esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Strength Characterization
### Effect of Slurry Concentration on the Strength of the Cemented Backfill
Under low slurry concentrations (Figure 7a,b), the specimens exhibited pronounced brittle failure characteristics, with vertical cracks running through the entire specimen and clear structural splitting. In contrast, the specimen with a high slurry concentration (Figure 7c) exhibited a more ductile failure behavior, where inclined shear cracks gradually developed during the loading process. Even after exceeding the peak load, the specimen retained a certain degree of structural integrity. This change in failure mode indicates that higher slurry concentration enhances particle bonding and improves the structural stability of the cemented backfill. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cement-Sand Ratio**} & \multirow{2}{*}{**Type of Fit**} & \multicolumn{4}{c}{**Curing Age**} \\ \cline{3-6}  & & **3 d** & **7 d** & **28 d** & **Average** \\ \hline
1:3 & exponential & 0.9727 & 0.9307 & 0.9977 & 0.967 \\
1:4 & exponential & 0.9641 & 0.9382 & 0.9731 & 0.9585 \\
1:5 & exponential & 0.9749 & 0.9045 & 0.9685 & 0.9493 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Correlation coefficient values (R\({}^{2}\) values) between slurry concentration and filler strength.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 6 Short-Term Clock Prediction Analysis  
The short-term clock prediction results are presented here to further evaluate the stability of the BeiDou-3 experiment satellite clocks. It has a significant meaning, considering the uneven distribution of the tracking stations around the world. Once the satellites run out of sight of the ground stations, the parameters of the orbit and clock will be predicted using the precise ephemeris. In this part, a simple linear model is selected for testing the accuracy of the 1-hour prediction with a sliding window of two hours of precise clock offsets. The previous analysis of periodicity is not considered in this short-term prediction.  
The median value and the interquartile range (IQR) of the prediction residuals are shown in Figure 8. Thanks to the new AFS applied on experimental satellites, the IQR of C32 is less than 0.1 ns, and the IQR of C31, C33, and C34 are less than 0.2 ns. These results mean that the degree of dispersion of the new atomic clock prediction results is distinctly improved compared with the old ones on BeiDou-2. For instance, the best performing C14 in the BeiDou-2 satellite is more than twice as much as C32. The precision of the sliding linear clock prediction is listed in Table 3. The Root Mean Square (RMS)of the new PHM and the improved Rb AFS is superior to 0.1 ns and 0.16 ns, respectively, better than that of BeiDou-2 satellites. The improvement of the stability of the new AFS does improve the precision of the clock prediction.  
Figure 7: Spectrum analysis obtained by the non-uniform fast Fourier transforms (NUFFT) for BeiDou-2 and BeiDou-3.
## 1 Introduction
In this paper, both methods are compared using the results of simulations and the inland water experiment. The values of correlation coefficients, which are the basis of the WSS indicator proposed in [6], are presented. These coefficients express the similarity of the autocorrelation and cross-correlation functions of the in-phase and quadrature components of the received signal and the impulse response estimated using this signal. However, the calculation of the TVIR requires performing matched filtration in the receiver, which in the case of long impulse responses, measured using dozens of repetitions of a single probe sequence, is time and energy consuming. The simulation and experimental tests have confirmed that a decision can be made if the UAC channel fulfils the WSS assumption on the basis of the analysis of the complex envelope of the received probe signal, instead of the analysis of TVIR of the channel. Skipping the TVIR calculation stage can significantly reduce the processing time and power consumption of the UAC receiver.
