esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 4 Numerical Simulation and Analysis
### Evaluation of Improved NSGA-III Algorithm
Without considering \(f_{4}\) in Equation (14), which is the loss of position estimation accuracy after a single-satellite loss, three algorithms were applied to optimize the approach configuration, and the solutions from the three algorithms were compared. As shown in Figure 9, the overall distribution trend of the solutions obtained from all three algorithms is similar, exhibiting a concave surface. This indicates that as positioning accuracy increases and maneuvering energy consumption decreases, the time cost of the maneuvers tends to increase. Figure 8: Convergence curve. Figure 7: Distribution of offspring. As Figure 9a shows, compared to the traditional NSGA-III, the improved NSGA-III algorithm performed better across the three objectives of positioning accuracy, maneuvering energy consumption, and maneuvering time cost, with particularly notable improvements in positioning accuracy. Furthermore, the solutions obtained by the improved NSGA-III algorithm were more concentrated around the optimal solution compared to the traditional algorithm, which demonstrates that the algorithm sufficiently searched in the vicinity of the optimal solution after the broad exploration in the early stage and the rapid convergence in the middle stage thanks to the cosine-like mutation distribution index. Figure 9b demonstrates the comparation results for NSGA-II and the improved NSGA-III. As we can see, the results of NSGA-II have many overlapping individuals, indicating poor population diversity, which confirms references [22; 23]. In terms of the distribution of solutions, the results of the improved NSGA-III were better across the three objectives and were closer to the true Pareto front, with a significantly higher number of high-quality solutions. Overall, the improved NSGA-III algorithm outperforms both traditional NSGA-III and NSGA-II.
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 5 Modeling Examples with Non-Atomic Registers
### Tournament Tree-Based Mutual Exclusion Solutions
#### 5.3.1 TT-P2's Solutions  
The TT-P2 algorithm under atomic registers or MWMR regular registers is shown in Figure 11. The subrange type of all the TT nodes is denoted by \(findex\) and contains all the integers from 1 to \(2^{\lceil\log_{2}N\rceil}+N-1\). The subrange type of the intermediate (ancestor) nodes is denoted by \(findex\) and contains the integers from 1 to \(2^{\lceil\log_{2}N\rceil}-1\). The TT is actually represented by the boolean array \(flag[findex]\) which stores the process advancements in the TT path toward the root. In addition, the array \(findex\)\(turn[findex]\) is introduced. The register \(turn[j/2]\) regulates the arbitration between the process in position \(j\) and its sibling, if there are any, whose position is calculated by the \(s()\) function. P2 CS is the virtual critical section after an arbitration, modeled as an urgent location. The time unit elapses in the CS location of the overall TT.  
Figure 11: The basic TT-P2 algorithm based on atomic or MWMR regular registers.  
The TT-P2 basic algorithm satisfies all the mutual exclusion properties. Its overtaking factor was found to be: \(ov=2^{\lceil\log_{2}N\rceil}-1\), which becomes \(N-1\) when \(N\) is a power of 2. The model was also studied in [11] under flickering (using the \(Simple\) technique) but without scrambling (the MWMR \(turn[.]\) variables were made SWMR registers). Also in this case the TT-P2 model proved to be fully correct.  
The most general version of the TT-P2 algorithm when both scrambling in the \(turn[.]\) safe registers and flickering (e.g., expressed by the _Decoration_ technique) in the output \(flag[.]\) safe registers are allowed, is proposed in Figure 12.  
The TT-P2 model of Figure 12 is without deadlocks, confirms the overtaking factor but violates the mutual exclusion property. Scrambling in the \(turn[j/2]\) register, in fact, can allow multiple processes to enter simultaneously their critical section CS.
## 3 The 6G Technology
### Sixth-Generation Requirements
#### 3.2.5 High Reliability and Availability  
The 6G network will provide support for critical or no-fail application scenarios. Consequently, its reliability and availability are critical for effective deployment. High reliability and availability are essential requirements for 6G networks [15]. The network components will be built with redundancy and failover mechanisms to ensure its continual operations [79; 80].
## 5 Discussions on Practical Applications  
For a real bridge, the vector \(\eta+\chi^{\prime}\) can be obtained from the deflection difference influence matrix of the completion stage of the bridge and the deflection difference at the anchorage point of each hanger and tie-beam under the damage state. Then, the location of the damaged hanger can be located. If a single hanger is damaged, the reduction ratio of cable force of the damaged hanger can also be obtained.  
After the successes of numerical and experimental studies, further verification on large-scale and full-scale structures will help to learn more about the method's capabilities under different field conditions. Given the application on the actual bridge, the following aspects are worth discussing.  
