esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Development of Machine Learning Model
#### 2.3.2 Model Construction  
As deep learning algorithms, some models were created using a CNN, as well as KNN, RF, SVM, and logistic regression (LR); a CNN was adopted as it had the highest validation accuracy. The CNN was implemented using Python, TensorFlow (ver. 2.18.0), and the Keras (ver. 3.9) library. The CNN architecture was simple and was designed with reference to the LeNet-5 architecture. Specifically, multiple Conv2D and MaxPooling2D were used, and the data were flattened in a Flatten layer and connected to a fully connected layer.  
* Convolutional layers: The first convolutional layer used 16 filters (3 \(\times\) 3 kernel size) and applied the ReLU function as an activation function for feature extraction. This process extracts the local patterns from the image. L2 regularization was also applied, and the weights were penalized to suppress the overfitting of the model and improve stability;  
Figure 2: Individual scale dataset: (**a**) [PERSON], (**b**) [PERSON], and (**c**) [PERSON].  
* Pooling layers: A 2 \(\times\) 2 max pooling was performed to shrink the feature map and retain important information. Consequently, the computational load was reduced, and overfitting was suppressed;
* Additional convolutional and pooling layers: Convolutional layers with 32 and 64 filters were combined with subsequent pooling layers to extract higher-level features. L2 regularization was also applied to these convolutional layers;
* Fully connected layers: After converting the feature map to one dimension in the Flatten layer, it was passed through a fully connected layer of 256 units for the final classification. L2 regularization was also applied to this fully connected layer;
* Dropout layer: Randomly invalidating 30% (Dropout = 0.3) output of the fully connected layer suppressed overfitting and improved robustness;
* Output layer: The probability of each variety was calculated using the SoftMax activation function. This outputs the probability that the sample belongs to a particular variety, and the final prediction is made.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Receiver Design
#### 2.4.4 Iterative SIC  
As the name suggests, iterative SIC is based on multiple iterations of symbol detection to reduce interference among users. By exploiting the prior information about interference for estimation, the next iteration improves the performance of the receiver at the cost of processing delay. Thus, the complexity of the iterative SIC is directly proportional to the number of iterations. However, the drawback of iterative SIC is that the performance of the current iteration is dependent on the information coming from the previous iteration; therefore, any inaccuracy may result in the form of an error and can propagate throughout the detection process.
[36] proposed a new method for classification problems. Other techniques can be used to monitor mechanical systems [37; 38; 39; 40]. Hybrid Electric Propulsion Systems (HEPS) for aircraft applications are a rapidly developing field, with a lot of studies focusing on energy efficiency [41; 42; 43] and emission reduction [44; 45; 46]. The monitoring and diagnostics of HEPS is of fundamental importance. There have been several studies in the literature that address the monitoring of aircraft propulsion systems, but none have focused on accelerometric signals as input data to evaluate their performance. [PERSON] et al. [47] developed a Convolutional Neural Network based approach to classify faults in an aircraft engine (AE). [PERSON] et al. [48] proposed an overview on AE performance monitoring to estimate aircraft parameters. [PERSON] et al. [49] analyzed the bearing diagnostics of an AE during in transitory conditions using accelerometric signals. [PERSON] et al. [50] proposed a real-time model-based monitoring technique for the early fault diagnosis of an AE. Adaptive algorithms for AE performance monitoring were proposed in [51; 52]. An automatic method for fault detection in satellites and spacecraft systems was presented in [53]. In this paper, a novel technique to monitor the torque/speed equilibrium point of an aircraft HEPS through accelerometric signals combined with a classification Feedforward Neural Network (FNN) is presented. The FNN was trained using the inputs obtained by the processed accelerometric signals acquired from an accelerometer mounted on the load-bearing structure of a HEPS. The FNN output was the HEPS' operating mode; the ICE completely balanced the propeller resistance torque, the Hybrid Motor and Hybrid Generator modes in which the Electric Machine (EM) operated as a motor and as a generator, respectively. The classification results were used to realize a logical vector. From this vector, an RMS value was calculated. The experimental results demonstrated the index's capability to monitor the equilibrium point of a HEPS. The focus of this research was training an FNN to accurately classify the three operating modes of a HEPS within torque curve slope variation limits attributed to changes in air properties. If the classification results are incorrect, this implies that the torque/speed equilibrium point falls outside the set limits, and this can be detected using a statistical index calculated based on classification results. The paper is structured as follows: in Section 2, the HEPS and the tests are described; in Section 3, the workflow is reported; in Section 4, the FNN model and the RMS are briefly explained; and in Section 5, the results are discussed.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 5 Options for a BSI-Compliant Integration of EMS into Smart Metering Systems
### Partial Integration Using the SMGW Solely for the Transparent Data Tunnel  
To not be bound to SMGW-related restrictions regarding meter measurements, the measurements can be collected locally directly from the _Smart Meters_ by using a measurement collector. Such a measurement collector can be connected to the _Smart Meter_ directly and independently from the SMGW. The measurement collector may be a standalone component, or it may be integrated into the local EMS. Hence, in these integration options, the SMGW--if used at all--is not involved in the collection of meter measurements for energy management. Instead, it acts solely as a gateway providing a transparent data tunnel. This data tunnel can be used bidirectionally to transmit measurements and local information to the remote EMS in the WAN and control signals to the CLSs and the local EMS in the HAN. The motivation for these integration options is to be free from the SMGW-related restrictions regarding meter measurements. However, while this potentially increases the range of possibilities, it potentially also decrease the quality and reliability of the collected data.  
