esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 3 Theory and Algorithms
### FWS Link Budget

Table 1: Values of parameters in the link budget equation. where \(\rho_{0}=7.5\) g/m\({}^{3}\) is the standard ground-level water vapor density, \(h\) is the location altitude (km) and \(h_{0}=2\) km is the scale height. The dry air pressure can be found by subtracting the water vapor partial pressure from the total barometric air pressure (\(p_{\text{dry air}}=p_{b}-p_{\text{water vapour}}\)). The formulas for the line shape factors \(F_{io}\) and \(F_{iw}\), which also depend on the dry air and water vapor pressures, are given in detail in reference [29]. Thus, it can be seen from Equations (3)-(8) that by measuring the barometric air pressure (\(p_{b}\)) and the temperature (\(T\)) at the link location, the gaseous attenuation (\(A_{gas}\)) can be calculated. The barometric air pressure and temperature data have been collected over an 8-month period in 2022, between March and October, at the experiment location. The average air pressure (\(p_{average}\)) is 968.2 hPa with a standard deviation of 2.5 hPa, and the temperature varies between 20.4 and 36.3 \({}^{\circ}\)C. During these months, the weather is governed by low-pressure systems and high temperatures. We calculated the gaseous attenuation at a link distance of 150 m as the function of temperatures at 3 air pressure values: \(p_{average}\), \(p_{average}+2 SD\) and \(p_{average}-2 SD\), as shown in Figure 4. Thus, the value of \(A_{gas}\) should fall in the range of 0.899-1.087 dB. The final three parameters in Equation (1) are thermal noise, the total system noise figure and the margin, respectively. These parameters can be estimated for a given link as follows. The thermal noise is calculated as  
\[N_{thermal}=10\log_{10}\left(\frac{kBT}{1\text{ mW}}\right), \tag{9}\]  
where \(k\) is the Boltzmann's constant, \(B\) is the signal bandwidth (Hz) and \(T\) is the absolute temperature (K). \(N_{system}\), the total noise figure of the system, is a constant value for a particular link. Finally, the parameter \(M\) is an additional margin of the link budget. The values of losses in the link budget equation, excluding rain and wind-induced attenuation, are summarized in Table 1. These values are almost constant in the FWS link; thus, they have little effects on the dynamic performance. On the other hand, the losses due to rain (\(A_{rain}\)) and wind (\(A_{wind}\)) greatly depend on weather conditions, which will be discussed in the following sections. Figure 4: The range of gaseous attenuation calculated from the measured data.
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 1 Introduction
Eliminating systematic error is the basis for improving the accuracy of target geo-location since it directly affects the geo-location accuracy and the filtering algorithms [6; 13; 14; 15; 16; 17] cannot eliminate systematic error. The geo-location error will increase along with the increase of the distance to the target with systematic error. At a certain distance, it will become the main error that affects the geo-location accuracy. So, eliminating the systematic error is of great significance to improve the accuracy of geo-location. The method proposed here is one kind of boresight calibration. However, it is different from those methods. First, boresight calibration methods usually solve the misalignments between the image coordinate system and the body coordinate system established by the inertial instruments [26; 27]. While this method mainly focuses on airborne optoelectronic platform which always has additional rotation axes such as azimuth and pitch axes. Secondly, the transformations are usually established for various photogrammatic bundle adjustment systems [26; 27; 28]. While this method establishes those transformations through the distance measured by the laser rangefinder and one target for each measurement, so the method proposed in this article is a special boresight calibration with more axes and a laser rangefinder to measure the distance between GCPs and the imaging system. The primary contribution of this paper is that a geo-location systematic error correction method is proposed. This method has the following advantages:  
This method can correct the systematic error from the manufacture and assemble which can't be eliminated by laboratory equipment. For example, after the photoelectric payload is installed on the UAV, due to the size and other issues, it is difficult for the laboratory equipment to correct the installation error between the aircraft and the payload. This method is easy to implement. Unlike the other laboratory methods [13; 24], this method doesn't need special equipment and the systematic error can be obtained through flight experiments and control points on the ground. The rest of paper is organized as follows: Section 2.1 briefly presents the overall framework of the geo-localization system. Section 2.2 presents the reference frames and transformations required for the geo-location system. Section 2.3 presents the ground target geo-location model. Section 2.4 presents the geo-location error analysis and methods to get the systematic error using control points. Section 3 presents the results of systematic error correction methods from both Monte Carlo analysis and in-flight experiments. Section 4 presents the discussion and our conclusions are given in Section 5.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 2 Application Background and Method Principles  
### Survey Background  
The survey studied in this paper is from the Sanhu area of the Qaidam Basin, and its surface is mostly composed of aeolian monadnock, sand dunes, sandbag, saline-alkali land, hard-alkali land, salt marsh, etc. The Sanhu area is a biogas enrichment area. Affected by subsurface gas, as shown in Figure 1 the energy and frequency absorption of P-wave seismic data in the seismic anomaly area is serious, the data signal-to-noise ratio is low, and the structure is distorted [31]. It is difficult to obtain reliable imaging of low-relief structures.  
