esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Principle and Element Design of the CPFRA
### Geometry and Performance of the Metasurface-Based Element on the Primary Reflector Surface
#### 2.2.2 Performance of the Metasurface-Based Element  
The metasurface-based element on the primary reflector surface is simulated using the commercial simulation software HFSS19.0. As shown in Figure 4a, \(r_{un}\) indicates that the polarization direction of the incident wave is along the \(u\)-axis and the polarization direction of the reflected wave is also along the \(u\)-axis. \(r_{un}\) indicates that the polarization direction of the incident wave is along the \(u\)-axis and that of the reflected wave is along the \(v\)-axis. \(\mid\!r_{un}\!\mid\) and \(\mid\!r_{un}\!\mid\) represent the reflection amplitudes, and \(\angle\!r_{un}\) represents the reflection phase. The arrows in the curve point to the corresponding \(y\)-axis. Within the frequency band range of 22.4-30.1 GHz, \(\mid\!r_{un}\!\mid\) is always lower than \(-10\) dB, indicating that the energy of the \(u\)-polarized wave reflected back by the metasurface-based element is very low. \(\mid\!r_{un}\!\mid\) is greater than \(-1.5\) dB and close to 0 dB, indicating that the incident \(u\)-polarized wave is reflected as a \(v\)-polarized wave, suggesting that the metasurface-based element has excellent polarization torsional characteristics in the broadband range. Furthermore, from Figure 4a, we can also see that the \(\angle\!r_{un}\) phase curve changes almost linearly with frequency. This linear phase response is helpful for designing reflect  
Figure 3: (a) Three-dimensional view; (b) top view.  
As shown in Figure 4b, the metasurface-based element still has good polarization conversion performance under oblique incidence. Within the frequency band range, the amplitude and phase changes are small and remain relatively stable.  
The metasurface-based element achieves 360\({}^{\circ}\) reflection phase compensation by changing the length (L) of the microstrip lines. Figure 5 shows the result of phase compensation. When changing the length of the microstrip lines from 3.2 mm to 10 mm, the values of the reflective amplitude are more than \(-2\) dB. Moreover, more than a 360\({}^{\circ}\) phase range can also be obtained. The simulation results show that the element has good performance.  
Figure 6 shows the surface current distribution of a 2 \(\times\) 2 patch layer based on a metasurface-based element. It can be seen from the figure that the 2 \(\times\) 2 patch layer can be effectively excited to reflect and polarize EWs.  
Figure 4: Reflection amplitude and phase responses based on metasurface elements under (**a**) normal incident and (**b**) oblique incident conditions.  
Figure 5: Simulated amplitude and phase of the metasurface-based element on the primary reflector surface.  
Design and Analysis of the Transmissive Linear-to-Circular Polarization Converter on the Sub-Reflector Surface
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### Design of the Decoder Module
#### 2.4.2 Type II STDecoder-CD  
As seen in Figure 3, the Type II STDecoder-CD architecture has four components: a Siamese TransCNN backbone, a difference detector, an FPN module, and an FCN layer. A detailed description of the components is provided below.  
The idea of Difference Detector II is borrowed from [20]. It is a double convolution module involving two or three data sources: the output tensor of XA, the output tensor of XB, and the output tensor of the previous difference detector, if any. In this way, change  
Figure 3: Flowchart of the Type II STDecoder-CD architecture. The legends in this figure are consistent with those in Figures 1 and 2 if not specified.  
information at different stages can be fused to generate more discriminative and informative feature representations.  
FPN uses a pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost [31], and it shows significant improvement as a generic feature extractor in several applications, such as object detection and semantic segmentation. FPN consists of a bottom-up pathway and a top-down pathway. For the former, we chose to use the hierarchical difference detector; then, a top-down pathway with lateral connections was developed for building high-level semantic feature representations at all scales; finally, after the layer-wise upsampling and Conv3 ReLU operations, these hierarchy semantic representations are fused to generate a new feature representation with the same size and dimension as the first difference detector. Note that, here, we use Conv3 ReLU instead of the traditional 1 \(\times\) 1 convolution widely used in FPN [31] in order to promote nonlinear change responses. Additionally, before the upsampling of the 4 th difference detector, the ASPP (atrous spatial pyramid pooling) [32] module is added for the purpose of obtaining several multi-scale receptive fields, which may improve the networks' ability to handle both large and small changes. The idea for these minor modifications is borrowed from an open-source CD code, which is available at [[https://github.com/businiaoo/PRCV2021-Change-Detection-Contest-2](https://github.com/businiaoo/PRCV2021-Change-Detection-Contest-2) nd-place-Solution]([https://github.com/businiaoo/PRCV2021-Change-Detection-Contest-2](https://github.com/businiaoo/PRCV2021-Change-Detection-Contest-2) nd-place-Solution) (accessed on 20 February 2022).  
