esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Vibration Influence Law of Double-Shield TBM Tunnel Construction
## 4 Analysis of Vibration Monitoring Results of Double-Shield TBM Construction
### Time-Domain Analysis of Vibration Monitoring Results  
When the double-shield TBM reached the monitoring section, it was advancing toward the 1091 st ring. In order to get the magnitude of construction vibration as realistic as possible, the measuring point 03 of sampling hole Z-4 above the tunnel vault is selected to analyze the time-domain characteristics of the double-shield TBM excavation. The X, Y, and Z-directions of this monitoring point correspond to the signal channels 2-2, 2-3, and 2-3, respectively. The time-domain curve of monitoring point Z-4 03 is shown in Figure 7 below.  
Judging from the vibration acceleration amplitude in the X, Y, and Z-directions, the peak value of the vibration acceleration in the Z-direction at the vibration source is 207.60 mm/s\({}^{2}\), the peak value of the Y-direction is 81.16 mm/s\({}^{2}\), and X-direction is 133.54 mm/s\({}^{2}\). In other words, the vibration acceleration generated by double-shield TBM construction is largest in the vertical direction and smallest in the radial direction of the tunnel.  
As seen from the above time-domain curves, the vibration acceleration value is about 1-2 mm/s\({}^{2}\), from 0 to 1320 s and 3580 to 2608 s. At this time, the double-shield TBM is in a non-excavating state. However, from 1032 to 3580 s, when the double-shield TBM is in an excavating state, the vibration acceleration value increases first. Subsequently, it reaches a steady state. After 3500 s, it rapidly reduces to the non-excavating vibration level. It can clearly be seen that the vibration value of double-shield TBM in the excavating state is 100-150 times larger than that in the non-excavating state. That is to say, the vibration of double-shield TBM construction is mainly caused by excavation. Moreover, the vibration caused by the non-excavation construction process, including the operation of the muck truck, segment assembly, etc., can be ignored.  
From the time-domain curves of vibration acceleration in the excavating state, the vibration caused by the double-shield TBM construction can be divided into three stages: rising stage, stable stage, and falling stage. The vibration of the rising stage and falling stage is synchronized with the rotation speed; it may be related to tunneling parameters. Moreover, peak acceleration appears in the stable stage. Considering the most unfavorable factor of vibration during double-shield TBM construction, the peak acceleration is taken as the representative value of each ring excavation.
## 3 Results and Discussion
### Synthesis of MIP on Terbium Nitrate
#### 3.2.3. Sorption and Desorption Properties of the MIPs and NIPs
Based on the obtained results, the imprinting factor (IF) was calculated [71]:  
Figure 9:0  
For the calculations, the values of the Tb concentration in the MIP and NIP were taken. The IF is a key parameter used to evaluate the effectiveness of the synthetized MIP in selectively recognizing and binding the target molecule (template). It quantifies the enhanced binding affinity of the MIP compared to the NIP, which lacks the template-specific binding sites. Interpretation of the IF values can be performed as follows [71]:  
* IF = 1: No imprinting effect (MIP performs similarly to NIP). * IF > 1: Successful imprinting (MIP has higher affinity for the template). * IF < 1: Poor imprinting or non-specific binding issues. It can be concluded that the synthesized MIP on Tb has a high affinity for the target molecule, and it correlates with the results of the sorption experiments and SEM images. In order to evaluate the sorbents (MIP and NIP), the pseudo-second-order (PSO) kinetic model and Radushkevich kinetic model were used.
