esa-sceva/satcom-chunk-collection huggingface dataset:
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
### Feature Fusion
#### 2.3.1 R-IoU and Optical Flow Feature Fusion Model  
Assume that \(d_{i,j}^{\text{R-IoU}}\) is the value based on R-IoU, and \(d_{i,j}^{\text{OptFlow}}\) is the combined distance value based on optical flow. The final feature similarity distance can then be represented as \(d_{i,j}^{\text{SFusion}}\). The formula is shown in (9).  
\[d_{i,j}^{\text{SFusion}}=\alpha\cdot d_{i,j}^{\text{R-IoU}}+(1-\alpha)\cdot d _{i,j}^{\text{OptFlow}} \tag{9}\]  
where \(\alpha\) is a weighting factor used to balance the contributions of R-IoU and optical flow in the tracking task. The final feature similarity matrix is composed of \(d_{i,j}^{\text{SFusion}}\). Through this process, the R-IoU and optical flow information fusion model is obtained, which will be applied to the second association stage of the tracking framework.  
The R-IoU and optical flow feature fusion model is shown in Figure 3. In branch 1, the video frame image is input, and the detector detects the target boxes in the current frame. Meanwhile, the optical flow algorithm obtains the overall optical flow of the image and extracts the average optical flow vector of the target area. In branch 2, the estimator uses information from the previous frame to predict the bounding boxes for the current frame and then extracts the average optical flow vector within the predicted boxes. Subsequently, the R-IoU method is used to compute the complementary set between detection boxes and predicted boxes, and this value is compared with threshold \(\delta_{1}\). Values greater than \(\delta_{1}\) indicate that the detection box and prediction box do not match using the R-IoU method, while values less than \(\delta_{1}\) indicate a match. Similarly, the average optical flow vectors within the detection and prediction boxes are combined and measured using a certain method, and this value is compared with threshold \(\delta_{2}\). Values greater than \(\delta_{2}\) indicate that the boxes do not match using the optical flow feature method, while values less than \(\delta_{2}\) indicate a match. Then, the R-IoU feature similarity matrix and the optical flow feature similarity matrix are fused using a matrix weighting method to obtain the fused feature similarity matrix. The Hungarian algorithm (HA) is applied to this fused matrix to perform one-to-one matching between detection boxes and prediction boxes, ultimately obtaining the optimal tracking boxes.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results
### System Simulation
#### 6.2.3 Achievable Sum Rates versus Transmit Power  
In Figure 8, it is proven that the MIMO technique provides added value through a remarkable impact on the system performance compared to the SISO scheme. Moreover, the presented system outperforms the state of the art considering our simpler and cost-effective model, which has lower complexity and achieves higher EE and SE. Hence, we achieve the same target for the two users using relatively less power than allocated in previous research (e.g., [27]). This demonstrates the significance of using the energy-harvesting technique with the proposed strategy, representing additional gain and a significant contribution.  
In contrast, MIMO-NOMA systems might face some drawbacks, e.g., the computational complications of MIMO and procedural complexity of SIC-dependent NOMA, as SIC is widely thought to be implemented flawlessly, notwithstanding the possibility of procedural errors. Thus, it is critical to keep the number of users per cluster as low as possible, as user CSI acquisition has a significant impact on SIC implementation.
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures  
This section will present an overview of the best-performing TBML Architectures, which are widely utilized for both regression and classification tasks. These include Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and three prominent implementations of GBDT: XGBoost, LightGBM, and CatBoost. Figure 1 provides a comparative visualization of the structural differences between RF and GBDT. First, this study will discuss a foundational architecture in the realm of TBML: Random Forests.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 4 Experimental Results  
To verify the accuracy and stability of our method, we conducted a sea trial in July 2021. We then substituted the obtained sea trial data into the new method for processing and comparative analysis.  
The coherent processing interval (CPI) of the system was 0.5 s. According to [PERSON] theorem, it is obvious that the longer the CPI, the smoother the estimation of the system. However, in this case, considering that the target object was constantly moving, when the selected time was too long, it led to serious inconsistencies between the estimation results and the actual situation. Additionally, the longer the time, the larger the sample size and the required memory and system calculation also surged. Considering the tracking accuracy of the actual system combined with the actual demand and relevant experience, the time interval was set to 0.5 s, i.e., 33,280 sampling points.  
