esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Accuracy Verification of CORDIC Algorithm  
#### 3.2.1 Dynamic Filtering  
First, verify the output accuracy of the dynamic filter coefficient generation module based on the radix-4 CORDIC algorithm on FPGA. A set of 33 rd-order low-pass FIR filter coefficients is generated by Equation (1) based on the radix-4 CORDIC algorithm. Additionally, the filter coefficients with the same parameters as theoretical values are produced by the filter-Designer toolbox in MATLAB software (version number R2021b). Figure 8a shows the comparison of the 33 rd-order FIR filter coefficients generated by radix-4 CORDIC and filter-Designer, the coefficients of both are close to each other; Figure 8b shows the error of the output filter coefficients of the radix-4 CORDIC algorithm, it can be seen from the figure that the error of the output filter coefficients of the radix-4 CORDIC algorithm does not exceed \(4\times 10^{-3}\), which meets the requirement of FIR filtering accuracy in radial artery ultrasound signal processing.  
Next, the distributed FIR filtering module is analyzed. The design idea is first to simulate a single-point frequency superimposed input signal and a white noise input signal. The simulated input is then used as a test input, and the filter output is simulated. Finally, the output signal of the filter is analyzed, and the time domain and spectrum diagram of the input/output signal are compared. The test results of the dynamic FIR filtering performance based on the radix-4 CORDIC algorithm are shown in Figure 9. The time domain waveform and spectrum before and after filtering of a single-frequency signal synthesized by the FPGA simulation are shown in Figure 9a,c, respectively. The original signal is a mixed signal of 100 Hz, 300 Hz, 600 Hz, 800 Hz, 1200 Hz, and 1400 Hz. The sampling frequency is 3000 Hz, and the cut-off frequency is 400 Hz. It can be seen that the filtered synthesized single-frequency signal has been transformed into a regular single-frequency signal with a frequency of 100 Hz. Figure 9b,d show the time domain waveform and spectrum of the FPGA simulation of the white noise signal before and after filtering, respectively. The trend of the white noise in the time domain after filtering is significantly moderated, i.e., the high-frequency signal component is filtered out. Therefore, the dynamic low-pass FIR filtering based on the radix-4 CORDIC algorithm can effectively filter out high-frequency signals while retaining low-frequency signals, achieving a stop-band attenuation of more than \(-50\) dB. It demonstrates the superiority and practical application of the CORDIC algorithm to the dynamic FIR filtering technology of ultrasound systems, with good filtering and clinical diagnostic effects.  
Figure 8: Accuracy of dynamic FIR filter coefficients based on the radix\(-\)4 CORDIC algorithm. (**a**) The comparison of the 33 rd\(-\)order low\(-\)pass FIR filter coefficients generated by the radix-4 CORDIC and filter\(-\)Designer; (**b**) The error of the output filter coefficients of the radix\(-\)4 CORDIC algorithm.
## 2 Materials and Methods
### Modelling Scheme
#### 2.2.1 Pre-Modelling
(2019) was validated retrospectively and shown to be fit for purpose in the capture of the documented drought years (2006, 2009 and 2011). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Variable Description** & **1-Month Lag** & **2-Month Lag** & **3-Month Lag** \\ \hline NDVI\_Dekad & NDVI for last dekad of month & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ VCI\_Dekad & VCI for the last dekad of month & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ VCI1M & VCI aggregated over 1 month & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ RFE1M & Rainfall Estimate aggregated over 1 month & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ RFE3M & Rainfall Estimate aggregated over the last 3 months & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ SPI1M & Standardised Precipitation Index aggregated over & \(\boxtimes\) & \(\square\) & \(\boxtimes\) \\ SPI3M & Standardised Precipitation Index aggregated over & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\  & the last 3 months & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ RCI1M & Rainfall Condition Index aggregated over 1 month & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\  & Rainfall Condition Index aggregated over the last 3 & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\  & months & & & \\ Month 1 & Denotes the month of year & \(\square\) & \(\square\) & \(\square\) \\ VCI3M & VCI aggregated over the last 3 months. The & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\  & non-lagged value is the dependent variable & \(\boxtimes\) & \(\boxtimes\) & \(\boxtimes\) \\ \hline \hline \end{tabular}  
* Variable only used in GAM models but excluded from the corresponding ANN models. The lag for the predictor variables ranges from 1 to 3 months and thus for instance, for VCI3M we consider VCI3M\({}_{\text{t}-1}\), VCI3M\({}_{\text{t}-2}\), VCI3M\({}_{\text{t}-3}\).  