# Galileo Single Point Positioning Assessment Including FOC Satellites in Eccentric Orbits
## Materials and Methods
### Methodology
The set of Equations (3) is solved for \(\underline{\Delta X}\) by means of the Weighted Least Squares (WLS) method, and the solution is given by the equation:  
\[\underline{\Delta X}=(H^{T}WH)^{-1}H^{T}W\underline{\Delta\rho} \tag{4}\]  
where: \(W(nxn)\) is the weighting matrix. It can be set as the inverse of the measurement co-variance matrix \(R\), weighting the accurate measurements more and the noisy ones less. Here, all pseudorange errors were considered independent, thus the co-variance matrix was set equal to the diagonal matrix with the weights calculated as a function of the satellite elevation angle. \[W=R^{-1}\cdot diag(Weight) \tag{5}\]  
The weighted least squares estimation was used to update the a priori value:  
\[\underline{\hat{X}}=\underline{X_{a}}+\underline{\Delta X} \tag{6}\]  
where \(\underline{\hat{X}}\) is the updated least squares solution. In order to find the optimal solution, the process was iterated. During the software testing period, we found that residuals began to remain stable after the fifth iteration; thus, we stopped the process after five iterations. The dimensionless GDOP parameter represents the geometry contribution on positioning accuracy, and it is considered a figure of merit for Galileo system geometry analysis. Starting from \(Q\) matrix:  
\[Q=(H^{T}\cdot H)^{-1} \tag{7}\]The elements of the matrix \(Q\) are a function of the satellite and receiver geometry only. The GDOP parameter is defined in Equation (8):  
\[GDOP=\sqrt{Q_{1,1}+Q_{2,2}+Q_{3,3}+Q_{4,4}} \tag{8}\]  
Low GDOP values coincide with good geometries, while high values to poor geometries; satellite geometry with GDOP values up to 3 are considered excellent, while GDOP up to 6 are good. A GDOP value of 8 is considered the limit for a moderate geometry. Thus, GDOP takes into account the quality of overall solution, but we may prefer to look at the specific components such as the three-dimensional receiver position coordinates (PDOP), the horizontal coordinates (HDOP), the vertical coordinate (VDOP), or the clock offset (TDOP) [18], whose expressions are reported in Equations (9)-(12), respectively. \[PDOP=\sqrt{Q_{1,1}+Q_{2,2}+Q_{3,3}} \tag{9}\]  
\[HDOP=\sqrt{Q_{1,1}+Q_{2,2}} \tag{10}\]  
\[VDOP=\sqrt{Q_{3,3}} \tag{11}\]  
\[TDOP=\sqrt{Q_{4,4}} \tag{12}\]  
We developed a suitable software tool in the MATLAB(r) environment able to compute the single-point positioning solution using Galileo measurements on the E1, E5a, E5b, and E5 frequencies. The tool is provided with an orbital propagator able to use both transmitted and precise ephemeris in sp3 format. As output, the software provides also the DOP values.
# Frequency Response of Optically Pumped Magnetometer with Nonlinear Zeeman Effect
## 5 Conclusions and Outlook  
In this work, we present a quantitative model to describe the influence of the NLZ effect on the frequency response of the OPM and experimentally verify this model with a Bell-Bloom cesium OPM. While it is known that the magnetic resonance signal with an NLZ effect is a linear superposition of multiple Lorentz curves, we find that the frequency response of the Bell-Bloom magnetometer influenced by the NLZ effect is also the same linear superposition of contributions from these multiple Lorentz curves. This model can be extended to the OPMs based on other alkali atomic species by updating the gyromagnetic ratio \(\gamma\), the ground state hyperfine splitting \(\omega_{\text{HF}}\), and the total angular momentum quantum number \(F\).  
Our work is important for analyzing the frequency response of the alkali atomic magnetometer operated in an unshielded geomagnetic environment. Full and precise knowledge of the frequency response of the OPM can help to automatically optimize parameters of the closed-loop operation of the OPM and thus help to extend the bandwidth of the OPM without losing its sensitivity, or to improve the identity between the frequency responses of different OPMs to improve the noise rejection ratio of the gradiometer [26]. As closed-loop magnetic-noise-compensation setups require adequate knowledge of the system response, our work can also help to improve the performance of the active magnetic-field stabilization operated in the Earth's field [29; 37] which is attractive for ultralow-field nuclear magnetic resonance [7] and for recording bio-magnetic signals from human brain activities in unshielded environments [26; 38].  
Conceptualization, [PERSON], J.C., X.P., T.W., and H.G.; data curation, R.Z.; formal analysis, [PERSON] and T.W.; investigation, [PERSON] and T.W.; funding acquisition, J.C. and H.G.; methodology, [PERSON], T.W., J.C., X.P., and H.G.; resources, J.C., X.P., and H.G.; software, R.Z.; validation, [PERSON], T.W., J.C., X.P., and H.G.; supervision, [PERSON] and H.G.; visualization, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON], writing--review and editing, [PERSON], [PERSON], [PERSON], and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grants No. 61571018, No. 61531003, No. 91436210), the National Science Fund for Distinguished Young Scholars of China (Grant No. 61225003), and National Hi-Tech Research and Development (863) Program. [PERSON] acknowledges the support from the start-up funding for young researchers of Peking University.  
