esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Section
### Plant volatile collection  
Volatiles were collected from _F. sachalinensis_ leaves infested by _P. japonica_ (N = 6). Undamaged leaves were used as a negative control (N = 3). To identify the herbivore-induced volatiles from _F. sachalinensis_ leaves, a mature leaf excised from the base of its petiole was placed in a glass container (1 L), and then three _P. japonica_ beetles were introduced onto the leaf. The container was placed in a chamber at 25 \({}^{\circ}\)C under continuous light. Fifty microliters of acetone containing 20 \(\upmu\)g/uL _n_-octane as an internal standard was added to the container after 24 h and then the volatiles in the container were collected using a solid phase micro extraction (SPME) fiber (65 \(\upmu\)m Stable Flex PDMS/DVB, Supelco, PA, USA) for 30 min. The SPME fiber was then injected into the GC or GC-MS for 5 min and the volatile components were analyzed by GC or GC-MS.  
To determine whether the volatiles were induced specifically by insect feeding, a mature leaf was bored using a pipet tip and then the volatiles from the mechanically damaged leaf were analyzed by the same procedure (N = 3). A mature leaf was also enclosed with a paper disk containing 2 \(\upmu\)L MeJA without any contact and then the volatiles induced by exogenous airborne MeJA were analyzed by the same procedure as described above (N = 10).
## 3 Urban Water System Issues and Potential Smart Solutions
### Summary of Problems Potentially Addressed by Smart Water Grids  
Table 1 summarizes the connections between smart technology and problems address. Components such as smart valves have the potential to address multiple issues, especially as part of a smart system such as smart step testing. Other components, such as flood sensor, are more limited in their contribution to a smart water grid.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Component** & **Problem(s) Directly Addressed** & **Problem(s) Indirectly Addressed** & **Embedding System for Component** \\ \hline Smart Meters & Water losses, water quality, disasters, and drought & Energy consumption & Smart step testing and smart pressure management \\ \hline Contaminant Sensors & Water quality & Energy consumption & Contaminant isolation \\ \hline End-Use Sensing Devices & Water losses and drought & Energy consumption & N/A \\ \hline Flood Sensor & Disasters & N/A & Smart flood management \\ \hline Smart Valves & Water losses, water quality, and disasters & Energy consumption & Smart step testing, contaminant isolation, smart pressure management, and smart flood management \\ \hline Smart Pumps & Energy consumption and Disasters & N/A & Smart pressure management and smart flood management \\ \hline Smart Irrigation Controllers & Water waste/overuse & Energy consumption & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components of the smart water grid, problems each component can address, and the smart systems that components are part of.
## 4 Experimental Results
### Ablation Studies for Optimal Multi-Task Learning Setups
#### 4.1.2 Change Detection via Single Task Learning  
Table 3 lists the CD results from the six SOTA methods with four different network architectures and two backbone pre-training choices. It can be observed that our adapted Swin-based model outperforms Siam-UNet, Siam-UNet++, and ChangeStar. Additionally, the pre-training backbones on the WHU-CD are much better than those on ImageNet.
## 5 Discussion  
The maximum accuracy is reached when data are split into a considerable number of classes, as long as balanced samples are used (dataset B). Clearly, this is a remarkable capability of TL combined to SED networks, since classes including few more than 20 training samples deliver a very efficient diagnosis model. Training YAMNet from scratch would actually require millions of samples. Similarly, 100% accuracy is achieved also in the event of class imbalances (dataset A), provided that training classes are fed with more training data (Table 1). Interestingly, performances slightly deviate from 100% when class imbalances are superimposed to extremely few training data (dataset C).  
Although the accuracy achieved for the dataset C is quite promising, appreciable overfitting occurred in this case, as suggested by the validation and test sets (Table 5). For the diagnosis task of dataset C, training samples did not provide full diagnosis accuracy and the model did not effectively generalize knowledge to new data. Nevertheless, the implementation of a consistent dropout strategy markedly mitigated this effect and remarkable accuracies (higher than 90%) were reached.  