\(T_{CPI}\) was the coherent processing interval and the sampling rate \(fs\) of the received signal was 65 kHz, so the sampling points that were obtained in this time period were expressed as:  
\[N=T_{CPI}\cdot fs \tag{9}\]  
Assuming that the position in this period of time was a certain value \((r,\theta)\), each sampling point received an estimated value, which was set as \((r,\theta)\). Therefore, within the CPI, the variance \(\sigma^{2}\) was expressed as:  
\[\sigma^{2}=\frac{1}{N}\sum_{i=1}^{N}{(\hat{r}_{i}-r)^{2}} \tag{10}\]  
According to the Cramer-Rao theorem:  
\[\sigma^{2}\geq\frac{1}{N\cdot I(r)} \tag{11}\]  
where \(I(r)\) is the Fisher information for one data sample.  
Figure 11: Comparison diagram of the target tracking deviations between the Kalman filtering method and the LSTM–Kalman filtering method.  
As the underwater target moved nonlinearly, the comparison results of the UKF method and the LSTM-Kalman filtering method, along with the real trajectory, are shown in Figure 15.  
Figure 14: DEMON diagram of the receiving data.  
Figure 15: Comparison of the real trajectory, the tracking trajectory that was obtained using the Kalman filtering method, and the tracking trajectory that was obtained using the LSTM–Kalman filtering method.  
The LSTM-Kalman filtering method was still better than the UKF method in the actual marine environment and fit the actual motion trajectory more closely. The specific error between these two methods is shown in Figure 16.  
Compared to the real path, the deviations in the UKF were within 140 m, the average error was 42.45 m, and the variance was 21.08. The results from the LSTM-Kalman filtering method were better, the average error was reduced to 16.94 m, and the variance was 4.79. The relative error of the LSTM-Kalman filtering method was 72.25% lower than that of the UKF method. The results showed that the tracking error of the proposed LSTM-Kalman filtering method was significantly reduced compared to the traditional method and that the new method demonstrated a stable and fast tracking performance.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Simulation 4: Effect of Sparse Codes  
Figure 13 shows the effect of the overloading factor \(\lambda=\frac{N}{K}\) and the reflection coefficient \(\alpha\) for the BER. If the codewords of tags are designed in low density as depicted in Figure 6, these codewords can be compressed to efficiently overload multiple data by virtue of their sparsity. When the number of tags is \(N=10\), the minimum number of time slots is \(K=5\) for \(K_{1}=2\) since it satisfies the condition in Equation (17). Hence, \(\lambda\)-fold increase in the data rate can be achieved by compressing \(K\) in Equation (47). It is remarkable that although the data rate is improved by increasing the overloading factor, the BER performance is not degraded by the parameter \(\lambda\). When the codewords are sparsely encoded at tags and the iterative MPA-based decoding at the reader are employed, a higher data rate can be achieved in AmBC without loss of the detection performance. Thus, multi-dimensional signalling in the MSC-AmBC can realize high-rate communication by controlling the overloading factor \(\lambda\) and the modulation order \(M\) while long-range communication by controlling the duty cycle \(D\). Potential applications of the proposed MSC-AmBC include smart city and factory where a massive number of low-power IoT devices are concurrently connected to the Internet to monitor our daily routines.