We acknowledge useful discussions with [PERSON] and [PERSON] on the theoretical analysis of the OPM's frequency response.  
The authors declare no conflict of interest.
## 3 Results
### Maximum Radiated Power Angle of Electrons and the Trend of Corresponding Radiated Power  
The blue line in Figure 4 is the maximum radiation power angle \(\theta_{\mathrm{max}}\) corresponding to the electron in the case of different pulse widths, and we can find its maximum radiation power angle decreases continuously when \(\mathrm{L}<6\lambda_{0}\), i.e., the collimation increases; and when \(\mathrm{L}>6\lambda_{0}\), it stabilizes at 3, i.e., it reaches the saturation state. Corresponding to the result of Figure 3, the change of collimation of radiation is reflected.  
The red line in Figure 4 shows the radiated power corresponding to the maximum radiated power angle \(\theta_{\mathrm{max}}\) for electrons at different pulse widths. We can find that as the pulse width \(\mathrm{L}\) increases, the corresponding radiated power tends to rise and reaches saturation roughly at \(\mathrm{L}=6\lambda_{0}\). This indicates that as the radiation collimation is enhanced, the power released from it also increases and finally reaches saturation at \(\mathrm{L}=6\lambda_{0}\). Corresponding to the result of Figure 3, the variation of the magnitude of the radiated power is reflected.  
The reason why there is a situation such as the red line in the figure above is that when the pulse width \(\mathrm{L}\) is small, the contact time between the laser field and the electrons is short, and the energy of the laser field can't all be transferred to the electrons, so that the maximum radiated power of the electrons at the radiated power of the power is small. As the pulse width increases, the contact time between the laser field and the electrons becomes longer, and all the energy is transferred to the electrons, so the radiated power of the electrons reaches saturation.
## 4 Results: Manual Service Composition
### Perceived Satisfaction Towards Manual Service Composition  
Scales showed an excellent reliability (Cronbach alpha \(>\)0.90). Non-programmers' perceived complexity of manual service composition activities and satisfaction with the process editor differed from the programmers' in all dimensions: \(t\) (22) = -3.04, \(p\) = 0.006, as depicted in Figure 5. On average, the non-programmers (m = 2.42, SD = 1.16) found it less easy to define the condition than the programmers (m = 3.50, SD = 0.90). Moreover, they felt less confident (m = 3.00, SD = 1.34) using the process editor than the programmers (4.08, SD = 0.79), which may be due to their low self-efficacy. The non-programmers did not find it less difficult (m = 2.67, SD = 0.88) to understand the service composition notation than the programmers (m = 4.33, SD = 0.88). Overall, the non-programmers (m = 3.42, SD = 0.90) were less satisfied with the process editor than the programmers (m = 4.08, SD = 0.66). These satisfaction scores closely mirror the performance results and qualitative feedback, confirming hypothesis H1e.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Positive Aspect** & **Non-Programmers** & **Programmers** \\ \hline Ease of use of manual service composition & 20\% & 23\% \\ \hline Control and freedom of manual service composition & 20\% & 15\% \\ \hline Use of notations to represent activities and services & 3\% & 15\% \\ \hline Other disperse features & 57\% & 47\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Positive features of manual service composition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **Non-Programmers** & **Programmers** \\ \hline Guide users through service composition using, e.g., & 26\% & 12\% \\ wizard, tutorials & & \\ \hline Include more help and instructions & 26\% & 6\% \\ \hline Define complex terms & 6\% & 6\% \\ \hline Provide immediate feedback & 5\% & 18\% \\ \hline Improve visual appearance of the tool & 5\% & 25\% \\ \hline Other disperse suggestions & 28\% & 33\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Strategies for improving manual service composition.