At the same time, there are unique geological characteristics of the Quaternary system in the Sanhu area, such as weak diagenesis, a thin sand-mudstone interbed, an indiscriminate hydrocarbon source and reservoir, as well as the succession and relay of gas generation. Under this unique geological background, a high resolution is required for imaging the structure in this area, and SH shear wave imaging has a high stratigraphic resolution due to its low velocity and wide frequency band.
## 4 Control Strategy for Suppressing ZSCC
First of all, the three-phase current on the first converter line is collected, and the ZSCC is calculated. Then, through the PIQR controller, zero-vector duty ratio adjusting factor is initially generated, and the 3 rd, 9 th, and 15 th harmonics in the ZSCC are suppressed. Afterwards, the duty ratio of the nonzero vector in the SVPWM controller of the two converters is collected, and the disturbance variable \(\Delta D_{12}\) is calculated. After multiplying the disturbance by the coefficient, it is used as the feedforward compensation regulating factor. Finally, the switching signal of the second converter is modified by this regulating factor. Figure 7: Control block diagram of suppressing ZSCC in two paralleled converters using the proposed control strategy.
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
###### Abstract  
The research on the influence of radial stress on the mechanical properties of an anchorage structure has important theoretical and practical significance for optimizing the design of anchorage structures and saving support cost. Firstly, based on the phenomenon of concrete splitting found in the laboratory model test, the influence of radial stress on the mechanical properties of an anchorage structure is analyzed. Secondly, using the criterion of maximum tensile stress and the Mohr-Coulomb criterion, the influence of radial stress on the mechanical properties of rock around the borehole is analyzed. Finally, the influence of radial stress on the shear stress at interface and the ultimate bearing capacity of the anchorage structure is studied by numerical simulation. The results show that the existence of radial stress in the anchorage section greatly improves the bearing capacity of the anchorage structure. With the increase of confining pressure, the maximum value of interfacial shear stress increases obviously. The larger the confining pressure is, the faster the convergence speed of the finite element method (FEM) program is, which shows that the mechanical properties of the anchorage structure are improved obviously. With the increasing confining pressure, the adaptability of the anchorage structure to deformation is stronger and the anchorage structure is less likely to fail. The ultimate bearing capacity of the anchorage structure increases linearly with the increase of confining pressure, and the effect of confining pressure on the ultimate bearing capacity is very significant.  
**Keywords:** anchorage structure; radial stress; mechanical properties; model test; numerical simulation +
Footnote †: journal: Applied Sciences
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 2 The System Model  
This letter explores an RSE system within an uplink NOMA transmission model, aided by an active RIS. As depicted in Figure 1, the system consists of a single BS, equipped with \(G\) receive antennas, an active RIS with \(M\) reflective elements and \(N\) single-antenna sensors. The sets of sensors, sensor groups, and reflective elements are denoted as \(\mathcal{N}=\{1,2,\cdots,N\}\), \(\mathcal{G}=\{1,2,\cdots,G\}\) and \(\mathcal{M}=\{1,2,\cdots,M\}\), respectively.  
