esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 1 Introduction
However, the method is oriented towards mobile backpack LiDAR point clouds. It can be seen from the above research that the individual tree segmentation method starting from the trunk is more suitable for TLS point clouds. The high accuracy of trunk detection and the reduction in noise points such as brushes and branches play a decisive role in the segmentation method. In traditional prototype and hierarchical clustering algorithms, Euclidean distance, Minkowski distance and Manhattan distance are used to evaluate the similarity between two clusters, and the final clustering structure is mostly spherical or convex set [25]. These methods have difficulty detecting arbitrary shapes of clusters and are insensitive to noise data. In contrast, the DBSCAN method can divide regions with sufficient density into clusters and effectively filter out regions with low density point to achieve clustering of arbitrary shapes in datasets containing noise [26]. However, the DBSCAN algorithm requires manual input of neighborhood distance threshold \(\varepsilon\) and minimum points of clustering _MinPts_[27]. The rationality of parameter values directly affects the accuracy of trunk detection [28]. To sum up, there are still some challenges in individual tree segmentation for TLS point clouds: (1) In the face of complex forest, the robustness of individual tree segmentation method for TLS point clouds is low, and it is easy to miss small trees under tall trees. (2) Although the DBSCAN algorithm has a strong ability to detect trunk [13; 24; 29], it needs to manually determine \(\varepsilon\) and _MinPts_ which cannot fully automate the individual tree segmentation process. The objective of the study is to propose a method to segment individual trees directly from TLS point clouds to improve the recognition rate of small trees under tall trees in the vertical forest canopy and the segmentation accuracy of individual trees. We improved the DBSCAN algorithm to identify and segment individual trees directly and automatically from the TLS point clouds. This method fully considers the low efficiency of dense TLS point clouds processing and introduces spatial index for point clouds management. With the advantage of natural segmentation between trunks, the location of individual trees can be accurately obtained by analyzing the result of point clouds clustering at breast height. Then, the region growth clustering algorithm is used to cluster the point clouds layer by layer from bottom to top with the initial cluster center. Finally, the individual tree segmentation of point clouds is realized. We test the effectiveness of the proposed method using Chinese scholar tree plantation and German mixed forest datasets by calculating recall, precision, and F1-score from overall segmentation results, trunk detection results and small tree segmentation results. We also compare the total segmentation results of our method with the traditional DBSCAN method, the CHM method, and the method of [PERSON] et al. [11].
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 2 Analysis of Time Synchronization Models in Large-Scale WSN  
According to the time synchronization information exchanging methods, there is a hierarchical model and clustered model [19; 20; 21] in large-scale WSN. The hierarchical model is appropriate for protocols such as TPSN, in which information exchange occurs in pairs. In this model, the network is divided into a one-hop level structure, and nodes in one level synchronize with nodes in the next level. The clustered model is suitable for protocols such as FTSP, in which nodes obtain the synchronization information broadcasted by the reference nodes. In this model, a cluster structure is established at first and a head node is selected as the time reference in each cluster. Then, the head node broadcasts the time synchronization information to each node by multi-hop forwarding.  
A hierarchical model is shown in Figure 1. The first phase involves creating a hierarchy structure, and time synchronization is then carried out between levels. The root node launches a synchronization process and the information is delivered level by level. In this way, every node synchronizes with its parent node from the upper level by a one-hop process. In the level established phase, the level number should be the minimum, in order to reduce multi-hop error accumulation. This strategy will result in much redundant information exchange and aggravate the network traffic. Meanwhile, it strongly depends on the topology.  
A clustered model is shown in Figure 2. The synchronization is accomplished by cluster head and intracluster synchronization. After cluster heads synchronize, each head becomes a local root node and  
Figure 1: Hierarchical model.  
sends time information in its own clusters, where the synchronized nodes forward synchronization information to every node hop by hop. In the forwarding process, synchronization information is received by all of the neighbors. However, most of the information is redundant. This strategy is more adaptable to topology changes, but causes severe energy waste.
