esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Technologies
### Long-Range (LoRa)  
LoRa is a technology of wireless communication networks (radio frequency), which allows the communication of thousands of devices powered by batteries, over long distances and with a minimum consumption of energy. LoRa technology is part of a grouping of networks called Low Power Wide Area Networks (LPWANs), capable of communicating over long distances, even in adverse conditions, because of their simple way of organizing information [23]. LoRa's low energy consumption is essential for integration into devices that are intended to be installed over a long period of time and powered by a battery, while, for thousands of devices to communicate, the efficiency of the network and the use of a radio frequency spectrum is important to ensure that no information is lost. LoRa technology uses unlicensed Industrial, Scientific and Medical (ISM) bands, i.e., 868 MHz in Europe, 915 MHz in North America and 433 MHz in Asia. Bidirectional communication is provided by Chirp Spread Spectrum (CSS) modulation, which spreads a narrow band signal over a wider channel bandwidth [24].  
There are several works and applications of LoRa networks in the context of remote monitoring, namely in [25], where the authors propose an advanced architecture combining edge computing, fog computing, LoRa and other technologies based on IoT. The proposed architecture can help to overcome the limitations of existing IoT-based health monitoring systems (for example, drop detection or IoT-based electrocardiogram (ECG) monitoring systems) and satisfy the requirements of high data rate applications and the regulation of the LoRa work cycle, demonstrating the functionality of the proposed architecture through the presentation of a case study involving fall detection.  
The work presented in [26] shows the advantages and disadvantages of current communication systems and technologies, proposing new IoT architectures in the medical field, dedicated to home and hospital care services, based on LoRa technology.  
In [27], the authors studied the internal performance of LPWAN LoRa technology, using measurements in the context of real life. Measurements were performed using commercially available equipment on the main campus of the University of Oulu to test the suitability of LoRa LPWAN technology for health and well-being monitoring. In the study, authors analyzed the performance of LoRa communications used to monitor a person's well-being in the workplace during normal working days.  
In the study presented in [28], the authors show an irrigation monitoring system with practical application in precision agriculture on a Czech Republic farm, using LoRa networks, while evidencing the potential of IoT, in the case using LoRaWAN, in helping farmers, namely in irrigation control.
# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 5 Conclusions  
In this paper, we propose an improved YOLOv5 algorithm, named DMS-YOLOv5, to address the problem of insufficient semantic information extraction caused by small object sizes and low resolutions. The proposed method introduces the receptive field module in the feature extraction network. The CA attention mechanism, which combines spatial and channel attention information, is introduced in the feature extraction and feature fusion networks to reduce the interference of background information on object features and focus on the important information of the object. Additionally, a small object detection layer is added to compensate for the loss of small object information during multiple downsampling operations. Furthermore, the detection head network is improved using a decoupling network, which enhances the performance of the classification and bounding box regression tasks. Finally, the bounding box regression loss function and NMS algorithm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP\_0.5** & **Precision** & **Recall** \\ \hline YOLOv3 [27] & 25.7 & 34.2 & 30.5 \\ YOLOv5 [29] & 27.6 & 38.2 & 29.5 \\ YOLOv7 [31] & 31.2 & 40.1 & 38.9 \\ DMS-YOLOv5 & 39.7 & 48.2 & 46.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different algorithms on the datasets (%).  
Figure 17: Detection results of different algorithms in image examples. Subfigures show the detection results of (**a**) YOLOv3, (**b**) YOLOv5, (**c**) YOLOv7, and (**d**) DMS-YOLOv5 algorithms.  
are optimized to effectively alleviate missed detection caused by the concentration of small objects and mutual occlusion between objects. With these improvements, the proposed method achieves effective improvement in small object detection accuracy.  
In the future, the detection performance of small objects will be significantly impacted by complex and diverse real-world scenarios. To mitigate the effects of sample distribution differences between datasets, transfer learning can be applied using small object datasets from various scenarios. Furthermore, when new domain-specific datasets become available, pre-trained models can be leveraged for efficient and rapid adaptation to the new dataset, thereby enhancing the detection performance of small objects in the new domain.  
