esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of OSC Systems and LG Modes  
Compared to RFC, large bandwidth and high speed are offered by OSC due to the incorporation of high-frequency optical carriers, and these features make OSC a paramount data transmission candidate for space-based satellite communications. Moreover, high frequency in OSC enables manufacturers to design lightweight satellites due to smaller size antennas.
## 3 Proposed Methodology
### Support Vector Machine Algorithm  
Support Vector Machines (SVMs) are supervised machine learning algorithms that are commonly used for classification and regression tasks. The primary goal of SVMs is to find a hyperplane that separates different classes or predicts a continuous output based on input patterns [24]. One key aspect of SVMs is the use of kernel function that allows the algorithm to map the input patterns into a higher dimensional feature space. SVMs effectively perform linear separation by transforming the data into a higher dimensional space even when the original input data may not be linearly separable. The selection of kernel function depends on the characteristics of the data and the related problem [25]. The kernel parameters like width or the degree of polynomial are also carefully selected. Let the input patterns be \(\{A_{x},B_{x}\}_{i=1}^{P}\) with \(A_{x}\in R^{N}\) and \(B_{x}\in\{-1,+1\}\). The inputs are initially estimated onto a high-dimensional space \(\beta\) by nonlinear estimate \(\xi\) that inner attributes between estimated vectors are computed by kernel function \(k\big{(}A_{x},A_{y}\big{)}=\xi(A_{x})^{T}\,\xi(A_{y})^{T}\). The maximal margin linear classifier in \(\beta\), \(f(A)=sin\,\big{(}\partial^{T}\xi(A)+\theta\big{)}\) where \(\partial\) and \(\theta\) are the solution to  
\[\min_{\partial,\theta\omega_{x}}\frac{1}{2}\,\partial^{T}\partial+Z{\sum_{x=1 }^{D}\omega_{x}} \tag{1}\]  
related to  
\[B_{x}\big{(}\partial^{T}\xi(A)+\theta\big{)}-1+\omega_{x}\geq 0\quad\forall_{x }=1,\ldots.D \tag{2}\]  
The positive slack variables to deal with non-separable problems is denoted by \(\omega\_\)x. The penalty of patterns incorrectly classified or inside the margin is denoted as Z.  
Where SVM is the configured algorithm, \(\emptyset\) is the search space of the possible SVM configurations (Z, kernel type, and kernel parameters), \(\delta\) is the distribution of the set of instances, \(\mathrm{F_{C}}\) is the cost function, and \(\mathrm{\varphi}\)is the statistical data to minimize the cost function \(\mathrm{F_{C}}\) to obtain the solution sets over a set of problem instances to find  
\[\theta^{*}\in\mathrm{argmin}_{\theta\in\mathcal{O}}\frac{1}{|\delta|}.{\sum_{ \pi\in\delta}F_{\mathrm{C}}(\theta,\pi)} \tag{3}\]  
for each \(\theta\)\(\in\)\(\mathcal{O}\) denotes single possible configuration of the SVM and the output \(\mathrm{F_{C}}\) is obtained by testing SVM across many instances. The main function of the SVM is to find \(\theta\)\(\in\)\(\mathcal{O}\) and to optimize the cost function.
# Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
## 5 Results and Discussions
### Performance Evaluation
#### 5.2.3 Performance of Isolation Forest  
Unlike OCSVM and OCNN, IF uses tree ensembles to isolate anomalies from the dataset instead of learning the system's normal behavior. IF achieves high recall rates on test sets 1 and 2 at 91% and 97%, respectively. IF misclassifies about 40% of anomalies as normal instances in test set 3, which resulted in a reduced recall rate of 88%. In test set 2, IF classifies normal and anomalous data points almost perfectly with high confidence, thereby achieving a precision of 98%. Unlike OCSVM and OCNN, IF's decision scores on TN and TP are consistently high, which means that whenever IF correctly predicts a normal instance, it is certain about the detection decision. In addition, Figure 8 shows that for more than 25% of the time, whenever IF correctly detects anomalous instances, the associated decision scores are high, signifying high detection confidence. Figure 8 shows that IF's decision scores are far away from the decision boundary, which makes IF a stable model for detecting anomalies in the TLIGHT system. IF decision scores are confident, therefore, it is an attractive approach for ICS anomaly detection. Figure 8 shows a histogram of normalized FP, FN, TP, and TN decision scores made by IF on test sets 1-5.  
