esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Rician K-Factor Characteristics
### Rician K-Factor in Complex Propagation Environment
Also, although not shown in the figure, the radiation gain of the transmitting and receiving antennas in the x-axis direction, where Buildings B and C are located, is very small, so the effect of the reflected waves from Buildings B and C is suppressed. Therefore, as shown in Figure 13a, it can be confirmed that the Rician K-factor is large, and the variation in the Rician K-factor by multiple measurements is small. In addition, it can be confirmed that the difference in path length between the direct wave and the reflected wave from Building A at \(H\) = 30 m is shorter than that at \(H\) = 10 m, indicating that the difference in path power between the direct wave and the reflected wave from Building A at \(H\) = 30 m is small. As a result, as shown in Figure 13a, it is considered that the Rician K-factor gradually decreases with the increase in the UAV altitude. On the other hand, when the antenna is set to vertical installation, the directivity is a Figure 8 shape in the \(zy\)-plane. Since the transmitting and receiving directions change significantly depending on the UAV altitude, the radiation gain also changes. At \(H\) = 10 m, the direct wave and the reflected wave from Building A can be transmitted and received with relatively high radiation gain. In contrast, at \(H\) = 30 m, the radiation gain in the direction of both the direct wave and the reflected wave from Building A is small, so the received power of the direct wave and the reflected wave from Building A is low, even  
Figure 14: Radiation pattern of two antenna installations for each path angle in a complex propagation environment. though the path is the same as in the horizontal installation. In addition, since the directivity in the \(xy\)-plane is omnidirectional and the radiation gain is high, the path power of the reflected waves not only from Building A but also from Buildings B and C increases, so the effect of the reflected waves from Buildings B and C is large. This makes the Rician K-factor extremely low. It is concluded from these results that stable communication can be expected by setting to horizontal installation rather than vertical installation because the Rician K-factor is high.
## 2 Methods
### Search Strategy and Inclusion/Exclusion Criteria  
To select and research relevant papers, first of all, we selected databases, including _Scopus_ and _IEEE Xplore_ repositories. Then, we defined the searching terms, and the entry query was as follows: 'Machine Learning' AND 'Air Quality Predict\" OR 'Air Pollut\". The next step was year and source type restrictions by selecting journal papers and conference proceedings published since 2002. The output of this step provided 316 papers.  
It should be noted that recently [PERSON] and [PERSON] published a paper about 'Machine learning approaches for outdoor air quality modelling: A systematic review' [11]. By reviewing this paper, we have defined key features which were taken into consideration during our study. In the first place, we have narrowed the scope of the models by choosing only forecasting models, while the authors mentioned above also included papers concentrated on the estimation models. Another reduction is that we selected papers in which only sensor data in the smart cities context are being used. After these steps and after excluding duplicated manuscripts and reviewing titles and abstracts, the filtration output reached 131. Finally, applying quality assessment, irrelevant papers were excluded, and as a result, we had 41 selected papers. The key questions, on which we focused during the quality assessment, are listed below:  
1. Are the research aims clearly specified?
2. Was the study designed to achieve these aims?
3. Are the used techniques clearly described and their selection justified?
4. Are the data collection methods adequately described?
5. Is the purpose of the data analysis clear?
6. Are the findings convincing?
7. How clear are the links between data, interpretation and conclusions?  
The inclusion and exclusion criteria used during the review are listed in Table 1 and the overall workflow of the review is represented in Figure 1.  
Figure 1: Review workflow.
