esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved YOLOv8s Network Model
### ESE Attention Mechanism  
The obstacles encountered during driving in open-pit mines are complex and diverse. In the training of the network model, insufficient feature extraction leads to the problem of missed detection during detection. Therefore, it is necessary to boost the network model's capacity for obtaining features. The SE (Squeeze-and-Excitation) mechanism of attention is a method to determine weights proposed by SENet [22], which mainly includes the Squeeze part and the Excitation part. In the Squeeze part, the global average pooling decreases the original feature map \((N,\ C,H,W)\) to the dimension of \((N,C,1,\ 1)\), which is equivalent to compressing the feature map into one dimension. This step is to achieve the fusion of global context information; the Excitation part, where the compressed feature map \((N,C,1,\ 1)\) is passed through the fully connected layer to predict the importance of each channel. This prediction process will pass through a Sigmoid activation function to standardize the predicted value between \((0,\ 1)\). In this way, the importance of different channels is weighed. Then, the weights will be applied to the corresponding channels of the previous feature map so that each channel is given weights of different importance, which is more conducive to feature extraction. The schematic diagram of the SE module is shown in Figure 5.  
The scaling factor \(r\) (channel scaling parameter) is used in the SE module. Although it reduces the computational complexity of the model to a certain extent, it reduces the module's ability to extract weights, as shown in Figure 6a. In the CenterMask [23] algorithm, it is noted that the two fully connected (FC) layers of the SE module need to be compressed when processing the channel dimension. In the first FC layer, the number of channels of the input feature is reduced from \(C\) to \(C/r\), while the second FC layer must return the number of channels to \(C\). The procedure will result in information loss. The ESE (Effective Squeeze and Extraction) module replaces the original two FC layers with a single FC layer with a channel number of \(C\) in order to overcome this issue and prevent the loss of channel information. Figure 6b displays the schematic diagram for the ESE module.  
Figure 4: CGC2f module.  
Figure 5: SE module.
## 2 Data Acquisition and System Design
### Design of Edge Computing Device  
The hardware equipment of the citrus fruit recognition system is mainly composed of an edge computing development board, display screen required for visualization, external camera, and power supply module. We deployed the target detection model to Jetson nano for reasoning, which is a portable development version of artificial intelligence released by NVIDIA. It contains 128 core Maxwell architecture GPU and fast reasoning speed in 5 W/10 W low-power model, has rich AI programming neurons, and speeds up the speed of deep learning algorithm for equipment operation. The operating system is Ubuntu 18.04. The platform power supply module is 5000 mAh, 12 V equalizing rechargeable lithium battery; powerful computing power and small platform design are conducive to the design of portable citrus fruit target detection system. Figure 3 shows an example diagram of an edge computing device for detecting citrus fruits.  
Figure 2: Citrus image shooting and transmission process.
## 2 Materials and Methods  
### Numerical Model  
A 3D numerical model consisting of a CGCW and aquifer system is established to evaluate the effects of GMB holes based on _COMSOL Multiphysics 6.0_. A 0.0015 m HDPE GMB is embedded into the SB cut-off wall (SBCW). The upstream SBCW, downstream SBCW and the aquifer thicknesses are assumed to be equal to 0.6 m, 0.6 m and 10 m, respectively. The cross-section of the model is a 1 m\({}^{2}\) square and the GMB hole is located in the center of the cross-section. The Transport of Diluted Species in Porous Media and Darcy's Law Interfaces of COMSOL are used to simulate leachate migration in the CGCW and aquifer system. The influences of GMB thickness (\(T_{G}\)), cut-off wall hydraulic conductivity (\(k_{G}\)), hole radius (\(r_{G}\)) and head loss (\(H_{G}\)) on leakages through the hole are investigated.  
To obtain a simple method for predicting leakages for different shapes of holes, the Parametric Sweep option in COMSOL is used. Additionally, the empirical equation proposed in the study of [PERSON] and [PERSON] [22] is used as a basis equation. In other words, the empirical equations for different shapes of holes all follow the form:  
\[Q=ak_{G}H_{G}T_{G}^{b}r_{G}^{\text{cln}}T^{+d} \tag{1}\]  
where, \(a\), \(b\), \(c\) and \(d\) are all empirical coefficients and \(Q\) is the leakage.
