esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications
### Light Modulators
#### 3.2.1 Optical Limiters  
Optical limiting has been sought after for the protection of delicate sensors and electronics from intense laser threats. Since GST has a relatively large refractive index over a broadband range, by using the GST and SiO\({}_{2}\) as the high and low index films in a long-pass edge filter configuration, it has been possible to demonstrate the designed optical limiters has the over 30 dB extinction over a broad spectral band with \(\pm\)45 angles of incidence [15], as shown in Figure 17. The transition edge of the filter can be shifted by hundreds of nanometers by using the large refractive index difference between the amorphous and crystalline state of GST. It further demonstrates that GST can be used in multilayer configurations to realize complex optical filtering properties without inducing excessive stresses due to the phase change.  
Figure 16: Resistance distribution of a 7-level cell using single pulse programming. Adapted with permission from Reference [84]. ©IEEE.
## 3 Results  
### Changes in the Organic Matter of the Lake Sediments  
The content changes of TOC and TN, the atom ratio of TOC and TN (C/N), and \(\mathrm{\delta^{13}C_{org}}\) in the sediments of the North Aral Sea are shown in Figure 3. The TOC content in the core sediments was between 0.17% and 1.44%, with an average content of 0.93%. From the beginning of the 1950s to the end of the 1960s (corresponding to a depth of 30-21 cm), the TOC content fluctuated in a small range, and there was an obvious sudden change from 1970 AD (22 cm).  
The content of TN was between 0.01% and 0.20%, with an average content of 0.12%, and its change trend was more consistent with the change of the TOC. The average value of the C/N ratio was 8.4, with a maximum value of 13.9 and a minimum value of 6.9. The C/N ratio changed greatly in the lower part of the core (22-30 cm). In the upper part of the sediment core (0-21 cm), it slowly descended to the surface. The value of \(\mathrm{\delta^{13}C_{org}}\) ranged from \(-\)23.48% to \(-\)21.57%, with an average of \(-\)22.46%.  
The organic carbon burial rate (OCBR) varied from 7.36 to 72.82 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), with an average value of 28.78 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) (Figure 3). Before the 21 st century (9-30 cm), the overall change in the OCBR showed an increasing trend, ranging from 7.36 to 33.89 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), with a relatively small range of change. At 2010 AD (8 cm), the OCBR increased significantly and began to show significant fluctuations around 2010, first increasing from 29.20 to 61.41 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), then rapidly decreasing to 32.73 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), before rising to the highest value of the core profile, which was 72.82 g\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), and then continuing to decrease.
## 2 Results  
### Model Establishment and Accuracy Evaluation  
Figure 1 shows the correlation between the modeling variables and grassland AGB in Xinjiang. The correlation of environmental variables with grassland AGB, from highest to lowest, were EVI (0.48) > NDVI (0.45) > LAI (0.42) > ET (0.36) > GPP (0.35) > PRE (0.08) > TEMP (\(-\)0.07). Among these parameters, EVI exhibits the highest correlation with AGB (R = 0.48, \(p\) < 0.01).  
This study aimed to evaluate the accuracy of the grassland AGB estimation model. A total of 20% of the sample data was used to validate the accuracy, and a scatter plot was created to compare the estimated AGB from the model with the measured AGB in the field. In the test set, the estimated AGB values were concentrated between 0 and 200 g\(\cdot\)m\({}^{-2}\), with a coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE) of 0.55 and 64.33 g\(\cdot\)m\({}^{-2}\), respectively (Figure 2a). The AGB model tended to underestimate some high AGB values and overestimate some low values. However, there was a good linear relationship between the estimated and measured AGB values. When the measured grassland AGB was below 100 g\(\cdot\)m\({}^{-2}\), most of the estimated AGB values were higher than the measured values, while for measurements above 100 g\(\cdot\)m\({}^{-2}\), the estimated values tended to be lower. Figure 2b shows that the estimated grassland AGB and measured grassland AGB have nearly equal means of 85.33 g\(\cdot\)m\({}^{-2}\) and 85.39 g\(\cdot\)m\({}^{-2}\), respectively, with over 75% of AGB values being less than 100 g\(\cdot\)m\({}^{-2}\). In summary, the AGB model accurately predicted AGB values and provided a reliable estimate of grassland AGB in Xinjiang.  
