esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Healthcare
### Human Activity Monitoring
#### 3.1.2 Human Activity Monitoring Using RF Signals  
Referring to Figure 4, the OFDM transmitter model transmits multiple OFDM subcarriers, implemented using MATLAB/Simulink. The data generated are transmitted through the USRP, using an omnidirectional antenna operating in the frequency range from 2.4 to 2.5 GHz and from 4.9 to 5.9 GHz with 3 dB gain. The key advantage observed in using these omnidirectional antennas is that they can detect human activities performed in both line-of-sight and non-line-of-sight locations.  
The experiment carried out in this use case was conducted in the James Watt Building South at the University of Glasgow where volunteers from different age groups participated. The motive behind the study was to build a foundation and develop a prototype of a system that can continuously monitor in-home daily activities for elderly people. The trials were performed in a controlled laboratory environment, where each individual was asked to perform various activities including (a) walking, (b) sitting on a chair, (c) standing from a chair, (d) exercising and (e) bending down to emulate the picking up of an object laying on the ground. All activities were repeated 10 times and the test was performed in a 7 \(\times\) 8 m\({}^{2}\) room, consisting of typical office furniture, such as tables and chairs.  
The K-nearest neighbour (KNN) algorithm was used for the classification of five activities. Through a 10-fold cross-validation, the algorithm provided the optimal results, leaving one sample out for validation. In the experiment, 755,630 samples were processed for all activities. For the exercising activity, a total of 270,089 samples were correctly classified with 24,423 samples misclassified into other classes. A total of 13,337 out of the 24,423 misclassified samples were recognised as the activity \"Picking up an Object\", 5074 samples as \"Sitting Down\", 2577 samples as \"Standing up\" and 3435 samples as \"Walking\". The overall recorded accuracy of the KNN classifier in this experiment was 89.73%. Other classification algorithms were compared to the KNN classifier. For instance, the Decision Tree (DT) classifier recorded an accuracy of 81.20%. Other tested algorithms included the discriminant analysis and Naive Bayes, which had poor performance compared to both  
Figure 3: Activity monitoring using Ambient RF signals.  
the KNN and the DT, with an accuracy of 49.21% for the discriminant analysis and 23.28% for Naive Bayes.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 3 Methodology
### Machine Learning
#### 3.2.2 Support Vector Machine  
SVM is a supervised learning algorithm primarily used for classification problems, but it can also be applied to regression problems [46]. In classification tasks, the main objective of SVM is to find a hyperplane to segment the samples, with the principle of maximizing the margin. Support vector classification (SVC) is an important application of SVM in classification problems. When the input labels are categorical, SVC can be used for classification. Its basic model is defined as a linear classifier with the largest margin in the feature space. In other words, the learning strategy of SVM is to maximize the margin, which ultimately can be transformed into solving a convex quadratic programming problem. SVM has several notable advantages, such as strong robustness for small samples with a large number of feature attributes, strong learning capabilities for both simple and complex classification models, and the ability to avoid overfitting when adopting complex mathematical models. Additionally, SVM has strong convergence performance, a simple structure, few adjustable parameters, and is easy to implement. However, SVM also has some drawbacks, such as being sensitive to parameter selection, lacking interpretability, having low convergence accuracy, and having insufficient convergence speed in some complex problems. In practical applications, SVM is widely used in areas such as image recognition and text classification and is particularly suitable for small samples, high-dimensional features, and nonlinear problems.
## 1 Introduction
Another contribution of the paper is the implementation of an Adaptive Hysteresis Band (AHB) to fix the switching frequency according to the system dynamic, which improves current waveform in zero crossing and reduces \(THD_{i}\). The proposed AHB does not need PWM signals; hence, the SMC response is fast without compromising robustness. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Contributions** & **Drawbacks** \\ \hline
[12; 25] & Hybrid PI-SMC with constant hysteresis band. & 1. Variable switching frequency. \\  & & 2. DC voltage not considered in the SS. \\ \hline
[15] & GA hybrid PI-SMC & 1. DC voltage oscillations. \\  & & 2. High DC overvoltages. \\  & & 3. High \(THD_{i}\) near zero crossings. \\ \hline
[18] & Hybrid PI-SMC controller (fixed frequency). & 1. DC voltage oscillations. \\  & & 2. Slow linear control. \\ \hline
[23] & Hybrid PI-SMC controller (Totem-pole topology). & 1. Protection external limiter required. \\  & & 2. DC voltage over peaks. \\ \hline
[26] & Integral and double integral SMC. & 1. Integral: high \(THD_{i}\) \\  & & 2. Doble integral: DC current components. \\  & & 3. DC voltage not considered in the SS. \\  & & 4. DC voltage control was not implemented. \\ \hline
[27; 28] & Hybrid PI-SMC for current error reduction. & 1. AC-current and DC-voltage slow responses. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key points of the literature review. This paper is organized as follows. Section 2 corresponds to the SMC design which includes transversality, existence and equivalent control conditions. Section 3 describes the controllers implemented in this paper: PI control, PI-SMC and SMC. Additionally, the AHB is described for PI-SMC and SMC. Section 4 presents the simulation results, including a comparative analysis of the control schemes and a detailed analysis of the proposed sliding surface illustrating its effectiveness. Section 5 presents the experimental results that demonstrate the effectiveness of the proposed SMC. Finally, conclusions are presented in Section 6.
