esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions  
### Comparison to Previous Work  
In the research of [25], almost 80% of the 115 GNSS stations had their _WRMS_ decreased after subtracting GRACE from GNSS time series, while our analysis shows that GNSS signal _WRMS_ decreases at almost 80% of the 186 stations, indicating that we have 56 more stations presenting the expected improvements. Both the results from [25] and our analysis show that nearly 6% of the stations  
Figure 10: WRMS reduction statistics from the comparison of the IG2 time series and GRACE deformations (the red text above the histogram represents the average of WRMS reduction).  
have a negative correlation, and the mean correlation coefficient is 0.48 and 0.49 for [25] and our results, respectively. With regard to the results of [26], our _WRMS_ reduction indicates reasonable results (mean _WRMS_ reductions12% for both). Although the percentages of these parameters are close to each other, our analysis is based on 186 stations, which is 62% more than the 115 stations of [25] and 69% more than the 110 stations of [26]. All of these factors can confirm the reliability of the analysis.  
Moreover, we select results from [28] to probe the improvements of IG2 solutions relative to IG1 solutions. The research of [28] found about 64% of the 233 stations to have _WRMS_ decrease after subtracting vertical deformations from the GGFC loading model; our analysis demonstrates that 74% of the 186 stations have a _WRMS_ reduction. For the number of stations with _WRMS_ decrease greater than 10%, [28] found 26% of these stations to have a _WRMS_ decrease greater than 10%, while our analysis indicates that 53% of all the stations have GNSS signal _WRMS_ decrease. We found 37 more stations as having _WRMS_ decrease than that of [28]. Overall, we can find that our analysis with GNSS/GGFC solutions is more optimal than that of [28], and better results can be noted than previous research comparing GNSS with GGFC loading models. All improvements should be ascribed to the improvements from IG1 to IG2 solutions.
## 2 Design of TPMS Structures  
### Design of Uniform TPMS Structures  
TPMS is a surface that varies periodically in three directions along the coordinate axes and has an average curvature of zero [24]. Each type of TPMS can be represented by an implicit function. Gyroid and Diamond surfaces are widely used in engineering practice as the base surfaces of TPMS [25]. The types of TPMS lattice structures adopted in this paper are Gyroid and Diamond, whose implicit functions are as follows [26; 27]:  
Gyroid surfaces:  
\[f(x,y,z)=\cos\Bigl{(}\frac{2\pi x}{l}\Bigr{)}\sin\Bigl{(}\frac{2\pi y}{l} \Bigr{)}+\cos\Bigl{(}\frac{2\pi y}{l}\Bigr{)}\sin\Bigl{(}\frac{2\pi z}{l} \Bigr{)}+\cos\Bigl{(}\frac{2\pi z}{l}\Bigr{)}\sin\Bigl{(}\frac{2\pi x}{l} \Bigr{)}+t \tag{1}\]  
Diamond surfaces:  
\[\begin{array}{ll}f(x,y,z)&=\sin\bigl{(}\frac{2\pi x}{l}\bigr{)}\sin\Bigl{(} \frac{2\pi y}{l}\Bigr{)}\sin\bigl{(}\frac{2\pi z}{l}\bigr{)}+\sin\bigl{(}\frac {2\pi x}{l}\bigr{)}\cos\Bigl{(}\frac{2\pi y}{l}\Bigr{)}\cos\bigl{(}\frac{2\pi z }{l}\bigr{)}\\ &+\cos\bigl{(}\frac{2\pi x}{l}\bigr{)}\sin\Bigl{(}\frac{2\pi y}{l}\bigr{)} \cos\bigl{(}\frac{2\pi z}{l}\bigr{)}+\cos\bigl{(}\frac{2\pi x}{l}\bigr{)}\cos \Bigl{(}\frac{2\pi y}{l}\Bigr{)}\sin\bigl{(}\frac{2\pi z}{l}\bigr{)}+t\end{array} \tag{2}\]  
where, \(x,y,z\) are the independent variables along the three coordinate axis directions, \(l\) is the cell length, and \(t\) is the offset parameter of the surfaces.  
