esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanical Properties  
HA0 was a control group that did not contain PMMA, and it was observed that HA15 had the average values of the porosity and the apparent density similar to those of HA0. Although HA60 and HA75 were two types of scaffold with a porosity of more than 75%, HA60 was expected to exhibit relatively high mechanical properties. Thus, HA0 and HA60 were adopted to evaluate the mechanical properties among the 6 scaffold types. In addition, HA30 had the medium values between HA0 and HA60 for the properties such as porosity and density. Consequently, the three scaffold types HA0, HA30, and HA60 were selected. The compressive strength of HA0, HA30, and HA60 was 6.18, 1.73, and 3.23 MPa, respectively, and the porosity was 52.7%, 62.9%, and 81.9%, respectively. Compared with HA30, the scaffold HA60, which had approximately 30% higher porosity, exhibited mechanical properties approximately two times better. This result contradicts previous studies indicating that there is a linear inverse relationship between porosity and compressive strength. All three types of scaffolds satisfied the compressive-strength range of cancellous bone. On the other hand, HA60 had the highest compressive elastic modulus (Table 5). HA0 and HA60 were consistent with the modulus range of cancellous bone, and HA30 had a smaller modulus than real bone. Figure 9 shows the stress-strain curves obtained in the compression test of the three types of scaffolds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean Wall Thickness (\(\mu\)m)**} & **Difference in Means** \\ \hline
**PMMA Addition (vol \%)** & **Before Sintering (m \(\pm\)\(\sigma\))** & **After Sintering (m\({}^{\prime}\)\(\pm\)\(\sigma\))** & \(\left|m^{\prime}-m\right|\)(\(\mu\)m)** \\ \hline
0 & 50.42 \(\pm\) 16.22 * & 50.76 \(\pm\) 16.51 * & 0.34 ** \\
15 & 50.69 \(\pm\) 16.36 * & 51.65 \(\pm\) 17.20 * & 0.96 ** \\
30 & 51.13 \(\pm\) 16.81 * & 58.54 \(\pm\) 20.76 * & 7.41 ** \\
45 & 54.82 \(\pm\) 19.42 * & 54.62 \(\pm\) 19.21 * & 0.2 ** \\
60 & 54.39 \(\pm\) 19.44 * & 56.53 \(\pm\) 20.33 * & 2.14 ** \\
75 & 55.31 \(\pm\) 19.85 * & 60.91 \(\pm\) 23.06 * & 5.6 ** \\ \hline \hline \end{tabular}  
* Significant difference between groups (before and after sintering, respectively) at \(p>0.05\). ** indicates the statistical significance with \(p<0.01\) using the paired \(t\)-test for the difference in means.  
\end{table}
Table 4: Mean wall thickness of the scaffolds with respect to the PMMA addition amount, before sintering and after sintering.  
Figure 9: Compressive stress–strain curve for the HA scaffolds.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 2 Overview of the Traditional LDM Scheme  
The basic structure diagram of the transmitter and receiver of the two-layer LDM in the ATSC 3.0 system is shown in Figure 1.  
At the transmitting end, the original information bits sequentially go through the steps of forward error-correction (FEC) encoding and decoding, bit interleaving, mapping, injection level adjustment, and mixing, and then the signal is framed, modulated, and finally sent out. Each layer of an LDM signal shares the same orthogonal frequency division multiplexing (OFDM) signal structure, including inverse fast Fourier transform (IFFT) size, cyclic prefix (CP), and pilot frequency structure. The ATSC 3.0 physical layer standard only uses a two-layer LDM signal structure [27]. The specific implementation has been described in detail in [3].  
At the receiving end, a typical successive interference cancellation (SIC) detector is exploited to demodulate the LDM signal, as shown in Figure 1b. First, the most robust CL signal is demodulated, and then, the bit-decoded CL data are subjected to bit-interleaved coded modulation (BICM) (CL regeneration) again. The CL-reproduced signal is subtracted from the delayed received signal in the buffer area to obtain the EL signal and demodulate it. It is easy to see that in order to avoid the spread of errors between layers, the premise of this processing method is that the error rate of CL reception is extremely low, that is, the robustness of the CL signal is required to be very good.
