esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-packet Full-Duplex (FD) transmissions. To avoid the effect of channel estimation processes when using short-packet transmissions, this semi-blind algorithm was developed by taking into account only a small number (four at least) pilot symbols, which was integrated with the intended information sequence and used for the feedback loop of the estimation of the channels. The results showed that this semi-blind algorithm not only achieved nearly optimal performance, but also significantly reduced the processing time and computational complexity. This semi-blind algorithm can also improve the performances of the Mean-Squared Error (MSE) and Bit Error Rate (BER). The results of this study highlight the potential efficiency of this joint semi-blind iterative algorithm for 5G and Beyond and/or practical IoT transmission scenarios.  
full-duplex; digital self-interference cancellation; 5G LDPC codes; short-packet transmission; semi-blind channel estimation; recursive least-squares algorithm; sum of product algorithm 2022, 22, 2004. [[https://doi.org/10.3390/622062204](https://doi.org/10.3390/622062204)]([https://doi.org/10.3390/622062204](https://doi.org/10.3390/622062204))
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 2 Design Methodology of the UWB Antenna  
### Geometry of the Proposed Antenna  
The top and side perspectives of the presented UWB antenna are depicted in Figure 1. The antenna has a small footprint of only 15 mm \(\times\) 10 mm (L \(\times\) W). The antenna geometry has a relative electrical permittivity and a loss tangent of 2.2 and 0.002, respectively, with a thickness of 0.254 mm, and is etched on the Rogers 5880/Duroid substrate. The proposed UWB antenna consists of a Coplaner Waveguide (CPW) feedline and a rectangular patch with several stubs and slots. An additional stub is added to the modified rectangular patch radiator to enhance impedance bandwidth and get WLAN spectrum rejection. The proposed antenna is of the CPW feedline which allows for the inherent advantages of broadband performance, decreased dispersion, and ease of manufacture.  
The following are the optimal dimensions of the proposed notch band UWB antenna: W = 10, L= 15, H = 0.254, P\({}_{\text{I}}\) = 9.2, P\({}_{\text{W}}\) = 5.4, C\({}_{\text{L}}\) = 4, C\({}_{\text{W}}\) = 3.5, s = 0.75, x = 0.8, S\({}_{\text{L}}\) = 1.25, S\({}_{\text{W}}\) = 6, d = 0.4, G\({}_{\text{L}}\) = 3.19, and G\({}_{\text{W}}\) = 2.65 (All dimensions are in millimeters).
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 1 Introduction
For the remainder of this paper, we shall refer to the _Named Data Networking (NDN)_ architecture as an ICN instantiation, as it is today the only ICN approach that supports all of the IoT basic requirements [7]. It should be highlighted, nevertheless, that NDN has been built upon the CCNx original network semantics and thus, the considerations presented in this paper apply to approaches that share the same network architectural semantics. The remainder paper is organized as follows. Section 2 goes over the notion of pull and push communication models. Section 3 explains mechanisms that have been developed to support push-based communications in ICN, and in NDN in particular. Section 4 debates on guidelines for the integration of NDN push-based communication support in IoT. Section 5 describes related work, while conclusions are provided in Section 6.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 4 Emerging Applications
### Guesture Sensing
Classifiers are necessary to distinguish different action gestures, such as swiping the band from left to right or from right to left. Advanced machine learning and classifiers need to be developed to recognize micro gestures such as click and virtual slider. A recent advancement in interactive sensing is Google's Project Soli, which is a new interactive sensing technology for gesture recognition based on millimeter wave radar [47, 80]. The Soli sensor was designed as a 60 GHz FMCW radar and the entire system including antennas was integrated as a compact solid-state semiconductor device, i.e. a radar sensor chip. The radar chip is a miniature and low-power device that has no moving parts and can be manufactured inexpensively at scale. The Soli sensor has been demonstrated to be an indeed viable, powerful and promising technology that can enhance and improve user interaction. A broad range of applications of this technology can be envisioned, including wearable and mobile interaction, gaming control, smart home electronics, internet of things, augmented reality (AR) and virtual reality (VR), and other exciting applications. Soli envisions a future of a truly ubiquitous gesture interaction language that would allow users to control all smart computing devices with one universal gesture set, without bothering touching. Soli is still in early stage of development. However, once the technology is mastered, it is expected that the physical interface would disappear and our hand will naturally become the only interfacing device we would ever need for gesture sensing to control all things.
