esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 3 Literature Review  
In the literature, two types of collaborative scenarios are identified: _post-collision_ and _pre-collision_ scenarios [5]. In the first case, the collision could be dangerous if the robot limits are poorly defined or when the robot is equipped with sharp tools; moreover, any collision stops the task execution, thus affecting production time. Several techniques have been proposed to minimize the energy transmitted during the contact [6] by using robots endowed with sensors for assessing force exchange when the impact occurs, e.g., force or tactile sensors [7], or by using only proprioceptive measurements in the case of industrial manipulators with closed control architecture [8]. However, industrial safety standards prohibit the use of _post-collision_ systems, as they involve physical impact between the robot and the human operator before the machinery comes to a complete stop.  
In contrast, a _pre-collision_ approach uses exteroceptive sensors to detect the presence of humans and prevent potential collisions before they occur. This paper presents a novel strategy, which implements an SSM _pre-collision_ scenario. The proposed strategy complies with the most recent perspective of Industry 5.0, as described in [9], where HRC enables seamless collaboration between humans and robots, with a focus on enhancing worker safety and comfort. Indeed, this collaboration involves robots that are capable of understanding human actions, responding to their intentions, and adapting their behavior accordingly. To address this, three modules have been developed and integrated, each one proposing innovative technologies with respect to the state of the art: the human tracking module, the intention estimation module, and the robot control module.
## 4 Discussion
### Probiotics in Orthodontics  
The orthodontic treatment causes dysbiosis of the oral microbiome due to difficulty in hygienizing the orthodontic appliance [95]. Probiotics may be useful as supplements to hinder bacterial colonization and render the dental biofilm less virulent in patients with fixed or removable orthodontic appliances. Six studies related to the effect of probiotics on orthodontics were analyzed (Table S8).  
[PERSON] et al. [96] found a significant reduction of _S. mutans_ levels in dental plaque around the bracket evaluated after probiotic (undisclosed composition) administration with curd or toothpaste. In a study by [PERSON] et al. [97], four weeks ingestion of milk containing _L. paracasei_ SD1 in adolescents with non-syndromic lip-palatine cleft led to a significant increase of salivary _Lactobacillus_ spp. count and decrease of _S. mutans_ count at all times of evaluation when compared with baseline data. _L. paracasei_ SD1 temporarily colonized the oral microbiota, being detected in saliva up to 4 weeks after cessation. The results suggest that this probiotic may help to prevent caries after orthodontic treatment in this population.  
The effectiveness of 14-days milk consumption of _L. casei_ or _L. reuteri_ lozenges was investigated in young adults undergoing orthodontic treatment [98]. Periodantal condition was improved in both probiotic groups, better results being observed in the _L. reuteri_ group. [PERSON] and [PERSON] [99] assessed the effect of a systemic probiotic (_Lactococcus lactis_ subsp, _Leuconostoc_ spp., _Lactobacillus_ spp. and _S. thermophilus_ and yeasts isolated from cereal grains) or local intervention (bacteriocin extracted from lactic acid bacteria), for 6 weeks on salivary microbial colonization in orthodontic patients. There was a significant reduction in _S. mutans_ count at weeks 3 and 6 in both intervention groups. _Lactobacillus_ spp. counts decreased significantly at week 3 in the probiotic group and at week 6 in the local intervention group. In contrast, in a study by [PERSON] et al. [100] there were no oral benefits after two-weeks consumption of yogurt containing _B. animalis subsp. lactis_ DN-173010, and in [PERSON] et al. [101] there were no clinical and microbiological advantages to the use of _L. reuteri_ lozenges in patients with maxillary orthodontic appliance. In summary, daily consumption of some but not all probiotics may help to prevent caries and improve periodontal condition in patients on orthodontic treatment.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 6 Locomotion: Mobility Design
