esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fuzzy Sets and Similarity Measures  
In this subsection, the concepts of fuzzy sets, as well as their operators and similarity measures, are reviewed.  
In this paper, it is assumed that \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) is the universal set.  
**Definition 1** ([6]).: _A fuzzy set \(A\) on the universal set \(X\) takes the form_  
\[A=\{(x_{i},\mu_{A}(x_{i}))|x_{i}\in X\}\]  
_where \(\mu_{A}:X\rightarrow[0,1]\) is the membership function of the fuzzy set \(A\), and \(\mu_{A}(x_{i})\) is the degree membership of the element \(x_{i}\  
\begin{tabular}{l l} + & Subset: \(A\subseteq B\) if \(\mu_{A}(x_{i})\leq\mu_{B}(x_{i})\) for all \(x_{i}\in X\), \\ + & Equal: \(A=B\) if \(\mu_{A}(x_{i})=\mu_{B}(x_{i})\) for all \(x_{i}\in X\), \\ + & Intersection: \(A\cap B=\{(x_{i},\min(\mu_{A}(x_{i}),\mu_{B}(x_{i})))|x_{i}\in X\}\), \\ + & Union: \(A\cup B=\{(x_{i},\max(\mu_{A}(x_{i}),\mu_{B}(x_{i})))|x_{i}\in X\}\), \\ + & Complement: \(A^{\mathbb{C}}=\{(x_{i},1-\mu_{A}(x_{i}))|x_{i}\in X\}\), \\ + & For all real numbers \(\lambda>0\). \\ \end{tabular}  
\[A^{\lambda}=\Big{\{}\Big{(}x_{i},(\mu_{A}(x_{i}))^{\lambda}\Big{)}\Big{|}x_{i} \in X\Big{\}}. \tag{1}\]  
\(F(X)\) denotes a collection of all fuzzy sets on \(X\).  
**Definition 2** ([12]).: _Let \(A\), \(B\in F(X)\) be two fuzzy sets on the universal set. A similarity measure of \(F(X)\) is the function \(S:F(X)\times F(X)\rightarrow[0,1]\), which satisfies the following conditions:_  
1. \(0\leq S(A,B)\leq 1\) _for all_ \(A,B\in F(X)\)_,_
2. \(S(A,B)=S(B,A)\) _for all_ \(A,B\in F(X)\)_,_
3. \(S(A,B)=1\) _if_ \(A=B\) _for all_ \(A,B\in F(X)\)_,_
4. _For all_ \(A\)_,_ \(B\)_,_ \(C\in F(X)\)_, if_ \(A\subseteq B\subseteq C\)_, then_ \(S(A,C)\leq min\{S(A,B),S(B,C)\}\)_._
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**GPP**} & \multicolumn{2}{c}{**ET**} \\ \hline
**Parameter** & **F** & \(p\)-Value** & **F** & \(p\)-Value** \\ \hline Rg & 8.726 & \textless{}0.001 & 26.27 & \textless{}0.001 \\ \hline VPD & 35.744 & \textless{}0.001 & 3.51 & 0.016 \\ \hline SWC & 28.561 & \textless{}0.001 & 109.11 & \textless{}0.001 \\ \hline EVI & 30.053 & \textless{}0.001 & 38.71 & \textless{}0.001 \\ \hline Regression coefficient & R\({}^{2}=0.79\) & & \multicolumn{2}{c}{R\({}^{2}=0.84\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outcomes of GAM analysis with simple smoothing functions for each parameter. Figure 5: GPP and ET dependence on Rg during the period June to August, under optimal SWC conditions (SWC > 20%) and different VPD conditions. The circles (open for GPP and closed for ET) present means of instantaneous values in 50 W m\({}^{-2}\) Rg intervals for the period June to August during the entire study period (August 2019–December 2021). Black, red, blue and green colour corresponds to VPD \(<\) 10 hPa, 10 \(<\) VPD \(<\) 15 hPa, 15 \(<\) VPD \(<\) 20 hPa, VPD \(>\) 20 hPa, respectively. All regression models are significant at \(p<0.001\). The R\({}^{2}\) coefficient ranges from 0.92 to 0.97 for the relationships shown in plot (**a**) and from 0.92 to 0.98 for the relationships shown in plot (**b**). Consistent with GPP, ET also increased almost linearly with increasing Rg (Figure 7a) and the same response of ET was detected in increasing SWC (Figure 7c). However, ET was very little affected by VPD; it presented only a mild decline as VPD decreased to approximately 20 hPa and did not react to any further decrease in VPD (Figure 7b). Almost no response in ET was found up to EVI levels of around 0.58, while ET increased with higher EVI values (Figure 7d). Figure 6: Responses of daily GPP to Rg (**a**), VPD (**b**), SWC (**c**) and EVI (**d**), based on GAM analysis. All models are statistically significant (\(p<0.001\)). Details of GAM analysis are given in Table 1.  