## 3 Application to Synthetic Data
### Model 1
#### 3.1.2 MCSEM Data  
The synthetic data were contaminated with gaussian noise with an amplitude of 3% of the amplitude of the field at the greatest offset in each frequency, which is representative of real world surveys, such as the one shown in Section 4.  
Offsets ranging from 1 km to about 8 km were used. The cutoff of the data is at an amplitude of \(10^{-15}\) V/Am\({}^{2}\). This cutoff corresponds to less than 4 km for the highest frequency and 8 km for the lowest frequency, for the \(E_{y}\) data, and slightly over those same distances for the \(E_{x}\) data, as shown in Figure 5.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 5 Addition of Fault-Tolerance
### Problem Definition  
The problem statement for the addition of fault-tolerance is as follows:  
Given \(p\), \(\delta_{e}\), \(S\), \(spec\), set of program restrictions \(\delta_{r}\), \(k>1\) and \(f\) such that \(p\)\(k\)-satisfies \(spec\) from \(S\) in environment \(\delta_{e}\) and \(\delta_{p}\cap\delta_{r}=\emptyset\), identify \(p^{\prime}\) and invariant \(S^{\prime}\) such that:  
* every computation of \(p^{\prime}\|_{k}\delta_{e}\) that starts in a state in \(S^{\prime}\) is a computation of \(p[]_{k}\delta_{e}\) that starts in \(S\) and
* \(p^{\prime}\) is (failsafe or masking) \(k\)-\(f\)-tolerant to \(spec\) from \(S^{\prime}\) in environment \(\delta_{e}\) and
* \(\delta^{\prime}_{p}\cap\delta_{r}=\emptyset\)  
The problem statement requires that the program does not introduce new behaviors in the absence of faults (Constraint **C1**), provides desired fault-tolerance (Constraint **C2**) and does not include any transition in \(\delta_{r}\) (Constraint **C3**).
## 3 Data Analysis  
First, the evolution of the main parameters of each battery were analyzed individually; common parameters between them were identified and then subsequently linked to scientific explanation. A comparative analysis was then performed to identify which of these parameters most significantly affects battery performance [10].  
The studied parameters include internal resistance, relaxation voltage drop, depth of discharge, voltage dispersion between cells, the ambient temperature during operation, storage time between uses, discharge rate, and loss of capacity through the relationship between charged mAh and variation of operating voltage experienced in the battery during each charge. The goal of this study is not only to identify parameters that influence aging, but also to identify parameters on which a model should be based.
# Exploring Ephemeral Features with Ground-Penetrating Radar: An Approach to Roman Military Camps
## 2 Materials and Methods
### Case Studies
#### 2.2.2 Sueros de Cepeda (Villamejil, Leon, Spain)  
The Roman camp of Sueros de Cepeda (Figure 4(2a,2b)) was discovered using governmental satellite and aerial imagery in 2020 and later published among other new evidence on Roman temporary sites in north-western Iberia [7]. The camp is ca. 13.5 ha in size and was built on an elevated plain area (950 masl) 500 m to the West of the village of Sueros de Cepeda, in the Tuerto River valley. Although its canonical squared, playing-card outline can be easily recognised, no other morphological elements such as gates were detected. The Roman camp is located in a flat agricultural area which is nowadays devoted to cereal, legumes, and corn cultivation. The USAF imagery from 1956 (see Figure 4 top row) shows an important change in the organisation of land division. From the 1960s onwards, small properties were rearranged into larger plots, whereas irrigation infrastructure and new dirt roads were built. The use of heavy machinery in those works affected the preservation of ancient remains. Indeed, this site was detected thanks to crop marks observed through remote sensing, whereas no topographic observations indicate the presence of the Roman camp on the ground. The geological composition is a mixture of sandstone, conglomerates, and clay that originated in the Miocene era (Figure 6).  
In addition to geophysics using GPR and fluxgate magnetometer, the 2021 archaeological campaign in Sueros de Cepeda included aerial surveying using UAV, and pedestrian survey, followed by the excavation of two trenches at the site's defences. Investigations confirmed that a single V-shaped ditch limited the camp and that the earthen, defensive rampant had been razed by constant ploughing. Absolute dating and paleo-environmental samples were also collected. Results from the 2021 investigations have not been published yet, whereas preliminary field reports have been submitted for evaluation to the funding institutions and the regional heritage curator agency. Specifically, the GPR survey goals were to test this methodology in a previously unexplored geological context and to document the camp defences in an area corresponding with the western boundary of the camp, where aerial photographs had not yielded optimal results.
