esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Body-Hinge Frameworks
### Coning for Body-Hinge in \(\mathbb{P}^{d}\)  
_Coning_ a body-hinge framework \((G,H)\) in \(\mathbb{P}^{d}\) involves picking a point \(O\) in \(\mathbb{P}^{d+1}\) and adding it as a point on all bodies and expanding all hinges as \(H_{ij}\lor O\) to create a new body hinge framework \((G,H*O)\) in \(\mathbb{P}^{d+1}\). With the cone point \(O\in\mathbb{P}^{d+1}\), not in \(\mathbb{P}^{d}\), and any cycle \(C\) in the body hinge framework, a motion assignment \(\sum_{j\in C}\ \omega_{ij}H_{ij}=\mathbf{0}\) in \(\mathbb{P}^{d}\) implies \(\sum_{j\in C}\ (\omega_{ij}H_{ij})\lor O=\mathbf{0}\) or \(\sum_{j\in C}\ \omega_{ij}(H_{ij}\lor O)=\mathbf{0}\) in \(\mathbb{P}^{d+1}\). Therefore, every motion assignment of the original body-hinge framework becomes a motion assignment of the coned framework \((G,H*O)\).  
Figure 58: Two body–hinge cycles of length 6 in 3-space, one isostatic (**a**) forming an octahedron, and one geometrically singular (**b**) with all hinges meeting a single line through two vertices.  
Conversely, a motion assignment for \((G,H*O)\), \(\sum_{ij\in C}\ \omega_{ij}(H_{ij}\lor O)=\mathbf{0}\) in \(\mathbb{P}^{d+1}\) implies  
\[\sum_{ij\in C}\ \omega_{ij}(H_{ij}\lor O)=\sum_{ij\in C}\ (\omega_{ij}H_{ij})\lor O= \mathbf{0}.\]  
Since \(O\) is not in \(\mathbb{P}^{d}\), and therefore not in the span of \(\sum_{(ij)\in C}\ (\omega_{ij}H_{ij})\), this implies \(\sum_{(ij)\in C}\ (\omega_{ij}H_{ij})=\mathbf{0}\) for every cycle. Therefore the motion assignment transfers back to the original body-hinge framework. We summarize this as a theorem:  
Take a body-hinge framework \((G,H)\) in \(\mathbb{P}^{d}\) and its cone to \(O\in\mathbb{P}^{d+1}-\mathbb{P}^{d}\). An assignment \(\omega\) for the hinges is a motion assignment for \((G,H)\) if and only if it is a motion assignment for the cone body-hinge framework \((G,H*O)\).  
Therefore coning preserves infinitesimal rigidity and static rigidity of the body-hinge framework.  
What about a general cross-section of a body-hinge framework \((G,H*O)\) in \(\mathbb{P}^{d+1}\)? This cross-section of a hinge with a hyperplane \(A\) is the dimension of a hinge in \(\mathbb{P}^{d}\), with a weighted extensor. The cycle condition \(\sum_{ij\in C}\ (\omega_{ij}H_{ij}^{*})=\mathbf{0}\) transfers to the cycle condition \(\sum_{ij\in C}\ (\omega_{ij}H_{ij}^{*})\wedge A=\mathbf{0}\). We conclude that the cross-section inherits any motion from the original body-hinge framework.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 2 Background
### Deep Neural Networks for Drowsiness Prediction
#### 2.3.1 MobileNet-V2 Network  
MobileNet-V2 [20] is a significant improvement over MobileNet-V1, enhancing image recognition capabilities on mobile devices including classification, object detection, and semantic segmentation. Figure 4 illustrates the MobileNet-V2 network architecture. Overall, MobileNet-V2 models are faster with the same accuracy across the entire latency spectrum, using 2 times fewer operations, 30% fewer parameters, and being 30-40% faster than MobileNet-V1. MobileNet-V2 has the advantages of being fast, lightweight, and having high accuracy, which makes it suitable for training with limited datasets. MobileNet-V2 is trained on datasets such as ImageNet, COCO, and VOC [21], and has been applied in object classification such as Fruit Image Classification [22] and Flower Classification [23], with accuracy up to 85.12% and 96%, respectively. With these advantages and practical results, we decided to choose MobileNet-V2 for drowsiness prediction. Besides, we would like to apply this network to build a mobile application for driver drowsiness detection while driving.  
Figure 3: Example of 6 facial landmarks related to eyes.
###### Abstract  
With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target's actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.  
air combat; multi-agent deep reinforcement learning; maneuver strategy; network training; unmanned combat aerial vehicle +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Training Process  
Training our model for large-format images demands significant GPU memory and computational power, necessitating high-performance GPUs. It is difficult to use regular GPU for direct training. To address this challenge, we optimize the training process by breaking it down into three distinct steps, as shown in Figure 2. Details are as follows.  
