esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] [69] proposed an artificial intelligence approach called RF, which is an integrated model combining of tree predictors. This model has good generalization and is more robust in terms of noise control. Numerous scholars revealed the advantages of RF models in the field of geotechnical and tunneling engineering [70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. [PERSON] et al. [80] compared the classification performance of ANN, SVM and RF models for predicting the rockburst intensity. The results showed that the misclassification rates of these models are 0.1, 0.2, and 0, respectively. [PERSON] et al. [81] conducted a study on predicting the tunnel boring machine (TBM) penetration rate. The results showed that the RF regression algorithm has achieved a high accuracy prediction of the mean squared error (MSE) at 0.0504. [PERSON] et al. [18] investigated the geometric, geological, and operational parameters as input variables to train RF models for the ground settlement prediction. A good prediction accuracy has been achieved by means of valuable performance indices (R\({}^{2}\): 0.93, RMSE: 15). However, few RF models with particularly good results were developed to predicting ground settlement during tunnel excavation. Motivated by the above considerations, the main goal of this paper is to establish some hybrid RF models based on the three types of meta-heuristics named, Ant Lion Optimizer (ALO), Multi-Verge Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA). In practice, these metaheuristic techniques are meritocratic with regard to the RF hyperparameters and improve the predictive accuracy of the model. This paper is organized as follows: the RF model and three used meta-heuristic algorithms are introduced in Section 2. Section 3 describes the relevant parameters, four performance evaluation indices and model development (Matlab 2021 was used in this paper to complete the model construction). Subsequently, the performance evaluation results of all hybrid RF models for the ground settlement prediction are displayed and discussed in Section 4. Finally, the main conclusions and limitations of this paper are summarized in Section 5.
## 4 Experiment and Results  
To validate the proposed SMDT method, we first applied it to select significant features for the binary classification of MQTT traffic. Subsequently, we extend our analysis to a multiclass classification approach to identify the specific class of attacks (brute force, DoS, flooding, malformed data, and slowite). The selected features were used to train and evaluate seven machine learning models, including traditional techniques, i.e., Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and neural network-based models, i.e., Multilayer Perceptron (MLP) and Deep Neural Network (DNN), with a feedforward architecture.
## 3 Three-Dimensional Finite Element Model of Composite Foundations
### Loading Conditions and Material Parameters  
According to the results of the site survey, information about the layers of the composite foundation is shown in Table 1. The foundation adopted concrete bored piles with a diameter of 800 mm, spaced at 2.4 m \(\times\) 2.4 m intervals. The effective length of the piles was approximately 20 m to 30 m, and the base-bearing pressure was 400 kPa.  
Using elastoplastic statics, three-dimensional static numerical calculations of the composite foundation were conducted to simulate the stress distribution among the \"cushion layer-concrete pile-inter-pile soil-lower soil\" during loading, with the aim of obtaining detailed information on the stress, strain, and deformation of each structural component.  
In order to comprehensively consider the effects of the pile length, pile type, soil modulus, and pile modulus on the bearing capacity of the composite foundation, this study conducted calculations for 40 different scenarios. Specifically, simulations were performed for a pressure of 400 kPa while considering two pile lengths, two soil elastic parameters, five pile elastic modulus parameters, and two pile types. The computational conditions for the three-dimensional numerical simulations are listed in Table 2.  
Stress and Displacement Diffusion Laws of Pile Bodies and Soil in Composite Foundations with Different Types of Piles
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 3 Results
### The Traffic Safety of HSR with CAVs  
As evident from Figure 5, irrespective of the traffic flow conditions (free flow, saturated flow, or congested flow) and the utilization status of highway hard shoulders, the values of \(TTC\) consistently diminish with the escalating penetration rate of CAVs. This observation indicates that the increase in autonomous vehicle penetration rate will help to reduce the traffic flow safety risk. Furthermore, the figure also reveals that the impact of opening or closing highway hard shoulders on the \(TTC\) values of traffic flow varies across different traffic-demand scenarios. In the cases of free flow and stable flow, the \(TTC\) values are consistently lower when the hard shoulders are open compared to when they are closed, suggesting that the HSR in these conditions leads to a lower traffic safety risk. In contrast, in the case of congested flow, the \(TTC\) values are higher when the hard shoulders are open, indicating an increased traffic safety risk when utilizing hard shoulders during congestion.  