### Flapping Robot  
For the flapping robot, a bio-inspired indirect flapping mechanism was used. In this mechanism, the actuator makes reciprocal motion between the thorax roof and bottom, which induces the flapping motion of wings. In 2002, a biomimetic thorax-type flapping robot was built and tested [8]. In this robot, a stacked DEA is connected to the upper and bottom plates. Other flapping micro air vehicles using the same mechanism were proposed with folded DEA and rolled DEA [107]. Folded DEA with a dimension of 32 mm height and 25 mm width shows 0.2 mm axial contraction, which induces three-degree wing strokes. On the other hand, the rolled DEA which has an 11 mm diameter and 61 mm length shows five-degree wing strokes at 0.6 Hz. The flapping robot, which adopted resonance-based movement, was fabricated using a double cone shaped DEA in 2019 [108]. The structure of the robot is shown in Figure 10b. The overall mechanism of the wing motion is accomplished using a slider-crank mechanism. The double cone DEA acts as a slider, and the crank's rotational movement induces flapping of the wings. In this work, a rapid increase in power output at resonance frequency was observed. The robot provides a peak flapping amplitude of 63 degrees at the frequency of 18 Hz with a double cone shape actuator which is composed of 18 layers of 40 \(\upmu\)m silicone elastomer. The strategy of utilizing resonance frequency was also used in DEA based micro-aerial vehicles (MAVs) for flights that have succeeded in taking off (real flight) [109]. In this robot, multi-layered rolled DEA is used for linear actuation and with this linear actuation, four-bar transmission makes the wings rotate. For real flights, they used a light-weight airframe and abandoned the pre-strain of DEA which normally requires rigid and heavy supporting frames. Also, the resonance frequency is utilized for maximizing the performance of flapping motion and reducing the non-linearity of the actuator. Finally, a 100-mg MAV demonstrated open-loop lift-off at the resonance frequency of 500 Hz.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Machine Learning Techniques  
To detect and predict errors in the energy distribution process, a binary classification of the dataset instances was performed using two supervised ML techniques: RF and SVM. On one hand, an RF [45] combines multiple decision trees and obtains a final prediction through majority voting of these trees, using criteria such as entropy or the Gini index to determine the splits at each node. On the other hand, an SVM [46] aims to find the optimal hyperplane to maximize the separation between two classes, adjusting the regularization parameter \(C\) to balance the margin and classification errors, and configuring the parameter \(\gamma\) along with the kernel type to handle non-linearity in the data. The development of these techniques followed a sequence of necessary steps to design optimal models and configure an effective training, based on the use of various classes and methods provided by the open-source software library _scikit-learn_[[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)).
## 3 Experimental Results and Discussion
### Comparative Analysis of the Optimizing Diversity of the Parallel EGO Algorithm
#### 3.3.1 Population Diversity
This may be due to the fact that the spatial design information provided by GEK to the parallel EGO algorithm is sufficiently reliable, which makes the search directions for potential improvement points more well-defined.
## 2 Related Work
### Radiomic Features  
In medical image analysis, radiomics studies the extraction of quantitative features from the images to construct predictive models capable of being used to support the clinical decision [22]. Radiomic features can translate quantitatively, in a higher dimension space, the texture, shape, and size of the tumour [7; 10]. These features can then be used to construct predictive models based on machine learning and deep learning approaches to address different problems. The common radiomics pipeline follows a set of sequential steps: (1) image acquisition, (2) image reconstruction and pre-processing, (3) ROI segmentation, (4) feature extraction, (5) features quantification and post-processing and (6) predictive model construction [11; 24]. In the literature is possible to find different proposes of models constructed based on radiomic features capable of performing the classification of the tumour histological subtype using the acquired lung CT image [5; 6; 7; 8; 12; 22; 23].  
The use of radiomic features extracted from the lung CT images can be based on handcrafted features extracted from the images or learned from the data using DL methods [10; 24]. The features extracted from the ROI are used to construct ML classifiers, such as RF [7; 22]. Support-Vector Machine (SVM) [6; 8; 22], K-Nearest Neighbors (KNN) [8; 12; 22], or logistic regression [5; 23]. The classifier can also be based on DL methods, such as the MultiLayer Perceptron (MLP) [7]. The use of handcrafted radiomic features is dependent on the identification of the tumour ROI, commonly the gross tumour volume (GTV) [5; 6; 7; 22; 23]. The prediction of the tumour subtype can be based on a multi-class classifier [5; 6; 7; 12; 22], or in a direct comparison of two classes, e.g., ADC versus SCC [8; 23].
