esa-sceva/satcom-chunk-collection huggingface dataset:
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 3 Results
### Optimal and Control Dough Microstructure  
Dough microstructures obtained for dough samples with and without germinated soybean and lentil addition are shown in Figure 7.  
The images obtained show a dough structure with red areas interconnected with green areas in a homogeneous and continuous matrix. The red-colored areas indicate the presence of protein, whereas the green-colored areas depict the presence of starch. These different colors for dough compounds were determined by the two fluorochromes used in the EFLM technique namely rhodamine B and fluorescein. Rhodamin B is labeling in red the protein present in the dough system, whereas the fluorescein is labeling the starch granules in green [43]. From both images obtained it may be seen that starch granules are surrounded by a continuous protein network forming a fine dough matrix structure. For the optimal dough sample, it may be seen a slightly higher red area compared to the control due to the high protein content of this dough sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Optimal Sample** & **Control** \\ \hline A: SGF (\%) & 5.60 & 0.00 \\ B: LGF (\%) & 3.62 & 0.00 \\ FN (s) & 280.51 b  & 350.0 a  \\ WA (\%) & 53.25 b  & 54.30 a  \\ Tol (s) & 200.19 b  & 214.00 a  \\ D250 (mb) & 359.80 b  & 394.00 a  \\ D450 (mb) & 933.32 b  & 943.00 a  \\ P (mm) & 119.95 a  & 104.00 b  \\ L (mm) & 56.20 b  & 72.00 a  \\ W (10\({}^{-4}\) J) & 228.64 b  & 301.00 a  \\ P/L (adim.) & 1.99 a  & 1.43 b  \\ H’m (mL) & 70.50 a  & 65.90 b  \\ VT (mL) & 1684.98 a  & 1532.00 b  \\ VR (mL) & 1305.19 a  & 1228.00 b  \\ CR (\%) & 78.78 b  & 80.10 a  \\ G\({}^{\prime}\) (Pa) & 41,384.57 a  & 29,290.00 b  \\ G\({}^{\prime\prime}\) (Pa) & 16,296.27 a  & 10,780.00 b  \\ tan \(\delta\) (adim.) & 0.39 a  & 0.37 a  \\ T\({}_{i}\) (\({}^{\circ}\)C) & 51.20 b  & 51.90 a  \\ T\({}_{\text{max}}\) (C) & 74.67 a  & 73.40 b  \\ \hline \hline \end{tabular}  
* A: SGF—solvent germinated flour (%), B: LGF—lentil germinated flour (%), R\({}^{2}\), FN—lathing number, WA—water absorption, Tol—tolerance to kneading, D250—dough consistency after 250 s, D50—dough consistency after 450 s, P—dough tenacity, L—dough extensibility, W—baking strength, P/L—curve configuration ratio, H’m—maximum height of gaseous production, VT—total CO\({}_{2}\) volume production, VR—the volume of the gas retained in the dough at the end of the test, CR—retention coefficient, G\({}^{\prime}\)—elastic modulus, G\({}^{\prime\prime}\)—viscous modulus, tan 8—loss tangent, T\({}_{i}\)—initial gelatinitization temperature, T\({}_{\text{max}}\)—maximum gelatinization temperature, \({}^{\text{ab}}\) values followed by distinct letters in the same row are significantly different (\(p<0.05\)).  
\end{table}
Table 7: Optimal vs. control sample properties.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 3 Calculation Process of Smoothing Theory  
### Dft  
Firstly, the discrete signal is transformed from the spatial domain to the wavenumber domain by two-dimensional DFT. The reason for this is that it is more complicated to deal with discrete data in the spatial domain and simpler in the wavenumber domain. The specific steps are as follows.  
Equation (1) is a two-dimensional continuous Fourier transform formula which transforms the two-dimensional spatial signal \(p\) (\(x,y\)) to the wavenumber domain to obtain the angular spectrum \(P\) (\(k_{x}\), \(k_{y}\)).  
\[P\big{(}k_{x},k_{y}\big{)}=\int\limits_{0}^{L_{x}}\int\limits_{0}^{L_{y}}p(x,y )H(x,y)e^{-i(k_{x}x+k_{y}y)}dxdy \tag{1}\]  
in which \(H\) (\(x,y\)) is the spatial window function. \(L_{x}\) and \(L_{y}\) are the length and width of the test space, respectively.  
