esa-sceva/satcom-chunk-collection huggingface dataset:
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 2 Related Work
Instead of using an allometric function, the points were roughly segmented by a fixed bandwidth mean shift first, and the crown sizes were estimated by an iterative region growing at multiple layers of different heights. Then the varying crown size was used to guide the kernel bandwidth in the second round of mean shift segmentation. A spherical kernel was chosen instead of a cylinder-shaped kernel, as in previous studies. Both the segmentation and localization results were improved by detecting the tree trunks first, in order to complement the adaptive mean shift segmentation ([PERSON] et al., 2018). In addition to monochromatic wavelength lidar points, the algorithm was also employed for multispectral airborne lidar data by [PERSON] et al. [PERSON] et al. (2018), who firstly segmented the trees only in the spatial domain, then the SVM was used to detect those which had been undersegmented, which were then refined by a second round of mean shift segmentation, considering the multispectral domain. The cylindrical kernel followed the same design as in [PERSON] et al. (2018), apart from an extra weight on higher points in the kernel, which guided the kernel to move upwards. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Setting** & **[PERSON] et al. (2018)** & **[PERSON] (2018, 2018)** & **[PERSON] (2018)** & **[PERSON] ([PERSON] et al., 2018)** & **[PERSON] (2018)** & **[PERSON] ([PERSON] et al., 2018)** \\ \hline Kernel Size & Cylinder & Cylinder & Cylinder & Cylinder & Pollock & Sphere & Cylinder \\ Size & discrete & adaptive & fixed & fixed & adaptive & fix/adaptive & fixed \\ Weight & NA & Gaussian/Z & Gaussian & Gaussian & Gaussian & Flat/Gaussian & Gaussian/Z \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reported variations of mean shift used for tree segmentation. In summary, the mean shift algorithm has been a popular and effective method to segment individual trees from airborne lidar data of different types of forests. However, there are variations in terms of kernel shape, adaptiveness of kernel size, and weighting. Therefore, this paper will focus on a systematic assessment of the algorithm to provide a better understanding of the performance under different configurations and data conditions.
## 3 Wake-Up Radio Communication Protocols
### Single Hop and Multi-Hop Wake-Up Network
#### 3.1.2 Wake-Up Reception  
The energy harvesting process takes place at the wake-up receiver. While the entire node is in deep sleep, the wake-up receiver taps and harvests enough energy from the RF signal of the incoming wake-up message. Depending on the signal type, the wake-up radio checks the signal packet to determine if the wake-up call is destined to it. Once it is confirmed that the packet is meant for the radio, it uses the energy from the RF signal to become active and transmit an interrupt signal to wake-up the main radio. After activation, the main radio sends data and waits for an ACK and/or receives the data and reply with an ACK if the reception was successful and then returns to sleep. The wake-up radio will return to sleep after sending the interrupt trigger signal. The passive wake-up reception process is shown in Figure 5.  
Figure 4: Transmission process.
## 4 Optimization Strategy and Classification  
### Optimization Strategy of LPLC-HDL  
As the objective function in Equation (4) is not a jointly convex optimization problem for the variables \((D_{0},L_{0},Z_{0},D_{p},Z_{p})\), it will be divided into two sub-problems by learning the shared and class-specific dictionaries alternatively, that is, updating variables \((D_{0},L_{0},Z_{0})\) by fixing \((D_{p},Z_{p})\), then updating variables \((D_{p},Z_{p})\) by fixing \((D_{0},L_{0},Z_{0})\).  
We firstly use k-means algorithm to initialize shared dictionary \(D_{0}\in\mathbf{R}^{m\times k_{0}}\) by using all the training samples, then initialize \(i\)th class sub-dictionary \(D_{i}\in\mathbf{R}^{m\times k_{i}}\) by using the training samples \(X_{i}\) and concatenate all these sub-dictionaries as the class-specific dictionary \(D_{p}\in\mathbf{R}^{m\times k_{p}}\). Next, we compute the initialized coefficients \(Z_{0}\) and \(Z_{p}\) with the Multi-Variate Ridge Regression (MVRR) algorithm, and obtain the initialized matrix \(L_{0}\) by Equation (6). In line with the corresponding sub-dictionaries, the serial of matrices \(P_{i}\) and \(L_{i}\) (\(i=1,2,\cdots,C\)) are constructed with Equations (9) and (12), respectively. After finishing the initialization, we can optimize the objective function as the following steps.
