esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Analysis of Model Prediction Results  
The predicted and measured values of the model were used to draw the fitting diagram of the predicted result, and the fitting effects of CNN, SVR, CNN-LSTM, and GA-CNN-LSTM were further compared and analyzed.  
The prediction of soil moisture by each model is shown in Figure 6. The prediction accuracy rates of CNN and SVR models in the training set were 82.1% and 85.6%, respectively. The prediction effect of SVR model was better than CNN model, and the accuracy of CNN model after LSTM processing was only improved to 92.1%. However, the accuracy of  
Figure 5: One-dimensional convolutional neural network model structure.  
the CNN-LSTM model optimized by GA was greatly improved, and the accuracy of the prediction set reached 94.5%.  
The prediction of SN by each model is shown in Figure 7. The figure shows that the prediction effects of CNN, SVR, CNN-LSTM, and GA-CNN-LSTM models on SN were weaker than those on soil moisture. Similarly, the CNN-LSTM model optimized by GA had the best accuracy, and the accuracy of the prediction set reached 92.9%.  
Compared with CNN-LSTM and CNN model, the prediction value of the optimized GA-CNN-LSTM model was more closely distributed at about 1:1, and the data-fitting ability and stability were better than those of the CNN-LSTM model. The fitting effect of the training set was better than that of the validation set, and the sample points of the validation set were relatively scattered, indicating that the optimized GA-CNN-LSTM model can be used as an alternative to the prediction of soil OM and moisture.  
PRC shows the relationship between precision and recall. Figure 8 shows the effect of the optimized GA-CNN-LSTM model on precision and recall. Precision and recall can be selected according to the requirements of different soil identification tasks. For some moisture or organic matter with high accuracy requirements, high precision should be selected as far as possible. If one wants to identify as many specific soil moisture and organic matter areas as possible, then one should choose high recall. For example, high precision can be selected in the discrimination of identifying drought or excessive fertilizer  
Figure 6: Measured and predicted values of soil moisture content.
## 2 Materials and Methods
### Acquiring and Elaboration Unit  
The microcontroller board used as the acquisition, control and calculation unit is based on a 32-bit PIC32 MX (Microchip Technology Inc., Chandler, AZ, USA) architecture (120 MHz/150 DMIPS), inserted in an Arduino platform, maintaining its simplicity of use but enormously increasing its potential thanks to the 32-bit microcontroller and additional components. The strengths of the board (called Fishino32, designed by dr. [PERSON] [19]) are the possibility of being powered by a LiPo battery, integrates a dedicated audio codec, a flash memory of 512 KB which, combined with a RAM of 128 KB, and an A/D (analog to digital) converter 10-bit 1 Msps rate, allows the development of extremely  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Camera Height from the Floor (m)** & **X Max (m)** & **Y Max (m)** \\ \hline
1 & 0.7 & 0.5 \\ \hline
2 & 1.4 & 1 \\ \hline
3 & 2 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The range of marker visibility on the measurement scenario.  
Figure 4: The IR positioning camera (courtesy of www.dfrobot.com/product-1088.html, accessed on 27 November 2020).  
complex applications. On the card (68 \(\times\) 53 mm) there are also a Wi-Fi module, a slot for microSD, and a Real Time Clock and Calendar (Figure 5a).  
A shield board (Figure 5b) is positioned on the Fishino32 board containing the few hardware components to correctly interface the signals coming from the sensors (force sensor, IR camera, liquid crystal display (LCD)) and to produce the signals necessary to correctly actuate the solenoid (a direct current (DC)-DC converter and an opto-isolator).
## 1 Introduction  
The coupling of plasma response in metamaterials can mimic an electromagnetically induced transparency (EIT) effect, the physical mechanism of which is explained by plasma resonance, so it is called plasmon induced transparency (PIT) [1; 2; 3]. In the terahertz (THz) regime, the electromagnetic properties accompanying PIT effect, such as wave velocity delay and high refractive index sensitivity, mean that PIT metamaterials can be effectively used in a terahertz buffer [4] and refractive index sensor [5]. However, it is worth noting that most of these devices are passive, and people expect to realize more active and controllable terahertz PIT devices on this basis. In recent years, controlled active mediums, integrated into resonator structures, are reported for the active modulation of PIT metamaterials [6; 7; 8; 9]. Currently, graphene, as a novel active material, has attracted extensive attention of researchers.  