## 4 Computational Approach
### Mesh Studies  
A study of the influence on mesh size was performed on each previously described model, considering computation time and convergence criteria. In all cases, the geometry of the study permitted the creation of structured mesh composed of regular quadrilaterals.  
The sizes for analysis ranged from 0.2 mm to 2 mm. Table 4 shows, for each approach, the size and number of elements of the different mesh analyzed, and the selected option.  
The mesh selected for approaches A and B was 0.5 mm since the use of a smaller mesh size did not improve the results. On the other hand, a thicker mesh needs a higher quantity of iterations to converge, lengthening the time for its solution. The mesh for approach C was insensitive to resizing. Being a solid system, convergence was stable and quickly achieved in all cases. Additionally, the time step was varied to values above 15 s, maintaining the stability and accuracy of the calculation. Therefore, the mesh that was selected and applied had the finest mesh to provide the best resolution in the results.
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 3 Research on the Heat Transfer Model of the Subsea Connector
### The Heat Transfer Model of the Outer Surface of the Finger and the Press Ring  
Since the subsea connector has 12 fingers, the seawater between the fingers and the press ring will flow through the gaps between the fingers. This part of the seawater has strong fluidity, so it can be incorporated into the heat transfer model of the outer surface of the press ring for analysis. The heat transfer model on the outer surface of the press ring is shown in Figure 7.  
Figure 6: Relationship between \(\lambda_{e2}\) and the temperature.  
Figure 7: Heat transfer of the collet outer surface and the press ring outer surface.  
As shown in Figure 7, the outer surface of the press ring is in direct contact with the flowing seawater, and the main methods of heat transfer are convective heat transfer \(q_{2}\) generated by the seawater flowing through the outer surface and radiative heat transfer \(q_{3}\) with the outside world. The convective heat transfer loss, \(Q_{p}\), can be expressed as:  
\[Q_{p}=h_{p}S_{p}(T_{5}-T_{6}) \tag{8}\]  
where \(h_{p}\) is the natural heat transfer coefficient of seawater [20]; \(S_{p}\) is the outer surface area of the press ring; \(T_{5}\) is the outer surface temperature of the press ring (as shown in Figure 7); \(T_{6}\) is the temperature of seawater (as shown in Figure 7). In addition, the heat loss caused by thermal radiation, \(Q_{R}\), can be expressed as:  
\[Q_{R}=C_{0}\varepsilon S_{p}\left[\left(\frac{T_{5}}{100}\right)^{4}-\left( \frac{T_{6}}{100}\right)^{4}\right] \tag{9}\]  
where \(Q_{R}\) is the heat loss caused by thermal radiation from the outer surface of the press ring; \(C_{0}\) is the black-body radiation coefficient; \(\varepsilon\) is the emissivity of the press ring. Substituting the composite thermal conductivity of convection and thermal radiation surface, \(h_{e}\), into Equation (9), Equation (9) can be written as:  
\[h_{e}S_{p}(T_{5}-T_{6})=h_{e}S_{p}(T_{5}-T_{6})+C_{0}\varepsilon S_{p}\left[ \left(\frac{T_{5}}{100}\right)^{4}-\left(\frac{T_{6}}{100}\right)^{4}\right] \tag{10}\]  
By simplifying Equation (10), the expression of the composite thermal conductivity, \(h_{e}\), can be expressed as:  
\[h_{e}=h_{c}+\frac{C_{0}\varepsilon\left[\left(\frac{T_{5}}{100}\right)^{4}- \left(\frac{T_{6}}{100}\right)^{4}\right]}{T_{5}-T_{6}} \tag{11}\]  
The composite thermal conductivity, \(h_{e}\), which describes the heat transfer of the outer surface of the finger and the outer surface of the press ring, has nothing to do with the shape of the finger, and the press ring as can be seen in Equation (11). Since the outer surface of the finger is in full contact with seawater, the temperature of the finger is similar to the temperature of the press ring. Therefore, the two can be simulated and analyzed together. After calculation, the relationship between the composite thermal conductivity \(h_{e}\) and the temperature is shown in Figure 8.
