esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 2 Background and Related Work  
### Controller Area Network(CAN)  
The composition and format of a standard CAN bus data frame are shown in Figure 1. The structure consists of seven segments: frame start, arbitration segment, control segment, data segment, cyclic redundancy check (CRC) segment, acknowledge character (ACK) segment, and frame end. The two segments that are most relevant to this paper are the arbitration and data segments.  
The arbitration segment consists of 12 bits, the last of which is the remote transmission request (RTR), which is used to distinguish whether a frame is a data frame or a remote frame. The first 11 bits form the CAN ID, which identifies this CAN message's sending node (ECU). The bus can only have one ECU transmitting at a time, and each ECU can initiate message transmission. However, the bus does not specify the priority levels of the ECUs. The CAN bus determines the priority levels of the messages using the CAN ID, where the smaller the CAN ID is, the higher its priority, which is the arbitration mechanism.  
The data segment is the entire data field of the message and is 8 bytes in size. This short frame structure gives the CAN a high degree of real-time and interference immunity. The data field contains the control commands or status data sent by the ECU, thus enabling the different ECUs to work together to ensure the proper operation of the vehicle. In practice, the data fields are defined differently for each ID. For example, bits 9 to 12 of the data field corresponding to the message ID 0x100 are defined as data related to the lights, while bits 9 to 12 of the data field corresponding to the message ID 0x120 are related to the doors. These definitions are determined entirely at the discretion of the vehicle manufacturer and are non-public. The method described in this paper aims to quickly obtain the definition of the body state in the CAN message.
## 4 Computational Experiments and Results
### Performance of the Proposed Heuristic Method  
In this section, the proposed heuristic base on DBR is used to make the schedule of customer orders on CCR which are arriving in the production system on an every day planning horizon. The performance of the proposed heuristic based on DBR is compared with the famous scheduling methods in literature including NEH [31] and [PERSON] et al. [33] and CDS [34] heuristic algorithms. These heuristics are famous in flow shop scheduling problems. A production system consisting of five serially connected machines is considered in which of five different product models--A, B, C, D, and E--that are produced. Customer orders are arriving every day with different demand of product models. The data for the example problem are generated randomly and are explained below.
## 1 Introduction
Then, we utilize attention pooling to determine the contribution of the final scores and obtain sentence-scale local semantic features. (3) The relevance between the essays and their corresponding prompts is an important basis for scoring. We vectorize them by Doc2 Vec and calculate their similarities to obtain their relevance features. (4) In addition, to address the shortcomings of DNN models in extracting shallow features, such as grammatical errors and text richness, we use manually extracted features with the adaptive weight to obtain the shallow linguistic features of the essays. MSSF fuses global semantic features, local semantic features, prompt relevance features, and shallow linguistic features for essays. Experiments are conducted on the Kaggle ASAP competition with our proposed model. The experimental results show that our proposed AES model with multi-scale semantic hybrid features can effectively improve the performance of the automatic scoring of essays and obtain the optimal performance compared with the baseline model. Our main contributions are as follows: we add 18 typical manual features with adaptive weights to the distributed representation of essays. They can not only extract valuable quantitative information from essays that are difficult to extract from DNN-AES, but can also adjust the weight parameters adaptively according to different AES tasks. * We utilize SBERT as the sentence vectorization method of the essay. Compared with manually extracted sentence-level features, it can be fine-tuned according to specific tasks after pre-training the tasks and can make the final score more accurate. * We add the relevance feature between the prompt and the essay. This feature allows the model to learn the correlation between them, rather than just utilizing the essay for scoring. Furthermore, the features of point 2 and point 3 are easily extended to other AES models, since traditional AES models ultimately have a distributed representation layer for final scoring. The remainder of this paper is organized as follows: we will introduce different types of existing AES methods and discuss their limitations in Section 2; Section 3 will develop the various components of the model proposed in this paper; Section 4 will present the experimental results and analysis, with the validity of the proposed method being discussed based on the experiments; finally, we will summarize the conclusions and propose further work in Section 5.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 4 Performance of Different Antenna Configurations
### Different Performance of 2 \(\times\) 2 Antenna Configurations  
To compare and verify the difference of the 2 \(\times\) 2 antenna configuration between the arc array and rectangular array, MATLAB simulations were carried out at different \(\xi\) with the same SNR of 10 dB.  
