esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Uncertainty-Based Explanation Methods  
Explanation methods assist in identifying specific features or input values crucial for the model's predictions, potentially emphasizing image regions particularly relevant for detecting or diagnosing a lesion. This helps radiologists comprehend the model's decision-making process and recognize the most informative aspects of the image for lesion detection.  
In contrast, uncertainty-based explanation methods such as BayLIME and BLRP provide a measure of the model's confidence or uncertainty in its predictions. When the model exhibits high confidence in its predictions, the uncertainty-based explanation method may highlight the most important image regions for those predictions. Conversely, in cases of lower confidence, the uncertainty-based explanation method may display a more dispersed attribution map emphasizing multiple image regions.  
In this portion of the results, lesion detection performance using the explanation methods was compared to two types of baselines. The first baseline involved two explanation methods that did not include uncertainty quantification to determine any potential benefits of incorporating uncertainty. The second baseline featured the MRI image without any explanation to examine whether the presence of explanation methods enhances lesion recognition.  
Our results demonstrated that the improvement in sensitivity was not significant when using the explanation techniques compared to not using them. However, radiologists were more likely to misidentify lesions without the explanation techniques. By employing explanation techniques, radiologists more effectively detected true positive and true negative cases without negatively impacting the other. Moreover, radiologists reported that the uncertainty component was useful in situations where the explanation method emphasized multiple areas, as it assisted them in differentiating between relevant and irrelevant features in the image.  
As per the findings in Table 4, BayLIME received the highest score of 3.8 for radiologists' perceived usefulness, whereas BLRP scored 3.71. Radiologists emphasized the significance of clarity for understanding and regarded explanation methods with noise as less comprehensible.
## 2 Stress Field Inversion Method and Verification of Its Accuracy
### Verification of the Accuracy of the Method
Figure 7 shows that the error stabilizes after 175 iterations, converging from 165.0 \(\mu\)e to 6 \(\mu\)e. Considering the error, the inversion accuracy is good; to further validate the accuracy of the inversion, the errors in boundary stresses and measured strain values at the measurement points were analyzed (Tables 2 and 3). As shown in Table 2, the maximum error in the boundary stresses is 4.44%. In this case, the inverted stresses are as follows: \(\sigma_{x}\) = 19.30 MPa, \(\sigma_{y}\) = 17.69 MPa, \(\sigma_{z}\) = 12.28 MPa, \(\sigma_{xy}\) = 5.45 MPa, \(\sigma_{yz}\) = 3.76 MPa, and \(\sigma_{xx}\) = 7.34 MPa. From Table 3, the maximum error in the measured strains occurs at Point A. This can be attributed to the small reference strain at Point A (\(-\)20.31 \(\mu\)e). The maximum error in the strains at the remaining points is 3.34%, which is observed at Point E. In summary, based on Figure 7 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measurement Points** & \(\varepsilon_{x}(\mu\epsilon)\) & \(\varepsilon_{y}(\mu\epsilon)\) & \(\varepsilon_{z}(\mu\epsilon)\) & \(\gamma_{xy}(\mu\epsilon)\) & \(\gamma_{yz}(\mu\epsilon)\) & \(\gamma_{xz}(\mu\epsilon)\) \\ \hline A & 97.37 & \(-\)263.86 & \(-\)20.31 & 61.28 & \(-\)204.97 & 68.75 \\ B & 93.85 & \(-\)249.94 & \(-\)67.14 & \(-\)76.26 & \(-\)223.13 & 58.03 \\ C & 78.86 & \(-\)204.86 & \(-\)68.03 & 9.83 & \(-\)228.99 & 78.62 \\ D & 94.72 & \(-\)327.85 & \(-\)15.71 & \(-\)135.78 & \(-\)264.40 & 84.46 \\ E & \(-\)182.64 & 68.44 & \(-\)49.09 & \(-\)11.36 & \(-\)14.86 & \(-\)11.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measurement point target value during inversion. Figure 6: Stress–strain curve.