Finally, as displayed in Figure 3a, the deep supervision operation is conducted at four different places so that the weights of intermediate layers can be effectively updated.
# A Study of the Bond Strength and Mechanism between Basalt Fibers and Asphalt Binders
## 3 Results and Analysis
### Surface Energy and Contact Angle
#### 3.3.3 Adhesion Work of Asphalt Binders and Basalt Fibers  
The thermodynamic adsorption theory states that adhesion occurs under conditions of sufficient moisture. According to the basic theory of surface energy, the contact angle and adhesion work can be used as an index of the adhesion between asphalt binders and BFs. The adhesion work represents the bonding ability between asphalt binders and BFs. Generally, the adhesion work of two materials is expressed by Equation (4). The basic theory of surface energy indicates that the adhesion work and Gibbs free energy are opposite to each other, and the adhesion work is not less than zero, which means that the adhesion process can occur spontaneously.  
\[W_{a}=\gamma_{l}+\gamma_{s}-\gamma_{sl} \tag{7}\]  
According to [PERSON] theory and [PERSON] acid-base theory, if the small force between molecules is neglected, the adhesion work can be expressed by their dispersion component and polar component, as shown in Equation (5).  
\[W_{a}=2\sqrt{\gamma_{s}^{d}\gamma_{l}^{d}}+2\sqrt{\gamma_{s}^{p}\gamma_{l}^{p}} \tag{8}\]  
The rubber asphalt with BFs showed a higher value of adhesion work (53.160 mJ/m\({}^{2}\)) compared to that of the neat asphalt (42.046 mJ/m\({}^{2}\)) and SBS-modified asphalt (46.932 mJ/m\({}^{2}\))  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Asphalt Binders** & \(\gamma_{s}\) & \(\gamma_{s}^{d}\) & \(\gamma_{s}^{p}\) \\ \hline Neat asphalt & 18.58 & 16.59 & 1.99 \\ SBS-modified asphalt & 23.82 & 21.53 & 2.29 \\ Rubber asphalt & 28.57 & 25.04 & 3.53 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Asphal surface energy and its component (m)/m\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Contact Angle with Liquid (\({}^{\circ}\))** & **Surface Energy and Its Component (m)/m\({}^{2}\))** \\ \cline{2-5}  & **Distilled Water** & **Ethylene Glycol** & \(\gamma_{s}\) & \(\gamma_{s}^{d}\) & \(\gamma_{s}^{p}\) \\ \hline BFs & 71.103 \(\pm\) 8.305 & 39.594 \(\pm\) 5.074 & 37.98 & 11.39 & 26.61 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Surface energy of BFs.  
with BFs. Rubber asphalt is more suitable as a binder for basalt fibers, and this is consistent with the results of POT.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis
### Evaluation Metrics  
In the present work, the quality of enhanced speech samples was evaluated by five popular metrics, as shown below:  
1. PESQ: Perceptual evaluation of speech quality, which has values within the range of \([-0.5,\,4.5]\); a higher value indicates better quality.
2. STOI: Short-time objective intelligibility, which is within the range of \([0,1]\); the speech is considered to have been fully understood when it equals \(1\).
3. CSIG: Mean opinion score (MOS) [20] for the prediction of signal distortion, with a value ranging from \(1\) to \(5\).
