esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.  
deep learning; automatic modulation recognition; spatial transformer networks; signal processing +
Footnote †: journal: Applied
## 2 Materials and Methods  
### Overall Concept of THz Undulator  
The PolFEL device will be a superradiant source of a 0.5 THz-5 THz ranged radiation, based on the superconducting electron accelerator, capable of providing electrons of energy \(E_{e}\) equal to 54 MeV, and on a planar, permanent magnets, and tunable gap undulator [31; 32]. The final design of the THz undulator is presented in Figure 2. It is designed for the electron energy range between 20 and 70 MeV. Magnetic arrays are mounted in movable jaws armed with a system of adjustable holders. The jaws are driven by the servomotors through leadscrews and are additionally stabilized by means of linear guides. Their positions and motions are controlled using linear encoders, in a closed-loop control system. The entire system is supported with a stiff frame and foot settled on the experimental hall's floor. The electron guiding pipe is detached from the main body of the undulator and placed between the movable girders. The magnetic array contains 8 periods of magnets. Each period comprises 4 blocks of magnets with the dimensions 100 \(\times\) 100 \(\times\) 39.9 mm (width \(\times\) height \(\times\) depth). Due to the manufacturing process, a single magnet block is built of two stacked and glued NdFeB magnets, of N42 UH grade. At both array ends, the thicknesses of the magnets are reduced to 34, %, and 4 of the nominal thickness. Therefore, the disturbances of the magnetic field that influence the electron beam are minimized. Each of the arrays consists of 38 magnet blocks; therefore, the total length of the girder is 1560 mm long. The working range of the gap between both arrays varies between 100 mm and 200 mm, which corresponds to the \(B_{0}\) = 0.37 T field. Additionally, the gap between the arrays can be enlarged up to 600 mm, to diminish the \(B_{0}\) field below the earth's background level.  
An example of the magnetic structure used for the THz undulator, with the field vector distribution for particular magnets, is presented in Figure 3. The vertical magnets are oriented in the same direction on both girders, whereas horizontal magnets are oriented in the opposite direction to \"close\" the magnetic loop.  
Figure 3: Exemplary part of magnetic array with two periods of magnets with the direction of magnetization.  
Figure 2: General concept of THz PolFEL undulator. In the central part, jaws with magnetic arrays are visible.
## 3 Methods
### Crop Yield Detrending  
In this study, the weights of the CDI values were optimized using crop yield as the target variable. When comparing drought indices with yield, it is essential to remove the trend component from the original series. This trend is often associated with technological advancements, soil improvements, and fertilization effects in agriculture [88]. Common methods for detrending include polynomial fitting (linear or cubic) [89], moving average [90], and locally weighted regression model (LOWESS) [91; 92]. Due to certain assumptions associated with linear trends [93], the difficulties in using cubic splines with small datasets (<60) and their tendency to remove components other than the trend [94], this study opted for the nonparametric LOWESS method [94]. A standardization process was carried out to compare the detrended series with standardized series such as SPEI and scPDSI, and yield anomalies were obtained. The standardization process is given in Equation (6).  
\[\textit{detrend~{}yield}_{std}=\frac{\textit{detrend}_{i}-\overline{\textit{ detrend}}}{\sigma_{detrend}} \tag{6}\]  
where \(\textit{detrend}_{i}\) represents the detrended yield in the i-th month, \(\overline{\textit{detrend}}\) represents the temporal average of the detrended yield in a district, and \(\sigma_{detrend}\) represents the standard deviation of the detrended yield in a district.
## 3 Method Implementation
### Digital Meter Reading  
The process of digital meter reading encompasses image preprocessing, character segmentation, and digital recognition. This paper employs OpenCV for image preprocessing and character segmentation, followed by the application of the SVM module to achieve digital meter reading. The specific process is outlined as follows and illustrated in Figure 15:  
* Dataset preparation: Collect digital meter images from various environments to ensure that the images are clear and informative.
