esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines
## 2 Materials and Methods
### Computer-Interpretable Guidelines  
In 2019, WHO initiated a revision of its 2010 guidelines on physical activities and sedentary behavior. The revision aimed to be aligned with the guidelines with the latest evidences and clinical studies on the field. They also provided expert recommendations on the optimal amount of physical activity in children, adolescents, adults, and elderly people, (>64 years), as well as pregnant and postpartum women, and people living with chronic conditions or disabilities [23]. Alongside the revision of the WHO guidelines, there is a growing interest in incorporating WHO and other similar guidelines into information technologies. For instance, the Physical Activity Ontology (PACO) [24] focuses on the interoperability of physical activity data, while the HeLiS [25] ontology combines physical activity and nutrition for monitoring unhealthy behaviors. However, so far, no other works actually materialize the new WHO guidelines into a service. In general, the use of clinical guidelines in health care information system and health-related activities concerns a wide range of applications. For example, \"Computer Interpretable Clinical Guidelines\" [26] (CICG) is largely focused on interoperability and management of guidelines (i.e., hierarchies and adaptation to local clinical protocols), execution engines, and decision support systems for clinicians. Here, it is worth mentioning the Guideline Interchange Format (GLIF) in its third iteration, GLIF3 [27], and the GLIF Execution Engines, GLEE [28]. The GLIF ecosystem is tailored to the life cycle and consumption of clinical guidelines by health care institutions, from design, encoding, and validation to local adaptation, integration, application, and revision. This combination is used to automatiase data and clinical actions, using the guidelines to define clinical processes [29] and the interpretation of health care records [30]. This direction is promising and, overall, in support of a systematic production and revision of reusable and validated guidelines.  
A central application of this field are decision support systems, e.g., for coordinating health care services (personnel and facilities) [31] and for mitigating the risks of medical errors [32]. Moreover, CICG and logical reasoning have been applied to the construction and maintenance of corpora of guidelines, e.g., to identify and lower the risk of interference between different guidelines [33; 34], and to support authoring and formal verification of guidelines [35].  
To sum up, systems for CICG are growing from both a technical and application point of view. This trend connects the production of machine-readable guidelines with the clinical processes of adopting, adapting, and revising guidelines, outlining a sustainable and flexible approach, firmly grounded on clinical standards. However, the development of these systems is still oriented to a strictly clinical domain, leaving outside their scope well-being and consumer applications, such as digital coaching.
# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 3 Methodology
### Data Processing and Screening  
#### Data Source  
In this paper, the NGSIM (Next Generation Simulation) traffic dataset is used for the study as a source of simulation data [26]. Its main advantages are as follows:  
1. The NGSIM dataset contains a large amount of traffic flow data, including different time periods and different road types and traffic conditions, which can provide comprehensive traffic behavior information.
2. The NGSIM dataset has a high degree of authenticity, accuracy, and more comprehensive data [27], which can provide a reliable research basis and is widely used in microscopic traffic simulation modeling.
3. The NGSIM dataset has a sampling accuracy of 0.1 s, which provides high temporal and spatial resolution traffic data, and continuously records traveling data such as vehicle category, speed, acceleration, headway, and headway hourly distance, which accurately describes the traveling state of vehicles.  
In this paper, we selected the US-101 freeway collection section data from the NGSIM dataset to screen the suitable lane-changing vehicles, and the specific applicable conditions are as follows:1. Vehicle driving road conditions are freeways.
2. Only small cars are used as research objects.
3. Screening for free lane changing and single lane changing vehicles: only lane changing vehicles in lanes 1 to 5 are considered due to the impact of converging traffic on ramps 7 and 8.
4. Screening of vehicles with a distance of 5 m to 60 m between the following vehicles.
5. The process of lane-changing for a vehicle encompasses the leading vehicle in the current lane, the leading vehicle in the intended lane, and the trailing vehicle in the intended lane.  
