esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Random Forest  
RF is a special bagging method, which is an organic combination of bagging algorithm and decision tree. In each decision tree in the random forest algorithm, there is no connection between each other. After the model is established, whenever a new deformed input sample appears, each decision tree in the model will make a judgment and judge which type the sample belongs to, then the more frequent ones will be the selection result of the final data analysis [36]. The RF algorithm flow is as follows.  
1. Among N samples, T training sets \(S_{1},S_{2},\cdots,S_{T}\) are formed by Bootstrap sampling.
2. Each training set S generates a corresponding decision tree \(C_{1},C_{2},\cdots,C_{T}\).
3. The node of each decision tree is split, and an attribute is randomly selected from the attributes as the attribute set of the current node split, and a feature is selected for splitting based on the principle of minimum absolute average error or square average error.
4. After the decision tree is constructed, there is no need to perform pruning processing.
5. Testing set sample X, after testing with each decision tree C, get the predicted value.
6. The average value of all decision trees is used as the final predicted value.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Simulated Data  
In this section, three experiments have been carried out. The simulated data of about five seconds duration is generated, where the target speech \(s(t)\) is male speech, and two speech interferences \(s_{i}(t)\) are male and female speech, respectively. Moreover, the AURORA2 database [24] was used, which includes subway, babble, car, exhibition noise, etc. Without loss of generality, all of the speech sources are placed one meter away from the AVS.
# Artificial Empathy and Imprecise Communication in a Multi-Agent System
## 2 Definitions  
_Fuzzy Sets and Their Similarity_  
In the presented paper, the agent's states will be represented in the form of fuzzy sets. Fuzzy sets allow us to represent imprecise data, and since agents will communicate using a not ideal communication and will interpret the signals subjectively, fuzzy sets seem to be a proper tool to use.  
Let \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) be a crisp universal set. A mapping \(A\colon U\to[0,1]\) is called a fuzzy set (FS) in \(U\). For each \(1\leq i\leq n\), the value \(A(u_{i})\) (\(a_{i}\) for short) represents the membership grade of \(u_{i}\) in \(A\). Any crisp set \(X\subseteq U\) can be represented as a fuzzy set by its characteristic function \(\mathds{1}_{X}\). We say that fuzzy set \(A\) is a subset of fuzzy set \(B\) (\(A\subset B\)) if \(A(u_{i})\leq B(u_{i})\) for all \(u_{i}\in U\). Let \(\mathcal{F}(U)\) be the family of all fuzzy sets in \(U\), \(\mathbf{A}(u_{i})\) denote its subset and:  
\[\mathbf{A}(u_{i})=\{x\in[0,1]\colon x=A(u_{i}),\text{ for some }A\in\mathbf{A}\}. \tag{1}\]  
**Definition 1**.: _A similarity measure of fuzzy sets is defined as a function \(s\colon\mathcal{F}(U)\times\mathcal{F}(U)\to[0,1]\) such that:_  
1. _For each_ \((A,B)\in\mathcal{F}(U)\times\mathcal{F}(U)\)_, we have_ \(s(A,B)=s(B,A)\)_;_
2. _For each_ \((A,D)\in E\) _and_ \((B,C)\in E\) _such that_ \(A\subset B\subset C\subset D\)_, we have:_ \[s(A,D)\leq s(B,C);\] (2)
3. _For each_ \(X\subset U\) _such that_ \((\mathds{1}_{X},\mathds{1}_{X^{c}})\in E\)_, we have_ \(s(\mathds{1}_{X},\mathds{1}_{X^{c}})=0\) _and_ \(s(\mathds{1}_{X},\mathds{1}_{X})=1\)_._  
This definition coincides with the classical one proposed by [PERSON] [15] (see also [16; 17; 18]). The higher measure values indicate a higher similarity of its arguments. It is usually assumed that \(E=\mathcal{F}(U)\times\mathcal{F}(U)\), meaning that all fuzzy sets are comparable by a given similarity measure. However, some similarity measures (such as the Jaccard index) cannot be formally defined over the whole Cartesian product \(\mathcal{F}(U)\times\mathcal{F}(U)\).  
It is important to note that any fuzzy subset \(A\) of a finite universe \(U\) can be identified with a tuple \(\big{(}A(u_{1}),\cdots,A(u_{n})\big{)}\in[0,1]^{n}\), and therefore, \(\mathcal{F}(U)\) can be identified with the Cartesian product \([0,1]^{n}\).  
