esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Second Assay (70 Compounds) Results and Obtained TRPA1 Agonists  
By detailed interpretation of the first assay results, we decided to dig deeper into the clusters with two concepts: the first is to dig the P1 clusters (clusters 12, 33, 35, 36, 38, 48, and 98), and the second is to focus on some clusters, which contain the electron-withdrawing group (e.g., clusters 20, 21, 22, 34, and 100) and can facilitate the Michael addition reaction of cysteine residues in TRPA1, matching to the knowledge of the previously investigated TRPA1 agonists. Conclusively, we assayed 70 compounds, focused on certain clusters in the second assay (Table S2).  
As a result of the TRPA1 assay, 63 compounds were identified as TRPA1 agonists: their responses in the TRPA1-expressing cells were significantly higher than those in the parent cells; in addition, their Ca\({}^{2+}\) responses in the TRPA1-expressing cells were concentration-dependent (Table S4b, Figure S1c,d). As shown in Figure 3c, the focused clusters, which had characteristic electron-withdrawing groups, were extremely limited in the chemical space in Library707. These data indicate that if all assays are based on such prior knowledge, it would be difficult to investigate the wide range of structures for novel findings.  
By the agonist activities confirmed in compounds bearing electron-withdrawing groups (Table S4b), the second assay data showed that our image-based assay platform works effectively to investigate compounds that match the prior knowledge of TRPA1 agonists. Interestingly, it was shown that compounds 46 and 51 diminished their activity, even if they had an electron-withdrawing group in the structure. Since electrophilic moieties of TRPA1 agonists are suggested to form a covalent bond with cysteine residues of TRPA1 via a Michael addition [8, 9], their reactivity can be modified by peripheral substituents. Such knowledge accumulation should be important for avoiding the design of inactive agonists.
# Constant Envelope Modulation Techniques for Limited Power Millimeter Wave Links
## 4 Performance of AWGN Channel in the Presence of CCI  
In the presence of additive white Gaussian noise, \(n(t)\), in addition to the interference (see Figure 3), the received signal can be written as follows:  
\[\mathrm{r}(t)=\mathrm{s}(t)+\iota(t)+n(t). \tag{16}\]  
Due to the fact that \(\iota(t)\) and \(n(t)\) are independent random processes, that is, \(\sigma_{1+N}^{2}=\sigma_{1}^{2}+\sigma_{N}^{2}\), where the AWGN standard deviation is based on the following [26]:  
\[\sigma_{N}=\sqrt{\frac{\hat{q}^{2}}{2\frac{E_{b}}{N_{0}}N\log_{2}M}}. \tag{17}\]  
Here, \(E_{b}\) is the energy per bit of the transmitted signal and \(N_{0}\) is the power spectral density (PSD) of the AWGN. In order to calculate the SER analytical expression of this new scenario, the expression of (13) is used, leading to:  
\[SER\approx 2\bigg{(}\frac{M-1}{M}\bigg{)}Q\Bigg{(}\frac{d}{\sqrt{\sigma_{1}^{2}+ \sigma_{N}^{2}}}\Bigg{)}=2\bigg{(}\frac{M-1}{M}\bigg{)}Q\Bigg{(}2\pi h\sqrt{ \frac{6N_{chip}SIR\frac{E_{b}}{N_{0}}\log_{2}M}{(M^{2}-1)\Big{(}\frac{E_{b}}{ N_{0}}N\log_{2}M+N_{chip}SIR\Big{)}}}\Bigg{)}. \tag{18}\]  
A comparison is made between the analytical results obtained using Equation (18) and the simulation results of the CE-MTO and the CE-OFDM techniques in the presence of external interference and AWGN. Figure 5 shows the simulated results for the SER as a function of the \(E_{b}/N_{0}\). It is noticeable that there is a high correlation between the simulated results for both modulation techniques (CE-OFDM and CE-MTO).  