Taking ID64 vehicles as an example, the screening data is shown in Table 1.
## 2 Methodologies and System Description
### Experimental Procedure  
The flowchart of the experimental steps is depicted in Figure 11. The system was divided into pre-signal processing, waveform processing, frequency conversion, and calculation of physiological parameters.  
1. This study obtained images through a webcam, used dilb's 81-point face recognition algorithm to set up a region of interest to reduce the impact of the subject's head shaking, separated the images from the region of interest into three RGB channels and extracted the green channel, and averaged the extracted green channels for each image to form a sequence.
2. This study conducted a detrended fluctuation analysis (DFA) of the signal and normally adjusted the signal level, removed the waveform artifacts through ICA combined with particle cluster optimization, and, finally, used a band-pass filter to sort the bandwidths for heart rate and HRV.
3. The waveform filtered by the band-pass filter was transformed through the FFT algorithm to convert the time sequence of the heartbeat signals into a frequency domain to find and output the highest peak of the waveform. The heart rate variabilities of very low frequency, low frequency, and high frequency were presented according to their power spectral density (PSD).
4. Finally, the physiological signals measured were converted into understandable values through formula calculations.  
Figure 10: Architecture diagram of heart rate and heart rate variability measurement system.  
Figure 11: Flow chart of the non-contact physiological signal measurement system.
Therefore, the design of PersL materials must involve a careful choice of a suitable emitter and a host that can provide adequate trap centers to feed the long-lasting persistent luminescence process, which is often a major challenge [6; 12; 17]. Likewise, up-conversion is another appealing effect in luminescent materials, enabling near-IR light to be converted into visible radiation, which is of considerable significance for many research areas, especially in bioapplications, since it minimizes photo-damage to the biological tissues, as well as their autofluorescence background [18; 19; 20]. This is a nonlinear anti-Stokes process in which the emitted photons have higher energy than the absorbed ones, i.e., two or more low-energy photons are absorbed sequentially by long-lived intermediate energy states, resulting in spontaneous or stimulated emission at energies significantly higher than those of the excitation [18; 19; 21]. Up-conversion has been established as an efficient process, particularly when involving RE ions which have ladder-like energy level schemes, enabling the absorption of photons in equal increments [18; 21]. Analogously to the PersL, up-conversion materials are mainly composed of the inorganic host, which radically influences the radiative and non-radiative processes, and the dopant ions, which can act either as activators or sensitizers, being responsible for the energy of the emitted photons and for absorbing light and efficiently transferring energy to the activator, respectively [19]. The wide possibilities of selecting the host lattice and the dopant(s) can lead to a myriad of up-conversion properties, allowing plenty of room for creativity and imagination [19]. The variety of luminescence materials is almost infinite, from organic polymers, organic dyes, transition metal complexes (iridium, copper, platinum, gold), metal-organic materials, to inorganic phosphors, etc. [22]. In this Special Issue, we welcome research on all luminescence materials, with particular emphasis on defect-engineered inorganic materials, such as transition metal (TM) and rare-earth (RE) doped semiconductors and isolators, to tailor their emission and promote fascinating phenomena like PersL or up-conversion. Therefore, although research on luminescent materials has come a long way since the reports on the Bologna Stone, with many breakthroughs and a great maturing of the optical spectroscopy techniques used to assess them, there is still a vast path to be taken to respond to all the demands of the emerging technologies. In addition, many pieces of the puzzle are yet to be fitted to master the underlying physical phenomena, leaving researchers with an immense playground for improvement and novel discoveries. The author declares no conflict of interest.