As a typical representative of two-dimensional materials, graphene has been widely studied in many fields, such as physics, chemistry, energy, and materials, thanks to its unique mechanical, thermal, and electromagnetic properties [10; 11]. In the terahertz band, the conductivity of graphene depends on the intraband transition. By moving the Fermi energy through electrostatic gating and chemical doping, the conductivity of graphene can be changed, and then its electromagnetic response to terahertz wave can be adjusted flexibly. Based on the remarkable tunability of graphene conductivity in the terahertz band, many terahertz control devices are proposed with graphene as the active material [12; 13; 14; 15; 16]. The combination of graphene and metallic metamaterials, and the design of metamaterials based on a patterned graphene structure can be the basis of the construction of tunable PIT metamaterials [17; 18; 19; 20; 21; 22; 23]. However, it is difficult to tune the surface conductivity of patterned graphene resonators, which limits the scalability of these tunable approaches in practical application.  
In this article, an active control of the PIT resonance in a terahertz hybrid metal-graphene metamaterial is proposed. The hybrid PIT metamaterial consists of a pair of classic split-ring resonators(SRRs) array, in which an unpatterned graphene ribbon is laid directly under the bright element of the metallic metamaterial. By manipulating the electromagnetic response of the bright mode resonator via shifting the Fermi energy of graphene, the tunable PIT behavior can be obtained in the hybrid structure. The main advantage over the common dark mode control scheme is more spectrum range for actively controlling the terahertz waves, which is modulated by the two resonance dips beside the transparency peak. Thus, this work has opened up a new avenue for designing ultrafast real-time control functional devices in terahertz regime.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 6 Conclusions  
As the frequency spectrum gets more crowded and the demand for wireless communication increases, nonlinear distortions generated by passive elements become more relevant. Commercially-available nonlinear measurement systems are expensive and either lack the dynamic range needed to make the sensitive measurements or are fixed in frequency and do not provide the flexibility required. Robust feed-forward systems have been constructed that provide both the flexibility and dynamic range needed to made the sensitive measurements, but these systems are complex and require iterative tuning and optimization algorithms. This paper has presented an alternate method for characterizing nonlinear distortion from weakly nonlinear devices. The alternate method uses absorptive diplexers to separate the harmonic response from the high power probe signal, thus increasing the system's dynamic range. The method is also capable of measuring the reflected harmonic from devices. The method is relatively simple to implement and it is cost effective for measuring weak nonlinear responses of circuit elements. This paper has presented an implementation of this method with the capability of achieving 175-dBc dynamic range over a 22% bandwidth. The system is capable of producing over 10 W (+40 dBm) of probe signal power while measuring second harmonics generated by a DUT as low as \(-135\) dBm, resulting in the 175 dBc dynamic range. This paper has outlined system requirements that dictate the dynamic range of the harmonic measurement system and provided guidance to achieve the required dynamic range. Results are presented on passive RF circuit elements that are not traditionally thought to exhibit nonlinearities, which require a high dynamic range, nonlinear measurement system.  
All authors equally conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] provided the theoretical support; K.D.S. contributed the logistics and measurement facilities; all authors analyzed the data; [PERSON] wrote the first draft of the paper, and all authors contributed to its final form.  