## 1 Introduction
utilized grey relation analysis (GRA) to reconstruct the components decomposed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), thereby enhancing the model's anti-interference capability [14]. The model is improved with the introduction of the above decomposition technique, but the parameter selection mainly relies on empirical settings, which may limit the effectiveness of the decomposition and, consequently, affect forecasting accuracy. The Transformer model based on attention mechanisms performs excellently in load forecasting [15; 16; 17; 18]. iTransformer is a variant of the Transformer model that enhances its accuracy in prediction tasks involving multi-dimensional features [19]. To enhance load prediction accuracy, this paper proposed an ICEEMDAN-iTransformer forecasting model with the beluga optimization algorithm (BWO). BWO is used to optimize the ICEEMDAN to maximize its decomposition effectiveness. ICEEMDAN is then applied to decomposed load data to reduce its volatility. The IMFs and other influencing factors are used as inputs to the iTransformer to produce the final forecast results. The main contributions of this study are as follows:  
* A novel ICEEMDAN-iTransformer forecasting model based on the BWO is proposed. By integrating data decomposition, optimization algorithms, and an improved Transformer model, the accuracy of short-term electricity load forecasting is enhanced. * The data decomposition process is optimized by utilizing BWO to adjust ICEEM-DAN parameters, maximizing the decomposition effectiveness and reducing the non-stationarity of electricity load data. The organization of this paper is presented as follows, where each section addresses a specific aspect of the research in detail. Section 2 provides a detailed introduction to the relevant algorithms employed in the forecasting model. Section 3 outlines the design of the proposed forecasting model in detail. Section 4 describes the experimental setup and dataset, discusses the experimental results, and compares the load forecasting performance of different methods. Finally, the conclusion is presented in Section 5.
## 4 Design of Multi-Constraint Optimized Genetic Algorithm
### Multi-Dimension Constraints in Cyber-Physical Domain
#### 4.2.1 Constraints for Fundamental Capabilities  
After analyzing the cyber-physical characteristics, several capability constraints are briefly summarized and listed in this section.  
**Constraint 1 (Service Amount)**: Assuming a task set \(\xi_{type}^{\tau}\), where each task \(\tau_{i}\) requests some amount of several services, belonging to different service type denoted as \(type\), and \(type\) type service  
Figure 2: Chromosome Piece and its Coding for \(\tau_{i}\).  
corresponds to the \(CS_{ik}\) in the chromosome piece of \(\tau_{i}\), then, only when \(\sum_{i=first(\tau),id}^{last(\tau),id}(X_{ik})\leq\sum(Y_{ik})\) is satisfied, there possibly exists a feasible solution \(\pi\) for \(\zeta_{type}^{\tau}\).  
**Constraint 2 (Task Feasible)**: For any task \(\tau_{i}\), only when the amount of each type service left for it is greater than it required, \(\tau_{i}\) is feasible.  
**Constraint 3 (Service Types)**: Assuming a service set \(\zeta_{\tau_{i}}^{\varepsilon}\) required by \(\tau_{i}\), where the types of all services are need by \(\tau_{i}\), then, only if each type of required services is in \(\zeta_{\tau_{i}}^{\varepsilon}\), \(\tau_{i}\) is realizable.  
**Constraint 4 (Global Service Capability)**: For all tasks of mission \(M\), only when **Constraint 1-Constraint 3** are all satisfied, these tasks are realizable, namely, it is possible to find a plan for \(M\).  
**Constraint 5 (Reachable Range)**: For a distance-related service \(\varepsilon\), such as robot, missile etc., if the sum of its \(\varepsilon.MaxD\) and \(MaxD\) of \(\varepsilon.Cid\) is greater than the distance from \(\varepsilon.Cid\) to the position of \(\tau.tar\), where service \(\varepsilon\) is allocated to \(\tau\), then, that is to say that \(\varepsilon\) is usable to \(\tau.tar\).  
**Constraint 6 (Global Reachable Range)**: When a platform service is scheduled to orderly move to different targets, correspondingly, performing a series of services it carried, the global reachable range \(cD\) of this service must be not smaller than the sum of distances of planned positions of these adjacent targets.  
**Constraint 7 (Service Time Window)**: Every service must be performed in a predetermined time window, which depends on \(MaxV\), \(cPos\) of this service and its carrier.  
It should be cleared that a platform service is also a carrier service. Its reachable range mainly depends on the fuel or battery allowance, and sometime, the round trip must be considered. For other carried services, the reachable range is always a compound value as indicated within **Constraint 5**. Only when these constraints are all satisfied, is it possible to find a feasible task-service solution \(\pi\). Namely, these constraints can be used to adjudge whether the capability of current service pool is sufficient, and also, verify whether every solution found is valid.