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Results
#### 4.3.3 Detection Capacity  
In Figure 8, the \(y\)-axis represents the detection capacity ratio while the \(x\) axis illustrates the three different network setups of sizes with 400, 700, and 1000 nodes. We can observe that the detection capacity of AEF on average is similar to that CCEF and DEF for 50% FTR. For any FTR, the marginal performance of each scheme exhibits similar trends. The detection capacities for CCEF, DEF, and AEF are 99.00%, 98.47%, and 98.31, respectively. Therefore, with trivial difference, we can claim that detection capacity is maintained. A summary of energy-efficiency is presented in Table 4.
# Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
###### Abstract  
Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dao, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties \(\times\) 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models (GSM) were constructed (budburst: \(d=-0.6\), \(e=8.6\); flowering: \(d=-0.6\), \(e=13.7\); veraison: \(d=-0.5\), \(e=13.2\)). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is \(<7\) days, while the EF (efficiency coefficient) is \(>0.5\). Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. These results can be used to more accurately monitor and predict grapevine phenology during a given season, thus supporting decision making in the Portuguese wine sector.  
grapevine; phenology modeling; sigmoid model; wine regions; Portugal 2020
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 4 Results
### Results of Confusion Matrix for CNN  
Although the current results showed an accuracy of over 95%, further analysis of the classification pattern of each participant and posture and of the physical characteristics of the participants is required to improve the accuracy. Further analysis is presented only for CNN as the results of CNN were superior to those of the other algorithms.  
The results of the confusion matrix for each participant using CNN is shown in Figure 9. As shown in Figure 9, the classification accuracy of all the participants was above 0.900. In particular, in the case of #10 participants, the classification accuracy for all the postures was 1.000. For each participant, in the case of recall, the value in the posture (c) of the participant #4 was significantly lower than that of the other postures (Rc = 0.804). Participant #7 recorded values lower than 0.9 in postures (a), (c), and (e) (Ra = 0.823; Rc = 0.865; and Re=0.881). The precision value for posture (a) of participant #6 was the lowest as compared with that of the other postures (Ra = 0.803). This is because posture (b) is often classified as the posture (a) (\(n\) = 45). In the case of the participant #4, the precision value of posture (e) is low (Re = 0.798) because postures (b) and (c) are classified as posture (e) (\(n\) = 36, 24).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **CNN** & **NB** & **MLR** & **DT** & **NN** & **SVM** \\ \hline
1 & 0.995 (0.994) & 0.848 (0.900) & 0.776 (0.993) & 0.873 (0.974) & 0.938 (0.994) & 0.981 (0.986) \\
2 & 0.950 (0.997) & 0.704 (0.922) & 0.740 (0.990) & 0.716 (0.978) & 0.839 (0.997) & 0.890 (0.989) \\
3 & 0.908 (0.994) & 0.872 (0.931) & 0.800 (0.992) & 0.743 (0.979) & 0.912 (0.995) & 0.941 (0.990) \\
4 & 0.916 (0.997) & 0.902 (0.914) & 0.835 (0.994) & 0.726 (0.970) & 0.895 (0.996) & 0.926 (0.990) \\
5 & 0.986 (0.997) & 0.968 (0.928) & 0.894 (0.993) & 0.797 (0.980) & 0.983 (0.997) & 0.976 (0.989) \\
6 & 0.934 (0.995) & 0.853 (0.934) & 0.737 (0.990) & 0.776 (0.972) & 0.888 (0.997) & 0.901 (0.982) \\
7 & 0.902 (0.995) & 0.656 (0.942) & 0.795 (0.992) & 0.711 (0.981) & 0.835 (0.995) & 0.824 (0.991) \\
8 & 0.981 (0.994) & 0.965 (0.917) & 0.977 (0.987) & 0.906 (0.972) & 0.968 (0.992) & 0.991 (0.985) \\
9 & 0.958 (0.997) & 0.943 (0.921) & 0.952 (0.989) & 0.937 (0.972) & 0.968 (0.994) & 0.990 (0.988) \\
10 & 1.000 (0.995) & 0.995 (0.912) & 0.948 (0.991) & 0.755 (0.968) & 0.986 (0.997) & 0.999 (0.986) \\ Ave. & 0.953 (0.995) & 0.871 (0.922) & 0.845 (0.991) & 0.794 (0.975) & 0.921 (0.995) & 0.942 (0.988) \\ Std. & 0.037 (0.001) & 0.113 (0.012) & 0.091 (0.002) & 0.083 (0.004) & 0.057 (0.002) & 0.057 (0.003) \\ \hline \end{tabular} Note: The numbers in parentheses are training accuracy. DT (minimum parent size = 15), NN (layer = 1, neuron = 40), SVM (c = 0.05, gamma = 0.05). The numbers in parentheses are training accuracy.  
