})\) are defined as the following equations, respectively. \[\mu_{r,t+1}{}^{*}=\mu_{r,t}-\Theta_{r,t}\times\frac{\partial\theta_{r}}{ \partial\mu_{r,t}} \tag{36}\]  
\[\sigma_{t+1}{}^{*}=\sigma_{t}-\Theta_{r,t}\times\frac{\partial\theta_{r}}{ \partial\sigma_{t}} \tag{37}\]  
Here, \(\Theta_{r,t}\) is a tuning coefficient. Therefore, to design the digital twin for the bearing, we have two steps: signal modeling and signal estimation. For the first step of the bearing'sdigital twin, signal modeling, the combination of autoregression (AR), Laguerre filter (LA), and support vector regression (SVR) (henceforth called SAL), was recommended. Next, in the second step of the bearing's digital twin (signal estimation), the combination of a Kalman filter (KF), the high-order variable structure technique for robustness (R), and the adaptive neuro-fuzzy technique (ANF) (henceforth called HRKF), which was modeled by the SAL technique, was recommended. Consequently, after modeling and estimation the bearing signal using the proposed digital twin (MS-HRKF), the residual signal is computed in the next section.
## Results
### Satellite-Scale: Results for Various Plant Functional Types
#### 3.4.3 Validation of Cl\({}_{\rm green}\)  
To test the potential of Cl\({}_{\rm green}\) to estimate P\({}_{\rm max\_capacity2000}\) in a different year, we used the empirical equations of year 2003 (Table 4) and Cl\({}_{\rm green}\) calculated from MOD09A1 for the CA-Let, JP-TMK,  
\begin{table}
\begin{tabular}{l l c c c} \hline
**Site Name** & **Plant Functional Types (PFTs)** & **a** & **b** & **R\({}^{2}\)** \\ \hline
**CA-Let** & C3 grass, arctic & 0.388 & \(-\)0.235 & 0.81 \\
**JP-TMK** & Needleaf deciduous trees (NDT) & 0.232 & \(-\)0.145 & 0.84 \\
**JP-TKY** & Broadleaf deciduous trees, temperate (BDT, temperate) & 0.169 & \(-\)0.355 & 0.67 \\
**JP-Mase** & Crops (paddy field) & 0.371 & \(-\)0.361 & 0.95 \\
**JP-FJY** & Needleaf evergreen trees, temperate (NET, temperate) & 0.179 & 0.182 & 0.70 \\ \hline \end{tabular}
\end{table}
Table 4: Coefficients, intercepts and correlation coefficients (R\({}^{2}\)) of the linear correlations (\(y=\rm{ax}\pm b\)) between Cl\({}_{\rm green}\) and P\({}_{\rm max\_capacity2000}\) for CA-Let, JP-TMK, JP-TKY, JP-Mase, and JP-FJY.  
Figure 11: Comparison of estimated P\({}_{\rm max\_capacity2000}\) (black line) and observed P\({}_{\rm max\_capacity2000}\) (red line) with error bars. (**a**) CA-Let 2002, (**b**) JP-TMK 2002, (**c**) JP-TKY 2004, (**d**) JP-Mase 2002, (**e**) JP-FJY 2004, and (**f**) TH-SKR 2002. Estimated P\({}_{\rm max\_capacity2000}\) calculated using empirical linear correlation of 2003 and Cl\({}_{\rm green}\) of 2002 and 2004. Observed P\({}_{\rm max\_capacity2000}\) estimated from EC flux tower data of 2002 and 2004 using light-response curve fitting by the least-squares method.  
