esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### Achievable Rate for IRS-Assisted Bidirectional FD-MTC System  
To find the best location of IRS to achieve better transmission for a proposed IRS-assisted bidirectional FD-MTC system, an achievable rate is calculated by placing IRS at a different distance from the machine as shown in Figure 4. The achievable rate is determined by the channel capacity, which is the maximum information rate that can be transmitted over a channel with an arbitrarily small error probability. The achievable rate can be lower than the channel capacity if the transmitted signal is not optimized for the channel conditions, or if the receiver does not have sufficient processing power to decode the signal. It is expressed as [37],  
\[P_{r}=N^{2}\frac{P_{t}\pi^{2}\rho_{h}^{2}\rho_{S}^{2}}{16} \tag{28}\]  
where \(P_{r}\) is the received power, and \(P_{t}\) is the transmitted power. The scenario assumes i.i.d. Rayleigh fading, where the average power of each entry in the fading vectors \(h_{i}^{(1)}\) and \(h_{i}^{(2)}\) is denoted by \(\rho_{h}^{2}\) and \(\rho_{g}^{2}\), respectively. It is assumed that the average power of the fading vectors as \(\rho_{h}^{2}=C_{0}d_{1}^{(-\xi)},\rho_{g}^{2}=C_{0}d_{2}^{(-\xi)}\), where \(d_{1}\), \(d_{2}\) is the corresponding link distance in meters (m), \(C_{0}\) is the path loss at reference distance and \(\zeta\) is the path loss exponent.  
Figure 4: Various locations of IRS in bidirectional FD-MTC system.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm  
The previous algorithm stops when the mobile node is placed in the needed position and becomes active. However, at some point, the current problem may be resolved, and the mobile node may no longer be needed. For this reason, we extended the previously mentioned algorithm (the Dynamic variation) in order to reuse the mobile nodes that are placed in the network. We call this extension Energy Node Placement algorithm (eNPA), and we refer to it as Dynamic MobileCC+. In this respect, we introduce energy considerations. Based on the energy levels of the mobile nodes, they can be either be reused or be replaced; in the latter, the mobile node returns to its initial position (near the sink) to recharge its battery.  
When a new problem occurs in the network, there is a need to use a mobile node to resolve it. The introduction of reuse provides the opportunity to first check among the in-use mobile nodes, that is, the mobile nodes that are already placed in the network, whether one of them is available to be used. The following conditions must be applied to reuse an in-use mobile node:  
1. The current problem for which the mobile node was sent to resolve has now been resolved, or the neighbor nodes of the mobile node can now find an alternative path.
2. The energy level of the mobile node does not exceed a certain threshold, which enables the mobile node to travel from its current position to the new position, and then from there to its initial position (near the sink) without running out of energy.  
We now present the actions of each node type: Mobile node, Sink node, and node (a static node or an in-use node acting as a relay). In Appendix A.3 you can find the flowchart of this algorithm.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Caching  
The Proxy can improve QoS by caching data at the edge: it attempts to serve Server requests taking the responses from the cache, thus reducing network load. Since caching may lead to staleness of information, there is the need of a refreshing scheme to guarantee freshness of data, considering that each update of the cache requires additional messages to be sent on the IoT network that can degrade system performance. A typical cache-refreshing scheme associates a validity lifetime to each cached data item. When the data item expires, it is not useful anymore and it must be discarded.