###### Abstract  
The design of wideband multi-port multiple-input multiple-output (MIMO) antennas and their optimization are very important for next-generation smartphones with the increase in massive connectivity. This paper offers the design, simulation, measurement, and specific absorption rate (SAR) analysis of a Pi-shaped ten-element MIMO antenna system for use in the upper mid-band, covering LTE 46 (5.15-5.925 GHz), LTE 47 (5.855-5.925 GHz), and n102 (5.925-6.425 GHz), thus meeting a good fractional bandwidth of 32.7% with a maximum peak gain of 2.89 dBi. Hence, it is well suited for high-isolation (\(<-10\) dB), compactness, and wideband (4.7-6.5 GHz) applications suitable for the current communication system needs. The overall size of the proposed system is 125 mm \(\times\) 70 mm, with a planar dielectric material Rogers RT/5880. Designing the proposed antenna with multiple units entails the preservation of the spatial features of the antenna alongside the reduction of the mutual coupling for adjacent elements by using a decoupling structure. Due to the high accuracy of the positioning elements and precise geometric transformations, the antenna system provides high-performance analysis based on reflection coefficients, radiation patterns, and each antenna's averaged efficiency values (76.12-91.57%).  
upper mid-band; MIMO; smartphone; LTE 46/47; n102; IoT; future generation Article
# A 3D Two-Phase Landslide Dynamical Model on GIS Platform
## 6 Conclusions  
The proposed real three-dimensional landslide dynamic model under the GIS platform in this study represents a significant advancement in simulating landslides in three dimensions. This model lays the foundation for more authentic simulations of actual landslide movements, thereby offering an opportunity to enhance our understanding of landslide dynamics. Through the three-dimensional simulation of landslide motion, we gain insights into its detailed dynamics.  
In this study, we propose a methodology for three-dimensional modeling, numerical simulation, and two-dimensional visualization of landslides on the GIS platforms. Specifically, we proposed a solution to achieve a three-dimensional simulation of landslide motion on the GIS platform. Simultaneously, drawing inspiration from SPH kernel functions, we convert the simulation results into two dimensions. To demonstrate the feasibility of our approach, we select the Yigong landslide case for three-dimensional landslide modeling, numerical simulation, and two-dimensional visualization of results.  
The case study results indicate that our model can capture numerous details in the landslide motion process, enabling numerical simulation of complex three-dimensional landslide dynamics on the GIS platform. However, constrained by the inherent limitations of the three-dimensional model, the model-solving process is susceptible to numerical oscillations and diffusion. The model's accuracy is contingent on grid partitioning, and striking a balance between model precision, solving time, and expediting the solving process poses a challenging problem. Furthermore, the model considers the friction between mass flows as fluid friction, resulting in faster landslide motion speeds compared to the depth-averaged model. Future developments should focus on devising new solvers to achieve more precise simulations of landslide motion processes.  
Conceptualization, Y.W., X.G. and A.T.; methodology, Y.W., X.G. and A.T.; software, Y.W. and A.T.; validation, Y.W. and A.T.; formal analysis, A.T.; writing--original draft preparation, A.T.; writing--review and editing, A.T.; visualization, A.T.; supervision, A.T.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of Innovation LREIS (KPI007) and a grant from State Key Laboratory of Resources and Environmental Information System.  
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 Background
### Rpl
#### 2.2.1 ETX Metric  
The expected transmission count [8] is based on the expected number of data packet transmission attempts required to transmit the packet successfully. It is a link-based metric that emphasizes the rate of packet delivery between communication devices and not node-level statistics. This metric is expected to increase efficiency, as retransmission attempts are minimized by choosing a better link. However, Expected Transmission Count (ETX) does not consider the path length and can select longer paths. Also, channel conditions are dynamic, which is why frequent switching from parents is highly likely.  
