esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 1 Introduction
In the system, the transmitting antenna adopts a planar SPA structure with three elements, and the distance between antennas is 1/12 propagation wavelength, which breaks through the limitation of half a wavelength of the traditional 2 \(\times\) 2 MIMO system. The experimental results show that the changing tendency of error rate with the signal-to-noise ratio shares the same regularity with traditional MIMO system. The single RF link MIMO system can not only guarantee the performance of traditional MIMO, but greatly reduces the complexity of the system and the power loss, which realizes the miniaturization of the antenna. The spatial multiplexing of MIMO system can be realized through the SPA switched far field patterns, but the process of far field pattern fails in the hardware realization. Subsequently, the theory of using switching parasitic antennas to achieve the diversity of far field radiation pattern has gradually been improved [24; 25]. Now, in 2 \(\times\) 2 MIMO systems, the space between the two switching parasitic antennas can reach 1/16 of the wavelength [26]. It is worth noting that three elements can achieve two radiation patterns conversion in a 2 \(\times\) 2 MIMO system. If the MIMO system size increased by one, then the number of the switched parasitic antenna will increase by two [27], which requires all parasitic elements around the feeding unit to be evenly arranged, and leads to an increase in space. In conclusion, a large single RF link MIMO system based on the switched parasitic antenna is more suitable for the base station rather than the portable terminal, especially for cognitive radio applications [28]. In this paper, system level simulation was established in SystemVue software and the performance of single RF link MIMO system was analyzed comprehensively. The SystemVue software can provide rich design modules and an open environment for complex algorithm development and system level analysis of digital signal processing, and it also has the ability to generate real signal system for testing and verification with the help of all kinds of Agilent signal sources. Therefore, it is suitable for the situation in this paper to implement the system-level simulation that can directly evaluate the component characteristics that have an influence on the overall performance of the system. The rest of this paper is organized as follows. Section 2 introduces the structure, principle, and key technologies of a single RF link MIMO system based on SPA. In Section 3, the proposed single RFlink MIMO system model was simulated in SystemVue software. Section 4 presents the performance analysis of the single RF link MIMO system. Finally, the paper is concluded in Section 5.
# AC Microgrids Protection: A Digital Coordinated Adaptive Scheme
## 4 Case Studies and Simulation Results  
In this study, a modified test system, as shown in Figure 9, is used. In the studied MG, three DGUs operated as droop-based grid forming power converters, controlled by hierarchical droop, voltage, and current controller, are employed. A current saturation block is utilized for safe operation of power electronic converters in the current control layer. The electrical parameters of the system are shown in Table 1. It is worth noting that some concepts and control strategies such as type of earthing system [44], hierarchical control and operation of MG systems have been explained and described before in most contexts such as [4]. In comparison with the comprehensive method in [40], the proposed method not only presents an adaptive scheme for coordination, but also hierarchical control as well as current limitation of DGUs are considered in the simulations, which leads the case studies to be closer to the real world operational MGs.  
In order to evaluate the merits and effectiveness of the proposed comprehensive MG protection scheme, the illustrated MG in Figure 9 is simulated in Matlab/sim-power systems environment. Then, all types of fault such as LG, LL, LLG, and three-phase fault are examined in different locations for both of operation mode, i.e., islanded and gird-connected modes. By employing the proposed protection scheme, all the PDs operate and isolate the faults in a coordinated scheme. Furthermore, DGUs and loads are allowed by the proposed protection scheme to service after clearing the fault.
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
## 4 Result Analysis  
### Joints Opening Analysis  
S3 and S4 were selected for analysing the joints opening phenomenon, and the locations of monitoring points are shown in Figure 7. The curves of joints' opening value in phase III and IV are shown in Figure 8a,b.  
Figure 8: Shield tunnel segment joint opening value. (**a**) Joints’ opening value at S3. (**b**) Joints’ opening value at S4. (**c**) Maximum opening value (PGA = 0.3 g). (**d**) Maximum opening value (PGA = 0.5 g).  