## 3 Case Study
### Evaluation Indicators  
For the multi-categorization problem, the following results may occur: true positive (\(TP_{i}\)), false positive (\(FP_{i}\)), and false negative (\(FN_{i}\)). In this study, four evaluation indicators were selected to evaluate the model comprehensively, including accuracy (\(A\)), Macro-Precision (\(P_{\text{macro}}\)), Macro-Recall (\(R_{\text{macro}}\)), and Macro-F1 score (\(F1_{\text{macro}}\)). The formulas are as follows:  
\[A=\frac{\sum\limits_{n=1}^{N_{\text{class}}}TP_{i}}{\sum\limits_{n=1}^{N_{ \text{class}}}TP_{i}+FP_{i}} \tag{6}\]  
\[P_{\text{macro}}=\frac{1}{N_{\text{class}}}\sum\limits_{n=1}^{N_{\text{class}} }\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{7}\]  
\[R_{\text{macro}}=\frac{1}{N_{\text{class}}}\sum\limits_{n=1}^{N_{\text{class}} }\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{8}\]  
\[F1_{\text{macro}}=\frac{1}{N_{\text{class}}}\sum\limits_{n=1}^{N_{\text{class}} }\frac{2\times TP_{i}\times TP_{i}}{TP_{i}(TP_{i}+FN_{i})+TP_{i}(TP_{i}+FP_{ i})} \tag{9}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Execution environment & Central processing unit \\ Optimization algorithm & Stochastic gradient descent with momentum \\ Max epoch & 120 \\ Mini-batch size & 128 \\ Initial learning rate & 0.01 \\ Shuffle & Every epoch \\ L2 regularization & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial parameters of all the compared methods including PSDSEF.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Labels** & **Operation** & **Number of the Training Dataset** & **Number of the Test Dataset** \\ \hline “57,344” & Expand 672 to 1263 & 855 & 408 \\ “65,536” & Select 1170 from 1835 & 793 & 377 \\ “73,728” & Expand 222 to 1248 & 845 & 403 \\ “98,304” & Deleted for insufficient number. & - & - \\ “106,496” & Deleted for insufficient number. & - & - \\ “114,688” & Deleted for insufficient number. & - & - \\ “122,880” & Deleted for insufficient number. & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(Cont.\)where \(N_{\text{class}}\) is the total number of categories categorized; \(TP_{i}\) is the number of samples correctly predicted to be categorized \(i\); \(FP_{i}\) is the number of samples incorrectly predicting other categorizations as categorization \(i\); and \(FN_{i}\) is the number of samples incorrectly classified as the true categorization \(i\) as other categorizations.
## 4 Discussion  
The present randomized controlled trial was aimed to compare implant and prosthetic success and survival rate, biologic and technical complications, marginal bone loss, patients' satisfaction, and peri-implant tissue health between three (Figure 1) or two (Figure 2) unsplinted, implant-retained, overdentures. The null hypothesis that there are no differences between groups can be accepted.  
Looking at the maxilla, the ideal number of implants required to retain an overdenture is yet to be clarified. However, the greater trend is to place at least four implants, splinted or not, in order to ensure a higher survival rate of implants, regarding stress distribution [7]. In the mandible, [PERSON] et al. [8], in an in vitro study on 3D finite element models of mandibular overdentures, demonstrated that the two-implant-retained overdentures exhibited lower stresses than the single- implant-retained overdentures. A similar study was conducted by [PERSON] et al. [9] concluding that placing a third implant placed between the classical two did not eliminate denture rotation around the fulcrum line. In addition, no strain concentration was found in cortical bone around the middle implant. These results are in agreement with another in vitro research revealing that the bone was insensitive to the number of implants or attachment type. After this observation, the authors recommended to use two implants in the canine region. The results of the present research are in agreement with the aforementioned studies, confirming that there are no benefits to placing one more implant, at least one year after loading.  
Looking into the future, a possible benefit to place three implants could be to remake the implant-retained overdenture with an implant-supported restoration, both fixed or hybrid (fixed/removable). The so called \"Exit Strategy\" allows to move from a removable to fixed denture, limiting the biologic and economic impact for the patients. In this way, the OT Equator attachments can work both as an attachment system and as an intermediate abutment for fixed restorations [10; 11; 12; 13].  
The main limitation of the present study was the small sample size and the kind of implant used, which may influence the primary and secondary outcome variables.  
This happened most likely due to the COVID-19 pandemic, which poses as a life-threatening risk to elderly patients, particularly when affected by co-morbidities and/or fragilities [14]. This allowed to enroll a minor number of patients compared to the exerted sample. Nevertheless, although the number of patients was unbalanced, a total of 34 patients were treated up to one year after loading, with no unbalancing between patients' and implants' characteristics. Most of the researches aimed to find the optimal number of  
Figure 1: Explanatory case (test group).  