Conceptualization, T.G. and M.W.; methodology, T.G.; software, T.G.; validation, T.G.; investigation, T.G.; resources, T.G.; writing--original draft preparation, T.G.; writing--review and editing, T.G., M.W. and G.T.; visualization, T.G.; project administration, T.G.; funding acquisition, M.W. and G.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Autonomous Region under Grant 2021D01C118, and in part by the Autonomous Region High-Level Innovative Talent Project under Grant 042419006.  
Not applicable.  
The datasets we used in this paper are publicly available, including VisDrone2019-DET.  
The authors declare no conflict of interest.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 4 Results  
### Vibration Measurement Result  
A total of 29 vibration measurement modules were installed. Because of installing the coordinator module at the farthest point from the blasting point, the observed values of all vibration measurement modules were monitored by the coordinator. Modules near the blasting point are over 400 m away from Coordinator 1, and each XBee 3 Pro communication module has a communication range of approximately 90 m in an indoor environment; therefore, direct communication between them is impossible. Particularly, it was confirmed that the router modules performed the relay activity of wireless communication. Although Coordinator 1 can be connected to all modules, two coordinators are installed to reduce data loss. Figure 7 shows the vibration measurement results for blasting obtained from the vibration measurement modules. Because the vibration measurement module is manufactured using an accelerometer, the acceleration due to gravity is measured even in an environment without vibration. Therefore, the gravitational acceleration was removed by applying a low-pass filter to the acquired data. In modules relatively close to the blasting spot, a little vibration was observed at around 4:40 pm, when blasting was made (Figure 7a-e). This is a small vibration, and the impact of the vibration propagation through the ground is not large because of the hard rock of the Samdo Mine. As the coordinator and the vibration measurement module are farther apart, data must be transmitted through routers; therefore, if the connection is lost somewhere in the network, stable data transmission may be difficult. For example, for V2, some data were missing (Figure 7f). In addition, there was a blank period during which data were not acquired. This means that the network connection of the vibration measurement module is momentarily disconnected, owing to the complex structure of the underground mine, which we discuss in detail in the conclusions.  
Figure 6: Technology roadmap of vibration and dust measurement modules.
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
###### Abstract  
Nonlinearity exists widely in civil engineering structures; for example, the initiation and growth of damage under dynamic loadings is a typical nonlinear process. To date, for the purpose of structural evaluation and a better understanding nonlinear characteristics of complicated structures, a number of parametric and nonparametric methods have been developed for the identification of nonlinear restoring force (NRF). However, due to the highly individualistic nature of nonlinear systems, it would be inefficient to attempt to express the structural NRF in a general parametric form. For many nonparametric techniques, their nonparametric models or approximations may result in undesirable results or oscillations around unsmooth points. In this paper, on the basis of extended Kalman filter (EKF), a model-free NRF identification approach is proposed to circumvent the limitations mentioned above. The NRF to be identified was treated as 'unknown fictitious input', and thus, no prior assumptions or approximations for the NRF model were required. With the aid of a projection matrix, a modified version of observation equation was obtained. Based on the principle of EKF, the recursive solution of the proposed approach was analytically derived. The NRFs provided by the nonlinear components were identified by means of least squares estimation (LSE) at each time step. Numerical examples, including building structures equipped with magnetorheological (MR) damper and shape memory alloy (SMA) damper, demonstrated that the proposed approach is capable of satisfactorily identifying NRF without knowledge or intuitive assumptions of any nonlinear model class in advance.  