## 3 Results and Discussion
### Numerical Validation of the Inverse-Laplace Approach
For the configuration in this work, obtaining a detailed impulse response (250 samples) took \(\approx\)\(1\) h of wall-clock time. This relatively long operation only has to be carried out once for a given geometry, and the response to any given signal can be obtained through a convolution integral, requiring \(\approx\)\(10\)-\(100\) ms. A final consideration about accuracy: truncation (and round-off) errors propagate over time during a time-marching simulation, and the truncation error magnitude depends on the time step length. This is not the case when using the inverse-Laplace approach, in that the solution at each instant is computed independently.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### Telehealth  
Local eHealth systems can provide deep learning capabilities for processing biomedical signals along with collecting the necessary raw data. Remote patient monitoring systems and telemedicine implementations often face difficulty remotely acquiring the necessary sensor data from the patient. Screening patients in person offers the possibility of using advanced sensors and specialized equipment. When this is unattainable, one solution is to extract new kinds of data from the existing connection the system has with the patient to be able to identify useful risk markers for conditions not yet recognized. Telehealth implementations could screen patients for different conditions by recording the video feed and analyzing it for potential markers. One such implementation, a sensor-less deep learning image-processing frailty meter [36] records 20 s elbow movement videos (flexion and extension) through the camera of a tablet and then the frailty phenotype and frailty index are calculated, thus screening the patient for physical frailty and having the means to triage patients with chronic obstructive pulmonary disease (COPD). Remote healthcare services for the treatment, management and remote monitoring of Parkinson's disease patients should be built, taking in consideration the fact that neurological examinations carried out in the hospital can sometimes offer less information about the patient's symptoms than prolonged observations, because some impairments can occur at specific times during the day, hence the need to use wearable devices that act as automated systems [37]. IoT systems that allow in-home health monitoring have been adopted in recent years thanks to their many advantages achieved by leveraging technologies such as dedicated communication protocols (LoraWan, Sigfox, NB-IoT), the 3 GPP standard, smarter medical devices and sensors (blood pressure, pulse, glucose meter, temperature, blood oxygen saturation, ECG, accelerometers, etc. ), devices supporting wireless technologies (Zigbee, Wi-Fi, Bluetooth, BLE, NFC, RFID, etc.) and cloud computing services for analyzing the previously collected data [38]. Accessible development boards such as the Raspberry Pi make excellent testing devices because of their low cost and versatility, translating to new possibilities for eHealth and telehealth systems. There are numerous commercially available ways of connecting a great number of sensors to the Raspberry Pi. To list the most useful features, these boards offer configurable general-purpose input-output pins (GPIOs), audio and video connectors, RJ-45 Ethernet or wireless connectivity and USB ports. In our previous work with EEG signals [39; 40], we used the same e-Health sensor platform as in [41], where arrhythmia detection is possible through ML algorithms using
## 3 Results
### Correlation Analysis  
In the correlation analysis, the minimum, median, mode and mean were significantly correlated with the Gleason score with \(p\)-value = 0.01, although other radiomic parameters did not significantly correlate with the Gleason score. The DCE parameters were not significantly correlated with the histopathology report: ktrans and kep were \(p\) = 0.9 and \(p\) = 0.9, respectively. After the prostates were divided into different zones, neither ktrans nor kep were significantly correlated with the histopathology report in PZ (\(p\) = 0.86 and \(p\) = 0.93, respectively); in TZ, ktrans was significantly correlated with the histopathology report (\(p\) = 0.02), whereas kep was not significantly correlated (\(p\) 0.5). In the PZ, four radiomic parameters (mean, median, mode and maximum) were significantly correlated with Gleason at \(p\) < 0.05, while in TZ, five radiomic parameters (mean, median, mode, maximum and SD) were significantly correlated with Gleason at \(p\) < 0.05. In the preclinical test, all radiomic parameters were significantly correlated with the gold standard (phantom tissue-mimicking material) with a \(p\)-value < 0.01.  
Figure 7: The DAPC and how it performed to separate both groups in the clinical data. Most patients in the healthy group accumulated a negative area, similar to the behaviour of the pre-clinical data. The \(x\)-axis is the number of clusters, while the \(y\)-axis is the coefficient of discriminant analysis.  
Table S2 shows the outcomes of the logistic regression analysis and predictive variables of clinically significant cancer. On univariate analyses, mean, median, mode, SD, and SV were all significantly associated with clinically significant cancer (\(p<0.05\)). The other radiomic features did not show any significance (\(p>0.05\)), and were thus excluded from further analyses. In the multivariate analyses, modes were statistically significant and independently predictive of clinically significant cancer (\(p=0.04\)).  
There were 15% of significant cancers missed by the mpMRI which were reported by radiomics, and 8% of lesions missed by radiomics which were reported by mpMRI.