\end{table}
Table 4: Results of CNN along with the best performance of the other algorithm.
## 4 Data Collection and Preprocessing
### Data Preprocessing Techniques  
In healthcare systems, managing extensive databases becomes challenging, and hence, data preprocessing techniques become necessary. Data preprocessing may involve data creating, transforming, data cleaning, and data reading to improve model performance. Data preprocessing may also involve image normalization, noise reduction, data splitting, and standardizing image sizes to ensure consistency. Data preprocessing is essential for the best representation of data in machine learning [168]. To ensure effective training models, techniques such as handling missing values, standard scaling (Standscale (SS)), MaxAbs, quantile transformer, normalization known as (zero-mean normalization), robust scaler, and min-max (MinMax) scaling can be employed on the dataset [30; 62; 169]. Other techniques such as replacing missing values with estimates, cleaning data, removing rows or columns with too many missing values, and predictive modeling can also be used for data preprocessing [170]. The work in [171] has excluded independent variables (symptoms), which may have minimal or no impact on the target variable (disease), to simplify the analysis. In general, the numerical features of the dataset are normalized. This prevents certain features from dominating the modeling process. Missing values are addressed by simply removing the corresponding rows from the dataset.  
In addition, data augmentation can involve techniques like rotation, scaling, and flipping to increase the training data and reduce the risk of overfitting [172]. Oversampling techniques like Synthetic Minority Over-sampling Technique (SMOTE), random oversampling (ROS), and adaptive synthetic sampling (ADASYN) can be used to address the imbalanced data for efficient cardiovascular disease prediction [173; 174; 175]. Data augmentation can also include creating composite features, data normalization, one-hot encoding categorical variables, and extracting relevant information from unstructureddata [30]. Split the dataset into training, validation, and test sets to evaluate model performance. Further discussion about data preprocessing for cardiovascular disease can be found in [168]. Figure 2 shows the structure of the data preprocessing and machine learning model applications in cardiovascular disease prediction.