\end{table} 
Table 2: Variables used for modelling. All the indicated variables are lagged predictor variables. The dependent variable is non-lagged values of VCI3M. The month of year was added to model seasonality.
## 4 Experimentation
### Experimental Results
#### 4.5.2 Model Robustness Analysis  
To validate the robustness of the HCAE model, we conducted TKGC experiments on an irregular timestamp dataset called ICEWS14-irregular. The goal was to evaluate the performance of HCAE in handling unevenly distributed timestamp intervals. The construction of the dataset follows [43], where a snapshot graph is randomly sampled from ICEWS14,ensuring that the time interval between two consecutive snapshots does not exceed 4. The experimental results are presented in Figure 5.  
From the experimental results, it can be observed that HCAE performs the best in all four metrics. Notably, it achieves significant improvement in Hits@1. For instance, it outperforms TeMP-SA by 11.5. This is because HCAE adopts the idea of multi-scale historical modeling, which allows it to explore temporal dependency patterns at different granularities without overly relying on a specific temporal regularity. On the other hand, TComplEx's temporal modeling approach is relatively simple, representing temporal influence through a single time weight parameter with limited expressive power. TNTComplEx does not delve deep into modeling temporal regularities and primarily relies on linear transformations, which fail to capture the semantic information related to entities and time effectively. TeMP-SA only utilizes graph information within a fixed short time range for embedding learning, disregarding the rich graph information in other time intervals. T-GAP [12] employs time-shifted embeddings to encode different time differences. However, HCAE's modeling of attribute information reduces the reliance on structural information and mitigates the impact of irregular timestamps. Moreover, mining high-order structural information enables the capture of more complex entity relationships, allowing HCAE to effectively withstand fluctuations in timestamp intervals and better adapt to irregular time gaps.
## 2 Materials and Methods
### Classification of the Vegetation Area by NDVI Value  
The envelope of Sentinel-2 satellite images varies according to the resolution. We used the highest resolution (10 m) which contained spectral bands of 2, 3, 4, 8, TCI (True Colour Image), etc. We used band 4 (red light, \(RED\)) and band 8 (near-infrared light, \(NIR\)) images in order to calculate \(NDVI\), as shown in Equation (1).  
\[NDVI\;value=\frac{NIR-RED}{NIR+RED} \tag{1}\]  
According to the equation, the \(NDVI\) value ranged from \(-1.0\) to \(1.0\). For the study area, however, no image existed that had an \(NDVI\) value of less than 0. This can be understood  
Figure 1: The study area, Geomunoreum, is located on Jeju Island in South Korea. The width and height of the study area are 1450 m and 1342 m, respectively.  
by the fact that the study area was clipped based on OpenStreetMap, in which only the green region remained.  
The study area had four distinct seasons. In this study, evergreen refers to the region where most tree species keep their leaves during all four seasons; deciduous is the region where leaves tend to fall off at maturity, usually in autumn; grassland is an area of land where vegetation is dominated by grasses. In order to examine the classification criteria, we collected images in winter and summer in the same year. Due to the unavailability of 2017, 2018, and 2022 in Sentinel hub, only 2020 and 2021 were successfully collected. Figure 2 shows the comparison of NDVI images in winter in the first column and summer in the second column for 2020 and 2021. For 2021, the image in August was not available; therefore, we used the image for September. The green area of the summer image was subtracted from the winter image to obtain the area of deciduous trees; and the green area of deciduous trees was calculated from the area of the summer image to determine the evergreen tree area based on NDVI with confirmation from experts who had screened this area. After subtraction, the NDVI threshold (0.6) was set based on the results. In this case, the green area of deciduous trees could contain the grass area. The manual survey map for Geomunoreum is publicly available from the Korea Forest Service ([[https://map.forest.go.kr/forest](https://map.forest.go.kr/forest)]([https://map.forest.go.kr/forest](https://map.forest.go.kr/forest)) (accessed on 21 October 2022)). We confirmed the correctness of our criteria based on Figure S1 in the Supplementary Materials.  