Figure 7: Joints opening monitoring point.  
It can be observed that in phase III, the opening value of the joints continuously varies with the fluctuation of seismic loads and is positively correlated with the seismic intensity. The maximum joints' opening value during the seismic stage was statistically, as shown in Figure 8c,d. It can be seen that as the level of preload loss increases, the joints' opening value exhibits an increasing trend and the opening value of S4 is greater than that of S3. When the PGA = 0.3 g for S3, as shown in Figure 8c, preload loss causes the opening value to increase by 1077% compared to the condition without preload loss, whereas for S4, it increases by 247%. When the PGA = 0.5 g, as shown in Figure 8d, the opening value is much greater than PGA = 0.3 g, for S3, preload loss causes the opening value to increase by 50% compared to the condition without preload loss, whereas for S4, it increases by 40%. In summary, the maximum opening value at the segment joints increases with the level of preload loss, and the increase in PGA during earthquakes further amplifies this deterioration, making the impact of preload loss more pronounced.
## 3 Proposed Solution to Predict the AoA
### System Model and Mathematical Formulation
#### 3.3.3 Snapshot Matrix and Predictive Normalisation  
In a multi-antenna system with time-varying user mobility, the signal received at the antenna array must be aggregated over both space (antenna elements) and time (snapshots) to form the observation basis for AoA estimation and subsequent prediction. To this end, we construct a snapshot matrix. \(X_{u}\in\mathbb{C}^{N\times T}\) for each user, u, capturing the spatio-temporal characteristics of their received signal. The matrix is defined as follows:  
\[R_{u}=\begin{bmatrix}r_{1,u}(t_{1})&r_{1,u}(t_{2})&\dots&r_{1,u}(t_{T})\\ r_{2,u}(t_{1})&r_{2,u}(t_{2})&\dots&r_{2,u}(t_{T})\\ \vdots&\vdots&\ddots&\vdots\\ r_{N,u}(t_{1})&r_{N,u}(t_{1})&\dots&r_{N,u}(t_{T})\end{bmatrix} \tag{2}\]where \(N\) is the number of antenna elements and \(T\) is the number of time-domain snapshots considered per estimation window. This matrix formulation serves as the input for spatial covariance analysis, subspace decomposition (e.g., via MUSIC), and predictive learning (e.g., LR, SVR). Each column in \(X_{u}\) corresponds to a spatial sample of the received waveform at a particular time instant, t, and the rows span the antenna array dimension.  
The average signal and noise power for the \(i\)th antenna element over the observation window is computed as follows:  
\[P_{r,i}=\frac{1}{T}\sum_{t}\left|r_{i,u}(t)\right|^{2} \tag{3}\]  
\[P_{r,i}=\frac{1}{T}\sum_{t}\left|n_{i,u}(t)\right|^{2} \tag{4}\]  
Here, \(r_{i}(t)\) and \(n_{i}(t)\) represent the instantaneous signal and additive noise at the \(i^{\text{th}}\) element, respectively. These quantities may be computed individually for each user or globally across all users and antennas, depending on the normalisation strategy.  
Once the power levels are estimated, the received signal is scaled to produce a normalised signal \(P_{r}(t)\) as follows:  
\[z_{i,u}(t)=\frac{r_{i,u}(t)}{\sqrt{P_{r,u}}}+\frac{n_{i,u}(t)}{\sqrt{P_{n,i}}} \times 10^{-\frac{SNR}{2}} \tag{5}\]  
This equation ensures that the signal component is unity-normalised, while the noise component is scaled according to the target SNR, expressed in decibels. The term \(10^{-\frac{SNR}{20}}\) translates SNR from dB to a linear scale, applying an attenuation factor to the noise to reflect realistic receiver conditions.  