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 3 Network Performance Analysis
### Network Performance
#### 3.2.1 Path Break  
Assume that within the channel competition phase of LSM-UVCN, the average number of relaying operations for the established transmission paths is denoted as \(m\). This numerical value implies that each transmission path comprises a total of \((m+2)\) nodes interconnected by \(m\) point-to-point communication links. During the Transmission stage, the integrity of the entire transmission path is critically contingent upon the connectivity of all its constituent links. Specifically, the disruption of even one single link among these \(m\) links, triggered by nodal mobility, will inevitably lead to the breakdown of the entire transmission path.  
Furthermore, the relative displacement distance \(r\) between any two nodes over a given time interval \(t\) adheres to a Rayleigh distribution. The probability density function that characterizes this statistical behavior is given as Equation (1).  
\[f(r,t)=\frac{r}{\left(v_{\text{rel}}t\right)^{2}}\exp(-\frac{r^{2}}{2\left(v_{ \text{rel}}t\right)^{2}}). \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Meaning** \\ \hline \(n\) & Total number of nodes \\ \(L\) & Side length of the node distribution area, and the area of the distribution \\  & area is \(L\times L\) \\ \(\rho\) & Node density, \(\rho=n/L^{2}\) \\ \(A\) & Maximum communication distance of nodes \\ BER & Bit error rate \\ \(T_{\text{clk}}\) & The time of each clock cycle \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters in the analysis.  
Let the initial distance between adjacent nodes be \(d_{0}\). Then, when \(r>A-d_{0}\), the link is broken. The probability of link breakage can be obtained as Equation (2):  
\[P_{\text{link\_break}}=\int_{A}^{\infty}f(r,t)dr=\exp(-\frac{A^{2}}{2(v_{\text {rel}}t)^{2}}). \tag{2}\]  
Furthermore, in two-dimensional random motion, the relative moving speed of any two nodes is \(v_{\text{rel}}\), with \(v_{\text{rel}}=\sqrt{2}v\). The probability that the transmission path remains connected during the data transmission phase can be expressed by Equation (3):  
\[P_{\text{path\_alive}}=(1-P_{\text{link\_break}})^{m}=\left[1-\exp(-\frac{A ^{2}}{4v^{2}t^{2}})\right]^{m},t\in(0,T_{\text{ref}}). \tag{3}\]
# Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit
## 2 Materials and Methods
### Modelling Blocking State
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Destination**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Mean LOS**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Coefficient of**} & \multicolumn{2}{c}{**WIC**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Degrees of Freedom**} \\  & & **Patients** & & **Deviation (LOS)** & **Variation** & & **Phases** & **(\(\textbf{\emph{df}_{\textbf{max}}}\))** & **Total WIC** & Gain in WIC \\ \hline \multirow{2}{*}{**6**} & _All_ & _50_ & **24.56** & 55.12 & 2.24 & _370.13_ & \(4\) & 7 & - & - \\ \cline{2-11}  & _Death_ & **17** & **16.41** & **40.62** & **2.48** & **98.52** & **2** & & & \\ \cline{2-11}  & _Other_ & **33** & **28.76** & **60.83** & **2.12** & **270.67** & **4** & & & \\ \hline \multirow{2}{*}{**14**} & _All_ & _51_ & 26.88 & 39.2 & 1.46 & _437.87_ & \(2\) & 3 & - & - \\ \cline{2-11}  & _Death_ & **21** & **10.9** & **14.73** & **1.35** & **141.1** & **2** & & & \\ \cline{2-11}  & _Other_ & **27** & **31.89** & **38.85** & **12.22** & **230.81** & **1** & 7 & 395.23 & 42.64 \\ \hline \multirow{2}{*}{**15**} & _Full_ & 3 & 93.67 & **67.48** & **0.72** & **10.32** & 2 & & & \\ \cline{2-11}  & _All_ & _53_ & 48.6 & _67.58_ & 1.39 & _516.56_ & 2 & 3 & - & - \\ \hline \multirow{2}{*}{**15**} & _Depth_ & **27** & **24.15** & **38.47** & **1.47** & **223.82** & 2 & & & \\ \cline{2-11}  & _Other_ & **24** & **74.17** & **85.36** & **1.15** & **257.48** & **1** & 7 & 490.63 & 25.93 \\ \cline{2-11}  & _Full_ & 2 & **74.17** & **85.36** & **1.15** & **9.33** & **2** & & & \\ \hline \multirow{2}{*}{**8**} & _All_ & _194_ & _24.07_ & _42.45_ & _1.76_ & _1586.02_ & 2 & 3 & - & - \\ \cline{2-11}  & _Death_ & **14** & **21.29** & **35.22** & **1.65** & **121.65** & 2 & & & \\ \cline{2-11}  & _Other_ & **174** & **22.28** & **40.5** & **1.82** & **1405.34** & **2** & 7 & 1585.46 & 0.56 \\ \cline{2-11}  & _Full_ & **6** & **82.33** & **63.22** & **0.77** & **67.48** & **1** & & & \\ \hline \multirow{2}{*}{**15**} & _All_ & _461_ & _41.96_ & _48.79_ & _1.16_ & _4363.16_ & 2 & 3 & - & - \\ \cline{2-11}  & _Death_ & **112** & **35.66** & **46.4** & **1.3** & **1028.5** & **1** & & & \\ \cline{2-11}  & _Other_ & **296** & **36.94** & **43.09** & **1.17** & **2732.54** & 2 & 5 & 4339.19 & 23.97 \\ \hline \multirow{2}{*}{**10**} & _Full_ & **53** & **83.34** & **62.02** & **0.74** & **578.15** & **1** & & & \\ \cline{2-11}  & _All_ & _176_ & _5.84_ & _11.16_ & 1.91 & _243.58_ & 2 & 3 & - & - \\ \hline \multirow{2}{*}{**10**} & _Death_ & **2** & **57.5** & **12.5** & **0.22** & **8.47** & 2 & & & \\ \cline{2-11}  & _Other_ & **173** & **4.8** & **7.77** & **1.62** & **873.57** & 2 & 7 & 890.83 & 33.75 \\ \cline{2-11}  & _Full_ & **1** & **81** & **0** & **0** & **8.79** & **1** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tree extension using the WIC-based splitting criteria (nodes and possible splits by the covariate discharge destination).
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 4 Design Procedures of the One-Sided Exponential AEWMA Scheme  
### The Joint Effect of Parameter \(\lambda\) and \(k\)  
To investigate the performance of the one-sided exponential AEWMA scheme, it is necessary to select the parameters \((\lambda^{+},k,UCL^{+})\) or \((\lambda^{-},k,LCL^{-})\). This paper will use the MRL as the performance metric to study the effects of parameters \(\lambda^{+}(\lambda^{-})\) and \(k\) on the performance of the scheme. When \(MRL_{0}=200\) and \(c\in\{1.04,1.1,1.2,1.4\}\), Figure 2 provides the contour plot of \(MRL_{1}\) values for the lower-sided exponential AEWMA scheme. From Figure 2, it can be observed that near the optimal parameter \(\lambda^{-}\) value of the scheme, the \(MRL_{1}\) value is also smaller. When monitoring smaller process mean shifts (e.g., \(c=0.9\)), the \(MRL_{1}\) value tends to increase as \(k\) decreases; on the other hand, when monitoring larger process mean shifts (e.g., \(c=0.01\)), the \(MRL_{1}\) value decreases as \(k\) decreases.  
Figure 2: Contour plot of \(MRL_{1}\) values for the lower-sided exponential AEWMA scheme when \(MRL_{0}=200,c\in\{0.01,0.3,0.5,0.9\}\).  
