esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2015**, _15_, 9404-9426; doi:10.3390/s150409404  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 2**  
\({}^{1}\) School of Computer and Information, Hefei University of Technology, Tunxi Road 193,  
Hefei 230009, China  
\({}^{2}\) School of Electronic and Information Engineering, Beihang University, XueYuan Road 37,  
Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (T.J.); [EMAIL_ADDRESS] (D.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-551-6290-1552.  
Academic Editor: [PERSON]  
_Received: 22 December 2014 / Accepted: 14 April 2015 / Published: 21 April 2015_  
**Abstract:** In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments.  
**Keywords:** interference detection; time-frequency (TF) analysis; cross-terms; Wigner-Ville distribution (WVD); smoothed pseudo Wigner-Ville distribution (SPWVD); reassignment method
## 2 Method
### Low Frequency Region f1
#### 2.2.2 Back Propagation
The ideal value for \(\Omega\) minimizes the reconstruction energy  
\[\Omega_{\text{opt}}=\min_{\Omega}\{E_{\Omega}\} \tag{11}\]  
where  
\[E_{\Omega}\propto\sum_{n}^{N}\left|\frac{\partial P(\mathbf{Y}_{n},\omega)}{ \partial\mathbf{n}}\right|^{2}. \tag{12}\]  
The energy \(E\) is proportional to the sum of the squared pressure amplitudes on the considered structure. In a first step, the linear equation system is solved for integers from \(\Omega=0\) to \(\Omega=10\) and the reconstruction energy is plotted over \(\Omega\). Around the local minimum, the linear equation system is again solved, this time in steps of 0.1. Typically, the iteration is truncated after the first decimal place. An example of reconstruction energy over \(\Omega\) is illustrated in Figure 8 together with the condition number of \(G\times\Psi\) in Equation (9). Near \(\Omega_{\text{opt}}\), both the signal energy and the matrix condition number tend to be low. Alternatively, the parameter \(\Omega\) can be tuned manually to find the best reconstruction visually; the correct solution tends to create the sharpest edges at the instrument boundaries, with pressure amplitudes near 0. This is a typical result of the truncation effect: the finite extent of the source causes an acoustic short-circuit. At the boundary, even strong elongations of the sound board create hardly any pressure fluctuations, since air flows around the sound board. The effect can be observed in Figure 9.  
The result of the MEM is one source term \(Q(\mathbf{Y}_{n},\omega)\) for each equivalent source on the harpsichord sound board. Below the Nyquist frequency, these 1500 equivalent sources approximate the sound field of a real harpsichord. This is true for the complete half-space above the sound board. The radiation of numerous musical instruments has been measured using the described microphone array setup and the MEM, like grand piano [35; 42], vihuela [23; 43], guitars [43; 44],  
Figure 8: Exemplary plot of reconstruction energy (black) and condition number (gray) over \(\Omega\). The ordinate is logarithmic and aligned at the minimum at \(\Omega_{\text{opt}}=3.6\). drums [23; 41; 45], flutes [41; 45], and the New Ireland kulepa-ganeg [46]. The method is so robust that the geometry of the instruments becomes visible, as depicted in \[G(r,\omega)=\frac{e^{i(kr)}}{r} \tag{7}\]0
