esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Application
### Process of Roadway Retaining  
According to the previous analysis, when the spacing was selected as 2.2 m, the tensile stress area between the columns was within the range of the anchor bolt support system; thus, the on-site spacing of the concrete-filled steel tubes was selected as 2.2 m. \(\Pi\)-beams with a length of 5000 mm and single-hydraulic props were installed 30 m ahead of the working longwall for temporary reinforcement of support in the roadway. Each \(\Pi\)-beam was matched with two single-hydraulic props and arranged vertically to the direction of the mining, as shown in Figure 11.  
Anchor-cable-suspended I-beams with a length of 1800 mm were used to strengthen the rock mass of the roof between the concrete-filled steel tube columns. In the roadway retained behind the working longwall, two 3000 mm long \(\Pi\)-beams were arranged on the both sides; each \(\Pi\)-beam was matched with three hydraulic props and the spacing of the \(\Pi\)-beams, perpendicular to the mining direction, were 200 mm and 2900 mm, respectively. The \(\Pi\)-beams + hydraulic props were arranged 60-100 m behind the working longwall, and whether to withdraw the \(\Pi\)-beams + hydraulic props was determined according to the field condition of pressure and deformation of the roadway roof. A double-deck steel wire mesh was laid in front of the five groups of hydraulic supports at the end of the working longwall, overlapping with steel wire mesh in the roadway. The construction process of concrete-filled steel tubes is presented in Figure 12.  
Figure 11: Process of gob-side entry retaining with large-diameter, concrete-filled steel tubes in a fully mechanized working panel. (**a**) Advanced section of working face; (**b**) CFST construction area; (**c**) retained roadway.
## 5 Case Studies: Validating the CILS Framework
### Case Study One: Busuu
#### 5.1.4 Insights and Implications from Busuu  
The embedding of the CILS in Busuu is an excellent case regarding the future of language learning systems in terms of cultural awareness and adaptive learning. The model example shows what AI systems can do to improve the traditional learning method by using an additional understanding of cultural intonations, which are vital for real language mastery.  
An important finding from implementing the CILS in Busuu is the substantial enhancement in learner participation and retention rates. When content is targeted not only in terms of the level of learning but also in terms of cultural interests and needs, the chances that the learners will pursue their studies are higher. This individualized learning leads to learners remaining motivated and involved, which is the key to long-term language acquisition success [39].  
Another significant consequence is the improved level of intercultural communication among learners. Cultural distinctions allow learners to have a deeper perception and comprehension of the cultural background of language-related environments. This knowledge is crucial for learners who utilize their language skills in an array of professional and personal contexts worldwide since it averts confusion and promotes adroit interactions across cultural divides.  
CILS use in Busuu represents an opportunity for AI-driven platforms to scale personalized learning efficiently. AI's ability to analyze large datasets rapidly and precisely results in the opportunity for millions of users' platforms to provide personalized learning experiences to each learner. This scalability is crucial for the worldwide organization of advanced educational technologies.
## 5 Coupling Analysis of the ME Signal and EMR Signal  
### Time Domain Characteristics Analysis  
Analysis of the experimental results in Figure 6 reveals a high degree of synchronization between the EMR and ME signals generated during the rock failure process in the time domain. Notably, the time difference between the ME and EMR signals is minimal, indicating the faster propagation speed of EMR compared to ME. Consequently, the EMR signal can serve as a reliable predictor for the arrival of the ME signal.  
It is observed that the attenuation rate of the ME signal is significantly higher than the EMR signal, which is attributed to the charge accumulation effect during the later stages of crack propagation. As the rock mass fractures and the crack tip accelerates, charge accumulation occurs on both sides of the crack, leading to the generation of EMR. This phenomenon is particularly pronounced in rock masses with impact effects. The much higher propagation speed of EMR compared to ME means that when ME signals (cracks) and charged particles are generated simultaneously, EMR is also generated. Before the main rupture occurs, small ME signals are generated, serving as the power source for EMR. The charge generated by the rupture of these small ME signals forms the physical basis of EMR. Consequently, the precursor EMR signal, preceding the main EMR rupture, often occurs before the arrival of the main rupture signal. This EMR precursor signal serves as an early warning signal for synchronous monitoring of ME and EMR.  
