esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Terrestrial laser scanning is a modern non-contact method of acquiring spatial data by measuring the distance between an instrument and an observed object using laser light. Measurements are made using terrestrial laser scanners with active sensors that operate in the visible and infrared spectrum of light.  
The market for measuring instruments involves many manufacturers of high-quality measuring and computing equipment. On account of competition, the technology has become increasingly affordable for engineers in various technical disciplines. In addition to the price, which is often a determining factor in equipment purchases, other important elements of quality have been identified, such as scanner performance, accuracy and precision, scanning speed, laser dot resolution, range, field of view, target usability, camera features, and associated software. The advantages in the technology include the speed and relative ease of making measurements in the field, which played a significant role in conducting experiments. Working with the Leica RTC 360 LT terrestrial laser scanner involved more than just collecting measurement data during the scanning process. A necessary step in terms of the applicability of the technology is the computer processing. This process was divided into several work phases in the following order: registration of the scans, georeferencing of the point cloud, filtering of the point cloud, grid construction and modelling, and analysis and interpretation of the vertical beam displacements.  
Based on the written results, and in line with the purpose of the article, terrestrial 3D laser scanning can be confirmed as a good laboratory alternative and non-contact method for determination of vertical displacements due to following advantages: (i) the method showed millimeter accuracy of the measurements performed, (ii) application in the field was fast and easy, and (iii) the results could be post-processed quickly using standard software and hardware.  
Conceptualization, B.K., L.S. and M.D.; methodology, B.K., Z.P. and L.S.; software, L.S.; validation, B.K., L.S., M.D. and Z.P.; formal analysis, B.K. and L.S.; investigation, B.K., L.S. and M.D.; resources, L.S. and Z.P.; data curation, B.K. and M.D.; writing--original draft preparation, B.K. and M.D.; writing--review and editing, B.K., M.D. and Z.P.; visualization, B.K. and L.S.; supervision, B.K.; project administration, B.K. and M.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available. The data were obtained on the basis of research within the dissertation of [PERSON] at the Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### A-VTTCES-KF Algorithm  
In this section, the noise terms in the clock drift estimations of the GNSS SDR are identified and quantified with the Allan variance. Some preliminary experiments are firstly conducted to provide an exhaustive analysis on what types of error sources influence the performance of the NCO in a VT architecture. Then, the time-correlated error due to the VT algorithm will be modelled with the 1 st-order GM process. Finally, an A-VTTCES-KF algorithm will be proposed to reduce the influence of the time-correlated error on the VT loop.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 6 Conclusions  
This study focuses on key challenges in MOT, particularly the high sensitivity of tracking to detection outputs, object occlusion and overlap, and complex motion. We propose an innovative approach that integrates predictive information to improve NMS, thereby reducing reliance on detection results. By applying secondary modulation to suppression scores and dynamically adjusting suppression thresholds based on tracking information, our method significantly enhances the retention of candidate boxes for occluded objects. To track occluded or overlapping objects more effectively, we introduce an adaptive measurement noise method. This approach adjusts measurement noise to not only reduce the impact of foreground occlusion on tracking but also mitigate the effects of object overlap on tracking accuracy. Additionally, for objects with complex motion, we incorporate height information into the association algorithm to enhance the calculation of the affinity matrix, thereby improving the stability of object association. Experimental results demonstrate that these improvements significantly enhance overall MOT performance, especially in busy and complex scenarios. These findings provide new research insights for the MOT field and open up new directions for future studies. We hope our work will contribute positively to the development of multi-object tracking.  
Conceptualization, X.C., H.Z. and Y.D.; methodology, X.C. and H.Z.; software, H.Z.; validation, X.C., H.Z. and S.S.; formal analysis, Y.D. and S.S.; resources, X.C. and S.S.; data curation, H.Z. and Y.D.; writing--original draft preparation, X.C. and H.Z.; writing--review and editing, X.C. and S.S.; visualization, H.Z.; project administration, X.C. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by Key R & D projects of the Guangxi Science and Technology Program (GuikeAB24010338), The central government guides local science and technology development fund projects (GuikeZY22096012), National Natural Science Foundation of China (32360374), an independent research project (GXRDCF202307-01).  
