esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System Overview  
Figure 1 presents the schema of the developed monitoring system.  
The main component of the monitoring system is the following measurement and observation equipment: multi-sensor devices, beehive weight scales, inside-beehive cameras, and outside-beehive cameras. Additionally, the following auxiliary equipment was utilized: Power-over-Ethernet (PoE) management switch, Wi-Fi access point, electric switchboard fitted with fuses, lightning arrester, AC-DC power converters, and cables (electric, Ethernet, and USB cables).  
Multi-sensor devices are powered via AC-DC power converters, and they use Wi-Fi connection for data transmission. The same solution was applied in the case of beehive weight scales. Wi-Fi devices are connected to the access point working in bridge mode.  
Cameras mounted outside as well as inside beehives use Ethernet cables for data transmission and power. They are directly connected to a PoE-managed switch.  
The roof location of the monitoring system allows utilizing the line-of-sight microwave transmission line for connecting the switch with the server-side net infrastructure, in case it is located not far away, e.g., in a neighbouring building.  
Data from all devices are transmitted online and presented on a dedicated website [35]. The measurement data from multi-sensor devices and beehive weight scales are transmitted as MQTT messages. They are also saved as *.csv text files to data partition in the internal memory cards of the devices as a backup, for further analysis. Live video streaming from all cameras is also displayed on the webpage. Additionally, the videos are recorded and stored over a selected period, for offline analysis.  
Figure 1: Schema of the monitoring system for urban bekeeping.
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 3 Methodology
### The Structural Complexity of Selected ML Algorithms  
A novel set of dimensionless metrics based on internal connections or weights was introduced to enable a fair comparison among machine learning algorithms with varying structural complexities. This approach balances the evaluation, accounting for each algorithm's unique structural intricacies. Here is how structural complexity was defined for each algorithm:  
**LR**: for multinomial logistic regressions, each class prediction relies on weights associated with input features and an additional bias (or residual) term for each class. Thus, the total structural complexity is determined by the number of weights and residuals required to compute probabilities across all classes.  
**DT**: decision trees partition the data using split nodes at each layer. Each split node evaluates one feature to split the data. Therefore, the model's structural complexity correlates with the number of split nodes. More nodes allow for finer granularity in the classification, enhancing performance but increasing complexity.  
**SVM**: the complexity of an SVM in multiclass classification is determined by the number of binary sub-classifiers required. Based on the discussion above, two ensemble learning strategies, one-vs-one and one-vs-all, are commonly employed in multiclass problems. These strategies result in different levels of complexity, corresponding to k(k \(-\) 1)/2 and k, respectively, where k is the total number of classes.  
**ANN**: for ANNs, structural complexity is straightforwardly determined by the number of connections, or weights, within the network. For a single-hidden-layer ANN with x input nodes, y hidden nodes, and z output nodes, the total number of weights is calculated as: \((x\times y)+(y\times z)\). This includes the connections between the input layer and the hidden layer and those between the hidden layer and the output layer. This measure reflects the model's ability to capture intricate patterns in the data, with higher complexity associated with larger networks.  
This complexity-based framework allows for a balanced assessment of model performance by normalising their predictive power in relation to their structural demands. This makes comparisons meaningful despite inherent differences in algorithm design.
## 2 Result and Discussion  
### System Framework and Working Mechanism of TENG  
The working framework of the SAVWS is shown in Figure 1a, which was composed of an integrated TENG, a MEMS switch, a power management system (PMS), and a wireless transmitter. The integrated TENG could harvest the vibrational energy and measure the vibration acceleration simultaneously, which consists of E-TENG, S-TENG, mass, and four supporting springs (Figure S1a). Therein, the E-TENG could transform vibrational energy into electrical energy with an alternating current (AC) output. Then, the PMS connected to the E-TENG could convert the AC into direct current (DC) which was supplied to the transmitter module. For vibration sensing, the S-TENG could convert the vibration information into electrical signals and control the operating state of the MEMS switch. When the S-TENG detected that the acceleration of vibration exceeds a specific threshold, the MEMS switch would turn on to wake up the transmitter module to send alarm signals. What is more, the acceleration threshold could be adjusted by regulating the output characteristics of S-TENG. Finally, the alarm signals could be analyzed and recognized by the remote monitoring. As a result, a fully self-powered remote acceleration monitoring has been realized.  