This modelling rigour makes the simulation environment highly suitable for evaluating the effectiveness of hybrid MUSIC-prediction techniques, adaptive regression models, and fallback mechanisms, like enhanced MUSIC scanning, under practical urban mobility patterns.
###### Abstract  
Quantitative evaluations of biomass accumulation after disturbances in forests are crucially important for elucidating and predicting forest carbon dynamics in order to understand the carbon sink/source activities. During early secondary succession, understory vegetation often affects sapling growth. However, reports on biomass recovery in naturally-regenerating sites are limited in Japan. Therefore, we traced annual or biennial changes in plant species, biomass, and net primary production (NPP) in a naturally regenerating site in Japan after windthrow and salvage-logging plantation for nine years. The catastrophic disturbance depleted the aboveground biomass (AGB) from 90.6 to 2.7 Mg\(\cdot\)ha\({}^{-1}\), changing understory dominant species from _Dryopteris_ spp. to _Rubusidae_. The mean understory AGB recovered to 4.7 Mg\(\cdot\)ha\({}^{-1}\) in seven years with the dominant species changing to invasive _Solidago gigantea_. Subsequently, patches of deciduous trees (mainly _Betula_ spp.) recovered whereas the understory AGB decreased. Mean understory NPP increased to 272 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) within seven years after the disturbance, but decreased thereafter to 189 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\). Total NPP stagnated despite increasing overstory NPP. The biomass accumulation is similar to that of naturally regenerating sites without increase of trees in boreal and temperate regions. Dense ground vegetation and low water and nutrient availability of the soil in the study site restrict the recovery of canopy-forming trees and eventually influence the biomass accumulation.  
aboveground biomass; carbon accumulation; overstory; sapling; understory; windthrow
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 1 Introduction
As the conveyor belt moves faster, cameras with a higher frame rate are needed to capture clear and stable images of the conveyor belt surface. Otherwise, missing detection or moving shadows may occur. At the same time, if the processing speed or prediction speed is not accelerated, the image input and signal output will be out of sync, which is easy to cause delay and lag, also affect the detection results. Therefore, this paper mainly discusses the application and performance of lightweight convolutional neural network in conveyor belt damage detection, which is suitable for scene resources with limited storage space and computing capacity, and also meets the needs of the development of high speed belt conveyor. There are various ways to achieve lightweighting of neural networks, in terms of the network structure, a target detection network could be divided into two parts, one is the backbone feature extraction network, and the other is the prediction network. The quality of the features extracted by the feature extraction network directly affects the prediction effect of the prediction network. Similarly, the number of parameters and calculations of the backbone feature extraction network also directly affect the detection speed of the target detection network. Generally, the number of parameters is positively correlated with the detection accuracy while negatively with the detection speed. At present, the tricks to reduce the amount of parameters in CNNs include: using separable convolution [33], depthwise separable convolution [33; 34; 35], group convolution [36]; using global pooling to replace the fully connected layers(FC) [37], using \(1\times 1\) filter to achieve dimensionality reduction [34], etc., which are more representative by SqueezeNet [38], MobileNet and ShuffleNet [39]. Among them, SqueezeNet adopts a well-designed compression and expansion structure, MobileNet uses a more efficient depthwise separable convolution, and ShuffleNet proposes a channel shuffling operation, which further reduces the computational complexity of the model. The lightweight measures taken in this article are based on MobileNet. By replacing the backbone CSPDarknet53 of Yolov4 with MobileNet, the use of depthwise separable convolution is directly realized, and a lightweight model of Yolov4 network is obtained,and then it is applied to the detection of conveyor belt damage. Theoretically, the detection speed should be faster, and can meet the needs of the development of high-speed conveyor. Section 2 explains the structure of the lightweight neural network and its implementation method, also with the operating environment and related parameter settings; Results are presented in Section 3, then the conclusion is highlighted in Section 4.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 2 Related Work
### Machine-Learning-Based Sentiment Analysis  
The machine-learning-based approach involves training sentiment classifiers using both labeled and unlabeled data. This approach utilizes machine learning algorithms for feature extraction and predicts the sentiment polarity of the input. It can be broadly categorized into three types: supervised machine learning, semi-supervised machine learning, and unsupervised machine learning.  