## 1 Introduction
FDA gave the best classification rate; however, all three models revealed that NIR is a powerful candidate method to use when determining the geographical origin of EVOOs. Geographical and botanical discrimination of 412 Andalusian EVOOs was achieved with Raman spectroscopy, based on MUFA, saturated fatty acids (SAFA) and PUFAs presence. PLS analysis was used to select the most appropriate variables, and using DA, high correct classification rates of the samples according to their place of origin, year of harvest, Protected Designation of Origin (PDO) and variety were achieved (89, 94.3, 86.6 and 84%, respectively) ([PERSON] et al., 2003). Furthermore, FT-NIR spectroscopy was applied to separate Tunisian VOOs that belonged to six different cultivars using the PCA analysis. The classification was achieved by the SIMCA method ([PERSON] et al., 2003). In a similar study, discrimination of EVOOs of different varieties was also achieved by NIR and MIR spectroscopy. Results were analyzed with the LDA and SIMCA methods, while the spectral region used was chosen by the SELECT algorithm. The best results were obtained by MIR spectroscopy, in which classification and prediction rates were 94.2 and 86.6%, respectively ([PERSON] et al., 2003). In addition, [PERSON] et al. (2007) [PERSON] et al. (2007) analyzed 84 samples of EVOO derived from different regions of Italy and classified them according to their geographical origin using ATR-FTIR combined with PLS and PCA analysis. Only the fingerprint zone of the spectra was used to classify samples, and that is, according to the authors, the region between 1720-700 cm\({}^{-1}\). Most of the samples were correctly classified, while for some, moderate classification was achieved, probably, as stated by the authors, because of the small number of the samples or due to lack of genetic variation between them. Botanical discrimination of Greek cultivars was achieved using ATR-FTIR combined with chemometric methods. The forward stepwise selection algorithm was chosen for the selection of the spectral region that discriminated the olive oil samples, followed by the application of the algorithms LDA and quadratic discriminant analysis (QDA) analysis. The classification was achieved for both chemometric models ([PERSON] et al., 2003). The geographical origin of 60 EVOOs was also studied by [PERSON] et al. (2003) [PERSON] et al. (2003) [PERSON] et al. (2003) using FT-IR and samples of different European countries. They developed two models of data analysis using PLS followed by LDA. After they combined LDA with a genetic algorithm (GA) based on the selection of eight varieties according to the first analysis, which generated a good prediction model. The second model gave better results regarding the cross-validation rate (100%), contrary to the first (96%).
## 4 ASK Modulation and Demodulation
### ASK Modulation for Complex Implants  
[PERSON] tested an ASK modulator with 200 Kbps carrier frequency to achieve high small-signal bandwidth with low power levels and this unit worked better for complex implants such as stimulating electrode arrays or visual implants [37]. A high performance and low power consumption unit without capacitor and resistor and with clock recovery circuit of 10 MHz carrier frequency, 0.35 \(\upmu\)m CMOS, 2 Mbps data rate and less than 84 \(\upmu\)w ASK demodulator was developed for complex implants [29]. The wireless receiver of the ASK demodulator consisted of a current source, a class AB operational amplifiers with common source, a track and latch stage and a Schmitt trigger. To reduce the CMOS size, [PERSON] and [PERSON] [38] developed a novel mixed-signal interface for bio-telemetry complex implantable systemd consisting of a digital processing circuit and analog front-end. The system included power amplifier, ASK demodulator, clock extraction and power recovery without passive elements, _i.e._, capacitor and resistor. The CMOS size is reduced to 0.18 \(\upmu\)m, which is less than that of other complex implant systems and in addition, the power supply is only 1.8 V.
## 3 Results
The equations, determination coefficients (R\({}^{2}\)), root mean squared error (RMSE), and \(p\)-value for the regression models are indicated for each plot. Figure 2A represents the relaxation curves for the T1 (the percentage of signal intensity vs. TR) and T2 (the percentage of signal intensity vs. TE) values for each of the oils obtained directly from the commercial bottles. The acquisition time for the T2 experiment was approximately 110 s, and acquiring the data for T1 required around 180 s. In the TD-NMR study, the T1 and T2 values were automatically provided by the equipment software upon completion of the acquisition. Figure 2A (top) illustrates the T1 relaxation profiles and Figure 2A (bottom) displays the exponential T2 decay curves, focusing exclusively on data collected for lineseed and olive oils, paralleling the information presented in the preceding figure (Figure 1A). Figure 2B illustrates the graphical representation of the T1 and T2 relaxation times obtained by TD-NMR in relation to the IV of the studied oils. The graph illustrates a clear linear relationship between the relaxation times and the IV across the analyzed oils, yielding correlation coefficients of 0.98 for T1 and 0.99 for T2 (\(p<0.05\)) (Table S2). Linear equations for the regression models with the mean and standard deviation for each variable are also shown. Furthermore, the TD-NMR measurements exhibited a high variation coefficient, with relaxation times varying by only 0.098%. This reproducibility was assessed by calculating the standard deviation as a percentage of the mean across three replicate measurements, confirming the reliability of the method. Figure 2: Results obtained from the TD-NMR study: (**A**) the longitudinal relaxation curves (top) and transverse relaxation curves (bottom) of two types of oils analyzed (lineseed and olive). (**B**) Linear regression models (dash line) obtained from MRI analysis. Top: the regression model between the relaxation time T1 (ms) and the iodine value (IV) obtained by NMR spectroscopy. Bottom: the regression model between the relaxation time T2 (ms) and the iodine value (IV) obtained by NMR spectroscopy. The signal intensity is shown in percentages. The mean and standard deviation (SD) of IV, T1, and T2 were considered. The equations, determination coefficients (R2), root mean squared error (RMSE), and \(p\)-value for the regression models are indicated for each plot.
# Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy
## 2 Materials and Methods
### Study Area
The collection of all these data supports a robust characterization of optical conditions across varying temporal and spatialscales. The study areas are presented in Figure 1: blue dots represent the validation points, while the blue boxes highlight the areas targeted for the applications and correspond to the identification numbers (IDs) listed in Table 1.
## 5 Attributes and Characteristics of FL
### Homomorphic Encryption in FL  
Homomorphic encryption (HE) is crucial in enhancing data security, especially within federated learning. HE allows computations on encrypted data without decryption, ensuring that the results are consistent with those from the original data once decrypted. In FL, where models are trained directly at the data sources, like edge devices or nodes, HE is essential in preserving data privacy. It enables computations and model training on encrypted data while ensuring that any information sent back to the central model remains secure and encrypted [70].  
Benefits of Using Homomorphic Encryption  
Enhanced Data Privacy: HE ensures that raw data remain encrypted throughout the computation process. This means that the data, even when being used for training or analysis, are never exposed in their raw form, thus significantly mitigating the risks associated with data breaches or unauthorized access.  
* Flexibility in Computation: Despite the data being encrypted, HE allows for meaningful computations, ensuring that the insights or model updates derived from such computations are accurate and reflective of the original data.
* Compatibility with FL: Given the decentralized nature of FL, where data do not need to be centralized, HE fits seamlessly, ensuring that data privacy is maintained without hampering the training process. Trade-Offs, e.g., Computational Costs: While HE offers a robust solution to many challenges posed by data privacy, it comes with its set of trade-offs.
* Computational Intensity: The very processes that allow computations on encrypted data can be computationally intensive, necessitating more robust computational resources and potentially extending the training or analysis time.
* Increased Latency: Especially in real-time applications or scenarios where a rapid response is crucial, the additional time required for HE computations can introduce delays.
* Complexity: Implementing and managing HE can be complex, requiring specialized knowledge and potentially complicating the deployment of FL solutions in certain environments.
## 1 Introduction
[21] employed a simulation method to discuss the surface roughness effect of the cooling channel on the heat convection coefficient. In different cooling channel patterns of the built-in spindle, the heat convection coefficient could be increased by increasing the surface roughness. [PERSON] et al. [22] investigated the thermal equilibrium relationship between heat exchange efficiency and thermal deformation of a machine tool. The on-off operation of a cooling system was employed to experimentally attain the minimum thermal deformation of the vertical machine tool and lathe-milling machine tool and indicate that the proposed method could effectively increase the efficiency of the cooling system. In the literature on the cooling channel of a built-in spindle, most studies adopted a simulation analysis for the cooling channel design. The cooling channel design methods mostly utilized random dimension combinations in simulation analysis that lacked a systematic design. In prior studies, the cooling channel design was mostly employed for spindle motor cooling but did not adopt the design of the experimental method to systematically optimize the cooling channel design before practical machining. In addition to the built-in motor, the bearings are one of the main heat sources in a spindle. The cooling of the front bearing, which will directly affect the accuracy of a spindle, is important. In this study, first, the cooling efficiencies of four different cooling channel patterns of a built-in spindle motor and front bearing were investigated by simulation to obtain a suitable cooling channel pattern. Second, the design of experiments (DOE) method was applied to obtain the optimized channel dimensions of the channel pattern. Last, practical machining experiments were conducted to verify the machining accuracy enhancement by the optimized cooling channel design.
## 2 Theory and Implementation  
### Design of Proposed \(\Delta\)-\(\Sigma\) Gas Sensing  
The working principle of a typical NDIR spectrometer is based on the Beer-Lambert's theorem to describe the relationship between the infrared wavelength (\(\lambda\)), gas concentration (C), absorption medium length (\(d\)), and intensity of light source (\(l\)), shown in Figure 1. The NDIR spectrometer consists of three basic components: an infrared light source; an optical tube containing target gas concentrations; and an infrared sensor with a specific bandpass filter.  
In the experiment, we used OIR-715 as an infrared light source with a wide wavelength band (visible to 4.4 \(\upmu\)m). The component that we use to achieve the NDIR selection feature is the thermopile sensor HTS-E21-F3.91/F4.26 which detects the band of infrared radiation absorbed by CO\({}_{2}\) (4.26 \(\upmu\)m) and the band non-absorption (3.91 \(\upmu\)m). Finally, in order to maintain a stable absorption medium length (\(d\)), we installed the infrared source and infrared detector at both ends of the 5 cm chamber [6; 7].  