Figure 5: The values of \(TTC\) at different penetration rates of CAVs in different scenarios of HSR. The black line represents the values of \(TTC\) without HSR. The red, yellow, and green lines represent the values of \(TTC\) when the maximum speed limits on the hard shoulder are set at 120 km/h, 80 km/h, and 60 km/h, respectively, during the opening of the hard shoulder. (**a–c**) show the \(TTC\) at different penetration rates of CAVs in the free flow, saturated flow, and the congested flow, respectively.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Experimental Details
#### 3.3.2 Training Details  
The experimental environment was built on the NVIDIA GeForce RTX 3090 computing platform (produced in Taiwan, China), using the PyTorch framework (v2.2.1) and CUDA 12.1 computing protocol. A dynamic learning rate strategy is applied during the training phase, with an initial learning rate of \(5\times 10^{-5}\). The learning rate decay stops when it reaches \(1\times 10^{-5}\). The small batch gradient descent method (batch size = 4) is used for parameter optimization. The model was trained for a total of 100 epochs, and the learning rate was updated using a cosine annealing schedule [48], as defined by the following formula:  
\[lr=lr_{min}+\frac{1}{2}(lr_{0}-lr_{min})\bigg{(}1+\cos\bigg{(}\frac{T_{cur}* \pi}{T_{max}}\bigg{)}\bigg{)} \tag{8}\]  
where \(lr\) represents the current learning rate, \(lr_{0}\) represents the initial learning rate, \(lr_{min}\) represents the minimum learning rate, \(T_{cur}\) represents the current number of training epochs, and \(T_{max}\) represents the max number of training epochs. This schedule allows a smooth decrease in the learning rate, balancing parameter exploration and convergence, which is particularly beneficial for this task.  
The loss function used in this study is a combination of Binary Cross-Entropy (BCE) Loss and Dice Loss, formulated as follows:  
\[loss=a*BCE_{loss}+b*Dice_{loss} \tag{9}\]  
where \(a=0.6\) and \(b=0.4\). BCE Loss focuses on achieving accurate pixel-level classification to ensure precise probabilistic predictions, while Dice Loss emphasizes the spatial alignment between predicted and ground-truth change areas, effectively addressing the challenges of detecting subtle and irregular morphological changes. This combination leverages the strengths of both losses, balancing pixel-level precision and structural similarity. The model is optimized using the AdamW optimizer, which incorporates weight decay regularization to prevent overfitting and ensure stable convergence.
# Low-Level Light Therapy in Orthodontic Treatment: A Systematic Review
## 4 Discussion
### Lllt and Root Resorption  
Root resorption (RR) can occur thanks to many factors, such as root morphology, tooth anatomy, genetic factors, therapeutic procedures, and malocclusion [35]. Several studies have shown that through LLLT, RR is reduced due to its biostimulatory and bioinhibitory benefits [12]. LLLT has an effect on the mitochondria of leucocytes and fibroblasts, increasing the production of ATP, growth factors, and reactive oxygen species (ROS) and promoting cell migration. An important role is played by RANKL and osteoprotegrins (OPG) that interact in hard tissue remodeling [48]. Morphological changes are assessed with CBCT [36]. Studies performed with high-reliability micro-CT have not shown a great difference between the groups with and without LLLT [53]. Mini-implants were used in research on intrusive movement, and LLLT verified that the groups treated with LLLT reduced both volumetric and linear root resorption by 10 to 12% [60].
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication
### Node Observation Block  
This block is executed when nodes are sending normal messages of sensed data to the BS. As a requirement for this block, each node X keeps a neighbor activity table to store the number of communicated packets to or from each of its neighbors in a certain period, the activity period discussed later. This table contains each neighbor ID, a counter value (p_counter), a flag identifying the uplink (UL) nodes and another flag identifying the downlink (DL) node. In case of an UL neighbor, the p_counter keeps track of the number of packets that node X forwarded from this neighbor through its DL node. In case of the DL neighbor, the p_counter indicates the total number of packets that node X sent and forwarded; in fact the actual number of packets initiated by node X is the difference between the p_counter to the DL neighbor and the sum of the p_counters of the UL neighbors.  