Figure 2: Explanatory case (control group).  
implants to retain an overdenture were conducted in vitro. On the contrary, this is one of the few in vivo, randomized, and controlled trials.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion  
### Nonstationary Analysis of Runoff Data  
The hydrological series is complex and changeable, which is mainly affected by two factors: climate change and human activities. As a result, it exhibits the characteristics of nonlinearity and nonstationarity. For obtaining key and useful regularities from the series accurately and quickly, abrupt change analysis, trend analysis, and period analysis are commonly used to analyze runoff series characterization.  
Figure 3: Overview of Chengbi River Karst Basin.  
The Mann-Kendall mutation test was adopted as the abrupt change analysis method. It is a statistical verification method with the obvious advantage of unnecessarily following a certain statistical distribution of parameters and is very suitable for ordinal and type variables [35]. The monthly runoff depth mutation at Bashou hydrological station is shown in Figure 4A. It shows that the monthly runoff Mann-Kendall test statistic UF curve of Bashou station has no large fluctuation overall, and it is relatively flat. Only in the period of 1993-1994, the monthly runoff depth trend appeared significant. Within the significance level line of \(\alpha\) = 0.05, the UF and UB curves showed mutation points during the period between 2015 and 2017.  
The linear trend method was employed as the trend analysis method. The linear trend method is a forecasting method used when the rise and fall of the trend are approximately linear [36]. The monthly runoff depth trend diagram of the Bashou hydrological station is depicted in Figure 4B. Trend lines generated from monthly runoff series showed an increasing trend, and the monthly runoff increased at a rate of 0.018 mm/mon.  
The Morlet wavelet analysis was applied as the period analysis method. Wavelet analysis has a powerful function of discriminating multiple scales in analyzing hydrological series, and a good localization function in time and frequency domains [37]. The time-frequency diagram of the monthly runoff depth and the monthly runoff depth variance is shown in Figure 4C,D. The analysis results indicate that the four main cycles of monthly runoff are: 18a, 6a, 3a, and 12a in descending order.  
The conclusions of the above three analysis methods show that the monthly runoff depth series has nonstationary characteristics. Therefore, it is worth proposing a hybrid model employing the \"decomposition-prediction-reconstruction\" mode to increase the single model performance.
## 3 Proposed Solution to Predict the AoA
### System Overview and Assumptions
#### 3.1.1 3 GPP Beamforming Limitations
By explicitly distributing, clustering, and fusing predictive intelligence across both layers while enforcing dynamic dominance (dominance-enforced override) when one model substantially outperforms the other, our architecture enhances AoA estimation accuracy, robustness, and beamforming adaptability. This approach is fully aligned with O-RAN's modular design principles and the high-performance low-latency demands of next-generation networks. To overcome these limitations and fully exploit the architectural flexibility of Split 7-2x, we propose a distributed DE-ACSW-R beamforming strategy. In our framework, the following factors are observed:  
* The O-RU performs real-time AoA tracking using a jump-aware, clustered, WLR model. Upon detecting an abrupt AoA jump within the window, the O-RU recentres its prediction on the most recent (post-jump) samples to maximise its responsiveness to non-linear user mobility. * The O-RU maintains per-user AoA history buffers and precomputes beamforming weights for the predicted angles, ensuring slot-level adaptation and low-latency response. * Concurrently, the O-DU executes a curvature-adaptive SVR prediction process using a broader AoA history, real-time SNR, and physical and statistical confidence metrics. The O-DU can override, validate, or dynamically fuse O-RU predictions using a dominance-enforced logic that always selects the most reliable predictor based on windowed mean squared error (MSE). * At every slot boundary, the O-RU transmits updated AoA histories to the O-DU. The O-DU replies with either a dynamically fused AoA prediction (a dominance-enforced confidence-curvature-weighted combination of LR and SVR, or an explicit override/fallback command if low confidence or outlier behaviour is detected). Whenever a sudden AoA jump or confidence drop is observed, the O-RU autonomously triggers a localised MUSIC scan, refining the AoA estimate with minimal latency and computational overhead. This dual-path approach ensures both ultra-fast adaptation to real-time conditions and robust, centrally validated prediction, delivering reliable beamforming under fast mobility and multipath environments. This layered prediction and control design guarantees that latency-critical low-complexity decisions are made directly at the O-RU, close to the antenna, while more advanced and computationally intensive predictions are performed centrally at the O-DU. When uncertainty or abrupt changes are detected, such as non-linear AoA shifts, mobility-induced Doppler jumps, or sudden SNR drops, the O-RU autonomously triggers a localised enhanced MUSIC scan. This scan operates over a narrowed angular window, dramatically reducing computational cost and delay compared to full-spectrum searches, and thus maintains real-time responsiveness without waiting for O-DU coordination.