4. CBAK: The MOS prediction of background noise intrusiveness, with a value ranging from \(1\) to \(5\).
5. COVL: The MOS prediction of the overall effect, which has a value within [1; 5]. A higher value of any of these metrics corresponds to higher performance.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 3 Materials and Methods
### Software Design
The wake-up window is, therefore, extended to compensate for the drift of individual device clocks. To synchronize its clock, each CTD has three sources, which it uses with decreasing priority, as shown:  
1. GNSS signal;
2. NB-IoT signal;
3. BLE mesh network.
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 1 Introduction
### Originality  
This work is original since it addresses intelligent 2C task classification and the security threats against it. First, to the best of our knowledge, this is the first work that addresses AI assistance to task classification for both communication and computing parts. Second, the applications and realization of poisoning attacks and AML threats in general are poorly covered for wireless communications. Finally, the study of a hybrid k-means and k-NN ML application technique, poisoning threats, and anomaly detection countermeasures using statistical tools makes this initial study unique.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Effect of Wind Power Generation on Transient Stability  
In this subsection, we will investigate the effect of wind power generation penetration level on transient stability of the IEEE 14-bus system, and comparisons are made by observing generator angles and generator speeds in time domain as shown below.
# Practical Inner Product Encryption with Constant Private Key +
## 2 Preliminaries
### Definition of Inner Product Encryption  
An IPE scheme consists of four algorithms: **Setup**, **KeyGen**, **Encrypt** and **Decrypt**. The details of the algorithms are as follows:  
* \(\mathbf{Setup}(1^{\lambda},1^{\ell})\). Take as inputs the security parameters \((1^{\lambda},1^{\ell})\), where \(\lambda,\ell\in\mathbb{N}\), and the algorithm outputs the system parameter params and the master secret key msk. The descriptions of the attribute vector space \(\mathfrak{A}\) and the predicate vector space \(\mathfrak{P}\) are implicitly included in params. Moreover, the inner product operation over \(\mathfrak{A}\) and \(\mathfrak{P}\) must be well defined.
* \(\mathbf{Encrypt}(\mathtt{params},\mathbf{x},M)\). Given the system parameter params, an attribute vector \(\mathbf{x}\in\mathfrak{A}\) and a message \(M\), the algorithm outputs a ciphertext \(\mathtt{C_{x}}\) for the attribute vector \(\mathbf{x}\).
* \(\mathbf{KeyGen}(\mathtt{params},\mathtt{msk},\mathbf{y})\). Given the system parameter params and a predicate vector \(\mathbf{y}\in\mathfrak{P}\), the algorithm outputs the private key \(\mathbb{K_{y}}\) for the predicate vector \(\mathbf{y}\).
* \(\mathbf{Decrypt}(\mathtt{params},\mathtt{C_{x}},\mathbb{K_{y}})\). Given the system parameter params, a ciphertext \(\mathtt{C_{x}}\) and the private key \(\mathbb{K_{y}}\), the algorithm outputs a message \(M\) or a error symbol \(\bot\).  
The correctness is defined as follows. For all \(\lambda,\ell\in\mathbb{N}\), let \(\mathtt{C_{x}}\leftarrow\mathbf{Encrypt}(\mathtt{params},\mathbf{x}\in \mathfrak{A},M)\) and let \(\mathbb{K_{y}}\leftarrow\mathbf{KeyGen}(\mathtt{params},\mathtt{msk},\mathbf{y})\); thus, we have  
\[\begin{array}{ll}M\leftarrow\mathbf{Decrypt}(\mathtt{params},\mathtt{C_{x}}, \mathbb{K_{y}})&\text{ if }\langle\mathbf{x},\mathbf{y}\rangle=0;\\ \bot&\leftarrow\mathbf{Decrypt}(\mathtt{params},\mathtt{C_{x}},\mathbb{K_{y} })&\text{ if }\langle\mathbf{x},\mathbf{y}\rangle\
eq 0,\end{array}\]  
where \((\mathtt{params},\mathtt{msk})\leftarrow\mathbf{Setup}(1^{\lambda},1^{\ell})\).