The TPMS divides the whole space into two disconnected subspaces, and the TPMS in the subspaces can be constructed to have different lattice structures. By thickening the surface of the subspace, a sheet-TPMS lattice structure can be obtained; by filling the subspace, a skeletal-TPMS lattice structure can be obtained [28]. The TPMS structures are modeled by implicit functions in MATLAB, namely the Skeletal-Gyroid TPMS structure (Sk-G), Skeletal-Diamond TPMS structure (Sk-D), Sheet-Gyroid TPMS structure (Sh-G), and Sheet-Diamond TPMS structure (Sh-D), as shown in Figure 1.
## 3 Topology of Proposed Method
### Decoupling between Shoulder and Elbow Joint  
The paper aims to achieve accurate trajectory tracking for ULRRE in the presence of external disturbances and modeling uncertainties such as parameter variations. The mathematical model in Equation (1) is further expressed as:  
\[\left\{\begin{array}{l}j_{11}\hat{q_{1}}+j_{12}\hat{q_{2}}+c_{11}\hat{q_{1} }+c_{12}\hat{q_{2}}+g_{1}+d_{1}=\tau_{1}\\ j_{21}\hat{q_{1}}+j_{22}\hat{q_{2}}+c_{21}\hat{q_{1}}+c_{22}\hat{q_{2}}+g_{2}+ d_{2}=\tau_{2}\end{array}\right. \tag{5}\]  
where \(\mathbf{D}=\left[d_{1},d_{2}\right]^{T}\), \(d_{1}\) and \(d_{2}\) represent lumped disturbances. Equation (5) in state space is expressed as follows.  
\[\left\{\begin{array}{l}\ddot{q_{1}}=\dfrac{1}{(j_{11}\dot{2}-j_{21}j_{12})} (j_{22}\tau_{1}-j_{12}\tau_{2}-F_{1})\\ \ddot{q_{2}}=\dfrac{1}{(j_{12}j_{21}-j_{11}j_{22})}(j_{21}\tau_{1}-j_{11}\tau_ {2}-F_{2})\end{array}\right. \tag{6}\]  
Figure 2: Topology of proposed ESO-based and FTSTD-based ADRC trajectory tracking control for ULRRE.  
where  
\[\left\{\begin{array}{l}F_{1}=(j_{22}c_{11}-j_{12}c_{21})\dot{q}_{1}+(j_{22}c_{12 }-j_{12}c_{22})\dot{q}_{2}+j_{22}g_{1}-j_{12}g_{2}+j_{22}d_{1}-j_{12}d_{2}\\ f_{10}=(j_{22}c_{11}-j_{12}c_{21})\dot{q}_{1}+(j_{22}c_{12}-j_{12}c_{22})\dot{q}_{2 }+j_{22}g_{1}-j_{12}g_{2}\\ f_{1d}=j_{22}d_{1}-j_{12}d_{2}\\ F_{1}=f_{10}+f_{1d}\\ F_{2}=(-j_{21}c_{11}-j_{11}c_{21})\dot{q}_{1}-(j_{21}c_{12}-j_{11}c_{22})\dot{q} _{2}-j_{21}g_{1}+j_{11}g_{2}-j_{22}d_{1}+j_{12}d_{2}\\ f_{20}=(-j_{21}c_{11}-j_{11}c_{21})\dot{q}_{1}-(j_{21}c_{12}-j_{11}c_{22})\dot{q} _{2}-j_{21}g_{1}+j_{11}g_{2}\\ f_{2d}=-j_{22}d_{1}+j_{12}d_{2}\\ F_{2}=f_{20}+f_{2d}\end{array}\right. \tag{7}\]  
Equation (6) after simplification becomes Equation (8) and is given as :  
\[\left\{\begin{array}{l}\ddot{q_{1}}=P_{f1}.(j_{22}\tau_{1}-j_{12}\tau_{2})+f_ {1}\\ \ddot{q_{2}}=-P_{f1}.(j_{21}\tau_{1}-j_{11}\tau_{2})+f_{2}\end{array}\right. \tag{8}\]  
where \(P_{f1}=\frac{1}{(j_{11}j_{22}-j_{12})}\), \(f_{1}=-(f_{10}+f_{1d})P_{f1},f_{2}=(f_{20}+f_{2d})P_{f1}\).  