When \(MRL_{0}=200\) and \(c\in\{1.04,1.1,1.2,1.4\}\), Figure 3 provides the contour plot of \(MRL_{1}\) values for the upper-sided exponential AEWMA scheme. Similarly, near the optimal parameter \(\lambda^{+}\) value of the upper-sided exponential AEWMA scheme, the \(MRL_{1}\) value of the scheme is also smaller, and the \(MRL_{1}\) value generally increases as \(k\) decreases.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 3 Methodology  
The methodology used for object detection is based on the application of artificial intelligence and incorporates a series of processes, methods, and techniques to obtain persistent objects called plots or contacts in radar images. The methodology comprises four phases: image preprocessing and enhancement, object detection model generation, hyperparameter tuning, and criteria filtering. The first phase consists of radar image preprocessing and enhancement using convolution techniques. The second phase covers automatic cropping. Labeling is assisted with a proprietary interface, and training is performed using the Faster R-CNN model. In the third stage, model tuning is performed, evaluating the relevant metrics obtained because of a series of combinations of hyperparameters, and in the fourth phase, we present the execution of the plot detector with deep learning for use in real-time radar execution. In this phase, filters, which the radar expert determines, are added and  
Figure 3: Radar system architecture.  
executed to determine whether a plot is persistent and real, aiming to avoid false positives and improve performance. Figure 4 shows the methodology flow chart.
# Influence of Suction on Capacity of Pile Foundation
## 3 Site Investigation
#### Type 2  
Using the same approach, as for the Type 1 pile, the calculations were performed in order to calculate the shaft capacity of the Type 2 pile. Table 2 shows the soil parameters.  
The contribution of the matrix suction can be found through the SWCC and degree of saturation is found through correlation with volumetric water content (\(\theta_{w}\)). Figure 1 represents SWCC, the variation in the \(S\) with matric suction.  
According to the curve, the parameters for matric suction and degree of saturation were found and represented in Table 3. Ground Water Table level was determined as 6.4 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Soil Type** & \(\boldsymbol{\gamma_{stat}}\)**, \(\mathbf{k}\)**N/\(\mathbf{m^{3}}\)** & \(\boldsymbol{\gamma_{bulk}}\)**, \(\mathbf{k}\)**N/\(\mathbf{m^{3}}\)** & \(\boldsymbol{E_{50}}\)**, \(\mathbf{kPa}\)** & \(\boldsymbol{\varphi}\)**, \(\mathbf{Degrees}\)** & \(\boldsymbol{c^{\prime}}\)**, \(\mathbf{kPa}\)** & \(\boldsymbol{s_{u}}\)**, \(\mathbf{kPa}\)** & \(\mathbf{N}\)** & \(\mathbf{m}\)** & **OCR** \\ \hline Silt & 18.1 & 18.1 & 6000 & 31 & 3.6 & 36 & 6 & 0.7 & 4 \\ Silt & 18.1 & 18.1 & 6000 & 31 & 3.6 & 36 & 6 & 0.7 & 4 \\ Clay & 16.7 & 16.7 & 12,000 & 25 & 7.2 & 72 & 12 & 0.7 & 4 \\ Clay & 17.7 & 17.7 & 28,000 & 25 & 16.8 & 168 & 28 & 0.7 & 4 \\ Clay & 17.6 & 17.6 & 25,000 & 25 & 15.0 & 150 & 25 & 0.7 & 4 \\ Silt & 18.7 & 18.7 & 34,000 & 31 & 20.4 & 204 & 34 & 0.7 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil parameters for Type 2 borehole [27].  