Figure 4: Comparison of the symbol error rate (SER) as a function of the signal to interference (SIR) power ratio for the interfered link, \(N_{chip}=16\), and \(N=14\): (**a**) M = 4, 8, and 16, \(2\pi h=0.6\), and (**b**) M = 4, \(2\pi h=0.4\), 0.5, 0.6. CE-MTO—constant envelope M-ary time orthogonal; OFDM—orthogonal frequency division multiplexing.  
The graphs in Figure 5 have been created for \(N=14\) orthogonal waveforms, each containing \(N_{chip}=16\) chips. The PAM constellation order is M = 4 and the modulation index is kept at \(2\pi h=0.6\). The signal to interference power ratio is assumed to be SIR = 15 dB. The \"Analytical AWGN only\" curve refers to the analytical expression obtained in our previous work [26] in Equation (21) for the case of the AWGN channel only. The \"Analytical Interference only\" and the \"Analytical Interference and AWGN\" curves refer to Equations (15) and (18), respectively. Figure 5 shows that for the case of SIR = 15 dB, M = 4, and \(2\pi h=0.6\), the SER for the \"Analytical Interference only\" is approximately equal to 0.025. Please note the \"Analytical AWGN only\" and the \"Analytical Interference only\" curves function as an asymptote for the \"Simulated\" and \"Analytical Interference and AWGN\" graphs in which the interference and AWGN exists.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods
### The CORINE Land Cover  
Coordination of Information on the Environment (CORINE) is a database of the European Environment Agency (EEA) and its member countries in the framework of the European Network for Information and Observation (EIONET). Currently, the CLC datasets are part of the European Copernicus program and EEA coordinate landscape monitoring services for the PAN-European region. Briefly, CORINE was specified to standardize data collection on land in Europe to support environmental development. The number of participating countries has increase over time--currently including 33 EEA member countries and 6 cooperating countries (EEA39) with a total area of over 5.8 km2[48].  
The first development of CORINE began in 1986 and lasted until 1998, and the first reference year was 1990 (CLC1990). New versions are released every six years. So far, five versions have been implemented, CLC1990, CLC2000, CLC2006, CLC2012, and CLC2018, respectively [48; 49]. CORINE maps have been traditionally obtained in vector format through photointerpretation of satellite imagery at 1:100,000 scale. Furthermore, CORINE mapping rules remain the same: a Minimum Mapping Unit (MMU) of 25 ha and a Minimum Mapping Width (MMW) of 100 m (Table 1) [44].  
CLC has a detailed hierarchical nomenclature on three levels. The most-detailed level is level 3, with a maximum of 44 categories. Level 2 and level 1 have a maximum of, respectively, 15 and 5 categories (see Table 2). The three levels are connected to each other since the level 1 nomenclature is a hierarchical stepwise aggregation of the level 2 and level 3 categories [48; 50].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **CLC1990** & **CLC2000** & **CLC2006** & **CLC2012** & **CLC2018** \\ \hline Source images & Landsat 4/5 & Landsat 7 & SPOT 4/5, IRS P6 & IRS P6, RapidEye & Sentinel-2, Landsat 8 \\ \hline Date of satellite & & & & & \\ image acquisition & 1986–1998 & 2000 (+/\(-\)1 year) & 2006 (+/\(-\)1 year) & 2011–2012 & 2017–2018 \\ \hline Geometric accuracy & \(\leq\)50 m & \(<\)25 m & \(\leq\)25 m & \(\leq\)25 m & \(\leq\)10 m (Sentinel-2) \\ \hline CLC MMU & 25 ha & 25 ha & 25 ha & 25 ha & 25 ha \\ \hline CLC MMW & 100 m & 100 m & 100 m & 100 m & 100 m \\ \hline Location accuracy & 100 m & \(>\)100 m & \(>\)100 m & \(>\)100 m & \(>\)100 m \\ \hline Thematic accuracy & \(\geq\)85\% & \(\geq\)85\% & \(\geq\)85\% & \(\geq\)85\% & \(\geq\)85\% \\ \hline Duration (years) & 13 & 5 & 4 & 3 & 1.5 \\ \hline Number of & & & & & \\ participating & 22 & 32 & 38 & 39 & 39 \\ countries & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evolution of CORINE.  