Figure 6: EMR and ME signal time domain diagram of the four groups of specimens after noise reduction. (**a**) J-1; (**b**) J-2; (**c**) J-3; (**d**) J-4.  
Through post-observation data processing, time domain images confirm the presence of EMR precursor signals in all four sets of experimental data, affirming the feasibility and reliability of such precursor signals for monitoring and early warning purposes.
###### Abstract  
This paper presents a circularly polarized hybrid cylindrical dielectric resonator antenna (HCDRA) over a modified Minkowski unit-cell-based metasurface for 5G n79 band (4.4-5 GHz) and IEEE 802.11n WLAN (5 GHz) applications. The location of the perturbed probe feed mechanism and the asymmetric nature of the metasurface are the factors that influence the circularly polarized (CP) radiation within the DR element. The magnitude of E-field distribution and parametric study of the antenna to obtain the optimized feed location are the pieces of evidence of CP radiation. The return loss (RL) and axial ratio (AR) bandwidths produced by the proposed antenna are 1.837 GHz and 750 MHz with a peak gain of 7.04 dBic. The gain obtained is more than 5 dBic across the offered bandwidth of the proposed antenna. The proposed antenna is fabricated and tested in an anechoic chamber for measured results, and these results closely match with the simulation results.  
hybrid antennas; dielectric antennas; metasurfaces; CP radiation; Minkowski fractal; 5G n79 NR band +
Footnote †: journal: Electronics Letters  
1
Footnote 1: [[https://doi.org/10.3390/electronics13163117](https://doi.org/10.3390/electronics13163117)]([https://doi.org/10.3390/electronics13163117](https://doi.org/10.3390/electronics13163117))
## 2 Methods
### Decoder Architecture
#### 2.2.2 Spiking Long Short-Term Memory (SLSTM) Architecture  
We used the SLSTM architecture, as described in [23]. This is comparable to the general LSTM architecture [37], but only one time step is simulated each time the cell is called. The forward pass is governed by the following equations:  
\[\begin{split} i_{T}=\sigma(W_{ii}X[T+1]+b_{ii}+W_{hi}U[T]+b_{hi}) \\ f_{T}=\sigma(W_{if}X[T+1]+b_{if}+W_{hf}U[T]+b_{hf})\\ g_{T}=\tanh\left(W_{ig}X[T+1]+b_{ig}+W_{hg}U[T]+b_{hg}\right)\\ o_{T}=\sigma(W_{io}X[T+1]+b_{io}+W_{ho}U[T]+b_{ho})\\ I_{syn}[T+1]=f_{T}\star I_{syn}[T]+i_{T}\star g_{T}\\ U[T+1]=o_{T}\star tanh\left(I_{syn}[T+1]\right)\end{split} \tag{4}\]  
In the original LSTM formulation \(h,c\) are the hidden and cell states, which correspond to \(U\) and \(I_{syn}\) in the SLSTM formulation. In the equation presented above, \(\sigma\) is the sigmoid function and \(\star\) is the Hadamard product. The input gate \(i_{T}\) determines the information added to the memory cells, the forget gate \(f_{T}\) determines the information to be forgotten, and \(o_{T}\) determines if the memory cell state \(I_{syn}\) should cause an output spike (as long as \(U[T+1]>U_{thr}\)). \(b\) and \(W\) correspond to the learnable biases and weights. In the SLSTM, the reset scheme is set to 'none'.