# Algorithms for Drug Sensitivity Prediction
## 4 Predictive Models
### Deep Neural Networks  
In this section, we consider the broad class of deep neural network-based algorithms that have recently been applied for predictive modeling tasks in chemoinformatics, which is closely related to the field of drug sensitivity prediction. As part of the molecular diversity analysis in chemoinformatics, we find the Quantitative Structure Activity Relationship (QSAR) as a popular methodology that intends to model the relationship of the chemical and physical properties of compounds with their chemical structure. All of these elements can be summarized as the pharmacokinetics properties of the compound: potency, absorption, distribution, metabolism, elimination and toxicity. Other than using the previously-described approaches of regularized linear regression, ensemble models and kernel-based models in QSAR modeling, Deep Neural Networks (DNN) is also being applied. DNN is starting to gain popularity since there have been methodology improvements that allow model training using smaller datasets with minor overfitting using weight penalties, early stopping criteria or new approaches, such as random dropout [98] and unsupervised complementary priors with stochastic binary communication [99]. The hidden layers inside the DNN models can additionally make use of the chain rule structure (shown in Figure 4) and Stochastic Gradient Descent (SGD) [100] to map the input feature to another domain where there is less redundancy and features are more orthogonal to each other. This characteristic creates sparsity training that can boost the performance of the DNN model.  
Figure 4: Structure of deep neural networks.  
Similar to the DREAM challenge for drug sensitivity prediction, Merck organized the Molecular Activity Challenge ([[https://www.kaggle.com/c/MerckActivity](https://www.kaggle.com/c/MerckActivity)]([https://www.kaggle.com/c/MerckActivity](https://www.kaggle.com/c/MerckActivity))) where the purpose is to analyze independent datasets and predict which molecules will have higher activity towards their desirable biological targets and avoid targets that may result in harmful or undesirable side effects. The 2012 winners of the challenge [101] used multi-task learning for QSAR in conjunction with several neural network-based models, improving Merck's internal criterion by 15%.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 3 Methodology
### Path Aggregation Network SPANet  
In deep learning, the low feature layer has a smaller receptive field than the high feature layer, and the backbone network obtains more low feature information, which helps to improve the accuracy of tiny object detection. However, the original YOLOv8s neck network feature information is not sufficiently fused, making the accuracy rate low. Therefore, building upon the original PANet architecture, this study incorporates the shallow feature layer P2, which is outputted from the backbone network, as an additional input to PANet. Introducing the detailed information from lower layers into PANet facilitates the fusion of deep semantic and shallow detail information. Like feature layers P3 and P4, P2 is concatenated with the up-sampled deep feature layer, enabling the fusion process to integrate deep semantic and fine-grained details information. The resulting feature map is then downsampled to match the size of P3', followed by feature concatenation and convolution operations. Moreover, the feature maps concatenated with P3 and P4 include not only the down-sampled feature maps from the preceding layer but also the feature maps (P3' and P4') obtained through previous up-sampling, concatenation, and convolution operations. This enrichment of gradient information renders the network more robust and effective.  
Compared with the original PANet, the improved model further shortens the distance between the top and lower layers and enhances the model's ability to predict tiny objects. To avoid adding P2 into the path aggregation network to make the model parameters too much, P4'' downsampling and feature concatenating and convolution operations are removed from the original PANet to achieve the purpose of reducing parameters and balancing the training speed. Finally, the output feature layers P2', P3'' and P4'' are taken as the input into yolo head to obtain the prediction box, class, and other information. The architecture of the SPANet (Small Path Aggregation Network) is shown in Figure 2.  
Figure 1: Processing of feature maps by SPD-Conv module when scale = 2; (**a**) unprocessed feature maps; (**b**–**d**) space-to-depth on the feature map; (**e**) the output feature map.