## 2 Related Work
### Network Architectures
#### 3.1.1 Segnet  
SegNet is made of an encoder network and a corresponding decoder network, followed by a final pixel-wise classification layer (Figure 3). The encoder network consists of the first 13 convolutional layers of the VGG16 [38] network designed for object classification, without considering the fully connected layer in order to retain higher resolution feature maps at the deepest encoder output. In such a way, the number of parameters to train is significantly reduced. Each encoder layer has a corresponding decoder made of 13 layers. The final decoder output is fed to a multi-class soft-max classifier to produce class probabilities for each pixel independently.
## 3 Feature Extraction of Internal Leakage Fault
### Fault Features Analysis  
To clearly cover the main frequency of pressure signal fluctuation in Phase 4 by the subbands of wavelet packet decomposition, the signal is decomposed by five layers of wavelet transform when the sampling frequency of simulated signal is 1000 Hz, and 32 subbands can be obtained. The high-frequency subbands are affected by the internal leakage damping. Therefore, the subband \(S_{5,0}\) is not considered when calculating the three energy features; the subbands \(S_{5,1}\) and \(S_{5,2}\) corresponding to the main frequency of pressure signal fluctuation are highly valued. The energy distribution of the wavelet packet decomposition of the rodless chamber pressure is shown in Figure 5. The energy value of the high-frequency subbands is small compared with that of the subbands \(S_{5,1}\) and \(S_{5,2}\), and then only the energy value of the subbands \(S_{5,3}\) and \(S_{5,4}\) is displayed for contrast. It is clear that the energy values of \(S_{5,1}\) and \(S_{5,2}\) gradually decrease with the increase in the internal leakage. As a result, the wavelet packet energy corresponding to the main frequency of pressure signal decreases with the increase in the internal leakage.  
Both the wavelet packet energy entropy and the wavelet packet energy variance can reflect the energy distribution of pressure signal. With the energy reduction in the main frequency subband when the internal leakage is increasing, the energy distribution in all subbands tend to be evenly distributed. As a result, and the wavelet packet energy entropy trends increase, while the wavelet packet energy variance trends decrease. As shown in Figure 6, the change trend of these two parameters is verified with the increase in the internal leakage, which is consistent with the description in [12; 31].  
Figure 5: The energy distribution of the wavelet packet decomposition of the rodless chamber pressure.  
The pressure slope and pressure increase rate are calculated based on low-frequency subband \(S_{5,0}\). Specifically, the wavelet packet coefficients of \(S_{5,0}\) is used to reconstruct the low-frequency pressure signal, and then the two time-domain features are calculated. As shown in Figure 7, when the internal leakage increases, both the pressure slope and the pressure increase rate gradually increase, which is consistent with the analysis in Section 2.  
To sum up, these five fault parameters (three energy features and two time-domain features) are indicated the internal leakage in the dual-cylinder parallel balanced oil circuit.  
Figure 6: Variation curves of wavelet packet energy entropy and energy variance of the rodless chamber pressure.  