As shown in Figure 1b, the E-TENG and S-TENG were both in the vertical contact-separation mode. Polytetrafluoroethylene (PTFE) and Cu were chosen as triboelectric materials of TENGs due to their excellent contact electrification properties [35; 36]. Owing to the excellent processability of the acrylic sheet, which was used as the base and support layer, the sponge between the Cu film and the acrylic sheet could increase the effective contact area between the triboelectric pairs. Figure 1c illustrates the working mechanism of the vertical contact-separation TENG, which is the coupling effect of contact electrification and electrostatic induction [37]. When the TENG was actuated by external mechanical vibration, the triboelectric layers began to contact and separate back and forth. Based on the triboelectric series [36], the electrons could transfer from the Cu film to the PTFE film, while they would obtain the same number of opposite charges. There was almost no electric potential difference between the two electrodes (Figure 1c(I)). Once the two films are separated, an electric potential difference would be generated between the two electrodes and drive electrons from Cu electrode to Cu film (Figure 1c(II)) until the distance between the PTFE film and Cu film reached the maximum (Figure 1c(III)). Then, the two triboelectric layers came close again. The electric potential difference between the Cu film and Cu electrode decreased gradually, and the electrons would be driven from Cu film to Cu electrode. (Figure 1c(IV)). Through contact-separation circulation, the electric potential difference constantly drove electrons to move between the Cu film and Cu electrode. In this way, the conversion of mechanical energy to electrical energy could be realized. The contact area size of TENG is shown in Figure S1b.
## 7 Performance Evaluation
### Simulation Results and Discussions  
In this section, we show the simulation results based on the simulation configuration discussed in the previous section to illustrate the effect of DRRF in terms of throughput, fairness, and delay.
###### Abstract  
Firewall packet classification is a critical component of network security, demanding precise and reliable methods to ensure optimal functionality. This study introduces an advanced approach that combines Artificial Neural Networks (ANNs) with various data balancing techniques, including the Synthetic Minority Over-sampling Technique (SMOTE), ADASYN, and BorderlineSMOTE, to enhance the classification of firewall packets into four distinct classes: 'allow', 'deny', 'drop', and'reset-both'. Initial experiments without data balancing revealed that while the ANN model achieved perfect precision, recall, and F1-Scores for the 'allow', 'deny', and 'drop' classes, it struggled to accurately classify the'reset-both' class. To address this, we applied SMOTE, ADASYN, and BorderlineSMOTE to mitigate class imbalance, which led to significant improvements in overall classification performance. Among the techniques, the ANN combined with BorderlineSMOTE demonstrated superior efficacy, achieving a 97% overall accuracy and consistently high performance across all classes, particularly in the accurate classification of minority classes. In contrast, while SMOTE and ADASYN also improved the model's performance, the results with BorderlineSMOTE were notably more balanced and reliable. This study provides a comparative analysis with existing machine learning models, highlighting the effectiveness of the proposed approach in firewall packet classification. The synthesized results validate the potential of integrating ANNs with advanced data balancing techniques to enhance the robustness and reliability of network security systems. The findings underscore the importance of addressing class imbalance in machine learning models, particularly in security-critical applications, and offer valuable insights for the design and improvement of future network security infrastructures.  