* Image preprocessing using OpenCV: Gaussian filtering, graying, binarization, tilt correction, character segmentation.
* Train SVM model: In this paper, the digital meter readings are categorized into multiple classes, with each class corresponding to a specific digital reading.
* Number recognition: A trained SVM model is employed to predict the readings of new digital meter images. First, the image is preprocessed and features are extracted in the same manner as in the training set, followed by classification using the SVM model to obtain the predicted digital readings.  
Figure 14: Projection line chart.
## 3 Results and Discussion
### Attention Behavior Analysis: Grad-CAM Visualization  
To further investigate the decision-making behavior of each model and interpret their differences in classification performance, Grad-CAM visualizations were generated using the output of the final block of layer 3 in the ResNet50 backbone. This layer was chosen as it captures mid-level spatial representations, balancing local and global visual features. Four representative samples are shown in Figure 6.  
In the same-domain scenarios, Model 1 achieved the highest accuracy (99.65%), while Model 2 followed closely (97.75%). Grad-CAM results confirm that both models consistently attended to morphologically meaningful regions, especially the central area of the lettuce leaves. Model 2 exhibited slightly more concentrated attention due to the visual simplicity of segmented images, but this marginal gain in efficiency did not surpass the high accuracy of Model 1, which demonstrated superior generalization on raw images with richer contextual information.  
A notable performance drop occurred in the cross-domain evaluations. Model 1-CDE maintained a reasonably high accuracy of 82.77%, indicating its moderate robustness when exposed to preprocessed images despite being trained on original ones. Grad-CAM in this case showed partially diffused attention, yet still captured key plant areas. In contrast, Model 2-CDE's accuracy fell dramatically to 34.15%, and its Grad-CAM visualization revealed a breakdown in localization--attention was misdirected toward irrelevant background regions, such as grid patterns and lighting areas, rather than the plant itself.  
These findings suggest that while segmentation can offer cleaner inputs for training, models trained exclusively on preprocessed images (Model 2) may overfit to such uniform visual conditions and fail to generalize when faced with real-world variability. Conversely, models trained on original images (Model 1) demonstrate better adaptability and spatial awareness across domains, leading to more stable performance and interpretable attention behavior in deployment settings.  
Figure 6: Grad-CAM visualizations for representative test images under training–testing scenarios: (**a**) Model 1; (**b**) Model 2; (**c**) Model 1-CDE (Model 1 under cross-domain evaluation); (**d**) Model 2-CDE (Model 2 under cross-domain evaluation). The heatmaps indicate regions contributing to the model’s prediction, where red represents higher activation (stronger attention) and blue represents lower activation.
## 3 PV-Grid Tied Charging Stations for EVs and PHEVs
### Structure of the System  
The typical structure for a PV/grid-tied charging system for EV/PHEV is shown in Figure 4. The structure is interfaced through three power converters; in the PV stage, there is a DC-DC converter with maximum power point tracking (MPPT), which is used to step up the voltage supplied by the PV array and at the same time provide the maximum power to the common DC bus; a bidirectional AC-DC inverter, which is part of the AC grid stage, aims to control the power factor and the current injected into the grid, and regulate the DC voltage to adapt to that required by the common DC bus [34]; and a bidirectional DC-DC converter, which belongs to the charger stage, serves as an interface for the EV/PHEV, controlling the DC charging current. All of these components are connected to the common DC bus, which has a voltage range from 200 VDC to 600 VDC, according to the SAE J1772 standard reviewed on Section 2.1[13]. A central controller is also required in the system to establish the power  
Figure 3: Conventional PV structures. (**a**) Centralized structure; (**b**) string structure; (**c**) module structure [31].  
flow and activation of the power converters based on different constraints, e.g., minimum charging cost, maximum profit, etc. [13; 35; 36; 37].  