# Scale-Free Random SAT Instances
## 5 Phase Transition in Scale-Free Random 2-SAT Formulas
### Scale-Free 2-SAT Formulas
\begin{array}{ll}\frac{1-2\beta}{(1-\beta)^{2}}n+\mathcal{O}(n^{ \beta})&\text{if $\beta<1/2$}\\ 4\,n\log^{-1}n+\mathcal{O}(n^{1/2}\log^{-1}n)&\text{if $\beta=1/2$}\\ \frac{1}{(1-\beta)^{2}\zeta(2\beta)}n^{2(1-\beta)}+\mathcal{O}(n^{1- \beta})&\text{if $1/2<\beta<1$}\\ \frac{1}{\zeta(2)}\log^{2}n+\mathcal{O}(n^{-1}\log n)&\text{if $\beta=1$}\\ \frac{\zeta^{2}(\beta)}{\zeta(2\beta)}+\mathcal{O}(n^{1-\beta})&\text{if $1< \beta$}\end{array} \right. \tag{13}\]  
The last two cases are meaningless since we have assumed that \(\beta<1\) in other parts of the proof. The first three possibilities prove the two statements of the theorem. In the second and third cases, since we cannot prove that the fraction of repeated clauses is meaningless, we obtain a bound on the number of distinct clauses. From [PERSON] et al. [12] and Theorem 4, we can conclude:  
**Corollary 1**. : _Scale-free 2-SAT formulas over \(n\) variables and exponent \(\beta<1/2\) have a SAT-UNSAT phase transition threshold when the variable/clauses ratio is_  
\[m/n=\frac{1-2\beta}{(1-\beta)^{2}}\]  
We have experimentally analyzed the fraction of satisfiable random scale-free 2-SAT formulas depending on the parameter \(\beta\) and fraction of clause/variable \(m/n\). The results are plotted in Figure 3, for formulas with \(n=10^{5}\) variables. We observe that the phase transition predicted by Theorem 4 is quite precise, except when \(\beta\gtrsim 1/2\). In the limit \(n\to\infty\), the fraction of satisfiable formulas with \(n\) variables and \(c\,n\) clauses tends to zero when \(c>0\). However, as the number of clauses needed to make the formula unsatisfiable grows as \(n^{2(1-\beta)}\), when \(\beta\) is close to \(1/2\) the confluence is very slow. In order to test experimentally the second statement of Theorem 4, we have analyzed the fraction of satisfiable formulas with respect to \(m/\alpha\), where \(\alpha=\frac{1}{(1-\beta)^{2}c(2\beta)}n^{2(1-\beta)}\). In Figure 4, we show the results for \(\beta=0.7\). We observe that, for distinct values of \(n\), the transition between SAT and UNSAT is around \(\alpha\). However, for increasing values of \(n\) the transition does not seem to become more abrupt. Figure 3: Fraction of satisfiable formulas as a function of parameter \(\beta\) and fraction of clause/variables \(m/n\). The number of variables is \(n=10^{5}\) and the fraction is approximated repeating the experiment for 10 formulas at every point. We also draw the theoretical threshold \(m/n=\frac{1-2\beta}{(1-\beta)^{2}}\).