## 2 Methods  
Robust watermarks can protect digital multimedia copyrights to prevent or prevent unauthorized copying and copying of digital media. To improve the ability of watermarks to resist JPEG compression, this paper proposes an invisible watermarking method based on deep convolutional generative adversarial networks. The architecture of the method, as shown in Figure 1, includes two modules, the Embedder and Decoder, and two deep neural networks: Generator \(G\) and Discriminator \(D\), and the proposed method is to train a unique generator for the multiple protected images, and one generator corresponds to one watermark.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Key Features** & **Limitations** & **How Our Work Addresses** \\ \hline [PERSON] et al. (2017) [24] & WGAN; uses Wasserstein divergence and RMSProp for stable training. & Requires careful tuning of hyperparameters for optimal performance. & Our method simplifies training by focusing on JPEG compression robustness and using a secret key for security. & Our method simplifies training by focusing on JPEG compression robustness and using a secret key for security. \\ [PERSON] et al. (2023) [25] & Blind watermarking for color images; uses DCT and deep residual networks. & Limited robustness under severe distortions (e.g., cropping, noise). & Our method achieves better imperceptibility (PSNR \(>\) 49 dB, SSIM \(\approx\) 0.95) and robustness under distortions. \\ [PERSON] et al. (2020) [17] & ReDMark; uses FCN for embedding and extraction, with an attack layer. & Poor performance under severe distortions (e.g., Gaussian blur, salt-and-pepper noise, cropping). & Our method outperforms ReDMark in robustness to JPEG compression, with low BER (\(\leq\)0.008). \\ [PERSON] et al. (2020) [18] & Deep neural network-based method; uses Encoder, Embedder, and Decoder modules. & Inferior performance under severe distortions (e.g., 65\% cropping, 90\% salt-and-pepper noise). & Our method maintains low BER (\(\leq\)0.008) and high PSNR (\(>\)49 dB) even under severe distortions. \\ Proposed Method & DCGAN-based semi-blind watermarking; uses embedder, decoder, and generator. & Focused on JPEG compression; future work is needed for other attacks. & Our method outperforms prior works in robustness to JPEG compression, with low computational cost and flexible training. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The framework diagram of the proposed model.
## Abstract  
In this study, we examined the robust super-twisting sliding mode backstepping control (SBSC) method, which employed a tracking differentiator and nonlinear disturbance observer for a hexacopter unmanned aerial vehicle (UAV) system. To realize robust tracking control performance for a highly coupled nonlinear hexacopter UAV system, a super-twisting sliding mode control method was combined with designing stabilizing controls of backstepping control (BSC) applied to the UAV system. Furthermore, the differentiation issue of the virtual control and compensation of transformation error in the conventional BSC design were bypassed via a new continuous tracking differentiator structure. Additionally, a new disturbance observer based on the proposed tracking differentiator was considered to estimate uncertainties of the hexacopter UAV. Comparative simulation results demonstrated that the proposed tracking-differentiator-based SBSC scheme (PTSBSC) blended with the tracking differentiator and nonlinear disturbance observer exhibits improved performance when compared to that of conventional BSC and disturbance observer systems.  
hexacopter unmanned aerial vehicle; backstepping control; super-twisting sliding mode control; tracking differentiator; nonlinear disturbance observer
# Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin
## 1 Introduction
The insertion of moiety with nitrogen, oxygen, or/and sulfur atoms affects biological activity because heteroatoms could create a hydrogen bond with the biological target and increase water solubility [31; 32]. An especially interesting moiety is the triazole scaffold which consists of two carbon and three nitrogen atoms in a five-membered unsaturated ring. The triazole ring is created between organic azide and alkyne derivatives in the reaction of 1,3-dipolar cycloaddition [33; 34; 35]. The triazole ring is found in many clinically used drugs, like Fluconazole, Bitteranol, Cyprocanazole, Trazodone, Triazolam, Ribavirin, Isavuconazole, and Cefatrizine [35; 36; 37; 38; 39; 40]. In the literature, the triazole moiety is often used as a linker between two compounds showing high biological activity, which allows hybrids with new biological properties to be obtained. In many cases, the modification of natural compounds by introducing a triazole substituent affects biological activities, like anticancer, antibacterial, antifungal, antiviral, antimalarial, and antioxidant [41; 42; 43; 44; 45]. Figure 1: Chemical structure of betulin. In our scientist group, we deal with the modification of the betulin skeleton by introducing various substituents at the C3, C28, and C30 positions [46; 47]. One of the important research directions was the introduction of the triazole moiety to the betulin scaffold. The obtained compounds exhibited anticancer, antibacterial, and antiviral activity [13; 48; 50]. Continuing research on the biological potential of triazole derivatives of betulin, we decided to designate the bioavailability parameters of mono- and bistriazole derivatives of propynoylbetulin. Lipophilicity was determined using experimental and theoretical methods. Moreover, the correlation between these two values was studied. The research was supplemented by in silico-determined bioavailability parameters.