# Neural Network-Enhanced Fault Diagnosis of Robot Joints
## 1 Introduction
[12] proposes a neural network-based fault diagnosis and fault-tolerant control method for mobile robot control where, firstly, the neural network state observer is trained by a real nonlinear control system, and then the faults in the control system are detected and judged based on the residuals between the actual system output and the neural network observer output. This approach is common to this study in that both identify errors by comparing theoretical and practical results, but the disadvantage is that fault identification based on non-linear models requires more calculations and has a certain delay in identifying faults in real time. There is a similar study that collects the trajectories of different faulty robotic arm movements to build a database and train it with the network to finally achieve fault classification [2]. The disadvantage of this research is that the results obtained by fault classification with a single feature have low credibility and it does not give the possibilities for industrial applications, i.e., it does not separate faulty situations from non-faulty situations, which becomes a disadvantage of the application of this research. In this paper, a BP classification neural network-based fault diagnosis method for robotic arms is developed with the goal of multi-stage and high-precision fault diagnosis of robotic arms, with robotic arm motion as the object of study. The neural network is fully capable of automatically weighting and classifying data with different characteristics by virtue of its ability to learn data samples to approximate the mapping relationship describing a nonlinear system, and the algorithmic feature of weight updating. The object of this study is the UR10 robotic arm [13], which has become one of the most widely used industrial robots due to its collaborative nature, ease of programming and flexibility. The rest of the study includes the following sections. Section 2 gives the forward kinematics algorithm for the generation of trajectories by the robotic arm and the neural network algorithm used for classification. Section 3 describes the methods and principles of training the network with data-sets consisting of different features. Section 4 demonstrates the diagnosis procedure with simulation operations, where the performance of the classifier is evaluated by more metrics. Section 5 summarises the study.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 2 Problem Statement
### Architecture of the Previous Virtual Linkage  
Figure 2 shows the hierarchical architecture of the original virtual linkage approach proposed in Reference [18]. The states of the virtual linkage are implemented in a virtual linkage server and broadcast to all the virtual link servers. Each virtual link's state is in turn calculated in the corresponding virtual link server and transmits to all the virtual link members. As can be seen in Figure 2, there is a demand for the virtual link server \(F_{vli}\) to exchange message with all the agents which belong to the corresponding virtual link. Meanwhile, the virtual linkage server \(F\) also has to communicate with all the virtual link servers. The disadvantages of this hierarchical architecture lie in two aspects. First, it does not scale well with the number of agents in the team with limited communication bandwidth. Second, the virtual linkage server might lose communication with some virtual link servers when the agent group covers a large area.  
Figure 1: (**a**) Principle of virtual structure and virtual linkage. (**b**) Different formation using a specified virtual linkage.  
Figure 2: Hierarchical architecture of the previous virtual linkage.
## 2 Frame Model Design and Structural Analysis
### Establishing Finite Element Model
#### 2.2.2 Establishment Process  
This article conducts simulation analysis and optimization design in the Hypermesh environment. The geometric model is imported into CATIA V5 to create the finite element analysis model. The steps are as follows:  
1. Establishing materials: The frame is made of 4130 steel, also known as 30 CrMo, with an elastic modulus of 2.0510 MPa, a Poisson's ratio of 0.33, a density of 7.8510 g/cm\({}^{3}\), and an allowable stress of approximately 390 MPa;
2. Unit selection: Using Hyperbeam, create a beam unit with a uniform outer diameter of 25.4 mm for pipes and individual pipes with an outer diameter of 20 mm. Following the rules of the competition frame design section, establish four different wall thicknesses for the steel pipes: 20 mm (outer diameter) \(\times\) 2.2 mm (wall thickness), 25.4 mm \(\times\) 2.4 mm, 25.4 mm \(\times\) 1.65 mm, and 25.4 mm \(\times\) 1.25 mm;
3. Establish attributes: Create four attributes with the same names as the previously established four beam elements (for easy matching). Ensure that they correspond one by one with the beam section;
4. Grid divisions: Divide the pipe fittings into grids and set the grid size to 10 mm for efficient calculation. The established finite element model of the vehicle frame is shown in Figure 1.