For a finite test space, the edges are discontinuous. In order to reduce the error caused by discontinuity, a spatial window function \(H\) (\(x\), \(y\)) is introduced. The 8-point Tukey window function can be written as follows:  
\[H(x,y)=\left\{\begin{array}{cc}1&x\leq\frac{L_{x}}{2}-w_{x}\;and\;y\leq \frac{L_{y}}{2}-w_{y}\\ \left[\frac{1}{2}-\frac{1}{2}\cos\left(\frac{\pi(x-L_{x}/2)}{w_{x}}\right) \right]\\ \times\left[\frac{1}{2}-\frac{1}{2}\cos\left(\frac{\pi\left(y-L_{y}/2\right) }{w_{y}}\right)\right]&\frac{L_{x}}{2}-w_{x}<x<\frac{L_{x}}{2}or\;\frac{L_{y} }{2}-w_{y}<y<\frac{L_{y}}{2}\\ 0&x\geq\frac{L_{x}}{2}or\;y\geq\frac{L_{y}}{2}\end{array}\right. \tag{2}\]  
in which \(w_{x}=8\Delta x\) is the width of the spatial window function along the \(x\) direction, and \(w_{y}=8\Delta y\) is the width of the spatial window function along the \(y\) direction.  
In engineering applications, the two-dimensional spatial continuous signal needs to be discretized, and then the discrete space is processed by DFT to transform the discrete data from the spatial domain to the wavenumber domain, which can provide the angular spectrum value,  
\[P(n_{x}\Delta k_{x},n_{y}\Delta k_{y})=\Delta x\Delta y\sum\limits_{m_{x}=0}^ {N_{x}-1}\sum\limits_{m_{y}=0}^{N_{y}-1}p(m_{x}\Delta x,m_{y}\Delta y)H\big{(} m_{x}\Delta x,m_{y}\Delta y\big{)}e^{-2\pi\left(\frac{n_{x}m_{y}}{N_{x}}+ \frac{n_{y}m_{y}}{N_{y}}\right)} \tag{3}\]  
in which \(p\) (\(m_{x}\Delta x\), \(m_{y}\Delta y\)) represents a two-dimensional spatial discrete signal. \(\Delta x\) and \(\Delta y\) are the sampling intervals along the \(x\) and \(y\) directions, respectively. \(m_{x}\) and \(m_{y}\) are the discrete spatial variables along the \(x\) and \(y\) directions, respectively. \(n_{x}\) and \(n_{y}\) are discrete variables along the \(k_{x}\) and \(k_{y}\) directions in the wavenumber domain, respectively. \(N_{x}\) and \(N_{y}\) represent the number of discrete points in the spatial domain (or wavenumber domain), respectively. \(\Delta k_{x}=2\pi/N_{x}\) and \(\Delta k_{y}=2\pi/N_{y}\) are wavenumber domain intervals, and \(k_{x}=n_{x}\Delta k_{x}\) and \(k_{y}=n_{y}\Delta k_{y}\) are variables. \(j\) is an imaginary unit.
## 3 Results
### Reliability  
The reliability of different software tools includes software availability, fault tolerance and recoverability.  
Software availability evaluates the accessibility of software, ensuring its consistent functioning without incidents. To evaluate software availability, incidents of 'not responding' are calculated from the error register. Moreover, connection and software stability are assessed. The only software that encountered the 'not responding' issue during the experiment was Ladybug. This issue occurred when setting high-quality simulations. Another software with low stability is AST, which relies on an internet connection. CitySim often lagged due to the large number of calculations required to perform simulations. Other software tools were rather stable, as no incidents were recorded during experiments.  