**Definition 2** ([19]).: _An \(n\)-argument aggregation operator is a mapping Agg\(\colon\ [0,1]^{n}\to[0,1]\) with the following properties:_  
1. _if_ \(x_{i}\leq y_{i}\) _for all_ \(i\in 1,\cdots,n\)_, then_ \(Agg(x_{1},\cdots,x_{n})\leq Agg(y_{1},\cdots,y_{n})\)_;_
2. \(Agg(1,\cdots,1)=1\)_;_
3. \(Agg(0,\cdots,0)=0\)_._  
If aggregation covers some indexed values \(x_{i}\), it can be more convenient to use the following notation:  
\[\text{Agg}(x_{1},\cdots,x_{i},\cdots,x_{n})=\bigoplus_{i=1}^{n}x_{i}. \tag{3}\]
## 3 Results and Discussion
### Analysis of Diversity Performance
#### 3.1.2 Diversity Gain  
The usefulness of diversity can be measured by an indicator called diversity gain, which is calculated using Equation (4) [24]. At a significance level of 1% and employing maximal ratio combining, the attainment of a diversity gain of 10 represents the maximum level. In order to ensure practical applicability, it is recommended that the parameter DG exceed a value of 9.99 dB. Throughout the entire range of operational frequencies, as shown in Figure 18, the diversity gain (DG) of this antenna remains within the permitted limit.  
\[\text{DG}=10\sqrt{1-\text{ECC}^{2}} \tag{4}\]  
Figure 16: Radiation pattern simulation and measurement: (**a**) (E plane) 9.5 GHz, (**b**) (H Plane) 9.5 GHz, (**c**) (E plane) 21.1 GHz, (**d**) (H Plane) 21.1 GHz, (**e**) (E plane) 26.6 GHz, (**f**) (H Plane) 26.6 GHz.
## 6 Installation Error Compensation for the Shearer's SINS
### Static Calibration Model of Installation Deviation Angle  
Firstly, the \(f\) coordinate system is defined as the right-hand coordinate system of the SINS installation base coordinate system. When the \(f\) system and the \(b\) system coincide completely, the installation deviation angle error can be guaranteed to be the minimum. However, in the actual installation process, it is extremely difficult to achieve the complete coincidence of the \(f\) system and \(b\) system. Even if the \(f\) system and \(b\) system completely coincide, the changes in such external factors as vibration and deformation of the carrier will lead to the non-coincidence of the \(f\) system and \(b\) system. Let the deviation of heading angle between the \(f\) system and \(b\) system be \(\Delta\varphi\), the deviation of roll angle be \(\Delta\gamma\), and the deviation of pitch angle be \(\Delta\theta\); the orientation relationship is shown in Figure 7. The transformation order from \(b\) system to \(f\) system is as follows:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{\(\varphi_{E}\)} & \multicolumn{2}{c}{\(\varphi_{N}\)} & \multicolumn{2}{c}{\(\varphi_{U}\)} \\ \hline  & **Error** & **Variance** & **Error** & **Variance** & **Error** & **Variance** \\ \hline EKF & \(-0.510\) & \(0.0103\) & \(0.325\) & \(0.0056\) & \(-18.478\) & \(9.4679\) \\ UKF & \(0.302\) & \(0.0118\) & \(-0.253\) & \(0.0068\) & \(8.935\) & \(7.6143\) \\ Optimized UKF & \(-0.123\) & \(0.0127\) & \(0.102\) & \(0.0059\) & \(-4.239\) & \(4.8291\) \\ \hline \end{tabular}
\end{table}
Table 1: Filtering Results of error angle.  
Figure 6: Filtering results of heading error angle.  
As shown in Figure 8, the accelerometers are three cylinders in the three coordinate systems in the figure. The black dot in the middle of each cylinder indicates a measuring point. The three sensing axes are \(X_{b}\), \(Y_{b}\), and \(Z_{b}\), respectively. If the measured accelerations on the three axes are \(a_{x}\), \(a_{y}\), and \(a_{z}\), respectively, then the acceleration of gravity \(g=a_{x}^{2}+a_{y}^{2}+a_{z}^{2}\).  