## 2 Materials and Methods
### Gene Expression Analysis  
Total RNA was extracted from three biological replicates, each consisting of shoots pooled from at least 5 plants. Plant tissue stored in an ultra-freezer was ground in liquid nitrogen and extracted with modified methods described by [PERSON] et al. [42]. At the same time, RNA extraction buffer (100 mM Tris-HCl pH 9.0, 2% \(\beta\)-mercaptoethanolhanol, 1% SDS) and Tri-Reagent were added to the samples homogenised in liquid nitrogen. The isolated RNA (5 \(\upmu\)g) was treated with DNase (DNA-free, Promega, Madison, WI, USA) and the synthesis of cDNA was performed with Superscript II Reverse Transcriptase (Invitrogen, Waltham, MA, USA) at 42 \({}^{\circ}\)C for 1 h using an oligo dT primer, according to the manufacturer's protocol. Oligonucleotides for _PsGolS_ mRNA (AJ243815: GenBank) were forward 5\({}^{\prime}\)CACGAAACTGA AACGTGCAT3\({}^{\prime}\)/reverse 5\({}^{\prime}\)-TCAGTTAAGCTGCCCAAGGT3\({}^{\prime}\); for _PsRS_ mRNA (AJ426475: GenBank): forward 5\({}^{\prime}\)GGAACAAACGGACACGAACT3\({}^{\prime}\)/reverse 5\({}^{\prime}\)AACTGGTTCCACCAGAGATGG3\({}^{\prime}\); and for the _EF1a_ (X96555: GenBank) an internal standard, forward 5\({}^{\prime}\)TTCCCTTCGTTCCCATCTCG3\({}^{\prime}\)/reverse 5\({}^{\prime}\)TACAAGCATACCGGCTTCA3\({}^{\prime}\)[43,44]. Semiquantitative PCR was performed on 2 \(\upmu\)L cDNA (equivalent to approximately 0.5 \(\upmu\)g starting RNA), 1 \(\upmu\)M each primer, 0.2 mM of each dNTP, 2.0 mM MgCl\({}_{2}\), GoTaq buffer, and 0.75 U of GoTaq polymerase (Promega) in 30 \(\upmu\)L total volume. The following conditions were used for the PCR amplification of _PsGolS_ (962 bp) and _PsRS_ (1287 bp): initial denaturation at 94 \({}^{\circ}\)C (4 min); touchdown [94 \({}^{\circ}\)C (30 s), 68 to 61 \({}^{\circ}\)C (30 s), 72 \({}^{\circ}\)C (60 s)] (one cycle for each temperature) and 25 cycles at 94 \({}^{\circ}\)C (30 s), 61 \({}^{\circ}\)C (30 s) and 72 \({}^{\circ}\)C (60 s) followed by extension at 72 \({}^{\circ}\)C (10 min). Amplification of _EF1a_ mRNA (236 bp) was carried out with 2 \(\upmu\)L of cDNA with the following conditions set for PCR: initial denaturation at 94 \({}^{\circ}\)C (4 min); touchdown cycles [94 \({}^{\circ}\)C (15 s), 68 to 61 \({}^{\circ}\)C (15 s), 72 \({}^{\circ}\)C (30 s)] (one cycle for each temperature) and 20 cycles at 94 \({}^{\circ}\)C (15 s), 61 \({}^{\circ}\)C (15 s) and 72 \({}^{\circ}\)C (30 s) followed by extension at 72 \({}^{\circ}\)C (5 min). The intensity of bands was evaluated in a gel image analysis system (Gene Tools, Syngene, Cambridge, UK). Expression levels of _PsGolS_ and _PsRS_ were normalised with _EF_ expression.
## 2 Materials and Method
### Calculation of the Simulated Spectrum  
For any input crystal, after entering the cell parameters \((a,b,c,\alpha,\beta,\gamma)\), space group number, and atomic coordinates occupancy, the equivalent atomic coordinates are automatically generated through Wyckoff position analysis to ensure compliance with space group symmetry. All possible crystal face indices \((hkl)\) are traversed, and the corresponding diffraction angle \(\theta\) is calculated according to [PERSON]'s equation \(n\lambda=2d_{hkl}\sin\theta\), where the interplanar spacing \(d_{hkl}\) is directly calculated from the cell parameters and crystal face indices. Based on the atomic scattering factor \(f_{j}\) and Debye-Waller factor \(B_{j}\), the structure factor for each \((hkl)\) plane is calculated:  
\[F_{hkl}=\sum_{j}f_{j}e^{-B_{j}(\sin\theta/\lambda)^{2}}e^{2\pi i(hx_{j}+ky_{j} +tz_{j})} \tag{1}\]  
Efficiency can be improved by calculating only the independent reflections through symmetry reduction, as described in [PERSON] et al. [42]. The squared modulus of the structure factor \(|F_{hkl}|^{2}\) is multiplied by the Lorentz-polarization factor, and the instrumental broadening function (such as a Gaussian/Lorentzian mixed peak shape) is overlaid to obtain the continuous diffraction spectrum.