Note that the two flags are extracted from the path fragment message sent by the BS (explained shortly). Also note that a node sends its packets to the BS only through its DL and drops any packet received from a node other that its UL nodes.  
However, before the Node Observations block can be initiated and every node can start forwarding its data to the BS, the Activity Report Accumulation Block should be initiated for the node to know its downlink neighbor.  
Table 2 shows an example of a Node Neighbor Activity Table, where Node X has Node Y as its DL and nodes Z and U as its ULs; whereas node V is just a neighboring node without any interactions with node X. Node X forwarded 4 packets for node Z and six packets for node U. In total node X sent 15 packets through its DL node Y, and thus it initiated five packets.  
Note that in case of an authentication scheme set, every normal message communicated in this block must be signed by its initiator to be verified in every hop until the BS. Also, every node forwarding this message encrypts the signature by its own key for its neighbor to verify that it is receiving a packet from an UL neighbor. Thus each node on the path decrypts the signature using the ID of its UL neighbor, and verifies the signature from the source then encrypts the source's signature by its own ID and forwards the packet. Any wrong signature causes the packet to be dropped.  
Upon receipt, the BS checks the authenticity of the packet in case authentication is applied, and then increments the number of packets received from the packet source. Then the BS analyzes the packet and if it is found to be malicious, it increments the number of bad packets received from the source.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials
### Nanocomposites Using Core-Shell Coated Particles and Metal Inserts  
There have been many studies of embedding nanocomposites mixing ceramic, ferroelectric particles into a host polymer to attain an increase in dielectric constant [28; 29; 30; 31; 32; 33; 34]. Most of the studies have been done using BaTiO\({}_{3}\) particles as they have a very high dielectric constant of 3000-5000. If BaTiO\({}_{3}\) particles are deposited in a very thin film (less than 1 \(\mu\)), the dielectric constant of the film can be 600-800 [25] with a low loss tangent. However, when BaTiO\({}_{3}\) particles are mixed into a host polymer, the dielectric constant of the composite [29; 30; 31] is in the range of 30-40 even at high loadings (30-40%). Higher dielectric constants can be achieved using CCTO particles [29] but with a large percolation effect after 25% loading. Coating the particles and changing their shape can result in modest increases in dielectric constant and a modest percolation effect [33; 34], resulting in a dielectric constant of 400-500.  
There have been some studies with particles embedded in a host polymer that have resulted in large increases in the dielectric constant. One study [35] used Ni and BaTiO\({}_{3}\) particles in PVDF. With only the BaTiO\({}_{3}\) particles, the maximum dielectric constant achieved was about 50, but by adding the Ni particles, a dielectric constant of 800 was achieved for a 20% loading of both the BaTiO\({}_{3}\) and Ni particles including percolation effects. In another study [36] with PVDF, carbon particles coated with silica were embedded in the host polymer. For a particle loading of about 9%, a dielectric constant of 1000 was achieved with an AC conductivity of 5 \(\times\) 10\({}^{-4}\) Ohm-cm. These data indicate that embedding conducting  
Figure 2: The percolation effect on the dielectric constant [23].(**a**) Dielectric Permittivity; (**b**) electricalconductivity of CNT/elastomer composites as a function of CNT loadings.  
particles in the host polymer is a promising approach to achieve large increases in dielectric constant.
## 6 Discussion
Accurate channel estimation is crucial for obtaining channel state information (CSI) for reliable detection. In the OTFS, the instantaneous delay shift and Doppler shift of channel information are discrete on the DD grid. The time delay resolution and Doppler resolution depend on the bandwidth and duration, respectively. In actual communication scenarios, the bandwidth is large enough to provide sufficient latency resolution, but due to the low latency requirements of future communication, the duration may be relatively small. Therefore, the fractional Doppler needs to be considered. In the case of the integer Doppler, the effective channels in the DD domain are sparse. On the contrary, for the fractional Doppler case, the effective DD main channel is distributed across all Doppler indices, which reduces the sparsity of the effective channel and may therefore reduce the channel estimation performance. Estimating the fractional Doppler enables us to improve channel estimation by reconstructing effective DD domain channels using estimated Doppler frequency shifts, which is essential for reliable detection. Furthermore, this can improve the accuracy of positioning. When OTFS modulation assists sensing, accurate estimation of mobile user speed can be obtained through accurate estimations of Doppler frequency shifts.