As shown in Figures 9 and 10, \(\tan\Delta\gamma=a_{y}/a_{z}\), \(\tan\Delta\theta=a_{x}/\sqrt{a_{y}+a_{z}}\) can be obtained from the geometric relationship, but \(\Delta\varphi\) cannot be obtained. Therefore, the orientation relationship of the SINS installation deviation angle of the shearer can only solve the static pitch deviation angle and roll deviation angle, but the system accuracy can still be improved to some extent.  
Figure 8: Installation deviation of SINS.  
Figure 7: Installation deviation angle of SINS.
## 2 Study Area and Data Preprocessing  
### Scope and Overview of the Study Area  
This paper takes the \"Silk Road Economic Belt\" from Xi'an to Urumqi in the Chinese Mainland, which is located in northwest China. As seen in Figure 1, the study area is located at 85\({}^{\circ}\)17\({}^{\prime}\) E-109\({}^{\circ}\)49\({}^{\prime}\) E, 33\({}^{\circ}\)42\({}^{\prime}\) N-45\({}^{\circ}\) N, with an area of about 604,518.13 km\({}^{2}\). It passes through five provinces or autonomous regions, including Shaanxi Province, Gansu Province, Ningxia Province, Qinghai Province, and Xinjiang Uygur Autonomous Region. There are 16 cities or prefectures, i.e., Xi'an City, Xianyang City, Baoji City, Pingliang City, Guyuan City, Lanzhou City, Baiyin City, Wuwei City, Zhangye City, Jiayuguan City, JiuquanThe study area has a temperate continental climate, with mostly arid and semiarid areas. In winter, it is cold and has little rainfall. In summer, it is hot and dry. The temperature in the hottest month is above 20 \({}^{\circ}\)C, and the temperature in the coldest month is below 0 \({}^{\circ}\)C. The annual average temperature from 2010 to 2020 is about 11.01 \({}^{\circ}\)C. From 2010 to 2020, the average temperature in winter was about \(-\)1.3 \({}^{\circ}\)C; the average temperature in spring was about 12.6 \({}^{\circ}\)C; the average temperature in summer was about 21.7 \({}^{\circ}\)C, and the average temperature in autumn was about 11.5 \({}^{\circ}\)C. Besides, the annual average precipitation from 2010 to 2020 was 130-330 mm. From 2010 to 2020, the average precipitation in winter was about 15.15-37.56 mm; the average precipitation in spring was about 173.25-220.5 mm; the average precipitation in summer was about 185.5-268.34 mm, and the average precipitation in autumn was about 104.11-167.39 mm. The terrain there is mainly plateau, mountain, and basin. The eastern part mainly consists of the Inner Mongolia Plateau and the Loess Plateau, while the western part is alternately  
Figure 1: Overview of the Belt and Road Initiative.  
Figure 2: Overview of the study area.  
distributed with mountains and basins, such as Qilian Mountains, Tianshan Mountains, Turpan Basin, and Junggar Basin. The vegetation there is mainly grassland and desert. The eastern plateau is mainly grassland and gradually transits to desert grassland and desert to the west. A large area of desert is distributed in the western basin. Due to the arid climate, there are few rivers, and most of them are seasonal rivers and mainly inland rivers and inland lakes. There is lots of sandy soil. Affected by natural conditions, people there mainly develop oasis agriculture, irrigated agriculture, and animal husbandry. Transportation is relatively convenient, including expressways, railways, subways, aviation, pipelines, and other transportation modes. Coal, oil, natural gas, and other mineral resources are abundant, and the potential for energy development is huge. The culture has distinct characteristics, such as regionality, nationality, and pluralism.
## 4 Analysis of the Basic Unit of GSMLI
### Capacitance Selection
 \tag{11}\]  
From Equations (5) and (11),  
\[C_{2 opt}\geq\frac{I_{0 max}}{2\pi f\times V\times p}[cos\ (0.75-\ \varphi\ )\ -sin\ ( \varphi)] \tag{12}\]  
From Equations (3), (7), and (10), we get \(Q_{C1}\) as  
\[Q_{C1}\ =\ 2\times\int_{I_{t8}}^{T/4}I_{0 max}sin(\omega t-\varphi)dt\]  
\[Q_{C1}=\ \frac{I_{0 max}}{2\pi f}[cos\ (1.21-\ \varphi\ )\ -sin\ (\varphi)] \tag{13}\]  
From Equations (5) and (13), we get the optimal value of capacitance \(C_{1 opt}\):  
\[C_{2 opt}\geq\frac{I_{0 max}}{2\pi f\times V\times p}[cos\ (1.21-\ \varphi\ )\ -sin\ ( \varphi)] \tag{14}\]  
For plotting the graph between the optimal capacitance and phase angle for different values of voltage ripple, we take \(I_{0 max}\) = 4A and \(V\) = 20 volt. Figure 5c,e show the variation in optimal capacitances (\(C_{1 opt}\) and \(C_{2 opt}\)) with phase angle for different voltage ripples. From these figures, it is clear that, for a particular value of voltage ripple, the optimal capacitance decreases as the phase angle increases.