Figure 1: SWCC used for determination of the degree of saturation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Depth (m)** & **Matric Suction**, \(\boldsymbol{u_{a}}\)**-\(\boldsymbol{u_{w}}\) (\(\mathbf{kPa}\))** & **Degree of Saturation**, \(S\) \\ \hline
0 & 64 & 0.45 \\ \hline
1 & 54 & 0.52 \\ \hline
2 & 44 & 0.6 \\ \hline
3 & 34 & 0.68 \\ \hline
4 & 24 & 0.77 \\ \hline
5 & 14 & 0.86 \\ \hline
6 & 4 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of matric suction and degree of saturation with changing depth.
## 3 Material and Methods
### Performance Indicators  
To measure the performance of the results obtained from the DT, RF, and AdaBoost models against each other and the expected results obtained from the GeoStudio software, a few performance indicators were used. These performance indicators were accuracy, precision, recall, F1-score, and ROC curve. All the models were subjected to the performance indicators to observe their effectiveness. Accuracy is the ratio of the number of correctly classified predictions divided by the total number of projections. It ranges from 0 to 1.  
Figure 5: Methodology flowchart for AdaBoost modelling.  
Equation (7) shows the calculation of accuracy where True Positive and True Negative are correct predictions made by the model.  
\[\text{Accuracy}=\frac{\text{TruePositive}+\text{TrueNegative}}{\text{Total number of samples}} \tag{7}\]  
Precision is the measurement of positive class predictions that actually belong to the positive class, which in turn calculates the accuracy of the minority class. This calculation is expressed in Equation (8) where the False Positive represents the false positive prediction made by the model.  
\[\text{Precision}=\frac{\text{TruePositive}}{\text{TruePositive}+\text{ FalsePositive}} \tag{8}\]  
Recall is a statistic index that measures how many accurate positive assumptions were made out of all possible positive expectations. Unlike precision, which only considers true positive predictions out of all predictions, considering the positive predictions that were wrong. This calculation is expressed in Equation (9) where the False Negative represents the false negative prediction made by the model.  
\[\text{Recall}=\frac{\text{TruePositive}}{\text{TruePositive}+\text{FalseNegative}} \tag{9}\]  
F1-score is a method for combining precision and recall into a single measure that encompasses both. Neither precision nor recall can provide the full picture on their own. We may have excellent precision but poor recall, or vice versa, poor precision but good recall. With the F1-score, all issues with a single score can be expressed (Equation (10)).  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{10}\]  
ROC curve or receiver operating characteristic curve is a graph of the false positive rate (\(x\)-axis) vs. the precision (\(y\)-axis) with a variety of candidate thresholds ranging from 0.0 to 1.0. The false positive rate is determined by dividing the total number of false positives by the total number of false positives and true negatives. With all the performance indicators mentioned above, the area under the ROC curve could be obtained for each model. This value will represent the effectiveness of each model.
## 5 Conclusions
Therefore, future research should focus on refining the modeling techniques, specifically adapting the quality criteria, to better predict the performance of the LSTLS and its components under varied operational conditions. Our main intention in the preliminary stage of the research was to underline the importance of the design criteria of the SPD of LSTLSs. As we showed in this manuscript, the most popular criteria do not allow for the best choice of SPD as many solutions have similar quality metrics values. The next step is the proposal of the methodology for the design of the LSTLS and the application of other criteria that are more suitable for the photometric design. Further steps will focus on the analysis of the impact of LED stability (degradation) and linearity, emphasizing the importance of LEDs with stable light flux responses to current and temperature changes, especially in precision applications. It is worth noticing that each LED emitter may degrade at a different rate due to various constructions and operating conditions, such as different supply currents and junction temperatures, even if the ambient temperature remains constant. These factors contribute to varying rates of optical power degradation