esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As the direct crystallization and remineralization triggered by the particles are independent of long-range fluoride ion diffusion from other sources, the ACMFP particles can offer local remineralization with a lower overall fluoride concentration. The rapid in vitro conversion from amorphous core-shell particles into crystalline fluorapatite-like mineral depicted in Figure 3 illustrates the bioactive and remineralizing properties of the ACMFP particles. Several mechanisms of the conversion process from ACP to apatite in aqueous media have been proposed in the literature, including dissolution-reprecipitation, reorganization of Posner's clusters, and surface-mediated transformation triggered by phosphate hydrolysis, leading to apatite nucleation and growth supported by surface ion migration [22]. It is plausible that all these processes occur to a varying degree at different stages of conversion of the particles, i.e., once apatite is nucleated at the particle surface, the rate of dissolution-reprecipitation increases. It is further established that this conversion process is accelerated in the presence of fluoride [22; 27]. The corresponding conversion was demonstrated in an in vitro mineralization and tubule occlusion test, showing that the ACMFP particles generated a more acid-resistant mineralization layer than the fluoride-free ACMP particles (Figure 4). It should be noted that similar mineralization and tubule occlusion results have been observed in previous studies [16; 18], but only when the ACMP particles have been applied in combination with an external fluoride source, e.g., successive application of a toothpaste containing sodium fluoride. Although the current findings are based on in vitro data from a limited set of dentin specimens, the results indicate that a similarly effective remineralization and dentin tubule occlusion effect can be obtained by the sole use of the ACMFP particles, eliminating the need for external or additional fluoride sources.
## 9 Conclusions and Perspectives  
Focusing on the obtained results, the proposed methodology, based on multibody and finite element modeling, can be successfully employed in contact analyses. Thanks to the proposed constraint formulae, the finite elements model and the sliding pendulum remain in permanent contact (bilateral). As we can see, we can express the investigated frictionless contact as a set of holonomic/scleronomic constraint equations. Additionally, we can successfully employ the coordinate partitioning technique to eliminate dependent coordinates and the Lagrange multipliers. The presented structure of the used equations convinced us that we could extend the above methodology to more complex systems, i.e., we can also effectively use the proposed procedure in the cases of models of contacts present between more complex mechanisms and more complex finite elements models.  
We have to point out certain limitations in the applicability of the proposed procedure. Many authors of contemporary commercially accessible algorithms/programs design them with the black-box philosophy. Access to details of the used functions is limited. There is no access to internal modifications. Therefore, the proposed equations may be challenging in applicability, mainly because of a lack of detailed information about the applied commercial algorithms.  
In future-coming investigations, we intend to extend the model with the friction phenomena and investigate the impacts at the contact point of the unilateral model of contacts (in both factionless and fictional contact cases). Non-punctual types of connections are in the scope of future investigations. We intend to investigate aspects connected with potentially overactuated configurations. We intend to propose methods for estimating the driving torques occurring in these configurations. We shall present the results of the optimization of these torques. We intend to suggest three-dimensional versions of these models. A complete model of the brachiation robot should be investigated numerically and in physical experiments done on material models.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable. All related data presented in this article.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 5: Time evolution of the investigated system: sketches of subsequent poses obtained by the system at different instants of time: initial position of \(t=0\) s (**a**); \(t=0.2\) s (**b**); \(t=0.4\) s (**c**); \(t=0.6\) s (**d**); \(t=0.8\) s (**e**); and \(t=1\) s (**f**).
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Application of Reinforcement Learning Algorithm in the Development of Mobile Robot  
The findings of this review show that several theories and algorithms of reinforcement learning have been developed, and the findings of the reviewed experimental research demonstrate the usefulness of reinforcement learning models in mobile obstacle avoidance, path planning and optimization, navigation and learning, motion control, and adaption in both known and unknown environments. The basis of this algorithm is routed in training the robot to learn from the information available in the environment. Evidence from all the review studies shows that mobile robots can successfully transit from the start point to the target point without collision using RL algorithms. RL models have been used in different mobile functions. Their applications are further explained below.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 4 Methodology
### Fusion and Coarse Estimation  
To exploit the complementary spatial information from azimuth and elevation radars, we design a cross-attention-based fusion module followed by a regression head for coarse 3D skeleton estimation. As shown in Figure 6b, this module enables bidirectional feature interaction and semantic integration across views.  
