esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hammerstein Model for DEAP Actuator
### Parameters Identification  
The goal of this subsection is to obtain the parameters values of the MPI model and the ARX model. Specific identification steps are as follows:  
Step (1): The asymmetric hysteresis characterization of the DEAP actuator is modeled by using the MPI model. A low frequency input signal \(v\) of 0.1 Hz is given to the DEAP actuator, and \(N\) pairs of discrete input and output sequences \((v,y)\) are measured. Then, based on the input data \(v\) and the output data \(y\), LM algorithm is employed to identify the MPI model parameters.  
Step (2): After the MPI model is determined, a hybrid frequency input signal in range of 0.1 to 0.5 Hz is given to the DEAP actuator, and the discrete input and output sequences \((v,y)\) are measured. Based on the identified MPI model and \(v\), the intermediate unmeasurable variable \(u\) can be obtained. Then, based on the intermediate variable \(u\) and the output data \(y\), the parameters of the ARX model are identified by LM algorithm.  
The accuracy of the identified model can be expressed by root-mean-square error (RMSE) and mean absolute error (MAE), which are defined as follows:  
\[RMSE=\sqrt{\sum_{i=1}^{N}\left(y(i)-\hat{y}(i)\right)^{2}/N} \tag{12}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}(|y(i)-\hat{y}(i)|) \tag{13}\]  
DEAP actuator, respectively. Figure 6 shows the input-output relationship between the Hammerstein model and the DEAP actuator under different input frequencies. Table 4 shows the modeling errors. It can be seen that the maximal RMSE and maximal MAE are 0.0271 mm and 0.0182 mm, respectively. Therefore, the identified parameters of the Hammerstein model are correct, and the Hammerstein model is effective in characterizing asymmetric rate-dependent hysteresis of the DEAP actuator.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 4 Experimental Results and Analysis
### Experimental Settings
#### 4.1.2 Evaluation Metrics  
We use four commonly used evaluation metrics to assess the effectiveness of our network, namely F-measure (\(F_{\beta}\)) [21], Mean Absolute Error (\(MAE\)) [49], S-measure (\(S_{\lambda}\)) [50], and E-measure (\(E_{\gamma}\)) [51]. The detailed definitions of these metrics are as follows:  
F-measure (\(F_{\beta}\)) is a widely used comprehensive evaluation metric that considers both precision and recall. It is defined as follows:  
\[F_{\beta}=\frac{(1+\beta)^{2}\cdot Precision\times Recall}{\beta^{2}\times Precision +Recall} \tag{10}\]  
Among them, _Precision_ and _Recall_ represent the precision score and recall score, respectively, and \(\beta^{2}\) is set to 0.3 to emphasize precision.  
Mean Absolute Error (\(MAE\)) represents the average difference between the saliency map \(S\) and the ground truth \(Y\), and the detailed formula is as follows:  
\[MAE=\frac{1}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H}|S(x,y)-Y(x,y)| \tag{11}\]  
where \(W\) and \(H\) represent the width and height of the saliency map, respectively.  
S-measure (\(S_{\lambda}\)) evaluates the spatial structural similarity between the saliency map \(S_{r}\) and the saliency label map \(S_{o}\), taking into account the saliency of regions and boundaries. It is also known as the structured measure and is defined as follows:  
\[S_{\lambda}=\alpha*S_{o}+(1-\alpha)*S_{r} \tag{12}\]  
where \(\alpha\in[0,1]\) is the balance parameter, which is set to 0.5 by default [50].  
E-measure (\(E_{\gamma}\)) is an evaluation metric based on the enhanced alignment mapping between the prediction result map and the salient label map. It is defined as follows:  
\[E_{\gamma}=\frac{1}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H}\phi FM(x,y) \tag{13}\]  
The width and height of the saliency map are denoted by \(W\) and \(H\), respectively; \(\phi FM(*)\) represents the enhanced alignment matrix [51].