Step 1: Training the Global Feature Extraction Module  
In the first step, we train the global feature extraction module using downsampled images and their corresponding labels. The training process aligns with that of a traditional semantic segmentation network.  
Step 2: Training the Feature Encoding, Feature Aggregation, and Information Decoding Modules  
Building upon the fixed parameters of the global feature extraction module, we proceed to train the following three modules: the feature encoding module, the feature aggregation module, and the information decoding module. To facilitate this training, we introduce modifications to the information decoding module, incorporating a classification head. The classification head serves the dual purpose of generating classification results and computing the loss. In this step, we scale the input large-sized image down to a smaller 256 \(\times\) 256 image, and leverage the global feature extraction model to extract global features. Subsequently, these three modules are trained, with inputs comprising global feature information, small image patches, and their corresponding labels, while the output yields prediction results determined by the classification head.  
During the training process, we encountered high GPU memory consumption. To mitigate this issue, we implemented a gradient-accumulation training approach, following  
Figure 2: Training process.  
these specific steps: (1) Dividing the input image patches, corresponding labels, and global features into several small batches. (2) Performing backward propagation for each small batch, but refraining from updating the model parameters. (3) Updating the model parameters only after all batches have been processed.  
Step 3: Training the Lightweight Convolution Module  
In the final step, we train the lightweight convolution module based on the fixed parameters of the global feature extraction module, feature encoding module, feature aggregation module, and information decoding module. We begin by employing the global feature extraction module to extract global features from the downsampled image. Subsequently, we utilize the feature encoding module, feature aggregation module, and information decoding module to obtain feature maps with the same resolution as the original image. Finally, these feature maps are seamlessly stitched together into a large feature map mirroring the original image's dimensions. This unified feature map is then fed into the lightweight convolution module for parameter training.  
This partitioned training approach optimizes GPU memory usage and computational efficiency, making it feasible to train our model for large-format images on GPUs with more constrained resources.
## 4 Experiments and Discussion
### Experimental Setup
#### 4.1.1 Dataset Description  
In order to evaluate the performance of the IDBESW model, we carried out different experiments using three real OSNs datasets. Table 1 illustrates the datasets used in the experiments. The second column indicates the common name of the dataset used. The third and fourth columns show the number of nodes and edges of the OSNs datasets used. As shown in the table, we use a different number of nodes and edges. The average degree column indicates the average degree of nodes in the OSN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name** & **Nodes** & **Edges** & **Average Degree** \\ \hline
1 & Facebook & 4039 & 88,234 & 43.7 \\ \hline
2 & Twitter & 81,306 & 1,768,149 & 43.5 \\ \hline
3 & Epinions & 75,879 & 508,837 & 6.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 3 Optimization Algorithm
### Genetic Algorithm
#### 3.2.2 MATLAB Implementation  
Optimization was performed using MATLAB's GA Toolbox, which provides a platform for creating and using GAs. The function \(ga\) requires a series of input parameters to adapt the optimization problem. The most important inputs are described below:  
1. **Cost Function** (CostFcn): This parameter represents the function that needs to be optimized. It takes a set of input decision variables and generates a single value, the cost, to be minimized. As previously noted, the PSF is the cost function. It receives \(d_{rx}\) as an input and produces the PSF value related to the array configuration obtained from the combination of \(d_{tx}\)-\(d_{rx}\).
2. **Number of decision variables** (nvars): This parameter indicates the number of variables being optimized.
3. **Linear inequality constraints** (A and b): These constraints do not apply in our situation as the problem's intrinsic characteristics depend on nonlinear constraints.
4. **Linear equality constraints** (Aeq and beq): Similarly not applicable due to the use of nonlinear constraints.
5. **Lower and upper bounds for decision variables** (lb and ub): These arrays establish the boundaries of the search space by defining the range within which each variable is permitted to vary.
6. **Initial population** (\(x_{0}\)): This is the initial set of candidate solutions. A randomized starting point is chosen from the feasible variable values.  
Figure 3: Basic GA operation.  
7. **Options**: This structure contains a range of control settings for the behavior of the GA. It includes definitions for parameters such as population size, number of generations, genetic operators (parent selection, recombination, and mutation), and replacement strategy.
8. **Nonlinear inequality constraints** (nonlcon): An ad hoc function has been created to manage the constraints outlined in Section 3.2.1. Once the parameters have been configured, the GA is executed, and upon completion the algorithm generates two crucial pieces of information: * **x** represents the optimized solution and contains the decision variables. * **fval** indicates the minimum value of the cost function.