## 4 Discussion
With the self-drilling method, the distance between the miniscrew  
Figure 7: **(A)** The diameter of the neck and the head of a miniscrew (d1) is usually larger than the thread part (d2). (**B**) If the diameter of the guide hole is smaller than d1, the surgical guide cannot be removed after insertion. On the other hand, if the hole is as large as d1, the surgical guide will have too much space, and it will lose accuracy. (**C**) To maximize accuracy, a surgical guide must cover the screwdriver, which will make the surgical guide thicker and bulkier. (**D**) In the self-tapping method, the surgical guide is removed when the miniscrew is inserted. Thus, a guide for the self-tapping method can be much thinner than that for the self-drilling method. and the gingival tissue cannot be seen because the entire miniscrew is covered by the surgical guide. Therefore, the self-tapping insertion after the drilling means it is easier to position the flange of a miniscrew as close as possible to the gingiva in order to reduce patient discomfort. Further research needs to be carried out to assess the reproducibility of placing a miniscrew through the hole after the drilling. Although a drilled hole seems to be the most mechanically stable pathway for a miniscrew, to our knowledge, no research has been performed to evaluate the angular error between the drilled hole and the miniscrew placed through the hole. A randomized trial for dental implants showed that using an implant guide after a drilling guide was more accurate than using a drilling guide only [17]. They reported that using the drilling guide only resulted in 1.4 degrees more error. Applying this to a miniscrew, when the miniscrew is inserted to a depth of 6 mm, this would result in an error of 0.15 mm at the tip, which is more acceptable. However, dental implants are much larger than miniscrews, and it is still unclear whether the results can be applied to miniscrews. It might show unacceptable levels of error if the cortical bone thickness is thin or the hole diameter is too large or small compared with the diameter of a miniscrew. A limitation of this study was that the experiment was performed in vitro and the accuracy in a clinical situation might be lower than in the results shown here. Ultimately, the best way of knowing the actual accuracy in real patients is by comparing the CBCT of actual patients before and after the insertion. However, taking CBCT just for the evaluation is not beneficial for patients because of the radiation risk, especially when the interadricular space is not narrow and a miniscrew looks like it could be inserted successfully. Therefore, for clinical studies, another method with less radiation risk needs to be established, such as superimposing the post-insertion intra-oral scan over the pre-insertion CBCT and estimating the screw position in the bone.
## 5 Conclusions  
In this study, a method to analyse and improve the reliability of energy supply in two dwellings with their SAPV system and their ESS when they use a cooperative strategy has been proposed and validated. The method is based on the use of surplus generation and storage of one facility to supply energy to the other facility when needed, fixing a minimum amount of energy to stop cooperating in order to avoid losing energy needed to supply its own demand, defined by means of the key parameter \(SOC_{C}\). To test the method, it has been applied to two real neighbouring dwellings, using real generation and demand curves. The algorithm is based on the use of a random SMC simulation to find the stable values of the selected reliability indices. As the simulations show, the results of the cooperative strategy are better than the individual results in terms of reliability, but the improvement in both dwellings can be unequal. The method has proven to be useful to find the most even strategy adjusting the level of the minimum SOC needed to start cooperating.  
It is important to note that the data were restricted to 2019 due to the anomalous demand pattern shown during the pandemic situation, but the study can be repeated with any other data. In fact, the method shown in this work could be applied to a larger scale case study, i.e., to two neighbouring communities, with larger demand needs and a greater generation mix. As a future work, the authors propose to explore the application of a strategy such as the one proposed here for virtual power plants (VPP), since this would allow the use of more demand curves and different generation curves for each consumer, leading to higher differences between the analysed scenarios and probably to higher chances to improve the global reliability. Additionally, studying the economic impact of the proposed method is a research line that the authors propose to include in future studies.  
