esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Complexity Analysis of Recurrence-Analysis-Based Detector
### Complexity Analysis of Cyclostationary-Feature-Based Detector  
In a blind context, the CFD is based on the reliable estimation of the cyclic spectrum [13; 61; 62]. The crest factor \(F_{c}\) of the cyclic spectrum can be used as a decision statistic [13]:  
\[F_{c}(\alpha)=\frac{\max_{\alpha}D(\alpha)}{\sqrt{\frac{1}{2N+1}\sum_{k=1}^{2 N+1}D^{2}(\alpha)}}\begin{array}{c}\mathcal{H}_{1}\\ \mathcal{H}_{0}\end{array}\lambda_{CFD} \tag{53}\]  
where \(\alpha\) stands for the cyclic frequency and \(D(\alpha)\) denotes the Cyclic Domain Profile (CDP). The computation of \(F_{c}\) requires six steps [13]. First, a Hamming sliding window \(w(n)\) is used to apodize the observed signal \(y(n)\). Then, we apply a first Fast Fourier Transform (FFT) and calculate the complex demodulates of the apodized signal. After that, we compute the spectral correlation of the different complex demodulates and apply a second FFT. At the end, we obtain the cyclic spectrum, and we compute the crest factor \(F_{c}\) of the cyclic spectrum.  
Let \(N\) be the number of samples contained in the observed signal \(y(n)\) and \(N_{p}\) be the number of samples contained in each apodized block of \(y(n)\). \(L=\frac{N_{p}}{4}\) denotes the decimation factor, and \(P=\frac{N}{L}\) is the number of apodized blocks from \(y(n)\). The apodization is carried out with a Hamming window [13; 61; 63]. Figure 16 summarizes the essential steps of \(F_{c}\) computation.  
Figure 15: Energy Detector process. \(\lambda_{ED}\) denotes the detection threshold of the ED.  
The apodization equation is defined as follows:  
\[r_{k}=w_{k}y_{k};\qquad k=1..N_{p} \tag{54}\]  
where \(w_{k}\) denotes the Hamming window coefficients, \(y_{k}\) is the samples of the observed signal \(y(n)\), and \(r_{k}\) becomes the coefficients resulting from the apodization.  
The apodization step requires \(PN_{p}\) multiplication operations. The second step in the CFD detection process is the first FFT applied on apodized blocks. The complexity of FFT computation is known as \(N_{p}\log_{2}N_{p}\). Consequently, the \(P\) apodized blocks require \(PN_{p}log_{2}N_{p}\) multiplication operations. The complex demodulates computation requires \(PN_{p}^{2}\) multiplication operations, and the step of complex demodulates multiplication requires \(P^{2}N_{p}^{2}\) multiplication operations.  
The second FFT with \(P\) data points to obtain the estimation of cyclic spectrum requires \(N_{p}^{2}P\log_{2}P\) multiplication operation and the calculation of crest factor \(F_{c}\) alone requires \(2N+1\) multiplication operations.  
Finally, the algorithmic complexity of CFD, \(C_{CFD}\), is  
\[C_{CFD}=34N^{2}+64 NL+10N+2 \tag{55}\]
###### Abstract  
This paper presents rain attenuation effects on the performance of the full-duplex link in a tropical region based on one-year measurement data at 73.5- and 83.5-GHz E-band for distances of 1.8 km (longer links) and 300 m (shorter links). The measured rain attenuations were analyzed for four links, and the throughput degradation due to rain was investigated. The findings from this work showed that the rain attenuation for both frequencies (73.5 and 83.5 GHz) of E-band links are the same. The rain rates above 108 and 193 mm/h caused an outage for the longer and shorter links, respectively. The 73.5 and 83.5 GHz bands can support the full-duplex wireless back-haul link under rainy conditions with outage probability of \(2.9\times 10^{-4}\) and \(6\times 10^{-5}\) for the longer and shorter links, respectively. This work also finds that the heavy rain with rain rates above 80 mm/h for long link and 110 mm/h for short link causes about 94% and 0.90% degradation of maximum throughput. The application of these findings would help improve the architecture and service of full-duplex wireless E-band links that are established at other sites and in other tropical areas.  