This paper presents a method to identify the damage of the hanger by using the influence matrix of static deflection difference of tie-beam. Static deflection difference influence matrix of the tie-beam for hanger damage identification can be obtained using the FEM of the bridge under completion state. The difference of static deflection of each anchorage point in any damage state can be obtained from the line shape difference measured under non-traffic conditions (such as at night) and in the completed state. This method can detect bridges regularly or after extreme events such as earthquakes, floods, and hurricanes. When the deflection of the bridge needs to be obtained, it can be measured with a level gauge.
## 3 Numerical Validation  
### Simulation Model  
Numerical simulations were conducted to generate a database for the proposed automated structural damage detection method. A simply supported beam with a length of 50 m was modeled with a 10-element Bernoulli beam (see Figure 4). The cross section of the model was 4 \(\times\) 2 m (width \(\times\) height), the modulus of elasticity was 210 GPa, the density was 7850 kg/m\({}^{3}\), and the damping ratio was 2%. The three major natural frequencies of the beam without damage were 1.85, 7.52, and 16.8 Hz. Damage was simulated by reducing the flexural rigidity of the damaged element by 20%, 30%, and 50%.  
To excite the structure with different loading cases, two loading conditions--random excitation and traffic loading excitation--were considered. A random excitation was provided to validate the proposed method in an ideal condition where all frequency spectra are excited so as to obtain precise modal parameters. Traffic loading was considered for the actual excitation of a structure, which requires sufficient measurement time for accurate modal analysis.  
Figure 3: Global average pooling (GAP) layer.  
Random excitation was modeled with a normal distribution with a mean of 0 and a standard deviation of 20 kN at a single node randomly on the beam for each simulation case. Traffic loading was modeled as a three-wheel truck moving on the beam at different speeds from left to right (nodes 1 to 9). The force on the three wheels weighed 35 kN with 10% random force on the front and 145 kN with 10% random force added to two rear axles. The distance between the front and rear axles was 4.3 m, and that of the two rear axles was 9.0 m.  
Sensor noise was assumed to be white Gaussian noise with a noise density of 100 \(\upmu\)g/\(\sqrt{\text{Hz}}\). The sampling rate was determined to be 100 Hz, and responses were generated using MATLAB Simulink with the ODE8 solver.
## 2 Background
### Principles of Model Predictive Control  
MPC is based on the optimization of future system behavior with respect to future control actions [35]. For MPC design, a discrete nonlinear (or linear) model of the system is required to predict the future behavior; the future control actions \(\{u(k),u(k+1),...,u(k+N_{u}-1)\}\) are then calculated by optimizing an objective function with constraints on the manipulated and controlled variables. Please note that only the first action \(u(k)\) of the optimal future control is actually applied during the current time step. In the next time step, a new optimal sequence is determined (rolling horizon). In general, the MPC optimization problem solved at each instant is given by:  
\[\min_{u(k),\ldots,u(k+N_{u}-1)} \sum_{j=1}^{N_{u}}F_{y}\left(g(k+j),r(k+j)\right)+\lambda\sum_{j =1}^{N_{u}}F_{u}\left(u(k+j-1)\right)\] (10) s.t. \[\hat{y}(k+j)=f\left(g(k+j-1)\ldots,u(k+j-1),\ldots,\right),\quad j =1,\ldots,N_{y} \tag{11}\]  
where \(\hat{y}(k+j)\) is the \(j\)-step-ahead prediction for the controlled variable, which is given by a nonlinear prediction model \(f\), \(F_{y}\) is the cost associated with the predicted output and the reference \(r(k+j)\), \(F_{u}\) is the cost associated with the control action, and \(\lambda\) is the weighting factor. \(N_{y}\) and \(N_{u}\) are the prediction and control horizons, respectively.  
The next section describes the proposed EWMS using the MPC approach based on the models described above for the aquifer, wells, and irrigation demand.
## 2 The Characteristics of Endodontic Instruments
### Evaluation of Instrument Performance in Ex Vivo Settings  
Several ex vivo tests have been developed to investigate parameters that closely resemble those encountered in clinical practice. These tests aim to evaluate the shaping ability of various instrumentation systems in canals with specific shapes and dimensions, such as oval or curved canals. In the last decade, the use of micro-CT imaging technology has emerged as the gold standard for evaluating the shaping ability of NiTi systems. By employing micro-CT analysis, a precise three-dimensional assessment of the root canal is performed before and after mechanical preparation [5]. The assessment follows a systematic procedure: extracted teeth are carefully selected and subjected to micro-CT scanning to obtain an accurate three-dimensional image of the root canal prior to instrumentation. The teeth are then categorized into groups based on specific anatomical parameters, aiming to achieve optimal homogeneity and balanced group pairing. This approach enhances the validity of the method and minimizes potential anatomical bias, which could otherwise result in erroneous outcomes. The selected instruments are then used to prepare the root canals. After instrumentation, the teeth undergo another scanning process to obtain a three-dimensional image of the root canal post-preparation. The data from pre- and post-preparation scans are meticulously co-registered and compared to assess several parameters including changes in root canal geometry, volume, surface area, untouched walls, transportation, accumulated hard tissue debris, and dentin thickness [5; 55]. This assessment method offers several advantages, notably providing an accurate and objective means of comparing different instruments. Consequently, it aids clinicians in selecting the most suitable instrumentation system or specific instrument for each clinical scenario [55] (Figure 3).