## 1 Introduction
It includes geometric matching algorithms, probabilistic matching algorithms, tight coupling matching algorithms, and comprehensive matching algorithms [22]. Among these categories, geometric matching represents the most fundamental and intuitive approach, encompassing point-to-point matching. Point-to-point matching can be considered equivalent to a classical search problem, and a subset of feature-matching problems, which are based on similarity measures, falls within the domain of point-to-point matching algorithms. For instance, in reference [23], track irregularities are employed as a background map to correct train positioning errors. However, maps based on track irregularities are difficult to obtain, especially after railroad lines have been maintained and rebuilt. [PERSON] endeavored to attain train positioning through map matching. The essence of his approach revolved around matching the angular rate data derived from a known map's track segments with the measurements recorded by the onboard gyroscope. Nevertheless, in instances when the train was traversing a straight stretch, a successful matched proved elusive. The approach taken in Reference [17] primarily involves using map matching to vertically project the fused location onto the electronic track map. This projection method effectively constrains the lateral error of the train, but is less effective in correcting longitudinal errors. Compared with existing methods in the literature, the digital track-based map-assisted SINS/OD algorithm proposed in this paper can partially eliminate the role of balises and reduce the construction and operation costs; in addition, the construction of the basic layer of the track map requires only the three-dimensional discrete point coordinates of the track median, and it can effectively correct the bias error of the IMU. Lastly, the method in this paper can effectively constrain the lateral errors, which is independent of the curvature of the track. This paper has two main contributions. Firstly, it proposes a map-matching method using integrated weighting that can effectively constrain the lateral and longitudinal errors;secondly, it proposes a filtering framework that fuses the electronic track map with the existing SINS/DR, which can improve the positioning accuracy of the train while correcting for the inertial error. The remainder of this article is organized as follows. Section 2 presents the construction and characteristics of the digital track map that can be used for train localization. Section 3 describes the onboard train localization algorithm, which is the focus of this paper. Section 4 describes the experiments and performance analysis. Concluding remarks are summarized in Section 5.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Angle Determination Method  
When the IR intensities have been recorded on the photodiodes for each of the three beacons, the angles \(\varphi_{i}\) should be determined (see Figure 2). It seems natural to assume that the angle \(\varphi_{i}\) is the angle for which the measured spectrum of a given beacon reaches its maximum. Unfortunately, it cannot be assumed that this angle is equal to the angular position \(D_{j}\) of the j-th measuring photodiode for which the IR intensity is maximum because the measuring system consists of 32 photodiodes, which gives an intensity measurement every 11.25 degrees. In practice, the angle of the maximum value of the beacon spectrum will never be equal to \(D_{j}\) but will be somewhere between \(D_{j}\) and \(D_{j+1}\). It can therefore be seen that an analysis of the measured spectrum must be carried out in order to estimate the position of the maximum intensity measured by the receiver. In our research, we tested two approaches, the results of which will be presented in this chapter.  
In our triangulation system, we used BPV22 NF receiver photodiodes [49], for which the normalized radiant sensitivity profile plot can be found in the manufacturer's documentation. It turns out that this profile can be described by a sixth-order polynomial:  
\[I_{s}(\theta)=0.0706\theta^{6}-0.2216\theta^{4}-0.2899\theta^{2}+1 \tag{1}\]  
Figure 2: Principle of the STS triangulation system.  
as shown in Figure 3.  
The obtained polynomial allows us to simulate the measurements recorded by the receiver photodiodes and then analyze the selected methods for the angle determination.
## 1 Introduction
### Background
LQR results in a fixed feedback gain matrix that is computed offline, while MPC requires solving an optimization problem at each time step, which can adapt to changing conditions. Reinforcement learning (RL) differs from the previously mentioned techniques in that it typically does not require a model of the system to be controlled. The RL controller (also called the agent) trains control policies through trial and error by interacting with the environment. It continuously improves using rewards received from the environment, without requiring a model of the system. Deep reinforcement learning, in particular, is deemed to be particularly suitable to deal with complex real-life environments [28]. Thus, RL is a powerful tool to deal with highly variable scenarios, complex environments, or where a system model is difficult to define. Its downside is that it often requires extensive training effort [29].