E-band; wireless back-haul; 73 GHz; 83 GHz; rain attenuation; outage probability; tropical region +
Footnote †: journal: Applied Sciences
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 4 Optimal Power Dispatch with Fault Accommodation
### Fault Accommodation and Optimization
Through the objective function and its constraints, all the objectives of the optimal power dispatch strategy under generator faults can be achieved. The control diagram of the power dispatch is shown in Figure 10. When the WF controller receives the power demand from the TSO, the optimal power references will be calculated and then sent to all the WTs according to the proposed power dispatch strategy. All the WTs will track these power references by their original turbine controllers. The power output signals will be fed back to the WF controller for the optimization process in the next static state.
###### Abstract
Another flexible control approach is proposed in [19], where the FFC reference is determined by the fault condition and predefined constants from the grid code. Furthermore, the approach proposed in [20] guarantees safe operation of the inverter during voltage sags by calculating the appropriate reference FFC according to the equivalent impedance and the voltage sag characteristics, avoiding active power oscillations, and limiting FFC to the maximum value allowed by the inverter. The IIDG models discussed in [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20] are too complicated for protection studies in large networks. Therefore, IIDGs are, typically, modeled simply as a constant current source, showing the impact of their capacity and location on the coordination of overcurrent relays in distribution networks and micro-grids with different topologies [21; 22; 23; 24; 25]. More elaborate is the BFFC-based model proposed in [26], as a current limiting element in parallel with the filter capacitor. Ref. [27] uses this model to develop a protection strategy for microgrids. Furthermore, Ref. [28] uses current-limiting characteristics for grid-following and grid-forming IIDGs as constraints for the protection coordination in microgrids. Other protection studies [29; 30; 31; 32; 33] incorporate IIDG models with instantaneous FFCs. A BFFC injection, required by the Spanish grid code, is used in [29], presenting a communication-based directional relay system. Ref. [30] discussed protection coordination in distribution networks while considering UFFC injection. Ref. [31] enhances overcurrent protection coordination using the IIDG model that provides limited power by using orthogonal components of the grid voltages, while a current limitation approach has been proposed in [32] to restrict FFC injection. Ref. [33] uses an IIDG model that complies fully with the network code requirements for BFFC injection, to perform a sensitivity analysis of a test network's transient short circuit current response. Furthermore, Ref. [34] summarizes the FFC contribution by IIDGs and the network protection operation.
## 3 Results and Discussion
### Comparison of Positioning Deviations according to the Current State of the System
From the median point of view, it could be assumed that, for all the RTK receivers, the positioning deviations were significantly influenced by the current state of the system.
## 3 Numerical Analysis of Flow Behaviour inside the Steam Ejector
### Analysis of the Flow Structure inside the Steam Ejector
#### 3.3.1 The Division of the Entire Flow Region  
The Mach number contour of the static pressure is shown in Figure 5. The fluid flow characteristics of the shock train, the diamond wave, the primary jet core, choking position, shock-mixing layer, the normal shock wave and oblique shock waves can be observed. The flow region is divided into three zones as indicated by the static pressure distribution along the ejector axis in Figure 6. The first flow area is the \"inducing zone\". In this zone, the supersonic primary fluid forms a primary jet core after spraying from the nozzle [10]. A low-pressure region was generated at the nozzle outlet. The secondary fluid was pumped into the mixing chamber driven by the pressure difference. Then the secondary fluid was accelerated by the viscous transport capacity of a high-speed shock-mixing layer and reached sonic at the end of the zone. Meanwhile, the intensity of the diamond wave [56] decreased until it disappeared. Under the combined action of the pressure difference and viscous transport capacity of shock-mixing layer, the purpose of pumping and accelerating the secondary fluid has been accomplished.  
Figure 4: Wall pressure distribution under different back pressure.  
The second flow area is the \"chocking zone\". It can be seen from Figure 6 that the static pressure was constant, and the velocity shows no obvious changes in this zone. One reason is that the energy of the primary jet core had been partly consumed in the previous flow. The other reason was that the secondary fluid flowed in a fixed cross-sectional area (i.e., the effective area [57]). The kinetic and internal energy was continuously added with the increasing of the mass flow rate of the secondary fluid. The two streams began to mix and are at co-velocity at the end of this zone. The mixed fluid was choked and exceeded sonic. Besides, the size of the effective area played an important role in determining the pumping performance of the ejector.  
