esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Drying Strinkage
#### 4.1.3 Effect of Modulus on Drying Shrinkage  
Figure 7 shows the drying shrinkage of RPSG with different moduli within 28 days. The test results show that the drying shrinkage has a certain positive correlation with the modulus. When the alkali equivalent is constant, drying shrinkage increases with the increase of modulus, which is consistent with the conclusions drawn by other studies [29]. The 28-day drying shrinkage of RP-S30-M0.9N6, RP-S30-M1.1N6, RP-S30-M1.3N6, RP-S30-M1.5N6 and RP-S30-M1.7N6 are 0.42%, 0.82%, 0.93%, 1.17% and 1.32%, respectively. For the RPSG with fixed alkali equivalent, the main factor affecting drying shrinkage is the pore distribution in the binders. When the alkali equivalent is constant, the content of sodium silicate increases relatively with the increase in modulus. Generally speaking, the increase of sodium silicate will lead to the decrease of the total porosity of the slurry and the increase of mesopore ratio, while the increase of mesopore volume will lead to the increase of capillary stress, thus increasing the drying shrinkage of the specimen. In addition, excessive sodium silicate will not react completely, and it is very easy to lose water under dry conditions, resulting in dry shrinkage of the slurry.  
From the above analysis, it is clear that RPSG has large drying shrinkage, with maximum drying shrinkage reaching 2.98% after 28 days, which is far higher than the drying shrinkage of metakaolin-based polymer (approx. 0.4-0.6%) [64]. However, the drying shrinkage of RPSG could be reduced by optimizing the mix ratio, such as the specimen RP-S30-M1.3N3, whose drying shrinkage is only 0.40%  
Figure 6: Drying shrinkage results of RPSG with different alkali equivalents.  
Figure 7: Drying shrinkage results of RPSG with different moduli.
## 4 Analysis of Shield Tunneling Parameters
### Grouting Pressure and Grouting Volume  
In the process of shield tunneling, ring-shaped gaps are generated between the segment and the soil after the shield tail passing section, which leads to settlement of the soil above. Therefore, grouting is needed to supplement the gap.  
Due to the high permeability of the round gravel stratum, the grouting dissipation and slurry loss at the shield tail are more obvious than those in the soft soil layer, which makes the grouting volume and pressure difficult to control. The surface is also prone to a large settlement. As can be seen in Figure 15a, the grouting pressure of the south line is concentrated at 0.16-0.28 MPa, and the grouting amount is 20-53 m\({}^{3}\). In the stage of the shield tail pass section, the grouting volume and grouting pressure rise rapidly to varying degrees. This is because the earth pressure acting on the south line is greatly released. After the shield tail passing section, a large amount of grouting pressure and volume are needed right away to supplement the gap at the shield tail. Therefore, the grouting volume and pressure fluctuate greatly after the shield tail passing section.  
There is only vacant land above section B of the north line, so the earth stress released and the stratum are relatively small. As shown in Figure 15b, the grouting pressure of the north line is concentrated at 0.11-0.23 MPa, and the grouting volume is concentrated at 20-38 m\({}^{3}\), which is significantly smaller than that of the south line. The fluctuation degree is relatively small as well.  
Figure 14: Measured value of torque of cutter: (**a**) south line; (**b**) north line.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Comparison with State-of-the-Art Methods  
The subject-independent classification accuracy for the proposed model is 96.6%, outperforming all state-of-the-art methods (refer Table 5). Most existing methods are built on the EEGNet architecture [34], which was initially proposed to classify P300 data [35]. Hence, the classification accuracy is compromised for MI data. Furthermore, all previous state-of-the-art methods require more storage space and higher computational cost for the data than the proposed method, as they were not optimized for compressed features. The proposed method achieves higher subject-independent classification accuracy than the existing methods with less storage space and a computational time of 0.32 s, sufficient for real-time classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Accuracy (\%)** & **Testing Time (s)** \\ \hline SVM & 92.46 \(\pm\) 0.28 & 0.45 \\ \hline LDA & 68 \(\pm\) 2.98 & 0.78 \\ \hline k-NN & 56.2 \(\pm\) 2.74 & 0.76 \\ \hline CNN & 66.9 \(\pm\) 0.25 & 0.62 \\ \hline Proposed model & 96.6 \(\pm\) 0.18 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed model with different classification models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline Wavelet entropy & 93 \(\pm\) 0.28 & 110 & 0.34 \\ \hline Wavelet energy & 93.9 \(\pm\) 0.25 & 111 & 0.34 \\ \hline Kurtosis & 86.11 \(\pm\) 1.26 & 122.76 & 0.34 \\ \hline Wavelet entropy + Wavelet energy (Proposed) & 96 \(\pm\) 0.19 & 125.6 & 0.38 \\ \hline Power spectral density & 91.2 \(\pm\) 0.84 & 156 & 0.54 \\ \hline Common spatial pattern & 94.5 \(\pm\) 0.456 & 142 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the proposed method with different features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Accuracy (\%)** & **k-Coefficient Values** \\ \hline Deep recurrent CNN [40] & 70.64 & - \\ \hline EEGNet Fusion [42] & 83.8 & - \\ \hline CNN [43] & 65.73 & - \\ \hline CNN + electrode pair signals [44] & 72.51 & - \\ \hline EEGNet [33] & 65.07 & - \\ \hline Proposed method & 96.6 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with state-of-the-art methods.