Not applicable.  
The datasets presented in this study are accessible via the following link: [[https://github.com/DanceTrack/](https://github.com/DanceTrack/)]([https://github.com/DanceTrack/](https://github.com/DanceTrack/)) and [[https://motchallenge.net/data/MOT20](https://motchallenge.net/data/MOT20)]([https://motchallenge.net/data/MOT20](https://motchallenge.net/data/MOT20)), accessed on 8 September 2024.  
We are very grateful to the volunteers from Guilin University of Technology for their assistance in the experimental part of this manuscript.  
The authors declare no conflicts of interest.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Research Process of Path-Planning Problem  
In this part, we summarize the research process of the path planning. Figure 3 shows the flowchart of the conducted research. The first step is problem formulation. The UAV path-planning problem is formulated as a minimization optimization problem. In this study, we consider path energy consumption as the main objective to be minimized. The energy model is explained in Section 3.1, which includes different maneuvering activities, such as vertical, horizontal, and hovering flight. In our study, the drones fly at a fixedaltitude, simplifying the problem into a 2D path-planning problem. Therefore, we consider the horizontal and turn energy.  
Moving on, in each subsequent step, we apply meta-heuristic approaches. This involves setting the environment boundaries, obstacle locations, and their sizes. Next, we set the parameters of the selected algorithm and initialize its phase by initializing the parameters and populations. The population is then evaluated using the objective function. Following that, the iteration starts, and for each iteration, the population is updated based on the algorithm's evolving process. New populations are generated and evaluated, and the best path so far is stored. The iteration continues until the maximum iteration is reached, at which point the algorithm ends, and the optimal paths for all drones are generated. These steps are repeated for all algorithms.  
Figure 3: Flowchart of the study process.  
After collecting the results for all algorithms, we select metrics to evaluate their performance, including formation stability, convergence speed, energy consumption, straight line rate, and relative percentage deviation. Finally, we conduct a statistical analysis applying one-way ANOVA to determine significant differences between these algorithms, which are visualized in figures.
# Intelligent Simulation Technology Based on RCS Imaging
## 5 Conclusions  
Target simulation technology is very important in radar detection. This paper presented a cost-effective target drone design based on RCS characteristic simulations. The main conclusions are as follows:  
1. The PO-AP method was used to analyze the RCS of the variable corner reflector, which made up for the shortcomings of the traditional PO method that could not calculate a complex model. A simulation target of an aircraft was designed;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Maximum Level Speed** & **Utility Ceiling/m** & **Endurance Time** & **Cost/RMB** \\ \hline The aircraft simulator & 1.3 Ma & 15,000 & 2 h & About 10,000 \\ in this paper & & & & \\ \hline JC-80 & 0.78 Ma & 7000 & 30 min & Hundreds of thousands \\ \hline II-250 & 0.74 Ma & 11,000 & 60 min & Hundreds of thousands \\ \hline S-400 & 1.17 Ma & 10,000 & 60 min & Hundreds of thousands \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cost effectiveness analysis.  
Figure 21: Error analysis trend chart.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed** & 0 km/h & 12.5 km/h & 25 km/h & 50 km/h & 100 km/h & 200 km/h \\ \hline
**Error** & 0\% & 8.30\% & 4.80\% & 4.72\% & 3.16\% & 0.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analysis of Doppler frequency shift simulator.  
2. The distribution characteristics of sharp angle RCS, stationary space region distribution characteristics and fluctuations caused by multiple scatterers of an aircraft were simulated by combining the corner reflector, Luneberg lens reflector and metal sphere. The RCS error of the nose was 1.39 dBsm, while the error of other parts was less than 3 dBsm, which satisfied the design index;
3. A fidelity evaluation model for complex targets was proposed, and the designed simulation target drone scored up to 93.1 points, indicating a highly realistic level. This provided an objective and reliable fidelity evaluation rule for complex targets and provided a relevant theoretical basis;
4. The Doppler frequency simulator designed in this paper can simulate the motion characteristics of an aircraft, with errors of 8.3% and 4.8%, respectively, which can meet the test requirements.  