## 3 Background Theory
### Secret Sharing  
In this study, the use of secret sharing is aimed at supporting key management in the secure MQTT protocol. Secret sharing is a protocol scheme that supports multi-user communication by sharing sensitive information or a secret key into several parts. Such information can be recovered by a combination of part or all of the entity with a specified mechanism [41]. Secret sharing was first introduced by [PERSON] with the aim of providing a solution for sharing secret keys in cryptographic communication [42].  
Secret sharing protocols are widely used in our daily lives, such as in crypto currency wallets, for key management in vault by Hashicorp, and on Amazon Cloud HSM.  
Secret sharing research has developed both in terms of method and efficiency. In 2012, [PERSON] et al. designed a secret sharing to simplify the reconstruction process without involving other entities [43]. In 2017, [PERSON] and [PERSON] conducted a study to increase the efficiency of proactive secret sharing schemes in reducing communication traffic [44]. Another research project related to secret sharing was also carried out by [PERSON] et al. regarding secret sharing with the XOR model, which is intended for distributed systems with many users [19]. This secret sharing scheme proposes a formal method, multiple XOR-ED secret sharing approach, and One Time User Key.
# Numerical Assessment of the Seismic Vulnerability of Bridges within the Italian Road Network
## 1 Introduction
Given that the proper selection of the seismic input is fundamental to obtaining a realistic result from the assessment and that the records employed in the fragility analysis must be representative of the site conditions, recent research proposed the use of stochastic earthquake models to generate the seismic records [16] and studied the fragility of structures in near-fault regions [17]. The study of [PERSON] and [PERSON] [18] deals with the dynamic testing of a damaged bridge. The bridge is subjected to increasing damage levels. The natural frequencies of the structure vary as the damage progresses. Lower modes can be employed to gain information about the damage location. The retrofit of R.C. bridges can be performed in different ways. For instance, examples can be found in the literature on retrofit interventions using Ultra High Performance Fiber R.C. [19] or Stainless Steel Wire Mesh [20]. Clearly, these are not the only possible retrofit interventions that can be employed to improve the structural response of a bridge. [PERSON], [PERSON], and [PERSON] [21] investigated the effect of Buckling Restrained Braces and Self Centering Energy Dissipation devices on the seismic response of a bridge, while the study of [PERSON] and [PERSON] [22] treated seismic retrofit methods employing lead rubber bearings and rocking foundations. The most common criteria considered in the selection of a retrofit strategy or, in a more general sense, a bridge management strategy are associated cost, resilience enhancement, values of design parameters, risk, and network resilience [23; 24; 25]. The present research work aims at assessing the structural performance of five bridges belonging to the Italian Road Network, by using different techniques. The five case studies have been built around the same period, between the early 60s and mid-70s. At that time, there was a less developed understanding of the seismic behavior of structures, and crucial facets of the design process, such as bearing design and anti-seismic details, were addressed in a simplified way. For each of the case studies, fragility curves have been computed, by defining the target Limit States and the analysis method. The obtained results have been used then to evaluate the efficiency of the procedure set out by the new \"Guidelines for the classification and management of risk, safety evaluation, and monitoring of existing bridges\" [26], which were introduced in 2020 and will be herein referred to as \"the Guidelines\". In particular, the Level 2 assessment has been applied to the case studies, defining Structural and Foundational and Seismic Classes of Attention, allowing for evaluation of the proposed approach. The obtained results, in terms of fragility curves and Classes of Attention, are finally discussed.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
###### Abstract  
This article presents a circular polarized enclosed dielectric resonator antenna (DRA), operating at 5.8 GHz. The design consists of a twist DRA, which is enclosed in a box to give stability to the structure. The circular polarization of the antenna depends on the sense of twisting the top with respect to its base to achieve Left Hand Circular Polarization (LHCP) or Right Hand Circular Polarization (RHCP). The antenna was manufactured using 3D printing and low-loss dielectric filament. The measurement results show the two resonance frequencies and an axial ratio below 3 dB at the operational frequency, while exhibiting a bandwidth and gain compatible for unmanned aerial vehicle (UAV) applications.  