Analogously to Figure 2, Figure 3 shows three options for integrating an EMS by solely using the SMGW's transparent data tunnel:  
1. [label=_Option 2a_]
2. describes the direct access to the meter measurements via a measurement collector that utilizes a _Smart Meter_ interface. These measurements can be used by an EMS to control one or more CLSs. In this option, the local EMS is completely unaffected by the presence of an SMGW.
3. is similar to _Option 1b_ with the major difference that the meter measurements are collected by the local measurement collector. Via the transparent data tunnel, the measurements are provided to a remote EMS in the WAN which in turn sends control signals directly to one or more CLSs. Please note that this option still requires an EMP for setting up the transparent tunnel.
4. is a combination of both, _Options 2a_ and _Option 2b_. Similar to _Option 2b_, it uses the measurement collector to transmit the meter measurements to the remote EMS in the WAN. In this option, however, the remote EMS interacts with the local EMS, hence allowing a hierarchical control, as in the already presented _Option 1c_.
## 2 Methods
### Viscoelastic Model for Acoustic Wave Propagation
#### 2.1.1 Viscoelastic Model Construction of Fluororubber
In the field of ultrasonic research, all field variables (displacement, stress, strain) are represented as harmonic functions of time. Therefore, the expression is given by:  
\[\varepsilon=\hat{e}^{i\omega t},\sigma=\hat{\sigma}^{i\omega t} \tag{3}\]  
Substituting Equation (3) into Equation (2), the viscoelastic model of fluororubber can be expressed as:  
\[\hat{\sigma}=\bigg{[}\frac{(k_{1}+k_{2})\tau^{2}\omega^{2}+k_{2}}{\tau^{2} \omega^{2}+1}+\frac{\eta_{0}\tau^{2}\omega^{2}-(\eta_{0}+\eta_{1})}{\tau^{2} \omega^{2}+1}\omega i\bigg{]}\hat{\varepsilon} \tag{4}\]  
where \(\tau=\eta_{1}/k_{1}\). In this model, the elastic modulus exists in a complex form and is frequency dependent. The real part of the elastic modulus represents the energy storage capacity, while the imaginary part represents the energy dissipation capacity. Figure 1: Second-order generalized Maxwell viscoelastic model structure of fluororubber (**a**), and the step response curve of time to deformation (**b**).
###### Abstract  
Underwater wireless sensor networks (UWSNs) have applications in several fields, such as disaster management, underwater navigation, and environment monitoring. Since the nodes in UWSNs are restricted to inbuilt batteries, the effective utilization of available energy becomes essential. Clustering and routing approaches can be employed as energy-efficient solutions for UWSNs. However, the cluster-based routing techniques developed for conventional wireless networks cannot be employed for a UWSN because of the low bandwidth, spread stay, underwater current, and error probability. To resolve these issues, this article introduces a novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs. When using the CSRO-MHDT technique, cluster headers (_CHs_) are selected and clusters are prearranged, rendering a range of features, including remaining energy, intracluster distance, and intercluster detachment. Additionally, the chaotic search and rescue optimization (CSRO) algorithm is discussed, which is created by incorporating chaotic notions into the classic search and rescue optimization (SRO) algorithm. In addition, the CSRO-MHDT approach calculates a fitness function that takes residual energy, distance, and node degree into account, among other factors. A distinctive aspect of the paper is demonstrated by the development of the CSRO algorithm for route optimization, which was developed in-house. To validate the success of the CSRO-MHDT method, a sequence of tests were carried out, and the results showed the CSRO-MHDT method to have a packet delivery ratio (PDR) of 88%, whereas the energy-efficient clustering routing protocol (EECRP), the fuzzy C-means and moth-flame optimization (FCMMFO), the fuzzy scheme and particle swarm optimization (FBCPSO), the energy-efficient grid routing based on 3D cubes (EGRC), and the low-energy adaptive clustering hierarchy based on expected residual energy (LEACH-ERE) methods have reached lesser PDRs of 83%, 81%, 78%, 77%, and 75%, respectively, for 1000 rounds. The CSRO-MHDT technique resulted in higher values of number of packets received (NPR) under all rounds. For instance, with 50 rounds, the CSRO-MHDT technique attained a higher NPR of 3792%.  