Given the extracted temporal feature sequences \(\mathbf{F}_{\text{AZ}},\mathbf{F}_{\text{EL}}\in\mathbb{R}^{B\times T\times d _{\text{model}}}\) from each radar stream, we first project them into a shared embedding space using two separate linear layers. The projected features are then fed into a symmetric cross-attention mechanism: each view attends to the other by using its own features as queries and the counterpart as keys and values. This mechanism facilitates view-aware information exchange and highlights motion-sensitive and structurally relevant frames.  
The attention-enhanced outputs \(\mathbf{F}_{\text{AZ}}^{\prime},\mathbf{F}_{\text{EL}}^{\prime}\) are then temporally aggregated via average pooling, yielding global descriptors \(\mathbf{\hat{F}}_{\text{AZ}},\mathbf{\hat{F}}_{\text{EL}}\in\mathbb{R}^{B \times d_{\text{model}}}\). These are concatenated and  
Figure 6: Key modules of the mmHSE system. (**a**) Feature extractor module: used for processing radar input data. (**b**) Fusion and coarse estimation module: used for merging radar input branches and providing preliminary outputs of skeletal point coordinates. (**c**) Localization optimizer: used for fine-tuning the skeletal point coordinates.  
passed through a fully connected regression head, which consists of a ReLU activation and two linear layers, to produce the initial 3D keypoint prediction \(\hat{\mathbf{Y}}_{\text{coarse}}\in\mathbb{R}^{B\times N\times 3}\), where \(N=21\) denotes the number of keypoints. This coarse estimate provides a structurally plausible initialization for the final refinement stage.
# A Simplified Pointing Model for Alt-Az Telescopes
## 3 The Methodology for Creating the Telescope Pointing Model
### Telescope Pointing Error Correction  
In practical usage, astronomical ephemeris is initially employed to calculate the true azimuth and elevation angles of the observed target. These values are then input into the azimuth and elevation angle error functions to obtain the azimuth and elevation angle errors for that specific direction. The corresponding telescope azimuth angle, \(\Phi_{\rm T}\), is obtained by subtracting the azimuth angle error from the true azimuth angle, and the telescope elevation angle, \(\Theta_{\rm T}\), is obtained by subtracting the elevation angle error from the true elevation angle. By inputting \(\Phi_{\rm T}\) and \(\Theta_{\rm T}\) into the telescope control system, the accurate pointing of the telescope toward the desired target can be achieved. When a time series of \(\Phi_{\rm T}\) and \(\Theta_{\rm T}\) values are input into the control system of the telescope, the control system will order the telescope toward specific direction at a given time step. Currently, a time step of 0.1 s is adopted by the PAST telescope. For example, every 0.1 s the telescope control system will turn the telescope according to the position of the target at the current moment. The stabilization of the telescope is then achieved by the high-frequency-swing stabilizing mirror, and the movement of the target in the image is eliminated and the target will be fixed at a specific pixel, with uncertainty in the order of one pixel (0.5 arcs).
## 4 Detection Performance and Discussion
### Threshold Determination  
A suitable threshold is required for reliable detection. A victim is assumed to be present if \(\aleph\) is less than the threshold. The data for all six human subjects were considered for this step. For each subject, three sets of data were obtained at distances between the antenna and subject of 300 cm, 600 cm, 900 cm, and 1100 cm, for a total of 72 datasets. Further, 10 sets of data were obtained without a subject present using (23) and (28) based on the acquired 10 datasets.  
In theory, the error \(\aleph\) should be large when a subject is not present due to the random nature of the noise. A histogram of the range errors for the 72 sets of data is shown in Figure 24a, and the corresponding probability distribution is given in Figure 24b along with the associated Gaussian  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **FFT** & **MHOC** & **CFAR** & **AM** & **Proposed** \\ \hline Rate (Hz) & 0.116 & 0.177 & 0.0823 & 0.147 & 0.334 \\ SNR (dB) & \(-\)15.09 & \(-\)7.58 & \(-\)10.83 & \(-\)6.72 & 4.06 \\ Deviation (\%) & 65 & 47 & 75 & 56 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Actuator experiment results with five methods.  