# Inertial Parameter Identification in Robotics: A Survey
## 5 The Output Error (OE) and Input Error (IE) Identification Approaches
### Closed-Loop Input Error (CLIE)  
The CLIE method can be seen as a variation of the CLOE method where the simulated torque is being used instead of the simulated position in Equation (37), resulting in \(\mathbf{y}=\left[\mathbf{\tau}(t_{1})^{\top}\ \ \cdots\ \ \mathbf{\tau}(t_{N})^{\top}\right]\in \mathbb{R}^{n\cdot N}\) and \(\mathbf{y}_{s}=\left[\mathbf{\tau}_{s}(t_{1})^{\top}\ \ \cdots\ \ \mathbf{\tau}_{s}(t_{N})^{\top}\right]\in \mathbb{R}^{n\cdot N}\). The general principle of the CLIE method is illustrated in Figure 3. In this case, the error function which must be minimized at time \(t_{k}\) has the following expression  
\[\mathbf{e}_{CLIE}(t_{k},\mathbf{\beta})=\mathbf{\tau}(t_{k})-\mathbf{\tau}_{s}(t_{k},\mathbf{\beta })\,, \tag{42}\]  
and relation (40) thus becomes  
\[\mathbf{\Delta y}(\mathbf{\tau})=\mathbf{\Phi}_{CLIE}^{i}\mathbf{\Delta\beta}_{CLIE}^{i}+ \epsilon_{CLIE}^{i}\,, \tag{43}\]  
where \(\mathbf{\Delta y}(\mathbf{\tau})\in\mathbb{R}^{r}\) is the vector built from the sampling of \(\mathbf{e}_{CLIE}(t,\mathbf{\beta})\); \(\mathbf{\Phi}_{CLIE}^{i}\in\mathbb{R}^{(r\times b)}\) is the matrix built from the sampling of \(\mathbf{G}_{\mathbf{\tau}_{s}}=\left.\partial\mathbf{\tau}_{s}/\partial\mathbf{\beta}_{|\mathbf{ \beta}-\hat{\mathbf{\beta}}_{CLIE}^{i}}\in\mathbb{R}^{(n\times b)}\right.\), the Jacobian matrix of \(\mathbf{\tau}_{s}\) evaluated at \(\mathbf{\hat{\beta}}_{CLIE}^{i}\) (often referred to as _input sensitivity_); and \(\epsilon_{CLIE}^{i}\in\mathbb{R}^{r}\) is the vector built from the sampling of the residuals of the Taylor series expansion. Then, \(\mathbf{\Delta\beta}^{i}\), the LS  
Figure 2: Block diagram of the CLOE identification method.  
estimate of \(\mathbf{\Delta\beta}_{CLIE}^{i}\) at iteration \(i\) is calculated with (43). Once CLIE has converged, the covariance matrix of the estimate is given by  
\[\mathbf{\Sigma}_{CLIE}=\Big{(}\mathbf{\Phi}_{CLIE}^{\top}\mathbf{\Sigma}^{-1}\mathbf{\Phi}_{CLIE }\Big{)}^{-1}. \tag{44}\]  
This method was successfully implemented in [12] and formally compared to CLOE, DIDIM and IDIM-OLS. From this work, it appears that although CLIE outperforms both IDIM-OLS and CLOE in terms of accuracy, it has a similar computational complexity as CLOE, as a direct consequence of the finite difference Jacobian matrix computation. The authors demonstrated that CLIE could in fact be thought of as a frequency weighting of the CLOE method by the controller's gains. This property can be verified in practice by re-injecting (16) into the expression of the sensitivity matrix \(\mathbf{G}_{\tau_{s}}\). As a result, although the two estimators are asymptotically equivalent, CLIE proves in practice to be more sensitive to the changes in parameters than CLOE, thereby inducing better convergence properties.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol
### Cluster Formation Phase
#### 3.2.5 CH Re-Selection  
It is also feasible that the leaving node is the CH when considering the earlier standard for node exit from a cluster. In this scenario, the CH temporarily hands over to \(N_{r}\), a node selected at random, gathering data on all of the member nodes and conducting CH election similarly to Section 3.2.3.
