esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In the raw data of dam safety monitoring, the existence of gross errors will seriously affect the accuracy of the dam safety evaluation results. This paper proposes a novel method to efficiently identify outliers and predict the behavior of concrete dams, combined with the Least Median Squares method, the robust estimation IGG method and extreme learning machine method. The main contents are as follows:  
(1) In view of the gross errors problem in the dam safety monitoring data under the influence of complicated factors, this paper proposes a novel method to eliminate gross errors, which combines the advantages of the Least Median Squares method with strong robustness to multiple failure points, the robust estimation IGG method with the good robustness to outliers and extreme learning machine with strong generalization ability and fast operation speed when processing sample data.  
(2) Through the case study, the deformation monitoring data of a 305.0 m hyperbolic arch dam arch crown beam is utilized and compared with three other methods to verify the superiority of LMS-IGG-ELM method in eliminating gross errors. In the dam monitoring data sequences with 22 gross errors, the proposed method has an outlier identification rate of 100%, which is much higher than the traditional outlier identification method.  
(3) Meanwhile, the proposed method also enables the prediction of the dam's behavior. The dam safety monitoring model of LMS-IGG-ELM is utilized after eliminating gross errors by the four methods to verify that the proposed method has the best fitting effect and the highest prediction accuracy. LMS-IGG-ELM method is able to solve the problem of outlier identification in continuous time series data well, and can be applied widely to other projects such as roads and bridges.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Gross Errors Identification Method**} & \multicolumn{3}{c}{**Fitting Segment**} & \multicolumn{3}{c}{**Validation Segment**} \\ \cline{2-7}  & \(MAE\) & \(RMSE\) & \(R^{2}\) & \(MAE\) & \(RMSE\) & \(R^{2}\) \\ \hline LMS-IGG-ELM method & 0.0890 & 0.1261 & 0.9998 & 0.3284 & 0.4101 & 0.9720 \\ DBSCAN clustering algorithm & 0.3764 & 0.9526 & 0.9706 & 0.5572 & 0.6127 & 0.9375 \\ \(3\sigma\) method & 0.4104 & 1.1582 & 0.9863 & 0.5947 & 0.7316 & 0.9106 \\ Romanovsky criterion & 0.4684 & 1.5355 & 0.9762 & 0.8668 & 0.9107 & 0.8614 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison table of evaluation indicators.  
**Author Contributions:** Conceptualization, Y.W., H.G. and C.G.; methodology, Y.W.; software, Y.W.; validation, Y.W., H.G., M.Y., W.C., Z.F. and Y.H.; formal analysis, Y.W.; investigation, Y.W.; resources, Y.W.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and H.G.; visualization, Y.W.; supervision, H.G. and Y.H.; project administration, Y.W.; funding acquisition, H.G. and C.G. All authors have read and agreed to the published version of the manuscript.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study  
### Selection of the Numerical Simulation Model  
When simulating anchor bolts in FLAC3D 6.0 software, built-in anchor bolt units or pile units are generally used as linear structural elements. These units can effectively represent the structural response of a mechanical system composed of solid continuous media and supporting structures. It is worth noting that the pile unit can simulate the anchor bolt support characteristics by activating the \"pile property rock bolt on\" command. In this mode, the pile unit retains mechanical characteristics similar to those of the anchor bolt unit while also possessing unique functions: it can quantify the distribution of surrounding restraint stresses in the support body, characterize the strain softening properties between the anchor bolt and surrounding rock, and establish failure criteria for anchor bolt uplift and rupture. The pile unit in the software uses a one-dimensional linear model to simulate the axial mechanical behavior of the anchor bolt. Additionally, it employs a tangential and normal coupled spring mechanism to accurately reflect the bond and friction characteristics at the anchor bolt-anchorage-soil interface. The numerical simulation process for the anchor bolt's action mechanism in the software is shown in Figure 7.  
According to the software user manual, the shear force at the interface between the pile unit and the solid mesh unit is a function of the interface bond strength and the interface frictional force. Equation (5) describes the maximum shear force for a unit length pile, which can be used to estimate the relevant parameters. Additionally, FLAC3D software allows the definition of shear coupling springs through a table function programming, which models the softening of the cohesion and friction angle of the shear coupling springs. This, in turn, adjusts the effective lateral confinement stress defined by the parameters.  
\[\frac{F_{T\text{max}}}{L}=sc_{h}+\tau_{\text{u}}\times\tan(sc_{f})\times\text {perimeter} \tag{5}\]  
In the formula:  
\(F_{T\text{max}}\) is the maximum shear force of the anchor body, N;  
\(sc_{h}\) is the cohesion per unit length of the shear coupling spring, N/m;  
\(\tau_{u}\) is the shear strength of the anchor body, Pa;  
\(sc_{f}\) is the friction angle within the shear coupling spring, in \({}^{\circ}\);  
_perimeter_ is the outer perimeter length of the anchor body, m.