According to Figure 7a,b, when \(\xi\gg 1\), whether it is an arc array or a rectangular array, they can reach the maximum capacity. There are differences between the two configurations when the aspect ratio \(\xi\) is small, and they are basically the same when the aspect ratio \(\xi\) is large, which means that the performance of the two configurations is similar when the distance between the transmitting and receiving antennas is large. The result of the research on the 2 \(\times\) 2 MIMO system can be calculated as \(\xi=2n\) in [43], which uses an approximation of the Taylor expansion in sub-channel propagation paths. This paper gives a more precise expression \(\xi=2n-\frac{1}{8n}\) on the 2 \(\times\) 2 MIMO system, such errors cannot be ignored in short-distance transmission. However, the aspect ratio \(\xi\) is approximately equal to \(2n\) in long-distance transmission, where \(n\) is the ratio of array element spacing \(d_{s}\) to wavelength \(\lambda\).  
According to Equations (29) and (34), Table 2 gives the best value of the aspect ratio \(\xi\) and array element spacing \(d_{s}\) at the maximum channel capacity. Figure 8 shows the specific change trend.  
In Figure 8, two antenna configurations have the same trend of change. In Table 2, the last location of aspect ratio \(\xi\) at the maximum channel capacity is given, which corresponds to the last peak in Figure 8. After this peak, the channel capacity will continuously decrease until the lower capacity limit. However, there are still other peaks before the last peak in Figure 8, which are caused by the changes in channel phases in short-distance communications. In addition, the number of peaks occurs more frequently in the rectangular configuration, which means that the channel capacity will change greatly when the distance  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(d_{s}\) (\(\lambda\)) & \(\xi\) & **Antenna Configuration** \\ \hline
0.5 & 0.5 & Circular Arc \\
1 & 1.75 & Circular Arc \\
2 & 3.875 & Circular Arc \\
0.5 & 0.75 & Rectangular \\
1 & 1.875 & Rectangular \\
2 & 3.9375 & Rectangular \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of theoretical maximum capacity.  
Figure 8: Channel capacity under two different \(2\times 2\) antenna configurations: (**a**) circular arc antenna array; (**b**) rectangular antenna array.  
Figure 7: Channel capacity at different aspect ratios \(\xi\): (**a**) \(\xi=2n;(\textbf{b})\)\(\xi=2n-\frac{1}{8n}\).  
between the transmitting and receiving antennas changes slightly. Similar results are also given in [40], but this paper gives the location of the last peak, thereby designing the optimal antenna array.
###### Abstract  
This paper presents a finite element (FE) analysis for predicting the flexural behavior of reinforced concrete (RC) beams strengthened with Fe-based shape memory alloy (Fe-SMA) strips using a near surface mounted (NSM) method. Experimental results reported in the literature were used to verify the proposed FE model. FE analyses were conducted using OpenSees, a general-purpose structural FE analysis program. The RC beam specimens were modeled using a nonlinear beam-column element and a fiber element. The Concrete 02 model, Steel 01 model, and Pinching 04 model were applied to the concrete, steel reinforcement, and Fe-SMA strip in the fiber element, respectively, and the FE analysis was carried out in a displacement control method based on the Newton-Raphson method. The FE model of this study accurately predicted the initial crack load, yield load, and ultimate load. From parametric analyses, it was concluded that an increase in the compressive strength of the concrete increases the ductility of the specimen, and an increase in the level of recovery stress on the Fe-SMA strip increases the initial stiffness of the specimen.  
Fe-based shape memory alloy (Fe-SMA); recovery stress; near surface mounted (NSM)
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 3 Optimization of the Panel with Ribs and Frames  
### Optimization of Buckling  
The analysis in Section 2 shows that the natural frequencies and buckling eigenvalue of the structure are coupled together. Optimizing the first natural frequency of the panel with ribs and frames can improve its buckling eigenvalue.  