Fault tolerance considers errors and failures as the main aspects indicating software faults. The most severe faults were encountered when using CitySim, as the file could not be reopened due to errors in the model. The ENVI-met application for SketchUp also faced issues while reopening the files, leading to information and data loss. Ladybug and GreenScenario identified some errors in the scripts based on the visual script method. Although AST did not face any errors or failures, it does not integrate the function helping to identify errors.  
Recoverability analyses whether the software can recover data in case of interruption. AST software does not integrate a function allowing a project to be saved; therefore, data recovery is not possible. CitySim revealed issues while reopening files. ENVI-met had no failures or issues with data loss; however, the SketchUp application could not recover information after reopening the file. GreenScenario and Ladybug, which are based on the Rhino interface, can recover files in case of failure.
## 1 Introduction  
Wireless virtual reality (VR) offers a seamless user experience by removing the restricted mobility of conventional wired VR headsets. The greater freedom of movement offered by wireless VR allows users to more conveniently immerse in a virtual world which in turn results in improved quality of experience (QoE). The delivery of such VR video services over networks requires data rates of \(\geq\)1.6 Gbps [1] depending on the video resolution and anticipated level of interaction with the virtual world. Furthermore, the motion-to-photon (MTP) latency, i.e., the delay between the viewer's head movement and the head-mounted display's (HMD's) change reflecting this movement, must be kept as \(\leq\)20 ms [1]. In the context of home Wi-Fi, for example, network latencies of \(\leq\)10 ms for fair experience, \(\leq\)7 ms for comfortable experience, \(\leq\)5 ms for ideal experience have been recommended in [1]. As a consequence, the network contribution to this MTP latency budget also needs to be significantly improved to guarantee satisfactory QoE. To accommodate such high performance wireless data applications in terms of throughput and latency, several standards have been developed and are being worked on regarding advanced wireless and cellular network technologies. In particular, standardization of the required wireless connectivity takes place both in the field of wireless local area networks (WLAN), and broadband cellular networks such as fifth generation (5G), beyond fifth generation (B5G) and sixth generation (6G) networks.  
Contributing to the understanding of network-induced temporal disturbances in wireless VR systems, this paper reports an experimental study that focuses on the VR video frame freeze length characteristics of a commercial TPCAST wireless VR solution [2]. Similar as other wireless systems that target ultra-reliable low latency communications, the commercial TPCAST wireless VR solution also utilizes beamforming, higher order modulation constellations, and millimeter wave technology in the 60 GHz band. As such, the results and statistical models provided in this paper may be considered as representative for the freeze characteristics in such practical settings. In the following, an overview of the related wireless technologies targeting ultra-reliable low latency communications, experimental studies, and wireless VR testbeds are provided leading to the motivation, research questions, and contributions of the work reported in this paper.