The ETX of a wireless link is the estimated average number of data and ACK frame transmissions required for the successful transmission of a packet [9]. As we know, the transfer of data can be in both directions. From the source to the destination, the node is defined as the forward direction, and the data transmission rate is \(d_{f}\), which represents the probability of successful transmission in a positive direction. On the other hand, from the destination to the source node is defined as the reverse direction, and the data transmission rate is \(d_{r}\). The ETX value is defined as follows:  
point-to-point communication: \(ETX=\frac{1}{1-e_{pt}}\)  
where the error probability (\(e_{pt}\)) is: \(e_{pt}=1-d_{f}\times dr\)  
and the bidirectional communication is: \(ETX=\frac{1}{d_{f}\times d_{r}}\)
## 2 Results and Discussion
### Volatile Compounds  
Volatile compounds of raw and toasted black cherry seeds were extracted by head space solid phase micro extraction and analyzed by gas chromatography-mass spectrometry, using DB-5 and Wax capillary columns in order to detect a wide range of polar and non-polar compounds. A total of 59 and 99 volatile compounds were identified in the raw and toasted _P. serotina_ seeds, respectively (Table 5). The identified volatile compounds comprise aldehydes, alcohols, ketones, carboxylic acids, esters, hydrocarbons, and pyrazines. The predominant aldehyde identified in raw and toasted seeds was benzaldehyde. This compound has a characteristic pleasant almond-taste and aroma [33] and has been identified in bitter and sweet almonds [34], hazelnuts [33], and seeds of other species of the genus _Prunus_[35; 36]. Benzaldehyde is produced by enzymatic degradation of the cyanogenic glycosides amygdala and prunasin [35; 37; 38]. Considering on one hand that benzaldehyde content in toasted seeds was higher than that in raw seeds, and on the other, that bitterness attributed to the presence of amygdala [39; 40] diminished in toasted seeds, it is very likely that the toasting process may induce benzaldehyde formation from cyanogenic glycosides. Benzaldehyde might also be generated from phenylalanine during heating processes [41; 42] as was demonstrated by [PERSON] _et al._[33] in hazelnuts, pumpkin seeds [43], and peanuts [44]. It is worth noting that we have previously identified benzaldehyde as one of the major components in black cherry leaves essential oil, we also proved that this compound induces a significant concentration-dependent vasodilator effect [8]. Therefore, the presence of this compound along with oleic, linoleic, and \(\alpha\)-eleeostearic fatty acids indicate that black cherry seeds consumption might have beneficial effects on the cardiovascular system. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**AA**} & **FAO Reference** & **AAS *** & **AAS * \\  & **Pattern** & **Raw Seeds** & **Toasted Seeds** \\ \hline His & 19 & 1.37 & 1.12 \\ Thr & 34 & 1.55 & 1.74 \\ Met & 25 & 0.36 & 0.39 \\ Val & 35 & 1.30 & 1.33 \\ Phe & 63 & 0.77 & 0.82 \\ Ile & 28 & 1.40 & 1.44 \\ Leu & 66 & 1.38 & 1.24 \\ Lys ** & 58 & 0.15 & 0.19 \\ \hline \end{tabular}  
* * Amino acid score (AAS) = AA content of test protein/reference AA pattern, where reference AA pattern is the amino acid requirement for a preschool child (2–5 years) [30]. ** Limiting amino acid.  
\end{table} 
Table 4: Black cherry seeds amino acid score (AAS).
## 4 Experiment and Result
### PPP/INS Solutions for Tunnel and Long Trajectory
As shown in Table 4, compared with PPP solutions, the RMS of PPP/INS horizontal errors in the area marked by purple box 1 decreases by 49.37% from 11.419 to 5.782 m, and the RMS of PPP/INS horizontal errors in the area marked by purple box 3 decreases by 48.29% from 11.618 to 6.008 m. Meanwhile, the RMS of PPP/INS vertical errors also decreases significantly. In the area marked by red box 2, as shown in Figure 16, the deviations on the south direction between the trajectories of PPP/INS and reference increased rapidly after the GNSS signals were blocked, which can be caused by the scarcity of GNSS satellites before. Meanwhile, it can be found that the southward error of PPP is higher than 30 m in 373 epochs, as shown in Figure 17. However, compared with the trajectory of PPP/INS, the deviations of trajectory between PPP/INS-ADIS and reference are lower. In the area marked by red box 4, the deviations of trajectory of PPP/INS are approximate to those of PPP/INS-ADIS. For the long trajectory experiment, the RMS of PPP/INS and PPP/INS-ADIS horizontal errors are 6.01 and 5.73 m, respectively, which are significantly lower than those of conventional PPP. Therefore, with the higher reliability of initial alignment in dynamic mode and RKF parameter estimation model with equivalent variance matrix, some adverse factors, such as the effects of multipath, noise level, and observations gaps, related to GNSS signal quality of smartphones under GNSS-degraded environments were overcome to some extent. The convergence efficiency of the PPP/INS model is enhanced and the trajectory deviation of the smartphone can remain stable in some areas, such as tunnels, when GNSS signals are blocked. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multicolumn{6}{c}{**Huawei Mate 30 GPS/BDS Kinematic Positioning Error RMS (m)**} \\ \cline{2-7}  & \multicolumn{3}{c}{**150–250 (Epochs)**} & \multicolumn{3}{c}{**650–750 (Epochs)**} \\ \cline{2-7}  & **PPP** & **PPP/INS** & **PPP/INS–ADIS** & **PPP** & **PPP/INS** & **PPP/INS–ADIS** \\ \hline E & 9.837 & 5.252 & 2.267 & 9.072 & 3.532 & 3.788 \\ N & 5.799 & 2.417 & 5.998 & 7.258 & 4.860 & 6.574 \\ H & 11.419 & 5.782 & 6.412 & 11.618 & 6.008 & 7.587 \\ V & 23.190 & 4.507 & 5.083 & 19.446 & 2.784 & 7.498 \\
3D & 25.849 & 7.331 & 8.183 & 22.652 & 6.622 & 10.667 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The RMS of PPP, PPP/INS, and PPP/INS positioning errors using Huawei Mate 30 on the areas marked by purple boxes 1 and 3. Figure 17: Comparison of Huawei Mate 30 GPS/BDS PPP, PPP/INS, and PPP/INS–ADIS solutions with reference points. The boxes 1, 2, 3, and 4 corresponds to the boxes in Figure 16.