## 4 Results
### Class Granularity  
The first study examines whether the detection of bearing damage in the present application is implementable with the use of a CNN. At first, a binary classifier is developed, which distinguishes between healthy and damaged. The second class is composed of the distributed and localized classes. Then, the classification problem is extended to three classes. Thereby, the subclasses healthy, distributed and localized are differentiated. As shown in Table 3, the classes _dist 0_, _dist 1_ and _dist 2_ are combined for the distributed subclass. The subclass localized consists of _local 0_, _local 1_ and _local 2_. In the last step, the class granularity is increased to seven classes. Therefore, all classes shown in Table 3 should be detected. The investigation of the class granularity should provide information as to which granularity should be preferred in the case of a suitable trade-off between high-quality and high-information content. The results are shown in Table 4. Each model is trained ten times using tenfold cross validation, with the averaged BAC over all iterations and the results of the best and worst iterations listed, respectively.  
The result shows that the binary classifier achieves the overall best result with an average BAC of 0.99. The worse iteration is achieving a BAC of about 0.98. But the classifiers with higher granularity show very good results, too. With increasing class granularity, the BAC decreases. But the classifier with seven classes still reaches an average BAC of 0.96. The worst iteration produces a BAC of 0.70, which is far worse than the other granularities. However, this iteration could be seen as an outlier since the second-worst iteration reaches a BAC of about 0.98. In Figure 4, the result of the worst iteration of the classifier with seven classes is shown. A confusion matrix (CM) is used for visualization. On the horizontal axis, the predicted labels are shown, whereas on the vertical axis, the true labels are depicted. A good classifier should predict the true labels, and therefore most of the samples should be placed on the main diagonal. The CM shows that the distributed classes are classified nearly perfectly, while the healthy and all of the local classes show multiple misclassifications. It is noteworthy that all four classes are often predicted as class _dist 0_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Granularity** & **BAC\({}_{\text{mean}}\)** & **BAC\({}_{\text{max}}\)** & **BAC\({}_{\text{min}}\)** \\ \hline
2 & 0.99 & 1 & 0.98 \\
3 & 0.98 & 0.99 & 0.97 \\
7 & 0.96 & 1 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classifier results for different class granularities.  
In summary, the classification of all presented granularities is possible with sufficiently good accuracy. Depending on the requirements and the use case, it should be decided individually which misclassifications are acceptable, and the largest granularity that meets these criteria should be selected accordingly. For the presented use case, the result of the classifier with seven classes is sufficient.
## 3 Results and Discussion
### Optimization Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Design Point 1** & **Design Point 2** & **Design Point 3** \\ \hline \(\theta\) (\({}^{\circ}\)) & 76.166 & 75.398 & 77.616 \\ X (mm) & 1.001 & 1.0002 & 1.0003 \\ C (\({}^{\circ}\)) & 5.0217 & 5.0217 & 5.3439 \\ L (mm) & 15.005 & 15.005 & 15.005 \\ Efficiency & 94.61 & 94.609 & 94.61 \\ Total Pressure Ratio & 1.3985 & 1.3986 & 1.3985 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top three design points. Figure 16: Pressure compression ratio result curves of X, C, L, and \(\theta\) parameters, respectively. The graphical results of the optimum tandem blade design, tandem blade design, and conventional blade design at the same speed and different flow rates are shown in Figure 18.  
The analysis results of the optimum tandem-bladed impeller give better results than the classical tandem blade and conventional blade impellers.
## 2 Related Work
### Hybrid CNN-Transformer Methods  
To take advantage of the complementary advantages of CNN and transformers, a hybrid method for road extraction has been proposed. These methods combine the depth feature extraction ability of the neural network and the context information modeling from transformers. By integrating the two discriminative features, the hybrid model can capture fine-grained details and long-range dependencies, thus improving the performance of road extraction tasks. In recent years, many universal models for semantic segmentation have been proposed, such as TransFuse [25], TransUnet [26], Ds-TransUnet [27], and SegFormer [28], which have good results in the related fields. And some noteworthy hybrid CNN-transformer models for road extraction include TransLinkNet [29], Seg-Road [30], and DCS-TransUperNet [31]. In the above models, local and global features can be adaptively integrated through specific strategies and attention mechanisms, so that the model can better capture the structure and context information of the road.  
In summary, different approaches to road extraction have shown their strengths in handling specific aspects of the task. CNN-based road methods have excelled in capturing local features and spatial patterns, allowing them to accurately delineate road structures at a fine-gained level. On the other hand, transformer-based road methods have demonstrated the ability to model global context and long-dependencies, enabling them to understand the overall road layout and context in the entire image. Hybrid CNN-transformer methods have shown enhanced performance by leveraging the strengths of both approaches. The next section will delve into the details of the hybrid CNN-transformer mechanism and provide an in-depth analysis of its advantages in road extraction tasks.