dielectric resonator antenna; circular polarization; 3D printing Article
## 2 Related Work
### Edge Computing and Object Detection Model Lightweight  
Edge computing is a way to fill the insufficiency of cloud computing. With the sharp increase in the number of terminal devices in the future, cloud computing will not be able to meet the needs of future network and computing costs. Fortunately, the development of embedded devices has enhanced the capabilities of edge computing to help cloud computing with data processing. Edge computing offers the advantages of low latency, low bandwidth requirements, and low cost. However, compared with cloud devices, edge devices are much weaker in terms of computing power and storage. Therefore, in order to obtain a lightweight model that can meet the hardware requirements of front-end embedded devices, the neural network model needs to be lightweight. For object detection tasks, there are currently three main ways to implement the deployment of models on edge embedded devices: lightweight detection model design, model pruning, and model quantization.  
Lightweight Model Design: Lightweight detection model design is to apply a more efficient convolution method or a more efficient structure to reduce the computing power requirement of the object detection model. Aiming at real-time detection of surface defects, [PERSON] [20] proposed a reusable and high-efficiency Inception-based MobileNet-SSD method for surface defect inspection in industrial environment. [PERSON] [21] proposed a lightweight object detection based on the MobileNet v2, YOLOv4 algorithm, and attentional feature fusion to address underwater object detection.  
Model Pruning: Model pruning refers to evaluating the importance of model weights according to a certain strategy and eliminating unimportant weights to reduce the amount of computation. To address the threat of drones intruding into high-security areas, [PERSON] [22] pruned the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Aiming at the problems of low detection accuracy and inaccurate positioning accuracy of light-weight network in traffic sign recognition task, [PERSON] [23] proposed an improved light-weight traffic sign recognition algorithm based on YOLOv4-Tiny.  
Model Quantification: The essence of model quantification is to convert floating-point operation into integer fixed-point operation, which can drastically reduce model storage requirements. [PERSON] [5] proposed a data-free quantization method for the CNN-based remote sensing detection model by using 5-bit quantification. [PERSON] [24] proposed a hybrid fixed point/binary deep neural network design methodology for object detection to achieve low-power consumption.
## 7 Bioprocess for Xanthophylls Production by Microalgae
### Cultivation Systems
#### 7.1.1 Open Systems  
The cost of construction and operation in open systems are reported to be much lower than for closed PBRs and the cultivation process is also simpler. Open ponds is the most commercially used method for cultures of microalgae, in which the medium flow occurs through a system of paddle-wheels. The latter keeps the cells in suspension and provides better mass transfer [122]. The low deepness in these open systems ensures the light penetration efficiency. The system flow is continuous, so nutrients are continuously supplied and microalgae are harvested at the same time [123]. This system is ideal for the growth of microorganisms that tolerate and can grow under extreme environment conditions such as high alkalinity (_Spirulina_ sp.), high salinity (_Dunaliella salina_) and nutrient-rich media (_Chlorella_ sp.). Indeed, the photoautotrophic culture mode has been extensively employed for _Dunaliella_ with the aim of carotenoid overproduction [124]. A two-step system, namely \"intensive cultivation\", allowed large-scale carotenoid production in _Dunaliella_. The aim of stage one is to promote biomass accumulation with a weak \(\beta\)-carotene-chlorophyll ratio; and in stage two, _Dunaliella_ culture is diluted three times to increase the light penetration to cells, and carotenogenesis after nitrogen depletion [79]. Therefore, it is always recommended to use open and raceway ponds for cultivating microalgae using a photoautotrophic growing condition to minimize contamination issues. However, microalgae that cannot grow in these specific environmental conditions, such as _Tetraselmis_ sp., _Isochrysis_ sp., _Crypthecodimium_ sp., and _Skeletonema_ sp., should not be cultured with this type of approach [125]. Some drawbacks are observed from this method; for example, it is difficult to control the conditions around the tank, such as the temperature and light, and there is a high risk of contamination by other algal/bacterial strains [126]. Other crucial factors could have a great influence on these systems amongst other large losses of water after evaporation, CO\({}_{2}\) diffusion into the atmosphere, and the need for large land areas [122]. Therefore, the closed systems seem to be preferred.