Figure 2: NDVI images of Geomunoreum in winter (the first column) and summer (the second column) for 2020 and 2021. The third column represents only pixels with an NDVI value greater than 0.6 in the NDVI image for March.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Response Time and QoS-Node Content Store Size
#### 5.4.1 Response Time and QoS-Node Content Store Size for QoS Traffic  
The simulation evaluates the response time for QoS traffic corresponding to varying QoS-node content store sizes. The size of a content store of intermediate NDN nodes is set at 10 MB, and the size of a content store of QoS nodes varies between 50 MB and 250 MB. Observations from Figure 7 show that QLPCC outperforms EQPR in the range of 65% to 82%, PRR in the range of 61% to 78%, the probability cache scheme in the range of 79% to 88%, the LFF scheme in the range of 80% to 89%, and the LFU scheme in the range of 82% to 90%.  
Figure 7: Response time vs. QoS-node content store size for QoS traffic. (**a**) Response time vs. QoS-node content store size (QoS traffic) for \(|M|\) = **500**. (**b**) Response time vs. QoS-node content store size (QoS traffic) for \(|M|\) = **1000**.
# Radioactivity in Soils of Kosovo and Radiological Implications
## 3 Results and Discussion
### The Activity Concentrations of the Radionuclides
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Radionuclides** & **Data** & **Mean-SD** & **Median** & **Min-Max** & **Percentiles** \\ \hline \multirow{2}{*}{\({}^{226}\)Ra (Bq/kg)} & Actual & 22 \(\pm\) 5 & 24 & 8–30 & 19–26 \\ \cline{2-6}  & Simulation & 23 \(\pm\) 2 & 23 & 12–29 & 21–25 \\ \hline \multirow{2}{*}{\({}^{232}\)Th (Bq/kg)} & Actual & 21 \(\pm\) 5 & 21 & 7–31 & 19–25 \\ \cline{2-6}  & Simulation & 21 \(\pm\) 2 & 21 & 11–29 & 20–22 \\ \hline \multirow{2}{*}{\({}^{40}\)K (Bq/kg)} & Actual & 358 \(\pm\) 106 & 368 & 105–515 & 289–447 \\ \cline{2-6}  & Simulation & 370 \(\pm\) 48 & 369 & 181–541 & 337–406 \\ \hline \multirow{2}{*}{\({}^{137}\)Cs (Bq/kg)} & Actual & 13 \(\pm\) 12 & 10 & \textless{}MDA–43 & 3–15 \\ \cline{2-6}  & Simulation & 10 \(\pm\) 2 & 10 & 1–41 & 7–13 \\ \hline \multirow{2}{*}{\({}^{232}\)Th/\({}^{226}\)Ra} & Actual & 0.95 \(\pm\) 0.1 & 0.95 & 0.69–1.21 & 0.9–1.00 \\ \cline{2-6}  & Simulation & 0.93 \(\pm\) 0.07 & 0.93 & 0.68–1.33 & 0.88–0.97 \\ \hline \multirow{2}{*}{\({}^{40}\)K/\({}^{226}\)Ra} & Actual & 16.5 \(\pm\) 5.1 & 16.9 & 7.75–30.4 & 12.8–18.5 \\ \cline{2-6}  & Simulation & 16.4 \(\pm\) 2.4 & 16.3 & 9.75–29.4 & 14.7–17.8 \\ \hline \multirow{2}{*}{\({}^{40}\)K/\({}^{232}\)Th} & Actual & 17.4 \(\pm\) 5.2 & 18.5 & 7.13–29.9 & 13.4–20.5 \\ \cline{2-6}  & Simulation & 17.6 \(\pm\) 2.3 & 17.7 & 9.83–27.6 & 15.9–19.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the actual measurement and simulation of radionuclide activity concentrations in the soil of Kosovo. Figure 2: Correlation analysis of natural radioisotopes (\({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K) versus each other for actual and simulation data. (**a**) Correlation between \({}^{226}\)Ra and \({}^{232}\)Th activity concentration, (**b**) Correlation between \({}^{226}\)Ra and \({}^{40}\)K activity concentration, (**c**) Correlation between \({}^{232}\)Th and \({}^{40}\)K activity concentration. Figure 4 shows the performance metrics of the SGS-ANN estimation results. Explanations of these performance evaluation metrics are given in the Supplementary Material. According to the results of cross-validation, the Pearson correlation coefficients (significance, MAE in Bq/kg, RMSE in Bq/kg) for \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs were 0.34 (\(p<\) 0.05, 4.2, 5.0), 0.37 (\(p<\) 0.05, 3.9, 4.7), 0.42 (\(p<\) 0.05, 78.7, 95.6), and 0.40 (\(p<\) 0.05, 8.7, 11.4), respectively. Figure 5 shows the distribution maps of the activity concentrations of \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs for Kosovo, generated by the SGS-ANN method at 100 \(\times\) 100 m spatial pixel resolution. According to these distribution maps, it is noteworthy that natural radionuclide activities are higher in the northern, southern, and western parts of Kosovo compared to the inner and eastern parts. \({}^{137}\)Cs artificial radionuclide activities are observed to be more effective in the eastern parts of the country close to Chernobyl.
## 5 Conclusions  
To enhance the tracking robustness in signal attenuation and high dynamic environments, an adaptive KF-based carrier tracking approach is proposed. The proposed approach introduces the adaptive algorithm into the KF algorithm to self-tune the process noise covariance, which makes the tracking approach accommodate the time-varying signals. Moreover, the chi-square method is applied to detect system stability. The AKF algorithm is used when the tracking system is not steady, which can improve accuracy and computational efficiency.  
The algorithm has been applied to the GPS L1 software receiver. According to the test results, the proposed carrier tracking algorithm can enhance the tracking robustness in severe fading and high dynamic environments compared with other tracking methods. Accordingly, when the proposed AKF approach is implemented in the GNSS receivers, it can enhance the navigation and positioning accuracy of high-speed receivers moving through obstructions in complicated environments.  
For future research, the proposed tracking algorithm can be applied in complex conditions, such as automatic driving or unmanned aerial vehicle navigation systems experiencing high dynamic and signal fading. Moreover, the adaptability of the proposed algorithm can adjust the system noise covariance, which can also be employed in other nonlinear filtering methods, such as unscented KF (UKF), extended KF (EKF), and so on. Our work lays a theoretical foundation for nonlinear filtering and can enhance the filtering robustness in complex conditions. In addition, when the proposed AKF approach is implemented in the GNSS receivers, it can enhance navigation and positioning accuracy in complex environments.  
Conceptualization, Y.C. and S.Z.; methodology, Y.C., X.W. and H.W.; software, Y.C., H.W. and H.Y.; validation, Y.C., S.Z. and X.W.; investigation, Y.C., H.W. and H.Y.; writing--original draft preparation, Y.C.; writing--review and editing, S.Z.; supervision, S.Z.; project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the helpful comments of the anonymous reviewers.  