## 3 Experiments and Results
### Influence of Structural Parameters on Cavitation Performance
#### 3.1.2 Number of Teeth in A Single Row  
For a clearer comparison, while ensuring that the size of the teeth remains constant, single gear teeth numbers of 5, 10, 15, 20, and 25 were selected to study their impact on the cavitation performance of the hydrodynamic cavitation generator. As shown in Figure 13, the cavitation performance of the hydrodynamic cavitation generator is optimal when the single gear teeth number is 15. when the number of teeth exceeds 15 in the cavitation inducer of the same size, the decrease in cavitation performance is attributed to the reduction in fluid flow space between each tooth as the number of teeth increases. This results in a decrease in the cavitation occurrence area renders a deterioration in cavitation effectiveness. Conversely, when the number of teeth is less than 15, the continuous reduction in the number of teeth causes a decrease in shear velocity between the tooth sides. As a result, the low-pressure region cannot be generated, resulting in a decline in cavitation effectiveness. The degree of cavitation varies greatly for the structure difference in the hydrodynamic cavitation generator between the fluid domains. This change in cavitation is a very complex fluid dynamic result.
## 2 Data Availability and Collection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Location** & **Agency** & **Receiver Type** & \multicolumn{2}{c}{**Antenna Type**} \\ \hline ABJA & Nigeria, Abuja & iGMAS & gnss\_ggr & RINT-8 CH & CETD \\ GUA1 & China, Urumchi & iGMAS & gnss\_ggr & RINT-8 CH & CETD \\ HMNS & South Africa, & iGMAS & gnss\_ggr & RINT-8 CH & CETD \\ BJF1 & China, Beijing & iGMAS & CETC-54-GMR-4016 & LEIAR25.R4 & LEIT \\ BRCH & Germany, & iGMAS & CETC-54-GMR-4011 & NOV750.R4 & NOVS \\ DWIN & Australia, Darwin & iGMAS & CETC-54-GMR-4011 & GNSS-750 & NONE \\ KNDY & Sri Lanka, Kandy & iGMAS & CETC-54-GMR-4016 & GNSS-750 & NONE \\ LHA1 & China, Lhasa & iGMAS & CETC-54-GMR-4016 & NOV750.R4 & NOVS \\ WUH1 & China, Wuhan & iGMAS & CETC-54-GMR-4016 & LEIAR25.R4 & LEIT \\ ZHON & Antarctica, & iGMAS & CETC-54-GMR-4011 & GNSS-750 & NONE \\ ALBY & Australia, Albany & GA & SEPT POLARX5 & JAVRINGANT\_DM & SCIS \\ ARUB & Australia, Arubiddy & GA & SEPT POLARX5 & LEIAR25.R3 & NONE \\ CEDU & Australia, Ceduna & GA & SEPT POLARX5 & AOAD/M\_T & NONE \\ DAV1 & Antarctica, Davis & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ HOB2 & Australia, Hobart & GA & SEPT POLARX5 & AOAD/M\_T & NONE \\ KUNU & Australia, & GA & SEPT POLARX5 & JAVRINGANT\_DM & SCIS \\ MEDO & Australia, Meadow & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ Station & & GA & SEPT POLARX5 & JAVRINGANT\_DM & SCIS \\ NCLF & Australia, Northcliffe & GA & SEPT POLARX5 & JAVRINGANT\_DM & SCIS \\ NORS & Australia, Norseman & GA & SEPT POLARX5 & JAVRINGANT\_DM & SCIS \\ PTHL & Australia, Port & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ TFD & Hedland & GA & SEPT POLARX5 & AEIAR25.R3 & LEIT \\ STR1 & Australia, Canberra & GA & SEPT POLARX5 & ASH701945C\_M & NONE \\ THEV & Australia, Thevenard & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ TOMP & Australia, Tom Price & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ WILU & Australia, Wiluna & GA & SEPT POLARX5 & LEIAR25.R3 & LEIT \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on BeiDou-2- and BeiDou-3e- capable receivers. International GNSS Monitoring and Assessment Service (iGMAS).