Unfortunately, the thematic accuracy of CORINE (85%) is not high enough to enable its use for training a machine learning algorithm that would reliably detect LC. We, therefore, opted for a smaller, but more-accurate LC dataset when it comes to training the first stage of our pipeline.
## 4 The 3D Arbitrary Baseline Algorithm  
### The Principle of 3D Arbitrary Baseline Algorithm  
The direction finding principle for four antennas based on arbitrary baseline is shown in Figure 6. D is the fourth antenna placed in the space, and other conditions are the same as Figure 4.  
Figure 6: Four antennas direction finding schematic.  
The phase differences of the incident signal impinging on the four antennas A, B, C and D are represented respectively as follows:  
\[\phi_{\rm AB}+2k_{1}\pi=\frac{2\pi}{\lambda}[(x_{\rm B}-x_{\rm A})\cdot\cos\beta \cdot\cos\alpha+(y_{\rm B}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha+(z_{\rm B}- z_{\rm A})\cdot\sin\beta] \tag{11}\]  
\[\phi_{\rm AC}+2k_{2}\pi=\frac{2\pi}{\lambda}[(x_{C}-x_{\rm A})\cdot\cos\beta \cdot\cos\alpha+(y_{C}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha+(z_{C}-z_{\rm A })\cdot\sin\beta] \tag{12}\]  
\[\phi_{\rm AD}+2k_{3}\pi=\frac{2\pi}{\lambda}[(x_{D}-x_{\rm A})\cdot\cos\beta \cdot\cos\alpha+(y_{D}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha+(z_{D}-z_{\rm A })\cdot\sin\beta] \tag{13}\]  
Assuming \(\gamma_{1}=cos\beta\cdot cos\alpha,\gamma_{2}=cos\beta\cdot sin\alpha\) and \(\gamma_{3}=sin\beta\). Equations (11-13) can be expressed as the following matrix form:  
\[\frac{\lambda}{2\pi}\left[\begin{array}{c}\phi_{AB}+2k_{1}\pi\\ \phi_{AC}+2k_{2}\pi\\ \phi_{AD}+2k_{3}\pi\end{array}\right]=\left[\begin{array}{ccc}x_{B}-x_{A}& y_{B}-y_{A}&z_{C}-z_{A}\\ x_{C}-x_{A}&y_{C}-y_{A}&z_{C}-z_{A}\\ x_{D}-x_{A}&y_{D}-y_{A}&z_{D}-z_{A}\end{array}\right]\left[\begin{array}{c} \gamma_{1}\\ \gamma_{2}\\ \gamma_{3}\end{array}\right] \tag{14}\]  
Therefore, the solution of Equation (14) is:  
\[\left[\begin{array}{c}\gamma_{1}\\ \gamma_{2}\\ \gamma_{3}\end{array}\right]=\frac{\lambda}{2\pi}\cdot\left[\begin{array}{ ccc}x_{B}-x_{A}&y_{B}-y_{A}&z_{C}-z_{A}\\ x_{C}-x_{A}&y_{C}-y_{A}&z_{C}-z_{A}\\ x_{D}-x_{A}&y_{D}-y_{A}&z_{D}-z_{A}\end{array}\right]^{-1}\left[\begin{array} []{c}\phi_{AB}+2k_{1}\pi\\ \phi_{AC}+2k_{2}\pi\\ \phi_{AD}+2k_{3}\pi\end{array}\right] \tag{15}\]  
There are two ways to solve \(\alpha\) and \(\beta\). The first is:  
\[\theta=\arccos\gamma_{3} \tag{16}\] \[\varphi = \arcsin\left(\frac{\gamma_{1}}{\cos(\theta)}\right)or\varphi= \arcsin\left(\frac{\gamma_{2}}{sin(\theta)}\right) \tag{17}\]  
The second is:  
\[\varphi=\arctan\left(\frac{\gamma_{2}}{\gamma_{1}}\right) \tag{18}\] \[\theta=\arccos\left(\frac{\gamma_{1}}{\cos(\varphi)}\right)or \theta=\arcsin\left(\frac{\gamma_{2}}{\sin(\varphi)}\right)\]  
The codomain of \(\sin\beta\) is \([0,1]\) in the first method, but \(\gamma_{3}\) is larger than the codomain of \(\sin\beta\) at high frequency in practice. The real value of 2D-DOA can not be obtained because of so many ambiguous values at high frequency. As a result, the second method is selected in this paper. The codomain of \(\tan\alpha\) is \([-\infty,+\infty]\). All azimuth angles can be covered, which will make it convenience to solve the phase ambiguity.