###### Abstract  
Superconducting Josephson junctions have major advantages as detectors of millimeter wave radiation. Frequency of the radiation can be easily derived from the Shapiro steps of the current-voltage characteristics. However, system performance is highly sensitive to impedance mismatch between the antenna and the junction; therefore, optimization is essential. We analyzed and implemented an improved antenna structure, in which the junction is displaced from the antenna center and placed between the ends of two matching strips. Based on theoretical analysis and advanced electromagnetic simulations, we optimized strip dimensions, which affect both the detection magnitude and the frequency of the reflection coefficient dip. Accordingly, two Au bow-tie antennas with different matching strip widths were fabricated. Superconducting Yttrium Barium Copper Oxide (YBCO) thin films were deposited exactly at the bicrystal substrate misorientation points, forming Josephson junctions at the ends of two matching strips. We found a very high correlation between the simulations and the response to Radio Frequency (RF) radiation in the range of 145-165 GHz. Experimental results agree extremely well with the design, showing best performance of both antennas around the frequency for which impedance matching was derived.  
Keywords:THz sources and detectors; antennas; Josephson junctions; millimeter wave radiation +
Footnote †: journal: Applied  
_Applied_
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 2 Materials and Methods  
### Pedestrian Dead-reckoning (PDR) Module  
PDR technology mainly includes three parts: heading angle estimation, step detection, and step length estimation. As illustrated in Figure 1, if the initial position is known, PDR technology can utilize the measured heading angles and step length to reckon positions by using Equation (1). However, due to the factors like the accuracy of heading estimation and the error of the initial position, system error will accumulate rapidly with time, which directly causes the navigation trajectory to deviate seriously from the actual path. Therefore, other positioning methods are usually needed to weaken the influence of error accumulation on the positioning results.  
\[\left\{\begin{array}{l}N_{k+1}=N_{k}+l_{k}\bullet\text{cos}\,\psi_{k}\\ E_{k+1}=E_{k}+l_{k}\bullet\text{sin}\,\psi_{k}\end{array}\right. \tag{1}\]  
where \(N_{k}\) and \(E_{k}\) represent the coordinate values of the north and east at time \(k\), respectively; \(l_{k}\) and \(\psi_{k}\) represent the step length and heading angle at time \(k\), whose calculation methods will be described in the following sections.  
Figure 1: The framework of pedestrian dead-reckoning (PDR) technology in this research.
# Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy
## 1 Introduction
A classification model was established by combining a support vector machine (SVM) with a genetic algorithm (GA). The proposed method, called GA-SVM, was compared with other approaches including the support vector machine combined with grid search (Grid Search-SVM) and back-propagation (BP) neural networks. The results showed that GA-SVM outperforms the other methods and is, thus, an attractive potential identification tool for the determination of the geographical origins of tea samples.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
###### Abstract  
Existing Simultaneous Localization and Mapping (SLAM) algorithms provide precise local pose estimation and real-time scene reconstruction, widely applied in autonomous navigation for land vehicles. However, the odometry of SLAM algorithms exhibits localization drift and error divergence over long-distance operations due to the lack of inherent global constraints. In this paper, we propose a real-time geo-localization method for land vehicles, which only relies on a LiDAR-inertial-visual SLAM (LIV-SLAM) and a referenced image. The proposed method enables long-distance navigation without requiring GPS or loop closure, while eliminating accumulated localization errors. To achieve this, the local map constructed by SLAM is real-timely projected onto a downward-view image, and a highly efficient cross modal matching algorithm is proposed to estimate the global position by aligning the projected local image to a geo-referenced satellite image. The cross-modal algorithm leverages dense texture orientation features, ensuring robustness against cross-modal distortion and local scene changes, and supports efficient correlation in the frequency domain for real-time performance. We also propose a novel adaptive Kalman filter (AKF) to integrate the global position provided by the cross-modal matching and the pose estimated by LIV-SLAM. The proposed AKF is designed to effectively handle observation delays and asynchronous updates while simultaneously rejecting the impact of erroneous matches through an Observation-Aware Gain Scaling (OAGS) mechanism. We verify the proposed algorithm through R\({}^{3}\)LIVE and NCLT datasets, demonstrating superior computational efficiency, reliability, and accuracy compared to existing methods.  