over time. Therefore, the reliability of the stabilization systems (thermal and spectral feedback) will also be investigated. In our future research, we are committed to making significant contributions to the field of applied optoelectronics. The potential impact of this work includes refining simulation models, developing advanced evaluation metrics, and implementing methodological enhancements like machine learning techniques and real-time spectral feedback mechanisms. This forward-looking approach aims to address emerging challenges in LED photometry, and we believe it will significantly advance the field. Finally, the temperature management and stability of each channel's control system are worth mentioning. Our goal is to comprehensively analyze reliability and stability (also in the life cycle) and provide guides for the holistic optimization of the LSTLS systems. Conceptualization, U.J.B. and L.G. ; methodology, U.J.B. and L.G. ; validation, L.G. and U.J.B. ; formal analysis, U.J.B. and L.G. ; investigation, L.G. and U.J.B. ; resources, L.G. ; data curation, L.G. ; writing--original draft preparation, L.G. and U.J.B. ; writing--review and editing, U.J.B. ; visualization, L.G. ; project administration, U.J.B. ; funding acquisition, U.J.B. All authors have read and agreed to the published version of the manuscript. This research was financed by the state budget under the program of the Minister of Education and Science \"Polska Metrologia\", project number PM/SP/0069/2021/1, total funding 978312.00 PLN, total budget of the project 978312.00 (Poland). Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
## 2 System Model and Problem Formulation  
### System Model  
As depicted in Figure 1, we consider a UAV-enabled integrated radar sensing and wireless power transfer (RadWPT) system that senses \(Q\) radar targets while transferring power to \(U\) energy-harvesting devices (EHDs) on the ground. The UAV in the integrated RadWPT system is equipped with \(N\) transmitting antennas and hovers at an altitude \(h\in[H_{\min},H_{\max}]\), where \(H_{\min}\) and \(H_{\max}\) are the minimum and maximum altitudes, respectively. In addition, the EHDs on the ground are equipped with a single antenna and are located within the coverage area of the UAV at a horizontal distance \(r_{u}\), where \(u\in\mathcal{U}\triangleq\{1,\ldots,U\}\). In the UAV-enabled integrated RadWPT system, the radar signals transmitted by the UAV can be directly utilized to transfer power to the EHDs because the energy-bearing signals for wireless power transfer (WPT) do not need to carry any information [1; 2; 3; 4], in contrast to conventional integrated sensing and communication (ISAC) systems, which require integrated signals for both radar and communication operations. Therefore, the EHDs can harvest energy from the radar probing signals [25; 26].  
Let \(x_{n}(s)\) (\(s\in\mathcal{S}\triangleq[1,2,\ldots,S]\)) denote the discrete-time baseband waveform transmitted by the UAV's \(n\)-th (\(n\in\mathcal{N}\triangleq[1,2,\ldots,N]\)) antenna at time index \(s\), where \(S\) is the number of samples of each transmitted waveform [23; 24]. Then, the \(N\)-dimensional vector that consists of the \(N\) transmitted signals of the \(s\)-th waveform is given by \(\mathbf{x}(s)=[x_{1}(s),x_{2}(s),\ldots,x_{N}(s)]^{T}\). The covariance matrix of the waveform transmitted by the UAV, which is expressed as \(\mathbf{X}\in\mathbb{H}^{N}\), is obtained as follows:  
\[\mathbf{X}=\mathbb{E}[\mathbf{x}(s)\mathbf{x}(s)^{H}]\succeq\mathbf{0}, \tag{1}\]  
Figure 1: Illustration of UAV-enabled integrated RadWPT system.  
where \(\mathbb{H}^{N\times N}\) is the set of \(N\)-by-\(N\) Hermitian matrices. Constraining per-antenna power is common practice when determining radar waveforms [23; 24; 25; 26], so the covariance matrix of the transmitted waveform should meet the following constraint:  
\[[\mathbf{X}]_{n,n}=\frac{P_{T}}{N},\ \forall n\in\mathcal{N}, \tag{2}\]  
where \(P_{T}\) is the total transmit power of the UAV.  
The covariance matrix of the transmitted waveform represents the correlation between the signals transmitted from the antenna array in a MIMO radar system. It defines the power distribution of the signals transmitted by each antenna, the orthogonality between the signals, and the spatial distribution. The objective of signal design in MIMO radar is to optimize the covariance matrix to achieve the desired beam pattern or specific performance metrics [23; 24].