Figure 7: Slope and increase rate of pressure under pressure maintaining state.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Validation Techniques
#### 3.5.3 Simulated Data  
Simulated or phantom data has also been used to validate the motion correction. Amplitude, frequency and velocity of retinal motion can be simulated in OCT volumes. First, 3D-OCT scans can be deformed by applying a known transformation. Then, the inverse transformation is applied to feature points selected in the deformed volume. Finally, these points are matched with those in the original volume to calculate the voxel distance errors between both images.
## 2 Materials and Methods
### PML-V2.1 Model
#### 2.2.2 Canopy Transpiration (\(E_{c}\) ) and Soil Evaporation (\(E_{s}\))  
The transpiration at canopy scales (\(E_{c}\)) is coupled with the photosynthesis process (\(A_{gs}\)) via the dynamical modulation of the canopy stomatal conductance (\(G_{c}\)), and the soil evaporation (\(E_{s}\)) depends on absorbed energy flux and soil water deficit,  
\[E_{c}=\frac{\epsilon A_{c}+\ \left(\rho c_{p}/\gamma\right)DG_{a}}{\epsilon+1+G_ {a}/G_{c}} \tag{6}\]  
\[E_{s}=\frac{f\epsilon A_{s}}{\epsilon+1} \tag{7}\]  
where the surface available energy (\(A=R_{n}-G\)) is divided into canopy absorbed energy (\(A_{c}\)) and soil absorbed energy (\(A_{s}\)), \(A_{c}=(1-\tau)A\) and \(A_{s}=\tau A\), \(\tau=exp(-k_{A}LAI)\), \(k_{A}=0.6\). \(\epsilon=\frac{\Delta}{\gamma}\), and \(\Delta\) is the slope of the curve relating saturation water vapor pressure to temperature (kPa \({}^{\circ}\)C\({}^{-1}\)). \(\rho\) is the density of air (g m\({}^{-3}\)); \(c_{p}\) is the specific heat of air at constant pressure (J g\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)); \(D\) is vapor pressure deficit (kPa); \(G_{a}\) is the aerodynamic conductance (m s\({}^{-1}\)); \(G_{c}\) (m s\({}^{-1}\)) is the canopy conductance; \(f\) is a dimensionless variable that determines the water availability for soil evaporation.  
The canopy stomatal conductance (\(G_{c}\)) is calculated by the photosynthesis process (\(A_{gs}\)) for each PFT with the constraint of \(D\) at surface.  
\[G_{c}=\frac{mA_{gs}}{C_{a}(1+D/D_{0})} \tag{8}\]  
\[A_{gs}=\frac{P_{1}C_{a}}{k(P_{2}+P_{4})}\bigg{\{}kLAI+ln\frac{P_{2}+P_{3}+P_{4 }}{P_{2}+P_{3}\exp(kLAI)+P_{4}}\bigg{\}} \tag{9}\]  
\[V_{m}=\frac{V_{m,25}\exp[a(T-25)]}{1+\exp[b(T-41)]} \tag{10}\]where \(P_{1}=A_{m}\beta l_{0}\eta\); \(P_{2}=A_{m}\beta l_{0}\); \(P_{3}=A_{m}\eta C_{a}\); \(P_{4}=\beta l_{0}\eta C_{a}\); \(A_{m}=0.5 Vm\). \(I_{0}\) is the photosynthetically active radiation (PAR, in mol) from shortwave downward radiation. \(C_{a}\) is the atmospheric CO\({}_{2}\) concentration (in ppm or mol mol\({}^{-1}\)). \(V_{m,25}\), \(\beta\), \(\eta\), \(m\), \(D_{min}\), \(D_{max}\) and \(D_{0}\) are the parameters (see Table 1) in the PML-V2.1 model.  
Finally, the gross primary productivity (\(GPP\)) is calculated by  
\[GPP=A_{gs}f_{D} \tag{11}\]  
\[f_{D}=\begin{cases}1,&D<D_{min}\\ \frac{D_{max}-D}{D_{max}-D_{min}},&D_{min}<D<D_{max}\\ 0,&D>D_{max}\end{cases} \tag{12}\]  
where \(f_{D}\) is the \(D\) constraint function; \(D_{min}\) and \(D_{max}\) are the parameters (see Table 1).