## 5 Conclusions  
This study describes a novel distance estimate method that has undergone time-consuming, costly development, exact calibration, and thorough testing. This unique technique significantly lessens the computing load on edge application devices by utilizing mathematical models to produce reliable estimations. Our method represents a substantial development in the field since it incorporates modern object detection models. The testing results have shown that NFD estimate is accurate within the practical distance range. Based on the findings obtained from our experimental analysis, it is evident that the position-based model achieved a satisfactory mean absolute percentage error (MAPE) of 6.18%, whereas the size-based model yielded a slightly higher MAPE of 6.59%. Notably, both models exhibited commendable precision of 100% and a recall rate of 94.12% within a specified effective distance range of 2.4 to 10 m.  
In ideal circumstances, where elements, including the camera's alignment with the ground's surface, minimum lens distortion, a well-defined effective distance, and exact POI are guaranteed, NFD emerges as a very effective navigation tool. It is also important to note that our suggested approach for distance estimate holds significant promise outside of its intended use cases. It has enormous promise for autopilot applications for electric cars and drone racing navigation, which advances computer vision and autonomous systems by offering insightful information on distance estimation in a dynamic 3D environment.  
These promising findings imply that with further refinement, NFD can be embedded into a wearable device that provides real-time navigation support to those with visual impairments. This advancement allows visually impaired people to move and engage with their environment with greater independence and mobility. We are getting closer to achieving the full potential of this technology and changing the navigation help the market as we continue to refine it.  
Conceptualization, T.L.W., K.S.C. and K.L.W.; methodology, T.L.W., K.S.C. and K.L.W.; software, T.L.W., K.S.C. and K.L.W.; validation, D.A. and L.S.; formal analysis, T.L.W.;investigation, T.L.W., L.S., K.L.W. and K.S.C.; resource, R.T., S.-K.T. and G.P.; data curation, T.L.W.; writing--original draft preparation, T.L.W.; writing--review and editing, T.L.W., K.S.C., K.L.W., L.S., D.A., R.T., S.-K.T. and G.P.; visualization, T.L.W.; supervision, R.T., S.-K.T. and G.P.; project administration, R.T., S.-K.T. and G.P.; funding acquisition, R.T., S.-K.T. and G.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work is supported in part by the research grant (No.: RP/ESCA-04/2020) offered by Macao Polytechnic University.  
The authors declare no conflict of interest.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 4 Discussion
### Technological Issues  
Several technological factors influenced the adoption of contact-tracing apps. Accuracy and reliability were major factors [54], with concerns about errors having a greater impact on decisions even than on the privacy risks [126]. Smartphone-related issues, including compatibility, battery drainage, and technical difficulties [101], were significant barriers to adoption.  
**Recommendations for government**. Governments should provide funding for technical improvements to ensure app compatibility across all smartphone models, optimise battery efficiency, and address accuracy concerns like false positives/negatives in contact detection. This is because they must eliminate usability barriers to ensure inclusivity and reliability, as technical issues significantly impact user experience and adoption rates. Governments should also offer robust technical support, including frequently asked questions, troubleshooting helplines, and tutorials, to assist users in adopting and using the apps effectively. This is because accessible technical support mitigates frustrations and ensures a seamless user experience, making adoption more attractive.  
**Recommendations for app developers**. App developers should develop robust algorithms to reduce false positives and negatives and implement clear error-reporting systems. This is because accurate functionality builds trust, ensuring users perceive the app as reliable, which is essential for widespread adoption. Developers must deliver systems that users can depend on to perform critical public health functions. App developers should also design energy-efficient apps compatible with various devices, minimising battery consumption and technical barriers. This is because high battery use and incompatibility discourage adoption. Developers are responsible for creating apps that integrate seamlessly with users' devices, ensuring accessibility and usability for all.