## 2 Materials and Methods
### Gaussian Filter Analysis  
A dosimetric approach was implemented to evaluate the impact of the Gaussian filter (varying from 0 up to 12 mm) on reconstructed 90Y PET images. MIM Sure Plan (version 6.9.4, MIM Software Inc, Cleveland, OH) was used to generate absorbed dose maps, starting from the PET acquisitions of P2 and P3, using the MIRD-17 voxel S-values approach [25] and obtaining both integral experimental DVH (iDVHexp) and differential experimental DVH (dDVHexp). Integral reference DVH (iDVHref) and differential reference DVH (dDVHref) were also obtained starting from two reference PET/CT mathematical phantoms, representing the P2 and P3, filled with the ground truth activity concentrations measured with the TDCR. In order to verify the accuracy in quantitative data, for each PET/CT data acquisition, as proposed by [PERSON] et al. [21], the RMSE was evaluated as (3):  
\[RMSE=\sqrt{\frac{\sum_{i=20}^{80}\left(D_{i,exp}-D_{i,ref}\right)^{2}}{60}} \tag{3}\]  
where \(\text{D}_{\text{(i,exp)}}\) is the dose at i% volume coverage in DVHexp, whereas \(\text{D}_{\text{(i,ref)}}\) is the dose at the same i% volume coverage in DVHref. Moreover, considering the differential DVH (dDVH), a Gaussian-like shape distribution, the FWHM of the dDVH distribution was evaluated in each PET/TC acquisition as:  
\[\text{FWHM\_Ddvh}=2.35\cdot\sigma \tag{4}\]  
where \(\sigma\) is the standard deviation of the dDVH distribution from the Gaussian fit.  
For P2, this dosimetric analysis was performed only on the 37 mm sphere because of its clinical relevance and to avoid partial volume effects, which highly affect dose values of smaller spheres.
###### Abstract  
The performance of a piezoelectric actuator for active noise cancellation depends primarily on the quality of the actuator material and its design approach, i.e., single-layer or multi-layer actuators, stacks, benders, or amplified actuators. In this paper, material selection and multiphysics modeling were performed to develop an optimal piezoelectric plate actuator for active noise cancellation. The material selection process was analyzed using two multi-criteria decision making (MCDM) approaches for material selection, i.e., figure of merit (FOM) for actuators and the technique for order of performance by similarity to ideal solution (TOPSIS). Of the 12 state-of-the-art piezoelectric actuator materials considered in this article, PMN-28% PT is the best material according to TOPSIS analysis, while \(\left[Pb\left(In_{1}^{\ast}Nb_{1}\right)O_{3}\right]24\%-Pb\left(Mg_{1}^{ \ast}Nb_{1}\right)O_{3}-PbTiO_{3}\) (PIN24%-PMN-PT) is the best material according to FOM analysis. The ranking of state-of-the-art piezoelectric material categories for actuators according to the two analysis is consistent and the category of monocrystalline piezoelectric materials has the highest actuation performance. The multiphysics modeling was performed using ANSYS Mechanical using two different approaches: one using Ansys Parametric Design Language (APDL) command fragments, the other installing the PiezoAndMEMS ACT extension in ANSYS. Static structure, modal, and harmonic response analyses were performed to determine an optimal pair of piezoelectric plates to be used as an actuator for active noise cancellation. A pair of plates of the same materials, but of different dimensions turns out to be the optimal piezoelectric plate actuator for active noise reduction, according to the two multiphysics modeling methods.  
piezoelectric plate actuator; multi-criteria decision making; figure of merit; TOPSIS algorithm; multiphysics modeling; ANSYS MAPDL; piezo and MEMS extension; polarization Article  
Multiphysics Modeling and Material Selection Methods to Develop Optimal Piezoelectric Plate Actuators for Active Noise Cancellation  
[PERSON], [PERSON], [PERSON], & [PERSON], 2021  
11 Department of Mechanical Engineering, Wolayta Sodo University, Wolayta Sodo 138, Ethiopia;  
[EMAIL_ADDRESS]  
2
## 2 Materials and Methods
### Methods
#### 2.3.4 The Hurst Exponent  
The Hurst exponent was proposed by British hydrologist [PERSON] (1951), and later revised by [PERSON] and [PERSON] (1969), for determining the sustainability of the time series in the future. This method was used to assess the sustainability of vegetation growth by detecting whether the NDVI has long-term dependence [37; 38]. In this paper, the sustainability of CLP in the short term was judged bythe quantitative description of GPP dependence using the Hurst exponent. The calculation steps are as follows:  