Figure 1: Correlation between AGB and various environmental variables (** represent significance at the levels of 1%).
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection
#### 3.4.4 Rotating the Images  
Rotation, particularly around the head-foot axis in medical scans, is relevant, due to the asymmetrical nature of human anatomy along this axis. This rotation reflects plausible variations during patient positioning in the scanner. Unlike rotations in other planes, which might distort the image due to the anisotropic (non-cubic) nature of voxels in medical imaging, rotating around the head-foot axis preserves the clinical features and their spatial relationships in the scan. This type of rotation helps the model to be invariant to minor misalignment or rotations in the scanned images, accommodating common discrepancies that arise from different scanning protocols. Training with images rotated around this axis enhances the model's ability to correctly interpret images regardless of slight orientation differences, thereby improving diagnostic accuracy and reliability.
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## 4 Performance Evaluation  
Table 1 presents the basic parameters for evaluating the performance of CoMP. The parameters used in this analysis were set to typical values found in [18]. To analyze the average number of transmissions and retransmissions and the handover latency, we used the packet loss rate by SNRs with \(f\) fragments of a CoAP message in [17]. It is assumed that the wireless link bandwidth and wired link bandwidth were 250 Kbps and 100 Mbps, respectively.  
The quality of service requirements for the handover delay depends on the message type of the IoT application, such as discrete or continuous message data. Figure 3 displays the average number of retransmissions. As the probability of packet error rate is lower than 30%, the CoAP message is retransmitted only once.  
However, as the probability of packet error rate becomes 50%, the CoAP message is retransmitted approximately twice. Figure 4 indicates the average delay of SNRs by impact of transmission delay (\(T_{r}\)). As the retransmission count increases, the average delay also increases. If the CoAP message is retransmitted due to packet error or wireless network failure, the average delay is approximately 3 s. Figure 5 exhibits the total handover delay with the CoAP retransmission mechanism by impact of \(T_{r}\). As the packet loss rate is approximately 50%, the handover latency increases to 4.3-4.5 s. In this way, CoMP supports reliability in the handover operation. Conversely, the related standard mobility  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Param** & **Value** & **Param** & **Value** & **Param** & **Value** \\ \hline \(t_{DHCP}\) & 5000 & \(t_{(mCSN,ER)}\) & 4.4 & \(t_{(CWC,IMMS)}\) & 50 \\ \(t_{MD}\) & 100 & \(t_{(mCSN,IMMS)}\) & 79.4 & \(S\) & 113 \\ \(t_{L2}\) & 50 & \(t_{(mCSN,CWC)}\) & 79.4 & \(m\) & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for delay analysis (Kbps, ms, bytes).  
Figure 3: Average number of retransmission counts for \(f\) fragmented CoAP messages.  
protocols such as MIPv6 and HMIPv6 do not support reliability. As the failure of signaling message occurs, these mobility protocols do not support an automatic retransmission mechanism.  
Figure 6 shows the handover delay by retransmission counts. Although the handover delay due to the retransmission can be increased, the CoMP can guarantee the reliability of handover message by using CoAP retransmission mechanism. On the contrary, the related standard mobility protocols such as MIPv6 do not support the retransmission mechanism. As result, CoMP can be utilized to a high reliability service such as remote healthcare service on IoT environment.