###### Abstract  
The aim of this study was to compare the forces and moments exerted by thermoformed aligners (TFMs) and direct printed aligners (DPAs) on the maxillary left central incisor (21) and adjacent teeth (11, 22) during lingual bodily movement of tooth 21. Methods: An in vitro setup was used to quantify forces and moments on three incisors, which were segmented and fixed onto multi-axis force/moment transducers. TFM were fabricated using 0.76 mm-thick single-layer PET-G foils (ATMOS; American Orthodontics, Sheboygan, WI, USA) and multi-layer TPU foils (Zendura FLX; Bay Materials LLC, Fremont, CA, USA). DPAs were fabricated using TC-85 photopolymer resin (Graphy Inc., Seoul, Republic of Korea). Tooth 21 was planned for bodily displacement by 0.25 mm and 0.50 mm, and six force and moment components were measured on it and the adjacent teeth. Results: TC-85 generated lower forces and moments with fewer unintended forces and moments on the three teeth. TC-85 exerted 0.99 N and 1.53 N of mean lingual force on tooth 21 for 0.25 mm and 0.50 mm activations, respectively; ATMOS produced 3.82 N and 7.70 N, and Zendura FLX produced 3.00 N and 8.23 N of mean lingual force for the same activations, respectively. Bodily movement could not be achieved. Conclusions: The force systems generated by clear aligners are complex and unpredictable. DPA using TC-85 produced lower, more physiological force levels with fewer side effects, which may increase the predictability of tooth movement and enhance treatment outcome. The force levels generated by TFM were considered excessive and not physiologically compatible.  
tooth movement; aligners
## 4 Model Validation  
To verify the rationality of the model, the phase transition simulation calculation results of the spiral groove liquid film seal were compared with the results from [21], and the comparison is shown in Figure 3. The average vapor phase volume fraction calculated via default mass transfer coefficient (0.1) in this paper is consistent with the results obtained from the literature. Moreover, the overall agreement is high, which verifies the accuracy of the simulation results. However, under the same conditions, when the mass transfer coefficient was set to 2, 4, or 6, the calculated average vapor phase volume fraction increases from the original 3% to 25%, 32%, and 36%, respectively. This shows that the results obtained using the default mass transfer coefficient are not credible. Therefore, corresponding research and analysis on the influence of the change in the mass transfer coefficient on the average vapor phase volume fraction were conducted in this study. The corresponding simulation calculation was carried out by setting the mass transfer coefficient to 6, 18, 24, 30... 300. The calculation results are shown in Figure 4.  
Based on the data provided in Figure 4, with an increase in the mass transfer coefficient, the average vapor phase volume fraction rapidly increases until it reaches the value of 0.47. Then, between 0.47 and 0.5, slow growth is observed and eventually stabilizes. This indicates that the calculated value of the average vapor phase volume fraction increases  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary** & **Boundary Type** \\ \hline liquid film AD & pressure-inlet \\ liquid film BC & pressure-outlet \\ spiral groove EF & pressure-outlet \\ liquid film AB, CD & periodic boundary \\ the upper surface of the liquid film and the lower surface of the spiral groove & interface \\ spiral groove EH, GH, FG & moving wall \\ the upper surface of the spiral groove & moving wall \\ other walls & stationary wall \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary condition settings.  
Figure 3: Variation of average vapor volume fraction with helix angle under different mass transfer coefficients.  
with the mass transfer coefficient. Furthermore, the larger the mass transfer coefficient, the more similar the obtained value to the actual results, which is consistent with the conclusion obtained in [29]. After the mass transfer coefficient was set to 48, the average vapor phase volume fraction calculated each time had a growth rate of less than 1% compared with the previous calculation result. Considering the calculation efficiency and cost issues, 48 was selected as the mass transfer coefficient for the simulation calculation in this study.