## 3 Assessment of Spire Data Characteristics
### Precision
Although the SNRs from different emitter ranges are different, the mean differences for GPS, GLONASS, and GALILEO are all close to zero, with STDs of 1.81, 1.78, and 1.78, respectively. Using a similar approach, we further compare the precision of Spire with that of COSMIC-2 and COSMIC-1. Because the uncertainty of the mean difference may vary with the atmospheric moisture distribution, we limit our comparisons to the same period from October 1 to November 30, but with different years and over the same latitudinal zones (the tropical region from 20\({}^{\circ}\)N-20\({}^{\circ}\)S, mid-latitudes from 20\({}^{\circ}\)S-45\({}^{\circ}\)S and 45\({}^{\circ}\)N-20\({}^{\circ}\)N) (Figure 11). The Spire, COSMIC-2, and COSMIC-1 data are collected from 2022, 2019, and 2006, respectively. In general, the STD of Spire is very close to that from COSMIC-2 over the tropical region, especially in the troposphere (Figure 11b). With fewer observations in the lower troposphere, the COSMIC-1 STD is smaller than that of Spire and COSMIC-2. The COSMIC-2 STDs over mid-latitudes are slightly more significant than those from Spire, which may be owed to their lower SNR over the same regions (see Figure 5). The Spire STD above a 10 km altitude is marginally larger than that of COSMIC-2 and COSMIC-1.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Link Selection Based on Combined Weighting  
Regarding the algorithm that selects routing from multirouting metrics, the quality of the routing performance depends heavily on the scientificity of the weighting of routing metrics. No matter whether the subjective weighting method represented by AHP or the objective weighting method represented by EWM is used, there are incomplete data in ad hoc environments. A comparison of the single-weighting methods is shown in Table 3.  
The combined weighting method overcomes the disadvantages of subjective and objective weighting and includes the advantages of both. Thus, the authors of the current study propose that the combined method can obtain more reasonable weights.  
Finally, the multiplicative synthesis method was adopted to solve the problem of whether to assign weights using subjective weighting or objective weighting. The method is described in Equation (10):  
\[weight_{i}=\frac{\omega_{ij}\mu_{i}}{\sum_{i=1}^{n}\omega_{i}\mu_{i}} \tag{10}\]  
where \(weight_{i}\) is the combined weight of the calculated indicators, \(n\) is the number of metrics (\(n=3\)), \(\omega_{i}\) is the weight calculated by AHP (Section 3.2), and \(\mu_{i}\) is the weight calculated by EWM (Section 3.3).
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 5 Conclusions  
This article represents an advancement in the conventional trust model widely utilized in satellite network security routing. Given the open nature of satellite networks and their vulnerability to electromagnetic radiation interference, this paper takes a twofold approach. Firstly, it employs D-S evidence theory to mitigate these interference factors, thereby enhancing the accuracy of the trust vector. Secondly, it leverages the variational autoencoder architecture for identifying malicious behavior. Subsequently, the discriminant results are integrated into the ant colony algorithm to guide satellite network routing.  
Currently, research at the intersection of satellite network security routing and deep learning remains relatively limited, and this paper presents pioneering efforts in this direction. In the future, further enhancements can be made to optimize the speed of satellite network security detection, thereby reducing time consumption during satellite network routing. Additionally, introducing the energy utilization factor of satellite network nodes into the network's security model can strike a balance between energy efficiency and security detection. This can ultimately lead to an improved security routing strategy for satellite networks.  
Conceptualization, [PERSON] and J.R.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and L.Z.; investigation, [PERSON] and Y.J.; resources, [PERSON]; data curation, J.R.; writing: original draft preparation, J.R.; writing: review and editing, [PERSON], J.R. and Y.J.; visualization, J.R.; supervision, [PERSON] and L.Z.; project administration, [PERSON] and J.R.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not accessible.  
**Informed Consent Statement:** Not accessible.  
**Data Availability Statement:** The processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: The effect of transmission delay in increasing the data packet.