## 3 Proposed Solution to Predict the AoA
### Predictive AoA Framework and Decision Logic
This approach robustly mitigates the impact of outlier predictions and ensures optimal tracking across varying mobility patterns. In parallel, the O-RU continuously uses its LR (or clustered LR) prediction to pre-compute and update beamforming weights for immediate and near-future slots. These predictions are stored and indexed by user ID (UEID) to support rapid beam scheduling and handover decisions. The O-DU, using the fused or dominant AoA predictions, may override or refine these weights as needed based on a broader temporal and physical context, further increasing system adaptability. By explicitly combining the low-latency responsiveness of LR (including clustered and jump-aware variants) with the non-linear modelling capacity of curvature-adaptive SVR, the proposed framework achieves a substantial improvement in AoA prediction accuracy. The dominance-enforced fusion approach guarantees that, at every time step, the most statistically reliable predictor controls the beam update, providing both robustness to outliers and fast adaptation to sudden trajectory shifts. This aligns precisely with O-RAN's principles of modular and distributed intelligence, with the O-RU handling latency-critical decisions and the O-DU ensuring global accuracy and consistency through advanced predictive analytics and CSI-based validation. The resulting prediction pipeline is scalable, latency-aware, and robust for high-mobility multipath-rich urban environments. The proposed dual-layered jump-aware regression and dominance-enforced fusion not only support real-time prediction and fallback, but also maintain full compatibility with standard O-RAN Split 7-2x deployments, offering a practical path toward AI-native self-optimising beamforming in future RANs.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 4 Simulation Verification
### Experimental Results and Analysis
#### 4.2.1 Double-Lane-Change Scenario  
Using the corresponding covariance matrix from Equation (66) and the road adhesion coefficient of 0.85, assuming that the measurement noise and process are non-normally distributed. \(x_{0}=[40/3.6,0,0]\), \(P_{0}=0.01\times diag([1,1,1])\) are the system's initial state parameters. Figure 4 shows the input signals. Figure 5 shows the simulation results.  
Figure 5 displays the outcomes of the vehicle's simulated double-lane-change scenario. It can be observed from Figure 5a that both IMM-MCCKF and MCCKF maintain high accuracy in longitudinal speed estimation, and their performance is superior to that of CKF. However, compared to MCCKF, the estimates from IMM-MCCKF are more accurate, reducing the impact of individual outlier errors.  
According to Figure 5b,c, the IMM-MCCKF algorithm also ensures high accuracy under extreme conditions, with smoother estimation curves demonstrating better robustness. Due to the limited ability of MCCKF and CKF to resist noise variations, their estimation curves show some fluctuations, with CKF's results exhibiting significant oscillations and divergence trends. According to the Duggoff tire model, the tires enter a nonlinear operating zone when the vehicle's steering angle approaches extreme conditions, increasing tire forces. Additionally, the increase in outlier data from onboard sensors enhances the non-Gaussian nature of the noise, resulting in more significant estimation errors in CKF.  
Figure 4: Input signals for the double-lane-change scenario: (**a**) the vehicle trajectory, (**b**) the angle of the front wheels, (**c**) the longitudinal acceleration, and (**d**) the vertical acceleration.  
Figure 5: Simulation results for the double-lane-change scenario: (**a**) longitudinal velocity, (**b**) lateral velocity, and (**c**) centroid sideslip angle.