f  
firewall packet classification; artificial neural networks; SMOTE; network security; imbalanced data +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Land Cover Mapping of Japan  
A land cover map was produced that not only defined the areas as simple forests and non-forested areas but also provided more detail (_i.e._, subclasses) such as forest types (coniferous, deciduous and evergreen broadleaf), so the information could be used in many different ways. The need for a detailed digital land cover (or digital vegetation model) map, especially for the vegetation categories, is important for obtaining more precise estimations of CO\({}_{2}\) sequestration values. For this study, only single-scene data sets were used to develop the land cover map for each area. [PERSON] _et al._[5] states the usefulness of applying temporal data sets to improve land cover accuracy due to the clear difference of radiance values that change during different seasons of the year. In the case of forest classes, similar spectral signatures would be observed throughout a season after blooming finishes, but clear and distinguishable differences can be observed for different forest types, such as trees whose leaves fall in the winter. Such methods were not applied in this case because few cloud-free products were captured, which made it difficult to find a set of scenes with two different seasons for each location. Operation of Landsat 8 is still in its third year, so its scene database is still relatively small compared to Landsat 5 or 7, so such methods are currently limited to very small areas or must wait for more data to be collected over time.  
However, because of the lack of temporal data scenes, the classification process was performed not by increasing the number of images but by collecting as many detailed training sites as possible. This method led to the ability to discriminate between forest classes in more detail and with more precision, because constructing more detailed and precise land cover classes resulted in greater chances of unclear pixels being classified into the forest class to which they should correctly belong. For example, if the training sites of the forest classes are specified and classified only as \"coniferous, deciduous broadleaf and evergreen broadleaf\", deciduous and evergreen broadleaf forest classification drop inaccuracy by 33% and 36%, respectively, compared to when more detailed training classes of those forest types are constructed. Therefore, this methodology is effective when there are few scenes available for classifying a region. Constructing detailed training sites can improve the categorical map. In the future, we recommend reprocessing the classification of areas that had lower accuracy results or regions that were covered by snow/cloud to improve and correct the land cover types used here.
## 5 Conclusions  
In this paper, a new method based on the combination of transfer learning and deep learning to acquire the common features and unique features is proposed. First, we obtain a large number of DBNs trained by source domain data. In this paper, according to the hierarchical features of the DBN networks, we use a limited sample tag to fine-tune the network. This experiment studies the effect of the features at all levels on the final classification results. Finally, the features extracted from the first few layers of the DBN network are low-level features, and the source domain data and the target domain data have good transfer. As a result, the more complex the extracted features, the less effective the transfer learning. Based on the simulation experiment, this paper concludes that transfer learning is optimal when the number of hidden layers is five.  
Figure 16: The above four pictures are the SVM classification results, the PCA-SVM classification results, the DBN-SVM classification results, and the DBN-SVM spatial classification results.  
Figure 17: Confusion matrix. Without transfer (left), after transfer (mid), joint spe–spa (right).  
Hyperspectral data are tested for simulations to find a model that performs better with fewer samples. Transfer learning can be used to obtain the common characteristics of the target domain and the source domain data, but each data set will inevitably have its own unique characteristics. Using only transfer learning will lose this part of the characteristics. For this purpose, the main components extracted by the PCA algorithm are mostly characteristic of the target domain data. After the experimental simulation, it can be concluded that the more main component layers that are used, the higher the classification accuracy will be. However, after the main component layers reach six layers, the accuracy rate increases more slowly. Therefore, a six-layer principal component cell is used. For the algorithm structure we proposed in this paper, we use the LR and SVM to separately test it. It can be seen from the comparative experiments that the proposed structure in this paper has a good effect in both the LR and SVM algorithms. Our future work will focus on the use of more deep learning models to conduct the transfers, testing, and extraction of the characteristics of the deep learning model in the transfer learning model framework. The model performances will be assessed and compared to the other methods.  
Conceptualization: K.L. Data curation: W.L. Formal analysis: K.L., M.W., Y.L. Funding acquisition: K.L., W.L. Investigation: Y.L. Methodology: W.L., K.L. Project administration: [PERSON], K.L. Resources: K.L. Software: M.W., Y.L., N.Y. Supervision: [PERSON]. Validation: K.L., W.L. Visualization: K.L., W.L. Writing--original draft: [PERSON], K.L. Writing--review & editing: [PERSON], W.L.  