## 1 Introduction
These methods must strictly set up the structural parameters of the chaotic system and accurately judge the running state and the phase space trajectories of the system. That requirement makes them susceptible to many shortcomings, such as a low operating efficiency, a large amount of calculation, a difficult state determination, and a capability of being easily influenced by the change of parameters, and they cannot be applied directly to the noise reduction processing of bearing vibration signals. The synchronization of two chaotic systems with different initial states is an important characteristic of chaotic systems. Many scholars have put forward some data encryption methods based on this characteristic [14; 15]. In addition, some scholars studied the synchronization method of chaotic systems with unknown parameters or different structures [16; 17]. These related studies provide a theoretical basis for noise reduction via chaotic synchronization [18; 19]. Considering that the synchronization error of two chaotic systems can reflect the change in the input signal, a new method of noise reduction based on the coupled Lorenz system is proposed in this paper and applied to bearing fault diagnosis. Compared with the existing methods, the proposed method improves the real-time performance and adaptability, and has a good denoising effect on the tested vibration signal. This paper is organized as follows. In Section 2, the noise reduction mechanism and frequency estimation method based on the Lorenz system are discussed. In Section 3, the detection characteristics of the established model and the difference between the traditional chaotic detection method are simulated and analyzed. In Section 4, the feasibility of the new method applied to vibration data processing of a rolling bearing is discussed. This paper ends with some concluding remarks in Section 5.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 1 Introduction
### Background and Motivations
Therefore, exploring collaborative cooperation among satellites becomes imperative [16; 17; 18]. Additionally, for large-scale LEO constellations, processing massive data caching incurs high computational complexity and costs [19]. To tackle these challenges, the development of a rational LEO satellite data-caching scheme is essential. This scheme should focus on determining cache placement locations and enhancing cache efficiency [20; 21; 22].
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 4 Performance Evaluation
### Evaluation Measures
#### 4.1.2 Search Cost of the Routing Algorithms  
Figures 3 and 4 illustrate the search cost of our query routing algorithms RLQR-EGNS, RLQR-UCBNS, and RLQR-TSNS compared with CF and LPS. Indeed, we observe that the cumulative average overhead and the number of contacted peers per query of CF are high and stable over the simulation time at about 324 and 275, respectively. Furthermore, we note that the cumulative average overhead and the number of contacted peers per query of LPS decrease after the training phase. Indeed, LPS starts with an empty knowledge and it relies on CF during the training phase (i.e., first 1000 queries), which leads to achieving lower performance as CF. However, the cost search  
Figure 2: Evolution of the cumulative average recall.  
of LPS improves after updating the knowledge base of each peer to achieve a cumulative average overhead and number of contacted peers per query around 300 and 255, respectively. Moreover, we observe that the search cost of our reinforcement learning algorithms RLQR-EGNS, RLQR-UCBNS, and RLQR-TSNS going down rapidly allowing them to outperform CF and LPS. Indeed the cumulative average overhead per query of RLQR-EGNS, RLQR-UCBNS, and RLQR-TSNS decreases from 324 to 287, 272 and 268, respectively. This is owed to the fact that our reinforcement learning routing algorithms locate more peers holding pertinent documents for the query. The latter stops the query flooding process to avoid cycles then returns positive replies to requesting peers.
# Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data
## 3 Proposed Methodology
### Principal Component Analysis  
Meaningful data interpretation of a large dataset is quite challenging. The PCA is used to transform highly correlated data into a limited number of uncorrelated values comprising the actual information. It calculates Principal Components (PCs) for all variables contained in a dataset. Different numbers of PCs can be calculated such that the first PC encloses the peak variance, the next PC comprises the second uppermost variance, and so on. These PCs map the highly dimensional dataset into the simplest variables and make it easier to investigate the data for predictions purposes [27; 28]. The emotions tweet data is mostly gathered in raw text, and it must be separated from noisy data. We used the PCA technique to convert the complex tweet dataset into the simplest form of PCs. Mathematically, it represents the \(p\) dimensional data with discrete coefficients as given in Equation (1).  
