esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spectrum Auction Structures
### Simultaneous Multiple-Round Auction
#### 3.5.2 Free-Riding  
Another issue that may arise in a combinatorial auction is known as free-riding. Allowing combinatorial bids in addition to strictly individual submissions can introduce inefficiencies into the system. If a dispute occurs among more than two players, free-riding may become a significant factor.  
Expanding the game structure to allow for combinatorial bids, the game presented in Table 4 could proceed as follows: \(P_{1}\) is interested solely in License A, valuing it at USD 4. Player 2 has a symmetrical interest regarding License B. Player 3 is interested in both licenses, with \(V_{3A}=V_{3B}=1+\epsilon\) and \(V_{3 AB}=2+\epsilon\). Each player is constrained by their budget.  
It is clear that the optimal allocation for the auctioneer is to assign licenses A and B to \(P_{1}\) and \(P_{2}\), respectively, with \(p_{A}=p_{B}=3\). However, each player has an incentive to wait for the subgame between \(P_{3}\) and the other party.  
If \(P_{1}\) waits for \(b_{2B}=3\) to occur, such a bid would eliminate \(P_{3}\), as the combination AB would no longer be available. Consequently, Player 1 could acquire A for USD 2, increasing \(\pi_{1}\). Player 2's behavior is symmetrical. Solving this game through backward induction reveals that the optimal strategy for both Players 1 and 2 is to adopt a mixed strategy, where the bids are made with a probability \(\frac{2}{3}\) of bidding and \(\frac{1}{3}\) of bidding low.  
The problem arises when both players adopt the \"bid low\" strategy. In \((\frac{1}{3}\times\frac{1}{3})=\frac{1}{9}\) of scenarios, both players choose to bid low in hopes that the other player will remove \(P_{3}\) and will win the auction. If this situation occurs, it will lead \(P_{3}\) winning the auction with \(b_{3 AB}=2+\epsilon\), resulting in an inefficient allocation for all parties involved.  
As the free-riding problem is fundamentally a probabilistic error, it can be advantageous for the auctioneer to increase the number of participants in any given auction. One effective method, commonly observed in recent auctions such as FCC-101 and several others, is to implement special rules, such as offering bidding credits for new incumbents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Licensees**} \\
**Player** & **A** & **B** & **AB** & **Budget** \\ \hline
1 & 4 & - & - & 3 \\
2 & - & 4 & - & 3 \\
3 & \(1+\epsilon\) & \(1+\epsilon\) & \(2+\epsilon\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Free-riding.  
This approach inflates the number of participants, thereby reducing the likelihood that free-riding will have a significant impact.  
One solution to completely eliminate the possibility of free-riding is to simultaneously close bidding for all licenses. However, this approach introduces a new problem.
# A Fast and Reliable Luma Control Scheme for High-Quality HDR/WCG Video
###### Abstract  
The evolution of display technologies makes high dynamic range/wide color gamut (HDR/WCG) media of great interest in various applications including cinema, TV, blue-ray titles, and others. However, the HDR/WCG media format for consumer electronics requires the sampling rate conversion of chroma signals, resulting in a quality problem on the luminance perception of media, even without compression. In order to reduce such luminance perception problems, this paper proposes a fast and reliable luma control scheme which takes advantage of the bounds on the best luma value derived from the solution based on truncated Taylor series. Simulations performed for an extensive comparison study demonstrate that the proposed algorithm significantly outperforms the previous representative fast luma control schemes, resulting in almost the same quality of the iterative optimal solution with a fixed amount of computations per processing unit.  
H pacs: 8  
HDR (high dynamic range); WCG (wide color gamut); fast luma control; PQ (perceptual quantizer); luminance perception Article
## 4 Experiment
### Ablation Experiment
#### 4.4.5 Ablation Experiment of Loss Function  
To validate the effectiveness of the improved loss function, we conducted extensive experiments comparing it with various modified loss functions. In addition to the conventional evaluation indicators, such as the mAP value, the key indicator (Box_Loss) of the bounding box loss is worth paying attention to, which is measured as the difference between the predicted bounding box and the actual bounding box of the model. The lower the Box_Loss, the more accurately the model predicts the target location. The results of the ablation experiment under different loss functions are shown in Table 6, and the comparison of the test box loss experiment under different loss functions is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline CIoU & 98.3 & 76.0 & 68.2 & 1.31 \\ EIoU [63] & 98.2 & 75.8 & 67.9 & 1.48 \\ Focal-EIoU [63] & 98.3 & 74.8 & 68.0 & 1.29 \\ Alpha-IoU [69] & 98.3 & 74.4 & 67.2 & 3.09 \\ SIoU [70] & 98.2 & 75.2 & 67.8 & 1.32 \\ MPDIoU [71] & 98.0 & 76.3 & 68.1 & 1.30 \\ Inner-IoU [72] & 98.2 & 75.8 & 67.7 & 1.53 \\ WIoUv3 & 98.3 & 84.7 & 73.1 & 1.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments with different loss functions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **FPS** & **Parameters** & **FLOPs** & **Weights** \\  & **(\%)** & **(\%)** & **(\%)** & **(M)** & **(G)** & **(MB)** \\ \hline Detect & 98.4 & 68.6 & 44.85 & 5.54 & 15.5 & 11.3 \\ FasterDetect & 98.3 & 68.2 & 50.35 & 4.03 & 8.5 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FasterDetect based on PConv.  