## 3 Structure of the Proposed Biomodal Multitask Network
### Training and Testing Data  
One of the main problems that arise when building a multimodal biometric system is the lack of a database that contains enough data on multiple traits/modalities to train and test robust models. Publicly available multimodal databases may contain insufficient data for a particular trait, while private databases may not be accessible. To overcome these issues many authors resort to building their own multimodal databases that meet the requirements of their work. A large number of studies on multimodal biometric recognition are performed using virtual databases, i.e., databases created by combining datasets of specific traits and pairing their respective subjects [10; 39]. Hence, we created a virtual database by combining BIOMEX-DB and VidTimit databases.  
BIOMEX-DB is a multimodal database created by our research group, composed of speech, video, and electroencephalogram (EEG) biometric data. Speech data was recorded at a 16 KHz sample rate. The vocabulary is comprised of pronunciations in Spanish language of 20 random digit sequences. The first 10 sequences have a length of 10 digits, while the remaining sequences are 5 digits long. A video feed was recorded showing the faces as the volunteers pronounced their digit sequences. During the pronunciations, EEG signals were simultaneously acquired with a 14 channels Emotiw wireless headset. This database contains the biometric data of 51 subjects; 39 of them have information of the 3 modalities while the remaining 12 subjects lack video data. A complete description of the database can be found in Ref. [49]. VidTimit [50] is a database comprised of video and audio recordings of 43 subjects reciting 10 sentences from the TIMIT database. Volunteers have 10 audio recordings and several image frames extracted from their videos. The speech dataset of BIOMEX-DB has a maximum of 51 subjects, thus we used that amount as the population of our experiments. In order to complete our virtual database we pooled the image datasets from BIOMEX-DB and VidTimit. Then we randomly selected 51 subjects from the pooled set and paired them with the ones of the speech dataset. Our virtual database had a balanced gender representation: 26 males and 25 females. In the following subsections we describe the preprocessing of speech and face data.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 3 Background and Motivation
### Background  
DMA is an excellent feature of modern computer systems. With the help of DMA, applications in OS can transfer data between specific PCIe-based devices and the main memory independently, without involving the CPU. Without DMA, when a process performs I/O operations, it typically fully occupies the CPU for the entire life cycle of the I/O requests, making the CPU unable to perform other works. With DMA, the CPU only needs to initialize the data transfer, such as the data's direction, size, and location. CPU can then do other work. This feature is critical to improving the performance of asynchronous I/O requests because the CPU does not need to wait for relatively slow I/O data transfers.  
Compared to the DMA in kernel space, user-level DMA is a more lightweight data transfer mode. It allows users to access data for DMA transfer or control DMA transfer from the user space. Specifically, NVMe SSDs can be accessed directly through UIO (user space I/O) or VFO (virtual function I/O) at the user level. Based on UIO and VFO, users can implement user-level DMA, assigning a hardware device to a process that allows the process to operate and read/write the device. However, UIO and VFO must solve a critical problem: ensuring that physical memory is in place during the DMA process. Currently, the mainstream accepted method is manually pinning the physical memory pages. Pinning the memory pages makes these pages unable to be modified (e.g., page swapping). Pinning memory pages can cause a certain degree of I/O performance loss, especially for I/O-intensive applications.