This research was supported by U.S. Army Research Office (ARO) contract #W911 NF-12-1-0305 through a subcontract from Delaware State University (DSU).  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 2 Analysis of Bending Shape Based on Tendon Insertion Paths and Applied Tensions
### Mathematical Modeling
These variables can be adjusted through modifications in the mechanical design of the joint. In Figure 3, \(r_{s,i}\), which assists with bending, is similar to \(r_{m1,i}\) in the way it is calculated and has been omitted for brevity. Finally, the total potential energy \(\Pi\) resulting from the bending of the system due to the tensions applied to the tendons can be expressed as follows:  
\[\Pi=W_{m1}+W_{m2}+W_{s} \tag{8}\]  
Figure 2: Overtube with \(n\) rolling joints bent into different shapes due to the tensions applied to the inserted tendons. Based on the principle of minimum total potential energy, the angle \(\theta_{i}\) between each pair of joints that determine the bending shape of the system can be calculated using the following equation:  
\[\frac{\partial}{\partial\theta_{i}}\Pi=0,\ (i=1,2,\cdots,n-1) \tag{9}\]  
The rotational centers \(O_{i}\) of the joints can be determined from the angle \(\theta_{i}\) and the forward kinematics of the overtube. Among the equations, the main strategy of the proposed method is to adjust the values \(d_{s,i}\), which determine the path of the shaping tendon, to achieve the desired bending shape of the overtube.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
###### Abstract  
Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06\({}^{\circ}\) and 0.01\({}^{\circ}\), respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications.  
Pi-GRU model; multisource data fusion; wellbore trajectory prediction; directional drilling in coal mines; TensorFlow +
Footnote †: journal: Applied sciences  
Article
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 3 Results  
### Quantitative Evaluation  
In order to assess the synthesis results quantitatively we calculate the Mean Squared Error (\(MSE=\frac{\Sigma(pCT-CT)^{2}}{V}\)), where \(V\) denotes the total number of non-zero voxels) and Mean Absolute Error (\(MAE=\frac{\sum|pCT-CT|}{V}\)) between the synthesised pCT and the ground truth CT (Table 1). It can be seen that the MultiRes method without uncertainty performs the best in terms of MAE averaged across all inference subjects. The lowest MSE is achieved by the MultiRes\({}_{unc}\) network. A paired t-test was performed to show that the results of the MultiRes models are significantly better (\(p\)-value \(<\) 0.05) compared to the single resolution baseline methods. The MultiRes networks have a higher number of trainable variables than HighRes3 DNet and DBR, however, the afforded performance increase compensates for this. Results from our preliminary work show that training MultiRes and MultiRes\({}_{unc}\) with a single input channel leads to slightly superior results for MultiRes(MAE: 62.42 HU \(\pm\) 6.8 HU; MSE: 11,347.16 HU\({}^{2}\)\(\pm\) 3089.12 HU\({}^{2}\)) while the performance of MultiRes\({}_{unc}\) is inferior (MAE: 80.14 HU \(\pm\) 15.81 HU; MSE: 14,113.83 HU\({}^{2}\)\(\pm\) 3668.79 HU\({}^{2}\)).  
The results of the additional validation on the brain dataset are demonstrated in Table 2. It can be observed that all deep learning based methods outperform the more traditional multi-atlas propagation method. For the deep learning based methods, similar results are achieved compared to the results on the whole-body dataset. Both MultiRes models significantly outperform all baseline methods (_p_-value \(<\) 0.05) in MAE and MSE. When adding uncertainty to the MultiRes model, the performance is increased compared to its uncertainty unaware counterpart, suggesting that the network compensates for more uncertain regions during optimisation.
## 3 Design of Isl Network for IoT
### Network Topology  
Typically, the network topology is determined not only by the orbital configuration, but also by the network operating mode, desired QoS, and available resource differences between the nodes. The orbital configuration is the most heavily weighted factor affecting the basic network topologies. The simplest configuration consists of only very few S/Cs placed into the same orbital plane with short intersatellite distances, as in the case of Precise Formation Flying (PFF). In this case, the communication graph is fully connected. It can, thus, be approximated with a fully connected mesh (see Figure 7b) or, if one satellite is used as a base station (sometimes called the master), with a centralized star topology (see Figure 7a). As each pair of nodes can be interconnected directly or through a master S/C, no advanced routing techniques are necessary. In the case of the star topology, the channel synchronization can be organized by the master S/C. With a fully connected scheme, either a satellite with a master function is chosen (arbitrarily or by the ground station), or a random access protocol (e.g., pure ALOHA) can be used.  