The third flow area is the \"discharging zone\" where the momentum of the primary fluid decreased while the secondary fluid's momentum correspondingly increased. However, the primary fluid is still in the supersonic state. A normal shock wave which resulted in the velocity dropping and the pressure rising abruptly was generated to make the mixed fluid overcome the backpressure to discharge from the diffuser section. Meanwhile, the shock-mixing layer was broken as the normal shock wave appeared. The velocity of the two streams completely mixed after a series of oblique shocks. In this zone, the back pressure was the main influencing factor that directly determined the position and intensity of the normal shock wave and oblique shock.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 6 Discussion  
According to the AODV RFC [14], if a node obtains more replies for its RREQ message, it also processes them, and if they have a fresher sequence number, or the same sequence number, but the number of hops is smaller, then the routing table is updated with the new route. This is something to keep in mind when implementing the TARA method.  
If there are alternative paths and the route discovery is delayed according to the trust of the nodes along those paths, the first RREP message is the fastest, meaning that path is the most trusted one. RREP for the other alternative paths may take longer to arrive, implying that those paths pass through less trusted nodes. The less trusted path could be shorter (have fewer hops), and AODV will accept it. The goal of our method is not to change the implementation of AODV. Our interlayer that delays packets should discard the repeated RREP messages to the same RREQ message.  
Another aspect to consider is the behavior of the DSR protocol, which stores several route entries for the same destination. The problem is identical to AODV, and the interlayer can address it in the same way.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 5 Additive Manufacturing in Printing Nanocellulose Composites
### Microextrusion-Based Printing  
This technique has been employed for various applications in tissue engineering, for example, neural, cardiac, cartilage, skeletal muscle, and liver [127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138]. Microextrusion-based printing utilizes a thermally-regulated dispensing system, a video recorder, a piezoelectric humidifier, and a fiber-optic laser source to constantly dispense biomaterials and biological agents via a nozzle attached to the bioink cartridge.  
The microextrusion-based technique generates thick vertical structures, enhances cell density, increases viscosity, and facilitates different polymerization mechanisms. Nevertheless, they are susceptible to nozzle blockage, the accomplishing interlayer interaction is complex, and the nozzle shearing could decrease the viability of cells in high-resolution structures [139]. [PERSON] et al. fabricated a highly porous cell-loaded GelMA framework by utilizing a microextrusion-based printing method for the application in tissue engineering and achieved an increase in the viability of cells up to 97% due to the printing accuracy of the construct compared to their previous findings [140; 141]. Microextrusion printers effectively used nanocellulose-alginate biopolymers to generate human chondrocytes-loaded composites that sustained remarkable proliferation and cell survival under in vitro culture. This suggests that the nanocellulose-based bioink could be employed to construct articular cartilage tissue [142].
## 2 The Spectrum Signature of SMPSs
### Spectral Superposition Characteristics of Diode-Based Parallel Redundant SMPS System
Figure 4: The proposed LSTM model. For the actual output voltage of a power supply, the ripple contains many harmonic frequency components. The amplitudes and initial companions of these harmonics are different, making the instantaneous amplitude of a power supply's output voltage have strong randomness in the time domain. Mathematical modelling of this situation can be simplified to a mathematical model in which two sets of random variables take extreme values. Assume that the output voltage of each branch power supply obeys a normal distribution:  