## 3 Results
### Spread-F and Scintillations
Small deviations for the Moscow station are explained by its slightly higher latitude than that of Irkutsk and Mohe. There is no noticeable spread-F appearance dependence on the longitude. At Yakutsk, the spread-F is observed early, which we attribute to a higher latitude of that station as compared to others. The above (Section 3.1) substorm results in spread-F at Yakutsk latitude before the main storm onset. Figure 3: Experiment geometry. Panel (a) shows the GPS IPP (black lines) at 300 km and 10–20 min total electron content (TEC) variations (superimposed red lines) within 19:30–20:30 UT on 22 June 2015. Panel (b) shows the geometry in details. Black lines show the GPS IPP at 19:30–20:30 UT. Gray lines show the Cygnus A ionospheric pierce point at 100, 250, and 400 km. On both panels, the filled grey region is the ionosonde approximate radiation pattern, the filled blue region is the IISR approximate radiation pattern. Vertical segment in the right bottom part of the panel (**a**) shows 0.5 TECU variations. The Cygnus A was observed within the radar field of view 17:00 through 22:00 UT. The Cygnus A signal scintillation index S4 was calculated by using formula (1). Figure 4c shows the result. The black continuous line shows the 22 June 2015 data. On 22 June 2015, the S4 first increase was observed between 18:30 and 19:30 UT. A similar increase was recorded on 24 June 2015, the day that was chosen  
Figure 4: Effects of ionospheric irregularities on different-band radio signals. Panel **a** presents the spread-F at the Irkutsk ionosonde; panel **b** displays the \(\sigma\)\(\varphi\) for the L1 GPS signal; panel c reflects the Cygnus A radio amplitude scintillations from the Irkutsk Incoherent Scatter Radar (IISR) data; panel d shows spread-F. Panel **d** shows data for the ionosondes in Yakutsk (YA462, 62.0”N, 129.7”E; blue line), Mohe (MH453; 52.0”N, 122.5”E; black line), and Moscow (MO155; 55.47”N, 37.3”E; red line) during the 22 June 2015 magnetic storm. The horizontal line on panel \(d\) shows 0.5 MHz. The black lines and the histogram on panels _a-c_ show the 22 June 2015 data, the gray line on the panel c shows the 24 June 2015 data (reference day). The dashed-dotted line in panel c shows the angle \(\gamma\) between the “IISR—Cygus A” LOS and the Earth magnetic field. The dashed lines mark 19:30 UT, 20:15 UT, 21:30 UT. as a reference day (grey line). The increase corresponds to the angle \(\gamma\) minimum, i.e., it is the magnetic zenith effect. Such an effect is regularly observed in the IISR scintillation data and fades quickly, when the angle between the LOS and the magnetic field exceeds 10\({}^{\circ}\)[52]. The highest S4 for Cygnus A was observed around 20:00 UT. It coincides with the time, when the enhanced spread-F was observed. The scintillation intensity dropped at 20:30 UT, while the spread-F lasted for an extra hour.
## 5 Results and Analysis
### Safety Analysis of Path Planning  
For glacial lake environment V1 with stochastic wind disturbances and other potential environmental perturbations, path safety evaluation requires systematic analysis of spatial adjacency to ice holes. This study defines risk level 1 as path segments adjacent to one ice hole and risk level 2 for those adjacent to two ice holes (Table 2). Corresponding risk values are quantified as 0 for risk level 0 (no adjacent ice holes), 2 for level 1, and 4 for level 2.  
Expert strategy analysis reveals a total risk value of \(\delta=12*0+2*2=4\) for five typical paths. To evaluate algorithm safety performance, we establish the average risk metric  
\[\Delta=\frac{\sum_{i=1}^{n}C_{i}\delta_{i}}{n} \tag{10}\]  
where \(n\) denotes test iterations and \(\delta_{i}\) represents the risk value of the \(i\)-th path. And \(C_{i}\) denotes the number of grid cells with distinct risk levels along the \(i\)-th path. As shown in Table 3, paths 2-5 generated by EIRL demonstrate superior stability with \(\Delta=4\), matching expert-level safety standards. Notably, path 1 was excluded from typical EIRL analysis due to its predominant reliance on autonomous exploration (Low-dependency mode). In contrast, Q-learning exhibits significant divergence in safety propensity compared with expert strategies. Experimental results confirm that EIRL enhances path safety by over 100% compared with conventional reinforcement learning through expert knowledge integration.  