JP-Mase, and TH-SKR sites in 2002 and the JP-TKY and JP-FJY sites in 2004. Comparisons of estimated Pmax_capacity2000 and observed Pmax_capacity2000 with standard error bars are shown in Figure 11. Estimated Pmax_capacity2000 was obtained from the empirical equations in Table 4, and observed Pmax_capacity2000 was calculated from the flux data. Standard errors of estimated Pmax_capacity2000 were calculated from standard-error propagation of empirical relationships between CIgreen and Pmax_capacity2000. Standard errors of observed Pmax_capacity2000 were calculated from standard-error propagation of least-square fitting of the light-response curves. The standard errors of the estimated Pmax_capacity2000 from linear regression were larger than those of observed Pmax_capacity2000. Figure 11 shows that the observed Pmax_capacity2000 values for all plant functional types were within the standard error of the estimated Pmax_capacity2000, especially in spring, when most of the plant functional types showed better matching. These results indicate that the CIgreen and empirical equations successfully estimated Pmax_capacity2000 for all plant functional types. For TH-SKR, only five periods are shown because most CIgreen data were affected by clouds.
## 4 Characteristics of Different Time Distribution Systems
### Network Time Protocol
#### 4.1.4 Reliability and Security  
NTP is vulnerable to security issues, including the potential spoofing of NTP packets, which can lead to incorrect clock settings. There have been cases of NTP misuse and abuse, where servers have fallen victim to Denial-of-Service (DoS) attacks [77].  
NTP is highly susceptible to attacks from malicious servers, which can occur in two primary ways: (1) the attacker gains control over existing timeservers within the NTP server pool, and (2) the attacker introduces rogue timeservers into the server pool to manipulate time synchronization [78].
## 3 Training and Testing Results
### Evaluations Metrics  
During the experiments in this paper, we used \(mAP_{0.5}\) and \(mAP_{0.5-0.95}\) to measure the box and mask accuracy of the model and inference time to measure the inference speed of the model. \(mAP_{0.5}\) and \(mAP_{0.5-0.95}\) can be described as:  
\[mAP_{0.5}=\frac{1}{n_{c}}\int_{0}^{1}P(R)dR \tag{8}\]  
\[mAP_{0.5-0.95}=avg(mAP_{i}),i=0.5:0.05:0.95 \tag{9}\]  
where \(n_{c}\) denotes the number of the classes, \(P\) represents precision and \(R\) represents the recall, and they satisfy:  
\[\mathrm{P}=\frac{TP}{TP+FP} \tag{10}\]  
\[\mathrm{R}=\frac{TP}{TP+FN} \tag{11}\]  
where \(TP\) is true positive, which represents the number of the prediction boxes whose \(IoU>0.5\); \(FP\) is false positive, which represents the number of the prediction boxes whose \(IoU\leq 0.5\); and \(FN\) is false negative, which represents the number of the labels without prediction.  
In addition, weights, parameters and GFLOPs were used to evaluate the complexity of the model.
## 4 Discussion
### Data Distribution (Statistical Data Heterogeneity) Challenge
#### 4.3.1 Non-IID Characteristics
For instance, node two does not have the  
Figure 10: Non-IID from label distribution skew (prior probability shift) characteristic. **(a)** IID: The distribution of labels \(\mathcal{Y}\) exists in all nodes; **(b)** non-IID: the distribution of labels \(\mathcal{Y}\) varies between different nodes. For instance, node two does not have the labels \(y_{2}\) and \(y_{3}\) while node one has all labels. and \(x_{2}\) features while other nodes have those features. Missing features or missing data is a common occurrence in medical datasets. For instance, missing features can be caused by failures of measurement on medical images. Measurement in medical image acquisition requires the images to be in focus. Medical images that are not in focus or blur can cause missing pixel values. The absence of some features in one or several nodes in the features distribution skew can be a problem in the FL training process. Data imputation techniques such as probability principal component analysis (PPCA) and multiple imputations using chained equations (MICE) can be employed to mitigate the problem [58]. **Concept Shift Skew. ** There are two forms in the concept shift skew: the same label but different features \(P(x|y)\) and the same features but different label \(P(y|x)\). An illustration of concept shift skew is depicted in Figure 12. The same label but different features in non-IID characteristic is related to vertical federated learning data partition where each node shares the sample index \(\mathcal{I}\) but have different features \(\mathcal{X}\), while in the case of the same features but the different label in non-IID characteristics is not applicable in most FL studies. Figure 11: Non-IID from feature distribution skew characteristic. (**a**) IID case: the distribution of features \(\mathcal{X}\) exists in all nodes; (**b**) non-IID case: the distribution of features \(\mathcal{X}\) varies between each node. For instance, node two does not have the features \(x_{1}\) and \(x_{3}\) while the other nodes have those features.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 3 Smart Energy Campus