## 2 Modeling of Airgap Eccentricity Fault Using the Finite Element Method  
For the purpose of detecting the fault accurately, the proper and precise modeling needs to be established [15]. By considering the detailed real-world parameters and practical conditions, the finite element analysis (FEA) provides a reliable method. The FEA is based on the equivalent electric and magnetic circuits, which is close to the reality [16]. In this paper, the modeling of the induction motor under DE fault is established by the finite element method. The geometric model of all parts of motor, such as rotor, stator and shaft, are all included in the modeling. Besides that, the material property of all components in the motor, the decision boundary, the excitation source, the slot and winding condition are taken into consideration. In this model, the external voltage sources transient equations, the field equations and the motion equations are mutually coupled in the finite element model. Moreover, the nonlinearity of iron is also incorporated into this transient analysis [2]. Figure 2 shows the modeling of induction motor using the FEM.  
Figure 2: Modeling of the induction motor using FEM.  
The DE introduces the unbalance of the airgap resulting in the asymmetry of the motor structure. Therefore, the airgap between the rotor and stator is non-uniform due to the eccentricity fault.  
Figure 3 illustrates an induction motor model under the DE. \(R\) and \(r\) represent the radii of stator and rotor, respectively. The occurrence of DE introduces the shift \(a\) between the stator symmetrical axis and the rotor symmetrical axis. Hence, the airgap distribution in this case is non-uniform under the reference of stator axis and also time-variant. A most special noteworthiness is that the condition that the axis of the rotor is parallel to the stator axis is considered.  
Consequently, due to the DE, specific components appear in the stator current spectrum, at sideband frequencies around fundamental and principle slot harmonics (PSH), the expression is given as follows [7]:  
\[f_{de1}=|f_{s}\pm n_{d}f_{r}| \tag{4}\]  
\[f_{de2}=|f_{s}\pm(N_{r}\pm n_{de})f_{r}| \tag{5}\]  
where \(n_{d}=2,4,6,\cdots\).  
In most cases, the SE and DE tend to coexist and the mixed eccentricity needs to be considered in reality [10].  
The specific component under the mixed eccentricity can be detected around the fundamental frequency in the stator current, given by [1]:  
\[f_{mix}=f_{s}\pm mf_{r} \tag{6}\]  
where \(m=1,2,3\cdots\).
Although research focuses on improving the bio-based film characteristics of packaging materials, their mechanical, thermal, and physical properties are still non-satisfactory, and their use in industrial applications is often restricted [2]. To counteract these disadvantages by replacing fossil-based plastic packaging, research, and developments include active packaging based on bioactive polymers and composites obtained from renewable resources [1; 35]. There are several developments in this field, but up to now, a widespread use in the market is still missing. Today, a limited number of bio-based plastics with food packaging applications are commercially available mainly based on the following polymers: poly(lactic acid) (PLA), poly(hydroxyalkanoate) (PHA), poly(ethylene furanoate) (PEF), poly(butylene succinate) (PBS), thermoplastic cellulose derivatives, and starch-based films [1]. This review focuses on most recent developments of packaging materials (polymers and composites) that are produced from renewable resources and considered as promising alternatives for fossil-based plastics. Annual and perennial crops, herbs, flowers, lignocellulosic and agricultural residues are a major source of polysaccharides, proteins, and lignin. In addition, they can also be used to obtain plant extracts and essential oils (EO). Therefore, we consider their application in terms of sustainable packaging that contribute to the reduction of food waste. First, challenges along food supply chains related to packaging characteristics are discussed: advantages and disadvantages related to the characteristics of the most prominent fossil-based plastics packaging are compared. They are evaluated regarding food waste, recyclability, and sustainability issues (Section 2). Second, characteristics of selected packaging plastics (mainly plant-derived polymers) in terms of mechanical, thermal, and physical properties are highlighted. Biodegradability will not be addressed in detail but mainly properties, such as gas permeability with influence on the food quality, safety, and shelf life (Section 3). Third, natural additives with focus on plant extracts in the context of active food packaging are presented (Section 4). Incorporating extracts of herbs, flowers, trees, and their fruits is inevitable to achieve desired material properties that are capable to prolong shelf life, resulting in reduced food waste. Finally, in Section 5, the potential of renewable resources is evaluated from a bioeconomy perspective of a packaging core matrix as well as a source for natural additives for active packaging.
## 4 Study Limitations and Future Work  
The study has a few limitations. Experimentally, the number of samples was limited, and the tests were conducted under controlled laboratory conditions, which may not fully represent real-world gas generation environments.  
In the FEM modeling, certain assumptions were made--such as idealized boundary conditions, isotropic material behavior, and the exclusion of external environmental effects--which may affect the accuracy of the predicted results.  
Future work should address these limitations by testing a larger number of samples and more detailed simulation conditions.