Figure 7: Responses of daily ET to Rg (**a**), VPD (**b**), SWC (**c**) and EVI (**d**), based on GAM analysis. ET response to VPD is significant at \(p=0.016\). All other responses are statistically significant at \(p<0.001\). Details of GAM analysis are given in Table 1.
## 2 Materials and Methods
### Generation of Knockout Cells  
The generation of MCF-7 AHR\({}^{\text{KO}}\) cells has been described elsewhere [30]. MCF-7 PARP7\({}^{\text{KO}}\) cells were generated using CRISPR-Cas9. The guide RNAs (gRNAs) used to target PARP7 were designed to target exon 2 of the _PARP7_ gene (NC_000069.7) using the Broad Institute's gRNA design tool ([[https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)]([https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design](https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design)), accessed on 17 March 2017. The following guide oligos were designed to express the sgRNA: forward primer 5\({}^{\prime}\)-GGAGGCTGGCACTACACAGC-3\({}^{\prime}\) and reverse primer 5\({}^{\prime}\)-GACTGTGAGTGCAGCCTC-3\({}^{\prime}\). The gRNA was cloned into the pSpCCas9(BB)-2A-Puro (PX459) plasmid (Addgene, Watertown, MA, USA; plasmid #62988), containing _Streptococcus pyogenes_ (Sp) Cas9 and puromycin genes. The efficiency of the gRNA at targeting _PARP7_ was assessed with T7 endonuclease assays. The PX459 PARP7 containing gRNA plasmid was transfected into MCF-7 cells, using Lipofectamine 2000 (Thermo Fisher Scientific). Three days after transfection, the cells were exposed to 1 ug/mL puromycin for 4 days. The puromycin was then removed and the transfected cells were cultured for an additional 7 days before dilution in order to isolate MCF-7 PARP7\({}^{\text{KO}}\) clones. To confirm the knockout, genomic DNA was isolated from MCF-7 PARP7\({}^{\text{KO}}\) cells and the regions surrounding the gRNA binding site in _PARP7_ was PCR amplified using the primers: forward 5\({}^{\prime}\)-CATCTTCCCTTTCCTCTCTCTCTCA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-CTAAAACCCCCATCAAGTGAGC-3. The amplicon was cloned into pGEM-T Easy vector (Promega, Madison, WI, USA) and 45 individually cloned genomic DNA amplicons were sequenced to confirm knockout. Using this approach, we identified one clone containing insertions and deletions resulting in frameshift mutations.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 3 Proposed Algorithm
### GS Iterative Estimation  
When GS is used for signal detection at the receiving end of a massive MIMO communication, the GS scheme can be executed according to the following steps.  
1. Matrix decomposition. If there are fewer antennas at the user site than at the base station site for massive MIMO communication, the channels tend to be orthogonal to each other. The MMSE filter matrix is a positive definite matrix with diagonal dominance [26]. Matrix A can be divided into [26] \[A=E+F+D\] (7) where \(D\), \(E\), and \(F\) denote the diagonal matrix, the strict upper triangular matrix, and the strict lower triangular matrix of matrix A, respectively.