# A Vision-Based Bolt Looseness Detection Method for a Multi-Bolt Connection
###### Abstract  
Many vision-based bolt looseness detection methods that directly observe the bolts have been developed. However, these methods have many limitations in terms of the conditions and processes of their implementation. To address these problems, this paper proposed a fully automated vision-based bolt looseness detection method for a rigid multi-bolt connection. The proposed method combines digital shearing speckle pattern interferometry (DSSPI) and recurrent neural network (RNN) and involves capturing speckle fringe patterns under various looseness cases using the DSSPI system and classifying these patterns with an RNN model to detect the loose bolts. The proposed method can detect all the bolts within the measured surface at one time, which is efficient. On the other hand, it eliminates the need for prior information such as the initial angle and position of each bolt. It can even detect unseen bolts in multi-bolt connections, making it applicable for connections in complex structures in which occlusion often occurs. Additionally, the method eliminates the complex process of distortion rectification. These features make the method achieve a single-judgment time (four bolts at one detection) of only 4.70 millisecond with a detection accuracy over 99%, which has potential for the real-time detection of loose bolts in multi-bolt connections.  
structural health monitoring; multi-bolt connection; looseness; detection; shearography; recurrent neural network +
Footnote †: journal: Applied sciences
## 3 Results
### Validating the Quality of IMERG Data Based on Diurnal Variations
Figure 6: Diurnal precipitation cycle measured via the following parameters: Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI); the red and green lines represent POD and FAR, respectively, with ranges being shown on the left axis; the blue line represents CSI, with the range being shown on the right axis. The shadow represents the nighttime (20:00–08:00) for the local time. The diurnal cycle of FAR in all regions examined showed a similar pattern to that of POD. Similar CSI patterns were observed in North America, North Asia, and South Asia, with values peaking around 08:00 local time and decreasing in the afternoon. This may be due to morning weather conditions being conducive to lower errors in remote sensing observations.
# A Distributed RF Threat Sensing Architecture
## 4 Test Environment and Experiment Setup
### Experiment Setup and Measurements
#### 4.2.3 Drone Detection RF Profiling  
We also examined the potential use case of detecting RF activity of a malicious COTS drone, assuming transmitting within the typical regulation limits (EU WiFi band up to 20 dBm). In this scenario, after estimating the free space loss and considering the received interference levels due to wideband monitoring in crowded RF bands, the expected maximum detection range cannot exceed 50-100 m. Although such a detection range does not seem ideal for drone sensing activities, it may still serve the purpose for dual scope sensor use.  
To investigate the actual sensor behavior, we operated a DJI Mini 3 pro drone and recorded the RF activity (control signal and video streaming) at a distance of 150 m from the sensor, using an SMB200 20 GHz real-time Signal Hound spectrum analyzer that provides fast scanning speeds at 1 THz/s and increased sensitivity levels. During the first flight, the drone was configured on a dual band mode (2.4 GHz and 5.8 GHz), while on the second flight both control and video signals were set on the 2.4 GHz band. As depicted in Figure 11, the drone control signal appears with short duration spikes at \(-\)54.5 dBm, while the video streaming signal has a longer span but much lower power (<90 dBm), which cannot be detected by the proposed sensor. The subfigure on the left of Figure 11 represents the RF signals from the first flight where RF operation of the drone was restrained, from the remote control of the drone, to operate only in the 2.4 GHz band. As such, one can observe in the 2.4 GHz band both the RC signal as a spike and the video signal as a broadband RF  
Figure 10: Low-power jamming detection at 5.8 GHz band.  
Figure 9: High-power sweep jamming detection at 5.8 GHz band.  
signal. The subfigure on the right figure of Figure 11 represents the RF signals from the second flight where RF operation of the drone was allowed in both the 2.4 and 5.8 GHz bands. On this occasion, the RC signal remained in the 2.4 GHz band, but the broadband video signal in this occasion hopped into the 5.8 GHz band.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 1 Introduction  
Preprocessing is an age-old technique used for implementing algorithms, especially for difficult problems. In the field of parameterized complexity, preprocessing has gained formal recognition. A parameterized problem involves a parameter in addition to the input instance. The objective of parameterized complexity is to determine whether it is possible to design an algorithm that limits combinatorial explosion to the parameter while keeping the rest of the running time polynomial in the input size. Such an algorithm is known as a fixed-parameter algorithm, and a problem that can be solved by such an algorithm is called fixed-parameter tractable.  