## 4 Fittings of Synthetic Data
Best fit parameters and standard deviation indicated in Figure (**b**). Figure 9: Estimated values of the fitting parameters and standard deviations as a function of the SNR: (**a**) width \(w\), (**b**) height \(h\), (**c**), depth \(d\), and (**d**) angle \(\varphi\), corresponding to a square heat source (\(w=h=1\) mm), buried \(d=0.5\) mm and making an angle \(\varphi=45\) with the surface, excited with a \(\tau=5\) s burst. The continuous line represents the true value of each parameter. Inspection of Figure 10a-d reveals a slight decrease of the quality of the retrieved width \(w\), and depth \(d\) with increasing angle, in terms of both accuracy and precision. However, the estimation of the height of the heat source is strongly affected by the inclination, as the parameter of interest, i.e., the deepest side of the heat source is buried deeper with increasing angle. This loss of quality affects both accuracy and precision. As for the estimation of the angle, the accuracy stays very good, but the uncertainty increases dramatically for heat sources approaching the vertical. It is worth mentioning that in all cases analyzed, the accuracy and precision in the estimation of the emitted power is better than 5%. These results are in agreement with the sensitivity analysis summarized in Figure 6.  
As a summary, we can conclude that it is possible to determine the lateral dimensions, the depth and the inclination of buried rectangular heat sources. The duration of the burst does not significantly affect the quality of the retrieved parameters, whereas the SNR might eventually jeopardize precision of the retrieved values, mostly the height and the inclination of the heat source. For SNR above 50 and angles below 45\({}^{\circ}\), the uncertainty stays below 5%.
# Experimental Study on High-Energy Gas Fracturing Artificial Coal
## 2 Gas Generation Principle of Fracturing Agent and Fracturing Mechanism of High-Energy Gas
### High-Energy Gas Fracturing and Desorption Mechanism  
The fracturing agent is excited to produce high-temperature and high-pressure gas, which rapidly expands and impacts the borehole wall, generating a shock wave or stress wave inside the coal, thus creating a crushing zone or radial crack around the borehole. The high-temperature and high-pressure gas starts to expand and works based on the stress wave action. First, the gas wedges into the crack in the borehole wall, driving its steady-state expansion, followed by the quasi-brittle fracturing of the crack tip media, leading to continuous crack expansion under the combined effect of the gas quasi-static stress field and the original rock-stress field. Finally, the crack tip is again in small extension due to localized destruction. As a result, a large crack zone was formed around the borehole, which improved the development level of coal seam cracks, unblocked seepage channels, and improved coal seam permeability.  
[PERSON] [22] [PERSON] carried out an experimental study on the vibration desorption characteristics of gas-bearing coal and concluded that the vibration of gas-bearing coal greatly promotes the desorption of gas. [PERSON] et al. [23] carried out gas desorption tests at a series of temperatures (40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C) and verified that the gas desorption process is a heat absorption process and that the warming promotes the desorption of gas. [PERSON] et al. [24] tested the adsorption characteristics of single-component CH\({}_{4}\) and CO\({}_{2}\) gases using a high-pressure volumetric gas adsorption device and demonstrated that the adsorption characteristics of CO\({}_{2}\) were much greater than those of CH\({}_{4}\). The above literature illustrates that the vibration of the coal body, the increase in temperature, and the production of CO\({}_{2}\) in the coal body all promote the desorption of gas. Under the action of high-energy gas, the microscopic void structure of coal and the existing state of the gas is disturbed in the elastic zone outside the fracture zone, which breaks the dynamic equilibrium of gas adsorption and desorption in coal. As a result, it converts part of the gas in the adsorbed state into the gas of the free state. This constitutes a perturbative desorption effect. The fracturing agent burns vigorously in the borehole to produce a large amount of high-temperature gas; while the desorption process of gas needs to absorb heat, heat acts on the surrounding coal seam through heat transfer, promoting the desorption of gas in dynamic equilibrium. This constitutes a thermal effect desorption. The fracturing agent contains 95 % organic matter and complete combustion will produce a lot of CO\({}_{2}\). Because the adsorption effect of coal on CO\({}_{2}\) is twice that of CH\({}_{4}\), CO\({}_{2}\) can replace CH\({}_{4}\) to make it desorb into a free state. This constitutes displacement desorption.