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 3 The Proposed Method
### The Process of TP-DSDPC Algorithm  
The whole algorithm has seven main steps. The workflow chart of the TP-DSDPC algorithm is shown in Figure 3. Figure 4 displays the realization process of the tissue--like P system.  
* Input: the dataset \(x\) (\(x_{1}\), \(x_{2}\), \(\cdots\), \(x_{n}\)) and \(d_{c}\)
* Output: the clustering result
* Step 1: In \(cell_{1},\cdots,cell_{n}\), we first apply rule \(R_{1}\) to calculate the local density and relative distance of each data point at the same time.
* Step 2: The rule \(R_{2}\) is applied to obtain the distance matrix \(M\) in \(cell_{1},\cdots,cell_{n}\).
* Step 3: In \(cell_{1},\cdots,cell_{n}\), the rule \(R_{3}\) is used to calculate the _Score_ value of each point as a backup for selecting the clustering centers.
* Step 4: According to the number of clusters, use rule \(R_{4}\) to split \(cell_{n+1}\) into \(c\) new cells.  
Figure 2: The initial configuration of the tissue—like P system.  
* Step 5: Membrane fusion of clustering centers--apply the rule \(R_{5}\) to fuse the \(c\) clustering centers with \(cell_{n+1},cell_{n+2},\cdots,cell_{n+c}\), respectively.
* Step 6: In this step, rule \(R_{6}\) is used to allocate the remaining points, except clustering centers.
* Step 7 (Termination of calculation): The calculation is terminated when all of the points are allocated; the clustering result is output.
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 4 Power Allocation under Fairness Constraints  
In this section, the power allocation problem of maximizing system throughput is studied under the constraints of minimum intra and inter cluster fairness. Power allocation includes inter cluster power allocation and intra cluster user power allocation. The problem can be described as:  
\[\begin{array}{ll}P0&\max\limits_{p_{c}^{n},p_{n}^{n}}R_{sum}=\sum\limits_{n=1} ^{N}\left(R_{c-n}^{s}+R_{c-n}^{w}\right)\\ s.t.&C1:\sum\limits_{n=1}^{N}\left(p_{n}^{s}+p_{n}^{w}\right)=P_{t}\\ &C2:0<p_{n}^{s}<p_{n}^{w}<P_{t},n\left[1,N\right]\\ &C3:F_{c-n}\geq\varphi_{c-n},n\in\left[1,N\right]\\ &C4:F_{c}\geq\varphi_{c}\end{array} \tag{15}\]  
where \(\varphi_{c-n}\) represents the lower bound of fairness among users in the \(n\)-th cluster, \(\varphi_{c}\) represents the lower bound of fairness between clusters. C1 ensures that the power allocated to all user clusters is equal to the total transmit power of the BS; C2 represents the power magnitude relationship between strong and weak users; C3 gives the lower bound of fairness among users in the \(n\)-th cluster, \(\varphi_{c-n}\in\left[0,1\right]\); C4 gives the lower bound of fairness between clusters, \(\varphi_{c}\in\left[0,1\right]\).  