## 5 Future Issues in Next Forwarding Node Selection in UWSNs  
Forwarding nodes selection in UWSNs is a major challenge due to the use of acoustic waves, a communication medium that causes high energy consumption and low packet delivery ratio. Based on our comparative analysis and discussions, the future issues in next forwarding nodes selection can be listed as follows.  
* _Energy-efficient and reliable forwarding_: the use of different metrics during the process of selecting the next forwarding nodes has a direct impact on the overall performance of the protocol [4; 35]. Therefore, it is essential to design and develop an energy-efficient and reliable forwarding nodes selection algorithm based on residual energy and suitable link quality, to balance the energy consumption, improve the delivery ratio, and further optimize the network lifetime [7; 15].
* _Route cost calculation_: as discussed previously, an efficient cost calculation has a direct impact on selecting the best node among neighbors [11; 15]. Therefore, convenient route cost should be carefully designed using suitable metrics such as depth, link quality, and residual energy, which in turn improve the network lifetime and reduce network overhead.
* _Security-aware forwarding_: In military applications, security issues have become one of the major challenges in selecting the next forwarding nodes in UWSNs [14]. However, few studies have been conducted in this field. Hence, designing and developing a secure forwarding node selection algorithm is necessary to ensure data delivery [4; 13].
* _Congestion control-based forwarding_: congestion is another important issue that affects path selection during the forwarding process [3]. However, this problem has not been addressed in the literature. Therefore, it is recommended to design and develop a congestion control algorithm to control packet loss in applications that suffer from this problem, such as event-driven data reporting models [2; 8; 9].
* _Void-aware forwarding_: Communication void is another major issue in selecting the next forwarding nodes and has a direct impact on the packet delivery ratio. However, existing void-handling algorithms in UWSNs have been divided into two groups. The first group attempts to find a recovery path, whereas the second group broadcasts beacon messages provided by the sink. These two kinds of solution consume high energy, increase end-to-end delay, and reduce the network lifetime [25; 31]. Therefore, it is essential to design an efficient, void-aware forwarding algorithm that discovers the void nodes locally and avoids these nodes in the data forwarding process [15; 38].
## 6 Conclusions and Future Work  
This paper proposed an integrated flight system framework for the trajectory navigation of formation in a continuous differentiable and combined action space. Then, an affine-based DMPC module reference layer was developed to enhance the fast convergence of the proposed algorithm. To demonstrate the effectiveness of the proposed method, we conducted a series of simulations involving a formation with varying numbers of agents and obstacles. The goal was to navigate through square areas characterized by random obstacle motion while adapting configurations and avoiding collisions. The results show that our algorithm notably reduces the collisions' number during online learning, with the average number of collisions being approximately 1/500 of AMCOFS and MADDPG. Moreover, the success rate of the task operated in the learning phase is 41% higher compared to MADDPG, 47% higher compared to AMCOFS. These results indicate that the proposed method effectively facilitates formation navigation and obstacle avoidance, which can ensure the unrigid configuration change continuously.  
In future work, we will extend this approach to the three-dimensional environment to enhance practical applicability. Moreover, there are several important interests that require attention, which include variable topology structures and reconstruction response mechanisms to fit unknown environments.  
Conceptualization, C.K. and J.X.; methodology and writing--original draft, C.K.; writing--review and editing, Y.B. and W.T.; visualization C.K.; supervision J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 4 Numerical Experiments
### Impact of the Correlation Structure of the Arrival Process
#### 4.2.2 Frame Asynchronous Coded-Slotted ALOHA  
The same arrival process as in the precedent Section was used with frame asynchronous CSA. In this case, new packets do not wait for transmission (or wait randomly and independently until their first replica is transmitted). Similarly, there is not now a \"natural\" frame length for decoding the packets. As a consequence, it seems intuitive that the LRD or SRD correlations existing in the arrivals persist, without significant alterations, and could make performance much worse, either for PLR or for average delay. This is indeed the case, as Figure 13 shows, where we used moderated values of the parameters, \(H=0.75\) and \(r_{1}=0.5\) (we compare this case with respect to the case with no correlations, i.e., Poisson point arrivals, cf. Figure 8). We see clearly in these graphs that, in the lightly loaded regime, PLR is similar for \(k=1,2,3\) and a fixed \(\lambda_{a}/k\), but deviates when the normalized input traffic exceeds \(\approx\)0.7, and is worse for \(k\)-MUD with \(k>1\). To explain this, recall that due to LRD and SRD correlations, large bursts of packets can arrive in short time periods to the system (with non-negligible probability), and a fraction of these are replicated \(\ell\) times. Thus, \(k\)-MUD with persistent correlations can amplify the total number of replicas to be transmitted within the current transmission window, increasing the system load substantially.  