Conceptualization, C.R.-B. and C.R.-P.; Data curation, C.R.-B., E.Q. and G.E.-E.; Formal analysis, C.R.-P.; Funding acquisition, C.R.-P. and G.E.-E.; Investigation, C.R.-B. and C.R.-P.; Methodology, C.R.-B. and C.R.-P.; Project administration, C.R.-P. and G.E.-E.; Resources, G.E.-E.; Software, C.R.-B.; Supervision, C.R.-P.; Validation, C.R.-B., E.Q. and G.E.-E.; Visualization, C.R.-P. and E.Q.; Writing--original draft, C.R.-B. and C.R.-P.; Writing--review and editing, C.R.-B. and G.E.-E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Conselleria de Educacion, Investigacion, Cultura y Deporte (Grant No. AICO/2019/001).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(B_{r}^{j}\) (kWh) & **LOLP (\%)** & **Extra Capacity Needed (\%)** \\ \hline D1 & 26,000 & 2.21 & 13.04 \\ D2 & 20,000 & 2.02 & 11.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial design of batteries for scenario S1 to obtain the same results as in scenario S230.
## 3 Results  
Comparison of Chlorophyll a Contents of Extracts and Morphology of Spirulina with and without High-Pressure Homogenization  
Figure 1 shows the chromatograms constructed by analyzing two extracts against a chlorophyll \(a\) standard by HPLC. The amounts of chlorophyll \(a\) in the extracts from the conventional ethanol extraction and high-pressure homogenization with various input pressures were also compared and are shown in Table 1. The extract with the highest content of chlorophyll \(a\), at 9.85 mg/g, was the extract processed at 690 bar with a shear stress of approximately 20,000 (1/s) applied in passing through an orifice of 70 \(\upmu\)m diameter at a flow rate of 150 cm\({}^{3}\)/min at room temperature (HP-2).  
Interestingly, however, the extract processed at the high pressure of 1370 bar with the very high shear stress of approximately 50,000 (1/s) (HP-3) contained 6.32 mg/g of chlorophyll \(a\), which was much lower than the 9.85 mg/g obtained with HP-2 at 690 bar. At a lower input pressure of 350 bar (HP-1), the extract had only 4.24 mg/g of chlorophyll \(a\), which was also lower than the yield from the conventional process.  
Additionally, the electron micrographs of _Spirulina_ support the above results and the hypothesis by illustrating the structural changes in _Spirulina_ before (a) and after (b, c, and d) homogenization  
\begin{table}
\begin{tabular}{c c} \hline
**Extraction Process** & **Chlorophyll \(a\) Contents (mg/g) \\ \hline EE 1  \\ HP-1 2  \\ HP-2 3  \\ HP-3 4  \\ \hline \end{tabular}
\end{table}
Table 1: Estimation of chlorophyll \(a\) contents from _Spirulina plattnesi_ in the extracts from various extraction conditions.  
Figure 1: Comparison of HPLC chromatograms of chlorophyll \(a\) from different extraction conditions: (a) Standard chlorophyll \(a\); (b) High-pressure homogenization at 690 bar and room temperature; (c) 70% ethanol extraction at 40 \({}^{\circ}\)C for 24 h.  
under pressures of 350, 690, and 1370 bar, respectively. In contrast to untreated _Spirulina_ (Figure 2a), most _Spirulina_ in Figure 2c had their cell walls substantially disrupted after being processed at 690 bar; however, 350 bar of input pressure was not sufficient to break all of the membranes and cell walls of _Spirulina_ (Figure 2b).  
Consequently, the yield of chlorophyll \(a\) from extraction at 350 bar was lower than the yields from extraction at 670 and 1370 bar and even from conventional 70% EE as shown in Table 1. However, Figure 2d illustrates that, at 1370 bar, which was the highest pressure, most of the cell membranes and other organs within the cells were completely destroyed, which suggests that intact chlorophyll \(a\) within the cells was degraded as well due to a very high shear stress at a high input pressure.