If the power allocated to the \(n\)-th cluster by the BS is \(p_{nt}\), then the power allocated to strong channel gain user is \(p_{n}^{s}=\alpha_{n}^{s}p_{n}\), and the power allocated to weak channel gain user is \(p_{n}^{w}=\alpha_{n}^{w}p_{n}\), where \(\alpha_{n}^{s}\) and \(\alpha_{n}^{w}\) represent the power allocation factor for the strong channel gain user and the weak channel gain, respectively. For convenience, the problem P0 is decoupled into two sub problems: power allocation intra cluster and power allocation inter clusters.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 4 Methodology
### Instrument Development  
This e-health laboratory research used a quantitative approach. The survey instrument used was based on the indicators of each variable in accordance with the conceptual model in Figure 2. The indicators were based on existing supporting theories. Preliminary test was conducted on survey instruments in the form of validity and reliability tests. This was done to check the validity and reliability of research instruments. To check the internal validity and reliability of the instrument, Cronbach's Alpha and Corrected Item-Total Correlation were used. A pretest was also carried out to see whether the respondent understood each question [109].  
In the test, the results for each variable met the reliability test with a Cronbach's Alpha value above 0.8. With this instrument condition, it was ready to be shared online with e-health laboratory users in Indonesia.  
Figure 4: Methodology of research work.  
This study used a pretest sample of 50 people. The results of our analysis confirmed that all of our instrument indicators have met the validity test for the total items of the corrected correlations of each indicator above 0.2353. A value of 0.2353 was produced from the r-table with the value of degree of freedom (df) N-2 or 48 by considering the pretest sample of 50 respondents.  
The questionnaire in this study was developed using a 5-point Likert scale which had a range of (1) strongly disagree and (5) strongly agree [110].  
The indicators used from each variable in this study were adapted from previous studies. The task-driven model included three constructs such as technology characteristics and task characteristics whose measuring items were adapted from [PERSON] and [PERSON]'s study, [75] while the task technology fit was adapted from [48; 79; 80; 81]. Consequently, technology characteristics, task characteristics, and task technology fit comprised 4 items, 3 items, and 4 items each.  
The technology-driven model also included three constructs which were information quality, accessibility, and design. The measurement items used were taken from studies such as information quality from [PERSON] and [PERSON], 2016 [58], accessibility [57], and design adapted from the [PERSON] and [PERSON]'s study, 2016 [58]. As a result, information quality, accessibility, and design contained 5 items, 3 items, and 3 items each.  
The technology readiness index had only one construct, namely, personal innovativeness where the personal innovativeness measurement items were adopted from [70; 71] so personal innovativeness contained 4 items and 3 items each.  
The technology acceptance model had three constructs, namely, perceived usefulness, perceived ease of use, and adoption. Perceived usefulness and perceived ease of use were adapted from the measurement items of research [107; 108]. For adoption constructs, the measuring items were adopted from [36], so the total survey instrument was composed of 11 variables which were interpreted into 39 questions in the questionnaire which can be fully seen in the Appendix B.
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 3 Results
### Freeze-Thaw Resistance  
The relative UPTT (%) vs. number of freeze-thaw cycle curves for asphalt samples are plotted in Figure 4a and for paving tiles in Figure 4b. All the reference samples qualified for classification as RILEM 'good' resistance materials [52]. Photocatalytic samples S2-A, E1-A, E2-A, E3-A and T2 were also so qualified, albeit with some scatter, whilst S1-A exhibited 'intermediate' resistance. The poorest performance was observed in emulsion-coated paving tiles E1-T, E2-T and E3-T, which has been attributed to the photocatalytic layer cracking. The cracks might also explain the wider scatter observed for these materials. Even though there are a small number of samples, no clear deterioration can be seen in the bulk of any of them as no continuous rise in the relative UPTT has been observed in any  
Figure 3: (**a–d**) Differential pore size distribution in the samples.  