data transmission; routing; search and rescue optimization; chaotic concept; fitness function; underwater wireless sensor network +
Footnote †: journal: Sensors  
0000-0001-6000-0001-6000][PERSON]  
1 Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai 600123, India 1,*
## 2 Carbon Nanostructures as Templates for Titania Nanomorphologies
### Multi-Walled Carbon Nanotubes (MWCNTs)  
MWCNTs have been widely studied as templates for titania nanostructures, as can be seen from the many entries in Table 1; thus, only a few studies are discussed here. With a sol-gel process in ethanol using a tetrabutyl titanate precursor, it was possible to decorate CNTs with titania nanocrystals, and the materials were used to obtain a film for potential applications as a battery anode [119]. When the same precursor was used in alkaline aqueous solutions with wider, hydroxylated CNTs (average diameter of 50 nm), nanowires were obtained instead, which entangled with CNTs in a network and were envisaged for the same application [126]. The CNT/titanium relative ratio is one of the critical parameters that determines nanomorphology. For instance, when an excess of titania precursor was used in a solvothermal method, nanoflowers were obtained, while increasing the relative amount of CNTs yielded nanowires through which the titania anatase coated the CNTs [130].  
The hollow interior of MWCNTs can also be exploited for various functions. For instance, iron-filled nanotubes could harness a magnetic separation after use for the easy recovery of the photocatalytic system. The tubes were functionalized on their surface to display carboxylic acid groups as anchors for titania nucleation and growth, and a thermal treatment allowed for the formation of anatase nanocrystals on the surface of CNTs (Figure 7) [184].
# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 2 Related Works
### Targeted Sentiment Classification  
Targeted sentiment classification is an important research topic as well as a fine-grained task in emotion analysis which is also known as _opinion mining_[18]. The early works on specific targeted sentiment classification mainly focus on extracting features to train sentiment classifiers [19], such as bag-of-word features and sentiment dictionary features. Most of these methods are rule-based [20] and statistical methods [4]--all of which are extremely dependent on feature engineering. Feature engineering is a labor-intensive task. In recent years, recurrent neural networks (RNNs) have achieved great success in this task because the deep learning model is able to utilize distributed representation to automatically learn and obtain the relevant features of targets. In addition, the use of attention mechanisms also makes sentence representation more focused on important information given a specific target [21]. ATAE-LSTM [7] combines LSTM and an attention mechanism. The model embeds specific targets into the calculation of attention weights. RAM was proposed by [PERSON] et al. [11]; this work improves upon Mem-Net by representing memory with bidirectional LSTM (Bi-LSTM) and using a gated recurrent unit network to combine the multiple attention outputs for sentence representation. [PERSON] et al. [10] designed a model with a bi-directional attention mechanism, which learned the attention weights of the contexts and the specific target words in an interactive way. AEN [22] avoids recurrence, and multiple multi-head attention was applied between the contexts and the specific targets. [PERSON] et al. [23] developed a new direction named coarse-to-fine task transfer, which aims to use bidirectional LSTM and multiple attention layers to accomplish this task. However, these studies do not take the syntactic information into account and ignore the syntactic interdependence between words, which may lead to ambiguity when identifying the sentiment polarity of a specific target.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization
### Communication Range Outdoor Measurements
#### 3.2.1 Suburban Area  
These measurements were conducted in a residential area in the city of Montreal, in an open baseball field of a public park. The field measurement setup in the suburban area is presented in Figure 10.
###### Abstract  
This study explores the efficacy of magnetic abrasive finishing (MAF) with planetary kinematics for post-processing Inconel 939 components fabricated by laser powder bed fusion (LPBF). Given the critical limitations in surface quality of LPBF-produced parts--especially in hard-to-machine superalloys like Inconel 939--there is a pressing need for advanced, adaptable finishing techniques that can operate effectively on complex geometries. This research focuses on optimizing the process parameters--eccentricity, rotational speed, and machining time--to enhance surface integrity following preliminary vibratory machining. Custom-designed samples underwent sequential machining, including heat treatment and 4 h vibratory machining, before MAF was applied under controlled conditions using ferromagnetic Fe-Si abrasives. Surface roughness measurements demonstrated a significant reduction, achieving Ra values from 1.21 \(\upmu\)m to below 0.8 \(\upmu\)m in optimal conditions, representing more than a fivefold improvement compared to the as-printed state (5.6 \(\upmu\)m). Scanning Electron Microscopy (SEM) revealed progressive surface refinement, with MAF effectively removing adhered particles left by prior processing. Statistical analysis confirmed the dominant influence of eccentricity on the surface profile parameters, particularly Rz. The findings validate the viability of MAF as a precise, controllable, and complementary finishing method for LPBF-manufactured Inconel 939 components, especially for geometrically complex or hard-to-reach surfaces.  