Figures 14 and 15 show the effect of increasing the parameter that models the degree of SRD or LRD, respectively, maintaining the other parameter in a moderate value. We can see that in both cases the performance is degraded.  
Figure 12: Simulated PLR (**left**) and Mean Delay (**right**) for FS-CSA versus \(\lambda_{a}/k\), with \(\Lambda(x)=0.86x^{3}+0.14x^{8}\), \(n=200\) and \(k=\{1,2,3\}\), and correlated arrivals with marginal Poissonian distribution.  
Figure 13: Simulated PLR (**left**) and Mean Delay (**right**) for FA-CSA versus \(\lambda_{a}/k\), with \(\Lambda(x)=0.86x^{3}+0.14x^{8}\), \(n=200\) and \(k=\{1,2,3\}\), and correlated arrivals (\(H=0.75\) and \(r_{1}=0.5\)) with marginal Poissonian distribution.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### The Need for CR-Based UAVs
#### 4.1.1 Security  
Communication security is critically important for UAVs. These aircraft are also considered critical systems, a security issue, which can be used to manage confidential information, thus, in a UAV, may often represent a serious safety issue. Some conventional attacks, such as jamming and location spoofing (sometimes referred to as GPS spoofing or GNSS spoofing), could lead the base station to lose the UAV. This problem is particularly evident in overcrowded or hostile areas.  
Jamming is an attack in the physical layer which causes a high interference to a spectrum band by overloading it. It may provoke the attacked devices to present an excessive energy consumption due to package retransmission, or even interrupt its communication channel [7]. When a CR is under a jamming attack, it simply understands that spectrum band as being busy or overcrowded, and then it switches its transmission to a new channel, thus avoiding the attack. In order to succeed in their jamming attempt, the attackers would have to keep detecting the new CR frequency and switch to the same frequency. For this, however, the attackers themselves would need to also be equipped with a CR device and more sophisticated algorithms.  
Location spoofing attacks also happen in the physical layer, and they have become more frequent. It happens when an attacker uses a signal that is stronger than and mimics the attributes of a genuine location satellite signal to spoof the receiver [7]. By using a location spoofing attack, attackers may capture UAVs and/or take control of their flight path. Such attacks have become easy to launch [28]. However, location spoofing attacks are  
Figure 3: USRP B200 by Ettus Research. Its price is currently 5905.00.  
much more complex to execute to CR devices. The work in [29] provides some strategies for spoofing attack detection and countermeasure solutions in CR networks.  
Therefore, UAVs may benefit from CR technology security. Conventional jamming and GPS spoofing strategies may be easily avoided by CR. These attacks are only possible to CR when the attacker is also using a CR with different and specific strategies; therefore, attacking a CR device is a more complex problem than attacking conventional wireless devices. Herein, we define the most prominent aspects regarding the paramount importance of CR in the context of UAVs.
# Numerical and Experimental Analysis of Labyrinth Seals with Rhomboidal Cells
###### Abstract  
The labyrinth seals are devices commonly used in turbomachinery to reduce hot gas leakages through engine clearances, which adversely affect the gas turbine performance. For this reason, in the last decades, many in-depth analyses and optimization studies were carried out on this topic using experimental, analytical and numerical approaches. In this work, an innovative rhomboidal pattern is presented, obtained through Computational Fluid Dynamics (CFD) simulations, which is more dissipative than commonly used honeycomb cells. The experiments, performed using a Test Article that reproduces a stage and the next stator of a real low-pressure turbine suitably scaled, allowed to validate the numerical results in a situation that closely approximates the real one of use. The results obtained show that the leakages flow fraction of the total mass flow rate that bypasses the blade, which is 29.4% using a honeycomb pattern, is reduced to 27% with rhomboidal cells. The experimental results also made it possible to verify that the new pattern also behaves well from a thermal point of view, giving rise to temperature differences with respect to the honeycomb of less than 1%.  