After decoupling the system in Equation (8) by decoupling matrix \(\left[N\right]\)[52], it is written as  
\[\ddot{q}\;=\;f\;+\;\mathbf{\upsilon} \tag{9}\]  
Here, \(\mathbf{q}=[q_{1},\;q_{2}\;]^{T}\), \(\mathbf{f}=[\;f_{1},\;f_{2}\;]^{T}\), \(\mathbf{U}=[U_{1},\;U_{2}]^{T}=N[\tau_{1},\tau_{2}]^{T}\) if \(U\) is known, the control of input \(\mathbf{T}=\left[\tau_{1},\;\;\tau_{2}\right]^{T}\) is obtained as \(\mathbf{T}\;=\;\mathbf{N}^{-1}\mathbf{U}\;=\;\mathbf{N}_{\text{inv}}\mathbf{U}\). After separating the system, two ESO are generated for each joint using two distinct equations.  
\[\begin{array}{l}\ddot{q_{1}}\;=\;f_{1}+\;\frac{1}{(P_{f1}j_{22})}U_{1}\\ \ddot{q_{2}}\;=\;f_{2}\;+\;\frac{1}{(P_{f1}j_{11})}U_{2}\end{array} \tag{10}\]
## 4 Experiments
### Experiments on Registration Effectiveness
#### 4.3.6 Comparison between Registration Efficiency of Point Cloud
Based on the findings in Table 2, we can infer that our transformation estimation approach maintains high accuracy while being time efficient. According to Table 5, the registration approach based on our proposed feature descriptors takes just slightly longer than the registration approach based on PFH/FPFH. The registration approach based on PFH has the shortest registration time, which might be owing to the relatively easy and quick calculation of PFH descriptors. In contrast, registration methods based on 3 DSC have the longest registration times, typically due to higher descriptor dimensions, resulting in more complex and time-consuming generation and matching processes. As a result, it is clear that one of the most important elements influencing registration time is the descriptor dimension. Generally speaking, the larger the dimension of a descriptor, the more processing and storage resources it demands, resulting in a longer creation and matching time. Combined with the results shown in Table 3, it is clear that our feature descriptor achieves a relatively rapid registration time while being low dimensional, resulting in a good balance of efficiency and accuracy.
## 5 Conclusions  
In summary, the enzymes that cleaved linoleic acid to 1-octen-3-ol in _A. bisporus_ were not the common enzymes for the synthesis of oxylipids but multifunctional fatty acid dioxygenase _Ab_LOX and hydroperoxide lyase _Ab_HPL, and this may be the reason why the enzymatic pathway for the biosynthesis of 1-octen-3-ol in _A. bisporus_ has not been elucidated until now. The enzymatic pathway for the biosynthesis of 1-octen-3-ol in _A. bisporus_ discovered in this study will considerably contribute to the in-depth exploration of the biological functions of 1-octen-3-ol and oxylipids for mushroom fungi and the efficient production of the natural isomers of 1-octen-3-ol.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/jof8080827/s1](https://www.mdpi.com/article/10.3390/jof8080827/s1)]([https://www.mdpi.com/article/10.3390/jof8080827/s1](https://www.mdpi.com/article/10.3390/jof8080827/s1)), Figure S1: The amino acid sequence identify between _Ab_LOX in _Agaricus bisporus_ H97 and Cop4 in _Coperinus cinerea_. Figure S2: Domain structure and catalytic active site of the putative heme dioxygenase from _Agaricus bisporus_ H97 and other selected fungi. Figure S3: The amino acid sequence identified between _Ab_HPL in _Agaricus bisporus_ H97 and _Tm_HPL in _Tricholoma matsutake_. Figure S4. Protein purification of the putative enzymes involved in 1-octen-3-ol biosynthesis by nickel ion affinity chromatography column. Figure S5. Mass spectrum of 1-octen-3-ol from the enzymatic linoleic acid reaction by AbLOX+AbHPL. Table S1: Primers used in this study.  