The use of a bidirectional inverter in the AC grid stage, as shown in Figure 4, will allow the transfer of excess power from the PV array to the AC grid throughout the sunny hours of the day. Additional, with this proposed system, the flow of power from the EV battery to the grid (V2G) will be allowed. During low solar irradiation, the battery can draw energy from the grid. In order to facilitate the inverter and rectification modes of operation in the PV/grid-tied charging system, a bidirectional inverter is used [38; 39; 40; 41].  
To interconnect all the stages, the power converters must guarantee some characteristics to suit the system. For example, according to the grid needs, the power converters must provide a high power factor, with zero distortion and galvanic isolation to protect both, the grid and the EV [17]. As stated in the literature, this galvanic isolation can be implemented on the front-end of the AC-DC inverter through a low-frequency transformer. Another option to implement the isolation can be using an isolated DC-DC power converter at the charger stage by using a high frequency transformer [17; 34].  
Furthermore, at the charging stage, a power converter is required to control the charging power of the EV battery, so that the battery life can be maximized. It is also worth noting that the converters selected for this application must have high efficiency since high power is transferred to and from the common DC bus. [17].
## 3 Results
### Evaluation of Breeding Potential of RppSLN  
To evaluate the breeding potential of _RppSLN_ in hybrids, the resistant locus _RppSLN_ in N531_R was crossed with the elite parental lines of maize Zhengdan958 (C7-2\(\times\)Z58) and the susceptible inbred lines F35 and M35 to construct hybrids. Similarly, the susceptible locus within N531_S was used to construct hybrids with the susceptible inbred lines C7-2, Z58, F35, and M35. In the field, the hybrids carrying _RppSLN_ showed more resistance to SCR than the susceptible hybrids (Figure 6a,b). We investigated the yield-related traits. The results show that the _RppSLN_ gene enhanced maize resistance against SCR and increased ear weight by 9.98%-27.31%, kernel weight per ear by 10.10%-26.25%, and hundred-kernel weight by 4.03%-17.03% (Figure 6c). Overall, _RppSLN_ has important production applications for reducing yield losses under SCR conditions and contributed to a yield increase.  
Figure 5: Physiological and biochemical characteristics of N531_R and N531_S at 0 h, 12 h, 24 h, 48 h, and 96 h post-inoculation with SCR. (**–d**) The analysis of leaf samples at 0 h, 12 h, 24 h, 48 h, and 96 h post-inoculation is included in the study, including the measurement of malondialdehydedehyde (MDA), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), water-soluble sugar (WSS), and proline content. (**,f**) The analysis of defense enzyme activity, including catalase (CAT) and peroxidase (POD). For (**–f**), the values are presented as the means \(\pm\) standard deviations of the data. **\(p<0.01\); ***\(p<0.001\); ns, non-significant (Student’s \(t\)-test).  
Figure 6: _RppSLN_ enhances the yields of maize hybrids under the natural infection of SCR in the field. (**) The phenotypes of the hybrids C7-2, Z58, F35, M35 with N531_R, and N531_S under natural infection with SCR in the field. Scale bars: 30 cm. (**) The ear phenotypes of the field hybrids C7-2\(\times\)N531_R, C7-2\(\times\)N531_S, Z58\(\times\)N531_R, Z58\(\times\)N531_S, F35\(\times\)N531_R, F35\(\times\)N531_S, M35\(\times\)N531_R, and M35\(\times\)N531_S under conditions of SCR. Scale bars: 5 cm. (**) The ear weight, single-ear-grain weight, and hundred-grain weight of the hybrids C7-2\(\times\)N531_R, C7-2\(\times\)N531_S, Z58\(\times\)N531_R, Z58\(\times\)N531_S, F35\(\times\)N531_R, F35\(\times\)N531_S, M35\(\times\)N531_R, and M35\(\times\)N531_S. The values are presented as the means \(\pm\) standard deviations of the data. **\(p<0.01\) (Student’s \(t\)-test).