\[v_{i}\sim N(A_{0i},A_{1i}) \tag{7}\]  
Among them, \(A_{0i}\) is the output DC component of the \(i\)-\(th\) branch, and \(A_{1i}\) is the variance of the voltage change. Assuming \(A_{01}\geq A_{0i},i\geq 2\), the redundant power output can be expressed as:  
\[v_{o}(t)=a_{1}(t)v_{1}(t)+\sum_{i=2}^{n}(a_{i}(t)v_{i}(t)+(1-a_{i}(t)v_{1}(t))) \tag{8}\]  
where \(a_{1}(t)\) is 1 when the output voltage of \(v_{1}\) is not in the overlapping area, otherwise it is 0, specifically:  
\[a_{1}(t)=\left\{ \begin{array}{c}1,\ v_{1}(t)\
ot\in U_{1}(t)\\ 0,v_{1}(t)\in U_{1}(t)\end{array} \right. \tag{9}\]  
where \(U_{1}(t)\) is the part that overlaps with other branch supply voltages. For the overlapping region, the output voltage is determined by the branch voltage competition result, so  
\[a_{i}(t)=\left\{ \begin{array}{c}1,\ v_{i}(t)\geq v_{j}(t)\\ 0,v_{i}(t)<v_{j}(t)\end{array} \right.\ j\geq 1,j\
eq i \tag{10}\]  
In the overlapping area, the voltages of each branch compete fairly, and the result of the competition is related to the proportion of the voltage of each branch in the overlapping area of branch 1. Taking the dual power supply redundant system as an example, the proportion of each branch in the redundant output voltage in the overlapping area is 1/2, so the proportion of the voltage component of each branch in the redundant output voltage is:  
\[\frac{P(v_{0}(t)=v_{1}(t0))}{P(v_{0}(t)=v_{2}(t0))}=\frac{P(v_{1}(t)\
ot\in U _{1}(t))+\frac{1}{2}P(v_{1}(t)\in U_{1}(t))}{\frac{1}{2}P(v_{1}(t)\in U_{1}(t))} \tag{11}\]  
Figure 5: The defines of the sliding windows. It can be seen from the above formula that in the diode-based parallel redundant system, the total output voltage includes the voltage output components of each branch, and the proportion is related to the overlapping area of the actual output voltage range of the branch. At the same time, in this process, the redundant output voltage signal can be regarded as the signal obtained by recombining the power supply voltage of each branch after sampling. Based on the above characteristics, the spectral information of each branch power supply can be extracted from the total output ripple of a redundant power supply.
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
## 2 System Model  
### RC-LDPC Codes Based on Kite Codes  
Kite codes, refs. [24; 25] designed for noisy channels, are represented by \(C\in[\infty,k;p]\). These codes are defined by their \(k\) dimension and a p-sequence \(\mathbf{p}=(p_{1},p_{2},\ldots,p_{t},\ldots)\), where \(1<p_{t}<1\) for \(t\geq 0\). A codeword \(\mathbf{c}=(\mathbf{u},\mathbf{v})\in C[\infty,k;p]\) consists of an information vector \(\mathbf{u}=(u_{1},u_{2},\ldots,u_{k-1})\) and a parity sequence \(\mathbf{v}=(v_{1},v_{2},\ldots,v_{t},\ldots)\).  
The prefix Kite code with length \(n\) can be represented as \(C[n,k]\) and is a systematic linear code with \(r_{b}=n\times k\) parity bits. Its parity-check matrix \(\mathbf{H}\) can be expressed as  
\[\mathbf{H}=(\mathbf{H}\mathbf{u},\mathbf{H}\mathbf{v}), \tag{1}\]  
where \(\mathbf{H}\mathbf{u}\), corresponding to information bits, is a binary matrix of size \(r\times k\) and \(\mathbf{H}\mathbf{v}\), corresponding to parity bits, is a dual-diagonal matrix of size \(r\times r\). It can be seen that \(\mathbf{H}\mathbf{u}\) is a random-like binary matrix whose entries are governed by the p-sequence \(\mathbf{p}\):  
\[\mathbf{H}\mathbf{v}=\left[\begin{array}{ccccc}1&&&&\\ 1&1&&\\ &1&\ddots&&\\ &&\ddots&1\\ &&&1&1\end{array}\right]_{r\times r}. \tag{2}\]  
Therefore, a Kite code is a unique LDPC code with a degree distribution determined by the p-sequence. We can use the sum-product algorithm during decoding. The construction of Kite codes allows for the generation of specific parity-check bits corresponding to each coding rate, with the higher-rate codes being able to serve as prefix codes of lower-rate codes. From Kite codes, RC-LDPC codes can be obtained, but in order to ensure that all prefix codes are good enough, the optimization of \(p_{i}\), which determines the degree distribution of Kite code, is necessary.  
