esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Morris Results  
The Morris-at-a-time (MOAT) sampling method is used to generate model parameter samples and analyze model parameter sensitivity. The previous literature [60] showed that the MOAT method is relatively stable, which means that the influence of model calculation times on the results is not prominent. In this study, it was assumed that model parameters obeyed uniform distribution. Horizontal number \(p=4\) was used to generate 3100 samples of 100 groups of parameters (CN2 parameter sampling results are shown in Figure 3). Figures 4 and 5 depict the modified mean (\(\mu^{*}\)) and the standard deviationdistributions (\(\sigma\)) of each parameter, respectively, under the condition that NSE is taken as the objective function.  
Figure 4: Modified mean (\(\mu^{*}\)) of each parameter of the Morris screening method.  
Figure 3: Schematic diagram of parameter sampling of Morris-at-a-time (MOAT) sampling method, taking CN2 as an example.  
Figure 4 shows that the modified means (\(\mu^{*}\)) of SMTMP and SOL_Z parameters were 21.5627 and 16.3527, respectively, larger than those of others, indicating that they were the two most sensitive parameters and had a significant influence on the output results of the SWAT model. After them, SFTMP, HRU_SLP, ALPHA_BNK, SOL_AWC, SOL_BD, ALPHA_BF, GWQMN, SOL_K, RCHRG_DP, and CANMX were relatively essential parameters that had a specific influence on the model output. The modified means (\(\mu^{*}\)) of EPCO, OV_N, CH_N1, CH_N2, REVAPMN, SURLAG, and TLAPS were less than 0.1, indicating that they had a negligible effect on SWAT model results, i.e., insensitive parameters.  
Figure 5 shows that the standard deviations (\(\sigma\)) of parameters SOL_Z and SMTMP were 31.6447 and 28.8774, respectively, indicating that these two parameters interacted with other parameters except themselves. After them, the standard deviations (\(\sigma\)) of parameters ALPHA_BNK, ALPHA_BF, SFTMP, HRU_SLP, and SOL_AWC were relatively large, which proves that the interactions between the above and other parameters were relatively significant. The standard deviations (\(\sigma\)) of parameters REVAPMN, SURLAG, and TLAPS were 0, suggesting that there was almost no interaction between these and other parameters.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 5 Space-Time Relay Transmission
The authors in [27] proposed a distributed interference management technique for \(2\times 2\)\(X\) interference channel under a distributed manner. The two transmitters send the intended symbols to one desired receiver, where the interference signalsare aligned based on the delayed CSIT knowledge. The distributed STIA technique has high transmitter energy consumption and a single receiver has to decode multiple intended symbols over the time slots, which will have significant impact on the decoding complexity at the receiver-end. The proposed R-STIA transmission technique drastically decreases the interference misalignment for two intended receivers by the introduction of an additional receiver, efficient alignment and reconstruction of the interference signals. The transmitter energy consumption is subsequently reduced, since the proposed scheme activates only two transmitters over the time slots. The introduction of an additional (unintended) receiver aid in improving the desired signal strength drastically at the intended receiver-end. By considering the cooperation between the intended and unintended receivers, the decoding complexity is considerably reduced due to less interference misalignment at the intended receiver-end [28]. Although there are several other approaches for interference alignment, the proposed scheme has interesting methods for aligning the interference signals; the user 1 received signals from transmitters, \(Y_{(1)}^{[1]}\) and relay, \(Y_{(1)}^{[R]}\), are recovered and reconstructed at the unintended (additional) receiver during time slot 1. Using multiple antennas, the transmitters signals, \([Y_{(3)}^{[1]},Y_{(2)}^{[1]}]\) and the relay signals, \(Y_{(3)}^{[R]},Y_{(2)}^{[R]}\), are recovered at the unintended receiver-side, which assist in improving the receiver 1 signal strength drastically. Likewise, the received signals over time slot 2, \([Y_{(2)}^{[2]},Y_{(R)}^{[2]}]\) and time slot 3, \([Y_{(3)}^{[3]},Y_{(R)}^{[3]}]\), can also be recovered, respectively, from the interference signals received through the additional receiver.