nonlinear restoring force; model-free identification; Extended Kalman filter; least squares estimation; modified observation equation
## 3 Verification by a Two-Dimensional Finite Element Model
### Influence of Material Creep and Structural Stiffness Degradation  
Because creep and the degradation of the stiffness of the bridge will cause the deflection change, it is necessary to discuss their influence on the identification results. The creep can be simulated by the effective modulus method. That is, the effect of creep can be considered by reducing the elastic modulus. The stiffness degradation of the bridge can also be simulated by reducing the elastic modulus. Therefore, the creep and structural stiffness degradation are simulated by decreasing the modulus of elasticity. Four typical damage conditions are preset for verification, as shown in Table 4. The identification results are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hanger No.** & \multicolumn{3}{c}{**Damage Degree**} \\
**10\%** & **20\%** & **30\%** \\ \hline N2 & 0.0609/0.0609 & 0.1273/0.1273 & 0.1997/0.1997 \\ N3 & 0.0623/0.0623 & 0.1300/0.1300 & 0.2036/0.2036 \\ N4 & 0.0635/0.0635 & 0.1325/0.1325 & 0.2070/0.2070 \\ N5 & 0.0639/0.0639 & 0.1332/0.1332 & 0.2082/0.2082 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Actual cable force reduction ratio and identification results (one hanger damaged alone).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hanger No.** & \multicolumn{3}{c}{**Damage Degree**} \\
**10\%** & **20\%** & **30\%** \\ \hline N3 & 0.0622/0.0635 & 0.1297/0.1323 & 0.2027/0.2068 \\ N7 & 0.0622/0.0635 & 0.1297/0.1323 & 0.2027/0.2068 \\ N2 & 0.0615/0.0502 & 0.1303/0.1064 & 0.2072/0.1695 \\ N4 & 0.0639/0.0580 & 0.1341/0.1216 & 0.2112/0.1913 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Actual cable force reduction ratio and identification results (two hangers damaged simultaneously).  
It can be clearly seen from Figure 7 that regardless of the location of the failure, the damaged hanger can be accurately located. Another observation that can be drawn from this figure is that when creep and stiffness degradation are considered, the ratio change of tension force of the damaged hanger is increased. This may be because the influence matrix for identifying the damage of the hanger is obtained from the benchmark model. From the figure, it can be seen that the recognition result is ideal, and for the actual bridge, the specific values of creep and stiffness degradation cannot be obtained, so the damage recognition can only be performed based on the influence matrix under the benchmark state.
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Calculating the Optimal Solution for the Coordinate of the Circle Center  
The candidate circle center sample data set exhibits clear and common characteristics, and there are no different types of data points. In this case, clustering algorithms that converge at the maximum data density are suitable, such as the K-means and mean shift clustering algorithms. The K-means algorithm requires specifying the number of clusters (K) in advance and is sensitive to the initial cluster centers. The mean shift algorithm is a non-parametric probability density estimation method that utilizes the gradient of pixel feature points' probability density function. Through iterative computations, the mean shift algorithm converges to the local maxima of the probability density function, enabling target localization and tracking. Furthermore, it has the ability to track deformable objects in real time and exhibits robustness against variations, rotations, and other object movements. The mean shift algorithm is an automatic iterative tracking method where the mean shift compensation vectors continually move along the gradient direction of the density function. Under certain conditions, the mean shift algorithm can converge to local optimal points, accurately positioning moving objects. Therefore, the mean shift algorithm is chosen to compute the optimal solution for the density of the candidate circle center sample data [16].  
Figure 2: Two chords determine the circle center.  
This algorithm does not require specifying the number of clusters and is suitable for being driven by natural data. It performs well in terms of consistency and accuracy.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 4 Result and Discussion
### Performance in V2G Mode  
In V2G or discharging mode operation, the flow of power is from EV batteries to the grid. This process takes place in two stages. In the first stage, the EV batteries participate in achieving the \(V_{DC-link}\) from the \(V_{bat}\) using BSBD converter. Due to the higher voltage level of the \(V_{DC-link}\) then \(V_{bat}\), BSBD converter operates using boost mode control. As a result of the boost mode control, the performance of \(V_{DC-link}\) is shown in Figure 14 from \(t=2\) s to \(t=4\) s. Simultaneously, the performances of EV batteries in the boost mode are presented in the discharging section of Figure 15.  
