esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 2 Literature Review  
### Serial Communication  
With the development of microcomputer systems and the Internet of Things (IoT), the information exchange between computers, as well as between computers and other external hardware devices, is becoming increasingly important [7]. Serial communication is a communication method that transmits data bit by bit between an external hardware device and a computer. This process usually involves multiple interface protocols, such as data formatting, serial-to-parallel data conversion, and TTL (time to live)- and EIA-level (embedded logic analyzer) conversion. The entire communication process is realized using data signal lines, ground wires, and control lines [8]. In a distributed control system composed of multiple microcomputer processors and external devices, attributed to the natural advantages of serial communication for bit-by-bit data transmission, fewer data lines are needed, thus realizing low-cost, long-distance information transmission. However, in practical applications, the serial port method is widely used to control real-time, short-distance, and simple external devices such as terminals, printers, controllers, etc. [9].
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Reduced NDZ Based Islanding Detection [67]:  
With the use of the CART (Classification and Regression Trees) algorithm, the study reduces the non-detection zones in an islanded condition, thereby overcoming the large NDZ due to reduced power mismatch in case of multiple feeders network. CART is a decision tree building algorithm that uses if-then logical conditions to predict accurate conditions of cases. Without prior knowledge of relationships between multiple variables and theories governing them, this method can be effectively used for data mining and data's implementation to predict various cases [68]. With the reduction of 54% of total NDZ area, higher dependability and improved overall performance are achieved, though extensive field experiments are required before any practical device can be realized.
## 1 Introduction
On the other hand, by utilizing the connected car's sensor data while driving, it is possible to reduce the number of additional measuring devices to be installed in the RSU and to build an efficient C-ITS infrastructure. In addition, data collected directly from multiple vehicles on the road improves road situation awareness and the accuracy of traffic information. The central LDM, which collects vehicle data, has no analytics and processing capabilities implemented. Therefore, we intend to build a platform that provides connected car data by collecting and analyzing all big data of vehicles, such as sensor data and simple driving information collected by the existing central LDM. The platform created by separating the vehicle data collection function of the central LDM can be asynchronously interlocked with the central LDM to provide the processed data according to the central LDM standard to be delivered to the vehicle. In this paper, we propose a platform, as shown in Figure 1, that includes a big data processing system that collects and processes vehicle big data generated from connected cars and a messaging system that delivers large-capacity vehicle sensor data and traffic information in real time. The proposed system was built as a testbed, and its usefulness and scalability were verified through scenarios. The information analyzed by the proposed vehicle big data analysis system is delivered to the central LDM and vehicle LDM in near real-time to increase the safety of autonomous driving. The proposed big data processing system was built using the Hadoop ecosystem. Vehicle data are stored in the Hadoop distributed file system (HDFS) and MariaDB, and Apache Spark is used for big data analysis. MariaDB's structured data are periodically transferred to and loaded into the HDFS, and the big data and analysis results stored in MariaDB and HDFS are used for the development of various services and business models. Data transfer between the vehicle and the server is realized through the Apache Kafka messaging system. Data transmitted in real-time from the vehicle to the backend server through Kafka is analyzed in real-time using the Spark Streaming function. The remainder of this paper is structured as follows. Section 2 introduces research trends, e.g., LDM and big data platforms. Section 3 proposes the structure of the entire platform, and Section 4 shows an example of implementing a platform and using autonomous vehicle data. Section 5 concludes the paper.
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## Improvement of YOLOv7 Object-Detection Model  
### SPPCSC Module Improvements  
The SPCSPC module is separated into two primary components of the YOLOv7 network. The role of the SPP part is to be able to increase the perceptual field, making the algorithm adapt to different resolution images, which is obtained by maximum pooling for different perceptual fields. From Figure 2, it can be seen that, in the first branch of the SPCSPC module, the images are subjected to four maximum pooling processes, namely 5, 9, 7, and 1. These four different maximum pooling layers make it possible to distinguish the size of the targets and make it better at localizing small targets.  