Supervised learning algorithms typically require a substantial amount of manually labeled data to train the model. Common supervised machine learning algorithms include Support Vector Machine (SVM) [7; 8], Naive Bayesian (NB) [9], and K-Nearest Neighbors (KNN) [10; 11]. In a comparative analysis by [PERSON] et al. [12], the NB algorithm was found to outperform the SVM algorithm when training datasets had larger data sizes. [PERSON] et al. [13] utilized the KNN algorithm to classify input sentences into positive, negative, and neutral categories. They employed the N-gram algorithm for feature extraction and achieved better classification performance results compared to the SVM algorithm. [PERSON] et al. [14] compared the performance of different machine learning algorithms for sentiment classification on a movie review dataset, achieving a maximum accuracy of 82.9%. [PERSON] et al. [15] employed the KNN algorithm to measure the Euclidean distance of the input text, enabling successful classification.  
After applying the SVM algorithm to determine the sentiment labels of the input text, it was found that the KNN algorithm outperforms SVM in sentiment analysis due to its lower experimental dimensions. [PERSON] et al. [16] utilized the Latent Dirichlet Allocation Model (LDA), a machine learning algorithm, to analyze discussions related to the novel coronavirus. This approach helped identify the topics discussed and the sentiments expressed within those topics. [PERSON] et al. [17] employed an integrated-learning-based approach and the Synthetic Minority Oversampling Technique (SMOTE2) to extract features from sentiment words. Their final experiment yielded optimal results on the dataset. [PERSON] et al. [18] proposed the Emoticon Space Model (ESM), which utilizes a larger number of emoticons to construct word representations from a vast amount of unlabeled data. This model enables and enhances sentiment analysis by projecting words and microblog posts into the emoticon space. Unsupervised learning methods do not require a large amount of manually labeled data, but their accuracy is generally lower.
## 2 Materials and Methods
### Characterization of the Granules
#### 2.2.2 Fourier-Transform Infrared Spectroscopy (FTIR)  
FTIR spectra of the synthesized samples were recorded using an FTIR spectrometer equipped with an attenuated total reflectance (ATR) accessory (Alpha, Bruker, Bremen, Germany). Spectra were collected in the range of 1350-450 cm\({}^{-1}\) at a spectral resolution of 4 cm\({}^{-1}\), averaging 25 scans.
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 3 Underwater Radiation Noise Prediction Method for Cabin Structures
### Underwater Radiation Noise Prediction Method Based on the Wave Superposition Method  
The wave superposition method only needs to obtain the sound field distribution on the outer surface of the cabin structure to calculate the radiation noise at any position in the far field. The basic principle is to configure several equivalent point sound sources inside the structure, generally monopole or dipole sources. The source intensity of the configured equivalent source is calculated using the sound field distribution on the external surface of the structure. Finally, the radiation sound field of the structure is calculated by the linear superposition of the radiation sound field of the equivalent source.  
The radiated sound pressure of a series of simple sound sources located inside the structure at a field point \(r\) in space can be expressed as follows:  
\[p\left(\overset{\rightarrow}{r}_{m}\right)=\int_{R}j\rho_{e}c_{e}kq\left( \overset{\rightarrow}{r}_{0}\right)G\left(\overset{\rightarrow}{r}_{m}, \overset{\rightarrow}{r}_{0}\right)dR\ (m=1,2,\cdots M) \tag{18}\]  
where \(\rho_{e}\) is the fluid density, \(c_{e}\) is the speed of sound,\(k\) is the wave number, \(q\left(\overset{\rightarrow}{r}_{0}\right)\) is the source intensity of the simple source inside the structure, and \(G\left(\overset{\rightarrow}{r}_{m},\overset{\rightarrow}{r}_{0}\right)\) is the Green's function between point \(r_{m}\) and \(r_{0}\) under free-field conditions, which can be expressed as follows:  
\[G\left(\overset{\rightarrow}{r}_{m},\overset{\rightarrow}{r}_{0}\right)= \frac{e^{ikr}}{4\pi r} \tag{19}\]  
where \(r\) is the distance between the prediction point and the simple source, \(r=\left|\overset{\rightarrow}{r}_{m}-\overset{\rightarrow}{r}_{0}\right|\).  