In navigation scenarios with stochastic risks (e.g., glacial lake model V1), traditional path planning algorithms primarily handle deterministic obstacles but fail to address environmental uncertainties (e.g., wind-induced displacement). Our inverse reinforcement learning framework incorporates human expertise to enable rapid derivation of optimal paths balancing safety and efficiency. This approach proves particularly effective in environments with known obstacle distributions but unmodeled stochastic risks.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 2 System Model
### Achievable Secrecy Sum-Rate
#### 2.3.2 External Eavesdropping Scenario  
Eavesdroppers are assumed to be external nodes and their receiving operations are not restricted to the receive random beamforming technique. Generally, we consider the worst case (i.e., the best performance at the eavesdroppers) when the eavesdroppers' capabilities are not specifically revealed. Thus, we assume that the eavesdroppers exploit an ideal receiving signal processing (i.e., a theoretically optimal receiver) and achieve their channel capacity to intercept the information from desired transmitters. In this case, information leakage by all eavesdroppers (i.e., the achievable rate by eavesdroppers) is given by  
\[r_{\text{Ext}}^{[s]}=\max_{k\in\mathcal{K}}\left\{\log\left(1+\|\mathbf{g}_{ \ast k}\|^{2}\rho\right)\right\}=\log\left(1+\max_{k\in\mathcal{K}}\left\{\| \mathbf{g}_{\ast k}\|^{2}\right\}\rho\right), \tag{7}\]  
where subscript '\(\text{Ext}\)' indicates an external eavesdropping scenario.  
Similar to internal eavesdropping, the achievable secrecy sum-rate in the case of external eavesdropping is given by  
\[R_{\text{Ext}}^{\text{sec}}=\sum_{s}^{\text{S}}\left[r_{\text{BS}}^{[s]}-r_{ \text{Ext}}^{[s]}\right]^{+}, \tag{8}\]  
where \(r_{\text{BS}}^{[s]}\) and \(r_{\text{Ext}}^{[s]}\) are defined in (4) and (7), respectively.
## 5 Discussion of Results, Practical Implications, and Future Research
However, we acknowledge that machine learning (ML) algorithms and advanced analytical techniques, when data are available, can play a significant role in enhancing the predictive accuracy and adaptability of the framework. Although specific ML algorithms are not applied in this study, the modular design of the framework allows for the future integration of approaches such as Supervised Learning (e.g., regression models, decision trees) to model asset degradation; Unsupervised Learning (e.g., clustering) for identifying patterns in degradation data; or Reinforcement Learning for optimizing major maintenance scheduling based on feedback.
## 4 Results and Discussion
The experimental results showing small CTSI errors with a small RMSE imply that we can apply multispectral raw data aligned with limited biochemical tests to estimate the CTSI value without massive sampling requirements. However, the current work in this study is merely applied to a small pond or lake that is not directly related to a river or a natural water source whose monitoring condition is much more complicated. Further, more advanced requirements of detection facility that can produce high throughput of image data over a larger lake area will definitely increase in this application field. It is evident that more advances in both UAV and multispectral sensor (cameras) specifications, and intelligent algorithmic development for appropriate regression modeling, may continue to utilize the proposed methodology. To date, the proposed regression modeling approach is proven to work, so we are interested in extending these models more widely to find the appropriate and real-time expressions for more water quality parameters combined with emerging artificial intelligence (AI) feature recognition and big data analysis techniques in the near future. In addition, the visualization maps of individual water quality parameters, such as concentration distribution, temporal variation, etc., using the proposed method are currently in progress in our lab and these results will be disclosed soon in the subsequent manuscript.
# Internet of Things (IoT) and the Energy Sector
## 5 Challenges of Applying IoT
### Integration of IoT with Subsystems  
A main challenge includes the integration of an IoT system in subsystems of the energy system. Because subsystems of the energy sector are unique employing various sensor and data communication technologies. Therefore, solutions are needed for managing the data exchange among subsystems of a IoT-enabled energy system [132; 133; 134]. An approach for finding solutions for the integration challenge, taking into account the IoT requirements of a subsystem, pertains to modeling an integrated framework for the energy system [132]. Other solutions propose designing co-simulation models for energy systems to integrate the system and minimize synchronization delay error between the subsystems [135; 136].
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### Example Test Case  
Figure 6 shows the Teletest signal of Test 5 before processing, where the defect's signal region is in between the two dotted red lines. The SNR of a defect in this test is \(-0.98\) dB, which means that it is completely buried in the noise. The noise signal is calculated by removing the torsional waves (pipe end and defect signal) from the shown signal in Figure 6. However, since Defect 2 is not present in this test, the location in between 4-4.5 m is also considered as noise.  