Another common occurence is when the satellites are placed into the same orbital plane with separation distances approximately equal to the maximal possible communication range (i.e., a line topology; see Figure 7c). The most common and difficult case is the meshed topology (see Figure 7d), where the graphical representation of the network can change over time. The control algorithm of this topology is relatively difficult, because no centralized master can control the routing, and multi-hopping is necessary. However, such an algorithm has an important feature: It can be used to control each of the other above mentioned topologies, albeit not always with the optimal performance.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 2
#### 4.2.3 Comparison of Different Blade Sides  
In addition, fingerprints were analysed for sensors mounted on different sides of the same blade, i.e., pressure side (\(S14\)) and suction side (\(S4\)) of Blade \(A\) of Test Turbine 2 (see Figure 15). Sensors were mounted at the same radial position at the blade root and at 50% distance to the trailing edge. The comparison of fingerprints shows that fingerprints overlap for most Type 2 vibrations. This indicates that the vibrational characteristic of the blade is very similar on both sides of the blade in the root section.  
Differences can be noted for two vibrations: An additional vibration was detected by Sensor \(S14\) (fifth from the top) and covers a relatively large bandwidth in comparison to other vibrations in the same frequency range. This suggests that the vibration is not well-localised and is either nearly hidden in noise or falsely detected due to increased noise levels. In contrast, the second additional vibration detected by Sensor \(S4\) (first from the top) also shows a large bandwidth; however, it corresponds to other vibrations in the high frequency range, e.g., see Sensors \(S27\) and \(S29\). Hence, monitoring both sides of the blade is promising for evaluating changes in the stiffness of the blade and identifying weak spots for damage detection and prevention.  
Figure 15: (**Left**) Fingerprint comparison for sensors on pressure side (S14) and suction side (\(S04\)) on the same blade and radial position (Blade \(A\), Test Turbine 2). (**Right**) Comparison for tip sensors on Test Turbine 1 (\(S01\)) and Test Turbine 2 (\(S15\)). Solid lines represent vibrations of a single sensor, dotted multi-coloured lines represent jointly occurring vibrations. Error bars show locations of associated peaks with their length representing peak prominence.
## 5 Conclusions and Future Work  
In this work, we proposed an adaptive hybrid approach for routing in WSNs, called Multi-MAF. Multi-MAF aims at autonomously adapting the routing strategy to improve the energy efficiency, when the network presents different traffic profiles. An event detection model was proposed to support the adaptive decision behind Multi-MAF. As a result, we showed that when operating conditions of the network are unpredictable, adaptive approaches, such as Multi-MAF, are more suitable than a fixed routing strategy, leading to resource savings.  
To the best of our knowledge, there is no other solution for WSNs considering hybrid adaptive behavior for routing. Although this approach was first introduced in the MANET domain (see Section 2), this work differs from those, since we consider the traffic characteristic of WSNs to estimate event detections, and reduce the impact of reactive path discovery by choosing a proactive strategy. This is an interesting approach for resource-constrained networks operating with dynamic traffic profiles. In addition, Multi-MAF considers a simple reactive and a simple proactive routing solution for WSNs.  
As future work, we intend to improve both the reactive and proactive components of Multi-MAF. Recent contributions--such as BVR [14], VRR [15] and S4 [16]--present some optimizations to reduce the cost of routing updates. Consequently, these solutions are good candidates for composing the proactive component of Multi-MAF. Regarding the reactive component, the LRDE [19] solution proposes a heuristic to reduce the energy cost of path discovery. Thus, it could also be used as the reactive component of Multi-MAF. In any case, the adaptive model of Multi-MAF should be adjusted to determine the better value of event-detection estimation for changing the routing strategy. We intend to evaluate other techniques for event estimation, and evaluate the impact on our strategy when we have other functions, such as data aggregation and node scheduling. We also plan to implement and evaluate this solution in real sensor platforms and scenarios. The proposed approach can be used to the construction of new hybrid adaptive solutions that include other classes of routing algorithms, for instance, geographical and hierarchical algorithms.