Figure 23: **(a)** Frequency estimation using the proposed method at 400 cm, **(b)** frequency estimation using an FFT at 400 cm, **(c)** frequency estimation using the proposed method at 700 cm, and **(d)** frequency estimation using an FFT at 700 cm.  
distribution. The red line denotes the Gaussian distribution, while the blue stars denote the distribution of the 72 errors. Based on these results, the error can be considered to be Gaussian distributed as 95% of the error values follow this distribution. Further, 68 of the 72 values are less than 45 cm. This indicates that the range of the victim can be determined accurately using (30). Table 5 shows the error when there is no subject present. These errors are much larger than when a subject is present, so the existence of a victim can be determined based on the error compared to a threshold, i.e., a victim exists if \(\aleph\leq 0.5\) m.
# Numerical Study on Temperature Distribution of Steel Truss
## 3 Engineering Background and Thermal Parameters  
The steel truss aqueduct is in Hefei, Anhui province in China, and is now under construction. As shown in Figure 1, the channel connecting Yangtze River and Huai River will cut off the existing Pi River, and thus this aqueduct is built to reconnect the Pi River.  
Figure 1: Location of the steel truss aqueduct.  
The front elevation of the steel truss aqueduct is shown in Figure 2. The total length of the aqueduct is 246 m and consists of three spans, which are 68 m, 110 m, and 68 m long, respectively.  
The cross-section of the mid-span of the aqueduct is shown in Figure 3. The cross-section is 9 m high and 23 m wide. The check water level is 5.05 m, and the design water level is 4.0 m.  
The thermal parameters used in this study are listed in Table 1. The steel emissivity is available in the literature [3; 24; 28; 29].  
The steel truss aqueduct is between \(116^{\circ}20^{\prime}\) and \(117^{\circ}30^{\prime}\) in the east longitude and \(31^{\circ}30^{\prime}\) and \(32^{\circ}40^{\prime}\) in the north latitude. In this region, the highest temperature is reached  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{Latitude \(l\)} & \multicolumn{2}{c}{\(31.84^{\circ}\)} \\ \hline Specific heat of steel & 465 J (kg.\({}^{\circ}\)C) & Density & 7850 kg/m\({}^{3}\) \\ \hline Solar absorptivity \(a\) & 0.55 & \
\begin{tabular}{c} Thermal conductivity \\ \(\lambda\) \\ \end{tabular} & 45.01 W/(m.\({}^{\circ}\)C) \\ \hline Steel emissivity \(\varepsilon\) & 0.8 & Solar radiation \(A\) & 1416.58 W/m\({}^{2}\) \\ \hline Atmospheric extinction & & Atmospheric & \\ coefficient & 0.42 & cleanliness & 1.0 \\ \(B\) & & C\({}_{N}\) & \\ \hline Ratio of diffuse and & & & \\ direct radiation & 0.138 &
\begin{tabular}{c} Ground radiation \\ reflectance \(\rho_{g}\) \\ \end{tabular} & 0.35 \\ \(C\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal parameters for steel and other properties.  
Figure 3: Cross-section in mid-span of the aqueduct.  
Figure 2: Front elevation of the aqueduct.  
between July and August, while the coldest weather comes in January. Based on the weather station data (National Centers for Environmental information, [[https://www.ncei.noaa.gov/](https://www.ncei.noaa.gov/)]([https://www.ncei.noaa.gov/](https://www.ncei.noaa.gov/))), 16 to 19 July in summer of 2018 and 15 to 18 January in winter of 2019 are chosen as critical weather condition to carry on the numerical simulation of temperature fields on the aqueduct. The hourly air temperatures in summer and winter are shown in Figures 4 and 5, respectively.  
A water temperature experiment on the site was taken from 31 July 2018. It was found that water temperature has almost no change over a few days and thus set as 33 \({}^{\circ}\)C and 2 \({}^{\circ}\)C constantly in summer and winter, respectively.