Conceptualization, Y.G. and L.Q.; methodology, T.S., Y.C., H.L. and J.G.; formal analysis, Y.L. and Y.Q.; writing--original draft preparation, T.S. and Y.G.; writing--review and editing, L.Q.; funding acquisition, Y.G. and L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Department of Henan Province (202102110044 and 222102110302).  
Not applicable.  
The datasets are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Speech Dereverberation  
Furthermore, the quality of the dereverberated speech using the estimated LRSV is evaluated, and the log-spectral amplitude gain function [11] is adopted to suppress the late reverberant speech component. Besides, a method using recursive MSPP [10] is also evaluated as a reference. The measures are the segmental SRR and LSD (averaged over all frames) between the estimated and true early speech component [3,4], the short-time objective intelligibility(STOI) [13] and perceptual evaluation of speech quality(PESQ) [14]. The results are averaged over all AIRs and presented in Table 1.  
It can be observed that the proposed method achieves best performance in three measures and only performs slightly worse in LSD, which validates the superiority of the proposed estimator to conventional approaches. It also indicates that the LRSV estimator using proposed method performs even better than that using the measured ground truth \(\kappa\)(fullband and subband). It is worth mentioning that a single measure is not convincing, so this letter used four measures to jointly judge the performance of the proposed method. Hence, although MSPP has lower score than proposed method in LSD, considering all four measures, we still believe that the proposed algorithm is superior.
## 3 Results and Discussion
### Hydration Process and Microstructure of Ultrafine Calcium Silicate Cement Slurry
#### 3.5.2 XRD Analysis  
In order to study the influence of ultrafine calcium silicate on the mineral composition of oil well cement-based composite, the hydration products of cement with different ultrafine calcium silicate contents were studied by XRD analysis. The experimental results are shown in Figure 8. The calcium hydroxide (CH, 20 = 18\({}^{\circ}\), 47\({}^{\circ}\)) and hydrated calcium silicate (CSH, 20 = 28-35\({}^{\circ}\)) are the main hydration products of oil well cement slurry, and they are labeled in the XRD graph. The three types of cement samples have CH characteristic peaks after curing for 24 h. The CH characteristic peak of pure cement is the highest, and that of 8% ultrafine calcium silicate is the lowest, which is due to the SiO\({}_{2}\) contained in ultrafine calcium silicate powder reacting with Ca(OH)\({}_{2}\) to generate C-S-H gel, consuming a large amount of Ca(OH)\({}_{2}\)[25]. The normal hydration reaction of cement will consume C\({}_{2}\)S and C\({}_{3}\)S, and the characteristic peaks of C\({}_{2}\)3 and C\({}_{3}\)S of cement with the addition of ultrafine calcium are relatively weakened, which is an obvious manifestation of accelerating cement hydration. The results of XRD show that the ultrafine calcium  
Figure 7: Hydration temperature of ultrafine calcium silicate cement slurry.  
silicate can accelerate the hydration reaction process of C23 and C3S and consume Ca(OH)2 simultaneously to generate more hydration products.
## 2 Materials and Methods
### Sample and Experimental Setup  
The schematic diagram of the sample is shown in Figure 4. A pulsed Nd:YAG laser produces a series of laser pulses with a temporal width of \(\sim\)10 ns at a wavelength of 1064 nm. The laser is focused into a circular spot at a diameter of \(\sim\)0.2 mm on the plate as the pump laser. The Lamb wave is generated in a thermoelastic mechanism with a maximum pulse energy of \(\sim\)0.5 mJ. A laser vibrometer (wavelength 632.8 nm) is used as the detection mechanism; its output laser is focused on the upper surface of the plate as a probe laser, which is used to detect the out-of-plane displacement of Lamb waves. The sample is fully immersed in a glass water tank. The water layer is thick enough to reduce the influence of vibration or reflection from the edge of the tank. In Figure 4, 'P\({}_{\text{S0}}\)' represents the position of the pump laser for enhanced S\({}_{0}\) mode generation (described as 'lateral generation configuration'). In this configuration, the pump laser is focused to a diameter similar in size to the plate thickness after collimation, and the side of plate is irradiated vertically. Meanwhile, the pump laser is adjusted by stepper motor and focused to an appropriate position that is symmetric to the side of the plate, so as to generate the S\({}_{0}\) mode and restrain the A\({}_{0}\) mode. For comparison, the conventional laser-based A\({}_{0}\)-mode-dominant generation is also examined by focusing the pump laser at 'P\({}_{\text{A0}}\)' on the upper surface of the plate (described as 'top generation configuration'). As a result, the out-of-plane displacement of the generated A\({}_{0}\) mode is significantly larger than that of the S\({}_{0}\) mode generated at the same time, so that the out-of-plane displacement of the S\({}_{0}\) mode can be ignored.  