Figure 7 shows the assigned values to the first (black line) and the second version (red line) of thresholding, which is the inPhase and subtraction of the comparisonVal\(\ae\) from the inPhase, respectively. As can be seen both in the backward leakage signal and the noise region, the decrease in  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Test \#** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline SNR & 2.97 & 8.93 & 10.36 & \(-2.65\) & \(-0.98\) & \(-3.43\) & 18.57 & 27.63 & 28.05 & 14.73 & 24.00 & 23.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated signal-to-noise ratio (SNR) with respect to the coherent noises in the inspection (in dB).  
Figure 6: Example signal achieved using general routine, tested by 34 kHz where the first two red dotted lines shows the defect region, and the third and fourth ones show the pipe end.  
the assigned threshold is more than the defect and pipe end signals. In method two, a static percentage of either of the mentioned values is considered the threshold for each iteration; however, in method three, this percentage is assigned using the ratios of inPhase signals subtracted from its mean, as shown in Figure 8. The calculated threshold for each iteration using the average value of method three is shown in Figure 9. In the same figure, if method one was used, the threshold value would be a constant line for all of the iterations. The processed signal calculated from method three is illustrated in Figure 10, where it shows the locations at which the comparisonValue is higher than the threshold history. As expected, the pipe end is completely detected, since it is a complete axisymmetric feature, but only the center part of the defect could be detected.  
Figure 8: Calculated percentage of sensors with same phase, where the offset (mean values) are removed (orange); the black signal is showing the filtered version for better illustration.  
Figure 7: Comparison of the first and second thresholding versions on the Test 5 signal where the two zoomed regions are showing the backward leakage, defect, noise, and pipe end, respectively. The black and red signals are showing the first and second versions, respectively.
## 4 Simulation Environment Settings  
In the simulations, we considered the dynamics model of skid-steering vehicles with four independent driving wheels, which includes vehicle body dynamics, wheel dynamics, and the wheel-ground interaction model. It is important to note that we consider the vehicle as running on flat ground with friction coefficient \(\mu_{f}=0.85\). Table 1 provides the detailed vehicle dynamics settings used in the simulations.  
The KA-DDPG algorithm was implemented on the PyCharm IDE with Python 3.7, and ran on an Intel Core i5 computer. Based on the definition of the agent, the actor network and its target network were constructed, using two \(4\times 512\times 512\times 4\) fully connected neural networks, and the critic network and its target network were constructed using two \(8\times 512\times 512\times 1\) fully connected neural networks. The structures of the neural networks are shown in Figure 7, and the detailed parameters of the KA-DDPG algorithm are listed in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Symbol** & **Value** \\ \hline Vehicle mass & \(m\) & 2360 kg \\ Vehicle width & \(B\) & 2.4 m \\ Distance from the front axle to CG & \(L_{f}\) & 2.5 m \\ Distance from the front axle to CG & \(L_{r}\) & 2.5 m \\ Wheel radius & \(r\) & 0.2 m \\ Vehicle inertia around CG & \(J\) & 4050 kg\(\cdot\)m\({}^{2}\) \\ Friction coefficient & \(\mu_{f}\) & 0.85 \\ Rotation moment of wheel & \(j\) & 0.85 kg\(\cdot\)m\({}^{2}\) \\ Rolling resistance torque & \(T_{Z_{i}}\) & 11.8 N\(\cdot\)m \\ Max torque of wheel & \(T_{max}\) & 100 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle dynamics parameters.  
Based on the simulation environment design described above, training was carried out for a total of 5000 episodes, and the designed agent learned the torque distribution strategy. During training, the vehicle state was randomly initialized in each episode. Due to the two newly introduced knowledge-assisted learning methods, the training of the KA-DDPG converged quickly and stably. After completing the learning, only the parameters of the actor network were retained, which then received the current state of the vehicle in real-time and generated the optimal distributing action, in order to realize control of the vehicle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Random seed & 2 \\ Max episode & 5000 \\ Max steps per episode & 100 \\ Step size & 0.1 \\ Memory capacity & 1,000,000 \\ Batch size & 512 \\ Actor network learning rate & 0.0003 \\ Critic network learning rate & 0.001 \\ Exploration noise scale & 0.1 \\ Soft update rate & 0.01 \\ Discount factor & 0.999 \\ Proportional coefficient & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the KA-DDPG algorithm.  
Figure 7: Structure of the neural networks: (**a**) actor neural network; and (**b**) critic neural network.