Back-off algorithms and their impacts on LoRa MAC, however, have been barely studied and evaluated, unfortunately. Moreover, some parameters in the regulations are impractical for LoRa and must be adjusted to achieve optimal performance. For instance, the maximum EIRP (Equivalent Isotropic Radiated Power) is as low as 14 dBm in LoRa communication, and the sensing threshold is defined as \(-\)65 dBm in the regulations. In the listening step of LBT, this set of values implies that a node can detect a neighbor's signal only when it is closer than 50 m from the neighbor. Given that a LoRa signal can span several kilometers, this setting is certain to cause disastrous collisions. Thus, it is necessary to find the optimal value of the threshold in LoRa. We note that this paper does not intend to ignore the regulations but to derive recommendations for amendments. The last question is, \"how do application requirements affect LoRa performance?\" Different applications have disparate lists of performance requirements. In an autonomous vehicle system, for instance, data are processed in real time, and thus, the underlying communication technology must support it. Our campus testbed has more than 3000 nodes that transmit their sensor records every 3 min on average via LoRa communication. A node maintains 3-10 sensor points, meaning that each record contains 3-10 tuples of data. This determines a packet size of less than 32 bytes. The smart energy application runs machine learning algorithms and can thus tolerate a small amount of data loss, for example, 10%. Given this specification, how often does collision occur? Do we need more gateways to meet the packet loss requirement? If the packet size is halved, how many additional nodes can we install? To date, there are few experimental results showing the impacts of application parameters on LoRa MAC performance.
###### Abstract  
Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discrimination of genotypes in the field, which is crucial for improving the efficiency of breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables as input sets in ML models are still scarce in the literature. Against this backdrop, this study aimed: (I) to discriminate sorghum hybrids based on canopy reflectance in different spectral bands (SB) and vegetation indices (VIs); (II) to evaluate the performance of ML algorithms in classifying sorghum hybrids; (III) to evaluate the best dataset input for the algorithms. A field experiment was carried out in the 2022 crop season in a randomized block design with three replications and six sorghum hybrids. At 60 days after crop emergence, a flight was carried out over the experimental area using the Sensefly eBee real time kinematic. The spectral bands (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735) e NIR (790 nm, NIR_790). From the SB acquired, vegetation indices (VIs) were calculated. Data were submitted to ML classification analysis, in which three input settings (using only SB, using only VIs, and using SB + VIs) and six algorithms were tested: artificial neural networks (ANN), support vector machine (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) and logistic regression (LR, conventional technique used as a control). There were differences in the spectral signature of each sorghum hybrid, which made it possible to differentiate them using SBs and VIs. The ANN algorithm performed best for the three accuracy metrics tested, regardless of the input used. In this case, the use of SB is feasible due to the speed and practicality of analyzing the data, as it does not require calculations to perform the VIs. RF showed better accuracy when VIs were used as an input. The use of VIs provided the best performance for all the algorithms, as did the use of SB + VIs which provided good performance for all the algorithms except RF. Using ML algorithms provides accurate identification of the hybrids, in which ANNs using only SB and RF using VIs as inputs stand out (above 55 for CC, above 0.4 for kappa and around 0.6 for F-score). There were differences in the spectral signature of each sorghum hybrid, which makes it possible to differentiate them using wavelengths and vegetation indices. Processing the multispectral data using machine learning techniques made it possible to accurately differentiate the hybrids, with emphasis on artificial neural networks using spectral bands as inputs and random forest using vegetation indices as inputs.  