Dataset C resulted in a slightly harder form a diagnostic perspective. Contrary to the dataset A, fewer data per class were available and data imbalances affected performances. Depicting intra-class variability at a general level was a harder task for those training data.  
Figure 9: Test confusion matrices: (**a**) dataset A; (**b**) dataset B; (**c**) dataset C; (**d**) dataset C with dropout layer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Training Accuracy** & **Validation Accuracy** & **Test Accuracy** \\ \hline A & 100\% & 100\% & 100\% \\ B & 98.4\% & 100\% & 100\% \\ C & 96.9\% & 90.2\% & 83.0\% \\ C with dropout & 100\% & 93.9\% & 91.5\% \\ \hline \end{tabular}
\end{table}
Table 5: Performances for bearing fault detection using YAMNet and transfer learning.  
Indeed, when the dropout improvements are introduced, only the imbalanced OR classes present misclassification (Figure 9d).  
Future work can provide a systematic investigation on the performance variations with respect to the size of training sets for such a TL strategy. In this context, weighted loss function can be taken into account to deal with class imbalances. Further, the effect of additive gaussian noise on sample data can be investigated as well. The choice of network hyperparameters can indeed be affected by noisy signals. Automated tuning of hyperparameters is currently a challenging aspect and deserves further improvements. Future research will include additional experimental validation based on different datasets.
## 3 Results
### Delay and Phase Shift Impact on CD for Hybrid Two-Stage SD/SC  
The CD transfer function shows that the modulated signal has a time delay and a phase shift effect during propagation. In this section, we investigate these effects considering the hybrid two-stage SD/SC structure.  
Figure 7 shows these results. The blue curve (circle) shows the results achieved by applying only the time delay. The grey curve (diamond) shows the result of applying time delay and phase shift. These two curves show a similar performance, suggesting that the phase shift is not necessary. By applying only the phase shift (light green curve-square), instead, the performance worsens compared to using  
Figure 6: Compared performance of the complete hybrid optoelectronic system. The receiver topology considered in Figure 2 is highlighted inside the parentheses in the legend of the figures.  
only digital equalization (yellow curve-upper triangle). We can, therefore, conclude that delay is the most significant operation in the hybrid module to mitigate CD.
## 5 Experimental Analysis and Discussion
### QoS in Different Routing Protocols Corresponding to Mobility Models
#### 5.3.2 Average End-to-End Delay  
DSDV, being the proactive and table-driven protocol, performs way better in terms of average end-to-end delay for both mobility models. The graph above demonstrates the same and why this graph shows this behavior because DSDV stores a routing table along with the topology table where the routing table contains the best available paths, and the other two routing protocols are on-demand protocols, due to which their AE2 ED is more, as compared to DSDV. This graph takes this shape for different values because the increased number of the node delay continues to increase due to congestion in the networks, as shown in Figure 5.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 3 Satellite Signals Accessible and Decodable for Educational Purposes
* Scientific and Experimental Data Satellites: These include CubeSats that communicate using LoRa. * Communication Transponder Satellites: These include Immarsat satellites that communicate using Immarsat Aero and Immarsat STD-C data. * Aircraft Communication Signals: These include ADS-B [11] and air band voice communication signals [12]. Please note that in the information presented in Figure 3, some geostationary meteorological satellites cannot be received from Thailand, as they are located beyond the line-of-sight range. To receive signals from all geostationary meteorological satellites worldwide, at least three ground stations must be strategically placed at appropriate locations around the globe.