The sample is an aluminum plate with a thickness of \(\sim\)0.2 mm and a surface area of 150 \(\times\) 100 mm\({}^{2}\). In experimental measurements, the center frequency of Lamb waves generated by the pulsed laser is \(\sim\)5 MHz. The thickness is chosen to generate the S\({}_{0}\) mode of Lamb wave at 1 MHz\(\cdot\)mm, utilizing the dispersion curve shown in Figure 1. The artificial notch (\(\sim\)0.5 mm width on the \(x\)-axis, \(\sim\)0.1 mm depth on the \(z\)-axis, and \(\sim\)20 mm on the \(y\)-axis) is located 40 mm away from the left side of the plate, where the lateral generation source \"P\({}_{\text{S0}}\)\" is located. In the top generation configuration, the generation laser spot, indicated by 'P\({}_{\text{A0}}\)', is \(\sim\)40 mm away from the notch, as shown in Figure 4. In both the lateral and top generation configurations, the probe laser is focused on the upper surface of the plate in order to obtain the out-of-plane displacement of the Lamb waves. If the length of the artificial notch in the \(y\) direction is long enough, B-scan images can be extracted by scanning the probe laser along the \(x\)-axis near the center of the notch (blue stripe), as indicated by the yellow stripe shown in Figure 4.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 3 Code Generators
### Universal Code Generators Based on LFSR
#### 3.1.2 Area-Efficient Universal Code Generator  
The conventional universal code generator [5] is characterized by a sophisticated hardware architecture that configures specific initial ROMs, feedback ROMs, and output ROMs for each register bit. By inputting different signal types and satellite numbers, code generators SR1 and SR2 are assigned with their respective initial ROMs, feedback ROMs, and output ROMs. These feedback and output ROMs are utilized by a multiplexer to select different taps for XOR operations, ultimately generating the feedback and spread spectrum codes.  
Conventional universal code generators have the capability to generate multiple spread spectrum codes using a single LFSR. However, the configured ROM contains numerous redundant configuration items. The proposed multi-frequency universal code generator in Ref. [5] eliminates unnecessary ROM, MUX, and XOR resources by eliminating unused bits and directly connecting relevant configuration bits. In comparison to conventional multi-frequency universal spread spectrum code generators, this approach significantly reduces redundant hardware resources.  
Table 2 presents a summary of the binary ROM values for the spread spectrum code generator, using GPS as an illustrative example. Unused bits are represented as zero,  
Figure 8: Universal Ranging Code Generator.  
Figure 9: Merged Code Generator.  
utilized bits as one, and non-fixed bits as x. Based on the numerical summary, numerous bits are fixed at zero or one, enabling significant reduction in hardware resources. If the shift register corresponds to bit zero, the ROM, MUX, and XOR can be directly removed. On the other hand, if the shift register corresponds to bit one, the ROM and MUX can be eliminated by configuring a direct XOR connection. Therefore, the greater the number of fixed bits at zero and one, the more unnecessary hardware resources can be removed.  
As depicted in Figure 10, literature [5] proposes a compatible type code generator structure that enables the generation of spread spectrum codes for GPS, Galileo, GLONASS, and BDS navigation systems. This structure corresponds to four sets of independent code generator structures, one for each system. For example, the code generator designed for the GPS system can generate compatible spread spectrum codes such as GPS L1C/A, L2 CM, L2 CL, and L5I/Q. Similarly, the code generator tailored for the BDS system can produce compatible spread spectrum codes including BDS B1I, B2I, B3I, B2a, and B2B.