## 4 Problem Statement
### Collaborative Target Detection Problem  
The exploration of an environment \(E\) by M-UAV consists of detecting, analyzing, and finding the path to reach these targets. For a large-scale environment, the complexity becomes exponential with respect to the number of these elements. This becomes even more problematic when using a UAV based on a centralized architecture (UAV-to-GCS) because of the latency. In addition, the UAVs' limited flying autonomy must be considered. Therefore, to cope with these problems, a decentralized M-UAV system based on the divide-and-conquer [56] technique should be used. It consists in dividing the exploration problem into sub-problems according to the number \(k\) of drones used [12; 40; 49; 57].  
The division of the problem involves two challenges, which are (1) the partitioning of the environment \(E\), commonly called the k-partition problem, and (2) the use of M-UAV such that each UAV\({}_{i}\) explores its sub-environment \(E_{i}\) and computes the shortest path leading to a target of \(T_{i}\); it is known as the Single-Source Shortest Path problem [58].
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 5 Results
### Throughput  
The graph shown in Figure 6 illustrates the throughput performance, which indicates that increasing the number of WDs results in an increase in throughput with respect to time. However, as more WDs are added, the rate at which throughput improves slows down gradually. This outcome aligns with the analysis that suggests the throughput in a Wi-Fi network that employs the distributed coordination function (DCF) reaches a saturation point or begins to decline as the traffic load increases. Traditional schemes depending on RSSI exhibit the poorest throughput performance because they select the destination AP based on only one parameter and fail to balance the load among the APs. As a result some APs become overloaded, leading to packet collisions and degraded throughput. In the traditional RSSI scheme, the WDs remain associated with a particular AP unless they move out of range or there is an issue with the APs RSSI value. The RSSI scheme contributes just 40% of the total throughput.  
In contrast, the proposed ASLB balances the load among the APs and the controllers in a symmetrical way through a UC and selects the destination AP and RC that fulfills system and client parameter requirements. If an AP becomes overloaded, the UC prepares a de-association list for the overloaded APs and selects an underloaded AP as the destination for de-associated WD. This two-fold load balancing approach reduces contention and improves throughput performance. Additionally, the SDN controller de-associates the WD with the lowest RSSI values and associates them with the least loaded AP while considering packet loss rate, RSSI, and throughput. Consequently, this approach also enhances packet delivery rates.  
Among the other schemes evaluated, CMAS scheme outperforms the DASA and MPD schemes. MPD performs the worst due to unnecessary probing frames used to estimate the load values. This approach introduces delays in transmitting and receiving the probing frames, which can lead to larger probing delays and packet collisions even in lightly loaded networks. In the DASA scheme, selecting an AP with the best DL-SINR improves the end-to-end aggregate throughput by improving the transmission rate between the AP and the WDs. In CMAS, the load among the APs is balanced by estimating the bandwidth using the DCF mechanism, while taking frame aggregation and hidden terminal problems into account. When compared to CMAS, DASA, MPD and RSSI schemes, ASLB optimizes the throughput by 10%, 14%, 23% and 35%, respectively.  
Figure 6: Throughput Performance.