## 1 Introduction
divided the bridge into multiple sections, measured the short-term transmissibility of the axle acceleration time-history response, and then used the amplitude modulation program to connect the local mode shapes of each section in series to obtain the entire mode shape of the bridge [15]. In 2020, [PERSON] et al. established a finite element model for driving recognition of simply supported girder bridges. Through numerical simulation, they studied the effects of temperature changes, vehicle body speed, accompanying traffic flow, bridge deck roughness, and different damage on the driving test results of simply supported girder bridges. It further confirms the validity and application potential of the bridge indirect measurement method based on vehicle response [16]. In the past two decades, scholars have proposed a large number of structural damage identification methods based on the overall parameters of the bridge and the response of the bridge, and have been applied to a certain extent; [PERSON], [PERSON] et al. obtained bridge parameters through a vibration test of Indiana cable-stayed bridge in Florence, and used these parameters to establish a finite element model and analyze the influence of this model on static and seismic loads [17]. Although the results of traditional detection methods are accurate, there are shortcomings such as inconvenient detection and a lot of preparation. Bridge damage detection methods based on vehicle response can overcome the above shortcomings, so this paper combines the existing research results of predecessors, further explores the bridge damage detection method based on vehicle response, uses wavelet analysis as the main tool, analyzes and studies the dynamic response of vehicles when passing through the bridge, and proposes corresponding structural damage indicators. The method proposed in this paper is applicable regardless of whether the pile foundation adopts full-scale reinforced concrete piles or reinforced micropiles [18]. Although the detection method proposed in this paper can quickly and conveniently identify the bridge damage, its detection results are greatly affected by the accuracy of the sensor. In addition, the experiment using this method has not been carried out yet, and further research is needed.
## 5 Conclusions  
Summarizing the results of the experimental work on the development of a method for assessing the strength of concrete by complex maturity, the following conclusions have been made:  
1. The laboratory tests for measuring the concrete strength by compression method and non-destructive sclerometer method were performed on concrete samples according to ASTM C109, GOST 10180 and GOST 22690. The estimated current strength values of concrete were obtained by ASTM C1074 (Nurse-Saul) and the proposed complex maturity methods.  
Figure 11: Calibration dependence of the above methods in relation to the compressive strength of cubic samples.  
Figure 10: Summary results of concrete strength determined by various methods.  
Assessment of concrete strength by the proposed method was based on ASTM C1074 and developed by taking the influence of ambient temperature and relative humidity into account when calculating the complex maturity.  
The estimated and measured by various methods strength were analyzed on the 1 st, 3 rd, 7 th, 14 th and 28 th days of concrete curing and compared with the strength obtained from the results of compression tests of cubes with an edge size of 10 cm, according to GOST 10180.  
According to the calibration dependence of the existing and proposed methods in relation to the strength gained in cubic samples, the highest coefficient of determination R\({}^{2}\) = 0.976 was revealed for the proposed method of strength estimation by complex maturity, which indicates the reliability of the latter.  
Determination of the complex maturity of concrete makes it possible to assess the current state of its strength and, above all, to reduce the waiting time for concrete maturation and increase the economic effect during construction. The proposed method accounts for the influence of internal and ambient temperature and ambient relative humidity on the strength gain of concrete and provides with the most reliable data at an early stage of concrete curing.  
Conceptualization, Y.U.; methodology, A.S.; software, S.Z.; validation, A.-A. ([PERSON]); formal analysis, A.S.; investigation, Y.U. and S.Z.; writing--original draft preparation, A.S. and Y.U.; writing--review and editing, A.A. ([PERSON]); visualization, S.Z.; supervision, A.T.; project administration, A.T.; funding acquisition, Y.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP08052033).  
Not applicable.  
The data presented in this study are available on request from the first author. doi:10.1061/40558(2001)17.  
The authors declare no conflict of interest.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 4 Performance Evaluation
### Strengths and Efficiency
#### 4.3.2 Efficiency  
In order to evaluate the time consumption of the inverse process in this paper, we performed time statistics for the whole process. Since some parts involved in the inverse process need to be performed manually, we only retrieved accurate time statistics for the data processing and analysis part, as well as rough estimates for the manual parts. The experiment was divided into four parts: data collection, labeling and filtering, fuzzing and packet analysis, and bit localization. Data processing and analysis were performed on a 64-bit CentOS 7.4.1708 server using python 3.7.1. The server had 8G RAM and the Intel(R) Xeon(R) Gold 6248 CPU @ 2.50 GHz with four cores and four threads. All code was run in a single thread, and the final efficiency in terms of time consumption is shown in Table 5. The total time taken to retrieve the body-related messages from the reverse experimental vehicle was 4087.85 s.