By matching the external surface radiation sound pressure of the structure, the simple source strength configured in the structure can be solved, which can be expressed in a matrix form as follows:  
\[Y_{H}=G_{H}Q \tag{20}\]  
where \(Y_{H}\) is the response matrix of measuring points on the surface outside the structure, \(G_{H}\) is the Green's function from the equivalent source point to the measuring point on the surface outside the structure under the free-field condition, and \(Q\) is the equivalent stream to be solved. With the help of the singular value decomposition method, it can be expressed as follows:  
\[Q=V\Sigma^{-1}U^{\mathrm{T}}P_{H} \tag{21}\]  
where \(G_{H}=U\Sigma V^{\mathrm{T}}\).
# Investigation of Functionality of Vehicle Crumple Zones Recovered after a Traffic Accident
## 2 Experiments and Their Results
### Quasi-Static Investigation of Buckling of the Part of Crumpling Zone  
The investigation aimed to identify the effect of damages of crumple zone elements damaged during a traffic accident and subjected to post-accident repair on the amount of absorbed energy of the longitudinal members under different types of load. For the investigation, a fragment of the Toyota Echo crumpling zone with initial length \(L\) = 170 mm was chosen. The specimen consisted of two parts joined by spot welding. The diameter of the spot-welded joint was 5 mm. The specimen featured eight spot-welded joints. The spot-welded joints were positioned symmetrically (Figure 6a).  
The wall thickness of the parts was \(thk_{1}\) = 1.4 mm and \(thk_{2}\) = 0.7 mm. For accurate positioning and uniform distribution of the load of the sample during the experiment, 10 mm thick alignment plates had been welded to the ends of the specimen (Figure 6b). The experiments confirmed that the plates provided uniform and perpendicular load distribution in relation to the specimen cross-section during the experiment.  
According to Figure 7, the critical buckling force \(F_{\sigma,1}\) = 46 kN was reached during the first experiment. During repeated buckling of the sample, the critical buckling force achieved was \(F_{\sigma,2}\) = 37 kN, that is, 20% lower than during the first experiment. Meanwhile, the amount of absorbed energy calculated after specimen deformation at a distance of 17.5 mm distance decreased by only 5% (\(EA_{1}\) = 390 J, \(EA_{2}\) = 370 J).  
The quasi-static in situ experiments performed on the crumple zone part of a Toyota Echo showed the following:  
Figure 6: P1 specimen used for the experiment: before the test (**a**); after the first test (**b**); after the second test (**c**); (1—positions of the spot-welded joints; 2—fixing plates).  
Figure 7: Deformation forces obtained during quasi-static experiments: axial displacement diagrams (**a**) and absorbed energy amount-axial displacement diagrams (**b**); (1—original part; 2—part with the restored geometry).  
* Critical buckling force decreased by 20% during the quasi-static buckling of the specimen with the restored geometry;
* The amount of absorbed energy decreased by 5% after the specimen to the same length;
* Theoretical assumptions on the effect of minor geometric deviations on the amount of absorbed energy in case of considerable deformations were verified.  
Even where the crumple zone elements are carefully and thoroughly straightened back, they could be claimed to have unevenness which may be expressed in the numerical models as 1 mm deflections directed inside.