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 3 Example Application
### Model Setup  
According to the statistics of fracture geometric characteristics in Table 1, a hypothetical 2-D rock slope with two sets of discrete fractures is established in 3 DEC software using the DFN-DEM method. Figure 3 shows the slope geometry and the two sets of random fractures. The generated slope model is 70 m in length, 1m in width, and 40 m in height. The overall angle of the rock slope is 55.3\({}^{\circ}\). A tetrahedral discrete element is used in this slope. Smaller-mesh-size (0.5 m) elements are used around the slope (Figure 3), and the remaining elements are set to a larger mesh size (1 m) to reduce the computation time. From the bottom, the model has been fixed in all directions, while being constrained horizontally (e.g., in the x-direction) on both vertical sides. The top of the model is simulated as the free surface.  
It is assumed that the rock mass is the ideal elastic-plastic for the whole slope. The discrete fractures and rock blocks are considered to follow the Mohr-Coulomb constitutive model. The strength parameters of the rock blocks and fractures are obtained through a series of rock mechanics tests, shown in Tables 2 and 3, respectively. Note that jkn and jks are the fracture normal stiffness and fracture shear stiffness, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dip Direction/(\({}^{\circ}\))**} & \multicolumn{3}{c}{**Dip/(\({}^{\circ}\))**} & \multicolumn{3}{c}{**Trace Length/(m)**} & \multicolumn{3}{c}{**Spacing/(m)**} & \multicolumn{1}{c}{**Density**} \\ \cline{2-13}  & **Model** & **Mean** & **Std Dev.** & **Model** & **Mean** & **Std Dev.** & **Model** & **Mean** & **Std Dev.** & **Model** & **Mean** & **Std Dev.** & \\ \hline Set 1 & a & 54 & 4.5 & a & 3 & 1.6 & b & 8.5 & 2 & c & 2.7 & 1 & 0.0047 \\ Set 2 & a & 97 & 3.2 & a & 50 & 2.5 & b & 5.9 & 1.3 & c & 1.8 & 0.8 & 0.0819 \\ \hline \hline \end{tabular} a  
\end{table}
Table 1: Statistics of geometric characteristics for two sets of fractures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weight/(kN/m\({}^{-3}\))** & **Bulk/GPa** & **Poisson’s Ratio** & **Cohesion/MPa** & **Friction Angle/(\({}^{\circ}\))** & **Tensile/MPa** \\ \hline
23.5 & 9.07 & 0.26 & 0.22 & 40 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strength parameters of rock blocks.  