## 2 Materials  
### Variational Mode Decomposition  
VMD can decompose a complex signal into a series of sub-signals with specific bandwidths in the frequency domain, thereby excavating the features of complex nonlinear signals [17]. Assuming the frequency components of each modal component obtained from the VMD decomposition process are concentrated around the center frequency \(w_{k}\), the signal decomposition problem using VMD can be transformed into an optimization problem. The goal of the optimization is to find the optimal number of modal decompositions K that minimizes the total bandwidth of the decomposed modal sequences, while satisfying the constraint that the sum of all the decomposed sequences recovers the original signal \(f(t)\). The mathematical modeling of this optimization problem can be expressed as:  
\[\begin{array}{l}\underset{u_{k},u_{k}}{\min}\big{\{}\sum\limits_{k=1}^{K}|| \partial_{t}[(\delta(t)+\frac{j}{\pi t})*u_{k}(t)]\mathrm{e}^{-jw_{k}t}||_{2}^{ 2}\big{\}}\\ \mathrm{s.t.}\ \sum\limits_{k=1}^{K}u_{k}=f(t)\end{array} \tag{1}\]  
In Equation (1): \(f(t)\) represents the original load signal; \(\delta(t)\) is the Fermi-Dirac distribution function; \(t\) is time; \(u_{k}\) and \(w_{k}\) are the modal component and corresponding center frequency for the \(kth\) decomposition; \(K\) is the number of modal decompositions; \(j\) represents the imaginary unit. By introducing a quadratic penalty and Lagrange multipliers into Equation (1), the above constrained variational problem can be transformed into an unconstrained optimization problem, as follows:  
\[\begin{array}{l}L(u_{k},w_{k},\lambda)\\ \begin{array}{l}L(u_{k},w_{k},\lambda)\\ +||f(t)-\sum\limits_{k=1}^{K}u_{k}||_{2}^{2}+<\lambda(t),f(t)-\sum\limits_{k= 1}^{K}u_{k}>\end{array}\end{array} \tag{2}\]  
In Equation (2): \(\lambda(t)\) represents the Lagrangian multipliers, \(\alpha\) is the balancing parameter of the data-fidelity constraint, \(||f(t)-\sum\limits_{k=1}^{K}u_{k}||_{2}^{2}\) is the quadratic penalty term to accelerate the convergence rate. The optimization problem can be solved by finding the minimum of the augmented Lagrangian equation, using the alternating direction method of multipliers.
## 1 Introduction
Based on the above-mentioned literature and analysis, the main contributions of this paper can be summarized as follows: (1) instead of the cross-correlation coefficient, correlated kurtosis was used as a criterion to estimate the optimal iteration number of DC offset cancellation, since CK has the superior ability to detect the PIS signal and is more robust to the noise interference; (2) when PIS is very weak compared with the deterministic signal or existing bearing fault and gear fault simultaneously, even CK cannot detect weak PIS produced by the bearing fault effectively; in this circumstance, the maximum order of high-speed shaft mesh frequency was used as the iteration number of DC offset cancellation; (3) because of several existing merits, the S transform was used to combine with DC offset cancellation to display the time-frequency characteristic of various gearbox faults; it is very efficient and intuitive to find faults through time-frequency contours; in addition, it could be used for deep learning-based fault classification; (4) through the planetary gearbox case study, it was found that DC offset cancellation is less effective for those small fault frequency signals, such as the planet gear, ring gear, carrier, etc. Compared to the DC offset cancellation used in [24], the advantage of this study is that it used CK instead of the cross-correlation coefficient to determine the optimal iteration number of DC offset cancellation. According to the CK's value, it is more corrective to select the iteration number because it can detect PIS more accurately than the cross-correlation coefficient and is not easily interfered with by impulse-like noise. However, the disadvantage of this method is that it is less effective when the PIS is very weak. For the S transform, the advantages are that it can display the fault information more fruitfully and intuitively than the frequency domain. However, for some small fault frequencies, it is difficult to display them in the time-frequency contour because this needs a greater sampling length and will consume more computational resources, especially requiring a high ability graphic processing unit. The remainder part of the paper is organized as follows. In Section 2, a novel pre-whitening method and the S transform are briefly introduced. In Section 3, the experimental case study of an implanted bearing outer race fault is presented. In Section 4, the experimental case study of bearing and gear fault diagnosis in the gearbox is presented and explained in detail. In Section 5, the experimental case study of the planetary gearbox fault diagnosis using the proposed method is presented. In Section 6, the effectiveness and existing problem of the novel pre-whitening method are discussed. Finally, Section 7 concludes the work.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Information-Centric Networking (ICN) and Named Data Networking (NDN)
#### 3.2.3 Dtn-Icn-Vanets  
Due to the inherited NDN features, such as multicast or in-path caching, NDN (and CCN/ICN in general) has been recognized as an attractive solution for Vehicular Ad Hoc Networks (VANETs) [90; 91; 92]. In particular, [93] introduces the Multihop Multipath and Multichannel Vehicular NDN routing protocol (iMMM-VNDN) designed for the Vehicle-to-Vehicle (V2V) message exchange. In another work [94], a Vehicle-to-Infrastructure (V2I) communication architecture based on NDN was investigated, including a content discovery phase with the assistance of both vehicles and Roadside Units (RSUs). Along these lines, they broadcast beacon messages containing information about content sources and MAC addresses. The authors in [95] propose a Density-Aware Delay-Tolerant (DADT) interest forwarding strategy to retrieve traffic data in vehicular NDN environments and retransmit interests based on directional network density considerations. Moreover, the NDN Vehicular Internetworking (V-NDN) framework, as discussed in [91], enables a car/node to utilize various wireless interfaces in a V2V and V2I manner according to the requirements of specific applications.  