The second-order optical susceptibility \(\chi^{(2)}\) should be equal to zero in these crystals [19; 20]. Therefore, the SHG process is forbidden in centrosymmetric crystals because SHG is a typical second-order nonlinear optical phenomenon relying on the second-order optical susceptibility \(\chi^{(2)}\). Compared with SHG, the direct third-harmonic generation (THG) process, where the laser beam directly generates the third harmonic wave, can be found in any material because the third-order optical susceptibility \(\chi^{(3)}\) does not vanish regardless of the symmetry of the material even in the air [21; 22]. It has also been proved that THG has a significant angle dependence in both the non-centrosymmetric crystals such as \(\beta\)-BBO [23] and the centrosymmetric crystals such as fused silica and calcite [24; 25]. The conversion efficiency of THG reaches 2.5% when the femtosecond laser has an intensity of 10\({}^{11}\) W/cm\({}^{2}\), though it is a weaker process than SHG [26]. Since the angular dependence of THG is generally sensitive near the phase-matching angle [23; 24; 25; 26], it is expected to be applied to angle sensors. In this research, we propose two angle measurement methods based on THG, including the intensity-dependent method and the wavelength-dependent method. This paper presents the proposed methods with centrosymmetric birefringent crystals. The non-centrosymmetric crystals are not discussed in this paper because they are seriously affected by the non-phase-matched second-order \(\chi^{(2)}\) optical processes [26]. The simulation results of investigating the angle measurement performance of different centrosymmetric crystals are reported in this paper. Firstly, the angle dependence principle of direct THG in the centrosymmetric crystal is illustrated for an intuitive understanding. Then, two angle measurement methods are introduced, including the intensity-dependent and wavelength-dependent methods. For theoretical investigation, three centrosymmetric crystals are selected, which could achieve a considerate THG conversion efficiency. Their material specifications are also supported by the literature. A wide wavelength range from 900 nm to 2500 nm is considered in the simulation for angle measurement. In this range, the measurement sensitivity is evaluated to choose the crystals that are suitable for angle measurement. In addition, the effects of focusing parameters, including the focal length and the focused position, are discussed. The optimized measurement configurations are found, and the reasons are briefly discussed.
# Fast Path Planning of Autonomous Vehicles in 3D Environments
## 2 Assumptions and Definitions
The first condition shows that a frontier point corresponds to the border between a space covered by footprints and a space covered by no footprints. If every sensor has no frontier point, then footprints of all sensors cover the entire workspace. The second condition indicates that as \(R\) moves from \(n\) to any point in \(f(n)\), \(R\) does not collide with an obstacle boundary. The third condition is required to avoid the case where \(R\) collides with an obstacle when \(R\) arrives at a frontier point. The second and third conditions assure that a frontier point is not inside an obstacle. See Algorithm 1 for generation of both candidate frontier points and frontier points. Figure 3 illustrates \(R\) deploying a new virtual sensor. In this figure, \(R\) is depicted as a sphere. \(R\) moves inside a tunnel in this figure. The tunnel boundaries are depicted with red curves, and the trajectory of \(R\) is depicted with a yellow curve. The large dots on the trajectory of \(R\) indicate the virtual sensors deployed by \(R\). The footprint boundary of each virtual sensor is depicted as a dotted sphere. Frontier points are marked as large dots on the footprint boundary of one sensor. ```
1:\(initPhase=TRUE\);
2: Generate a virtual agent \(R\) at the position of a real AV;
3:repeat
4:\(R\) deploys a new virtual sensor, say \(n\), at its position;
5:if\(initPhase==TRUE\)then
6: Let \(n_{init}\) define a virtual sensor deployed at the real AV position;
7:\(initPhase=FALSE\);
8:endif
9: Update \(I\) utilizing the newly deployed sensor \(n\);
10: Enable the sensing capability of \(n\). 11:\(FrontierGenerate(n)\) in Algorithm 2;
12: Every frontier point within \(r_{s}\) distance from \(n\) is removed;
13:\(R.FrontierJump\) in Algorithm 3;
14:until\(L(\mathbf{n},\mathbf{g})\) is a collision-free path;
15: Set \(n\) as \(n_{g}\), representing the sensor nearest to the goal;
16: Generate a shortest path from \(n_{init}\) to \(n_{g}\) utilizing \(I\);
17: The real AV moves along the generated shortest path;
18: The real AV moves along \(L(\mathbf{n}_{g},\mathbf{g})\) until reaching \(\mathbf{g}\);
```  
**Algorithm 1** Path Plan Strategy