Figure 2: The rock slope with two sets of fractures.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Protocol** & **Details** \\ \hline \multicolumn{3}{c}{**Protocols for Air Interface/Communication**} \\ \hline \multirow{4}{*}{802.11/Wi-Fi and variants} & **Band:** 2.4/5/60, 700/800/900 MHz and 800-1000 GHz (light). **Channel Width:** 20, 40, 60, 80, 160 MHz, 8 GHz @ 60 GHz, 1–16 MHz @700/800/900 MHz. **Modulation:** OFDM, MIMO-OFDMA, DSSS-FHSSS, single carrier OFDM, O-OFDM. **Range:** 10–70 m. **Data rate:** 1.7–450 MBPS, for 60 GHz data rate could reach up to 20 GBPS. **Scope:** Local area \\ \hline \multirow{2}{*}{802.15.4. PAN for resource-constrained devices} & **Band:** 868/915/2450 MHz. **Channel Width:** 868-898.6, 902-928, 2400-2483.5 MHz. **No. of Channels:** 1, 10, and 16. **Modulation:** DSSS with BPSK, AAK, O-QPSK, and CSSS with DQPSK. **Chip Rate:** 300, 400, 600, 1000, 1600, 2000 Kchips/s. **Data rate:** 20, 40, 100, 250, 1000, 2000 kbps. **Scope:** Home area \\ \hline \multirow{4}{*}{ZigBee} & **PHY/MAC layer:** 802.15.4 **Maximum Transmission Unit (packet size):** 127. **Foot Print:** 9 kB. **Networking protocol:** custom. **Transport protocol:** object-based with exposed interfaces. **Application development:** fixed profile applications with data exchange interface. **No. of Devices:** 1024. **Scope:** Home area \\ \hline \multirow{4}{*}{6 LoWPAN} & **PHY/MAC layer:** 802.15.4 **Maximum Transmission Unit (packet size):** 127. **Foot Print:** 264 kB. **Networking protocol:** custom adaptation to support TCP/UDP. **Transport protocol:** TCP/UDP. **Application development:** Legacy UDP/TCP socket programming. **No. of Devices:** 264. **Scope:** Home area \\ \hline \multirow{4}{*}{BLIP} & **PHY/MAC layer:** 802.15.4 **Maximum Transmission Unit (packet size):** 127. **Networking protocol:** IP on top of 6 LoPWAN. **Transport protocol:** TCP/IP and UDP. **Application development:** Legacy TCP/IP socket programming. **No. of Devices:** 264. **Scope:** Home area \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Protocols for IoT and smart meters. Air interface protocols are defined by bandwidth, band, modulation and some medium access, whereas data communication protocols are defined by how they distribute data (architecture) and the support of different communication functionalities such as transport protocol, etc.
## 5 Evaluation
### Experimental Result
#### 5.2.2 Finding the Fault by the Fixed Symbols (in the Case of the Fault Index #1)  
This fault means that the symbols required to be updated by function operations are not updated. In Table 3, the fault index #1 causes the \"Front Turn Signal\" function to the malfunction due to a consecutive command. The \"Front Turn Signal\" has three unit tests as left, right and emergency lamp. Therefore, the fault candidates include the IDUA of three unit tests. Figure 11 shows the fault candidates of the fault index #1. In (a), it switches from \"Left Turn Signal\" into \"Right Turn Signal,\" and (b) switches from \"Emergency Signal\" into \"Right Turn Signal\" (i.e., mCanLampModeSet: in (a), 32 \(\rightarrow\) 64 and in (b), 96 \(\rightarrow\) 64). The test condition values are input in #290 and #790, respectively and have to be updated in the same order as the IDUA of unit test but the flagIndicatorLamp is not updated. When cmdLeftSignalLamp of 1 in (a) or cmdEmergencyLamp of 1 in (b) is changed to 0, cmdRightSignalLamp is changed to 1 but all status* symbols of 2 are not updated. This shows that there is a malfunction in flagIndicatorLamp between 1 and 2. The fault index #1 uses OBEA to mutate the assignment operator into a bitwise assignment operator (see the Table 3). Therefore, if a new value comes in when a value already exists, the \" |=\" operation is performed and it malfunctions.  
This fault was found in the test that gave the 12 consecutive commands for 8 s. Of the 3714 addresses allocated on N1 (see the Figure 9, Node of handle steering and forward functions), this test has 187 memory addresses updated. Of the 187 updated memory addresses, the fault candidates that the developer has to check include only nine memory addresses (9-IDUAs of Figure 11). Moreover, without checking a total of 800 frames (generated during 8 s), this test only checks 120 frames, using IDUR for each of the 12 inputs. In summary, developers can find the fault by checking the value at IDUR for 4.8% of the total memory symbols used. The fault localization ratio can be defined by Equation (7).  
\[Fault\ Localization(\%)=\frac{\#\ of\ Fault\ Candidates}{\#\ of\ Updated\ Memory\ Symbols}\times 100 \tag{7}\]
## 3 Experiments and Results
### Performance Evaluation Metrics of the Network Model
#### 3.2.5 Statistical Significance Analysis  
To ensure that improvements were not attributable to random variation, each model was trained three times with different random seeds, and results are reported as mean \(\pm\) standard deviation. In addition, bootstrap resampling with 1000 iterations was applied to estimate 95% confidence intervals for mAP values. Pairwise comparisons with the YOLOv11 baseline were performed, and improvements were deemed statistically significant at \(p<0.05\). This evaluation design ensures both reproducibility and statistical reliability.
