esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Analysis of volumetric changes between the period of maximum injection in October 2019 and the period of maximum extraction in April 2020 showed a strong correlation with time series results, highlighting two zones with different behaviours (Figures 8 and 9): (i) the area above the storage facility, which shows a trend correlated with the gas in storage (A1 in Figure 9) and (ii) the south-eastern area with an opposite trend (A2 in Figure 9). The changes in volume were determined through analysis of displacement data obtained using the InSAR technique (Figure 10). However, it is important to note that, compared with the actual values, volumes derived from ground point displacements in the Yela area should be regarded as underestimations. This discrepancy can be attributed to the limited spatial density of the available monitoring points (MPs) used in this study. The volume computation performed in CC estimated a negative volumetric change of 1.074 m\({}^{3}\) in the area designated as A1 (Figure 9), which spans approximately 1.5 km\({}^{2}\) and includes the Yela plant (Figure 10B,C). These results support the hypothesis that only a small deformation in the dolomite reservoir is evident on the surface. The short-term cyclic subsidence/uplift associated with UGS is limited to the chosen area and is maximum above the 11 wellheads (see Figure 7A,B). Figure 9: Vertical ground displacement maps constructed using IDW interpolation of motion data from (**A**) the period of maximum injection in 2019 (October) and (**B**) the period of maximum extraction in 2020 (April). The black dotted lines represent the boundary of the structural trap at a depth at which natural gas is stored. The red dashed circle represents the Yela field area, and the blue dashed circle represents the external area (A2) with different and opposite behaviour.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 4 Fault Diagnosis Algorithm Based on MSPSO and FCNN  
### Structural Analysis of FCNN  
The FCNN network model proposed in Section 2 contains 1 DCNN and 2 DCNN. The structure of 2 DCNN is: input layer-convolutional layer (\(C_{1}^{2}\))--pooling layer (\(S_{1}^{2}\))--convolutional layer (\(C_{2}^{2}\))--pooling layer (\(S_{2}^{2}\))--convolutional layer (\(C_{3}^{2}\))--pooling layer (\(S_{3}^{2}\))--fully connected layer (\(F^{2}\)). The structure of 1 DCNN is: input layer--convolutional layer (\(C_{1}^{1}\))--pooling layer (\(S_{1}^{1}\))--convolutional layer (\(C_{2}^{1}\))--pooling layer (\(S_{2}^{1}\))--fully connected layer (\(F^{1}\)). To fuse and classify the features extracted from the two dimensions of convolutional neural networks, the FCNN also contains: convergence layer--dropout layer--fully connected layer (\(F\))--Softmax.  
Figure 7: Improved particle boundary processing method.  
In FCNN, apart from parameters that require training like weights and biases, it also contains some hyperparameters that cannot be obtained by training. These hyperparameters include the filter quantity, filter size, filter step size, activation function, loss function type, learning rate, etc.  
FCNN reduces the number of parameters during network training by combining three techniques: local sensing, weight sharing, and pooling, but some hyperparameters still need to be set manually, and the choice of these hyperparameters directly determines the structure of FCNN, thus affecting the performance of the network [35]. Due to the experimental environment, the step size of all convolutional layer filters is set to 1. The FCNN hyperparameters are shown in Table 1.
## Abstract  
The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm deals with imbalance and concept drift problems through the hybrid sampling and dynamic weighting phases, respectively. In the hybrid sampling phase, adaptive spectral clustering is proposed to sample the data after clustering, which can maintain the original data distribution; then the sample safety factor is used to determine the samples to be sampled for each class; the safe samples are oversampled and the unsafe samples are under-sampled in each cluster. If the data stream is extremely imbalanced, the sample storage pool is used to extract samples with a high safety factor to add to the data stream. In the dynamic weighting phase, a dynamic weighting method based on the G-mean value is proposed. The G-mean values are used as the weights of each base classifier in the ensemble and the ensemble is dynamically updated during the processing of the data stream to accommodate the occurrence of concept drift. Experiments were conducted with LB, OAUE, ARF, BOLE, MUOB, MOOD, CALMID, and the proposed HSDW-MI on 10 multi-class synthetic data streams with different class ratios and concept drifts and 3 real multi-class imbalanced streams with unknown drifts, and the results show that the proposed HSDW-MI has better classification capabilities and performs more consistently compared to all other algorithms.  
data stream; multi-class imbalance; concept drift; hybrid sampling; classifier weighting
## 3 Numerical Simulation Analysis
### Constant Velocity Motion (CV)  
First, we evaluate the performance of CCF-MSS under constant velocity conditions. Given that the channel inherently exhibits time-varying Doppler (TVD) shifts, we directly add noise to the received signal to simulate the communication process under constant velocity conditions. Figure 5a,b illustrates the BER curves for different reception methods under constant velocity conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transmitter** & \\
**Parameters** & \\ \hline Carrier center frequency \(f_{\mathrm{C}}\) (kHz) & 6 \\ bandwidth B (kHz) & 4 \\ SS code index library capacity M & 16 \\ length of the SS codes & 511 \\ Number of the SS codes & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmitter parameter settings.  