2. Calculate the initial value \(s^{(0)}\).
3. GS iterative estimation. The signal-estimation formula of the GS iterative algorithm is [26] \[s^{(i)}=(D-F)^{-1}(Es^{(i-1)}+y_{MF})\] (8)
# Improved Design of LNG Marine Loading Arm Docking Method Based on TRIZ Theory
## 4 Innovative Design of Problem-Solving Method Based on TRIZ
### Su-Field Model and Standard Solutions  
Su-Field model analysis is a crucial analytical tool in TRIZ. This approach involves analyzing the technical system's components and their interrelations to develop a Su-Field model. Through the use of symbolic language, this model helps to accurately and precisely describe the various contradictory problems that arise within the system [26].  
During the automatic docking process, the effect between the LNG marine loading arm and the LNG ship pipeline is not sufficient due to the influence of wind and waves, which leads to the inability to dock quickly and accurately. A Su-Field model for the problem was established, as shown in Figure 13. For a complete model with insufficient effect, there are three general solutions based on the Su-Field model: (1) replace the original initial field (or the initial field and substance) with another field (or another field and substance); (2) add another field to strengthen the useful effect; (3) insert the substance and add another field to improve the useful effect.  
Based on the causal chain associated with problem two, two improvement plans are proposed using solutions (1) and (3).  
Plan 1: According to solution (1) and the causal chain analysis, the structure of the LNG marine loading arm is redesigned to reduce the windward area affected by the wind. This will reduce the impact of wind on the LNG marine loading arm and improve the difficulty of accurately positioning the target. The improved Su-Field model is shown in Figure 14a.  
Plan 2: According to solution (3) and the causal chain analysis, a device named \(S_{3}\) is designed to address the small target issue. The device \(S_{3}\) is installed at the end of the marine loading arm. Through the mechanical force \(F_{2}\) generated by this device, it assists the LNG marine loading arm and the LNG ship pipeline in completing the docking quickly and accurately. The improved Su-Field model is shown in Figure 14b.
## 1 Introduction  
High intensity focused ultrasound (HIFU) treatment is regarded as one of the most promising cancer treatment technologies in the 21 st century due to its high penetration and non-invasiveness [1; 2; 3]. Its principle is to use ultrasonic energy to focus on the tumor tissues, the tumor tissues absorb the ultrasonic energy, and the temperature rapidly increases to above 65 \({}^{\circ}\)C in a short time, which results in coagulative necrosis of cells and achieves the purpose of killing the tumor tissues [4; 5; 6]. Additionally, it has no obvious effect on the tissues outside the focus [7]. In addition, some scholars believe that HIFU can destroy the double-stranded DNA to inhibit the growth of tumor cells [4; 8]. Therefore, it is particularly important to monitor and evaluate HIFU treatment accurately and efficiently.  
So far, researchers have studied the ultrasound echo signals during the HIFU treatment in many ways to reflect the effect of treatment. For example, many researchers studied ultrasonic echo signals and extracted parameters that could accurately reflect the characteristics of the tissues, such as ultrasonic attenuation coefficient, sound velocity and entropy [9; 10; 11]. Some scholars studied the effects of temperature and thermal dose on the attenuation of ultrasound in muscle, liver and kidney [12]. Additionally, some scholars used characteristic parameters of ultrasonic echo signals to identify denatured tissues [13; 14; 15]. Other scholars also have used the harmonic frequency of ultrasonic echo signals to explore tissue characteristics [16].  
However, all the above studies require a diagnostic ultrasound source to monitor and obtain the ultrasound echo signals. Few scholars directly used the HIFU echo signals for research. One of the reasons is that it is difficult to correctly judge whether the HIFU echo signal is received, because the HIFU echo signal is sampled mainly by manual observation of the waveform or spectrum. When conducting follow-up research, if the observer cannot accurately judge that the sampled signal is the HIFU signal, it may cause misjudment and affect the research results. The judgment result depends on the observer's experience, the judgment result of the different observer may be different.  