## 11 Discussion
Despite being widely used, the traditional RQD method [1,2] has limitations, primarily concerning its sensitivity to the orientation of the borehole relative to the fracture orientation and its inability to account for the quality of core pieces smaller than 10 cm. Because high RQD values may not always correspond with high-quality rock masses and vice versa, these restrictions may occasionally result in misrepresentations of rock mass quality [11; 13; 14]. Several modifications to the RQD method have been proposed to address these limitations: weighted joint density (RQD\({}_{\text{wjd}}\)), modified RQD with weighted joint density (RQD\({}_{\text{M-wjd}}\)), Modified RQD (MRQD), corrected RQD (RQD\({}_{\text{c}}\)), improved RQD (RQD\({}_{\text{i}}\)). Each modification offers unique advantages and addresses specific limitations of the traditional RQD method [18; 19; 20; 21; 22; 23]. The choice of method should be based on the specific conditions of the borehole and the rock mass being assessed. For instance, RQD\({}_{\text{wjd}}\) and RQD\({}_{\text{M-wjd}}\)[18; 19] are particularly useful in environments with varying joint orientations, while MRQD and RQD\({}_{\text{I}}\)[20; 22] provide more detailed assessments in fractured and weak rock masses. The improved methods demonstrate the importance of considering multiple factors in rock mass quality assessment, including joint orientation, weak zones, and the quality of core segments. By integrating these factors, the modified RQD methods offer more reliable and comprehensive measures of rock mass quality, which is essential for geological and rock engineering applications.
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 3 Proposed Method
### Proposed Inverse Panoramic Imaging Model  
#### 3.2.1 Relative Orientation between a Camera and the Panoramic Camera  
According to the structure of our designed system in Figure 1, the relative orientation relationships are derived in this section. Let \(C_{i}\) be the optical central point of the \(i^{th}\) camera (\(i=1,2,\ldots,8\)), and \(C_{i}-xyz\) be the image space coordinate system. The panoramic cylindrical coordinate system is defined with its origin, set as O, located at the optical center shared by all cameras. The \(X\), \(Y\), and \(Z\) axes of this coordinate system are aligned parallel to the y, z, and x axes, respectively, of the image space coordinate system of the first camera, as illustrated in Figure 2. Thus, the orientation rotation matrix of the \(i^{th}\) camera can be derived by Equation (1).  
\[R_{i}=R_{i}^{\prime}\Lambda \tag{1}\]  
where \(R_{i}\) is the rotation matrix from the panoramic cylindrical coordinate system to the camera coordinate system of the \(i^{th}\) camera. \(R_{i}^{\prime}\) is the rotation matrix from the image space coordinate system of the first camera to that of the \(i^{th}\) camera, and \(\Lambda\) is a matrix, \(\Lambda=\begin{pmatrix}1&0&0\\ 0&0&-1\\ 0&1&0\end{pmatrix}\).  
In addition, it is required to calculate the relative translation vector, set as \(t_{i}\), between point \(O\) and point \(C_{i}\) by Equation (2).  
\[t_{i}=\frac{\sum_{i=1}^{8}{(R_{i})^{T}t_{i}^{\prime}}}{8}-{(R_{i})^{T}t_{i}^{ \prime}} \tag{2}\]  
where \(t_{i}^{\prime}\) is the pre-calibrated relative translation vector between the optical centre point of the first camera and that of the \(i^{th}\) camera.  
Aiming at removing empty pixels and pixel aggregation in virtual panoramas, an inverse panoramic imaging model is further derived in this paper.
## 3 The Impact of the Electromagnetic Interference on Drones
### Influence of Electromagnetic Interference of Converter Station on Electronic Circuit of UAV  
High-power electromagnetic interference has high coupling efficiency for the backdoor of typical electronic equipment, and can produce significant electromagnetic radiation effects on UAV targets in the corresponding frequency range [34; 35].  
Instantaneous high-power electromagnetic interference can cause serious interference to the attitude detection or attitude calculation module of the UAV. The key components that are susceptible to interference in UAVs include the main flight control chip, PPM decoding chip, GPS, three-axis magnetometer, airspeed meter, and AD chip.  
When the drone is severely interfered, the received signal code cannot be parsed correctly, the remote-control signal cannot control the drone's flight, and the drone's flight is out of control. In 2.4 GHz communication technology, the signal duty cycle of the PPM signal is changed after interference, the analytic command signal is wrong, the control signal is disturbed, and the drone loses control. In a more serious case, the unmanned aerial vehicle cannot maintain its stable attitude due to the imbalance of the lifting torque provided by the motor, insufficient torque or the motor stalls, and the drone will overturn and crash. After the electronic governor was interfered by electromagnetic pulses, the field effect tube of the three-phase control circuit was damaged or the motor could not be driven to rotate, causing the drone to overturn and crash [36].  