Figure 5: The BER curves for different reception methods under two types of channels with varying motion states are presented: (**a**) represents the BER curve for constant velocity motion under the KAU1 channel; (**b**) represents the BER curve for constant velocity motion under the KAU2 channel; (**c**) represents the BER curve for variable velocity motion under the KAU1 channel; (**d**) represents the BER curve for variable velocity motion under the KAU2 channel.  
It is evident that under the conditions of speeds of \(-1.14\) m/s and \(1.28\) m/s, CCF-MSS exhibits the best performance, followed closely by FDE-MSS, though the decoding performance of the two methods shows only a slight difference. When the BER curve converges to \(10^{-2}\) the performance loss of CDE-MSS compared to CCF-MSS is approximately \(3.6\) dB, while the performance of WDE-MSS, which does not employ Doppler estimation, is the poorest. Based on the simulation results, it is evident that CCF-MSS exhibits slightly better noise and Doppler resistance under constant velocity motion compared to FDE-MSS, and significantly outperforms CDE-MSS while demonstrating a clear superiority over WDE-MSS. This indicates that under conditions of large-scale relative motion, Doppler estimation and compensation are effective methods for mitigating the decline in spreading code correlation caused by frequency shifts.
## 3 Hardware Design  
According to the needs of the system, it is necessary to monitor the vibration acceleration, speed, and height information of an elevator car; analyze the posture information of an elevator car in real-time through the microcontroller system; carry out real-time monitoring and judgment on any relevant faults that may occur in an elevator; send the information to the Internet of Things platform through the Internet of Things module; and then carry out the design of the relevant hardware in response to the above requirements.  
The structure of the hardware part of the system is shown in Figure 2. The hardware part of the device includes the STM32 main control chip, the barometric pressure sensor, the three-axis acceleration sensor, the NB-IoT module, and the power supply part. The barometric pressure sensor and the three-axis acceleration sensor collect the barometric pressure and acceleration data of an elevator car, and the STM32 main control chip processes the collected data and then packages the data to be transmitted to the IoT platform through the serial port and the NB-IoT module communication. After processing the collected data, the STM32 main control chip packages and transmits the data to the IoT platform through the serial port and NB-IoT module.
## 3 Results and Discussion
### Self Repairing Performance of Water-Based Coating Film with Microcapsules  
The coating film containing microcapsules and the coating film without microcapsules were prepared by the best coating technology. The results of self-repairing experiment were shown in Figure 11. Figure 11A,C show coating films without microcapsules, and Figure 11B,D show coating films containing microcapsules. After one week, the crack width of the coating film hardly changed without adding microcapsules, and the crack width of the coating film was been reduced with the addition of microcapsules, and the repair rate was about 20.6%. This was because the microcapsules in the coating film were broken by the scratches, and the core water-based acrylic resin flowed out, filling and repairing the scratches. Therefore, it can be proved that when the water-based emulsion microcapsules are added to water-based coatings, the coating film can repair micro-cracks to a certain extent by the coating technology of two coats of primer, three coats of topcoat, and microcapsules added in primer.  
Figure 11: Optical images of coating films with and without microcapsules before and after self-repairing: without microcapsules (**A**) before self-repairing, (**C**) after self-repairing; with microcapsules (**B**) before self-repairing, (**D**) after self-repairing.  
Figure 10: FT-IR of the coating film containing microcapsules.  
Comparison of Self-Repairing Properties of Dulux Water-Based Emulsion Microcapsules and Nippon Water-Based Emulsion Microcapsules  
The comprehensive properties of the coating film prepared by Nippon water-based emulsion microcapsules with two coats of primer, three coats of topcoat, and microcapsule added in primer were the best. In the early stage [25], it was proved by the same experiment that the comprehensive performance of the water-based coatings with Dulux water-based emulsion microcapsules (core-wall ratio 0.58:1, particle size about 5 \(\upmu\)m) was the best with three coats of primer, two coats of topcoat and microcapsule added in primer. These two kinds of microcapsules were added to the water-based coatings for comparison. Figure 12 shows the surface cracks of two kinds of self-repairing coating films before and after scratch test. The self-repairing rate of coating film with Dulux water-based emulsion microcapsules was about 3.5%, and that with Nippon water-based emulsion microcapsules was about 17.7%, and the self-repairing performance of coating film with Nippon water-based emulsion microcapsules was better. The diameters of these two microcapsules were 5 \(\upmu\)m, and the amount of microcapsules in the water-based coatings was 10.0%. The quality of the microcapsules in the water-based coatings was also consistent. Nippon water-based emulsion microcapsules have larger core-wall ratio and contain more repairing agents, so the repair performance of the coating film was better.