###### Abstract  
In order to control the tower deviation in the cable hoisting process of long-span concrete-filled steel tubular arch bridge during construction monitoring, a practical method of tower deviation correction was studied and established. In the paper, based on the studies about the deviation error formation mechanism of tower in the process of cable lifting, the relevant formulas of the arch rib elevation changes caused by the change of tower state were deduced. The traditional control methods including increase of tower stiffness and strength of the anchor cable, are not effective, costly, and require a longer construction period. To overcome these defects, in virtue of the Beidou GNSS measurement system and hydraulic jack active control system, the active control technology of the CFST (Concrete Filled Steel Tube) arch bridge tower deviation was thoroughly studied. Besides, a perfect active control theory was established. Finally, the author puts forward the idea of reverse pulling tower deviation. The field measurement and comparative study show that after the optimization of this method, the tower deviation is within 2 cm, and the error meets the specification requirements. The proposed method can accurately control the tower deviation in the process of arch bridge cable hoisting, and establish a set of perfect active control related systems and theories, which is especially suitable for the construction monitoring and adjustment in the construction stage of arch bridge, and can provide reference for the construction control of tower deviation of the same type of bridge.  
CFST arch bridge; Beidou GNSS; cable lifting; tower top displacement; automatic correction +
Footnote †: journal: Applied Sciences  
_Applied Sciences_
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 4 Experiments
### Discussion  
Our method achieves good performance, but there are still some actions that are difficult to recognize. Figure 4 shows the recognition accuracy of our method on the NTU RGB+D X-Sub dataset. We have observed that there are three actions with a relatively low accuracy (less than 70%), which are reading, writing and playing with a phone. Figure 5 presents the five key frames of the actions writing and playing with a phone. It is easy to find that both of the actions are performed by two hands, and they are very similar. Moreover, the hands are abstracted into two joints in the dataset, which causes difficulty in recognition. How to accurately model the finer correlations between the joints to better recognize these actions is the next problem we plan to solve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **X-Sub120 (\%)** & **X-Set120 (\%)** \\ \hline ST-LSTM [11] & 55.7 & 57.9 \\ GCA-LSTM [17] & 61.2 & 63.3 \\ Sked Motion [48] & 67.7 & 66.9 \\ \hline SGN [49] & 79.2 & 87.5 \\
2s-AGCN [15] & 82.9 & 84.9 \\ Shift-GCN [37] & 85.9 & 87.6 \\ DC-GCN+ADG [50] & 86.5 & 88.1 \\ MS-G3D [52] & 86.9 & 88.4 \\ \hline MSAAGCN & 87.1 & 87.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparisons of the Top-1 recognition accuracy with the state-of-the-art methods on the NTU RGB+D120 dataset.  
Figure 4: The recognition accuracy of 60 action classes on NTU RGB+D X-Sub.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline ST-GCN [13] & 30.7 & 52.8 \\ AS-GCN [16] & 34.8 & 56.5 \\
2s-AGCN [15] & 36.1 & 58.7 \\ DGNN [54] & 36.9 & 59.6 \\ Shift-GCN [37] & 37.5 & 60.2 \\ \hline MSAAGCN & 37.7 & 60.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons of the Top-1 and Top-5 recognition accuracy with the state-of-the-art methods on the Skeleton-Kinetics dataset.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 2 Experimental Outline
### Test Cases  
Three test groups were designed, which considered the alteration of soil parameters and void dimensions. All test cases are listed in Table 1.  
First, the soil mass density was considered. In this test group, the water content was set to the natural water content \(\omega_{1}\) = 0.3%. In case D1, the density was \(\rho_{1}\) = 1578 kg/m\({}^{3}\) when the soil was naturally accumulated. Then, the soil was rammed until the density was \(\rho_{2}\) = 1629 kg/m\({}^{3}\), and again until the density reached \(\rho_{3}\) = 1656 kg/m\({}^{3}\). The density was calculated by weighing the mass and measuring the filled soil volume. A void condition D3v was designed only for the highest density case, as embedding an empty box between the soil and concrete plate would disturb the soil if its density was low. Using this test group, the influence of mass density on the concrete plate responses was studied.  
Next, the water content was considered. In this test group, the dry density was set to \(\rho_{3}\). Three different water contents were tested: \(\omega_{1}\) = 0.3%, \(\omega_{2}\) = 8.2% and \(\omega_{3}\) = 15.3%. For each case in this test group, comparisons with void conditions W1v, W2v and W3v were designed. The box dimensions remained as \(V_{1}\) = 150 mm \(\times\) 150 mm \(\times\) 100 mm. Using this test group, the influence of the water content on both the concrete plate responses and void detection were studied. It should be noted that cases W1 and W1v were the same as cases D3 and D3v.  