Conceptualization, X.W.; methodology, J.H.; investigation, S.Y.; formal analysis, H.G.; software, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Education of the People's Republic of Chinagrant number (20190209400011).  
Not applicable.  
The authors declare no conflict of interest.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method
### Amplitude Synthesis of Antenna Array Radiation Pattern under Constraints
#### 3.1.3 Antenna Array with 24 Elements  
In this example, the network factor is described by the equation  
\[AF(\theta)=2\sum_{n=1}^{12}a(n)cos[\beta d(n)cos\theta]. \tag{28}\]  
The fitness function is chosen based on the following optimization objective:  
\[fitness=min(max\{20 log|AF(\theta)|\}) \tag{29}\]  
Under constraint \(\theta\in[0^{\circ},82^{\circ}],[102^{\circ},180^{\circ}]\).  
To use the Taguchi method, we need to follow these steps:  
* Step 1: Determine the number of parameters (\(k=12\)).
* Step 2: Determine the number of levels (\(s=3\))
* Step 3: Determine the strength (\(t=2\)).
* Step 4: Determine the OA experimental design (\(27,12,3,2\)).
* Step 5: Determine the reduced function (\(rr=0.8\)).
* Step 6: Determine the convergence value \(=0.001\).  
By following the same procedure as for examples 1 and 2, the results obtained in this example can be summarized as follows:  
* The optimized maximum SLL found by the Taguchi method is \(-39.2263\) dB (Figure 6a).
* The gain is \(25.2718\) dB, as the SLL of the uniform array is \(-13.9545\) dB.
* The number of iterations is \(73\) (Figure 6b).
* The excitation weights of the antenna array optimized with the Taguchi method are listed in Table 7.
* The comparative study between the results obtained by the Taguchi method and those obtained by PSO indicates a considerable gain of approximately \(3.7\) dB.  
In summary, the results of these three examples lead us to conclude that there is proportionality between the number of elements on one hand and the convergence speed on the other, such that the convergence speed will be faster with a higher the number of elements.  
Figure 5: Radiation pattern of sixteen-element linear antenna array and fitness function.
# Solid-State Foaming Process Optimization for the Production of Shape Memory Polymer Composite Foam
## 2 Experimental Method
### Foaming Temperature Determination  
Samples were prepared with pure CC and CC + 2 wt.% Fe\({}_{3}\)O\({}_{4}\) NPs, as well as pure JSD and JSD + 2 wt.% Fe\({}_{3}\)O\({}_{4}\) NPs. The main goal of this experiment was to test for the best foaming temperature. The foaming temperatures tested were 200, 240, 260, and 290 \({}^{\circ}\)C. Selecting these values was far from random, as they had to be optimum to avoid burning and to assure the polymerization of the polymer powder. The temperature at which the lowest density was obtained was considered to be the best foaming temperature.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Weight in Air** & **Weight in Water** & **Absolute Density (g/mm\({}^{3}\))** \\ \hline CC & 3.83 & 3.77 & 0.064 \\ \hline JSD & 3.19 & 3.13 & 0.053 \\ \hline CC + 2\% NPs & 3.36 & 3.29 & 0.048 \\ \hline JSD + 2\% NPs & 2.46 & 2.33 & 0.019 \\ \hline \end{tabular}
\end{table}
Table 2: Absolute density of the prepared foamed samples.  
Figure 6: Samples (**a**) before machining, (**b**): being machined and shaped into uniform shape for easier calculations with a CNC turning machine.  