labyrinth seal; rhomboidal cell; low-pressure turbine +
Footnote †: journal: Applied Sciences
## 3 System Description
### Communication Protocol  
In order to gather information from all the panels in the solar farm, a tree-structure using XBee modules is implemented. XBee modules have been chosen because they are easy to work with and have proven adequate performance in different kinds of applications [36; 37; 38].  
There is a _Monitoring Center_ attached to a central computer that stores and displays relevant data from all the panels. The _Monitoring Center_ is aware of the solar farm distribution: how many towers there are, how many panels in each tower, _etc._ This information must be passed on to the other nodes during the setup stage. Therefore, it is the _Monitoring Center_ that must be powered first, _i.e._, before connecting the Central Nodes in the towers or the so-called _Smart Modules_.  
Once the _Monitoring Center_ is powered, the towers' Central Nodes must be connected one at a time. This will allow the coordinator to identify them and assign them an individual name as well as the most adequate communication channel (CH) and network identifier (PAN). Other relevant information such as number of panels in the tower is also passed over from the _Monitoring Center_ to the Central Nodes (Figure 5). In this way, all the towers are connected to the coordinator and ready to provide it with the information requested.  
The following step is similar to the one just described; only it takes place in each of the towers in the solar farm. Each Central Node \"knows\" how many PV panels are associated to its tower, and a process begins by means of which all the _Smart Modules_ are named as they are connected (one at a time). An appropriate communication channel and a PAN are also defined so that information can be exchanged as reliably as possible. A protocol that prevents nearby towers from using the same communication channel is also implemented to avoid interference. However, even if this was not done, having all the nodes named differently would guarantee that no cross-talking is produced.  
Figure 5: Central node prototype.  
After the configuration process is complete, the Central Nodes will periodically request each of its _Smart Modules_ to send data (polling). This information will be stored until requested by the _Monitoring Center_. Finally, all the data are transferred to the main computer, where they are stored, displayed and/or analyzed. All the processes outlined above are associated to protocols and digital streams that will be briefly commented on later.
## 4 Acquisition and Analysis of VMF Experimental Data
### Conclusion Analysis  
From Table 1, it can be shown that the elevations of the CHAN, TWTF and URM stations are larger, and the interpolation accuracy of the CHAN and TWTF stations is higher, with the root mean square error less than 40 mm. Therefore, there is no direct relationship between station elevation and interpolation accuracy.  
From the elevation difference in Figure 5, using the VMF1 grid, the elevation differences of URUM and BJFS are 1069.34 m and 691.35 m, respectively, and using the VMF3 grid, the elevation difference of URUM station is 901.52 m, which is relatively large. According to the data, Beijing is located in the North China Plain, adjacent to the Yanshan Mountains in the northwest, with an elevation difference of 1200 m; Urumqi is at the north foot of the Tianshan Mountains. To the south is the towering Tianshan Mountains, and to the north is the vast Junggar Basin, with an elevation difference of 1000 m. In the above terrain, the elevation difference between the four grid points around the selected survey  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station Name** & **Longitude/(\({}^{\circ}\))** & **Latitude/(\({}^{\circ}\))** & **Height/m** \\ \hline SHAO & 121.20 & 31.10 & 22.11 \\ JFNG & 114.49 & 30.51 & 71.30 \\ TCMS & 120.99 & 24.80 & 77.30 \\ BJFS & 115.89 & 39.61 & 87.40 \\ HKSL & 113.93 & 22.37 & 95.26 \\ CHAN & 125.44 & 43.79 & 268.30 \\ TWTF & 121.16 & 24.95 & 203.10 \\ URUM & 87.6 & 43.81 & 749.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Station information.  
Figure 4: ZTD root mean square error under four solution schemes.  
station and the interpolation station is large, so there will be large errors in the tropospheric delay elevation correction, resulting in the low accuracy of the interpolation results.