## 3 Results
ference of mean root elongation between patients who underwent an orthodontic treatment duration for less than 24 months (ten subjects; 62.5%; mean RE = 0.48 \(\pm\) 0.16 mm (SD)) and patients who had an orthodontic treatment lasting 24 months or more (six subjects; 37.5%; mean RE = 0.55 \(\pm\) 0.21 mm (SD)) was not statistically significant (95% confidence interval; \(p\) = 0.27). In the non-impaction group, all 26 teeth went through apical root resorption (Table 3). In female patients (twelve subjects; 46.15%), the mean apical root resorption was \(0.87\pm 0.24\) mm (SD) while in male patients (fourteen subjects; 53.85%) it was 0.76 \(\pm\) 0.34 mm (SD) but the difference between the two genders was not statistically significant (95% confidence interval; \(p\) = 0.18). With regard to root shape, the mean apical root resorption was 0.80 \(\pm\) 0.24 mm (SD) for canines with a normal root (twelve teeth; 46.15%); 0.68 \(\pm\) 0.29 mm (SD) for canines with a blunt root (six teeth; 23.08%); 0.84 \(\pm\) 0.43 mm (SD) for canines apically bent (five teeth; 19.23%); and 1.04 \(\pm\) 0.15 mm (SD) for canines with a pipette-shaped root (three teeth; 11.54%). In subjects who underwent an orthodontic treatment that lasted less than 24 months (nineteen patients; 73.08%), a mean apical root resorption of 0.72 \(\pm\) 0.26 mm (SD) was found, while subjects who had an orthodontic therapy that lasted 24 months or more (seven patients; 26.92%) went through a mean apical root resorption of 1.04 \(\pm\) 0.24 mm (SD). These values were not significantly different from each other (95% confidence interval; \(p\) = 0.10). The mean apical root resorption was 1.01 \(\pm\) 0.33 mm (SD) for the impaction group and 0.81 \(\pm\) 0.29 mm (SD) for the non-impaction group. It was slightly greater in the impaction group, with a significant difference of 0.2 mm (95% confidence interval; \(p\) = 0.04). The difference of mean apical root resorption in impacted teeth and physiologically erupted teeth in relation to their root shape was not statistically significant (95% confidence interval; \(p\) = 0.07) (Table 4). \begin{table}
\begin{tabular}{l l l l} \hline  & &
\begin{tabular}{l} **Mean** \\ **ARR** \\ \end{tabular} & **SD** \\ \hline \hline \multicolumn{4}{c}{(mm)} \\ \hline Gender & Female (N = 12; 46.15\%) & 0.87 & 0.24 \\ \hline \multicolumn{4}{c}{Male (N = 14; 53.85\%)} & 0.76 & 0.34 \\ \hline \multicolumn{4}{c}{\(p\)} \\ \hline Root shape & Normal (N = 12; 46.15\%) & 0.80 & 0.24 \\ \hline \multicolumn{4}{c}{Blunt (N = 6; 23.08\%)} & 0.68 & 0.29 \\ \hline \multicolumn{4}{c}{Apically bent (N = 5; 19.23\%)} & 0.84 & 0.43 \\ \hline \multicolumn{4}{c}{Pipette-shaped root (N = 3; 11.54\%)} & 1.04 & 0.15 \\ \hline \multicolumn{4}{c}{Orthodontic treatment} \\ duration & \textless{}24 months (N = 19; 73.08\%) & 0.72 & 0.26 \\ \hline \multicolumn{4}{c}{\(>\)24 months (N = 7; 26.92\%)} & 1.04 & 0.24 \\ \hline \multicolumn{4}{c}{\(p\)} \\ \hline \end{tabular}
\end{table} 
Table 3: Mean apical root resorption of non-impaction group according to gender, root shape, and orthodontic treatment duration. NS = not significant.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 3 The Proposed HMIPS  
Prior to describing the HMIPS, we formulate the process of installing an HMIPS in a subway station. Distributing the HMIPS to subway stations requires a number of steps. Figure 1 illustrates the overall process of deploying a marker-based indoor positioning system in a subway station. In the first step, an indoor map of the subway station is prepared to provide an indoor positioning service and it is then registered in the system. In the second step, indoor space modeling proceeds using more advanced techniques for marker generation and installation, after which markers are generated and printed for the modeled area using a marker-making tool (step three). Once the generation of markers has been completed, a positioning engine is installed in step four. Afterward, testing and evaluation are performed to ensure the quality of the provided indoor positioning system (step five). The following sections describe the detailed activities required at each step.