It is clear from Figures 7 and 8 that the increment in the foaming temperature caused an increment in the foaming ratio, resulting in larger pores in both polymer matrices. The foaming temperature's impact on the CC polymer was at its maximum between 260 and 290 \({}^{\circ}\)C, but the difference between these values was not significant. On the contrary, JSD showed a different range of working temperatures at which the highest foam ratio could be obtained. However, 260 and 290 \({}^{\circ}\)C were selected as the two levels for the foaming temperature because of their obvious positive impact on the ratio of CC foam.
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 2 Materials and Methods
### Sun-Induced Fluorescence
While in that study chlorophyll fluorescence was divided by reflectance and transmittance at fluorescence's wavelengths (representing the structural and biochemical influences on its signal) we will define SIF-Yield as the quotient of SIF by corresponding NDVI, since NDVI is a known proxy of chlorophyll content and /APAR [61]. Thirdly, we have defined a tentative productivity-related modification to SIF (SIF-Prod) following from the well-known scheme of Gross Primary Productivity (GPP) from [PERSON] [62], [PERSON] [63] described in the following formula:  
\[\text{GPP}=f\text{APAR}*\text{PAR}*\text{LUE} \tag{1}\]  
where /APAR is the fraction of absorbed photosynthetically active radiation (PAR), PAR is the incident PAR and LUE (Light Use Efficiency) is a term relating to photosynthesis physiology that has been defined in different ways in scientific literature [64]. This classic equation has been frequently used to estimate GPP through the LUE-paradigm [65] and SIF has been proposed as a suitable proxy for LUE [2; 10], although studies testing this proposition have reached confounding results [11; 59]. However, preliminary results from another study testing different biochemistry- and structure-related adjustments to SIF has found that the product of SIF by a proxy of APAR showed the highest observedcorrelation to FLUXNET GPP data in comparison to various tested formulations [66]. The findings of that study suggest that SIF is not proportional to LUE by itself but rather to the instantaneous light-use efficiency under a given light incidence level (i.e., PAR \(*\) LUE). Thus, we decided to used it here in the same formulation as defined by [PERSON]'s seminal work, assuming that SIF encapsulates the product of LUE by PAR, and using NDVI as a proxy to _f_APAR. Beyond SIF\({}_{FR}\) and the above described SIF-SZA, SIF-Yield and SIF-Prod formulations we also included SIF at the red wavelength peak, SIF\({}_{R}\) (supplied with GOME-2 data), the ratio between SIF at both wavelengths, SIF\({}_{R/FR}\) (calculated from extracted GOME-2 data) and finally, SIF daily average based on a clear-sky proxy of PAR, _d_SIF\({}_{FR}\) (supplied with GOME-2 data). All tested SIF-related response variables are listed in Table 1. Due to the lower spatial resolution of its data and the lower signal-to-noise ratio of its data, SIF\({}_{R}\) was not used for testing the spectral adjustments discussed above (i.e., SIF-SZA, SIF-Yield and SIF-Prod) beyond preliminary tests that produced similar effects as those observed with SIF\({}_{FR}\) (results not shown).
## 4 Case Study
### Result Analysis
#### 4.2.3 Estimation of Spoil Volume Changes  
Using quarterly Sentinel-1 C-band SAR imagery, this study examines the Yagongshan SDA as an example, employing the D-InSAR method to analyze elevation changes. Combined with the analysis of the disturbance boundaries, the dynamic changes in volume of earth and rock from the SDA were estimated.  
1. Elevation changes of SDAs  
The design height of the Yagongshan SDA was set at 25 m. During the period from May 2018 to June 2018, the actual height of the SDA increased by an average of 3.77 m. However, the monitoring imagery generated using the D-InSAR method indicated an average height increase of 4.13 m. This shows that the D-InSAR method overestimated the elevation by 0.36 m, or 9.5%. Despite this overestimation, the method's efficiency in estimating elevation changes in SDAs is noteworthy, demonstrating significant application potential.