Therefore, it is of great significance to effectively judge whether the sampled signal is the HIFU echo signal [17]. In this paper, a method based on an auto-regressive (AR) model and spectrum information entropy is proposed to judge the HIFU echo signal. The consistency between this method and empirical judgment is compared, and the segment number for power spectrum is analyzed and discussed. The optimal segment number is determined, and the advantages of this method are verified by comparing with the classic power spectrum estimation method.
## 3 Biological Activities
### Antioxidant  
The antioxidant activity of citral has been previously explored (Figure 6). In this way, different studies have demonstrated that citral can scavenge the DPPH radical, presenting IC\({}_{50}\) values between 6.9 and 3700 \(\upmu\)g/mL [11; 119; 120]. A study performed by [PERSON], [PERSON] [121] found that citral exhibited the ability to stabilize the DPPH radical (IC\({}_{50}\): 67.31 mg/mL) and reduce power (concentration of 2 to 10 mg/mL of citral showed an absorbance value between 0.15 and 0.55; high concentration means high absorbance). [PERSON], [PERSON] [122] also reported the capacity of citral to stabilize the DPPH radical (EC\({}_{50}\): 263.33 \(\upmu\)g/mL) and reduce metals (FRAP, EC\({}_{50}\): 125 \(\upmu\)g/mL), as well as the capacity to inhibit the oxidation of linoleic acid (\(\upbeta\)-carotene/linoleate system, inhibition of 71.27%). In the same regard, [PERSON], [PERSON] [123] tested the capacity of citral to inhibit the DPPH radical and linoleic acid oxidation at a concentration between 5 and 100 \(\upmu\)g/L. DPPH results showed that citral had a low antioxidant effect (inhibition lowest to 1.10%). However, the evaluated compound could inhibit linoleic acid oxidation (inhibition between 5.6 and 38%). [PERSON], [PERSON] [12] studied the antioxidant potential of citral by several methods. The reduction potential showed a dose-dependent effect, presenting absorbance values between 0.8 and 1.9 at a dose between 0.05 and 0.40 mg/mL. In addition, citral effectively stabilizes the superoxide (IC\({}_{50}\): 0.67 mg/mL) and hydroxyl (IC\({}_{50}\): 0.5 mg/mL) radicals. Additionally, citral presented the potential to inhibit lipid peroxidation based on the ferric thiocyanate method, where the analyzed compound at 1 mg/mL reduced linoleic acid oxidation by 47% after 8 days of incubation compared to the control (without citral). The thiobarbituric acid (TBA) method demonstrated that citral reduced malondialdehydeebra formation by 81%, compared with control (without citral).  
The reported results demonstrated that citral may use different mechanisms of action, such as stabilizing free radicals and reducing metals, which is associated with its ability to donate hydrogen, generating more stable radicals due to the presence of double bonds in its structure, promoting the resonance effect [122; 124]. Furthermore, citral has shown the ability to effectively stabilize synthetic and biological free radicals, having an essential implication on health since it is well known that oxidative stress plays an important role in the development of chronic degenerative diseases such as cancer, cardiovascular diseases, and neurodegenerative diseases, illnesses that lead the main causes of death worldwide [125; 126]. Additionally, it is possible to suggest that citral can act as an antioxidant agent via free radical inhibition, preventing continuous free radical formation and interrupting free radical formation once initiated. On the other hand, the effect of reducing metals is important because metals can induce the formation of free radicals, with a potential oxidative stress generation [127].