The CSP module divides the whole SPCSPC module into two parts. The first part performs the regular processing of the feature map to ensure the speed of image processing. The second part is the aforementioned SPP module, which uses more than one group of large pooling modules to process the feature maps, making YOLOv7 adaptable to targets of various sizes with better generalization.  
As shown in Figure 2, the SPP module processes the input feature map four times and obtains the target size in the maximum pooling channels of different scales to locate and process the target. However, the disadvantage of the SPP module is obvious, that is when processing the feature map in different pooling channels, this processing will ignore the target that does not correspond to the number of channels, then the final result will result in the missing features and affect the accuracy. This phenomenon is very serious, resulting in the missing of small targets during the processing. In addition, because the feature map is  
Figure 2: SPPCSPC module structure diagram.  
divided into four branches here and pooling is performed once for each branch, this leads to the slow processing speed of the YOLOv7 algorithm.  
Therefore, the SPPFCSPC module was designed with reference to the SPPF structure in YOLO's past series (as shown in Figure 3). In the new SPPFCSPC module, the four pooling channels in the original SPP module are integrated so that each time they undergo pooling, the results are passed to the next pooling layer while passing them to the final connecting layer. In this way, because of the continuous pooling process, the YOLOv7 model can be preserved to the maximum extent to ensure the localization of various small targets when they experience the difficulty of the field, thus improving the detection of small targets. In addition, because the feature maps passed into the next pooling layer have already been processed first by the previous pooling layer, the processing speed of this pooling layer can be improved to a certain extent, and eventually, the overall processing speed of the YOLOv7 model will also be improved to a certain extent.
# Free Vibration Analysis of Functionally Graded Sandwich Plates with a Homogeneous Core
## 5 Conclusions  
In this paper, a numerical method for the free vibration analysis of FG sandwich plates with homogeneous cores has been developed, using hierarchical models and the 2-D natural element method. The reliability of the proposed method was examined through the comparison with the reference and the 3-D elasticity. Furthermore, the free vibration characteristics of FG sandwich plates were parametrically investigated using the developed numerical method. Through the verification and parametric experiments, the following major observations were drawn:  
* With respect to the 3-D elasticity, the relative differences in the calibrated fundamental frequencies of hierarchical models monotonically decreased with the increase of the model level, \(q\). Meanwhile, the relative differences between the hierarchical models uniformly decreased as the plate thickness, \(h\), became smaller.
* From the comparison with the reference, using the (1,1,0) hierarchical model, the present method showed a reasonable agreement with the maximum relative error of 7.961%. Meanwhile, the softcore and the hardcore showed a similar trend in the relative errors with respect to \(n\), but both cores exhibited the contrary trend with respect to \(b/h\)
* In the FG sandwich plates with softcores, the calibrated fundamental frequencies increased in proportion to the volume fraction index, \(n\), and in reverse proportions to the plate aspect ratio. Furthermore, the frequency curves became lower as the pseudo thickness ratio was changed from (2-2-1) to (6-6-1). However, these trends became entirely reversed for the plates with hardcores.
* In (2-2-1) FG sandwich plates with softcores, the calibrated fundamental frequencies were lower-bounded with respect to the thickness of the top facesheet. However, the frequency curves were upper-bounded for the plates with hardcores. Both cases commonly approach the \(\Delta\mathscr{L}\) -\(\Delta\mathscr{L}_{2}O_{3}\) bi-material plate as \(H_{1}\) and \(n\) tend to \(+\infty\).  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C1100924).  
The author declares no conflict of interest.
## 1 Introduction
### Review of State-of-the-Art Detection Methods  
Several peak detection techniques have been proposed in the literature to address the aforementioned challenges. These methods range from simple fixed-threshold approaches to more sophisticated subspace and learning-based techniques.
## 2 The Qubit and Bloch's Sphere  
A qubit is to a quantum computer what a bit is to a digital one: it is a basic unit for representing information. The state of a qubit is characterised within a two-dimensional vector space, known as 2D Hilbert space. The canonical basis vectors in this space are notated as \(\left|0\right\rangle\) and \(\left|1\right\rangle\), which is an abbreviated way to represent such vectors. In quantum mechanics, the bra-ket notation, or [PERSON]'s notation, is used to represent quantum states.  