## 3 Results and Discussion
Therefore, initially, C\({}_{\text{gs}}\) was larger and more influenced by the changes in the IFP structure. Figure 11 summarizes the frequency and capacitance variations as a function of IFP lengths in order to verify the dependence of the frequency upon the capacitances. The drain-side IFP increased C\({}_{\text{gs}}\) to moderate f\({}_{\text{T}}\), as shown in Equation (4). A dominated sum of C\({}_{\text{gs}}\) and C\({}_{\text{gd}}\) is responsible for the degradation in f\({}_{\text{T}}\). To alleviate this factor, the IFP length should be reduced. However, this also results in an increase in R\({}_{\text{g}}\) that affects the f\({}_{\text{max}}\), as shown in Equation (5). Consequently, to achieve both high f\({}_{\text{T}}\) and f\({}_{\text{max}}\), the gate-head optimization is inevitably required. Hence, 0.2-0.3 \(\upmu\)m IFP structures were found to be the final optimum structures and are thus highly recommended for a comprehensive usage of the HEMT device. A Si\({}_{3}\)N\({}_{4}\) layer was used below the T-shaped gate because it is a good surface passivation film which provides a high dielectric constant and breakdown strength and minimizes the current collapse [2]. The frequency variation from differences in Si\({}_{3}\)N\({}_{4}\) passivation thickness was also analyzed. Figure 12a shows that the passivation thickness changed from 30 to 100 nm, with consequential cut-off and maximum frequency effects, as shown in Figure 12b. The value of C\({}_{\text{gs}}\) decreased with an increase in the Si\({}_{3}\)N\({}_{4}\) passivation layer thickness, which consequently increased the f\({}_{\text{T}}\). However, C\({}_{\text{gd}}\) showed only a little increase with an increase in the Si\({}_{3}\)N\({}_{4}\) layer thickness. The highest f\({}_{\text{T}}\) and f\({}_{\text{max}}\) were 14 and 37.8 GHz with Si\({}_{3}\)N\({}_{4}\) layer thicknesses of 100 and 70 nm, respectively. Hence, because of the dependence of device's RF parameters on the passivation layer thickness, the Si\({}_{3}\)N\({}_{4}\) layer thickness was also regarded as an important factor for power applications of the HEMT device. Figure 9: Gains as a function of frequency for various IFP lengths: (**a**) cut-off frequency at 0 dB; (**b**) maximum frequency at 0 dB.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Windowing Based Methods
#### 3.2.5. Martin-Martin's Method  
In [130], a window designing method for cosine-modulated transmultiplexers is proposed, which is called generalized windowing method for transmultiplexers (GWMT). A 4 th order generalized cosine window function is expressed as  
\[w(n)=\sum_{i=0}^{3}(-1)^{i}A_{i}\cos\left(\frac{2\pi in}{N-1}\right), \tag{52}\]  
where \(n=0,1,2,\cdots,N-1\), and \(A_{i}\) are the weights of the terms for \(i=0,1,2,3\). The designed window function is normalized as  
\[\sum_{i=0}^{3}A_{i}=1. \tag{53}\]  
Figure 4: Frequency responses of Hamming window, Gaussian window and the new window proposed in [127].  
Figure 5: Frequency responses of Gaussian window and the new window proposed in [129].  
The weights \(A_{i}\) and the cut-off frequency \(\omega_{c}\) of the ideal low-pass filter can be adjusted by minimizing the objective function  
\[\phi(\mathbf{x})=-\frac{1}{F}\sum_{f=0}^{F-1}\frac{E_{f}(\mathbf{x})}{\beta E_{ \text{ICI}}^{(f)}(\mathbf{x})+(1-\beta)E_{\text{ISI}}^{(f)}(\mathbf{x})}, \tag{54}\]  
and  
\[\begin{cases} E_{f}=\frac{1}{\pi}\int_{0}^{\pi}\left(|T_{ff}(e^{i\omega})|^{2} \right)d\omega,\\ E_{ICI}^{(f)}=\frac{1}{\pi}\int_{0}^{\pi}\left(\sum_{l=0,l\
eq f}^{F-1}|T_{ ff}(e^{i\omega})|^{2}\right)d\omega,\\ E_{ISI}^{(f)}=\frac{1}{\pi}\int_{0}^{\pi}\left\{\|T_{ff}\|_{1}-|T_{ ff}(e^{i\omega})|\right\}^{2}d\omega,\\ \|T_{ff}\|_{1}=\frac{1}{\pi}\int_{0}^{\pi}|T_{ff}(e^{i\omega})|d\omega,\end{cases} \tag{55}\]  
where \(F\) is the channel number, \(\beta\) is the compromise factor satisfying \(0\leq\beta\leq 1\), \(T\) describes the transfer function, \(\mathbf{x}=[A_{0},A_{1},A_{2},\omega_{c}]\) denotes the adjustable parameter vector, and \(A_{3}\) can be obtained by the Equation (53). This optimization problem is solved by the Nelder-Mead simplex minimization algorithm.  
Figure 6 displays the frequency responses of [PERSON] window, [PERSON] window and the generated window by GWMT method. The length of the filters is \(N=2 KF\), where \(K=3\), \(F=32\). It is seen that the GWMT method based window has the highest signal-to-overall-interference ratio levels. The comments, pros and cons of the above windowing based methods are summarized in Table 3.  