case. The apparent inconsistent decline in relative UPTT after more cycles might denote peeling away of the photocatalytic layer that makes the matrix arise in the surface. E1-T exhibited the lowest resistance of all the photocatalytic samples, followed by E3-T and T1, in that order. Scaling was detected in only four samples (Figure 5), most severely in E2-T and S1-A. After a single slight rise early in the test, the weight of the scaled material in samples Ref-A and E3-T flattened.  
Figure 4: (**a**) Relative UPTT (%) in all (**a**) asphalt and (**b**) paving tile samples.  
Even to the naked eye, the photographs of the photocatalytic specimens reproduced in Figure 6 clearly revealed that the coatings had worn away after freeze-thaw testing. When the asphalt was coated, the samples initially turned light grey, whilst after the cycles the colour reverted to the original black. The same effect was observed on the paving tiles, although the difference was less dramatic. The slurries also disappeared from the surface, especially in sample S2-A, in which the pre-test whiteness vanished, unveiling the black asphalt across the entire surface after the cycles.  
The optical micrographs in Figure 7 show that in addition to the change in colour, the slurry-based samples, showed some cracks, whilst the white photocatalytic layer was missing altogether in the emulsion-coated materials. Most of the samples bore an obvious resemblance to the reference materials.  
Figure 5: Weight of scaled material in selected samples.  
Figure 6: Photocatalytic specimens (**a**) before and (**b**) after the freeze-thaw test.
## 7 Experimental Setup
### Idle Time for Agents in the HRC Scenario  
Figure 9 compares the human idle time (H-IDL) and robot idle time (R-IDL) in the screwdriver usage HRC task (i.e., Scenario 1) to highlight the benefit of integrating motion prediction into HRC. The robot is triggered when the human's action is detected to be 'pick up' and is stopped when the 'take the screw' action is detected. In the proposed screw-driver usage task, the original length of this HRC task is 14 s, which is the time used in human-human collaboration. Red blocks indicate when the operator is in an idle state, waiting for the screw to arrive. White blocks are the times the robot is idle for computation and data loading. Green blocks are the times when both agents are executing the commanded task. When employing motion-prediction-based control, the R-IDL consists of the time for motion prediction and path planning. The robot's execution time accounts for the majority of the total time when employing the motion predictor and embedded optimization engine. In the HRC task implemented without motion prediction, the robot  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Max Acc** & **Min d (cm)** & **Avg Max Acc** & **Avg Min d** \\  & **(m/s\({}^{2}\))** & & **(m/s\({}^{2}\))** & **(cm)** \\ \hline NMPC & 12.41 & \(-\)3.26 & 2.73 & \(-\)1.62 \\ \hline NMPC-ECBF & 2.70 & 0 & 1.42 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Controller performance for 100 HRC task instances.  
Figure 8: (**a**) The plot of \(V(z1)\); (**b**) The plot of \(\dot{V}(z1)\).  
has to start path planning after the human operator moves their right hand to the target interactive position.  
After repeating the screwdriver usage task 100 times, Table 3 compares the average human idle time (H-IDL) and robot idle time (R-IDL) for the human-robot collaboration task without motion prediction. In this work, the HRC without motion prediction can be treated as a strict turn-taking task, where each agent strictly takes action after the partner finishes. The H-IDL rate is reduced by 4.1% and the R-IDL rate by 13.4% in the HRC task using the motion-prediction-based approach. Without motion prediction, the H-IDL rate and R-IDL rate are 26.3% and 7.1%, respectively. Therefore, using motion prediction reduces the idle time for both the human and the robot in the HRC task. As a result, the total execution time is reduced from 19.0 to 14.6 s, a saving of 23.2%.