## 3 Results
### Grape Yield per Vine and Must Parameters
#### 3.3.1 Fermentations at Location M
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vinage/Crop Load (C, CT) 1**} & \multicolumn{2}{c}{\(p\) Values 2**} & \multicolumn{2}{c}{**Vinage/Ferm. Type**} & \(p\) Values \\  & **2020** & **2021** & & & & & & 2021 \\ \hline
**B-C** & **B-CT** & **B-C** (**B-C\_iAF**) & **B-CT** & **T** & **V** & **T**\(\times\) **V** & **B-C\_aAF 3** & **T** \\ \hline Ethyl esters of strain chain fatty acids (EEFA) (mg/L) & & & & & & & & \\ \hline Ethyl butyrate (20) & \(132.3\pm 2.21\) & \(128.7\pm 28.7\) & \(178.7\pm 22.25\) & \(184.3\pm 10.2\) & ns & **\({}^{**}\)** & ns & \(72.9\pm 10.7\) b & **\({}^{**}\)** \\ Ethyl deny diconate (200) & \(71.3\pm 1.5\) & \(74.0\pm 1.0\) & \(68.0\pm 8.7\) & \(78.7\pm 2.1\) & ns & ns & \(89.9\pm 1.6\) & ns \\ Ethyl deny diconate (2) & \(277.7\pm 5.0\) & \(283.0\pm 1.0\) & \(232.0\pm 39.0\) & \(299.7\pm 12.6\) & ns & \({}^{*}\) & ns & \(86.6\pm 11.0\) & **\({}^{**}\)** \\ Ethyl dodecanoate (640) & \(5.00\pm 0.00\) & \(6.00\pm 1.00\) & \(17.3\pm 2.5\) & \(18.6\pm 1.2\) & ns & \({}^{*}\) & ns & \(144.0\pm 0.9\) & ns \\ Ethyl hexanoate (5) & \(256.7\pm 7.6\) & \(255.7\pm 4.2\) & \(163.3\pm 2.8\) & \(163.3\pm 4.2\) & ns & ns & \(89.7\pm 4.7\) & ns \\ Ethyl palmitate (1.5) & \(50.0\pm 3.46\) & \(40.0\pm 1.00\) & \(38.0\pm 40.9\) & \(52.3\pm 7.8\) & ns & ns & \(30.9\pm 3.1\) & ns \\ SUM EFAs & \(748.0\pm 26.5\) & \(750.3\pm 27.3\) & \(697.3\pm 91.4\) & \(787.0\pm 16.5\) & ns & ns & ns & \(334.4\pm 18.8\) & ns \\ \hline Acetates (mg/L) & & & & & & & & \\ \hline Hexyl acetate (670) & \(5.63\pm 2.29\) & \(3.63\pm 1.12\) & nd \({}^{**}\) & ns & ns & ns & nd & / \\ Isomany acetate (30) & \(5.70\pm 0.52\) & \(5.83\pm 0.50\) & \(3.40\pm 0.61\) & \(4.30\pm 0.17\) & ns & \({}^{**}\)** & ns & \(3.79\pm 0.43\) & ns \\ \(2\)-Phenyl acetate (250) & \(33.7\pm 0.6\) & \(37.3\pm 1.2\) & \(34.3\pm 4.0\) & \(42.3\pm 2.5\) & ns & \({}^{*}\) & ns & \(54.3\pm 2.0\) a & **\({}^{**}\)** \\ SUM acetates & \(45.0\pm 3.2\) & \(46.8\pm 0.3\) & \(37.7\pm 4.6\) b & \(46.6\pm 27.2\) & \({}^{*}\) & ns & ns & \(58.1\pm 2.0\) a & **\({}^{**}\)** \\ \hline \hline \end{tabular}   
Alcohols (mg/L)  
1-Hexamid (8) & \(1.57\pm 0.02\) & \(1.64\pm 0.003\) & \(0.79\pm 0.02\) c & \(0.87\pm 0.04\) b & **\({}^{**}\)** & **\({}^{**}\)** & ns & \(0.92\pm 0.08\) & ns \\ Benzyl alcohol (200) & \(0.76\pm 0.00\) & \(0.67\pm 0.006\) & \(0.56\pm 0.01\) & \(0.58\pm 0.03\) & ns & **\({}^{**}\)** & \({}^{**}\)** & \({}^{**}\)** & \({}^{**}\)** \\ \(2\)-3-hexami-1 (0.4) & \(0.03\pm 0.001\) & \(0.01\pm 0.001\) & \(0.00\pm 0.001\) & \(0.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Cloud Server Configuration  
The trend of big data and cloud computing in recent years has led to the construction of cloud servers. Cloud servers can achieve centralized processing, which is beneficial for data maintenance and management. The crowd sensing crack collection system collects cracks through participants, and the centralized processing of the cloud server is conducive to user management, crack information processing, and utilization, providing decision-making support for bridge maintenance-related units. The issues mainly involved in cloud server configuration are the selection of the cloud platform and the hardware configuration of the server.