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 3 Calibration Method Design
### Design of Self-Calibration Coprocessor Based on LMSED
\begin{array}{l}err_{k}=u\cdot\cos(\varphi_{k})-v\cdot\sin(\varphi_{k}), \varphi_{0}=0,\\ \varphi_{k+1}=\varphi_{k}+P\cdot(err_{k}-err_{k-1})+l\cdot err_{k}.\end{array}  \tag{11}\]  
The calculations for the reference sine and cosine signals use the look-up table and interpolation method to obtain values in one clock cycle [21]. The purpose of the look-up table is to quantify the operands and obtain the exact values at the nodes. Interpolation is to obtain a more accurate output using a linear fitting method. The look-up table uses a ROM for data storage and data access via address lines. The read data is interpolated by multiplication and addition operations, and all designs are implemented by combinatorial logic. The number of iterations is set to 30 in the design. A module clock of 32 times the main drive clock is also used. Among them, the first clock cycle is used to latch the operand, 30 clock cycles are used to complete 30 iteration calculations, and the last clock cycle is used to latch the calculation result and provide an output valid signal. With the root number and arctangent operations, parameter identification can be performed after the LMSED process. Based on the identified parameter values, the signal calibration module eliminates  
Figure 4: Architecture of arctangent operation module based on a proportional-integral (PI) controller. the phase differences of the two measurement signals and equalizes the amplitudes. The module performs the following numerical operations:  
\[ \begin{array}{l}U_{1}(k)=a_{2}\cdot(U(k)-b_{1})=a_{1}\cdot a_{2}\cdot\sin( \omega_{c}k+\varphi),\\ V_{1}(k)=a_{1}\cdot(V(k)-b_{2})=a_{1}\cdot a_{2}\cdot(\cos(\omega_{c}k+\varphi )\cdot\cos(\beta)-\sin(\omega_{c}k+\varphi)\cdot\sin(\beta)),\\ U_{2}(k)=U_{1}(k)\cdot\cos(\beta),\\ V_{2}(k)=V_{1}(k)+U_{1}(k)\cdot\sin(\beta).\end{array}  \tag{12}\]  
where \(U_{2}(k)\) and \(V_{2}(k)\) are calibrated signals. These signals are fed into the angle calculation module to obtain calibrated angle information. The calculations processed by the calculation module are multiplication and addition and subtraction operations. The design uses two levels of registers to buffer the input operands and the output calculation results. The combination logic is used to complete all operations.
## 2 Advanced Oxidation Processes
### Sonochemical Methods
#### 3.1.2. Parabens Abatement by Photocatalysis
[58] had 80% parabens mixture degradation using TiO2/NT, [PERSON] and [PERSON] [92] had 45.88% of EP degradation in 6 h using TiO2, [PERSON] et al. [97] determined about 34% and 43% EP degradation for TiO2 and N-TiO2 in 10 min and achieved total removal in 60 min with N-TiO2 catalyst, and [PERSON] et al. [99] used Fe3O4/BiVO4 reaching 75% of MP degradation in 60 min. In all these studies, solar light was used as an energy source. Comparing these studies, the study of [PERSON] et al. [58] had better results than [PERSON] and [PERSON] [92] and [PERSON] et al. [99], and although total degradation was never achieved, contrary to what happened in [PERSON] et al. 's [97] work, it should be noticed that in this study, it was used a parabens mixture, while in the others, only single parabens degradation was evaluated. Thus, the complexity of the reaction mixture highly influences the process efficiency. Table 3 summarizes photocatalysis studies in parabens abatement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pollutant**} & \multirow{2}{*}{**Catalyst**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Results and**} \\  & & & **Conditions** & **Conclusions** & **Reference** \\ \hline \hline \multirow{8}{*}{Bisphenol A, Bisphenol B, Diamylphthalate, TiO2 \%IP-7000} & \multirow{8}{*}{*} & \multirow{8}{*}{Sun radiation in pilot plant. 0.3 mg/L of each pollutant in real wastewater effluent. 200 mg/L of catalyst} & \multirow{8}{*}{B, BP, and DP respectively, TiO2, P2S, while for 100%, 40%, 40%, and 45%, respectively.} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \multirow{8}{*}{MP, EP, and PP} & \multirow{8}{*}{Metal free graphite-like carbon nitride TiO2 P25} & \multirow{8}{*}{*} & \multirow{8}{*}{0.08 mM of each paraben in water. 200 mg/L of catalyst dose of 1 g/L} & \multirow{8}{*}{For parabens mixture, the degradation was about 99\%} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parabens removal by photocatalysis.