geo-localization; autonomous navigation; SLAM; cross-modal matching; Kalman filter +
Footnote †: journal: Applied sciences  
Article Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Z. Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery. _J.phi. Sci._**2025**, 15, 8257. [[https://doi.org/10.3390/app15158257](https://doi.org/10.3390/app15158257)]([https://doi.org/10.3390/app15158257](https://doi.org/10.3390/app15158257))
## 3 Results  
Figure 2 presents the Bland-Altman plots for throw speeds measured by the Stalker and Bushnell radar guns. Across the 320 valid throws, the mean speed recorded by the Bushnell radar was 63.43 \(\pm\) 14.6 km/h, while the Stalker radar recorded a mean speed of 64.82 \(\pm\) 14.93 km/h. The mean difference in throw speeds between the two devices was 1.39 km/h (95% CI: 1.20, 1.59). The upper limit of agreement, calculated as the mean difference plus 1.96 times the standard deviation, was 4.80 km/h (95% CI: 4.47, 5.13), and the lower limit of agreement was \(-\)2.02 km/h (95% CI: \(-\)2.35, \(-\)1.69). Additionally, the scatter plot shown in Figure 2 displayed an R\({}^{2}\) value of 0.976, with the Pearson correlation coefficient revealing a strong positive correlation of r = 0.988 (95% CI: 0.985, 0.990).  
Figure 3 presents the Bland-Altman plots comparing the kick speeds measured by the Stalker and Bushnell radar guns. For the 320 valid kicking trials, the mean speed recorded by the Bushnell radar was 67.3 \(\pm\) 12.5 km/h, while the Stalker radar recorded a mean speed of 68.6 \(\pm\) 12.8 km/h. The mean difference in kicking speeds was 1.38 km/h (95% CI: 1.20 to 1.56). The upper limit of agreement, calculated as the mean difference plus 1.96 times the standard deviation, was 4.55 km/h (95% CI: 4.24 to 4.86), while the lower limit of agreement, calculated similarly, was \(-\)1.79 km/h (95% CI: \(-\)2.10 to \(-\)1.49). The scatter  
Figure 1: Illustration of radar guns positioned by the setup.  
plot, also shown in Figure 3, had an R\({}^{2}\) value of 0.947, and the Pearson product-moment correlation test yielded a value of r = 0.973 (95% CI: 0.967 to 0.978).  
Figure 3: Bland–Altman plots, scatter plots, and raincloud plots show the relationship between Bushnell and Stalker radar guns concerning ball speed in kicking: (**a**) Bland–Altman plots; (**b**) scatter plots; (**c**) raincloud plot.  
Figure 2: Bland–Altman plots, scatter plots, and raincloud plots show the relationship between Bushnell and Stalker radar guns concerning ball speed in throwing. (**a**): Bland-Altman plots; (**b**): scatter plots; (**c**): raincloud plot.  
Table 1 presents the results of the concurrent validity analyses for both throwing and kicking movements. The agreement, assessed through intraclass correlation coefficients (ICCs), demonstrates excellent consistency for both throwing ball speed (ICC: 0.989 [95% CI: 0.986 to 0.991]) and kicking ball speed (ICC: 0.986 [95% CI: 0.983 to 0.989]). The analysis of variance (ANOVA) revealed significant differences for both throwing (\(p\) < 0.001; d = 0.799) and kicking (\(p\) < 0.001; d = 0.851), with a percentage difference of 2.17% in throwing and 1.91% in kicking. The standard error of estimate (SEE) was calculated to evaluate the average magnitude of error between the Stalker and Bushnell radar guns for both throwing and kicking measurements. The SEE values were 0.194 km/h for throwing and 0.357 km/h for kicking.
# Improved Recommendations Based on Trust Relationships in Social Networks
## 5 Experiment and Analysis
### Experimental Setup  
In our experiments, we selected the well-known Epinions dataset as the test dataset, which includes 49,290 users who rated a total of 139,738 different items at least once. The rating matrix contains 664,824 ratings records, and its density is less than 0.01%; that is, the matrix is large, sparse, and hence suitable for our experiments.  