Figure 1: A screenshot of the QISynth in ‘Simple’ mode.  
The state \(\ket{\psi}\) of a qubit is expressed mathematically as a linear combination of basis vectors as follows: \(\ket{\psi}=\alpha\ket{0}+\beta\ket{1}\) with \(\alpha,\beta\in\mathbb{C}\) and \(|\alpha|^{2}+|\beta|^{2}=1\). This linear combination expresses a state of superposition.  
Simply put, a quantum computer processes information with qubits in a state of superposition. But it returns binary numbers (0 s and 1 s) when we read the qubits. In quantum computing terminology, the act of reading qubits is referred to as _projective measurement_[12].  
A single qubit is represented visually as a sphere with opposite poles, with \(\ket{0}\) at the north pole and \(\ket{1}\) at the south. This sphere is called _Bloch sphere_. From its centre, a unitary vector \(\ket{\psi}\) can point to anywhere on the surface. This vector, which represents the state of the qubit, is referred to as a _state vector_.  
In quantum mechanics, the variables \(\alpha\) and \(\beta\) represent _amplitudes_. In practice, we can say that \(|\alpha|^{2}\) and \(|\beta|^{2}\) encode the probabilities for the possible results of measurements made on a quantum system.  
Quantum computers are programmed by applying sequences of operations to qubits. Programming languages for quantum computing provide a number of operations, referred to as _gates_, which act on qubits. For instance, the \(\mathbf{X}\) gate rotates the state vector of a qubit by 180 degrees around the x-axis of the Bloch sphere geometry: if the qubit vector is pointing to \(\ket{0}\), then this gate flips it to \(\ket{1}\), or vice versa (Figure 2).  
Essentially, quantum gates perform rotations. A generic rotation gate \(\mathbf{U}\) is typically available for programmers, with three angles called _Euler angles_. Thus, any rotation gate can be specified in terms of \(\mathbf{U}\). For instance the gate \(\mathbf{X}\) is equivalent to \(\mathbf{U}(\frac{\pi}{2},-\frac{\pi}{2},\frac{\pi}{2})\).  
A quantum program is often depicted as a circuit with sequences of quantum gates operating on qubits (Figure 3). Typically, the qubits start in the ground state \(\ket{0}\).
###### Abstract
[20] noted the presence of bands from amino acid side-chain vibrations attributed to tyrosine at approximately 1515 cm\({}^{-1}\), phenylalanine at about 1498 cm\({}^{-1}\), and proline at around 1454 and 1438 cm\({}^{-1}\). Chemometrics plays a crucial role in classifying cheeses by analyzing huge loads of chemical data. During chemometric modeling, multivariate data analysis takes place. The application of chemometric methods usually helps to identify key variables in distinguishing between different milk types. Variable selection is another important step that shows the most relevant spectral regions for predicting the desired properties. Moreover, cross-validation is necessary to ensure the robustness of calibration models by validating them on subsets of the data. By combining chemometrics and spectroscopy, a comprehensive database of authentic sample spectra can be created for comparison with unknown samples. Model updating, thus periodically updating calibration models, is important to account for variations in raw materials or production processes. For regulatory compliance, validation of the chemometric models to meet regulatory requirements may take place [21]. The problem of food adulteration is noteworthy, even in cases where it does not directly impact public health. Adulterating food has consequences for manufacturers' profits and the raw materials used in production. Extensive scrutiny has been directed at fraudulent practices in dairy products [16; 21]. One prevalent form of adulteration is the substitution of more valuable milk, like sheep's or goat's milk, with lower-value cow's milk. Hence, the precise identification of the milk species is imperative, especially for products like pure sheep or goat cheeses. This illicit practice yields economic advantages but leads to unethical competition involving the compromise of product quality [6]. This study is related to a previous one conducted by [PERSON] and [PERSON] [22]. The goal of this study was to obtain the FTIR and \({}^{1}\)H-NMR spectra of various cheese products of different geographical origins and use chemometrics to classify the samples based on milk species' origin. In addition, another aim was to check if both spectroscopical techniques are important or if only one could serve this purpose. Subsequently, such an outcome could be used as a tool against counterfeiting and adulteration.