## 5 IoMT Security Requirements
To simplify even further, an entity cannot deny completing a task after completing it and must take responsibility for any action or its consequence. The easiest approach to achieving this criterion is to use digital signature techniques [9]. * **Authentication:** When a user logs into the system, the user's identity should be verified. Message authentication, on the other hand, is the act of confirming that a user is the original source of the provided data from a previous time. Mutual authentication is the most secure type of security; before transferring secure keys or data, the client and server must first authenticate each other. Lightweight authentication algorithms are becoming increasingly common as a result of a shortage of memory capacity in certain IoMT devices or a lack of CPU (Central Processing Unit) strength to conduct the cryptographic operations required by classic authentication protocols [14]. * **Authorization:** As mentioned before, medical data must be protected from unauthorized access due to the sensitivity of such data [43]. Hence, in our context, only trusted parties (with the required skill or expertise) should be given permission to complete certain actions, such as giving commands to medical IoMT devices or updating the software or installing security patches on medical IoMT devices. * **Anonymity:** When unauthorized users engage with the system, this requirement guarantees that the identity of the patient or physician stays concealed, i.e., both the patient and the physician should remain anonymous. When a patient and a physician are communicating, their identities should not be revealed [35]. Passive attacks are only able to observe what a person does, not who a person is.
## 2 Materials and Methods
### Development and Training of the Artificial Neural Network  
The optimization of the neural network in terms of the number of hidden layers and neurons was based on a convergence test. The aim was to develop a neural network with the least number of neurons, as the complexity of the empirical equation increases with the number of neurons. The activation functions used include the hyperbolic tangent function (Equation (3)) at the hidden layers and a linear function (Equation (4)) at the output node.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grade** & **Specimen** & \(d\) **(mm)** & \(l\) **(mm)** & \(w\) **(mm)** & \(\sigma_{l}\) **(MPa)** & \(s_{l}\) **(mm)** & \(s_{c}\) **(mm)** \\ \hline \multirow{3}{*}{X52 [36]} & Test 1 & 5.15 & 243 & 154.5 & 0.0 & - & - \\ \cline{2-7}  & Test 5 & 3.09 & 162 & 30.9 & 48.0 & - & - \\ \cline{2-7}  & Test 6 & 3.09 & 162 & 30.9 & 84.0 & - & - \\ \hline \multirow{3}{*}{X80 [37]} & IDTS 2 & 5.39 & 39.6 & 31.9 & - & 0.0 & 0.0 \\ \cline{2-7}  & IDTS 3 & 5.32 & 39.6 & 31.9 & - & 20.5 & 0.0 \\ \cline{1-1} \cline{2-7}  & IDTS 4 & 5.62 & 39.6 & 32.0 & - & 0.0 & 9.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of burst test details by [PERSON] et al. and [PERSON] et al.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Burst Pressure (MPa)** & **FEA Failure Pressure (MPa)** & **Percentage Difference (\%)** \\ \hline Test 1 & 23.2 & 22.95 & \(-\)1.08 \\ \hline Test 5 & 28.6 & 28.35 & \(-\)0.87 \\ \hline Test 6 & 28.7 & 27.00 & \(-\)5.92 \\ \hline IDTS 2 & 22.68 & 22.40 & \(-\)1.23 \\ \hline IDTS 3 & 20.31 & 20.12 & \(-\)0.94 \\ \hline IDTS 4 & 21.14 & 20.62 & \(-\)2.46 \\ \hline \hline \end{tabular}
\end{table}
Table 7: FEM validation against full scale burst tests by [PERSON] et al. and [PERSON] et al.  
The failure pressure of the corroded pipes obtained using FEM was normalised using the intact pressure (Equation (2)) of the pipe before it was fed to the ANN for training [4, 6, 38].  
\[P_{i}=\frac{\sigma_{UTS}^{*}\ t}{r_{i}} \tag{2}\]  
\[a(x)=\frac{2}{(1+e^{-2x})-1} \tag{3}\]  
\[f(x)=x \tag{4}\]  
Seventy percent of the dataset was used for training ANN, while 15% of each of the remaining dataset was reserved as the validation and test dataset to prevent overfitting [4, 39, 40]. The training process starts with the random initialization of the weights and biases of the ANN. After each iteration, the algorithm calculates the mean square error of the validation dataset. The iteration was stopped upon reaching the maximum number of epoch or validation checks. The weights and biases at the epoch that produces the best validation performance were chosen and applied to the ANN. The ANN was validated based on its ability to produce results close to the training data. This was measured using the coefficient of determinant (R\({}^{2}\)) of the ANN with a value of 0.99 deemed acceptable. Based on previous literature, it was identified that the chosen algorithm is most suitable and efficient for the failure pressure of corroded pipes. Hence, the results were not compared with other algorithms [4, 27, 41, 42].