[PERSON] et al. partitioned coding rates from 0.05 to 1 into 19 intervals in their work [25], assuming \(p_{t}\) to be a step function of the coding rates. They enforced \(p_{t}=q_{l}\) whenever \(t\) satisfied the coding rate \(k/(t+k)\in(0.05l,0.05(l+1)]\), utilizing 19 possible different values \(q_{l},1\leq l\leq 19\). Therefore, the optimization process can be simplified to selecting \(\mathbf{q}=(q_{1},q_{2},\ldots,q_{19})\) for use in the greedy optimization algorithm. As \(q_{l}\) represents a check-node degree distribution varying with coding rates, the RC-LDPC codes based on Kite codes (RC Kite codes) perform well after optimization. [PERSON] et al. also provided an optimization algorithm in [25] that achieved the approximate right-regular distribution.
## 3 Comparison Traditional Access Control vs. Security Access Control Based on Three Layers  
This section will highlight the primary differentiation between traditional BYOD security policies and contemporary approaches to BYOD security, as depicted in Figure 1. According to [PERSON] [31], traditional security policies primarily focus on protecting devices, networks, data, and applications. These policies encompass network access control, mobile device management, mobile application management, and enterprise mobility management policies, as described in more detail in Section 3.1. While these access control policies may provide security mechanisms and solutions to protect the company's network, data, and devices, they often overlook risk control access policies. Consequently, they fail to meet the requirement for attack detection. To address this limitation, [PERSON] et al. [1] proposed an enhanced BYOD access control approach based on three security layers, aiming to comprehensively address security and privacy requirements that fulfill organizational security needs and user privacy. This three-layered protection approach, as described in Section 3.2, includes an access control policy and corresponding mechanism within each layer. A comparison between this security approach and traditional security policies and their respective shortcomings is outlined in Table 2:  
* Traditional security approaches are often deemed insufficient and potentially vulnerable in providing the required level of protection [1,31]. For example, network access control policies in traditional security heavily rely on mobile device management (MDM) technology for authenticating and managing BYOD devices within the organization. In contrast, ideal BYOD security emphasizes identification access control methods, such as biometrics and two-factor authentication. Additionally, ideal security policies for information protection encompass advanced measures like communication access control, encryption, virtual private networks (VPNs), and data wiping. On the other hand, traditional security approaches utilize mobile information management (MIM) to enforce information management policies.
* Traditional security policies lack the inclusion of risk access control policies, despite their importance in BYOD security, as recommended by [PERSON] et al. [1].
* Traditional security policies fall short in adequately addressing all privacy requirements due to limitations in policy techniques and defense mechanisms [31]. These limitations serve as a motivation to address the challenges surrounding privacy and security and enhance security policies through the implementation of three layers of security policies, thus meeting the privacy requirements of BYOD security.  
Figure 1: Comparison between traditional security policy and BYOD security.
## 3 Numerical Simulations
### Parameter Identification under Gaussian and Non-Gaussian Noises
#### 3.3.2 Parameter Identification under Non-Gaussian Noises  
Parameter estimation at different non-Gaussian noise levels (low noise level conditions) is also conducted, as shown in Figure 9. The identified results of the stiffness and damping also can converge fairly fast. However, the identified damping has deviated from the true value when the non-Gaussian noise level reaches 10%. Furthermore, the comparison of the estimation results between the EKF with  
Figure 8: Identification results under different Gaussian noise levels: (**a**,**c**,**e**,**g**) are the stiffness \(k_{1},k_{2},k_{3}\), and \(k_{4}\), respectively; (**b**,**d**,**f**,**h**) are the damping \(c_{1}\), \(c_{2}\), \(c_{3}\), and \(c_{4}\), respectively.  
Gaussian and that with non-Gaussian is listed in Table 1. It can be clearly seen that the identification accuracies of the stiffness and damping with EKF are affected more by the non-Gaussian noise than that by the Gaussian noise. When the non-Gaussian noise level reaches 10%, the maximum estimation error of damping reaches 19.436%. Hence, the identified values by EKF may be inaccurate when subjected to a certain level of non-Gaussian noise.  
Figure 9: Identification results under different levels of non-Gaussian noise: (**a**,**c**,**c**,**g**) are the stiffness \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\), respectively; (**b**,**d**,**f**,**h**) are the damping \(c_{1}\), \(c_{2}\), \(c_{3}\), and \(c_{4}\), respectively.