Figure 1: Active RIS-assisted uplink NOMA model.  
This letter explores an RSE system within an uplink NOMA transmission model, aided by an active RIS. As depicted in Figure 1, the system consists of a single BS equipped with \(G\) receive antennas; an active RIS with \(M\) reflective elements; and \(N\) single-antenna sensors. To facilitate decentralized decision-making, the \(N\) sensors are partitioned into \(G\) sensor groups, each served by the BS equipped with \(G\) antennas. Note that the grouping does not imply a one-to-one mapping between each group and a specific antenna; instead, it is designed for efficient multi-agent training and resource allocation. The sets of sensors, sensor groups, and reflective elements are denoted as \(\mathcal{N}=\{1,2,\cdots,N\}\), \(\mathcal{G}=\{1,2,\cdots,G\}\), and \(\mathcal{M}=\{1,2,\cdots,M\}\), respectively.
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 4 Experiments in a Real Room
### Observations in Different Ambient Noise Levels  
This experiment was conducted under different ambient noise amplitudes. Figure 15 evaluates the mapping. We observed different threshold values under different noisy conditions: they increase in noisy conditions and decrease in quiet conditions, as expected.  
\begin{table}
\begin{tabular}{c c c} \hline
**RT30 (s)** & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) \\ \hline
0.65 & 12720 & 11450 \\
1.33 & 11690 & 11940 \\
8.50 & 10070 & 13360 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the reverberant characteristics (RT30) and the average delay line lengths during the first 5 minutes, using the proportional (\(L_{1}\)) and reflected (\(L_{2}\)) mappings.  
Figure 15: High threshold curves measured when the volume level of ambient noise is measured as (**a**) \(-\)25 dB, (**b**) \(-\)30 dB, and (**c**) \(-\)47 dB. The range of the high threshold is constrained between 0.7 and 1.0.
## 1 Introduction
Although certain results have been achieved, the machine learning techniques have obvious flaws: manually extracted features are insufficient and cannot further complicate the emotion of the paragraph, and more complex tasks require more complex function derivation, thereby limiting the system's capacity to handle complex tasks. In the early years, a lot of effort was made to seek answers to these limitations; then, neural networks were developed, and increasing attention was focused on deep learning. Deep-learning-based sentiment analysis models avoid the need for the manual extraction of paragraph features and do not require more complex function derivations. However, deep learning models require a large amount of data for training, and the annotation of these training data needs to be carried out manually. This problem is present not only in the field of sentiment analysis, but also in other research that uses deep learning techniques. The advent of transformer networks and the advancement of transfer learning have opened greater possibilities for natural language processing. With the training of a large number of unlabeled datasets, models have a more comprehensive semantic understanding and can be fine-tuned for specific tasks to improve the accuracy of feature extraction. Network models based on Bert are starting to be employed [7]. For sentiment prediction, increased sentiment granularity can more correctly capture public impressions, enhance the credibility of social survey data, and simplify decision-making. Nevertheless, with more emotional content, trying to strike a balance between sentiment granularity and prediction accuracy is another huge obstacle. The field of Chinese small-sample sentiment analysis is plagued by insufficient model training and the insufficient interpretability of deep learning models. We propose a progressive prediction approach to address these issues. This method does not directly predict sentiment classification; rather, it first predicts whether the sentence contains sentiment, then predicts again with the predicted information and features to detect whether the statement is positive, negative, or neutral, and finally combines the predicted results with the output of BiLSTM-MultiHeadAttention and feeds them into the neural network to obtain the final multi-classification results. We have made the following contributions:  
* To improve the network's interpretability, a progressive prediction structure is proposed;
* An idea is provided for multi-category sentiment analysis tasks;* A solution is offered to the problem of inadequate datasets for sentiment analysis jobs;
* For the Bert model's fine-tuning, a novel approach is proposed that produces good results with a significantly smaller dataset.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 3 Experiment and Analysis
### Angle Verification and Analysis
#### 3.4.2 Processing  
The intermediate process is shown as Figure 19. Firstly, the collected depth map is manually segmented and the pole skeleton is randomly transformed. Then, using the camera projection matrix, the frustum is generated through 3D reconstruction and the pole point cloud is obtained after the frustum is preprocessed and segmented by the PointNet. Finally, the central axis of the pole is fitted, and then the inclination angle is obtained.  