**Keywords:** artificial neural networks; high-throughput phenotyping; random forest; spectral bands; vegetation index
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 1 Introduction
In the context of this work, the block-diagonal mass matrices of enriched elements ought to similarly be positive definite. Additionally, the Gauss points have to match the element nodes in order to perform nodal quadrature (see also [56]). To allow for these constraints, a residual error was allowed in the moment-fitting equations, and weights were computed via the solution of a quadratic programming problem in order to guarantee the properties of the mass matrix while minimizing the integration error. The remainder of this work is organized as follows: In Section 2, the weak form of the elastodynamics problem is reviewed. In Section 3, the novel method is presented and studied, with a brief review of the SEM offered in Section 3.1, followed by a presentation of the PU enrichment functions used in this work in Section 3.2. Building on the nodal quadrature method (Section 3.4), the proposed mass lumping technique for enriched SEs is presented in Section 3.6, and its accuracy and stability are studied. In Section 4, this method is deployed in three numerical examples to highlight its capabilities. In Section 4.1 a cracked panel is used to benchmark the accuracy of the method in comparison with the consistent mass matrix (CMM) formulation. In Section 4.2 a similar comparison is offered through the study of a riveted aluminum plate, involving multiple complex damage configurations. In Section 4.3, the effectiveness of the numerical modeling is assessed by simulating composite delaminations of a glass-fiber-reinforced polymer (GFRP) plate, for which reference experimental data were obtained via a scanning laser Doppler vibrometer (SLDV). Concluding remarks are presented in Section 5.
## 3 Results
### Redundancy Analysis (RDA) of Gases and Matrix Physicochemical Properties  
Redundancy analysis (RDA) was used to investigate the relationship between the emission rate of the gases and the physicochemical properties of the compost (Figure 7). The physicochemical indices variously affected the emission dynamics of the gases. The first two axes of Figure 7a-d produced averages of 97.3%, 95.3%, 91.7% and 93.7% variance between the gases and the physicochemical indices. The temperature and total nitrogen were identified as the main contributors to CH\({}_{4}\), CO\({}_{2}\), N\({}_{2}\)O and NH\({}_{3}\) emissions. The permutation test for NO\({}_{3}\)\({}^{-}\)-N correlated positively with the CH\({}_{4}\), CO\({}_{2}\) and NH\({}_{3}\) emissions (Table 2). While NH\({}_{4}\)\({}^{+}\)-N correlated positively with the CH\({}_{4}\) and CO\({}_{2}\) emissions, that of the TOC correlated positively with CO\({}_{2}\) and negatively with N\({}_{2}\)O gas emissions. In contrast, the EC and pH negatively correlated with the emission of CH\({}_{4}\), CO\({}_{2}\), N\({}_{2}\)O and NH\({}_{3}\) gases.  
Figure 6: Temporal emission of ammonia (a,b) and nitrous oxide (c,d) gas during composting under different moisture levels.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{\(p\)-Value} \\ \cline{2-5}
**Properties** & **CH4** & **CO2** & **N2O** & **NH3** \\ \hline Nitrate nitrogen & 0.026 * & 0.020 * & 0.068 NS & 0.008 ** \\ pH & 0.350 NS & 0.366 NS & 0.082 NS & 0.552 NS \\ Temperature & 0.008 ** & 0.044 * & 0.032 * & 0.024 * \\ Total organic carbon & 0.302 NS & 0.040 * & 0.352 NS & 0.616 NS \\ Total nitrogen & 0.004 ** & 0.012 * & 0.002 ** & 0.024 * \\ Ammonium nitrogen & 0.006 ** & 0.008 ** & 0.304 NS & 0.258 NS \\ Electrical conductivity & 0.124 NS & 0.312 NS & 0.394 NS & 0.152 NS \\ \hline \hline \end{tabular}
* Significant correlations at \(p<0.001\).  
\end{table}
Table 2: Permutation results between gaseous emissions and physicochemical indices of compost.  
Figure 7: Redundancy analysis (RDA) between gaseous emissions and physicochemical indices of compost (**a**), methane (**b**), carbon dioxide (**c**) nitrous oxide and (**d**) ammonia.