The following two objective functions can achieve the goal of maximizing the first-order frequency:  
\[\begin{array}{c}\textit{Maximize }\omega_{1}\\ \textit{Minimize }\frac{1}{\omega_{1}}\end{array} \tag{16}\]  
In order to maintain non-negativity during the calculation process, the second objective function should be selected.  
The formulation of this topology optimization problem can be written as  
\[\begin{array}{c}\textit{Find }\mathbf{D}=(\mathbf{D}_{1},\ldots,\mathbf{D}_{n})^{\mathbf{T}}\\ \textit{Minimize }\frac{1}{\omega_{1}}\\ \textit{S.t. }\mathbf{K\phi_{j}}-\lambda_{j}\mathbf{M\phi_{j}}=0,\quad j=1,\ldots,m\\ \mathbf{D}\subset\mathcal{U}_{\mathbf{D}}\\ \textit{V}<\overline{\textit{V}}\\ \textit{C}<\overline{\textit{C}}\end{array} \tag{17}\]  
Here, \(\mathbf{D}_{i}(i=1,\ldots,n)\) is the design variable for the \(i\)th rib. In the constraint function, \(\mathbf{K}\) and \(\mathbf{M}\) are the stiffness matrix and mass matrix, respectively. \(\lambda_{j}=\omega_{j}^{2}\) is the eigenvalue. \(\mathcal{U}_{\mathbf{D}}\) represents the feasible region of \(\mathbf{D}\), while \(\overline{\textit{V}}\) and \(\overline{\textit{C}}\) represent the volume constraint and deflection constraint, respectively.  
Compared to traditional topology optimization methods, the MMC method has a natural advantage in optimizing the beam ribs of the panel. The ribs of the panel can be reflected as components in MMCs. Therefore, this method will be developed to optimize the buckling resistance of the panel with ribs and frames.
## 2 Materials and Methods
### Experimental Analysis  
#### 2.5.1 Total Phenolic Content (TPC) and Antioxidant Capacity  
The same procedures described in our earlier study for measuring the total phenolic content (TPC) and antioxidant capacity (FRAP and CUPRAC) of the samples were followed in this study [18]. For TPC, an aqueous gallic acid solution concentrated in a range between 20-100 mg/L was used as the standard. The plotted calibration curve using pure gallic acid standards showed good linearity (R\({}^{2}\) = 0.9964), with the equation y = 0.0095x + 0.0064. This equation was utilized to determine the TPC values in the studied samples, and the values were recorded as mg of gallic acid equivalent (GAE)/100 g of dry sample weight.  
Two separate methods, FRAP (ferric reducing antioxidant capacity) and CUPRAC (cupric reducing antioxidant capacity), were used to determine the antioxidant capacity of the samples. Both methods used a Trolox-ethanol solution as a standard. The samples' FRAP metrics were estimated via the equivalent absorption of a Trolox standard solution with concentrations ranging between 10-150 mg/L, while the CUPRAC metrics were estimated via the equivalent absorption of a Trolox standard solution concentrated in a range between 50-500 mg/L. For FRAP, the calibration curve of the Trolox standards had an R\({}^{2}\) value of 0.9981. From this curve, the derived equation was y = 0.0056x + 0.071, which served to compute the comprehensive FRAP metric within the samples. For CUPRAC, the calibration curve of the Trolox standard also revealed good linearity (R\({}^{2}\) = 0.9985), with the accompanying equation y = 0.0014x + 0.1698 applied to calculate total CUPRAC metrics in the samples. For both methods, the outcomes were denoted in mg of Trolox equivalents per 100 g of moisture-free sample weight (mg TE/100 g DW).
## 4 Results and Discussions  
The results are based on the PID approach in the domain of control systems. The drug concentration is calculated through a two-fold system of negative feedback loops, as shown in Figure 3. The first loop is targeted towards the analysis of MDM2 concentrations, which are directly involved in the computations of the reference Nutlin 3a dose. The reference dose is obtained through the outer loop PID controller. In order to maintain appropriate dose concentrations, the second loop comes into play to provide the information about the Nutlin 3a levels already present within the body. With the inclusion of this final step, the dose concentration becomes the function of the error between the reference dosage \(n_{ref}\) and the actual Nutlin concentration present in the cells.  