2. Estimation of dynamic changes of earth and rock in SDAs  
Through the disturbance boundary identification method proposed in this study, the disturbance area of the Yagongshan SDA was determined to be 16,600 m\({}^{2}\). Between May and June 2018, the elevation change estimated using D-InSAR was 4.13 m, resulting in a calculated earth and rock volume change of 68,600 m\({}^{3}\). A review of the construction process records during this period showed that the actual cumulative amount of earth and rock volume was 59,400 m\({}^{3}\). The estimated data exceeded the actual data by 9200 m\({}^{3}\), an overestimation of 15.5%. Although the accuracy of estimating dynamic changes in earth and rock volume is limited, this represents a new technique application that will revolutionize SDA monitoring and management.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 5 Numerical Analysis  
We represent the numerical analysis in two parts. First, we enumerate the throughput for the PHY layers supporting lower data rates, such as PHY I and PHY VI. Then we collectively analyze PHY II and PHY III that support larger data rates up to 96 Mbps.
# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes
## 3 Results and Discussion
Thus, our presented method, based on the analysis of changes in the internal state of the UAV, makes it possible to accurately detect the presence of anomalous behavior and diagnose a possible spoofing attack. The novelty and advantages of our method are as follows. * It is versatile and can be applied to various data sets obtainable from the particular UAV's sensor system. * It determines only that a change has occurred in the system and does so quite accurately; it is then up to the decision-making system to determine whether the change is an attack or not. * It does not require information about reference and normal values, or inputs from external sources. The UAV independently analyzes the changes in indicators and compares its own state at different intervals. If the state is stable or quickly becomes stable, it means that there are no anomalies.
## 4 Results and Discussion
### Performance Analysis
#### 4.3.2 Analysis with NSL-KDD Dataset  
The proposed work has been evaluated in accordance with four standard metrics in relation to the NSL-KDD dataset and the equivalent outcomes are presented in Table 4.  
Table 4 shows that the proposed system achieved a 99.92 Accuracy rate, a 99.54 F1-Score, a 99.97 Precision rate and a 99.38 Recall rate. Following this, the accuracy values for different weights in the NSL-KDD dataset have been evaluated, and the acquired analytical results are outlined in Table 5.  
From Table 5, we see that the accuracy value changed in accordance with the weight. Correspondingly, when weight = 1, the accuracy value was 99.92. This rate gradually increased and decreased with the increase in weights, and it reached 42.5 when weight = 9 and 99.92 when weight = 10. The results of k-fold cross-validation for the NSL-KDD dataset are explored in Table 6.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
99.92 & 99.38 & 99.97 & 99.54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis with NSL-KDD dataset.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Folds** & **Accuracy** \\ \hline Fold 1 & 0.9971 \\ \hline Fold 2 & 0.9973 \\ \hline Fold 3 & 0.9964 \\ \hline Fold 4 & 0.997 \\ \hline Fold 5 & 0.9969 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of k-fold cross-validation for UNSW-Bot.  
From Table 6, it can be seen that the accuracy value differed for each of the folds. Accordingly, the accuracy value was 0.9375 for fold 1, 0.956 for fold 2, 0.9375 for fold 3, 0.94 for fold 4 and 0.9375 for fold 5. Hence, the accuracy value varied based on the folds. Furthermore, a confusion matrix for the NSL-KDD dataset has been constructed to expose the correct and misclassification rates of the proposed work. The equivalent outcomes are projected in Figure 5.  
Figure 5 shows the number of correct classifications of attack classes to be 15,524, while the number of correct classifications of normal classes is 14,165. Concurrently, 13 normal classes have been misinterpreted as attack classes. Contrarily, two attack classes have been misinterpreted as normal. In this case, as the correct classification rate appears to be higher than the misclassification rates, the proposed system has been exposed to be effective.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Folds** & **Accuracy** \\ \hline Fold 1 & 0.999 \\ \hline Fold 2 & 0.997 \\ \hline Fold 3 & 0.985 \\ \hline Fold 4 & 0.983 \\ \hline Fold 5 & 0.981 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of k-fold cross-validation for NSL-KDD.  
Figure 5: Confusion matrix for (**a**) binary classes and (**b**) different classes in NSL-KDD.