In Figure 15, the performance of EV batteries in discharging section is presented in terms of SoC, discharging current, and terminal voltage, where positive current and negative slope of SoC indicates the discharging process. Similarly, the discharging mode section in Figure 16 have positive instantaneous battery power, \(P_{bat}\), from \(t=2\) s to \(t=4\) s, which shows the power discharge from the EV battery towards the grid in V2G mode. At the end of the first stage of V2G mode, the DC power is transferred to the grid using GSBD converter in inverter mode. In the inverter mode of operation, the obtained \(V_{DC-link}\) acts as a DC input (voltage and current) and provides AC output (voltage and current) for the grid using inverter control. Figure 17 illustrates the feed in voltage and current for the grid. As expected, the targeted THD according to IEEE standard 519 and unity PF are achieved using inverter control strategy, as shown in Figures 18 and 19. Similarly, Figure 20 illustrates that the targeted transferred active and reactive powers that are \(P_{rat}\) and zero, respectively, have been achieved from the developed bidirectional  
Figure 16: EV batteries power during G2V and V2G mode.  
Figure 15: Dynamic performance of EV battery’s voltage, current, and SoC during G2V and V2G mode.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 8 Conclusions  
Due to rapid technological advancements, UAVs have grown increasingly popular, attracting an increasing amount of attention in the field of wireless networks. Numerous articles on UAV-based network architectures have been included in the literature review. Particular attention was given to the development of HO for UAVs as well as the expansion of networks that make use of UAV technology. Several aspects of HO were considered by examining various available studies. Several key research problems, including 3D deployment and energy efficiency, were discussed. New methods to resolve the mentioned issues have been introduced, including algorithm-based learning, experimental works, etc. The challenges, potential solutions, and future research directions were examined. The fundamental problem with UAV-connected wireless networks is their 3D mobility. This study provides comprehensive information on the shift from the standard 2D mobility and 3D mobility to 5G and 6G networks. A conceptual explanation of numerous elements was also highlighted to aid in identifying the optimum HO decision.  
**Funding:** This research has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C276) conducted at Istanbul Technical University (ITU), and it was also supported in part by Universiti Teknologi Malaysia (UTM), Malaysia.  
**Acknowledgments:** This research has been produced benefiting from the 2232 International Fellowship for Out-standing Researchers Program of TUBITAK (Project No: 118C276) conducted at Istanbul Technical University (ITU), and it was also supported in part by Universiti Teknologi Malaysia (UTM), Malaysia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
###### Abstract  
A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 \(\times\) 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol's position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.  
Keywords:constellation diagram; FPGA; K-nearest neighbors; machine learning; optical communications; QAM +
Footnote †: journal: _Electronics_
## 4 Discussion
### Functional Diversity and Forest Structure in DGVMs  
Dynamic global vegetation models (DGVMs) are valuable tools for estimating forest productivity and carbon budgets at the large scale [37]. Traditional DGVMs simulate vegetation dynamics with a resolution of 50 kilometers and are not able to represent forest structure, individual trees, and high species diversity. With a few exceptions, such as ED2 [22], most DGVMs use only one shade-tolerant PFT per biome for the simulations of tropical forests [11; 20]. Consequently, it is challenging to reproduce forest successions with these models, as competition between trees is only partly represented. In this study, we showed that this can lead to a relevant bias in forest productivity estimates.  
Local forest models have the ability to include functional diversity and structural realism due to their individual- and process-based approach. Such models are capable of providing reliable estimates of forest dynamics [25; 33]. This advantage has been recognized and the concept of forest gap models has been tried to incorporate within the DGVM framework, such as the SEIB-DGVM [11], LPJ-GUESS [38], and the ORCHIDEE-CAN model [39]. This means that gap dynamics are included, but species diversity is still only defined by one or two PFTs per biome depending on climatic conditions [40].  
A novel strategy is to include functional diversity in vegetation models by using trait distributions [41]. This is a promising approach for large-scale vegetation modeling. However, please note that the trade-offs between traits of tropical tree species are challenging to estimate. Enhancing databases on plant traits [42] will play a major role for this modeling approach in the future. Another novel approach allows for including more species diversity into large-scale vegetation modeling by applying traditional forest models on entire landscapes or biomes at the individual tree level [43; 44]. Such forest models are particular important to investigate forests through all successional states, investigate disturbed forests [45] and show potential in linking simulations with remote sensing [46; 47].