## 2 Materials and Methods
### Classification of EN Events  
Using the SST data, the Nino 3.4 index can be obtained by calculating the anomaly of the mean SST relative to the climate mean value (1979-2022) in the Nino 3.4 region (5\({}^{\circ}\) S-5\({}^{\circ}\) N, 120\({}^{\circ}\) W-170\({}^{\circ}\) W). An EN event occurs when the three-month moving average of the Nino 3.4 index, consistent with the length of the averaging window used in the Oceanic Nino Index (ONI, [[https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php](https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php)]([https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php](https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php)), accessed on 1 November 2023), exceeds 0.5 \({}^{\circ}\)C for five consecutive months. The statistical time range is from 1979 to 2022 for a total of 44 years. The EN events are classified into eastern (EP) and central (CP) types according to the spatial pattern of SST anomalies of EN events, as well as into spring (SP) and summer (SU) types according to the established time. The specific classification criteria are as follows [48-50]:
## 3 Results
### Parameter Sensitivity Analysis  
Regarding DGA, six parameters are subject to investigation, namely, \(\lambda\), \(\alpha\), \(\beta\), \(k\), \(m\), and \(\gamma\). Based on our experiments and the experience of others, it has been observed that setting the nearest neighbor parameter \(k\) to either 3 or 5 yields superior results. In this study, we set the value of k to 5. As for the learning rate \(\gamma\), we make adjustments based on the observed convergence effect of the loss function. The remaining parameters are obtained by searching from \(\left\{10^{-3},\;10^{-2},\;10^{-1},\;1,\;10^{1},\;10^{2},\;10^{3}\right\}\). The performance of the ACC performance on six datasets, namely COIL20, Isolet, ORL, WarpAR10P, warpPIE10P, and colon, is reported in Figures 8-11, taking into account the limited space available.  
Figure 8: Parameter sensitivity in relation to ACC for the parameter m (\(\lambda=0.01\), \(\alpha=1\), \(\beta=1\)).  
Figure 7: Convergence curves of the objective function value.  
Figure 10: Parameter sensitivity in relation to ACC for the parameter \(\beta\) (\(\lambda=0.01\), \(\alpha=1\), m = 5).  
Figure 9: Parameter sensitivity in relation to ACC for the parameter \(\lambda\) (\(\beta=1\), \(\alpha=\)1, m = 5).  
In Figure 8, it can be observed that as the hidden layer m changes, the clustering accuracy fluctuates to some extent. But, in general, as the variable m increases, there is a decrease in the value of ACC. A smaller m means that the lower the dimensionality space of the mapping, the more sufficient the measurement of the significance of each feature. Hence. In the subsequent experiments, m will be held constant at 5.  
Additionally, the performance of DGA varies under the influence of parameters, as illustrated in Figures 9-11. As can be deduced from the figure, the experimental performance of the model is significantly influenced by these three parameters, and the sensitivity of the parameters varies across different datasets.  
The parameters \(\beta\) and \(\alpha\) exhibit greater fluctuations across each dataset. Overall, the clustering accuracy initially increases and then subsequently decreases as the values of these two parameters increase. A lower number indicates inadequate preservation of data and feature structures, whereas a higher value may overfit the manifold structure of the data. Overall, there is a decrease in the number of features that correspond to the optimal value of clustering accuracy with the increase in parameter values. Increasing the parameters \(\beta\) and \(\alpha\) means that more of the manifold structure of the data and features are preserved. This allows for the selection of fewer features that can contain more complete information.  
As a result, parameter selection is critical for our approach.
## 1 Introduction
### Contributions  
The contributions of the proposed scheme can be described as follows:  
* The proposal of a trust-based multipath QoS routing algorithm for enhancing the QoS metrics in tactical ad-hoc networks.
* The trustworthiness and QoS guaranteed owing to the deployment of multipath routing algorithms to meet the mission-critical data requirements.
* The use of a flexible threshold method that considers data usage and link bandwidth for enhancing malicious node detection.  