As discussed earlier, there are two ways in which p53 can be triggered depending on the level of stress. If the stress levels are not very significant, then the response of the p53 pathway is oscillatory, resulting in several subsequent downstream reactions that stop the call cycle and mend the cell. If the damage is repaired, then the p53 oscillations are halted and the cell cycle continues in its normal fashion. However, when the damage to the cell is quite significant, the p53 pathway results in sustained oscillations, resulting in apoptosis.  
However, it has been reported that p53 is not present or mutated in 50% of cancer cases. This is due to the over-expression of MDM2, which binds with p53 through a negative feedback, resulting in the degradation of p53 through the ubiquitination process.  
The problem could be countered in such way that another molecule can be introduced that binds with MDM2 in the same way as p53. Nutlin, a drug molecule, possesses similar characteristics. The process of achieving the required response of the p53 pathway due to Nutlin can be divided into two strategies. The first one is sustained response, while the second one is incorporating the p53 response.  
In order to repair the cells, when the damage to the DNA is miniscule, p53 should respond in an oscillatory manner. The degeneration rate of MDM2 and p53-MDM2 increases and decreases, respectively, in response to DNA damage.  
The effectiveness of the suggested control method was assessed using closed loop simulations, as shown in Figures 4-7. The concentrations of all state variables are clearly approaching their desired equilibrium values. The convergence time was less than an hour, which may be too short given the substantial overshoot of the Nutlin dose. This can be decreased by modifying the gains of the controllers according to therapeutic needs.  
Figure 4: Required level of MDM2 achieved by the feedback controller and Nutlin activity. It can be seen the the MDM2 concentration reduces to the minimum level required to achieve the higher p53 levels.  
Figure 5: Desired sustained level of p53 achieved by feedback-controller-based Nutlin activity. p53 settles to a constant level without unwanted oscillations. According to the literature, the concentration of p53 is found to be in the range of 400 to 600 nM (nanomoles) in healthy cells.
## 3 Materials and Methods
### Analysis of GC-MS  
GC-MS analyses were conducted using an HP7693 MSD interfaced to an HP7890A gas chromatograph fitted with a 30 m \(\times\) 0.25 mm i.d. WCOT fused silica capillary column covered with a 0.25 um film of 5% phenyl-methyl silicone. Helium was used as the carrier gas. The initial temperature of the GC oven was 50 \({}^{\circ}\)C holding for a minute and ramped from 50 \({}^{\circ}\)C to 100 \({}^{\circ}\)C at a heating rate of 20 \({}^{\circ}\)C/min. Finally, the GC oven was ramped from 100 \({}^{\circ}\)C to 300 \({}^{\circ}\)C at a heating rate of 3 \({}^{\circ}\)C/min and held for 8 min. The MS acted as ionization energy of 70 eV in full-scan mode (\(m/z\) 50-800), with a source temperature of 230 \({}^{\circ}\)C, with an electron multiplier voltage of 1800 V.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**(a) Soxhlet Extraction Workflow** & **(b) Components Separation Workflow** \\ \hline Put the blockage into an oven and dry at 40 \({}^{\circ}\)C & Take 50 mg chloroform bitumen \"A\" in a triangular flask \\ \(\downarrow\) & \(\downarrow\) \\ \hline Pulverize the dried blockage to 100–120 mesh & Add 1 mL of dichloromethane and 20 mL of petroleum ether in the triangular flask and leave for 12 h to precipitate out the bitumen \\ \(\downarrow\) & \(\downarrow\) \\ \hline The powder of blockage is subjected to 72 h & After filtration of asphaltene, the filtrate is concentrated to 1 mL \\ Soxhlet extraction with chloroform & \(\downarrow\) \\ \(\downarrow\) & \(\downarrow\) \\ \hline Chloroform bitumen “A” is air-dried at room temperature & The filtrate is transferred into a chromatographic column filled with 2.5 g \\ \(\downarrow\) & 100–200 mesh neutral alumina \\ \(\downarrow\) & \(\downarrow\) \\ \hline The air-dried chloroform bitumen “A” & The saturated hydrocarbon is eluted with \\ is weighed & 10 mL of n-hexane \\ \hline \hline \end{tabular}
\end{table}
Table 1: The workflow of Soxhlet extraction components’ separation.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Multi-Channel MAC Protocols
#### 4.5.3 Tsgs Mac  
A Transmission Scheduling Greedy Search (TSGS) algorithm provides a contention-based scheduling mechanism with less time complexity. The algorithm is based on setting a time for each transmission with CSMA/CA. By minimizing the overlap of transmission, the number of collisions is reduced, thereby minimizing the probability of activation of the back-off timer.  