###### Abstract  
In predicting the impact of vibrations propagated in a ground on newly designed construction objects, it is extremely important to reliably measure the time histories of velocity or acceleration at the site of the planned investment. As some studies show, the method of coupling accelerometers and geophones to the ground can affect not only the level of peak particle accelerations (PPA) or peak particle velocities (PPV)--commonly used in this type of evaluation--but also vibration frequency distribution of recorded signals. This makes it difficult to compare and analyze the results obtained by various research teams. Conclusions based on this type of comparison may be wrong. For this reason, it is extremely important that reliable dynamic measurements related to the propagation of ground surface vibrations should be carried out using not only appropriately selected measuring equipment (with the required sensitivity and measurement ranges--both in the time domain and frequency domain), but also the measurement bases/setups used for mounting various types of transducers, whose natural frequencies will be outside the frequency range relevant to the possible impact of vibrations on buildings and human beings inside. The paper presents the results of modal analyses carried out with the use of Ansys Engineering Simulation and 3D Design Software (based on the Finite Element Method) for three different types of measuring bases used to coupling accelerometers to the ground. Measuring bases selected for numerical analyses were in the form of a stiff steel spike of an X-shaped cross section (a measuring base No. 1) and two steel spikes of L-shaped cross-sections (a measuring bases No. 2 and No. 3). In the places of screwed accelerometers (three different types of IEPE/ICP transducers of varying sensitivity and a weight) point masses were attached to the measuring bases. As a result of the analyses, it was possible to determine the impact of individual methods of coupling of accelerometers to the ground on the reliability of recorded ground surface accelerations. Among others it was concluded that in each analyzed case the lowest natural frequencies of the measuring bases with attached accelerometers significantly exceeded 100 Hz. The widest frequency band free of resonance vibrations can be provided by the measuring base No. 3 (L\(50\times 50\times 5\)).  
Keywords:modal analysis; measuring bases/setups; coupling to the ground; surface accelerations +
Footnote †: journal: Applied Soft. 2020, 10, 3128
# Experimental Study on Renewable Porous Carbon Dioxide Adsorbent Materials for Space Shuttles
## 2 Experimental Section
### Experimental Method  
#### 2.2.1 CO\({}_{2}\) Adsorption in the Solid Amine  
The adsorption calorimeter system used the PCT Proe and E and Calvet calorimeter device (PCT and C80, French) [28], and the accuracy of the adsorption calorimeter system was validated in refs. [29; 30]. The solid amine particles in the C80 battery were heated by a vacuum C80 at 50 \({}^{\circ}\)C for 20 h to remove the water and gas adsorbed by the solid amine particles in the air, serving as a pretreatment. The gas supply system was connected to the adsorption system which had three high-purity gas supply lines: carbon dioxide (CO\({}_{2}\)), nitrogen (N\({}_{2}\)), and helium (He). Nitrogen was used to drive the pneumatic valve in the PCT, and the sample cell in the C80 was volumetrically calibrated with helium. CO\({}_{2}\) was then fed into the sample cell as an adsorbent gas to meet the set pressure. Different target temperatures were controlled by C80. During the adsorption process, PCT and C80 were used to measure the adsorption capacity and heat synchronously. The test signal was recorded by the data acquisition system. Then, the isosteric heat of CO\({}_{2}\) adsorption in the solid amine could be measured directly by the software in C80.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Adsorbent** & **Solid Amine** \\ \hline Type & Sphere \\ Particle size (cm) & 0.02 \\ Particle density (g-cm\({}^{-3}\)) & 0.728 \\ BET (m\({}^{2}\).g\({}^{-1}\)) & 30.37 \\ Pore diameter (nm) & 2.5-40 \\ Micropore volume (cc\(\cdot\)g\({}^{-1}\)) & 0.167 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the adsorbent.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 5 GeoSOT-AS Based Regional and Time Segment ADS-B Data Extraction Algorithm
### Finest Granularity Matching  
This step of the operation consists in matching the finest granularity of the profile scale with the span range of latitude and longitude heights, as well as time to output the location of the finest level of coding retrieval, as described in Section 3. Determining the longitude values for extracting the maximum and minimum longitude surfaces of the spatio-temporal representation area, denoted as \(Dis_{L}=L_{max}-L_{min}\), the latitude of the maximum and minimum latitude surfaces of the spatiotemporal representation area, denoted as \(Dis_{B}=B_{max}-B_{min}\), the maximum and minimum values of the elevation of the spatiotemporal representation area, denoted as \(Dis_{H}=H_{max}-H_{min}\), and the span of the time range of the spatiotemporal representation area, denoted as \(Dis_{T}=T_{max}-T_{min}\).  