## 3 Setup and Results
This can be done by correlating the pulse energies measured by the XGM with the photon energy, that is by scanning the K parameter of the radiator segments. The results of the scan are presented in the upper plot in Figure 4, where each measure is found by averaging each point over 40 measures (4 s at 10 Hz). The lower plot in the same figure shows the actual pulse energies during the scan. Figure 3: Results of a scan of the output photon energy performed on a range of 100 eV. Four radiator segments were tuned at resonance. The scan was performed by changing the \(K\) values corresponding to the highest photon energy and to the output photon energy. Figure 2: Comparison of the transverse distribution of radiation with the radiator segments detuned (left plot) and on resonance (right plot). It should be remarked that the bandwidth provided by this scan does not correspond directly to the bandwidth of the output radiation. To illustrate this point, during one of the various experiments, Exp. 2 (see Table 1), we resolved the frequency-mixed signal in frequency with the help of a spectrometer [12]; see Figure 5. The figure corresponds to the case of three closed radiator segments. In Figure 6, upper plot, we show the color-coded spectra as a function of the \(K\) parameter: in the inset we extract the same information as in a K-parameter scan done with the pulse energies measured by the XGM (see Figure 6, lower plot). Note that the FWHM bandwidth is about 0.6%, which is larger than intrinsic bandwidth of SASE at this photon energy, but is comparable to the typical performance of SASE3 that is usually strongly influenced by electron energy chirp. Figure 4: Pulse energy versus the radiator \(K\)-parameter. Top plot: Amplification bandwidth around the nominal output photon energy at 600 eV. Each point is found by averaging over 40 single SASE3 pulses (4 s at 10 Hz). Lower plot: actual pulse energies during the scan. Figure 5: Spectrum of the frequency-mixed radiation from Exp. 2 in Table 1. Black line: single pulse. Red line: average over 10 pulses.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
###### Abstract  
This paper presents Sillcom, a high-performance secure indoor localization scheme designed to minimize both communication and computational costs while preserving participants' privacy. Unlike existing privacy-preserving indoor localization techniques, which suffer from high computational overhead and excessive communication, Sillcom integrates replicated secret sharing and function secret sharing in an outsourcing model to achieve significantly lower online communication overhead. A multi-branch tree structure and multi-thread parallelism further optimize both the offline and online phases. Experimental results demonstrate that Sillcom outperforms the state-of-the-art online-efficient scheme FAPRIL, reducing online communication by a factor of 15 and end-to-end query time by 75%.  
indoor localization; privacy-preserving; function secret sharing +
Footnote †: journal: Applied sciences  
0
Footnote 0: _Article_  
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (W.P.)
## 2 Overview of AC Analysis Methods
### Non-Linear Least Squares Sinewave Fitting (NLSQ)  
Linear and non-linear least-squares curve fitting algorithms have been used for AC signal analysis to reduce random noise and eliminate the effects of systematic distortions that could affect the amplitude and phase spectrum, as depicted in [31]. A major difference from the previous paragraph is the use of a non-linear model for the sinewave fitting. Here the model is assumed as the sum of \(N_{f}\) sinewaves, with amplitudes \(A_{k}\) and phases \(\varphi_{k}\) as shown in (19)  
\[x_{fit}[n]=\sum_{k=1}^{N_{f}}A_{k}sin(2\pi f_{k}n\cdot\delta t+\varphi_{k}) \tag{19}\]  
By setting a variable vector \(\beta\) at iteration (i), \(\beta_{(i)}=\begin{bmatrix}A_{1}&\ldots&A_{N_{f}}&\varphi_{1}&\ldots&\varphi_ {N_{f}}\end{bmatrix}\), (19) becomes (20)  
\[x_{(i),fit}[n]=\sum_{k=1}^{N_{f}}\beta_{(i)}[k]sin(2\pi f_{k}n\cdot\delta t+ \beta_{(i)}[k+N_{f}]) \tag{20}\]  
By iterating (21) several times until a maximum number of iterations MAX_ITER is reached, or until a convergence criterion (25) is achieved, the final value of \(\beta_{(i)}\) stores the values of the amplitudes and phases.  
\[\beta_{(i)}=\beta_{(i-1)}+(J_{(i|i-1)}^{T}I_{(i|i-1)})^{-1}I_{(i|i-1)}^{T}r_{(i)} \tag{21}\]with \(J(i|i-1)\) is the Jacobian Matrix in iteration (i) which takes the following form (22):  
\[J_{(i|i-1)}=\begin{bmatrix}\frac{\partial r_{(i)}[1]}{\partial A_{1}}&\dots&\frac {\partial r_{(i)}[1]}{\partial A_{N_{f}}}&\frac{\partial r_{(i)}[1]}{\partial \varphi_{1}}&\dots&\frac{\partial r_{(i)}[1]}{\partial\varphi_{N_{f}}}\\ \vdots&&\vdots&\vdots&&\vdots\\ \frac{\partial r_{(i)}[N]}{\partial A_{1}}&\dots&\frac{\partial r_{(i)}[N]}{ \partial A_{N_{f}}}&\frac{\partial r_{(i)}[N]}{\partial\varphi_{1}}&\dots& \frac{\partial r_{(i)}[N]}{\partial\varphi_{N_{f}}}\end{bmatrix} \tag{22}\]  
and \(r_{(i)}[n]=x_{(i)}[n]-x_{(i),fit}[n]\) is the residual at time instance \(n\) to the actual data, at iteration (i). Their partial derivative at time \(n\) with respect to the amplitude \(A_{k}\) and phase \(\varphi_{k}\) are depicted in Equations (23) and (24), respectively.  