Figure 6: Frequency responses of [PERSON] window, [PERSON] window and the new window by the GWMT [130].
## 3 Results and Discussion
### XRD and TEM  
The crystal structure of the formed C-NMs is the same as that of the bulk graphite target, as proven by the XRD patterns of Figure 3. Particularly, they correspond to graphite with a hexagonal crystal structure (Space group: P63/mmc) [28].  
Figure 2: (**a**) The ratio of the integrated intensity of the absorption peaks from the polyynes to that of the background absorption versus pulse energy. (**b**) The integrated intensity of the absorption peaks from the polyynes, C-NMs and background absorption versus pulse energy.  
TEM images of the formed C-NMs are shown in Figure 4 (see also Figures S6 and S7). A variety of C-NMs with different morphologies are produced, such as large NPs with sizes ranging from \(\sim\)50 to 260 nm (Figure 4a) (the plot in Figure 4b provides an indication of the deviation of the NP shape from the perfectly spherical shape); small spherical NPs with sizes ranging from \(\sim\)2 to 5 nm (Figure 4c); and sheet-like structures with sizes ranging a few hundred nanometers (Figure 4d). The small NPs are formed by the nucleation of the plasma plume carbon species from the target material ablation, as explained in detail in the Introduction of this paper, while the larger NPs of Figure 4a may correspond to material melted droplets that are directly ejected from the target material during ablation due to the high fluence used [2]. The flake-like structures are single- or multilayer graphene (or rGO) (multilayer structure similar to the bulk graphite target) that are formed from the exfoliation of the target from the laser beam [11]. The band at \(\sim\)262 nm observed in the UV-vis spectra is due to the formed C-NMs.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 2 FDA-MIMO Basic Principle and Jamming Model
### Jamming Model against FDA-MIMO  
DRFM can realize the functions of storage, modulation, and forwarding of radio frequency signals. By modulating the received signal, it can cover the real target and even generate a large number of false target signals, forming an electronic countermeasure technology that integrates deception, suppression, and interference. The false target generated by DRFM can be expressed as  
\[\begin{array}{ll}y_{m,n}&=\xi_{j}e^{2\pi\left[-f_{0}\frac{2(j+\Delta r)}{c} -m\Delta f^{2}\frac{2(j+\Delta r)}{c}+\frac{n\sin\theta_{j}}{2}+\frac{n\sin \theta_{j}}{2}\right]}\\ &=\xi_{j}{}^{\prime}e^{2\pi\left[-m\Delta f^{2}\frac{(j+\Delta r)}{c}+\frac{n \sin\theta_{j}}{2}+\frac{n\sin\theta_{j}}{2}\right]}\end{array} \tag{14}\]  
where \(\xi_{j}\) is the jamming complex coefficient, and \(\xi_{j}{}^{\prime}=\xi_{j}e^{-2\pi f_{0}\frac{2(j+\Delta r)}{c}}\). Figure 3 shows the signal processing flow related to the range information at the receiving side of the FDA-MIMO radar.  
Figure 3: The flow of signal processing related to range information at the FDA-MIMO receiver.  