Similar to OCSVM and OCNN, IF has an outstanding training performance. IF has an excellent performance on test sets 1 and 2 on all evaluation metrics. IF performance on test sets 3 and 4 are similar at an average recall value of 87%, whereas it has its lowest performance on test set 5. Test sets 4 and 5 consist of timing bits anomalies, and IF achieves recall rates of 86% and 82%, respectively. Results indicate that IF can detect timing bits errors better than OCSVM and OCNN. Table 7 shows a summary of evaluation results for IF.  
Figure 8: IF results of the normalized TP, TN, FP, and FN values on test sets 1–5.
## 4 Results and Discussion
### Effects of the Injection Timing on Engine Performance  
#### 4.2.1 Combustion Characteristics  
The influence of the injection timing on the average in-cylinder pressure and heat release rate when burning P15 are shown in Figure 13. The comparison of the spray development process for various injection timings is shown in Figure 14. For the injection timing at 696 \({}^{\circ}\)CA, 702 \({}^{\circ}\)CA, 708 \({}^{\circ}\)CA, and 714 \({}^{\circ}\)CA, each set of five figures corresponds to the spray development pattern in the interval of 20 crank angle degrees after the start of injection. As can be observed from Figure 13, the crank angle of the peak in-cylinder pressure advances as the fuel injection timing advances, which means the combustion phase advances. When the injection timing advances from 715 \({}^{\circ}\)CA to 695 \({}^{\circ}\)CA, the crank angle corresponding to the peak cylinder pressure advances from 730.9 \({}^{\circ}\)CA to 723.7 \({}^{\circ}\)CA. The peak heat release rate increases by 221.9% when the injection timing is at 695 \({}^{\circ}\)CA  
Figure 11: In-cylinder soot mass fraction distribution when burning P15.  
compared with that at 715 \({}^{\circ}\)CA, while the crank angle corresponding to the peak heat release rate advances from 728 \({}^{\circ}\)CA to 712 \({}^{\circ}\)CA. In the process of injection timing advancing from 715 \({}^{\circ}\)CA to 705 \({}^{\circ}\)CA, the peak heat release rate does not change much, while in the process of injection timing advancing from 705 \({}^{\circ}\)CA to 695 \({}^{\circ}\)CA, the peak heat release rate increases sharply. As can be seen from the slope of the heat release rate curve, the slope increases with the advance of the injection time, which means that the combustion process is accelerated. The reasons accounting for the changes above are as follows: the advance in the injection time will make the ignition delay period longer, and Figure 14 shows that when the injection timing is at 696 \({}^{\circ}\)CA, the longer ignition delay period makes the spray evaporate more quickly and the fuel droplets consumed more quickly. By contrast, the fuel in the form of a liquid bundle occupies a considerable amount of time when the injection timing is at 714 \({}^{\circ}\)CA, so it can be concluded that the evaporation of fuel rises comparatively and increases the volume of the mixture with the advance in injection timing, which results in more efficient and rapid combustion. However, the dramatic increase in peak heat release rate and heat release velocity caused by premature injection timing can cause the engine to run rough, thus deteriorating performance.  
Figure 12: Comparison of gas phase volume fractions of different fuels in the range of 0.6\(\sim\)1.0 for the cross-section at 0.2 mm from the nozzle inlet.  
Figure 13: Average in-cylinder pressure and heat release rate. (**a**) In-cylinder pressure. (**b**) Heat release rate.
## 5 The ATLAS MAC Protocol
### Medium Access Design
#### 5.2.2 Sensor to Cluster-Head (S-to-Ch) Communication  
The medium access control for the S-to-Ch communication is somewhat dependent on the traffic load-status and the mode of MAC operation currently used in Ch-to-G communication. However, the operational procedure is asynchronous and also receiver-driven. Although the sensor nodes are synchronized and maintain the same superframe structure, they operate asynchronously during the available CAP and IP modes of the superframe. Similar to the basic receiver-initiated MAC operation [12], whenever a sensor node has some data to send, it maintains _low power listening_ (LPL) and waits for the receiver's beacon (_i.e._, the data request message from the cluster-head). In contrast, performing a random back-off, the cluster-head sends the data request beacon to the sensors only during the available CAP and IP of the superframe. Thus, the S-to-Ch data transmission would not interrupt the CFP operation of the superframe during Ch-to-G communication. Note that if the medium is found to be BUSY while attempting beacon transmission, the cluster-head waits until \(T_{to}\) plus a data packet transmission time and makes another attempt for beacon transmission. In ATLAS, the maximum bound for such a beacon transmission attempt is set to four per superframe, considering the short packet retry limit of the CSMA/CA protocol.  