# Calibrating of a Simulation Model to Predict the Flexural Capacity of Pre-Stressed Concrete Beams
## 2 Materials and Methods
### Finite Element Modeling
#### 2.3.1 Boundary Conditions  
* RESTRICTIONS. At three levels: supports, symmetry and contact between elements. 1. Supports: a restriction is applied to the centerline of the support plate, coincident with the support point of the beam in the real test. The movement is restricted for the X and Y axes, avoiding that the piece moves vertically. 2. Symmetry: The simulation model is built for half a beam. On the face corresponding to the vertical symmetry axis, a restriction to the surface is introduced, restricting the movement in the X and Z axes. This allows to reproduce the symmetry of the model taking into account the continuity of the analyzed element. 3. Contacts: To ensure contact between the top and bottom faces of the beam with the corresponding surfaces of the support plates and of application of the load, it is applied contacts between these elements.
* LOADS. 1. External load application: the load step is introduced by means of a prescribed displacement in the vertical Y axis, applied at the center of the load plate. 2. Initial horizontal deformation due to prestressing: to simulate the effect of the initial prestressing force, an initial stretch deformation of 0.006375 mm/mm is applied.
* MONITORING POINTS 1. Control of the applied force: One at the same point of application of the prescribed displacement (center point of the load plate), to collect the output data of the Y-axis reactions. 2. Strain control in the center of the beam: located in the lower part of the central section of the beam. 3. Control of the deformation at the point of application of the load: which records the millimeters that the load arm descends during the analysis.  
Figure 3 shows a summary of all the boundary conditions assigned to each of the pieces used for the simulation.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 3 Enhanced Non-Coherent Pre-Filter Design  
As discussed in Section 2, the non-coherent pre-filter takes the ATAN/ANAT2/NC-EMLE discriminator outputs as observations. However, there are three problems when we implement the non-coherent pre-filter. Firstly, as will be shown in the following parts, the Equations (18) and (19) are not accurate enough to calculate estimation noise variances of ATAN/ATAN2 discriminator in weak signal condition. Secondly, the estimation noise variance of NC-EMLE discriminator is difficult to express in analytical form, and thus is difficult to calculate. These two aspects hinder the correct setup of an observation noise variance matrix for a non-coherent pre-filter. Thirdly, the working range of a non-coherent pre-filter for carrier phase error estimation is constrained to (\(-0.25\) cycle, \(0.25\) cycle) because of the limited effective working range of the ATAN discriminator. To address these three problems, this section will first analyze estimation noise variances of the ATAN/ANAT2/NC-EMLE discriminator through Monte Carlo simulation and then discuss the application of FDE structure in non-coherent pre-filter design.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches  
### 2D-Information-Based Approaches  
Compared with 3D information, 2D information can be obtained more easily by simpler devices such as Charge-Coupled Device (CCD) cameras, Complementary Metal Oxide Semiconductor (CMOS) cameras, or even color cameras that can obtain the color information of objects. There is much 2D information that can be used to measure the 6D pose of objects, for instance, geometric information, texture information, color information, and so on. Scale-invariant feature transform (SIFT) features [49] and speeded up robust features (SURF) [50] are the early and classical features for pose estimation based on the texture of objects. SIFT and SURF features are both reliable and can achieve precise matching; however, they rely on the texture information of objects. Therefore, they cannot be used to solve the pose estimation of textureless objects. [PERSON] et al. [51] used geometric information, which does not rely on texture, to solve the pose estimation problem of textureless objects. [PERSON] et al. [52] combined the color information in the 6D pose estimation to improve accuracy and robustness. 2D information is extracted for the matching of the template. According to the dimensions of the template, approaches based on 2D information can be divided into two categories: 1. CAD image-based methods; and 2. real image-based methods.
## 2 The Elementary Principle and Design of the Method
### Functional Safety: Theoretical Basis  
ISO 26262 is an adaptation of the more general IEC 61508 standard for the automotive industry. According to ISO 26262, a hazard is a potential source of harm. Functional Safety  
Figure 1: The system architecture of the proposed method.  
is defined as the \"absence of unreasonable risk due to hazards caused by malfunctioning behavior of Electrical/Electronic systems\". Part 3 of ISO 26262 is dedicated to hazard and risk analysis guidance, which determines the risk of harm/injury to people and property damage. Part 3 primarily introduces the requirements for risk analysis and risk assessment to determine the Automotive Safety Integrity Level (ASIL), a critical component of ISO 26262, and the level of risk reduction needed to achieve a tolerable risk is used to represent the stringency of the safety requirements [44].  