# Fault Tolerant DHT-Based Routing in MANET
## 3 Related Work
Similarly, if lookup queries arrive earlier than the updates (i.e., before selecting a new AN), the queries are buffered by the neighboring nodes and are forwarded towards the newly designated AN as soon as the updates arrive. This scheme has several problems; for example, it introduces an enormous delay (i.e., buffer delay, forwarding delay, and routing table updating time by the neighboring nodes) and overhead in the lookup resolution process. Further, the delay and overhead get worst as mobility and network size increase. Similarly, no buffer management is provided. However, mobility (high failure ratio) and network size would cause a buffer overflow, and lookup queries would get lost. Furthermore, DART mentioned the problems of partition and merging implicitly, using certain assumptions, without implementing algorithmic detail. These issues limit its scope in adversarial environments. Another closely related work is [5], In [5], the merging problem is investigated in the context of mismatch [3; 6] problem, and a leader election-based merging approach is utilized for merging DHT networks. However, the work assumes the existence of evenly distributed disjoint instances of the network, but this assumption does not hold in practice (for details, please see Section 2). Moreover, we use rank-based merging as a post-failure technique to recover from the failures and heal the logical, structured network. Additionally, FTDN ensures the existence of evenly distributed disjoint instances before initiating the merging process.
## 6 The Case for Cross-Layer Design
### Methodology
#### Merging of Adjacent Layers  
Although there are numerous cross-layer schemes that couple, for example, the MAC and physical layers [75, 76], these schemes still retain the traditional stack architecture. Efficiencies can be gained by creating a new super-layer that effectively merges the functions of both layers without the traditional overhead of two entirely separate layers. This union of the two layers services would not require new stack interfaces but rely on the existing interfaces to the rest of the protocol stack.
# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 1 Introduction
Due to imbalanced data between attack category instances, the previously proposed NIDPS becomes less reliable if their proposed methods do not solve the class imbalance ratio while training ML or DL models. The class imbalance problem normally remains ignored in many of the previously proposed NIDPS and created a big question for those NIDPS results, which does not solve it and made a biased NIDPS [15]. A class imbalance problem could also be defined as an improper distribution of categorical or binary data. It usually occurs in network-attacked datasets when the instance data against positive class exist with a lower ratio as compared to negative class data [16]. It creates a huge problem when it is fed to ML and DL classifiers by excess training of models against a single negative class only. The under-sampling and over-sampling are two ways to solve the class imbalance problem. The under-sampling technique reduces the majority class samples or instances to balance the majority and minority class instances. The over-sampling technique increases the minority class instances to solve the class imbalance problem [17]. However, these method needs vary from case study to case study of datasets. To solve the class imbalance problem, a few of the data and algorithm-interpreted methods are proposed, which solved the minority class problem by oversampling their instance numbers. They make a new dataset and give a more authentic NIDPS by solving class imbalance problems. However, the more efficient, minority class oversampled, and robust automated method of feature selection is needed to give a more precise, efficient, reliable, and confident NIDPS [18; 19]. Therefore, the proposed study uses a UNSW-NB15 dataset that contains a big class imbalance problem [20] against each category of network attack data as compared to the normal category. The proposed framework contributed in the following ways:  
* A searching potential and automated method of feature selection 'ISSA' is applied instead of using manual features selection, which uses hit and trial methods. * The class balancing is performed using majority and minority class balancing methods via SMOTE-Tomek on the most appropriate feature subset using ISSA. * An improved and more efficient NIDPS is developed after solving feature selection and class balancing problems. The rest of the article is divided into four sections. The related work, proposed methodology, results and discussion, and conclusion at last with future directions in it.
## 3 Results and Discussion  
### Effect of pH on AuNP Synthesis  
The analysis of AuNPs biosynthesized by different bacterial strains indicated that at a pH of 7.0, _P. aeruginosa, B. cereus, E. coli, and B. subtilis_ exhibited higher yields after 24 h of incubation than after incubation for 48 and 72 h. Furthermore, at a pH of 6.0, _B. lactis_ and _L. acidophilus_ exhibited higher yields after 72 h of incubation. The colloidal AuNPs biosynthesized by _E. coli_ (called ECAuNPs) were bluish violet and exhibited a high optical density (1.45) at 530 nm. The colloidal AuNPs biosynthesized by _B. lactis_ (called BLAuNPs) were wine red in color and exhibited a high optical density (1.63) at 540 nm. Among all tested strains, the optical densities of only AuNPs synthesized by _E. coli_ and _B. lactis_ were higher than 1.4, which indicated the potential of these two strains to synthesize AuNPs.  