This research received no external funding.  
The authors are supported by the Chinese National Natural Science Foundation (No. 61773039), the Aeronautical Science Foundation of China (No. 2018 XXX), and 'Fanzhou' Scientific Funds (No. 2017 XX).  
The authors declare no conflict of interest.
## 1 Introduction
The novelties and main contributions of this study are as follows:  
1. Aiming at the problem of the large fluctuation in wind speed series with strong non-linear characteristics, the CEEMDAN of wind speed series is performed to decompose the complex wind speed time series signal into multiple simpler eigenmode functions so that it effectively eliminates the noise and nonlinear perturbations in the signal, improves the robustness of the model to wind speed data noise, and enhances the expressive and generalization abilities of the model;
2. The RIME algorithm optimizes the hyperparameters of the BiLSTM model through iteratively updating and adjusting the weights and biases of the BiLSTM layer. This prevents the model from becoming stuck in local optimal solutions and allows it to quickly adjust to changes in the wind speed data, better fit the training data, and enhance its generalization ability;
3. A more accurate wind speed prediction model was designed by combining deep learning and machine learning. In order to improve the global feature extraction capability of the model, the BiLSTM model was improved by utilizing the multi-head attention mechanism. The MHSA assigns different attentional weights according to the correlation between the time steps and learns the local and global features in parallel in different representation subspaces. It extracts key information in long sequences and captures time-frequency information and complex dependencies at different scales in wind speed series, which enables the model to capture the dynamic change pattern of the wind speed more accurately, improves the ability to capture wind speed time series data, and enhances the model's expressiveness. The rest of this study is organized as follows: Section 2 provides the theory of the proposed prediction method. Section 3 describes the basic structure of the proposed model and the evaluation metrics. Section 4 presents the experimental data, analysis, and a discussion of the results, and the final conclusions are presented in Section 5.
## 3 Materials and Methods
### Learning Step for Models \(f\)
#### 3.3.2 Parsimonious PLSR (Plsr-P)  
For some data, selecting the PLSR dimensionality \(a\) by minimal RMSEP\({}_{\text{CV}}\) can generate overparameterization (excessively large values for \(a\)). For instance, this can occur when the error rate curve reaches a plateau without increasing values on larger values \(r\) or even shows a continuously decreasing trend, instead of a clear \"U-shape\", such as that in Figure 4. To get parsimonious PLSR models, a simple heuristic criterion is the \"Wold's ratio\" \(R\)[35; 36; 37]. This ratio is defined by  
\[R=1-\text{RMSEP}_{\text{CV}}(r+1)/\text{RMSEP}_{\text{CV}}(r)\]  
Figure 4: Example of RMSEP\({}_{\text{CV}}\) curve (mean over 30 repetitions) for PLSR: \(X\)-data TROP1 and y-variable ADF. Red point: \(a=22\) LVs selected by minimum RMSEP\({}_{\text{CV}}\); Green point: \(a=14\) LVs selected with Wold’s criterion.  
\(R\) represents the relative gain in prediction efficiency after a new LV is added into the model. When selecting \(a\), the iteration \(r\to r+1\) continues until \(R\) becomes lower than a threshold value \(q\). In this paper, \(q\) was set to 1%. In general, using this ratio returns a dimensionality (\(a\)) lower than with the usual selection approach (Figure 4).