The latitude and longitude heights, as well as the temporal finest granularity are described in Section 3, and setting them to \(\Delta p,\Delta h,\Delta t\) constants give the coordinates of the key finest granularity corner points by a similar interpolation method (Equation (4)):  
\[\begin{split} x_{i}&=L_{min}+Dis_{L}/\Delta p\\ y_{i}&=B_{min}+Dis_{B}/\Delta p\\ h_{i}&=H_{min}+Dis_{H}/\Delta h\\ t_{i}&=T_{min}+Dis_{T}/\Delta t\end{split} \tag{4}\]  
Figure 8: ADS-B data extraction algorithm flow chart.  
where the most fine-grained matching process \(T\) is defined as follows: for the region corner point information \(P(x_{i},y_{i},h_{i},t_{i})\), where if \(P(x_{i},y_{i},h_{i},t_{i})\) is within the cell of grid dissection \(Cell_{i}\), i.e., there is \(P(x_{i},y_{i},h_{i},t_{i})\in\ Cell_{i}\), then it is indicated that \(Cell_{i}\) represents the retrieval area of range span \((x_{i},x_{i}+\Delta p),(y_{i},y_{i}+\Delta p),(h_{i},h_{i}+\Delta h)\), and \((t_{i},t_{i}+\Delta t)\) (Equations (5) and (6)):  
\[T(P(x,y,h,t))=\left\{\begin{array}{l}Cell_{i},P(x,y,h,t)\in Cell_{i}\\ 0,P(x,y,h,t)\
otin Cell_{i}\end{array}\right. \tag{5}\]  
Under this condition, the specific code form \(Code_{i}\) of \(Cell_{i}\) can be obtained according to the code conversion method in Section 4, which results in the mapping relationship \(f\) between the fine-grained grid set, and the retrieval range span.  
\[f:(\Lmin,\Lmax),(\Bmin,\Bmax),(\Hmin,\Hmax),(\Tmin,\Tmax)\sim\ <\cdots Cell_{i}, Cell_{i+1}\cdots>_{code} \tag{6}\]
# Compact Aberration-Free Relay-Imaging Multi-Pass Layouts for High-Energy Laser Amplifiers
## 2 The ABCD-Matrix Model
Therefore, we concentrate on the special case of a symmetrical system, where the layout is mirrored at the second imaging element. Hence, the additional constraints \(f_{3}=f_{1}\), \(d_{4}=d_{1}\) and \(d_{3}=d_{2}\) reduce the degrees of freedom. Furthermore, in a practical setup, such a layout is very advantageous, as it only requires two different imaging elements. In this case, the ABCD-model results in the following constraints:  
\[d_{1} = \frac{f_{1}^{2}+2f_{1}f_{2}}{2f_{2}} \tag{6}\] \[d_{2} = f_{1}+2f_{2} \tag{7}\]  
Similar to the Type I system, both \(f_{1}\) and \(f_{2}\) have to be positive to obtain positive distances. Furthermore, solving these results for \(\
icefrac{{1}}{{f_{1}}}\), one obtains the imaging equation for an ideal lens:  
\[\frac{1}{f_{1}}=\frac{1}{d_{1}}+\frac{1}{d_{2}} \tag{8}\]  
Hence, this system will generate an image of the original beam on the second optical element. This reverses the angular distribution, and the beam is then imaged back to the original image plane by the first element. The diameter of the second element only has to be large enough for supporting the beam itself, as the individual round-trips will not be spatially separated on this optic. Such a system will be referred to as Type II in the following. Schematic examples are given in Figure 2 for three round-trips also. It should be mentioned that Type I systems are identical to Type II systems for \(d_{3}=0\) and half the focal length of the second element, to consider the double pass on this element. Under these conditions, Equations (3), (4) and (6), (7) are equivalent. Figure 2: Schematic drawings of Type II systems with three round-trips based on lenses (**a**) and on mirrors (**b**) as imaging elements. The numbers in the arrows denote the sequence of passing beams.