## 3 Modeling and Simulations of the Assembly Error Effect on the Stability Accuracy
### Definition and Modeling of Stability Accuracy Based on Pointing Error  
Based on the kinematics of space mechanisms, those disturbances can be decomposed into angular motions around three vertical axes of inertial space, and they will all be coupled to LOS. To stabilize LOS, at least two of those motions must be compensated for, and typically they are the rotations around the y- and z-axes of inertial space [17]. The compensations will be realized by the two motor-driven frames, namely the frames of azimuth and pitch, respectively. In this research, they are abbreviated as the azimuth and the pitch hereafter.  
UAV photoelectric detection commonly adopts this two-axis stabilization mechanism because of advantages of simplicity and low weight [18]. Based on the above background, an index of stability accuracy \(\triangle\theta\) is proposed for evaluation of the angular deviations. \(\triangle\theta\) is defined as the root mean square (RMS) of the deviations between ideal angles and real angles. In a statistical sense, the value of \(\triangle\theta\) can be estimated by sampling within a certain total sampling time \(t_{s}\) (with sampling period \(T_{s}\) and sampling number \(n_{s}\)). In that case, \(\triangle\theta\) can be obtained by using Equation (1), where \(\triangle\overline{\theta}\) is the mean of the sampling values, \(\triangle\theta_{si}\) is the sample value of the ith sample, and \(\sigma_{\triangle\theta}\) is the standard error.  
\[\left\{\begin{array}{c}\vspace{-0.1 cm}\ \As shown in Figure 4, in practice, the value of \(\Delta\theta_{si}\) is calculated by using sampling values, which include the angular deviations along the y-axis \(\Delta\theta_{syi}\) and that of z-axis \(\Delta\theta_{szi}\). The calculation method is expressed as Equation (2). \(\Delta\theta_{syi}\) and \(\Delta\theta_{szi}\) will be obtained by using the signals of the two gyroscopes assembled on the frames.  
\[\Delta\theta_{si}=\sqrt{\triangle\theta_{syi}^{2}+\triangle\theta_{szi}^{2}}, \tag{2}\]
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 3 Preparation of Molecularly Imprinted Microspheres
### Controlled/Living' Radical Precipitation Polymerisation (CRPP)  
As mentioned above, precipitation polymerisation has proven versatile for preparing MIMs, since it does not require a stabiliser or surfactant and is easy to operate. Most precipitation polymerisation reactions involve free radical polymerisation, which is slow to initiate but has quick chain propagation. However, the rate cannot be controlled, and it is susceptible to chain transfer and termination, leading to a broad size distribution of the polymer [69]. More advanced polymerisation techniques, such as controlled/living' radical polymerisation (CRP), have drawn interest for producing MIMs due the improved control regarding composition, molecular weight and end group functionality [57]. The principle of CRP lies in the equilibrium of growing free radicals or active species and dormant species, which can minimise chain breaking reactions and the instantaneous initiation of all chains [123; 124]. The CRP methods that are considered most efficient are atom transfer radical polymerisation (ATRP), stable free radical polymerisation (SFRP), nitroxide mediated polymerisation (NMP) and reversible addition-fragmentation chain transfer (RAFT).  
The presence of CRP-living' groups on the polymer surface are important since the 'living' groups are useful for advanced surface modification (e.g., surface modification by the incorporation of a hydrophilic comonomer). However, the MIMs obtained via a precipitation polymerisation mechanism based on traditional free radical polymerisation commonly do not possess 'living' groups on their surfaces. As a developing polymerisation technique, controlled/living' radical precipitation polymerisation (CRPP) occurs by the incorporation of the CRP mechanism into the precipitation polymerisation system, thus merging the superiority of CRP with a traditional precipitation polymerisation system. Several CRPP methods that have been developed will be discussed below.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 7 Conclusions  
The class-imbalance data is a major factor to lower the performance of software defect prediction models [31; 32]. Many class-imbalance learning methods have been proposed to address this problem in previous studies, especially the synthetic based oversampling methods, e.g., SMOTE [40]. Since previous synthetic based oversampling methods treat the artificial minority class samples equally with the real minority class samples, the unreliable synthetic minority samples may interfere the learning on real samples and shift the decision boundaries to incorrect directions. To address this limitation, we propose a credibility based imbalance boosting (CIB) method for software defect proneness prediction. To demonstrate the effectiveness of our CIB, experiments are conducted on 11 cleared NASA datasets and nine PROPMISE datasets. We compare CIB with previous class-imbalance learning methods, i.e., MAHAKIL [44], AdaC2 [45], AdaBoost [46], SMOTE [40] and RUS [35], in terms of three performance measures including AUC, \(F_{1}\), AGF, and MCC based on the well-known classifier (i.e., J48 decision tree). The experimental results show that our CIB is more effective compared with the baselines and is very promising to address the class-imbalance problem for software defect proneness prediction.  