Although there are 487,181 issued trust statements in the Epinions dataset, they cannot be used directly because in the dataset there are only positive trust statements and because the trust statement value is always 1. In order to initialize trust relationships between users, we establish a direct trust relationship between two users by setting the trust degree as 1 while they occur in a trust statement. Then, based on these direct trust relationships, indirect trust relationships can be extracted.  
The evaluation metric of recommendation performance that we used in our experiments is the Root Mean Squared Error (RMSE), which is defined as follows:  
\[RMSE=\sqrt{\frac{\sum_{u_{n},j}\left(r_{nj}-t_{nj}\right)^{2}}{N}} \tag{7}\]  
where \(r_{nj}\) is the actual rating value on item \(i_{j}\) by user \(u_{n}\), \(t_{nj}\) is the predicted value that \(u_{n}\) may give to \(i_{j}\), and \(N\) is the number of recommended items. Obviously, the smaller the value of RMSE is, the higher the accuracy of recommendations is.  
To evaluate the performance of our proposed method we considered two comparison partners: TrustWalkerList [10] and Normalized Cut for neighbor Selection (NCut) [17]. TrustWalkerList is a method combining the collaborative filtering and trust-based approach, which can exploit a trust network to improve the quality of top-N recommendation by performing a random walk on the trust network, considering the similarity of users in its termination condition. NCut is essentially a user-based collaborative filtering method that outperforms other standard state-of-the-art techniques in terms of ranking precision based on graph partitioning. These two approaches are partly similar to our proposed method in terms of the algorithm principles.  
We used the leave-one-out method [10] to evaluate the proposed recommendation method. Specifically, we first withheld a real rating value and tried to predict it through the three recommendation approaches. Then, we compared the accuracy of these approaches by analyzing the RMSE.
## 3 Numerical Validation
### Data Generation for Training  
For training of the proposed model, 24,000 datasets were generated with 50% random excitation and 50% traffic loading excitation, 20,000 datasets were generated for training, and 4000 datasets were used for validation. Two training cases were conducted to figure out the effect of data normalization pre-processing step (see Table 3). The first case consisted of datasets with sampling time of 60 s while varying sampling times from 20 to 60 s were applied to the second case. Each case includes random excitation and traffic loading datasets. Note that 20 to 60 s data was created by randomly truncating 60 s data. These two cases were compared to find out how long data length, i.e., beam excitation, affects the learning outcome and how data normalizing affects. To label the location of damage, 11 categories were prepared. Label 0 indicates an intact condition, whereas labels 1 to 10 indicate a single damage location that corresponds to the element number. Because there were 10 labels for damage (i.e., elements 1 to 10) and one label for the intact condition, the number of intact datasets was increased so that the number of intact datasets was equal to the number of damage datasets to handle data unbalancing [32; 33; 34; 35].  
Figure 5 shows the random excitation and resulting acceleration response measured at node 1. To simulate traffic loading, three to five AASHTO trucks were designed to pass the beam randomly with velocities of 30, 40, and 50 km/h, as summarized in Table 3. Trucks are set to depart randomly so that various acceleration amplitudes can be generated (see Figure 6).  
Figure 4: Simply supported beam for numerical validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \cline{2-5}  & \begin{tabular}{c} **Random** \\ **Excitation** \\ \end{tabular} & \
\begin{tabular}{c} **Traffic Loading** \\ **Facitation** \\ \end{tabular} &
\begin{tabular}{c} **Random** \\ **Traffic Loading** \\ \end{tabular} \\ \hline Sampling Time (s) & 60 & 60 & 20–60 & 20–60 \\ Damage Severity (\%) & 0/20/30/50 & 0/20/30/50 & 0/20/30/50 & 0/20/30/50 \\ Velocity (km/h) & & 30/40/50 & - & 30/40/50 \\ No. of Vehicles & & 8–10 & - & random \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configurations of training cases.  
The simulated acceleration measurements were normalized by NExT to an input layer of 81 \(\times\) 1024 and fed into a 1-D CNN.
## 2 Materials and Methods
### Model Performance Criteria  
The accuracy of the modelling results can be quantified by using the statistical variable root-mean-square error (RMSE), which is defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}Y_{i}-Y_{i}^{\prime}\bigr{)}^ {2}}, \tag{1}\]  
where \(N\) is the number of samples and \(Y_{i}\) and \(Y_{i}^{\prime}\) are the measured and calculated values, respectively. The best fit between the experimental and predicted values would have an RMSE = 0.