## 3 Results and Discussion
### Characteristics of DC Arc
For the presented type of electrical discharge (free-burning arc), the positive column was characterized by a relatively low and approximately constant electric field strength along the discharge length. The relationship between the steady-state values of the arc voltage and current for a constant \(x\) is called static VCC. To determine the reasons for the significant difference in the VCC characteristics at arc length \(x<125\) mm, the dependence of the arc voltage on time was investigated (Figure 5). It was found that the arc burns in laminar flow mode; however, it looks like a rod. At \(I=150\) A, the laminar arc reached a distance of \(x=170\) mm, and noise and crackling appeared due to arc fluctuations. In order to determine the reasons for a difference in the voltage-current characteristics in laminar and turbulent modes, the dependence of arc voltage on arc length was investigated. It was found that a short arc burns in a laminar mode, whereas it looks like a rod and burns noiselessly. For example, at \(I=150\) A, the laminar arc reaches the distance of \(x=170\) mm. At \(x>170\) mm, noise and crackling appear due to the arc fluctuations, and a part of the arc operates in a turbulent mode and becomes a multifilament, as presented in [17]. The turbulent regime was also characterized by higher values of arc voltage. The steady arc in the reactor, as an energy consumer, and the arc power source, such as the transformer and rectifier, form an interconnected energy system. There exist two modes of operation of this system: (1) static, when the values of voltage and current in the system do not change for a sufficiently long time; (2) transient or dynamic, when the values of voltage and current in the system are continuously changing. The current, the voltage, the size of the gap between the electrodes, and the connection between them determine the burning mode of the arc. In the arc gap, three areas exist: anode area, cathode area, and area of the arc column. The voltage drop in the anode and cathode regions is almost constant for these conditions. It was found that the arc voltage was higher using the graphite anode than that using the melt anode. The arc voltage decreases when the melt anode is used because melted material vapors are in the arc. However, the length of the laminar part of the arc remains almost the same in both cases. Figure 5: Dependence of the electric arc voltage on the plasma-chemical reactor operating time: 1 (black dots)—recycled zeolites, 2 (white dots) —recycled clay. A is the arc voltage after the melt is released. The air flow through the plasma torch was 1 g/s, and the protective gas was 2.63 g/s.
## 5 Experimental Results
### Analysis of Results  
#### 5.3.1 Schedule Results Table  
The partial scheduling results generated by the optimized NSGA-III are shown in Table 5. The table records the scheduling results of the vessels, including the actual scheduling of movement control time, arrival time at the berth, berthing location, and departure time for each ship. By optimizing with the NSGA-III algorithm, the scheduling arrangement is made more efficient.  
Figure 3: Convergence plot of berth deviation cost for optimized NSGA-III.
# Blind Recognition of Frame Synchronization Based on Deep Learning
## 2 Algorithm Description
### Image Sample Construction  
According to Section \(A\), when \(n\) matches \(L_{\alpha}\), the constructed images will be used as positive samples; when \(n\) dis-matches \(L_{\alpha}\), the obtained images will be used as negative samples. Negative samples also contain image samples constructed from randomly generated binary sequences to enhance the generalization ability of the network model.  
The neural network we will use requires the input image size to be \(224\times 224\), so the grayscale images should be resized before being fed into the network. The interpolated  
Figure 3: Images when the number of matrix columns is not equal to the frame length. (**a**) The number of columns is close to the frame length; (**b**) The number of columns differs greatly from frame length; (**c**) Image with faint stripes visible; (**d**) Images generated from a sequence using flat-topped interpolation.  