It is well-known (see, for example, [1]) that a decidable parameterized problem is fixed-parameter tractable if and only if the input instance can be reduced in the polynomial time to an equivalent instance, whose size is a function of the parameter. Such an equivalent instance is said to be a kernel. The size of the kernel can be exponential or worse, resulting in a quest for fixed-parameter tractable problems with polynomial-sized kernels. Machinery to show the non-existence of polynomial-sized kernels [2, 3] and lower bounds for kernel sizes [4] under complex theoretic assumptions make the theory more interesting.  
In this article, we surveyed a tool called expansion lemma that helps reduce the size of the kernel. Its early origin was in the form of crown decomposition, used for obtaining the linear kernel for the Vertex Cover problem [5]; the specific lemma was identified as the tool behind the optimal \(O(k^{2})\) kernel for the Undirected Feedback Vertex Set problem [6]. Since then, several variations and extensions of the tool have been discovered. In this paper, we surveyed them along with their applications. We provided sketches of as many results as possible wherever the main (and likely only) tool for the application was the expansion lemma variation. For several problems, there were several other ideas involvedin obtaining the improved kernel beyond the tool. Moreover, wherever the main tool was the expansion lemma, we explained the algorithm in a self-contained way. For others, we indicated how the tool was applied; we often did not go into the other complicated technical details as they distract from the main purpose of the survey.  
**Organization of the paper.** In Section 2, we introduce basic terminologies and notations. We begin with the technical part of the paper in the following three sections. In Section 3, we introduce the crown decomposition technique, basic lemmas, and their application to obtain some improved kernels. In Section 4, we introduce the expansion lemma and its use for several problems in kernelization. Finally, in Section 5, we discuss the recent developments, including generalizations of the (basic) expansion lemma, along with their applications. We conclude with some possible future research directions in Section 6.
## 5 Conclusions  
This study focuses on the regional feedback of vegetation in the Northeast China (NEC) to the obvious climate change using GLASS GPP products as indicators. Results shows that, from 1982 to 2015, GPPGS across the NEC has an overall increasing trend (4.95 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.01\)) within which great spatial heterogeneity exists. Vegetation productivity of crops distributing in western NEC is the smallest but has the largest increment (7.47 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.01\)). On the contrary, vegetation productivity of forests is obvious larger than that of crops but only has slight increments in northern NEC (GKM: CR = 3.28 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.05\); LKM: CR = 5.35 g C/m\({}^{2}\). yr\({}^{-2}\), \(p<0.01\)) and non-significant variation in southern NEC (CBM).  
Correlation analysis between GPPGS and meteorological factors indicates that warming can improve vegetation productivity of crops as well as forests located in temperature-limited high latitude (GKM and LKM), while this positive effect vanishes in mid-latitude CBM. In addition, for crops of the NEC, apart from the facilitating effect of warming, human intervention plays a decisive role in vegetation productivity increasing over the last 30 years, which directly leads to the difference of crops and forest in GPPGS trend.  
Contrary to the slight or even non-significant variation of vegetation productivity in forested region, an obvious vegetation greening is found across the whole NEC. Correlation analysis between GPPGS and LIAGS over the last three decades illustrates that the simultaneously increment of GPPGS and LIAGS only exists in high-latitude NEC (GKM and LKM), while in other region, facilitating effect of greening on GPPGS may be offset by other factors, such as the shortage of solar radiation in CBM. The different variation trend of \(\mathrm{GPP_{GS}}\) and \(\mathrm{LAL_{GS}}\) reminds us that apart from focusing on vegetation growth dynamics (greening or browning), more attention should be paid on vegetation's feedback to climate as well as its role in carbon cycle.  
Conceptualization, L.H. and W.F.; methodology, L.H.; formal analysis, L.H.; writing--original draft preparation, L.H.; writing--review and editing, W.F., W.Y., H.R., and Y.C.; funding acquisition, W.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant No. 41971301) and the Major Research Development Program of China (2017 YFE0122400, 2016 YFD0300601).  
The data presented in this study are available on request from the corresponding author or from the reference website.  
We thank \"anonymous\" reviewers for their original insights. We are also immensely grateful to the editor for their comments on the manuscript.  
The authors declare no conflict of interest.