The damage modes of electromagnetic pulse to electronic components mainly include instantaneous interference, high voltage breakdown, device burnout, and microwave heating [37; 38] (Table 3).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Damage Mode** & **Damage Process** & **Damage Result** \\ \hline instantaneous interference & The low coupling power is equivalent to adding noise or interference signals to the system. & Affect the normal operation of electronic components. \\ \hline high voltage breakdown & The electromagnetic energy coupled into the electronic components is converted into high voltage and high current. & Electrical breakdown occurred in electronic components. \\ \hline device burnout & Pneumic heating causes the component surface temperature to rise sharply, and the surface material produces a series of complex physical and chemical changes. & Ablation of semiconductor devices or fusing of wires, etc. \\ \hline microwave heating & The electromagnetic pulse heats the device temperature to exceed the temperature limit of normal operation. & The device is malfunctioning. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electromagnetic pulse damage classification of electronic components.
# Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis
## 1 Introduction
[18] come up with an algorithmic fusion method that decompose the wavelet packet to acquire 8 energy ratios as features input to different classifiers, which can acquire their respective local diagnosis results. Finally, fused with DS evidence theory, and this method improved the rolling bearing diagnosis accuracy. [PERSON] et al. [19] proposed to base on collects real-time multi-sensor signals using vibration, current, and sound sensors. The convolutional neural network (CNN) algorithm is used for different fault signals to classify faults in 3D printed products with an accuracy of about 94%. The multi-source information fusion technique can improve the accuracy, but when feature selection is carried out, only a certain part of time-domain features or frequency-domain features are considered, and the common time-frequency-domain features are not unified for feature selection. In addition, the GA-BP algorithm and DS fusion theory are fused at the data level and feature level to improve the fault diagnosis accuracy, but they are not fused at the decision level. Therefore, this study tries to select 19 common time-frequency domain feature indicators as features, and then use the DS theory and the local diagnosis results obtained by the GA-BP neural network algorithm for multi-sensor decision-level fusion, and apply the method to gear fault diagnosis as well. The fault diagnosis of the bearing inner ring of the bearing public data set of Case Western Reserve University has been used as a verification method. Moreover, the verification results have proven the effectiveness of the method. Then, a test bench for gear fault diagnosis was established to monitor the health of gear with varying pitting degrees. Both experiments and public datasets are able to verify that the proposed method is more accurate than traditional single-sensor fault detection.
## 3 Results
### Metabolic Protein Analysis of the Tear Fluid Samples  
Four metabolic proteins out of 5, namely leptin, insulin, active ghrelin and C-peptide, were present in all of the tear samples assayed (i.e., 100% of subjects) for both visits (Table 2; Supplementary Materials Table S1). While GIP was detected in 70.6% of the samples for V1 and in 52.9% of the samples for V2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**N**} & \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**OSDI**} & \multicolumn{2}{c}{**fTBUT**} & \multicolumn{2}{c}{**Schirmer**} & \multicolumn{2}{c}{**CFS**} & \multirow{2}{*}{**Test Eye**} \\  & & & & **OD** & & **OS** & & **OD** & **OS** & **OD** & **OS** \\ \hline
1 & 38 & M & 15.9 & 6.93 & 16.67 & 26 & 22 & 1 & 2 & OS \\
2 & 26 & M & 9.09 & 15 & 16.34 & 32 & 17 & 3 & 2 & OS \\
3 & 27 & F & 0 & 6.  
Overall, the concentrations of leptin, insulin, GIP, active ghrelin and C-peptide were determined as above the MinDC (Figure 1). Insulin was the most highly expressed metabolic protein (mean \(\pm\) S.D.; 1466.2 \(\pm\) 607.6 pg/mL in V1; 1336 \(\pm\) 408.6 pg/mL in V2), followed by leptin (379.2 \(\pm\) 11.23 pg/mL in V1; 381.5 \(\pm\) 7.87 pg/mL in V2), C-peptide (233.2 \(\pm\) 46.26 pg/mL in V1; 211.9 \(\pm\) 64.18 pg/mL in V2), active ghrelin (126.7 \(\pm\) 28.26 pg/mL in V1, 113.4 \(\pm\) 40.9 pg/mL in V2) and GIP (1.5 \(\pm\) 1.58 pg/mL in V1; 1.08 \(\pm\) 1.35 pg/mL in V2).  