## 2 Finite Element Modeling of the Cutting Head Cutting Rock
### Determination of the Material Parameters  
Drawing upon rock-breaking theory and the principles of explicit dynamic analysis, a finite element model of the soft rock, pick, pick holder, and drum was established. The soft rock was based on the HJC constitutive model of coal and rock. It can be seen from Figure 3 that the compressive deformation of coal can be divided into three stages: elastic deformation (O-A), plastic deformation (A-B), and compaction deformation (B-C). In Figure 3, \(p\) refers to the pressure and \(\mu\) is the volumetric strain.  
1. Elastic deformation (\(0\leq\mu\leq\mu_{c}\)): \[P=K\mu,\] (1) \[K=P_{c}/\mu_{c},\] (2)  
where \(K\) is the elastic bulk modulus of the rock, \(P_{c}\) is the crushing pressure of the rock, and \(\mu_{c}\), is the volume strain of the rock.  
Figure 2: Three-dimensional model.  
2. Plastic deformation (\(\mu_{c}\leq\mu\leq\mu_{1}\)): During this stage, the pores in the rock are broken, and the structure of the rock starts to become damaged, but it is not completely broken. Cracks appear in the rock. Loading process: \[P=P_{c}+K_{1}\left(\mu-\mu_{c}\right)\] (3) \[K_{1}=(P_{c}-P_{1})/(\mu_{c}-\mu_{1}),\] (4) In the above formula, \(K_{1}\) is the plastic bulk modulus of the rock, \(P_{1}\) is the compaction pressure of the rock, and \(\mu_{1}\), is the compacted volume strain of the rock. Unloading process: \[P=P_{0}+\left[(1-F)\,K+FK_{1}\right](\mu-\mu_{0}),\] (5) \[F=(\mu_{0}-\mu_{c})/(\mu_{1}-\mu_{c}),\] (6) \(P_{0}\), here, refers to the maximum pressure before the rock is unloaded, and \(\mu_{0}\) is the maximum volume strain before the rock is unloaded. 3. Compaction deformation (\(\mu\geq\mu_{1}\)): At this stage, the rock is completely crushed and compacted. Loading process: \[P=k_{1}\overline{\mu}+k_{2}\overline{\mu}^{2}+k_{3}\overline{\mu}^{3},\] (7) \[\overline{\mu}=(\mu-\mu_{1})/(1+\mu_{c}),\] (8) \(k_{1}\), \(k_{2}\) and \(k_{3}\) are the pressure coefficients. Unloading process: \[P=k_{1}\overline{\mu},\] (9) The HJC-based numerical simulation parameters are shown in Table 1. In this paper, the rock samples are taken from a certain engineering site, and all parameters required for simulation are obtained through experiments and literature reviews [26].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(\rho\) (kg/m\({}^{3}\)) & 1594 & \(\varepsilon_{f\mathrm{min}}\) & 0.01 \\ G (Pa) & \(5.8\times 10^{8}\) & T (Pa) & \(1.86\times 10^{6}\) \\ F\({}_{c}\) (Pa) & \(9\times 10^{6}\) & P\({}_{\mathrm{crush}}\) (Pa) & \(3\times 10^{6}\) \\ A & 0.4 & \(\mu_{crush}\) & \(8\times 10^{-4}\) \\ B & 0.7 & P\({}_{\mathrm{lock}}\) (Pa) & \(1\times 10^{9}\) \\ C & 0.005 & \(\mu_{lock}\) & 0.12 \\ \hline \end{tabular}
\end{table}
Table 1: HJC numerical simulation parameters.  
Figure 3: HJC model-based pressure–volume strain curve.