The basic MAC operations for S-to-Ch communication is cluster-head controlled and described using the pseudo code of Algorithm 1. As given in lines 1-13, under low (\(LN=00\)) and moderate (\(LN~{}=~{}01\)) loads when the Ch-to-G communication follows either CAP or IP mode, cluster-heads contend for data-request beacon transmission and accordingly acknowledge the data reception from the sensors. If there is no data, a cluster-head simply sleeps or performs LPL for possible beacon reception from the gateway. A similar procedure is followed by the cluster-heads when the network traffic load is high (\(LN=10\)) (lines 14-26). The only exception is in line 22, the cluster-head simply goes to sleep when there is no data to send. Finally, when the load-status is over-load (\(LN=11\)) (lines 27-29), the cluster-heads defer the data request beacon transmission to the sensors. This approach prevents data reception at the cluster-heads, thereby avoiding buffer drops; this is a type of trade-off that avoids packet drops due to buffer overflow at the cluster-heads.  
Figure 3: _Cont._The overall MAC operations of ATLAS for the Ch-to-G and S-to-Ch communications are totally dynamic and solely depend on the estimated traffic load. This load adaptive feature of ATLAS helps to achieve the primary goals of energy efficiency and high-throughput in diverse traffic environments.
## 2 Problem Formulation  
### Formation Rendezvous of Multi-UAVs  
Given the problem of formation rendezvous for \(N\) UAVs, suppose the starting pose (position and direction) at initial location of UAV \(i\) is given as \(p_{si}=(x_{si},y_{si},z_{si},X_{si},\gamma_{si})\) and the formation pose \(p_{t}=(x_{t},y_{t},z_{t},X_{t},\gamma_{t})\) at the rendezvous point is also known beforehand, where \((x,y,z)\) is the location of UAV or formation and \((x,\gamma)\) are the horizontal and vertical angles, respectively. The formation configuration at the rendezvous point is defined with the virtual structure approach [25] by a series of relative coordinates \(\{(x_{ti},y_{ti},z_{ti})|i=1,\ldots,N\}\) in the moving frame \(p_{t}X_{t}Y_{t}Z_{t}\), which is attached to the formation center as shown in Figure 1. Therefore, the desired pose \(p_{di}=(x_{di},y_{di},z_{di},x_{di},\gamma_{di})\) of UAV \(i\) around the rendezvous point can be calculated with coordinate transformation. Path-planning for multi-UAV formation rendezvous involves producing a group of paths \(r_{i}(q)\) connecting \(p_{si}\) and \(p_{di}\). Mathematically, this can be represented as:  
\[p_{si}=(x_{si},y_{si},z_{si},\chi_{si},\gamma_{si})\overset{\text{II}\,r_{i}(q )}{\rightarrow}p_{di}=(x_{di},y_{di},z_{di},\chi_{di},\gamma_{di}),\quad i=1, \ldots,N \tag{1}\]  
where \(r_{i}(q)\) is the resulting path of UAV \(i,q\) is defined as a path parameter, and \(\coprod\) represents the constraints, including the kinematic constraints of UAVs, safety requirements, simultaneous arrival with other UAVs, and so on. In view of this, all UAVs should have the same speed when formation is achieved at the rendezvous point before entering into the next stage of formation keeping. We assume that all UAVs have the same constant speed during the formation rendezvous for simplicity. Hence, simultaneous arrival can be achieved by generating a group of paths with equal or approximately equal length.  
As mentioned, the total cost of all the generated paths should achieve the minimum or second minimum, as well as satisfying a variety of constraints. There are some commonly used criteria for evaluating a UAV path, including fuel consumption, path smoothness, radar threats, and other safety costs, i.e., terrain avoidance, obstacle avoidance, and no-fly zone avoidance. In view of the limited flexibility of a single PH path, the rendezvous environment is supposed to be much simpler than the combat environment, with only a few obstacles and no-fly zones.  
Figure 1 shows the schematic of cooperative path planning for a 4-UAV formation rendezvous in the top view. A diamond configuration is defined at the rendezvous point, where \(O_{g}X_{g}Y_{g}Z_{g}\) is the inertial frame. No-fly zones are represented by rectangles and obstacles are represented by circles in the 2-D plane and cylinders in the 3-D environment.  
Figure 1: Cooperative path-planning for unmanned aerial vehicle (UAV) formation rendezvous.