The paper by [96] focuses on NDN-based content caching within dynamic and delay-constrained network topologies. They introduce DeepNDN, an architecture that combines probabilistic techniques with convolutional neural networks to optimize content caching. Similar contexts have been explored in V2V [97] and V2I [98] scenarios and in [99] where cache refreshing schemes have been proposed in rational order or updated upon requests with a defined probability.  
Efforts to increase link stability and improve content delivery timeliness have also been discussed. The importance of prioritizing neighboring vehicles with more stable links is highlighted in [100], while ref. [101] suggests sharing time-critical content among RSUs and vehicles using a publish/subscribe-based message propagation approach to prevent accidents.  
To conclude, while ICN/NDN and DTN initially addressed distinct networking challenges, i.e., content retrieval in fixed networks and data delivery in deep space communications, respectively, they can complement each other in scenarios involving mobility and intermittent or disrupted connectivity. In these cases where the usual end-to-end paths may not be available, the content-centric approach of ICN can improve content delivery, as expressions of interest (subscriptions) can be satisfied long after they have been issued and may be served from any node that has a copy of an object that matches the interest.
# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 3 Methodology  
In our proposed model, we frame the sentence segmentation task as a character-level sequence labeling problem. Each character is classified based only on features learned from a fixed-length context. During both training and prediction, the model processes multiple n-gram sequences derived from this fixed context. UnifiedCut, our proposed model, learns the classification features for each target character from these sequences and performs classification independently for each character. This independent classification allows for parallel processing, which improves segmentation efficiency.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & & & & & \\ \hline
**Methods** & **Holding Cost** & **Ordering Cost** & **Variable Order** & **Total Profit** & **Total Demand** & **Total Procerned** & **Number of Orders** \\ \hline Actual & 743,752 & 19,000 & 4,823,299 & 38,466,476 & 4077 & 4043 & 35 \\ \hline EOQ & 486,386 & **15,500** & **4,863,861** & 39,057,150 & 4077 & 4077 & 31 \\ \hline Q-Learning & **288,944** & 19,500 & **4,863,861** & **39,250,686** & 4077 & 4077 & 39 \\ \hline Deep Q Network & 395,598 & **13,500** & 4,896,072 & 39,117,821 & 4077 & 4104 & **27** \\ \hline \multicolumn{8}{c}{**Item-2**} \\ \hline Actual & 2,313,696 & 17,500 & 6,840,540 & 20,199,613 & 853 & 854 & 35 \\ \hline EOQ & 2,937,364 & 38,500 & **6,832,530** & 19,571,740 & 853 & 853 & 77 \\ \hline Q-Learning & **860,750** & 17,500 & **6,832,530** & **21,660,569** & 853 & 853 & 35 \\ \hline Deep Q Network & 2,741,836 & **5500** & 6,960,690 & 19,672,109 & 853 & 869 & **11** \\ \hline \multicolumn{8}{c}{**Item-3**} \\ \hline Actual & 238,537 & 16,000 & **1,142,766** & 9,848,969 & 363,233 & 362,783 & 32 \\ \hline EOQ & 114,418 & **8000** & 1,144,183 & 9,997,689 & 363,233 & 363,233 & **16** \\ \hline Q-Learning & **83,026** & 13,000 & 1,144,183 & **10,020,013** & 363,233 & 363,233 & 26 \\ \hline Deep Q Network & 155,494 & 9,000 & 1,206,746 & 9,888,982 & 363,233 & 383,094 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for non-deteriorating item with time-varying demand.