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 4 Experimental Analysis
### Experimental Setup and Evaluation Metrics  
Based on the computing power of the GPU and the parameter adjustment process, the initial parameters of the YOLOv7 model are set as follows: epoch = 100, batch_size = 16, momentum = 0.937, first learning rate = 0.01, last learning rate = 0.1, warmup_epochs = 10, and weight_decay = 0.005. During the training process, the learning rate gradually increases from 0.01 to 0.1 in a linear fashion to help the model adapt to the training process. To compare with other advanced methods, we used the mAP@50-95 and FPS as the evaluation metrics of the model performance.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles
### Convolutional Neural Network
#### 2.1.3 Pooling Layer  
In a convolutional neural network (CNN), the pooling layer is designed to reduce computation by compressing the feature dimensions. It generalizes the higher-order features extracted by the convolutional layer, improves the network iteration efficiency, and endows the features with translation invariance. The common pooling methods include max pooling, which takes the maximum value in the perceptual domain, and average pooling, which takes the average value. The following formulas are used,  
\[y^{l}_{i,j,k}=max\Big{\{}x^{l}_{i,j,k^{\prime}},s\Big{\}} \tag{2}\]  
\[y^{l}_{i,j,k}=\frac{1}{N}{\sum}x^{l}_{i,j,k} \tag{3}\]  
where \(y^{l}_{i,j,k}\) is the \(k\)th feature map output in the \(l\)th layer, and \(x^{l}_{i,j,k}\) is the \((i,j)\)th element in the \(k\)th feature map output from the \(l\)th convolutional layer.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 6 Performance Analysis and Computational Complexity
### Performance Analysis
#### 6.1.4 Performance with Large Buffer Threshold  
Case 1: If buffer threshold \(B_{c}\) is greater than \(2B_{1}\), the transmission action is shown in Figure 9. The energy consumption from \(t_{0}\) to \(t_{5}\) is  
\[E_{1}=\int_{t_{1}}^{t_{5}}\bigg{(}\mu-\frac{1}{c_{t}}\bigg{)}dt =\int_{t_{1}}^{t_{5}}\bigg{(}\mu-10^{-\frac{h_{t}}{10}}\bigg{)}dt \tag{45}\] \[=(t_{5}-t_{1})\mu+\frac{10}{k_{h}ln10}\bigg{(}\frac{1}{c_{t_{5}}} -\frac{1}{c_{t_{1}}}\bigg{)}.\]  
The average transmission energy consumption per unit of data is  
\[AE_{1}=\frac{E_{1}}{\lambda(t_{5}-t_{0})}. \tag{46}\]  
The average buffer length is  
\[AB_{1}=B_{c}-B_{1}. \tag{47}\]  
The average transmission delay is  
\[AD_{1}=(B_{c}-B_{1})/\lambda. \tag{48}\]
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 4 Results  
### Earthwork Design Review  
The main purpose of the design review is to check whether or not the initial design applies to the site. Two digital terrain models were compared, and the difference between the DEM and UAV-based 3D point cloud map is approximately 70 cm. Due to the elevation discrepancy, the earthwork volume of the initial design was imbalanced; the cut volume was 816.7 cubic meters and the fill volume was 102.25 cubic meters. After raising the building pad from 75.0 m to 75.7 m, the cut and fill volumes were changed to 481.21 cubic meters and 349.54 cubic meters, respectively (see Figure 4a,b). Eventually, by reviewing and revising the building pad, the earthwork balance between the cut and fill was significantly reduced by 82%. Figure 4c shows the as-built model of the design. Figure 5 shows the drainage flow of the site. Because the northern part of the site has a higher ground level, the main drainage pipe was buried along the access road (see Figure 1b), with the installed drainage duct wrapped around the bottom of the slope.  
Figure 3: The UAV’s flight path setting; a screenshot of the Pix4D app.
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 5 Simulation Results
### Number of Packets Received by the BS  
Figure 4a,b show the number of data packets received by the BS. When the ratio of advanced nodes is \(\alpha=0.1\), it can be seen from Figure 4a that the network throughput of HMGO increased by  
Figure 3: Network lifetime with respect to the number of rounds. (**a**) The network lifetimes of HMGO, SEP, DEEC, M-SEP, and FIGWO when \(\alpha\) is 0.1; (**b**) The network lifetimes of the HMGO, SEP, DEEC, M-SEP, and FIGWO when \(\alpha\) is 0.2.  
Figure 2: Network stability period with respect to the number of rounds. (**a**) The ratio of the advanced node is 0.1, and the HMGO network stability period is compared with SEP, DEEC, M-SEP, and FIGWO; (**b**) The ratio of the advanced node is 0.2, and the network stability period of HMGO relative to SEP, DEEC, M-SEP, and FIGWO.  
150.4%, 142.5%, 70.5%, and 17.6%, respectively, relative to SEP, DEEC, M-SEP, and FIGWO. When the ratio of advanced nodes is \(\alpha=0.2\), it can be seen in Figure 4b that the network throughput of the proposed algorithm improved by 142.7%, 124.8%, 70.9%, and 32.8%, respectively, relative to SEP, DEEC, M-SEP and FIGWO. This is because the HMGW algorithm adopts a more reasonable CH selection mechanism, and the nodes have a longer survival time. When the number of rounds is the same, the number of surviving nodes in the network is higher than that of other algorithms, so more data groups are generated, and the number of data packets received by the BS also increases.