## 4 Results and Discussion
### Seasonal Characteristics of Ground Deformation in the Subsidence Funnel Area  
Due to the influence of multiple factors, raw cumulative time series of ground deformation often exhibit non-periodic behavior, characterized by the superposition of trend, seasonal, and noise components. The amplitudes and phases of both the annual and semiannual cycles were calculated (Figure 6). The dominant annual amplitude within the subsidence funnel area ranges from 10 to 18 mm, with maximum values up to 22 mm observed in the Beijing Town area and the high-speed railway development zone within the county center. In contrast, the semiannual signals are weak, with an average amplitude of only 2.3 mm. Therefore, this study focuses exclusively on analyzing the annual periodic characteristics of land deformation within the subsidence funnel zone.  
The phase of the annual cycle in the cumulative deformation time series represents the timing of the maximum annual rebound. By converting the phase into months, the timing of the maximum annual rebound can be clearly identified. As shown in Figure 6, the maximum rebound values in the subsidence funnel area occur around mid-February and mid-May. In the subsiding area of the county center, the maximum annual rebound typically occurs in mid-February. This region is primarily composed of urban residential areas. From July to September, which is the main precipitation period, surface water replenishes shallow groundwater. This process slows down surface subsidence by November, and by mid-January of the following year, the ground begins to uplift, reaching the maximum rebound value in mid-February. In contrast, the maximum annual rebound in the subsiding areas of Beijing Town (southern part) and the high-speed rail development zone (central area) occurs in mid-May. These regions are predominantly agricultural areas. During the spring irrigation peak in March, shallow groundwater levels in agricultural areas rise due to irrigation, causing surface deformation to reach the maximum rebound in mid-May.  
Figure 6: Amplitude and phase of seasonal deformation cycle in subsidence funnel area (**a**) Annual cycle amplitude of the deformation time series; (**b**) Semiannual cycle amplitude of the deformation time series; (**c**) Annual cycle phase of the deformation time series; (**d**) Semiannual cycle phase of the deformation time series.
## 3 Experimental Results
### Ultrasonic Wave Analysis
\(A\) was the amplitude when the rock was subjected  
Figure 4: Characteristics of different types of waveform: (**a**) a excited ultrasonic waveform; (**b**) a received P-wave waveform; and, (**c**) a received S-wave waveform; (**d**) a received AE waveform; the blue dotted line is the symmetrical centerline of the waveform; the red dotted line is the recorded time of arrival of ultrasonic signal. to external stress and \(A_{0}\) was the initial amplitude before the test. As can be seen from Figure 5c, the P-wave amplitude rose at first, then remained constant, and then decreased with the development of rock damage. What was interesting about the data in this figure was that the P-wave amplitude began to decrease after entering the plastic deformation stage. The attenuation did not happen at the brittle-ductile transition or the beginning of the plastic deformation stage. This meant that there were not enough new cracks in the sample to hinder the ultrasonic wave propagation at that moment, even though the sample had a large deformation. The ultrasonic wave attenuation coefficient, \(\alpha\), is used to indicate the degree of attenuation of sound waves as they propagate through the medium. It is defined using Equation (2):  
\[\alpha=\frac{20}{x}\lg\frac{A}{A_{0}} \tag{2}\]  
where \(x\) is the propagation distance and \(\lg\) is the logarithm. Figure 5d provides the relationship between the ultrasonic wave attenuation coefficient and the axial stress-strain curves. The attenuation coefficient \(\alpha\) had an increasing trend at the beginning of the test, remaining stable at the brittle-ductile transition stage. Subsequently, it slightly decreased when the sample entered the plastic deformation stage, and the slope began to increase as the test progressed. These results were consistent with the change in the ultrasonic amplitude.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.4 Gradient Boosting Regression (GB)  
Gradient Boosting is a powerful ensemble learning algorithm used for regression and classification. It combines many weak learners (usually decision trees) to form a strong predictive model [47]. At each step, a new weak learner is added to correct the errors of the existing model. This process can be summarized by the following formula:  
\[f_{m}(x)=f_{m-1}(x)+\gamma h_{m}(x) \tag{9}\]  
where \(f_{m}(x)\) is the model created at the m-th stage, \(\gamma\) is the learning rate, and \(h_{m}(x)\) is the m-th weak learner. GB regression provides a flexible and highly accurate prediction by offering multiple adjustable hyperparameters and loss functions.