## 3 An ILP Model for the MWDDS Problem  
Our ILP model for the MWDDS problem requires the following sets of variables. First, we introduce a binary variable \(x_{ij}\) for each node \(v_{i}\) (\(i=1,\ldots,n\)) and each possible disjoint set \(D_{j}\) (\(j=1,\ldots,\delta(G)+1\)). Hereby, \(\delta(G):=\min\{deg(v)\mid v\in V\}\). (Remember that the maximum number of disjoint dominating sets in a graph is \(\delta(G)+1\).) When \(x_{ij}=1\), node \(v_{i}\) is assigned to the \(j\)-th dominating set. Second, a binary variable \(y_{j}\) (\(j=1,\ldots,\delta(G)+1\)) indicates whether or not the \(j\)-th set is opened. Finally, a real-valued variable \(z_{j}\in[0,M]\) (\(M\in\mathbb{R}^{+}\)) stores the weight of the \(j\)-th dominating set. Hereby, \(M:=\max\{\mathit{lifetime}(v)\mid v\in V\}\). Based on these variables, the MWDDS can be stated as an ILP in the following way.  
\[\textbf{max} \sum_{j=1}^{\delta(G)+1}z_{j} \tag{5}\] \[\textbf{s. t} \sum_{j=1}^{\delta(G)+1}x_{ij}\leq 1 i=1,\ldots,n\] (6) \[\sum_{x_{k}\in N(v_{i})}x_{kj}\geq y_{j}-x_{ij} j=1,\ldots,\delta(G)+1\] (7) \[y_{j}\geq x_{ij} i=1,\ldots,n\text{ and }j=1,\ldots,\delta(G)+1\] (8) \[x_{ij}\cdot\mathit{lifetime}(v_{i})+(1-x_{ij})\cdot M\geq z_{j} i=1,\ldots,n\text{ and }j=1,\ldots,\delta(G)+1\] (9) \[y_{j}\cdot M\geq z_{j} j=1,\ldots,\delta(G)+1\] (10) \[y_{j}\geq y_{j+1} j=1,\ldots,\delta(G)\] (11) \[z_{j}\geq z_{j+1} j=1,\ldots,\delta(G) \tag{12}\]  
The objective function sums over the weight values of all dominating sets. Note that, for this objective function to be correct, the weight value of those dominating sets that are not opened must be set to zero by respective constraints. Constraints (6) require that each node of the graph is assigned to at most one dominating set. This assures the disjointness of the dominating sets. Next, constraints (7) are the so-called dominating set constraints, that is, they ensure that, if the \(j\)-th set is opened, then the set of nodes assigned to the \(j\)-th set form a dominating set of \(G\). Further, constraints (8) make sure that nodes can only be assigned to opened dominating sets. Constraints (9) are responsible for correctly determining the weights of the opened dominating sets. In other words, the value of variable \(z_{j}\) of the \(j\)-th dominating set (if open) is set to the minimum of the lifetime-values of all nodes assigned to this set. Next, constraints (10) ensure that the weight values of dominating sets that are not opened are set to zero. Finally, the last two sets of constraints--that is, constraints (11) and (12)--are not necessary for the correctness of the ILP. They are tie-breaking constraints that ensure that (1) if \(k\) dominating sets are opened, they are assigned to sets \(1,\ldots,k\) and (2) the opened dominating sets are ordered according to a non-increasing weight value.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 7 Results and Findings
In particular, the results indicate that in order to obtain a rate of 4 bits/s/Hz, the proposed design achieves a again of 3 dB and 12 dB over the conventional designs with \(\alpha_{\mathrm{A}}=5^{\circ}\) and \(\alpha_{\mathrm{A}}=15^{\circ}\), respectively. Overall, the results presented in this paper indicate that a feasible DTS for the FDD MaMi systems with colored noise and limited coherence time can be achieved without increasing in the system overhead and design complexity. The findings of this paper are underpinned by accurate analyses, which tightly agree with the simulated results. These analyses support the contributions of this work. Figure 5: Normalized MSE versus SNR using the Laplacian channel model based on uniform planar array (UPA) BS with different levels of correlations, i.e., with an azimuth angular spread of \(\alpha_{\mathrm{A}}=\{5^{\circ},15^{\circ}\}\).