## 4 Conclusions  
In this study, potassium metakaolin-based geopolymer composites containing alumina platelets and glass frit powder were formed into cylindrical crucibles. Composites were then heated to 900 \({}^{\circ}\)C in air for five hours using slow (2.5 \({}^{\circ}\)C/min) and fast (10 \({}^{\circ}\)C/min) heating/cooling rates. It was found that slow rates of heating and cooling caused samples to have an increased density, loss of nearly all open porosity, and develop a uniform glassy glaze of approximately 200 \(\upmu\)m. This material can be called an amorphous self-healing geopolymer composite (ASH-G) as microcracks that occurred during heating were filled with glass phase. It is hypothesized that cracked samples can be slowly re-heated to high temperatures and the cracks healed with the molten glassy phase. Composites heated and cooled using 10 \({}^{\circ}\)C/min did not densify, did not produce a surface glaze, and deformed irregularly. It is speculated that sintering failure occurred during heating rather than cooling.  
The as-produced amorphous self-healing geopolymer composites were able to contain molten Group 1 chlorides for 240 h. Significant findings include:  
* The density and open porosity of the ASH-G samples increased and decreased, respectively.
* Molten sodium chloride and potassium chloride did not chemically react with the ASH-G container after 240 h
* A total of 4 wt% of molten salt was lost after 240 days in air
* ASH-G samples were more affected by temperature than exposure to molten salt  
Analysis of the sodium and potassium chlorides indicates an ion exchange of potassium and sodium, respectively. It is shown that this occurred due to diffusion rather than the dissolution of the container material. The dissolution of any phase of the container material would result in the presence of inert crystalline alumina in the recovered salt. Future work includes mechanical evaluation to determine embrittlement or reduced flexural strength, as well as exposure to sodium/potassium chloride eutectic (T\({}_{\rm melt}\) = 665 \({}^{\circ}\)C) to determine salt degradation and thermocycling.  
Conceptualization, P.F.K., R.J., M.B. and F.B.; Methodology, P.F.K.; Formal analysis, P.F.K. and R.J.; Investigation, P.F.K.; Resources, F.B.; Data curation, P.F.K. and R.J.; Writing - original draft, P.F.K.; Writing - review & editing, P.F.K., R.J., M.B. and F.B.; Supervision, R.J., M.B. and F.B.; Project administration, M.B. and F.B.; Funding acquisition, F.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Australian Government Research Training Development grant, The Alexander von Humboldt Foundation Early Career Post Doctorate Grant, and The Australian Solar Thermal Research Initiative P22 Grant. The APC was provided by Applied Sciences.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflict of interest.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 2 Literature Methods
[25] employed a fully convolutional network (FCN) that enhanced the segmentation of brain MR images by changing ischemia lesion dimensions and forms, resulting in a Dice symmetric coefficient of 0.75. The liver in the synthesized images is located using the YOLOv3 detector and ResNet block. Furthermore, DeeplabV3+ with the InceptionResNetv2 model was used for better liver segmentation. Ref. [26] used UNet for segmenting the liver and its tumors and achieved DSC values of 0.96 and 0.74,respectively. Ref. [27] developed a novel network named residual attention aware UNet (RA-UNet) for 3D segmenting the liver and its lesions. With an accuracy of 99.9, Ref. [17] classified liver tumors using a semantic pixel classification network called SegNet. Automatic segmentation may be challenging due to the liver's abdominal location and the tumor's overlap with the liver. Extensive liver and tumor segmentation studies have been conducted utilizing various methods, including semiautomatic, automatic, and manual approaches. The authors, [PERSON] et al. [28], conducted research for liver tumor segmentation; they employed a ResUNet model for automatically segmenting liver tumors in CT images. Infect their approach resembles ours, but our study introduces unique advancements, particularly in model training and testing, performance evaluation, and time efficiency using the Monai framework. This framework facilitates the integration of the ResUNet architecture and eliminates the need for manual script development.