## 2 Materials and Methods
### cDNA Preparation and Real-Time Polymerase Chain Reaction (PCR)  
Expression levels of osteoblast marker genes after 7 days of culture were measured using real-time PCR. Using Easy DNA Extraction kit version 2 (Kaneka, Minato City, Tokyo, Japan), cDNA was prepared from cultured cells according to the manufacturer's protocol. Real-time PCR was performed using a CFX96 PCR detection system (Bio Rad, Hercules, CA, USA) with TB Green(r) Premix Ex Taq II (Takara Bio, RR820, Kusatsu, Shiga, Japan). Osteoblast-related genes including alkaline phosphatase (ALP), run-related transcription factor 2 (Rumx2), type I collagen (Col I), vascular endothelial growth factor (VEGF)-A and VEGF receptor 1 (VEGF-1) were amplified using forward and reverse primers. The primers used were: 5\({}^{\prime}\)-GCAGATTAGATTGAATCGGAACAC-3\({}^{\prime}\), 5\({}^{\prime}\)- ATGGCCTGGTTCCACTCTC-3\({}^{\prime}\)(ALP); 5\({}^{\prime}\)- TGCAAGCAGATTTTAACAACAGGG-3\({}^{\prime}\), 5\({}^{\prime}\)- GGCTCACGTCCATCTT-3\({}^{\prime}\)(Rumx2); 5\({}^{\prime}\)- TCAGTGCAATTTGTTGCTGAAA-3\({}^{\prime}\), 5\({}^{\prime}\)- GATACCAAACTGGGGTGTG-3\({}^{\prime}\)(Col I); 5\({}^{\prime}\)- ACATTGGCTCACATTCAGAACAC-3\({}^{\prime}\), 5\({}^{\prime}\)- TGTGTGAACCGGACTTTTA-3\({}^{\prime}\)(VEGF-A); 5\({}^{\prime}\)- TCAGGTGCCAGAAG-3\({}^{\prime}\), 5\({}^{\prime}\)- CCCAATTGCTGGAATCTGGATG-3\({}^{\prime}\)(VEGF-1); and 5\({}^{\prime}\)- TGGTCCGTCGATGATG-3\({}^{\prime}\), 5\({}^{\prime}\)- TTGCTTGAGTCGCAGAG-3\({}^{\prime}\)(GAPDH).  
Relative expression ratios of markers were calculated based on the comparative threshold cycle (ddCt) method. The levels of mRNA expression were calculated and normalized to the level of GAPDH mRNA expression.
## 2 Remaining Useful Life (RUL) Prognostics Methodologies  
Remaining useful life (RUL) is defined as the time when equipment performance degrades to the failure threshold for the first time, or the first arrival time [7]. If the RUL can be predicted accurately, predictive maintenance of the equipment can be implemented. Preventive maintenance before degradation is helpful in reducing failure rates and maintenance costs. Therefore, RUL prognostics has become a focus of researchers globally. RUL prognostics methodologies can be divided into the mechanism analysis method and the data-driven method [8]. The degradation of lithium-ion batteries is a nonlinear and time-varying dynamic electrochemical process. Though mechanism analysis is clear in physical significance and concepts, it involves a lot of parameters and complex calculations for accurate modeling. In consequence, it is not suitable for real-time monitoring, which severely limits general applicability of the mechanism model. Instead, mechanism analysis is used more in theoretical research and battery designation than in practical engineering [9].  
The data-driven method of modeling batteries does not require an accurate mechanism of the system. Data-driven methods use the battery state of health data, which can be measured through advanced sensor technology. These methods extract effective feature information and construct the degradation model to predict RUL. These methods are able to describe degradation-inherent relationships and trends based on data [10]. Therefore, data-driven methods have become the focus of RUL prediction in the world [11]. Data-driven RUL prediction methods can be divided into three groups based on the artificial intelligence filtering techniques and stochastic process degradation, respectively. Figure 1 shows the main RUL prognostics methodologies of vehicle lithium-ion batteries.  