The remainder of this paper is organized as follows. In Section 2, we cover related work on trustworthiness estimation and trust-based routing for mobile ad-hoc networks and wireless sensor networks. Section 3 describes the proposed scheme in detail, and Section 4 provides a performance evaluation via comprehensive simulation studies. Finally, we conclude our work in Section 5.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### MRS Middleware Layer Functions, Services, and Requirements  
The middleware layer can be used to provide important services and functions between the lower layers and the application layer in MRS system architectures. These services and functions need to satisfy the requirements for different types of tasks and services that can be considered in MRS systems. The following are examples of such services:  
* R2R communication.
* Robots acting as relay nodes.
* Robots acting as gateway nodes (R2I links).
* Robots assisting in sensing operations.
* Robot providing data storage services.  
Figure 3: The networking protocols at the various layers of the nodes using robot-to-robot (R2R) communication.  
Robots providing processing services.
* Implementation of distributed or centralized control of robot teams.  
Additional ones can be added depending on the particular MRS application that is being considered.
## 4 Results and Discussion
### Statistical Calculation
#### 4.3.1 Total Weight and Load Calculation  
With a system diameter of \(\mathcal{O}\) 500 mm, inner wall thickness of 1.5 mm, rock wool thickness of 80 mm, and an outer wall thickness of 0.5 mm, the total weight of the system was determined as \(m=400\) kg.  
The total static load\(f_{g}\) can be obtained by multiplying the weight \(m\) and the gravitational acceleration \(g\), which was found based on Figure 12. The total static load\(f_{g}\) is given in Equation (10).  
\[f_{g}=m\cdot g \tag{10}\]  
Here, with \(m=400\) kg and \(g=9.81\) m/s\({}^{2}\),\(f_{g}\) was found to be 3.924 N.  
(Note: The weight and impact force on the bottom elbow were neglected in this force calculation as a separate support system was designed for that part.)
## 4 Research Opportunities in O-RAN
### Network Management  
As mobile networks become increasingly complex, there is a growing need for advanced solutions to effectively manage network operations. Although research has explored the application of ML techniques for automating network management, further efforts are required to enhance various aspects of network management. O-RAN, with its open architecture, presents a convenient platform for leveraging ML techniques in network management. The flexibility and openness of O-RAN enable the seamless integration of ML-based approaches, providing opportunities to enhance and optimize various aspects of network management.  
For supporting different network slices (e.g., eMBB, URLLC, and mMTC slices), the efficient placement of VNFs of slices onto the network infrastructure is crucial. The necessity of the optimization of the functional split of individual RAN slices between CU, DU, and RU entities, based on the functional split options defined by 3 GPP, is studied in [81]. In addition, the optimal placement of RAN slices in a multi-tier 5G Open RAN architecture, including multi-tier aggregation sites, has been emphasized in [81] by demonstrating that a flexible functional split can lead to enhanced utilization of physical network resources. Considering the various types of data available, such as network traffic patterns, resource usage availability, and future resource demand, these can be leveraged to determine the VNF split for each network slice. By harnessing the power of ML, characterized by its remarkable ability to forecast future patterns and make data-driven decisions, the flexible function split can be dynamically adjusted in response to evolving network environments, thereby optimizing target objectives encompassing resource utilization, data rate, power efficiency, and cost-effectiveness.
## 3 Results and Discussion
### Non-isothermal Flow  
Looking at Figure 18, which compares the isothermal to the non-isothermal simulation of the modified airfoil, there is little difference in \(C_{L}\), \(C_{D}\), and consequently their ratio. A similar result is observed in Figure 19, showing the isothermal versus non-isothermal results for the NACA 0012. Here, the performance is also identical. The Mach number of the flow is largely below 0.3; as such, the flow can be regarded as incompressible and the effect of temperature is small. Based on these results, the use of an isothermal flow model is valid. This is not surprising, as the non-isothermal NACA 0012 simulation already showed good agreement with the experimental \(C_{L}\) results.