In the future, we plan to further demonstrate the effectiveness of our CIB on more defect datasets and modify CIB to use in cross-project defect prediction.  
Conceptualization, H.T. and S.W.; methodology, H.T.; data curation, G.L.; writing--original draft preparation, H.T.; writing--review and editing, H.T. and G.L.; supervision, S.W. All authors have read and agreed to the published version of the manuscript.  
This work is funded by Field Fund of Equipment Development Department under grant #61400020404.  
We would like to thank the editors and anonymous reviewers for their constructive comments and suggestions for the improvement of this paper.  
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted
## 2 Influence of OH- Groups on Material Characteristics  
During the processing of fused silica components, along with the chemical bond breakage in the silicon-oxygen tetrahedral network structure [SiO4], a large amount of chemical structure defects will occur in the material, mainly due to oxygen deficiency and doping of other elements (such as Cl, -OH, etc.) [17]. The chemical stability of fused silica depends on the firmness of the Si-O-Si network connection structure, and the hydroxyl defects break the silicon-oxygen (Si-O) bonds, change the continuity of the network structure, and reduce its chemical stability.  
The introduction of impurity ions in the SiO\({}_{2}\) network structure leads to breakpoints in its complete grid structure. Figure 1 shows the chemical structure defects generated by the introduction of OH- and F- ion impurities [18]. Chemical structure defects can form energy absorption points in high-flux laser irradiation and reduce the LIDT. The absorption effect of individual defects is equivalent to that of the Ce element, which can absorb a large amount of energy from the laser photons, causing a sharp temperature increase and producing a plasma effect, resulting in surface damage. Therefore, it is necessary to strictly control the content of OH- groups inside fused silica to improve its optical uniformity and increase its LIDT under 351 nm ultraviolet laser irradiation [19].
# Strain Measurement Distributed on a Ground Anchor Bearing Plate by Fiber Optic OFDR Sensor
## 2 Fabrication of Fiber Optic OFDR Sensor  
A schematic diagram of a fiber optic OFDR sensor is shown in Figure 2a. Light launched from a tunable laser source (TLS, Agilent 8164B) is divided by a 1:99 coupler (C1). The TLS light is swept from 1545 to 1551 nm, i.e., a 500-GHz sweep range (\(\Delta\)v). A small portion of light (1%) is sent to an auxiliary interferometer (AI), while the remaining light travels to a main interferometer. The AI is a simple Mach-Zehnder interferometer, which has 100 m delay between two arms. It creates an interfered signal which has same frequency spacing. With this interfered signal, a clock signal is generated to remove tuning nonlinearity. The main interferometer is constructed by a 50:50 coupler (C2). The Fresnel reflection generated by one fiber end of C2 can be termed a local oscillator (LO). When the light of the tunable laser source scans another arm, which is termed a sensing fiber, the Rayleigh backscattering (RBS) and connector reflections are generated. The signals of the LO and the sensing fiber are recombined at C2, and this produces a beat signal, which has a different frequency resulting from the difference in the length between the LO and sensing fiber. This beat signal is divided by a polarization beam splitter (PBS) using a polarization-diversity scheme [11]. Two polarization controllers (PC1 and PC2) are used to optimize the signal to noise-ratio (SNR) of the RBS signal of the OFDR. This fabricated OFDR sensor system is shown in Figure 2b.
## 3 Link Level Parameters and Channel Coefficient Generation
### Small Scale Parameters
#### 3.3.4 Cross Polarization Power Ratio  
The last step for small scale parameters is to generate the cross polarization power ratios (\(XPR\)) for each ray \(m\) of each cluster \(n\). \(XPR\) is log-Normal distributed as follows  
\[\mathcal{K}_{n,m}=10^{X/10} \tag{53}\]  
where \(X\sim\mathcal{N}(\mu_{XPR},\sigma_{XPR}^{2})\) is Gaussian distributed, \(n=1,\ldots,N_{cl}\) and \(m=1,\ldots,M_{ray}\). The \(XPR\) average \(\mu_{XPR}\) and variance \(\sigma_{XPR}\) are listed in Table 7.5-6 of [21] for each scenario.