\[\frac{\partial r_{(i)}[n]}{\partial A_{k}} = -sin(2\pi f_{k}n\delta t+\varphi_{k})\] \[= -sin(2\pi f_{k}n\delta t+\beta_{(i-1)}[N_{f}+k])\] \[\frac{\partial r_{(i)}[n]}{\partial\varphi_{k}} = -A_{k}cos(2\pi f_{k}n\delta t+\varphi_{k})\] \[= -\beta_{(i-1)}[k]cos(2\pi f_{k}n\delta t+\beta_{(i-1)}[N_{f}+k]))\]  
The algorithm would achieve either a maximum number of iterations MAX_ITER or the convergence criterion shown in (25):  
\[\left\|\beta_{(i-1)}-\beta_{(i)}\right\|<\varepsilon \tag{25}\]
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Simulation Analysis
#### 4.2.2 Analysis of Scenario Two  
In order to minimize the overall operating cost of the industrial park, based on the ultra-short-term power forecasting results of photovoltaics and rigid loads, the controllable load's start-stop and output modification plans during the day are provided, optimized every 15 min, and the rolling optimization time domain is 4 h. Similarly, ignoring the response characteristics of controllable loads will also cause significant control execution deviation. The scheduled instructions for controllable loads and the actual power curve for the industrial park during the day are shown in Figure 8.  
Similar to the day-ahead dispatch, due to ignoring the response characteristics of controllable loads, there is a significant response delay and amplitude difference in the actual power curve compared to the intra-day dispatch plan. When ignoring the response characteristics of controllable loads, the actual power curve cannot follow the intra-day dispatch command well.  
The rolling optimization curve for the power of the interconnection line in the industrial park during the day and the actual operating curve are shown in Figure 9.  
Due to the inability of the actual power curve of controllable loads to follow the intra-day dispatch plan well, there is also a significant deviation between the expected and actual power of the interconnection line in the industrial park for the intra-day period. However, due to more accurate prediction of solar and rigid loads during the day, the deviation between the expected and actual power on the interconnection line is smaller than that of the day-ahead, resulting in lower assessment costs than the day-ahead.  
The expected cost of purchasing electricity based on the rolling optimization strategy for the industrial park during the day is Y21 = CYN 399,991.38, and the assessment cost is Y22 = CYN 15,063.10, with a total cost of Y2 = CYN 415,054.48; the actual cost of purchasing electricity is S21 = CYN 402,262.37, the assessment cost is S22 = CYN 72,245.10, and the total cost is S2 = CYN 474,507.47.  
Figure 8: Intra-day dispatch plan and the actual power curve of controllable load in industrial park.
## 5 Proposed Solutions and Policy Recommendations
### Policy Recommendations
#### 5.1.3 Modernizing Spectrum Policies for Equitable Access  
Regarding spectrum policy sharing, spectrum policies ought to be modernized to allow for spectrum sharing. National agencies like the NTIA therefore need to coordinate the spectrum needs given the fact that more individual agencies are working on individual spectrum priorities.  
Furthermore, in order to avoid the issue of warehousing radio frequencies, the United Nations Specialized Agency for Satellite Radio Spectrum recently updated its regulatory framework [106] to deal with this issue independently. Through this, a balance will be struck between the prevention of spectrum warehousing, the proper functioning of coordination mechanisms, and operational requirements for the deployment of NGSO systems. The flexible timelines and objective criteria will also regulate the phases of deployment of the satellites.