At the estimation level of the range bin, the range parameter is jointly determined by the time delay \(\tau\) and the pulse repetition period \(T_{p}\), that is  
\[r=\text{mod}(c\times\tau/2,R_{\text{max}}) \tag{15}\]  
where \(\text{mod}(a,b)\) represents the remainder of \(a/b\) and \(R_{\text{max}}=T_{p}\times c\) is the maximum unambiguous range. The array factor of the signal at the receiving side can be expressed as  
\[AF=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}e^{j2\pi(-m\Delta f\frac{2\pi}{c}+m\frac{ \text{min}\theta}{2}+n\frac{\text{min}\theta}{2})} \tag{16}\]  
Thus, its normalized antenna pattern can be expressed as  
\[\begin{array}{ll}B(r,\theta)&=\left|\frac{\mathbf{w}_{0}^{\text{H}}\cdot \mathbf{S}(r,\theta)}{MN}\right|^{2}\\ &=\left|\frac{1}{N}\sum_{n=0}^{N-1}e^{j2\pi n\frac{(\text{min}\theta-i\text{ min}\theta_{0})}{2}}\right|^{2}\times\left|\frac{1}{M}\sum_{m=0}^{M-1}e^{j2\pi[-m \Delta f\frac{2(r-r_{0})}{c}+m\frac{(\text{min}\theta-i\text{min}\theta_{0})} {2}]}\right|^{2}\end{array} \tag{17}\]  
When the adaptive beamforming algorithm is used for processing, the normalized gain is  
\[G=B(r,\theta)\left|\sum_{r=r_{j},\theta=\theta_{0}}=\left|\frac{1}{M}\sum_{m= 1}^{M}e^{j2\pi(-m\frac{2\alpha r}{c})}\right|^{2}\times\left|\frac{1}{N}\sum_ {n=1}^{N}e^{\varrho}\right|^{2}<1 \tag{18}\]  
Without considering the time delay modulation, which is \(\Delta r=0\), there are  
\[G=B(r,\theta)\left|\sum_{r=r_{j},\theta=\theta_{0}}=\left|\frac{1}{M}\sum_{m= 1}^{M}e^{\varrho}\right|^{2}\times\left|\frac{1}{N}\sum_{n=1}^{N}e^{\varrho} \right|^{2}=1 \tag{19}\]  
In Equations (18) and (19), the gain at the desired target is the maximum gain. The gain obtained at the side lobe position where the jamming is located is reduced, and its power will be greatly reduced, the interference performance is poor.
# Design and Analysis of Systematic Batched Network Codes
## 5 Inner Code for Systematic Batches
### Side Information for Message Packet Recovery  
We discuss some general properties involved in the recovery of message packets at the node \(u+\), which provide guidance for the design of new inner codes. The recoverability of a message packet depends on the knowledge of \(\mathbf{H}^{(u)}\), which is delivered by the coefficient vectors. Note that the original purpose of the coefficient vectors is for the destination nodeto decode the batches. A natural question to consider is the following: if more information is delivered from node \(u\) to \(u+\), could more message packets be recovered at node \(u+\)?  
**Proposition 3**.: _Suppose that \(\mathbf{X}\), \(\mathbf{H}^{(u)}\), \(\Phi^{(u)}\) and \(\mathbf{E}^{(u)}\) in (6) are mutually independent. \(\Phi^{(u)}\) and \(\mathbf{X}\) are conditionally independent given \(\mathbf{H}^{(u+)}\) and \(\mathbf{Y}^{(u+)}\)._  
Proof.: See Appendix A.  
The above proposition states that \(\Phi^{(u)}\) and \(\mathbf{X}\) are conditionally independent at the node \(u+\). The next proposition further shows that knowing \(\Phi^{(u)}\) at the node \(u+\) does not help to recover more message packets at node \(u+\). It actually shows a stronger result that knowing any variable that is independent with \(\mathbf{X}\) given \(\mathbf{H}^{(u+)}\) and \(\mathbf{Y}^{(u+)}\) at the node \(u+\) does not help in recovering more message packets at the node \(u+\).  
**Proposition 4**.: _Suppose that \(\mathbf{X}\), \(\mathbf{H}^{(u)}\), \(\Phi^{(u)}\) and \(\mathbf{E}^{(u)}\) in (6) are mutually independent. Let \(S\) be any random variable that is conditionally independent with \(\mathbf{X}\) given \(\mathbf{H}^{(u+)}\) and \(\mathbf{Y}^{(u+)}\). Given the instance of \(\mathbf{H}^{(u+)}\) and \(\mathbf{Y}^{(u+)}\) at the node \(u+\), further knowing the instance of \(S\) at the node \(u+\) does not help to recover more message packets at \(u+\)._  
Proof.: See Appendix A.  
Based on the above analysis, we know that the existing coefficient vectors are sufficient for the recovery of message packets at the intermediate nodes. In other words, it is not necessary for a network node to add further information to assist the recovery of the message packets in the following nodes.