## 1 Introduction  
The management of urban drainage is a critically important challenge and sustainable drainage systems (SuDS) are a key tool in managing extremes of rainfall [1; 2]. SuDS consist of a range of technologies and techniques used to drain stormwater and excess surface water in a manner that is more sustainable than conventional solutions [3]. They are based on the philosophy of replicating \"as closely as possible the natural, pre-development drainage from a site with installations based on natural hydrological processes which utilise vegetated land surfaces\" [4]. Typically, SuDS are configured as a sequence of stormwater practices and technologies that work together to form a management train that is designed to store, attenuate and treat surface water and in so doing, reduce runoff and flooding. SuDS have a secondary role in greening the environment. They are one way in which ecosystems are used to address mounting urban sustainability challenges, giving people access to nature to improve health and wellbeing.  
Despite the range of drivers supporting the use of SuDS, their implementation is a complex process and requires buy-in from multiple stakeholders. Buy-in is often undermined by a lack of practical evidence and monitoring of existing schemes to demonstrate their value and best practice.  
In this paper, we report one of the first holistic studies of the impact of retrofit SuDS on runoff in an urban environment. Initially, we present the current debates around SuDS, we then outline the study design using a suite of interventions, implemented in collaboration with a range of stakeholders. The results and emerging challenges for assessing the effectiveness and implementation of SuDS are then discussed. The results demonstrate that SuDS can result in a significant reduction in runoff, but this study also highlights the importance of partnership working, and the added value it can deliver in implementing sustainable drainage interventions at the property and the street scale. We also outline the challenges with obtaining high quality data in urban and rural areas.
## 5 Analysis of Training Results  
### Performance Analysis of the SE-TransUnet Model  
(1) loss function  
The loss function for each training epoch is shown in Figure 7, indicating that the model converges around epoch 85.  
Figure 7: The loss function of each round of experiments for the SE-TransUNet model.  
Analysis of k-fold cross-validation results  
Various metrics of the validation set from five rounds of experiments are presented in Table 5. Some of these metrics are visualized as boxplots in Figure 8, where the mean values are marked with red asterisks and the median values are indicated by red lines. As shown in Table 5, the mean values of IoU, Precision, F1-score, and FPS for the model are 0.8318, 0.8480, 0.9218, and 6.53, respectively. The standard deviations of the model's various accuracy metrics are generally around 0.02, indicating a low overall level of variability.  
As shown in Figure 8, among the precision metrics, Recall achieved a mean value of 0.9394 (SD = 0.0221) with the largest interquartile range (0.92-0.97), reflecting effective false-negative control. Precision attained a mean of 0.8480 and exhibited the second-smallest box height after Recall, indicating robust accuracy. However, its performance was marginally lower than Recall due to limited false-positive introductions accompanying high recall rates. IoU and Dice demonstrated narrower box distributions (ensuring prediction consistency) but lower mean values. Their mean scores were similar (0.8318 and 0.8304, respectively), with distributions generally below Recall and Precision. Dice showed the most compact box distribution (SD = 0.0204), indicating stable regional similarity assessment. Conversely, IoU displayed the largest dispersion among all metrics (SD = 0.0224), suggesting greater susceptibility to interference in intersection-over-union predictions between actual leakage regions and masks.
## 4 Discussion
### Importance of Features  
The application of machine learning algorithms (MLR and RFR) in our study involves six input features (D\({}_{50}\), C\({}_{\text{u}}\), C\({}_{\text{c}}\), \(f_{\text{d}}\), \(\sigma_{\text{n}}\), \(\rho_{\text{r}}\)) and one output which is the shear strength of sand. In Figure 17, both MLR and RFR models, when using the train test splits, identified normal stress as the principal factor, underlining its essential role in governing shear strength, while dry density followed as the second most influential parameter, highlighting its significance in determining the mass per unit volume. However, when using the 10-fold cross-validation method, mean particle size showed the highest feature importance, followed by coefficient of uniformity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Performance** & \multicolumn{3}{c}{**MLR**} & \multicolumn{3}{c}{**RFR**} \\ \cline{2-7}
**Metrics** & **Training Data** & **Testing Data** & **10-Fold CV** & **Training Data** & **Testing Data** & **10-Fold CV** \\ \hline _MAE_ & 8.31 & 7.67 & 9.28 & 3.79 & 5.68 & 9.83 \\ _RMSE_ & 11.87 & 10.08 & 13.57 & 6.55 & 7.37 & 15.8 \\ _RMSLE_ & 0.29 & 0.17 & 0.35 & 0.07 & 0.09 & 0.19 \\ \(R^{2}\) & 0.95 & 0.94 & 0.93 & 0.98 & 0.97 & 0.90 \\ \hline \end{tabular}
\end{table}
Table 6: Comparative performance of multiple linear regression and random forest regression on training, testing, and 10-fold cross-validation datasets.