1. To define the time series \(GPP(t)\), \(t=1\), \(2...n\).
2. To calculate the mean of the GPP time series, \[\overline{GPP}_{(\tau)}=\frac{1}{\tau}\sum_{t=1}^{\tau}GPP_{(t)}\quad\tau=1,2, \ldots,n.\] (7)
3. To calculate the accumulated deviation, \[\mathrm{X}_{(t,\tau)}=\sum_{t=1}^{t}\Bigl{(}GPP_{(t)}-\overline{GPP}_{(\tau )}\Bigr{)}\quad 1\leq t\leq\tau.\] (8)
4. To acquire the level difference, \[\mathrm{R}_{(\tau)}=\max_{1\leq t\leq\tau}GPP_{(t,\tau)}-\min_{1\leq t\leq \tau}GPP_{(t,\tau)}\quad\tau=1,2,\ldots,n.\] (9)
5. To acquire the standard deviation sequence, \[\mathrm{S}_{(\tau)}=\left[\frac{1}{\tau}\sum_{t=1}^{\tau}\Bigl{(}GPP_{(t)}-GPP _{(\tau)}\Bigr{)}^{2}\right]^{1/2}\quad\tau=1,2,\ldots,n.\] (10)
6. To acquire the H exponent, \[\frac{R(\tau)}{S(\tau)}=(c\tau)^{\mathrm{H}}.\] (11)  
According to previous research [39], the value of H ranges from 0 to 1, and there are three types: When H = 0.5, it indicates that the GPP time series is a random sequence without sustainability, and it is inferred that the trend of the CLP in the future is unstable. When H > 0.5, it means that the GPP time series is a sequence with sustainability, the future trend is consistent with past changes, and the trend of the CLP in the future is sustainability. When H < 0.5, it means that the GPP time series is a sequence with anti-sustainability, the future trend is opposite to past changes, and the trend of the CLP in the future is anti-sustainability.
###### Abstract  
Background: Some bandages with Kinesiotape have demonstrated some potential as means of prevention for Anterior Cruciate Ligament (ACL) injury in men. Objective: The main objective of this pilot study was to observe if a Kinesiotape bandage at the knee could potentially have an immediate preventive effect against ACL injuries by improving proprioception and balance and reducing knee valgus and anterior translation of the tibia during certain functional tests in female athletes. Materials and Methods: A cross-over clinical trial including 10 female athletes (football and rugby) was conducted, where the two lower limbs of the 10 participants (\(n=20\)) were randomly assigned to the intervention group (IG) or to the control group (CG). A Kinesiotape bandage was placed on one knee with a tension of 75% (IG, \(n=10\)) and another bandage on the other knee with a tension of 10% (CG, \(n=10\)), as a placebo. Ultrasound assessment and functional tests were performed before (T1) and after (T2) bandaging according to group assignment. A videographic analysis was carried out with the ImageJ Software version 2.0. Results: A statistically significant reduction in knee valgus was observed in the Lateral Step Down test in the IG, compared to the CG (\(p<0.05\); \(\eta\)2p = 0.26) (IG-T1: 151.40 \(\pm\) 11.04\({}^{\circ}\); IG-T2: 157.10 \(\pm\) 10.18\({}^{\circ}\); versus CG-T1: 156.96 \(\pm\) 5.44\({}^{\circ}\); CG-T2: 158.68 \(\pm\) 6.12\({}^{\circ}\)). In the other tests, no significant differences were found in terms of time \(\times\) group interaction. Conclusions: A Kinesiotape bandage was able to reduce knee valgus in a functional test compared to a placebo bandage but was not more effective at reducing the anterior translation of the tibia or improving balance or proprioception. Based on these results, it could have a modest preventive effect against an ACL injury in female athletes.  
Kinesiotape; women; athletes; ultrasound; anterior cruciate ligament +
Footnote †: journal: Applied Sciences
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 3 Experimental Results
### Radiation Pattern and Surface Current Distribution  
The far-field behavior of the proposed filtenna is investigated at 2.45 GHz, as shown in Figure 10. It is observed that the filtering antenna exhibits bi-directional radial patterns in the E-plane while an omni-directional pattern in the H-plane is observed for the selected frequency, where strong agreement is noticed for the simulated and measured results. However, slight variation between simulated and measured results is mainly attributed to the fabrication and installation tolerance and test errors.