Figure 2: Vertical stripes when the number of matrix columns is equal to the frame length or an integer multiple of the frame length. (**a**) The number of columns equals the frame length. (**b**) The number of columns is an integer multiple of the frame length.  
frame length \(L_{\alpha}\) can be obtained from the slope of the fringe \(1/(n-L_{\alpha})\). However, the image scaling process will cause \(n\) lost. To solve this problem, except for the grayscale images we need to add additional parameters to compensate for the information loss during image scaling. We will use RGB images as the input of the neural network. Both R and G channels will be filled with the grayscale images. For B channel, we introduce a scaling factor to fill in each pixel. The scale factor is given by  
\[\beta=\frac{n}{224}. \tag{2}\]  
where \(\beta\in[\beta_{min},\beta_{max}]\) with  
\[\begin{cases}\beta_{min}=\frac{L_{min}-d}{224}.\\ \beta_{max}=\frac{L_{max}+d}{224}.\end{cases} \tag{3}\]  
where \(L_{min}\) and \(L_{max}\) respectively represent the minimum \(L_{\alpha}\) and the maximum \(L_{\alpha}\) in the training set.  
Considering that the data matrix is built from a binary sequence, it is necessary to map the value of \(\beta\) to [0, 1] using the following formula:  
\[\beta^{{}^{\prime}}=\frac{\beta-\beta_{min}}{\beta_{max}-\beta_{min}}. \tag{4}\]  
After the scaling process using bilinear interpolation, the original pixel values in the R and G channels will be stretched, leading to blurriness or distortion in the image, while the pixel values in the B channel remain the same and are not affected by the bilinear interpolation operation. As a result, frame length feature of the image, represented by the scale factor, are preserved.  
Some positive samples are shown in Figure 4. These images show obvious stripes, and each row contains at most one complete frame, with scattered spots in the stripes due to the existence of error bits. In addition, RGB images with different scaling factors exhibit differences in color.
## 3 Results and Discussion
### HPLC Analysis of Organic Acids
niger_ strains are known to produce succinic acid [58], as well as a minor co-product production of citric acid [59]. Succinic acid was directly produced from minimally pretreated lignocellulosic biomass with employing a mixed lignocellulosic and acidogenic _A. niger_ and _T. reesei_ co-culture [60]. At elevated carbohydrate concentrations, organic acids of malic, citric, succinic, and fumaric acids were identified mostly in liquid cultures, while oxalic acid was dominant among the acids produced by _A. niger_, and its production was decreased by _P. cittrinum_ at high carbohydrate concentrations [61]. Carbohydrates, especially fructose, are mainly involved in biosynthetic processes [62], which is suitable for the formation of organic acids in the Krebs cycle, such as fumaric, malic, and succinic acid synthesis. _T. harzianum_ was reported to decompose cellulose and other carbohydrates to glucose within the parenchyma cells and the pits, while it lacks ligninolytic activity [63; 64]. Another study showed that no cell wall degradation signs appeared in the wood samples inoculated with _T. harzianum_[8; 65]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Organic Acid**} & \multicolumn{6}{c}{**Amount (ug/g o.d. Sample)**} \\ \cline{2-7}  & **Control** & _A. flavus_ & _A. niger_ & _A. tenuissima_ & _F. culmorum_ & _T. harzianum_ \\ \hline Oxalic acid & 293.34 & 362.08 & 1202.53 & 167.33 & 431.85 & 245.01 \\ \hline Citric acid & ND & 110 & 2499.63 & ND & ND & ND \\ \hline Tartaric acid & ND & ND & 1150.98 & ND & ND & ND \\ \hline Succinic acid & ND & ND & 2096.28 & 32.33 & ND & ND \\ \hline Glutaric acid & ND & 23.34 & 2497.59 & ND & 27.88 & 64.46 \\ \hline Acetic acid & ND & ND & 2.04 & ND & ND & ND \\ \hline Propionic acid & ND & ND & 1.79 & ND & ND & ND \\ \hline Butyric acid & 0.24 & 0.64 & 1.99 & 0.04 & ND & 0.35 \\ \hline \hline \end{tabular} ND—not determined.  
\end{table} 
Table 2: High-performance liquid chromatography (HPLC) analysis of wood organic acids as affected by growing five fungi. Figure 1: _Cont._  
Figure 1: HPLC chromatograms of changes in the organic acids of _A. saligna_ wood inoculated with (**a**) the control, (**b**) _A. flavus_, (**c**) _A. niger_, (**d**) _A. tenuissima_, (**e**) _F. culmorum_, and (**f**) _T. harzianum_.