magnetic abrasive machining; Inconel 939; additive manufacturing; abrasive finishing; vibratory machining; LPBF post-processing; surface integrity Article
## 3 Results
### Analysis of Feature Importance via SHAP
#### 3.3.1 SHAP Bar Plot  
The bar plot delineated the hierarchical structure of the most significant features influencing the predictive process. Both validation methods exhibited nearly congruent behavior in feature importance when classifying depression. The only noticeable variation between the validation methods was the classification of SCRES and TTP features within the importance hierarchy, with TTP assuming marginally higher importance when using the cross-validation technique. It became evident that the coefficient of variation of the ECG exerted the most profound influence on the overall classification, followed, in order, by the physiological coherence of the TTP, zero HRV crossings, the global coherence index, and the physiological coherence in respiration. These five characteristics represented the most influential variables in the decision-making process of the Random Forest algorithm, applicable to both the combined dataset and for both the train-test split and the stratified  
Figure 4: This figure contains both SHAP Bar plot and Beeswarm plot of the Random Forest for the combined dataset but, in this case for the Stratified \(k\)-fold trial. (**1**) Bar plot; (**2**) Beeswarm plot.  
Figure 3: (**1**) This SHAP bar plot represents in descending order the features with the greatest to least impact on the classification process of the Random Forest for the combined dataset in the train–test split method. These are the mean absolute values of the SHAP scores calculated. (**2**) On the right there is a Beeswarm plot also for the train–test split method, which has a colored point for each data point within each variable, with the colors indicating whether the value is high (in red) or low (in blue). Similarly, the plot has a positive and a negative section, indicating the type of impact of each data point on the classification. If it is on the positive side, it contributes positively to the classification, and vice versa.  
\(k\)-fold strategies. Moreover, the differences in influence among these characteristics were relatively minor, maintaining a consistent and progressive hierarchy. Although individual variables demonstrated varying degrees of impact, no single feature unequivocally overshadowed the contributions of others. Collectively, the top ten characteristics contributed approximately 73% of the performance of the model, while the remaining ten less significant features contributed approximately 27%.
## 3 Numerical Modeling and Novel Technologies and Materials
### Superconducting Material Based Electrical Machines
Therefore, besides the common constraints in machine design, including volume and material costs, cryogenics and the AC loss phenomenon also constitute design optimization challenges [167; 278]. Synchronous machines are the most common topology in high-power applications (typically greater than 1000 hp ) [279]. For these machines, they can be coreless or ferromagnetic cores can be used. Generally, the design is supported by finite element modeling [280] and optimization algorithms [277]. One major concern to finite element modeling is the long computation time that is required. The design optimization of superconducting machines includes the optimization of efficiency, the power factor, the length of superconducting windings, as well as machine and cryostat volumes. For the sake of cost savings, the length of superconducting coils and the machine volume should be reduced, whilst for the electrical characteristics, efficiency and the power factor should be as high as possible. A design example considering these criteria was presented in [281]. Modeling the Meissner effect and the related thermal properties of superconducting materials increases the complexity of the multiobjective optimization of superconducting material based electrical machines. A typical design problem considers, at least, the minimization of the superconducting winding material and the maximization of the power density, the solution of which generally requires a multiobjective algorithm [167]. Moreover, it is hard to formulate these design problems analytically without an FEM calculation. Furthermore, the superconducting materials are generally non-linear; their electrical and magnetic properties depend on their temperature, current density, and magnetic field. The applied FEM code has to handle this non-linearity as well. Another numerical problem is related to the thickness of the superconducting coating on the tapes, which are very thin (this can fall below the micrometer range). This can significantly increase the numerical complexity of the design problem. An hp-adaptive meshing strategy can significantly increase the accuracy of the calculation [282; 22; 283]. Due to the unique properties of the superconducting materials, there are novel possibilities. Design considerations should be made before they are applied to the electrical machine; it is not enough to follow the old design principles. For example, it is possible to reduce the short-circuit current in the conductors. Therefore, the short-circuit properties of large machines should be revised.