\[w_{(k)}=\left(w_{1},w_{2},\ldots,w_{p}\right)_{(k)} \tag{1}\]  
where \(w\) indicates the single dimension and k is the linear figure. It converts every instance (\(a_{i}\)) in 'emotions' data to a form of PCs values as given in Equation (2).  
\[t_{(i)}=\left(t_{1},t_{2},\ldots,t_{l}\right)_{(i)} \tag{2}\]  
Figure 1: Emotions Classification using a hybrid approach of Deep Learning based on TensorFlow\(-\)Keras and Principal Component Analysis (PCA).  
where \(t\) describes the mined variations from every data instance. Equation (3) shows the factors' weights.  
\[t_{k_{(i)}}=x_{(i)}w_{(k)} \tag{3}\]  
where \(i\) = 1,..., \(n\) and \(k\) = 1,..., \(l\). The \(x\) represents highest conceivable variance with w factor for the respective variance. Mathematically, the PC1 is expressed as given in Equation (4).  
\[w_{(1)}=\text{arg}_{w=1}\text{max}\{\sum_{i}\left(x_{(i)}w\right)^{2}\} \tag{4}\]  
where \(w\) is the PC and \(x\) means the number of instance in tweets. It gives scores in the standardized form in a range of \(-1\) to \(+1\)[29]. There are 12 different PCs extracted from the 'emotions' dataset, as shown in Table 1. We select eight PCs for a further experiment as it covers the maximum variance of the 'emotion' dataset.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 3 Results  
Spatio-Temporal Variations of AOD, PAR\({}_{\mathrm{dir}}\), PAR\({}_{\mathrm{dir}}\) and PAR\({}_{\mathrm{total}}\)  
The global mean values of AOD, PAR\({}_{\mathrm{dir}}\), PAR\({}_{\mathrm{dir}}\), and PAR\({}_{\mathrm{total}}\) had large spatial differences from 2000 to 2014 (Figure 2). The high values of AOD and PAR\({}_{\mathrm{dir}}\) (>0.8 and 50W m\({}^{-2}\), respectively) were found in regions with rapidly developing economies such as eastern China and northern India, mainly due to man-made pollution (Figure 2a,c); the moderate values were found in desert areas (e.g., the Arabian Peninsula and North Africa) likely due to frequent sandstorms; the low values (<0.2 and 20W m\({}^{-2}\), respectively) were found in the Qinghai-Tibet Plateau and Andes Mountains. The PAR\({}_{\mathrm{dir}}\) distribution patterns (Figure 2b) showed the opposite pattern to those of AOD and PAR\({}_{\mathrm{dir}}\) on the Plateau, in the Himalayas, and in the Andes. In addition, PAR\({}_{\mathrm{total}}\) and PAR\({}_{\mathrm{dir}}\) values had similar spatial patterns (Figure 2b,d), and the influence of PAR\({}_{\mathrm{dir}}\) on PAR\({}_{\mathrm{total}}\) was greater than that of PAR\({}_{\mathrm{dir}}\).  
The trends of AOD, PARdir, PARdiff, and PARtotal mean values over the period 2000-2014 varied across regions (Figure 3). For example, in India and southeastern China, values for AOD and PARdiff exhibited significant growth trends (Figure 3a,c), while those for PARdir and PARtotal showed significant declines (Figure 3b,d). The value for PARdiff showed a significant growth trend, while that for AOD dropped noticeably in tropical regions (e.g., Indonesia and Congo Basin) (Figure 3a,c). Values of PARdiff, PARdir and PARtotal all had increasing trends in most parts of the Southern Hemisphere (Figure 3b,d). At the global scale, the annual averages of AOD and PARdiff showed upward trends (0.03 Wm\({}^{-2}\) and 0.1 Wm\({}^{-2}\)) (Figure 3e,g), while the annual averages of PARdir (\(-\)0.3 Wm\({}^{-2}\)) and PARtotal displayed downward trends (Figure 3f,h) during the period 2000-2014.