Figure 19: Intermediate process of the pole inclination angle. (**a**) Pole depth map; (**b**) Image segmentation; (**c**) Frustum point cloud; (**d**) Preprocessing and segmentation; (**e**) Pole segmentation results, where the pole points are in the white dot line box; (**f**) Central axis fitting.  
Figure 18: The experimental environment of the simulated pole. Where, 1. pole model, 2. depth camera, 3. image processing system, 4. rotating platform, 5. inclined platform, and 6. angle measuring instrument.
## 4 The Proposed TOCA-IoT Framework
### SMOTE Technique  
The SMOTE is utilized to address the problem of class imbalance. SMOTE generates synthetic instances of the minority class, effectively increasing its representation in the training data. The purpose of this technique is to enhance the models' capability to accurately classify both the majority and minority classes. To mitigate the class imbalance inherent in network intrusion datasets, where anomalies represent a significantly smaller portion of the data compared to benign traffic, the SMOTE was applied.  
SMOTE produces synthetic instances for the minority class (anomalies) by interpolating between existing samples of the minority class inside the feature space. This technique helps to balance the class distribution, improving the accuracy of subsequent machine learning algorithms by preventing them from being biased towards the majority class. SMOTE can introduce noise into the dataset if the synthetic samples are generated from noisy or outlier instances. Consequently, this can negatively impact the model's generalization ability. To address this issue, outliers are eliminated during the preprocessing step to reduce SMOTE's sensitivity to noise.
# A Voxel Generator Based on Autoencoder
## 4 Performance Evaluation
### Comparison of Different Decoders  
In this experiment, we used three decoders, i.e., deconvolution (labeled as Deconv), mixed-single convolution (labeled as MSConv), and mixed-multiple convolution (labeled as MMConv) in VoxGen, respectively. In addition, we used the modified-VGG16 and the modified-ResNet18 encoders and evaluated the performance of the two encoders combined with the three decoders. The flattened layer of VoxGen was global average pooling. The experimental results are shown in Table 2. When the encoder was MVGG16, MSConv was better than Deconv, while MMConv was no better than Deconv for the performance of VoxGen in any of the four classes. By contrast, both of MSConv and MMConv outperformed Deconv in all four classes when the encoder was MResNet18. Moreover, MMConv was superior to MsConv in the chair, lamp, and table classes. On the other hand, the combination of MVGG16 and MSConv was the best for the car and lamp  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoder** & **Car** & **Chair** & **Lamp** & **Table** \\ \hline V3 DOR-AE & 0.827 & 0.551 & 0.491 & 0.599 \\ MVGG16 & 0.861 & 0.63 & 0.571 & 0.697 \\ MResNet18 & 0.858 & 0.65 & 0.564 & 0.711 \\ MResNet34 & 0.851 & 0.623 & 0.541 & 0.688 \\ MResNet50 & 0.86 & 0.634 & 0.565 & 0.705 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IOU values of VoxGen using different encoders.  
classes, while the combination of MResNet18 and MMConv was the best for the chair and table classes.  
According to these experimental results, we can conclude that mixing one convolution layer is beneficial for the performance of the voxel generator, while mixing more convolution layers is unnecessary. MMConv seems to be more suitable than MSConv for a deeper encoder network such as MResNet18. When the shape of objects such as chairs and tables was complex and irregular, the combination of MResNet18 and MMConv was better in terms of the quality of generated voxel models.