Regarding the effect of pH on AuNP synthesis, the results revealed that _B. lactis_ produced the highest yield (OD: 2.49) of AuNPs at pH 5.0 after 72 h of incubation (Figure 1a), and _E. coli_ produced the highest yield (OD: 1.95) at pH 8.0 after 24 h of incubation (Figure 1b). The optimal growth pH values of _E. coli_ and _B. lactis_ were 7.0 and 6.0, respectively, which indicates that the optimal growth pH might not be the optimal pH for AuNP synthesis. [PERSON] et al. reported that the optimal pH was found to balance the reactivity of the gold precursor to promote rapid nucleation while ensuring a good electrostatic stabilization of the particles [18].
## 2 Related Work
In that way, the first UAV is exhausted later, after completing more tasks, resulting in a smaller average delay and at the same time, has a better opportunity to fly closer to a charging station so that the energy required for recharging is decreased. Task delegation is extremely important for the performance effectiveness of multi-UAV systems. However, the algorithm is comparatively complex, which is its only disadvantage. Some multi-drone systems also appear in the literature where the task delegation method is used from one drone to another [33; 45]. A single drone system in the context of a dock station is also used for this kind of purpose [46]. Hence, the trajectory of the drone is planned based on the heading angle and return points of agricultural UAVs are considered for the optimization of UAV paths. Heading angle and return points of agricultural UAVs are considered for optimization of UAV paths in [47]. It applied techniques such as gravitational search and A\({}^{\star}\) for path planning where it was shown that incidents such as non-spraying on fields have been reduced by 23.18%, whereas excess coverage of pesticide could be kept within the upper limit of 64.47%. However, for the detection of certain phenomena, some portions of a large agricultural land have to be considered to have greater importance, which has been ignored in this article [47]. The authors in [48] focus on the partial aspect that a UAV cannot actually position itself at least in the very beginning during the flight process. Therefore, the UAV applies a genetic algorithm as well as the A\({}^{\star}\) algorithm (A\({}^{\star}\)G) to plan its trajectory so that always the shortest route is followed to change position from one place to another. A theoretical framework for cooperation between multiple UAV systems appears in [49], where a large agricultural land is divided into multiple regions with one UAV (MULT-UAV) dedicated to each of those regions. However, the method is very expensive because of the requirement to deploy such an average number of aerial vehicles. Flight path planning based on the artificial potential field method appears in [50; 51], where the environment is considered, consisting of many obstacles and there are different threat coefficients associated with different parts of an obstacle. The trajectory of UAVs is planned in such a manner that chances of collision are minimized.
## 3 Methods
### Multiple Linear Regression  
The studies carried out on the interpretation of the dynamics of the backscatter coefficients, as well as on their modeling, showed that the radar signals observed in bare soil conditions depend on several variables. For each of the satellite configurations studied in this paper, multiple linear regression was performed. This method aims at estimating the linear relationship between the backscatter coefficients (variable to be predicted, or Y) and the surface descriptors, classically considered explanatory variables in modeling approaches (explanatory variables, or X\({}_{1}\), X\({}_{2}\),..., X\({}_{\rm N}\)). The significance of the multiple linear regression coefficients was determined, along with their corresponding p-values. The surface and satellite descriptors were retained in the algorithm when the p-value was below the significance level of 0.05 for a considered explanatory variable (which allows the null hypothesis to be rejected and the hypothesis of a non-zero correlation to be accepted).
## 4 Antennas and Antenna Related Technologies for Future Internet
### Antennas for User Devises, (IoT--mMTC and UEs)
The design exhibits dual polarization and dual-band operation in the 28 GHz and 38 GHz millimeter-wave bands, with a total bandwidth ranging from 27 GHz to 40 GHz. In [257, 258, 267], 8-, 10-, and 12-port UE antenna arrays operating at various multiband frequencies are presented. Novel techniques for enhancing MIMO antenna array performance in the sub-7 GHz band are introduced in [268]. A shared-aperture three-antenna module utilizing a single floating metal structure with minimal clearance is the focus of [259]. This design maximizes space efficiency while maintaining high performance, making it ideal for compact 5G mobile terminals. A dual characteristic mode antenna design integrated into the smartphone back cover is proposed in [260], supporting multiple 5G frequency bands efficiently. Photonic reconfigurable electromagnetic band gap (EBG) antenna arrays focused on beam-steering enhancement are presented in [261]. Furthermore, other works focus on topics such as heat dissipation [269], user interaction mitigation [270], which refers to strategies aimed at improving handset antenna performance when affected by the human body, which can degrade radiation efficiency metasurface-based phone cases [271], and extremely low-profile tunable multiport handset antennas [262]. The photonic reconfigurable electromagnetic band gap antenna arrays discussed in [261] are illustrated in Figure 14.  
Figure 13: An all-polarization, beamforming, and DoA-ready, \(\Gamma\)_Dipole_ Antenna [250].