# Drilling Condition Identification Method for Imbalanced Datasets
## 2 Feature Selection Algorithm Based on Weighted Symmetric Uncertainty
### Sequential Backward Selection  
In feature selection, search algorithms can be divided into exhaustive search, greedy search, and heuristic search. Exhaustive search evaluates all possible feature combinations and, in theory, can find the optimal feature subset in terms of performance. However, its computational complexity grows exponentially with the number of features \(n\) (requiring traversal of \(2^{n}-1\) combinations), making it impractical for high-dimensional drilling data. Greedy algorithms include forward search and backward search: forward search starts with an empty set and, at each step, adds the feature that provides the greatest improvement to model performance. While it has low computational cost, it is prone to getting stuck in local optima due to its disregard for feature interactions. Backward search starts from the full feature set and gradually removes features with the least impact on performance, allowing for better consideration of inter-feature relationships. However, since each feature removal requires evaluating its effect on performance, the method involves training on numerous feature combinations, leading to a relatively high computational cost when applied to high-dimensional datasets. Heuristic search methods, such as simulated annealing and genetic algorithms, attempt to find a near-global optimum with lower computational complexity. Simulated annealing avoids local optima by accepting slightly worse solutions, gradually converging over iterations. Genetic algorithms optimize feature subsets through selection, crossover, and mutation operations, but require multiple iterations, leading to higher computational overhead. Considering both computational cost and effectiveness, this paper adopts the sequential backward search algorithm, using random forests to evaluate the quality of the feature set and progressively eliminate redundant features, ensuring a balance between computational efficiency and performance.  
The steps of the weighted symmetric uncertainty feature selection process are shown in Figure 1:  
1. Calculate the weighted symmetric uncertainty between each feature and class, generating the candidate feature subset \(Q\);For the \(k\) features in \(Q\), remove one feature to form all possible \(k\)\(-\)\(1\) subsets. These subsets are input into a random forest model for training, and the subset with the best classification performance is selected;
3. Eliminate the features that were not selected and update the candidate feature subset to \(Q^{\prime}\);
4. Repeat the above steps until removing features no longer significantly impacts the model's performance, at which point the iteration stops.  
The final output feature set not only fully considers the classification needs of minority classes but also effectively incorporates the interactions between features, achieving the goal of precise and efficient feature selection.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 5 Numerical Examples
### Trade-off Tracking Performance Comparison  
As a controlled plant, the following transfer function is used:  
\[P_{d}(z^{-1})=\frac{0.0231+0.0114z^{-1}}{1-0.9753}z^{-14} \tag{36}\]  
The transfer function is the discrete time representation of the following continuous time system with \(T_{s}=0.03\) s:  
\[P(s)=\frac{1.4}{1.2s+1}e^{-0.4s} \tag{37}\]  
Using the proposed method, the PID parameters are decided based on Equation (36) with \(M_{s}^{d}\in\{1.4,1.6,1.8,2.0\}\). The simulation results obtained using the PID parameters are shown in Figure 6, where the reference input is 1.0 and the control input is disturbed by a unit step signal after 15 s. Figure 6 shows that the servo design is superior to the regulator design with respect to reference tracking performance. On the other hand, the regulator design is superior to the servo design with respect to disturbance rejection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(M_{s}^{d}\)} & \multicolumn{2}{c}{**Servo**} & \multicolumn{2}{c}{**Regulator**} \\ \cline{2-5}  & **min** & **max** & **min** & **max** \\ \hline
1.4 & 1.3923 & 1.4088 & 1.3904 & 1.4216 \\
1.6 & 1.5836 & 1.6130 & 1.5819 & 1.6183 \\
1.8 & 1.7725 & 1.8256 & 1.7738 & 1.8266 \\
2.0 & 1.9518 & 2.0359 & 1.9527 & 2.0356 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum and minimum values of obtained \(M_{s}\).  
The control performance is evaluated using \(J_{s}\) and \(J_{r}\), where \(J_{s}\) denotes the SAE value from the start until 15 s, and \(J_{r}\) also denotes the SAE value from 15 s until the end. Here, \(J_{s}\) and \(J_{r}\), as well as the PID parameters and \(M_{s}\) in the servo and regulator, respectively, are summarized in Table 4. In the servo design, \(J_{s}\) is smaller than \(J_{r}\). In the regulator design, \(J_{r}\) is smaller than \(J_{s}\).  