## 3 Methodology
### Spatial Modeling
#### 3.3.1 Dynamic Global Attention Network  
The correlation among nodes will likely change over time. The idea is to use a dynamic spatial attention mechanism to adaptively capture the dynamic spatial correlation strengths  
Figure 2: The architecture of STGNN. The architecture adopts the end-to-end trainable network for passenger flow prediction. The pipeline of STGNN is mainly divided into two parts: spatial modeling and temporal modeling. Spatial modeling is primarily composed of the L-Spatial Encoder and the R-Spatial Encoder, which update the spatial features of nodes through their respective DAGN and GCN. Temporal modeling is primarily composed of series decomposition blocks, a Temporal Encoder, and a Temporal Decoder. The Temporal Encoder primarily updates the seasonal features of the passenger flow sequence, while the Temporal Decoder updates these seasonal features and uses the trend component of the passenger flow sequence to assist in prediction. Node embedding and temporal embedding are respectively achieved through MLP, implementing the embedding of node features and time point features.  
among nodes in the spatial dimension. The dynamic global attention is calculated as follows:  
\[S=V_{s}\cdot\sigma\Big{(}\Big{(}X^{(0)}W_{1}\Big{)}\cdot W_{2}\cdot\Big{(}W_{3} X^{(0)}\Big{)}^{T}+b_{s}\Big{)} \tag{3}\]  
\[\text{DGAN}\Big{(}X^{(0)}\Big{)}=\text{MatMul}\Big{(}\text{softmax}(S),X^{(0)} \Big{)}+X^{(0)} \tag{4}\]  
where \(X^{(0)}=(X_{1},X_{2},\cdots,X_{T})\in\mathbb{R}^{T\times N\times C}\),\(S\in\mathbb{R}^{T\times N\times N}\), \(T\) is the length of the temporal dimension, \(C\) is the number of features, \(V_{s},b_{s}\in\mathbb{R}^{N\times N}\), \(W_{1},W_{2}\in\mathbb{R}^{C\times C}\), and \(W_{3}\in\mathbb{R}^{N\times N}\) are learnable parameters, and \(\sigma\) is the activation function. A softmax function is used to dynamically compute the attention matrix. In Equation (3), the attention matrix adjusting the correlation between nodes is to capture the global influence from other nodes over time, while the residual connection preserves the information of the centered nodes.
# Cluster-Based Approaches toward Developing a Customer
## 2 Related Research
### Private Security Companies and Hypotheses  
The presented studies indicate that loyalty programs are prevalent across various industries, with distinct approaches stemming from diverse variables. Managers should keep in mind that customer behaviors are in a constant state of flux, as are their needs [10]. A loyalty program should not be regarded as a one-time action but rather as an ongoing process. A cluster analysis can assist businesses in developing a robust loyalty program, and the private security sector is no exception.  
Despite the current literature lacking studies that specifically address the development of a loyalty program or even customer segmentation within this industry segment, based on the results of the presented studies, the following hypothesis is proposed for the creation of loyalty programs in private security companies:  
1. \(H1\). Private security companies can establish loyalty programs for their customers similar to those in any other industry.  
[PERSON] et al. [20], in their study conducted in the health and beauty industry, proposed a model that grouped customers into segments based on RFM. As a result, grouping customers into different segments helped decision-makers identify market segments more clearly and develop more effective marketing and sales strategies for customer retention. In order to identify key customer clusters in the banking sector and create more efficient marketing strategies, [PERSON] and [PERSON] [21] employed cluster analyses based on RFM variables. However, [PERSON] [9] used financial, behavioral, and geographical variables to create customer segmentation in a telecommunications company. Therefore, the following hypotheses were proposed:  
1. \(H2\). RFM variables are sufficient for the creation of a loyalty program.
2. \(H3\). Behavioral and geographical variables, combined with variables for building an RFM model, yield better results in loyalty program creation.  
According to [PERSON] et al. [22], a loyalty program can reward customer purchasing behavior or select profitable customers and provide them with privileges and additional services. [PERSON] et al. [12], in their study analyzing the implementation of loyalty programs in the retail industry and their effectiveness, argue that loyalty programs can help allocate marketing budgets more efficiently if customers differ substantially in terms of profitability. Consequently, the following hypothesis was formulated:  
1. \(H4\). It is possible to create marketing strategies for specific customer segments identified through cluster analysis techniques.