## 3 Materials and Method
### Proposed Method
#### 3.3.1 Overview
In the field of network security, attack data often appear sparse and incomplete; diffusion models, through their generative process, can simulate and augment real attack behavior data. By introducing controlled noise into the data and progressively reducing this noise throughout the generation process, the models learn to fill in missing portions of the data, thereby restoring attack behavior features more completely. Feature Extraction: During the feature extraction phase, diffusion models are applied to high-dimensional data, aiding in the capture of deep (often unobservable) features. This level of feature extraction is crucial for understanding complex attack patterns and behaviors, providing more precise information than traditional deep learning models. Denoising and Anomaly Detection: Noise data or false alarms are frequent issues in network attack detection. Diffusion models exhibit superior performance in denoising, effectively distinguishing and isolating the differences between real attack actions and noise through the model's reverse generation steps (transitioning from noisy data back to original data). Generating Adversarial Attack Samples: To enhance the model's robustness and defensive capabilities, diffusion models are also employed to generate adversarial attack samples. These samples simulate unseen and potential attack methods, aiding in training the model to predict and counter these potential new threats. The detailed operational procedures are as follows:  
1. Initialization: An appropriate noise distribution is selected (represented by \(\epsilon\) in the diagram) to serve as the starting point for the model (typically a Gaussian distribution). 2. Forward process: Noise is incrementally introduced into the actual data at each step, with each addition of noise calculated to ensure reversibility. This process generates a series of data states transitioning from entirely real to completely noisy. 3. Reverse process: Starting from the noisy data state, the model's learned inverse transformations are progressively applied, recovering increasingly clearer data states until a state close to the original data is reached. During the application of this process, precise control over the amount and quality of noise at each step is essential if useful information is to be recovered from noisy data. Through this method, diffusion models enhance the accuracy of detection models, providing deeper insights and greater adaptability when dealing with complex data and attack patterns.
# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 8 Case Study  
### Task  
In order to validate the proposed method, a telecommunication network (TN) was considered as a complex monitoring object. The network is represented as a hierarchy of network devices--routers that accumulate network traffic from user devices. The devices are either stationary or mobile. Mobile devices can move between the coverage areas of different routers. In case the traffic on a router exceeds a threshold value, an emergency situation on the router is defined. The operator has a task to predict the internet traffic on network routers.
## 6 The Spectrum Methods for Incoherent Ground Displacement Excitations
### The Response Spectrum Method for Incoherent Ground Displacement Excitations  
By substituting Equation (45) into Equation (46), \(\mu_{z_{\max}}\) can be written as  
\[\mu_{z_{\max}}=\left(\sum_{k=1}^{m}\sum_{l=1}^{m}\sum_{i=1}^{n}\sum_{j=1}^{n}b_ {ki}b_{lj}\rho_{\delta_{ki}b_{lj}}p_{z}^{2}o_{\delta_{ki}}o_{\delta_{lj}} \right)^{\frac{1}{2}}. \tag{52}\]  
According to the definition of the response spectrum, a response spectrum value equals to average response of a single degree-of-freedom system subjected to the same ground excitation. Hence,  
\[D_{k}(\omega_{i},\zeta_{i})=p_{\delta_{ki}}o_{\delta_{ki}}. \tag{53}\]  
Substituting Equation (53) into Equation (52), \(\mu_{z_{\max}}\) can be written as  
\[\mu_{z_{\max}}=\left(\sum_{k=1}^{m}\sum_{l=1}^{m}\sum_{i=1}^{n}\sum_{j=1}^{n} b_{ki}b_{lj}\rho_{\delta_{ki}b_{lj}}\frac{p_{z}^{2}}{p_{\delta_{ki}}p_{\delta_{ij}}}D _{k}(\omega_{i},\zeta_{i})D_{l}\big{(}\omega_{j},\zeta_{j}\big{)}\right)^{ \frac{1}{2}}. \tag{54}\]  
Because \(p_{z}^{2}/\big{(}p_{\delta_{ki}}p_{\delta_{lj}}\big{)}\) is near to 1, then  
\[\mu_{z_{\max}}=\left(\sum_{k=1}^{m}\sum_{l=1}^{m}\sum_{i=1}^{n}\sum_{j=1}^{n} b_{ki}b_{lj}\rho_{\delta_{ki}b_{lj}}D_{k}(\omega_{i},\zeta_{i})D_{l}\big{(} \omega_{j},\zeta_{j}\big{)}\right)^{\frac{1}{2}}. \tag{55}\]  
Equation (55) is the expression of the mean of the structural peak response under incoherent ground displacement excitation.  