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 3 Data Analysis  
In this section, the wind speed dependence on the CNR threshold value is discussed. All illustrations are shown for a height of 126 m above the mean sea level, corresponding to the lowest observation height of the wind lidar, and it is also the most relevant for today's wind turbines. However, many are also illustrated for 626 m height. The analysis of the observations include all available data from the period September 2013 to September 2014, and, thus, represent a period of slightly more than a year.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
###### Abstract  
The Global Navigation Satellite System (GNSS) capability in smartphones has seen significant upgrades over the years. The latest ultra-low-cost GNSS receivers are capable of carrier-phase tracking and multi-constellation, dual-frequency signal reception. However, due to the limitations of these ultra-low-cost receivers and antennas, smartphone GNSS position solutions suffer significantly from urban multipath, poor signal reception, and signal blockage. This paper presents a novel sensor fusion technique using Precise Point Positioning (PPP) and the inertial sensors in smartphones, combined with a single- and dual-frequency (SFDF) optimisation scheme for smartphones. The smartphone is field-tested while attached to a vehicle's dashboard and is driven in multiple real-world situations. A total of five vehicle experiments were conducted and the solutions show that SFDF-PPP outperforms single-frequency PPP (SF-PPP) and dual-frequency PPP (DF-PPP). Solutions can be further improved by integrating with native smartphone IMU measurements and provide consistent horizontal positioning accuracy of <2 m rms through a variety obstructions. These results show a significant improvement from the existing literature using similar hardware in challenging environments. Future work will improve optimising inertial sensor calibration and integrate additional sensors.  
PPP; smartphones; IMU; single- and dual-frequency combination; ionospheric constraints; GNSS outage Article
# Software for Estimation of Stochastic Model Parameters for a Compacting Reservoir
## 4 Results
### Estimation Results of Compaction Coefficient \(C_{m}\) and \(tg\beta\)  
The established algorithm allowed parameters \(C_{m}\) and \(tg\beta\) to be determined. The estimation results in profile A-A and the deviation are shown in Figure 7. The compaction coefficient determined by the software for the Jurassic aquifer was \(C_{m}=8.0\times 10^{-4}\pm 0.1\) MPa\({}^{-1}\) at the range expressed by \(tg\beta=0.50\pm 0.05\) at correlation coefficient \(R^{2}\) of 0.77. According to [PERSON] [46], the compaction coefficient for sandstone may oscillate between \(5.0\times 10^{-4}\) MPa\({}^{-1}\) and \(15.0\times 10^{-4}\) MPa\({}^{-1}\), which indicates high reliability of the obtained results. The \(tg\beta\) value is responsible for the range of surface compaction. For instance, a value of \(tg\beta\) of 0.10-0.20 was assumed in modeling for the gas field in Groningen, which is located at a depth of over 2000 m [47]. The determined value of \(tg\beta=0.50\) for the Jurassic strata in the coal mine at 500-600 m also seems credible.  
Figure 6: Division of aquifer into cuboid reservoir elements whose bases measured 1000 m, 200 m, and 50 m. Owing to the large number of base elements measuring 50 m, a buffer was used for profile A-A. Changes in reservoir element pressure are shown in the color scale.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### PS-D2D Scheme
#### 3.1.2 Outage Probability of U\({}_{2}\)
 \tag{39}\]  
Adopting the \(L\)-step staircase approximation approach, we can rewrite the probability of case 4 as  
\[ \begin{split}\phi_{4}&\approx\sum\limits_{i=0}^{L-1 }\biggl{\{}F_{\Lambda_{su2,ps}}\biggl{(}\frac{i+1}{L}\gamma_{p}\biggr{)}-F_{ \Lambda_{su2,ps}}\biggl{(}\frac{i}{L}\gamma_{p}\biggr{)}\biggr{\}}\\ &\quad\quad\times F_{\Lambda_{u1u2,ps}}\biggl{(}\frac{L-i}{L} \gamma_{p}\biggr{)}-F_{\Lambda_{u1u2,ps}}(\gamma_{p}-\gamma^{\prime}{}_{p})F_{ \Lambda_{su2,ps}}(\gamma_{p}).\end{split}  \tag{40}\]  
Substituting Equations (33) and (36) into Equation (40), we can obtain \(\phi_{4}\). Then, substituting Equations (38) and (40) into Equation (37), we can obtain the OP of \(U_{2}\) for the PS-D2D scheme.
# Optimal Base Station Density of Dense Network:
## 5 Numerical Results and Discussion
### Effect of SmBS Density on the Channel Efficiency  
Figure 6 indicates how the SmBS density influences the channel efficiency, which is described by the fraction of time for successful transmission. The user density demonstrates the load of the network, for example busy or idle. The results are generated by (15). For different user densities, there are optimal SmBS densities maximizing the channel efficiency. When SmBSs are sparse, the average load of them will be high, which can lead to frequent collision in the channel and degrading the performance. As we deploy more SmBSs, the performance will improve. However, if there are too many SmBSs, the performance will decrease again due to the low utilization of the channel. It can also be observed that the optimal SmBS density increases with the user density. This is because more SmBSs are needed to suffer the heavy load of network. However, note that always increasing the SmBS density in busy networks is not correct, since excessive SmBSs result in huge interference. We will consider the balance later in this paper.