ASILs, as previously stated, are essentially classification levels for hazardous events. In the event of a malfunction at the vehicle function level, a hazard and risk analysis is used to determine the ASIL. According to ISO 26262, an ASIL shall be determined for each hazardous event based on the classification of the probability of exposure, the severity, and the controllability, as shown in Table 1. Exposure is defined as the \"state of being in an operational situation that can be hazardous if coincident with the failure mode under analysis\". Severity is defined as the \"estimate of the extent of harm to one or more individuals that can occur in a potentially hazardous event\". Controllability is defined as \"an ability to avoid a specified harm or damage through the timely reactions of the persons involved possibly with support from external measures\". Each has corresponding levels. ASILs have five levels: QM, A, B, C, and D, where level D implies the highest safety requirement, level A means the least strict safety requirements, and level QM implies no special safety requirements [45].  
As risk can be described as a function of the probability of exposure, the severity, and the controllability, based on ISO 26262, we introduced functional safety as our theoretical basis for testing scenarios. We aimed to quantify how risky scenarios are based on the three factors. The quantified value is defined as \(\mathcal{ESC}\). Scenarios in which \(\mathcal{ESC}\) exceeds the threshold are called challenging scenarios.
## 5 Test Materials and Equipment
### Rheological Models of Cement Paste after CO\({}_{2}\) Absorption  
An accurate rheological model is the premise for evaluating the rheological characteristics of cement paste. A rheological model of cement paste after CO\({}_{2}\) absorption was derived from a comparative analysis of the rheological curves (Figure 4) and the rheological models of several fluids (Figure 1) [23; 24; 25].  
(1) Rheological models of cement paste under mechanical vibration  
It can be seen from Figure 4a that under mechanical vibration, shear stress is positively related to shear rate, and the rheological curve shows three stages of increase. The rheological curve of cement paste carbonated under mechanical vibration (Figure 1) has a similar shape as the H-B model; hence, this model was selected for fitting to the observed data.  
The rheological parameters of cement pastes carbonated under mechanical vibration were fitted using the H-B rheological model. The fitted-model parameters of cement pastes carbonated under mechanical vibration are shown in Figure 5. It can be clearly seen from the fitting data that the power exponent (\(n\)) in the parameters in Table 6 meets the H-B rheological model, \(n\leq 1\) and \(n>1\). It approaches to the H-B model (\(n>1\)) at CO\({}_{2}\) AA = 0.44% and 0.88%. It approaches to the H-B model (\(n=1\)) at CO\({}_{2}\) AA = 1.32% and 1.76%. However, it conforms to the H-B model (\(n<1\)) at CO\({}_{2}\) AA = 2.20%. Specifically, the R2 coefficient is close to 1 in all cases, indicating accurate modelling. In short, the rheology of cement pastes carbonated under mechanical vibration conforms to the H-B rheological model.  
(2) Rheological models of cement paste carbonated under ultrasonic vibration  
In this study, the rheological characteristics of cement paste carbonated under ultrasonic vibration were modelled based on the Bingham, M-B and H-B models (Figure 6). It can be seen from Figure 6 and Table 7 that all three models have low accuracy with \(R^{2}\) coefficients < 0.8. Therefore, none of these three models can accurately characterise the rheological characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **CO\({}_{2}\) AA** & \(\tau_{0}\) **(Pa)** & **K** & \(n\) & \(R^{2}\) \\ \hline A1 & 0\% & 7.21 & 0.0032 & 2.0639 (\(n>1\)) & 0.997 \\ A2 & 0.44\% & 21.13 & 0.0043 & 2.1563 (\(n>1\)) & 0.994 \\ A3 & 0.88\% & 26.03 & 0.00022 & 2.631 (\(n>1\)) & 0.995 \\ A4 & 1.32\% & 30.41 & 0.00307 & 1.438 (\(n\approx 1\)) & 0.981 \\ A5 & 1.76\% & 44.45 & 0.182 & 1.272 (\(n\approx 1\)) & 0.992 \\ A6 & 2.20\% & 49.38 & 3.941 & 0.794 (\(n<1\)) & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rheological curve-fitting parameters of cement slurry carbonated under mechanical vibration.  