In consideration of the effect of the support degrees of freedom, the expression of the mean response spectrum method should be modified. Therefore, a structural response, \(z(t)\) can be expressed by the nodal displacement, \(\mathbf{X}_{a}\) and \(\boldsymbol{q}_{a}\),  
\[z(t)=\mathbf{q}_{a}{}^{T}\mathbf{X}_{a}=\left\{\begin{array}{c}\mathbf{q}\\ \mathbf{q}_{g}\end{array}\right\}^{T}\left\{\begin{array}{c}\mathbf{X}\\ \mathbf{X}_{g}\end{array}\right\}=\mathbf{q}^{T}\mathbf{X}+\mathbf{q}_{g}{}^{T }\mathbf{X}_{g}, \tag{56}\]\[z(t)=\sum\limits_{k=1}^{m}q_{gk}x_{gk}(t)+\sum\limits_{j=1}^{m}\sum \limits_{k=1}^{m}\beta_{jk}\mathbf{q}^{T}\boldsymbol{\varphi}_{j}\delta_{jk}(t)= \sum\limits_{k=1}^{m}q_{gk}x_{gk}(t)+\sum\limits_{j=1}^{m}\sum\limits_{k=1}^{m} b_{jk}\delta_{jk}(t), \tag{57}\] \[\mu_{|z,\text{max}|}= \left[\sum\limits_{k=1}^{m}\sum\limits_{l=1}^{m}q_{gk}q_{gl}\rho_{ x_{gk}x_{gjl}}\mu_{x_{gk,\text{max}}}\mu_{x_{gk,\text{max}}}+2\sum\limits_{k=1}^{m} \sum\limits_{l=1}^{m}\sum\limits_{j=1}^{n}q_{gk}b_{lj}\rho_{x_{gk}x_{lj}}\ D \!\!\left(\omega_{j},\zeta_{j}\right)\right.\] (58) \[\left.+\sum\limits_{k=1}^{m}\sum\limits_{l=1}^{m}\sum\limits_{i= 1}^{n}\sum\limits_{j=1}^{n}b_{lk}b_{lj}\rho_{x_{gk}x_{gjl}}\mu_{x_{gk,\text{ max}}}D_{k}(\alpha_{i},\zeta_{i})D\!\!\left(\omega_{j},\zeta_{j}\right)\right]^{ \frac{1}{2}}\]  
where \(q_{gk}\) is the conversion factor of \(x_{gk}\) in the conversion vector, \(\mathbf{q}^{T}\).  
It should be noted that the structural response derived from Equations (57) and (58) needs to be used to construct the structural displacement vector, \(\mathbf{X}_{a}\) like the modified MSRS method utilizing Method 1.
## 4 Results and Discussion
### Results of the Tank Sensors  
Following, the results of the calibration from the tank sensors (water level, illumination, and presence sensors) are detailed. The data from the water level sensor is obtained from [38] because the same level sensor is used in this system. The calibration of the illumination sensor is shown in Figure 13a. This calibration was done using a _Vin_ of 3.3 V the LDR as \(R1\) and an \(R2\) of 2 k\(\Omega\). The model presented in Figure 13a presents a correlation coefficient of 0.9855 and can be seen in Equation (3). Considering the resolution of the node, the minimum variation of light that can be detected by the light sensor goes from 0.1 lux in conditions with low illumination to 42 lux when the illumination exceeds the 1500 lux. In the code introduced in the node to read the _Vout_ from the light sensor and transform this voltage into illumination in lux value is shown in Figure 13b. The formula that relates the turbidity and the voltLig value is extracted from Equation (3).  
Figure 11: (**a**) Data and (**b**) code of the turbidity sensor.  
Figure 12: (**a**) Data and (**b**) code of the oil layer sensor.  
Figure 13: (**a**) Data and (**b**) code of the light sensor.  
\[Light\ (lux)=3.19+\frac{176}{-54.85-0.37\times Vout\ (\mathrm{V})} \tag{3}\]  
The results of the presence sensor are shown in this paragraph. As for the oil sensor, this sensor is not a quantitative sensor, it is a qualitative sensor. It offers information about if there are or there are not people around the tank. Thus, the calibration of this sensor is done comparing the _Vout_ of the sensor in two scenarios. In the first scenario, there is no presence of any worker. In the second scenario, there is a worker at 50 cm of the tank. The _Vin_ in this test is 3.3 employing an _R2_ of 2 M\(\Omega\) and the IR photodiode as _R1_. The _Vout_ in both scenarios is presented in Figure 14a. The values of _Vout_ without workers are between 1.5 and 1.6 V, while with the presence of workers the _Vout_ increases to 1.7 V. The code of the sensor can be seen in Figure 14b.