Additionally, we determined the intra-subject CV in the metabolic protein concentrations between V1 and V2 and compared them to the coefficient of variation reported by the manufacturer's values (CVm; Merck Millipore, UK). The CV of the metabolic proteins leptin (1.09%), C-peptide (1.71%) and active ghrelin (3.51%) were low and were within the manufacturer's values. However, the CVs of GIP (60.92%) and insulin (25.62%) were higher than those provided by the assay manufacturer (Table 3).  
Moreover, ICC were determined for all metabolites, and Bland-Altman plots and LoA were used to assess agreement between metabolic tear protein levels among visits. Regarding the ICCs values, Leptin had a strong reproducibility rating, whereas the ICC for the other molecules was poor or low (Table 4).  
There were no correlations between tear fluid metabolic protein concentrations and (1) ocular surface parameters, including NITBUT and TER values (\(p\) > 0.05); and (2) relative humidity and temperature in the examination room (\(p\) > 0.05).
## 3 Methodology  
### Extreme Gradient Boosting (XGBoost)  
To explore the complex regression relationship between the multi-dimensional factors and PC (the target variable), this study adopts the Extreme Gradient Boosting (XGBoost) algorithm [29]. XGBoost is developed based on the Gradient Boosting Decision Tree (GBDT) framework and iteratively optimizes weak regression or classification models, constructing a robust predictive system. By incorporating parallel computing techniques, it efficiently handles large-scale datasets, thereby substantially reducing training time.  
Theoretically, XGBoost exhibits superior performance in both classification and regression tasks. By leveraging the second-order Taylor expansion of the loss function, it captures nonlinear relationships more accurately, thereby enhancing prediction accuracy. This capability has enabled XGBoost to be widely applied across various domains, including financial risk assessment, medical diagnosis, and industrial fault detection [30].  
In this study, XGBoost integrates regularization terms into the objective function optimization process. This not only controls model complexity but also alleviates overfitting  
Figure 3: Correlation matrix plot. (An asterisk (*) indicates a correlation between data points. The greater the number of asterisks, the stronger the correlation between the two variables.)by penalizing excessively large weights, thereby enhancing generalization performance. XGBoost acts as a fast and reliable tool for complex simulations. Its data prediction mechanism constructs an ensemble model through iterative residual fitting, as shown in Equation (1).  
\[Obj^{(r)}=\sum_{i=1}^{m}L(y_{i},\hat{y}_{i}^{(r)})+\sum_{k=1}^{r}\Omega(g_{r}) \tag{1}\]  
In this equation, \(y_{i}\) represents the measured value, \(\hat{y}_{i}^{(r)}\) representing the \(r\)-th prediction result, \(g_{r}\) is the term of the decision tree, \(L(y_{i},\hat{y}_{i}^{(r)})\) representing the loss function, and the regularized term \(\Omega(g_{r})\) is given by Equation (2):  
\[\Omega(g_{r})=\gamma\mathrm{T}+\frac{1}{2}\lambda\sum_{j}^{T}w^{2} \tag{2}\]  
In Equation (2), \(T\) denotes the root node of the decision tree, while \(w\) indicates the weight associated with each root node. The default values for the coefficients \(\lambda\) and \(\gamma\) are set to \(\lambda=1\) and \(\gamma=0\), respectively.  
In practical applications, the performance of the XGBoost regression model is highly dependent on the optimal configuration of key parameters [31]. As shown in Table 1, num_trees, max_depth, and eta act as the core control parameters of the model, and their values directly affect the model's fitting capacity and generalization ability. Although traditional manual parameter tuning can improve model performance to some extent, this approach is constrained by human experience and subjective judgment. It not only tends to introduce bias but also risks, causing the model to converge to a local optimum rather than achieving a globally optimal parameter configuration, thereby impacting the accuracy and reliability of the regression analysis.