## 4 Simulation Results
### Analysis on the PKU Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Vehicle Conditions** & \begin{tabular}{c} **Input Image** \\ **Resolution (pixels)** \\ \end{tabular} & **No. of Images** & **No. of Plates** & \begin{tabular}{c} **Plate Height** \\ **(pixels)** \\ \end{tabular} \\ \hline G1 & \begin{tabular}{c} Cars on roads; ordinary \\ environment at different \\ daytimes; contains only one \\ vehicle/license plate per image. \\ \end{tabular} & 1082 \(\times\) 728 & 810 & 810 & 35–57 \\ \hline G2 & \begin{tabular}{c} Cars/tracks on main roads at \\ different daytimes with sunshine; \\ only one vehicle in each image. \\ \end{tabular} & 1082 \(\times\) 728 & 700 & 700 & 30–62 \\ \hline G3 & \begin{tabular}{c} Cars/tracks on highways during \\ night; one license plate per image. \\ \end{tabular} & 1082 \(\times\) 728 & 743 & 743 & 29–53 \\ \hline G4 & \
\begin{tabular}{c} Cars/tracks on main roads; \\ daytimes with reflective glare; \\ one license plate in input images. \\ \end{tabular} & 1600 \(\times\) 1236 & 572 & 572 & 30–58 \\ \hline G5 &
\begin{tabular}{c} Cars/tracks at roads junctions \\ with crosswalks; several vehicles \\ per image. \\ \end{tabular} & 1600 \(\times\) 1200 & 1152 & 1438 & 20–60 \\ \hline \multicolumn{4}{c}{**PKU dataset**} & **3977** & **4263** & **20–62** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The PKU dataset description. Overall, on the PKU dataset, the proposed method ranks 1 st at achieving vehicle detection in terms of the mAP as listed in Table 5. The method by [40] ranks 2 nd by yielding 99.86% accurate vehicle detection accuracy. The works developed in [30; 31] also yield slightly over 99.75% vehicle detection accuracy. In addition, the methods shown in Table 5 report over 97% detection accuracy, which we believe is encouraging in solving real-world traffic problems. * To best of our knowledge, we observe that vehicle detection challenge is almost solved on the PKU dataset. However, we observe that non-uniform illuminations or high glare at the night could still affect vehicle detection accuracy. Similarly, the researchers who aim to solve the other object detection problems, such as license plate detection or recognition, may need to perform additional preprocessing or postprocessing to achieve reliable detection results. Figure 7: Vehicle detection results on the PKU dataset from G1 to G5 categories
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
## 3 Formulations  
### Cost Functions  
The generalized path cost functions of solo-driving travelers, carpooling travelers, and taxi passengers are defined respectively as follows:  
\[c^{sd}_{rs,p}=\sum_{a\in A}\left[\lambda_{t}t_{a}(x_{a})+b_{s}l_{a}\right]\cdot \delta^{rs}_{a,p},\ r\in R,s\in S,p\in P \tag{1}\]  
\[c^{h}_{rs,p}=\Delta+\Delta_{f}+\sum_{a\in A+\overline{A}}\left[\lambda_{t}t_{a }(x_{a})+\frac{b_{h}}{n}l_{a}\right]\cdot\delta^{rs}_{a,p},\ r\in R,s\in S,p\in P \tag{2}\]  
\[c^{o}_{rs,p}=\lambda_{o}W^{o}_{r}+b^{0}_{o}+\sum_{a\in A+\overline{A}}\left\{ \lambda_{t}t_{a}(x_{a})+b^{1}_{o}l_{a}+b^{2}_{o}[l_{a}(x_{a})-l^{0}_{a}]\right\} \cdot\delta^{rs}_{a,p}, \tag{3}\]  
where \(n\) is the average occupancy in a carpooling vehicle; \(\Delta\) is the aggregate carpooling additional cost, which includes convenience, privacy, and so on [30]; and \(\Delta_{f}\) is the financial carpooling cost. \(W^{o}_{r}\) is an endogenous variable representing the customer waiting time in origin \(r\). It is worth noticing that the taxi cost function is not strictly linearly increasing due to the flag-fall price. However, the inaccuracy can be eliminated by longer distance. For simplicity, we adopted the linear assumption for the taxi cost function [9; 37; 38]. The waiting time can be calculated as:  
\[W^{o}_{r}=\frac{\eta Z_{r}}{N^{v}_{r}w^{v}_{r}},\ r\in R \tag{4}\]  
where \(\eta\) is the corrected parameter, \(N^{v}_{r}\) is the number of vacant taxis in origin \(r\), and \(w^{v}_{r}\) is the searching time of vacant taxis in origin \(r\). If vacant taxis have access to HOV lanes, the cost function of vacant taxis is denoted as:  
\[c^{v}_{a}=\lambda_{v}t_{a}(x_{a})+b_{v}l_{a},\ a\in A+\overline{A} \tag{5}\]  
On the contrary, if vacant taxis are forbidden from using HOV lanes, the cost function of vacant taxis is:  
\[c^{v}_{a}=\lambda_{v}t_{a}(x_{a})+b_{v}l_{a},\ a\in A \tag{6}\]  
Then the total generalized path cost function of vacant taxis is:  
\[c^{v}_{sr,p_{v}}=\sum_{a\in A+\overline{A}}c^{v}_{a}\cdot\delta^{sr}_{a,p_{v} },\ r\in R,s\in S,p_{v}\in P_{v} \tag{7}\]