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### Virtual Sensor Networks (VSNs)  
This project is carrying out by Computer Network research laboratory by Colorado State University [41]. The project team has simulated a top-down clustering scheme, cluster tree based routing schemes, VSN self organization scheme on top of the cluster tree, and VSN based subsurface chemical plume monitoring system. Currently the project team members are working on random routing, virtual coordinates, and VSN support functions [41, 42, 43]. Virtual Sensor Networks are an emerging form of collaborative Wireless Sensor Networks (WSNs). It supports collaborative, resource efficient, and multi-purpose WSNs. These networks may involve dynamically varying subset of sensor nodes and/or users. A Virtual Sensor Network can be formed by providing logical connectivity among collaborative sensors. Nodes can be grouped into different VSNs based on the phenomenon they track (e.g., rock slides _vs._ animal crossing which is discussed in the Section 6.2) or the task they perform. Virtual Sensor Networks are expected to provide the protocol support for formation, usage, adaptation,and maintenance of subset of sensors collaborating on a specific task(s). It should make efficient use of intermediate nodes, networks, or other Virtual Sensor Networks to deliver messages across members of it. The main idea of Virtual Sensor Networks is collaboration and resource sharing. By doing so, nodes achieve application objectives in a more resource-efficient way.
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 1 Introduction
After the NSST, the size of each direction sub-band at each scale is the same as the original image, which greatly improves the image redundancy. The enhancement of the image coefficient redundancy allows the NSST to have the translation invariance. The NSST has been successfully applied in many fields. In the field of image processing, the NSST has been widely used. [PERSON], et al. (2013) proposed a new multi-focus image fusion algorithm based on the NSST [24]. [PERSON] et al. (2016) adopted the NSST and the fully constrained least squares unmixing (FCLSU) to the denoising of hyperspectral images [25]. [PERSON] et al. (2017) used the NSST based on the multislice fusion to the edge enhancement of liver CT images [26]. [PERSON] et al. (2018) presented an image enhancement method based on the NSST and the directional information measurement [27]. [PERSON] et al. (2019) performed the enhancement of hyperspectral remote sensing images based on improved fuzzy contrast in the NSST domain [28]. [PERSON] et al. (2020) applied the NSST to the fusion of multiple exposure images [29]. [PERSON] et al. (2021) made a change detection in SAR images based on the improved NSST and the multi-scale feature fusion CNN [30]. The NSST also has been increasingly applied in the seismic exploration field in recent years. [PERSON] et al. (2018) presented a denoising method of microseismic data noise by the NSST based on singular value decomposition [31]. [PERSON] et al. (2018) applied the NSST to petroleum seismic exploration denoising [32]. [PERSON] et al. (2020) proposed a seismic random noise attenuation based on PCC classification in the NSST Domain [33]. The existing traditional denoising methods have achieved certain effects in the process of attenuating coherent noise but they also have some problems. For example, some methods will produce some new interference and some will lead to uneven energy after attenuating, even damaging the effective signal. Based on the coherent noise characteristics of the vibrator seismic data and the multi-scale and multi-directional properties of Shearlet transform, coherent noise removal of the source array in NSST domain is proposed in this article. The method is applied in both the synthetic seismic data and the filed seismic data. After processing with this method, the coherent noise in the seismic data is greatly removed and the effective signal information is greatly protected. The analysis of the results demonstrates the effectiveness and practicability of the proposed method on seismic coherent noise removal.
###### Abstract  
Proton exchange membrane (PEM) fuel cells represent one of the most interesting systems for converting hydrogen from fossil or renewable fuels into electric power at low temperature. To prevent poisoning of fuel cell anodes, CO concentration has to be reduced to 10-100 ppm. To this aim, the preliminary catalytic preferential oxidation of CO may be used, provided that the catalyst effectively oxidizes CO, limiting as much as possible the oxidation of H\({}_{2}\). Presently, both high selectivity and CO conversion cannot be simultaneously achieved. In this work, a novel strategy for CO removal from H\({}_{2}\)-rich streams based on a CuO/CeO\({}_{2}\) reactive trap is proposed, exploiting both catalytic and adsorption properties of this material. The process occurs in two stages. In the first stage, one reactor, fed with a CO-containing stream, works as a CO-reactive adsorber, providing a CO-free mixture. In the second stage, the adsorbed CO is converted to CO\({}_{2}\) by O\({}_{2}\). By this approach it is possible to simultaneously get CO lower than the limiting value and avoid any H\({}_{2}\) oxidation with no O\({}_{2}\) in the feed stream to PEM. Experimental tests allowed the evaluation of the kinetic parameters of all the reaction mechanism steps. Model simulations were performed at varying operating parameters, showing that the positive effect of high contact times and low CO inlet concentration is significantly affected by the non-linear behavior of the CO reactive adsorption.  
CO preferential oxidation; hydrogen; purification; CO trap; CuO/CeO\({}_{2}\)