## 2 Motor Topology and Simulation Analysis
### Simulation Analysis for On-Load Condition  
The torque of the PMA-SynRM consists mainly of permanent magnet torque and reluctance torque. In order to verify the torque characteristics of the proposed model after the MAS effect, the torque of the two models is decomposed using the frozen permeability method. The torque distribution of the two models at different current phase angles for a current RMS value of 2A is shown in Figure 7. It should be noted that in Figure 7, the orange markers are the components of the permanent magnet torque and the reluctance torque at the maximum total torque, and the green markers represent the components of the permanent magnet torque and the reluctance torque at the maximum total torque. The maximum total torque of the proposed model is improved by 22.3% compared to the conventional model. As seen in Figure 7a, the components of the maximum permanent magnet torque and the maximum reluctance torque of the conventional model have a phase difference of 45 degrees. In Figure 7b, the components of the maximum permanent magnet torque and the maximum reluctance torque of the proposed model display only 15 degrees  
Figure 5: Comparison of no-load air-gap flux density for the two models. (**a**) Air-gap flux density waveform. (**b**) Harmonic decomposition of the air-gap flux density.  
Figure 6: Comparison of no-load back EMF for the two models. (**a**) Back-EMF waveform. (**b**) Harmonic decomposition of back EMF.  
of phase difference. Permanent magnet torque and reluctance torque utilization can be expressed by Equation (7).  
\[\left\{\begin{array}{l}\xi_{PM}=\frac{T_{PM}}{T_{PM-MAX}}\times 100\%\\ \xi_{RE}=\frac{T_{RE}}{T_{RE-MAX}}\times 100\%\end{array}\right. \tag{7}\]  
where \(\xi_{PM}\) and \(\xi_{RE}\) are the permanent magnet torque utilization and the reluctance torque utilization, respectively; \(T_{PM}\) and \(T_{RE}\) are the permanent magnet torque and the reluctance torque components at the maximum total torque, respectively; and \(T_{PM-MAX}\) and \(T_{RE-MAX}\) are the maximum permanent magnet torque component and the maximum reluctance torque component, respectively.  
From Table 2, it can be seen that the utilization of both permanent magnet torque and reluctance torque is higher in the proposed model compared to the conventional model. This proves that the proposed model with the MAS effect can shift the permanent magnet torque and reduce the phase difference between the permanent magnet torque and the reluctance torque. Figure 8 shows the flux density cloud diagrams of the conventional and proposed models under load conditions. From Figure 8, it can be seen that the flux density of the proposed model is significantly higher than that of the conventional model in flux circuit B.  
Figure 8: Flux density cloud plots of the two models under load conditions. (**a**) Conventional model. (**b**) Proposed model.  
Figure 7: Comparison of the torque characteristics of the two models. (**a**) Conventional model. (**b**) Proposed model.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments  
We conducted comprehensive qualitative and quantitative evaluations of the proposed architecture on six real-world datasets. We also performed ablation studies to evaluate the different components of our suggested model.  
Figure 1: Overview of the proposed architecture. Input time series could be either of univariate or multivariate. The details of the “weak-stationarizing” block and “non-stationarity restoring” block are shown in the boxes at the bottom. The structure of the ConvMixer is shown in the dashed box.
## 4 Discussion
It is also concluded that PECVD grown graphene nanowalls on G substrates are more effective against Gram-positive _S. aureus_ than Gram-negative _E. coli_. As a result, it is evident that the antibacterial activities of 3D graphene nanowalls grown on G substrate are stronger than 2D graphene nanosheets grown on G substrate against _E. coli_ and _S. aureus_. According to the experimental results, we could also say that the antibacterial activities of PECVD grown graphene samples are more effective against _E. coli_ and _S. aureus_ than CVD-grown graphene samples. It can also be said that both the nanowall (3D structure) morphology, which changes significantly with the presence of RF power, and the defects created on the graphene surface using the PECVD method are more effective against _E. coli_ and _S. aureus_ than CVD-grown graphene-based samples (2D-structure). It may also be thought that PECVD grown graphene against _S. aureus_ is more effective than against _E. coli_. The differences observed between the viability of the _E. coli_ and _S. aureus_ could be related to differences in the cell wall structure, cell physiology, metabolism, or degree of contact. Indeed, while peptidoglycan counts for 90% of the cell wall in Gram-positives, it counts only for 5-10% of the cell wall in Gram-negatives. In addition, unlike Gram-positive bacteria, Gram-negative bacteria have an outer lipid membrane [31].