The authors declare no conflicts of interest.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 2 Related Works
### The State of the Art of the Routing Algorithm Based on Energy  
The energy-based routing algorithm optimizes the performance of opportunistic networks from the perspectives of energy harvesting, residual energy evaluation, and energy consumption. Energy harvesting pays attention to the energy acquisition approaches of mobile nodes, including charging, solar energy, wind energy, etc. [PERSON] et al. [35] analyzed the achievable throughput of opportunistic spectrum access in wireless cognitive radio networks with energy harvesting. This considers the cognitive radio network under each application user and optimizes the method of energy harvesting by assessing the joint influence of multiple sensing probabilities. Beside, [PERSON] et al. [36] recommend a middle position dynamic energy algorithm, which develops an efficient communication link between the source node and the destination node in WSN, which is known as MDOR [37]. Energy evaluation will be analyzed in communication links. [PERSON] and [PERSON] [38] introduced a PRoPHET-based routing algorithm, which proposed that the end-to-end data transmission between source and destination could be developed base on the remaining battery and delivery predictability. They provide a comparison between PRoPHET [39] and PRoPHET with periodic sleep. The PRoPHET-based routing algorithm leverages the remaining battery to determine the delivery predictability and select suitable next hops.  
For the application scenario of wireless social network, a routing algorithm can solve the problem of limited energy from the aspects of node similarity, community division, node centrality, and node trust. [PERSON] et al. [40] developed a rotated-algebraic path-time codes (RA-PTC) routing algorithm to face the technological challenges in networks with energy-harvesting mobile devices, in which a complete path from source to destination can harvest enough energy for data transmission. [PERSON] et al. [41] recommended a privacy protection mechanism, which is constructed based on the trust value of mobile vehicles. The energy consumption of data routing and forwarding can be employed to evaluate the incentive scheme and trust value of nodes. [PERSON] [42] proposed an energy-saving routing approach based on K-means and FAH, namely the KAK routing algorithm. The objective of this algorithm is to handle the issue of node energy constraints, low delivery ratio, and sort network cycle, with the support of energy consumption reducing mechanism. [PERSON], et al. [43] introduces a novel analytic model optimizing the number of discovered peers for a given energy budget, which aims to optimize the minimal network detection energy consumption by dynamically tracking the optimum even in non-stationary state. [PERSON] et al. [44] constructed a neighbor discovery algorithm based on the duty cycle length to the time-varying context and contact duration. The goal of this approach is mainly to make a tradeoff between energy consumption and network performance.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 2 Materials and Methods
### Plasma-Cladding Equipment and Process  
The plasma-cladding device primarily consisted of five components: the DML-V03 BD plasma welding machine, a three-axis moving platform for the welding torch, a positioner platform, a powder and gas feeding system, and a control computer. It allowed for the adjustment of eight parameters: the cladding current, cladding speed, powder feeding rate, cladding distance, longitudinal displacement, ion gas, shielding gas, and powder feeding gas. Prior to cladding, a pre-programmed procedure was uploaded to the control computer, and the workpiece, which had undergone rough surface grinding, was placed at a designated position on the positioner platform. The welding torch was then moved to the starting position specified by the program. Upon activating the start switch, the plasma beam was instantly ignited. The alloy powder, upon entering the plasma beam, was rapidly heated and accelerated to form a particle stream, which was subsequently sprayed onto the surface of the workpiece. On the surface of the workpiece, the powder underwent physical and chemical transformations such as melting, mixing, and solidification, ultimately forming a metallurgical bond with the surface of the part.  
The experiment was conducted using argon as the protective gas, with the powder feeding gas, ion gas, and protective gas flow rates set to 3.5 L/min, 1.5 L/min, and 10 L/min, respectively. The longitudinal displacement was 2 mm. The experimental setup is illustrated in Figure 1.  
After cladding, a metallographic specimen with dimensions of 12 \(\times\) 12 \(\times\) 6 mm was machined from the central part of the clad workpiece using a DK7735 CNC wire electrical  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**C** & **Cr** & **Ni** & **B** & **Si** & **Mo** & **Fe** \\ \hline
4.71 & 34.66 & 3.78 & 2.16 & 2.28 & 0.13 & Bal \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the Fe5 powder (wt.%).  
Figure 1: Plasma-cladding device.  
discharge machining (WEDM) machine. The specimen was then subjected to mounting, grinding, polishing, and etching for microstructural observation and hardness testing. The etchant used was a 4% nitric acid alcohol solution, with an etching time of 4 s.