The implementation of TSGS provides an optimal time slot for connection, thereby providing better packet delivery ratio. The major demerit is the protocol designed is RSU centric, so there is communication overhead on RSU. The protocol does not address hidden terminal problem and scalability when the number of vehicles increases in the network.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 3 System Model and Problem Definition
### Problem Definition  
The problem of energy-aware path planning through the use of an AGV is classified as the travelling salesman problem (TSP) [39; 40; 41]. Classical search algorithms and evolutionary algorithms are two primary approaches that are usually utilized in the process of resolving the TSP. The artificial potential field approach, greedy algorithm, and quick progress algorithm are all examples of algorithms that fall within the previously mentioned category. Methods such as biological algorithms like the Genetic Algorithm, Grey Wolf Optimizer algorithm and Nearest Neighbour algorithm are included in the latter category.  
The most prominent NP-hard optimization problem is the TSP [40; 41]. The TSP algorithm generates an optimized itinerary for a salesman, starting from their house, visiting multiple places, and returning with the minimum travel time, ensuring that each city is visited only once [42].  
In a TSP problem with \(m\) sensor nodes, let \(c_{ij}\) be the node distance from node \(i\) to node \(j\). Indicate by \(x_{ij}\) a binary variable that assumes the value of 1 if node \(j\) is visited right after node \(i\). If neither of them exist, it uses 0. In this case, the energy-aware path planning problem can be seen as an NP-hard TSP in the way that follows [43]:  
**Problem 1**.: _Minimizing the following cost function:_  
\[\min_{x_{ij}}\sum_{j=1}^{m}\sum_{i=1}^{m}c_{ij}x_{ij} \tag{1}\]  
_where_  
\[\sum_{i=1}^{m}x_{ij} = 1,j=1,\ldots,m\] \[\sum_{j=1}^{m}x_{ij} = 1,i=1,\ldots,m\] \[\sum_{i\in K}\sum_{j\in K}x_{ij} \leq |K|-1,\forall K\subset\{1,\ldots,m\}\]
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Machine-Learning-Based Technique  
Classifying the machine learning techniques used for financial fraud detection is an effective method for determining the appropriate methods for this research domain [123]. Research gaps can be identified by investigating why particular methods were selected and why others were not given more attention. Based on the literature, many learning algorithms that have received more attention and are used in other fields have not been popularly applied in financial fraud detection [5]. For instance, active learning can address the issue of insufficient data and improve the learning cost, incremental learning can dynamically add sample data to accuracy, and transfer learning can use the knowledge acquired from learning tasks to enhance the learning effect on other tasks. In future, these learning methods can be given more attention in financial fraud detection.  
Another future direction that is worth considering in machine learning is the concept of drift [124]. Many times, due to the continuous development and evolution of financial fraud, the accuracy of the algorithms diminishes. This phenomenon is called concept drift, [124], which is a problem that scholars have been attempting to address in the area of ML. Although the concept drift issue can be addressed by applying new datasets for  
Based on the findings of our study, we categorized financial fraud into four different categories, such as financial statements bank fraud, insurance fraud, and others. The number of reviewed papers and the type of fraud are provided in Figure 4. It can be shown that out of the review papers, 50% articles focus on bank fraud, 29% addressed financial statements, and 21% focused on insurance fraud and other fraud at 14% and 7%, respectively. This show that the majority of studies have a significant focus on bank fraud and financial statement fraud, while insurance frauds that include health insurance and auto insurance are not as frequently identified in the reviewed articles. It also shows that the two most common frauds include credit card and financial fraud, which appear to be the most prevalent types of fraud. On the other hand, our review did not cover money laundering, stock, commodities, or mortgage fraud for some reason. One of the factors is the difficulty in acquiring these data and the inability to reveal results if they are related to sensitive subjects.