# Evaluation of the Nasolabbial Angle in Orthodontic Diagnosis: A Systematic Review
###### Abstract  
Background: This study is a systematic literature review aiming at identifying the variation of the average nasolabbial angle (NLA) in various orthodontic situations. The NLA is one of the key factors to be studied in an orthodontic diagnosis for the aesthetics of the nose and facial profile. Methods: Out of 3118 articles resulting from four search engines (PubMed, Cochrane Library, Turning Research Into Practice (TRIP) and SciELO), the final study allowed the analysis and comparison of only 26 studies. These included studies have considered the NLA in the following cases: teeth extraction, class II malocclusion, class III malocclusion, rapid palatal expansion (RPE), orthognathic surgery, and non-surgical rhinoplasty with a hyaluronic acid filler. Results: The results indicate that teeth extraction and the use of hyaluronic acid fillers significantly affect the NLA. Conclusions: This systematic review shows that a statistically significant change in NLA values occurs in: extractive treatments of all four of the first or second premolars in class I patients; in class II patients with upper maxillary protrusion; in patients with maxillary biprotrusion, except for cases of severe crowding; and in patients undergoing non-surgical rhinoplasty with a hyaluronic acid filler. Trial registration number: PROSPERO CRD42020185166  
nasalbial angle; soft-tissue profile; facial profile; orthodontic profile; rapid palatal expansion; orthognathic surgery; headgear treatment; hyaluronic acid; hyaluronic acid filler
## 2 Materials and Methods
### Data Filling  
Classifications were undertaken on temporal stacks of ALOS-2 PALSAR-2 data, each representing a ScanSAR observation cycle of 42 days, with 9 such cycles per year. To handle areas of no data in the coverage, caused by missed acquisitions during certain cycles, a post-processing data-filling procedure was performed. The classified products were examined in turn and areas of no data would be filled with class values based on that pixel's classified value in the preceding and subsequent observation cycle. Where the preceding and subsequent classified outputs had the same pixel value, this value was inserted into the no-data region.  
When the preceding and subsequent classified outputs have differing classified values, the classified value to be filled was determined based on the date of the current observation. As the Amazon River Basin typically experiences peak inundation sometime between April and July, and peak dry conditions in October-November, if the observation with no data was acquired between November and April, the classified value from the preceding image was used. If the current image was acquired after April but before November, the no-data pixels were replaced with the classified value of the subsequent classified output.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Private (Non-Public) Networks  
In the shorter term, 5G private networks can fulfil many of the requirements of Network Slicing for verticals, as well as offering additional security that cannot be provided by public networks. Private 5G networks comprise Radio Access Networks (RAN) and core elements. The 5G base station (gNB) can scale from low to high capacity and power output according to needs. They connect to private core networks (in contract to operator's public core) that provides security, authentication, session management and QoS-control. The private 5G core network could be deployed on edge compute nodes, installed locally on premises to ensure high reliability, enable low-latency, ultra-security, and privacy, or be virtualized and reside on cloud servers. In the longer term, 5G private networks may coexist with Network Slicing or they may be entirely overtaken by slicing. Global companies are quickly recognizing the potential afforded in private 5G networks, and the additional revenue streams it offers. Deloitte expects more than 200 mobile network operators investing in 5G standalone networks in 2023, which is at least double from 2022 [12]. Early adopters for private 5G networks will be large seaports, airports and other logistical hubs. The 5G Private networks are very likely to spawn the first wave of IoV with multiple private networks belonging to different verticals, and/or enterprises are linked and orchestrated together thanks to ultra-reliable, ultra-secure, and deterministic wide area networks.