To build the \(\Delta\)-\(\Sigma\) modulation architecture, shown in Figure 2. Firstly, the infrared light source emits thermal radiation that is proportional to the fourth power of the temperature of light, \(T_{b}\). The temperature was raised and controlled by the heating power \(P_{e}\) as the bottom line in Figure 2. Then, the thermal radiation would travel through the chamber and the radiation intensity was multiplied by the factor of Beer-Lambert's law as \(\otimes\) in the figure. Due to the reading signal being too small to be observed and susceptible to external disturbance. Therefore, we use an integrator to amplify it and high-frequency sampling to filter out the noise, and then quantize it from analog signal into digital signal through a comparator with an adjustable threshold voltage (\(V_{th}\)), shown in middle part of the figure. Finally, the signal is transmitted to the microprocessor which monitors the signal by preset scan time (\(t_{s}\)) meanwhile counts working duty of light source to obtain \(m\) (heating duty of light source) and \(N\) (total duty of light source) through the \(\Delta\)-\(\Sigma\) conversion algorithm to obtain the correct gas concentration.  
In this study, it is practical to develop measurement of gas concentration with long term stability based on temperature compensation. The analytical function for the reading of first-order delta-sigma modulation and sensor's temperature was built to compensate the drift of measurement of gas concentration. To keep good long term stability for measurement, it is also feasible for higher-order  
Figure 1: [PERSON]–[PERSON]’s theorem.  
delta-sigma architecture with the same approach because it follows the same thermal behavior and phenomenon. The sampling time of NDIR gas concentration is about few seconds and it is quite long enough to adopt the first-order delta-sigma architecture with low noise.
## 4 Application Characteristics
### Satellite Networking for Observation of Large Ocean Areas  
The four launched satellites possess unique characteristics and can work in synergy for improving the observational coverage of Hainan Province. HN-1 01 and WC-1 01/02, with their wide swath widths of 110-115 km, can be used for general surveys over large  
Figure 5: Examples of the observation data of HSC.  
ocean areas to identify key research objects, such as ships. For instance, the size of a single-track image by HN-1 01 satellite is approximately 110 km \(\times\) 1900 km, covering an area of ~200,000 km\({}^{2}\). The multi-satellite network, i.e., by networking with WC-1 01 and WC-1 02, provides high-coverage observations of Hainan Province. Additionally, HN-1 02, with high spatial resolution and 4K HD video imaging capability, can be utilized to elucidate key objectives from a general survey. Integrating satellites into a network aid in achieving the mission's key goals, including the discovery (via HN-1 01 and WC-1 01/02) and identification (via HN-1 02) of key targets.
## 2 Related Works and Main Contributions
### Main Contributions  
Inspired by the above research, we focus on the downlink transmission design of the massive MIMO LEO satellite multigroup multicast communication system. In scheme design, comprehensively considering the influence of CSI errors caused by the residual Doppler shift and the phase disturbance, we mainly investigate the robust joint user scheduling and hybrid beamforming design to maximize the system EE. Meanwhile, we take the constraints of the transmission power and QoS into account. The main works are summarized as follows:  
* We establish the downlink transmission system model and channel model of the massive MIMO LEO satellite multigroup multicast communication system and analyze the CSI errors.
* Based on the CSI, we adopt a low complexity hierarchical clustering algorithm based on the _Ward_ connection method to group users, which can lay a foundation for the joint user scheduling and beamforming design.
* We establish the joint user scheduling and hybrid beamforming design problem model based on EE maximization, and binary variables are defined to represent whether the user is scheduled or not. Then, we transform the optimization problem into a Boolean fractional programming (BFP) problem, which is also a quadratic constraint quadratic programming (QCQP) form problem.
* For the BFP problem in QCQP form, we invoke the quadratic transformation algorithm to handle the fractional programming form problem in the objective function. Meanwhile, the SDP algorithm is invoked to convert the objective function in QCQP form into a concave function, and some nonconvex constraints can be converted into linear constraints. In addition, we adopt the relaxation and penalty algorithm to deal with the Boolean constraint. Then, the optimization problem is equivalently transformed into a difference of convex (DC) programming problem.
* For the DC programming problem, an iterative optimization algorithm based on the CCCP framework is proposed. For the rank-one matrix constraint introduced by the SDP algorithm, a penalty iterative algorithm is adopted.  
For the solution of the digital beamforming matrix and the analog beamforming matrix in the hybrid beamformer, the MM-AltOpt algorithm is proposed.