Finally, in order to analyse the influence of void dimensions on the NDT results, a poor soil-concrete plate contact was designed with three different void sizes.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 3 System Model  
### DL Scenario  
Consider a wireless network with four DL NOMA users and a 64 \(\times\) 64 MIMO system (as depicted in Figure 1). \(U1,~{}U2,~{}U3,\) and \(U4\) are the four users with different bandwidths of 80 and 200 MHz [43]. Let \(d1,~{}d2,~{}d3,~{}\text{and}~{}d4\) represent their various BS distances, \(d1>d2>d3>d4\) indicating the preferred order. Depending on the distance, \(U1\) is the weak/far user while \(U4\) is the strong/near user from BS. Let \(h_{T1},~{}h_{T2},~{}h_{T3},\) and \(h_{T4}\) identify which selective Rayleigh fading coefficients they correspond to \(\left|h_{T1}\right|^{2}<\left|h_{T2}\right|^{2}<\left|h_{T3}\right|^{2}< \left|h_{T4}\right|^{2}\).  
The total Rayleigh fading channel for each user is given by [44]:  
\[h_{Ti}=\sum_{i=1}^{M}h_{Ti} \tag{1}\]  
where, \(i=1,2,3,4\) is the number of users, and \(M=64\) is the number of channels. \(\alpha_{1},~{}\alpha_{2},~{}\alpha_{3}\) and \(\alpha_{4}\) show their respective power coefficients. According to the NOMA (power domain) principles, the lower user must have more power and the better user should have less power [45; 46]. As a result, the power coefficients must be modified as \(\alpha_{1}>\alpha_{2}>\alpha_{3}>\alpha_{4}\). Let \(x_{1},~{}x_{2},~{}x_{3}\) and \(x_{4}\) be the QPSK-formed messages to send to BS \(U1,~{}U2,~{}U3,\)_and_\(U4\). The BS's encoded overlay signal is then given by as in [47].  
\[x=\sqrt{p}(\sqrt{\alpha_{1}}x_{1}+\sqrt{\alpha_{2}}x_{2}+\sqrt{\alpha_{3}}x_{ 3}+~{}\sqrt{\alpha_{4}}x_{4}) \tag{2}\]  
\(U1\) decodes \(y_{1}\) directly when it has maximum power, interfering with the 2 nd, 3 rd, and 4 th \(U\) signals. As a result, the first possible \(U\) rate is  
\[R_{1}=\text{log}_{2}\Bigg{(}1+\frac{\alpha_{1}P|h_{T1}\,|^{2}}{\alpha_{2}P|h_ {T1}|^{2}+\alpha_{3}P|h_{T1}\,|^{2}+\,\alpha_{4}P|h_{T1}\,|^{2}+\sigma^{2}} \Bigg{)} \tag{3}\]  
The obtained rate is for \(U2\) after SIC eliminated \(U1\) data.  
\[R_{2}=\text{log}_{2}\Bigg{(}1+\frac{\alpha_{2}P|h_{T2}|^{2}}{\alpha_{3}P|h_{ T2}\,|^{2}+\alpha_{4}P|h_{T2}|^{2}+\sigma^{2}}\Bigg{)} \tag{4}\]  
Figure 1: Illustrates the wireless network consisting of 4 users 64 \(\times\) 64 MIMO-DL-NOMA (power domain).  
The achieved rate is for \(U\)3 after SIC deleted \(U\)1 and \(U\)2 data.  
\[R_{3}=\log_{2}\left(1+\frac{\alpha_{3}P|h_{T3}\,|^{2}}{\alpha_{4}P|h_{T3}\,|^{2}+ \sigma^{2}}\right) \tag{5}\]  
The acquired rate is for \(U\)4 after SIC deleted \(U\)1 data, \(U\)2 data, and \(U\)3 data.  
\[R_{4}=\log_{2}\left(1+\frac{\alpha_{4}P|h_{T4}|^{2}}{\sigma^{2}}\right) \tag{6}\]  
To calculate the spectrum efficiency.  
\[SE=\frac{Th}{BW} \tag{7}\]  
where the \(SE\) is spectrum efficiency, _Th_ is the throughput and _BW_ is the bandwidth.