Figure 5: Rheological models of cement slurry carbonated under mechanical agitation. Note: A1–A6 represent the models at CO\({}_{2}\) AA = 0%, 0.44%, 0.88%, 1.32%, 1.76% and 2.20%, respectively.
## 3 Dataset Collection
### Dataset Comprising Single Images  
To detect and identify executors of KMNs during the turnaround process, a dataset consisting of 3175 images was first constructed. The dataset mainly contains civil aircraft and mobile aircraft landing stairs. Their numbers are presented in Figure 3a. To reflect a realistic airfield operational environment, images of the dataset cover several special cases, such as multi-scale changes in the targets, different visibilities, and occlusions. All targets are labeled manually, and each ground truth includes the location of the bounding boxes and their class names. The location and scale distribution of the objects in the dataset are shown in Figure 3b. This figure shows that the objects to be detected are mainly  
Figure 2: Relationship between components of the proposed framework and our datasets.  
distributed in the central region of the image, and the scale distribution of the instances is relatively uniform.
## 2 Materials and Methods
### Mixture Proportion  
Optimizing particle components is a critical step in designing UHPC mixtures [26; 27]. The cement content and other supplementary mineral additives can be determined based on the required structural strength. The content of mineral additives should not be less than 10% of the cement, and the total content of binders should not exceed 50% of the total mass of UHPC components [28].  
The dosage of dispersed steel fibers is also determined at this stage depending on the specific requirements in terms of tensile and flexural strength, which can be adjusted at  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials** & **SiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** \\ \hline Cement & 17.23 & 3.45 & 64.56 & 2.11 & 3.98 & 4.33 & 0.15 & 0.89 \\ SF & 91.6 & 0.2 & 0.3 & 0.4 & 0.58 & 0.1 & 0.16 & 0.65 \\ RHA & 87.4 & 0.63 & 1.15 & 0.58 & 0.82 & 0.41 & 0.38 & 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cement, SF, and RHA (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **Specific Weight** & **Porous** & **Humidity (\%)** & **Loss on Heating** \\  & **(g/cm\({}^{3}\))** & **Volume (kg/m\({}^{3}\))** & **Humidity (\%)** & **(\%)** \\ \hline SF & 2.22 & 310 & 2.6 & 0.6 \\ RHA & 2.26 & 410 & 2.3 & 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of testing the physical of silica fume and rice husk ash (%).  
different levels. The even dispersion of steel fibers in the concrete mix is crucial for creating an effective reinforcement network. Steel fibers help prevent and limit the development of microcracks, thereby enhancing the crack resistance and load-bearing capacity of the concrete. This is particularly significant in applications requiring high durability, such as bridges, high-rise buildings, and heavy-load structures. Table 3 below presents the mixing ratios of the UHPC samples.  
The composition of UHPC mixtures is designed based on an absolute volume balance approach, assuming a certain amount of air voids, from which the maximum amount of chemical additives is determined. The next step is to adjust the volume balance and ensure volume density. The UHPC mixture must simultaneously meet three requirements regarding workability and various mechanical properties, especially in compressive, tension, and flexural strength [28]. UHPC is implemented at 140 MPa compressive strength, using different percentages of steel fibers (SF) as follows: 10% (SF10), 15% (SF15), and 20% (SF20) of the binding material. In this study, RHA was added and used to replace SF with corresponding ratios of 25% (SF15 RHA5), 50% (SF10 RHA10), 75% (SF5 RHA15), and 100% (RHA20).
## 4 System Model
### Network Assumptions  
Some of the network assumptions to be followed before entering into the proposed model are:  
* It is assumed that the centralized controller in the proposed environment is considered to be secure and the malicious attackers are not able to manipulate it.
* The proposed encryption algorithm is secure and cannot be bypassed by the attackers.
* The utilized WSN nodes in the environment are of a mobile and heterogenous nature that cannot pose any severe challenges to the network. Furthermore, managing mobility was conducted by organizing the nodes into clusters that allow the leaders to optimize routing updates and isolate any changes resulting from mobility within a specific cluster.
* The channel among the controllers and other entities (i.e., control messages provided by the controller) are secure in the proposed environment.
* Finally, the proposed routing links have enough bandwidth to impose routing in a reliable manner.