From both Figure 6 and Table 4, the larger \(M_{s}^{d}\), the better the control performance. Therefore, the trade-off design is achieved using the proposed method.
# Effect of Cement Content on the Deformation Properties of Cemented Sand and Gravel Material
## 4 Conclusions  
In this study, a series of triaxial compressive experiments were carried out on CSG materials for cement contents of 20, 40, 60, 80, and 100 kg/m\({}^{3}\) with a confining pressure range of 0.3-1.2 MPa. Based on the experimental results, the stress-strain behavior, initial modulus, and the initial Poisson's ratio of CSG material were analyzed. The conclusions obtained are as follows:  
1. The predictive model for the failure strain considering the effects of the confining pressure and the cement content is established. The model shows that the failure strain increases linearly with increasing confining pressure under a certain cement content; the failure strain decreased, and the brittleness of CSG material increased with increasing cement content.
2. The expression for initial modulus of CSG material considering the influence of cement content and confining pressure was proposed. This revealed that the modulus of the CSG material increases exponentially with increasing cement content.
3. Expressions for peak volumetric strain and its corresponding axial strain with the amount of cement content and confining pressure were proposed. These revealed that the peak volumetric strain and its corresponding axial strain increase linearly with increasing confining pressure, and the peak volumetric strain and its corresponding axial strain decrease with the increase in cement content.
4. The predictive model for the initial tangent volumetric ratio is established, and this value can be determined by the peak volumetric strain and its corresponding axial strain.
5. The expressions in this study are applicable when determining deformation properties of cemented granular materials with a cement content less than 100 kg/m\({}^{3}\) and a maximum particle size of the aggregates greater than 20 mm under different confining pressures and cement contents.  
These conclusions can help construct a reasonable constitutive model for the various CSG materials under different cement contents and can promote the application and utilization of CSG materials in dams, embankments, roadbeds, and so on.  
Conceptualization, X.C.; data curation, J.Y.; funding acquisition, X.C.; investigation, J.Y. and X.G.; validation, X.C. and X.G.; writing--original draft, J.Y. and J.Z.; writing--review & editing, J.Z.  
This research was funded by the National Key Research and Development Program of China (2018 YFC0406804), and the National Natural Science Foundation of China (51179061).  
The authors declare no conflict of interest.
## 5 Experimental Results and Discussion
### ASIC Implementation Results
That was attributed to the modest amounts of its latency and area at these word sizes. The proposed multiplier-squarer fulfilled a decrease in AT at word sizes of \(k=16\) and \(k=32\) by at least 32.3% and 70.4%. From Figure 13, we notice that the multiplier structure of Hua [60] had lower Power consumption (P) at word sizes \(k=8\) and \(k=16\) due mainly to the significant reduction in area. The decrease in area resulted in a reduction of the extracted parasitic capacitance and, hence, reduced the circuit's switching activities. The proposed multiplier-squarer structure had slightly higher power consumption over the multiplier structure of Hua [60] at the same word sizes of \(k=8\) and \(k=16\). In contrast, it had a slightly lower power consumption than the multiplier of Hua [60] at word size \(k=32\) due to its smaller area at this word size. Despite having lower power consumption at word sizes \(k=8\), and \(k=16\), the multiplier of Hua [60] had significant values of consumed energy, as shown in Figure 14, due to the tremendous amounts of its execution Time (T). The proposed multiplier-squarer had the lowest consumed energy, as shown in Figure 14, at all word sizes due to the reasonable values of its execution Time (T), as well as the lower values of the consumed power. The proposed multiplier-squarer reduced the consumed energy at word sizes \(k=8\), \(k=16\), and \(k=32\) by at least 70%, 72.4%, and 86.2%, respectively. From the previous discussion, we can conclude that the presented word-serial multiplier-squarer had a smaller AT for word sizes \(k=16\) and \(k=32\) and had less consumed energy for all word sizes. Thus, it is more suitable for realizing cryptographic primitives in RFID tags, especially semipassive and active tags with reasonable restrictions on area and delay and large restrictions on consumed energy. The design is more suitable for semipassive and active tags than battery-powered RFID tags (semipassive and active tags), which have restricted energy, contrary to energy-harvesting RFID tags (passive tags), which can accommodate endless energy with restricted power.