Moreover, the advantages and disadvantages of these three kinds met  
Figure 1: The main remaining useful life (RUL) prognostics methodologies of vehicle lithium-ion batteries.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 2 Background
### Research Gap and Motivation  
Most studies on safety management have focused on identifying the impact of safety management factors on business operations and production systems. However, quantitative analyses that explore the relationship between productivity and safety performance have received less attention. While the balance between productivity and safety has gained international interest, particularly in the context of ESG, few studies have specifically examined the relationship between productivity and safety in the safety management field. Government policy is also needed to enhance companies' capabilities by balancing safety performance with productivity. This study focuses on a quantitative analysis aimed at evaluating the efficiency of the analysis targets by comparing production and safety activities. To achieve this, we selected DEA to assess the relative efficiency between productivity and safety performance.  
The proposed approach, based on the DEA method, addresses this gap in three key areas, as illustrated in Figure 2: index, comparison, and variable. First, this study develops an integrated index that considers both productivity and safety performance to reflect socio-economic values. Based on this index, industries with an outstanding ratio between productivity and safety performance are identified. Second, the study conducts a relative  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Topic** & **Purpose** & **Level** \\ \hline R\&D & R\&D efficiency based investment and knowledge outputs (academic publication, patent, etc.) & National [30] \\  & & & Industry [31] \\ Energy & Energy efficiency based on raw material inputs and energy use & & Industry [33] \\  & & & Corporate/project [34] \\ Ecology & Eco-efficiency based on raw material inputs and emission of greenhouse gases & & Industry [28; 35] \\ Transport & Transportation service efficiency based on resources (e.g. capital, infrastructure) & & Industry [37] \\  & and quality of transportation services (e.g. speed, channel) & & Corporate/project [38] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Literature review of efficiency studies based on DEA.  
comparison among industries, rather than an absolute comparison, as the cap on the ratio of productivity to safety performance is unknown. This approach is more effective in providing benchmarking information for controlling and improving the resources of inputs and outputs. The benchmarking function of DEA, based on optimization, is effectively applied in this context. Finally, multiple inputs and outputs are utilized to capture all resources that influence both productivity and safety. While previous research typically analyzed individual inputs and outputs separately, making it difficult to identify interaction effects, this study uses DEA to analyze the relationship between productivity and safety performance by developing an integrated index that allows for relative comparison using multiple inputs and outputs.
## 2 Methodology  
### Experimental Site  
Manual chamber measurements were conducted in the Rzecin peatland (\(52^{\circ}45^{\prime}41^{\prime\prime}\) N, \(16^{\circ}18^{\prime}35^{\prime\prime}\) E), an ecosystem included in the Natura 2000 European Union habitat protection network (Area code: PLH300019; [14]). The peatland area is classified as a poor-rich fen with high vegetation heterogeneity. Measurements were conducted at two experimental sites (CR and CL), located 50 m apart, to represent stable and variable water table depth (WTD) conditions (Figure 1). The sites are equipped with meteorological stations providing continuous data, like the nearby tower continuously providing PAR with 30 min time steps [15]. The CR site is located on a floating _Sphagnum_ mat, and its surface oscillates within the range of 15-25 cm, depending on the year. The CL site is located close to the border between the floating mat and the part of the peatland underlined with 4-5 m of lake sediments. Hence, both locations present differences in their WTD regimes, which eventually developed into, among others, two distinct vegetation species dominating each site. The dominant species in CR are _Sphagnum_ spp. and _Carex rostrata_ with the presence of _Oxycoccus palustris_, while the CL site is a poor fen dominated by _Sphagnum_ spp. and Carex lasiocarpa with the presence of _O. palustris_, _Equisetum palustre_, and _Comarum palustre_[13]. The names of the sites are derived from the dominant vascular plant species: _C. rostrata_ for CR and _C. laciocarpa_ for CL [14]. CR and CL sites are divided into plots, and three replicates are used per site.