As shown in Table 7, the proposed WIoU\({}_{v3}\) loss function in the network achieved the best performance across all the evaluation metrics. Specifically, the [EMAIL_ADDRESS] remained stable, the [EMAIL_ADDRESS] increased by 8.7%, and the [EMAIL_ADDRESS]:0.95 even saw a growth of 4.9%. It is worth noting that, with the introduction of the WIoU\({}_{v3}\) loss function, the minimum average for the Box_Loss is 1.21.  
Figure 8 visually shows the superior performance of the Box_Loss using WIoU\({}_{v3}\) compared to the other loss functions and the low Box_Loss value. The improved loss function plays an important role in improving the prediction accuracy of the target position. This provides intuitive visual support for crosswalk and arrow detection research and further verifies the effectiveness of the improved loss function in target detection tasks.
## 3 Results and Discussions
### RSM Optimization of Hempesed Protein Extraction
#### 3.5.1 Model Fitting  
The impact of extraction pH (X\({}_{1}\)), time (X\({}_{2}\)), and temperature (X\({}_{3}\)) on two responses were investigated using multiple regression analysis on the experimental data. Box-Behnken design models for response prediction were established, which were expressed by quadratic polynomial equations as follows:  
\[\begin{split}\text{Hempesed protein concentration}\ (\text{mg}/\text{mL})=0.64\ \text{X}_{1}+0.56\ \text{X}_{2}+0.42\ \text{X}_{3}+0.22\ \text{X}_{1}\text{X}_{2}+0.02\ \text{X}_{1}\text{X}_{3}+0.06\ \text{X}_{2}\text{X}_{3}-\\ 0.20\ \text{X}_{1}^{2}-0.44\ \text{X}_{2}^{2}-0.28\ \text{X}_{3}^{2}+2.32\ \text{(Adjusted R}^{2}=0.9732,\ \text{predicted R}^{2}=0.9124 )\end{split} \tag{7}\]  
\[\begin{split}\text{Free thiol content}\ (\mu\text{mol}/\text{g})=-7.60\ \text{X}_{1}-14.25\ \text{X}_{2}-9.19\ \text{X}_{3}-2.72\ \text{X}_{1}\text{X}_{2}-2.09\ \text{X}_{1}\text{X}_{3}-1.35\ \text{X}_{2}\text{X}_{3}+6.71\ \text{X}_{1}^{2}\\ +12.89\ \ \text{X}_{2}^{2}+6.21\ \ \text{X}_{3}^{2}+31.04\ \text{(Adjusted R}^{2}=0.9894,\ \text{predicted R}^{2}=0.9261 )\end{split} \tag{8}\]  
The predicted R\({}^{2}\) values of these two models are in reasonable agreement with the adjusted R\({}^{2}\), and the predicted R\({}^{2}\) suggests that the fitting model can account for more than 90% of the variations.  
The \(p\)-value can determine whether the model factors are statistically significant (\(p<0.05\)). The significance of each factor is shown in Table 4. It can be observed that the three independent parameters (pH, time, and temperature) dramatically influenced the two responses. To further improve the quality of HPI, it may be necessary to manipulate multiple variables simultaneously.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Fault Detection, Diagnosis and MPC Reconfiguration Tests
#### 5.3.2 Scenario 3 Results
Looking at the main indicators, you can see the same behavior as in previous cases. For instance, by comparison of an MPC with a fault with FTCMPC, \(G_{\text{u}}\) is 67.28% vs. 68.81% and \(V_{\text{ov}}\) is 4.6066 \(\times\) 10\({}^{4}\) vs. 4.5926 \(\times\) 10\({}^{4}\) (m\({}^{3}\)). Therefore, FTCMPC improves the performance of the system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **No Control** & **Normal MPC** & \
\begin{tabular}{c} **MPC with h1** \\ **Fault** \\ \end{tabular} &
\begin{tabular}{c} **Reconfigured** \\ **MPC** \\ \end{tabular} \\ \hline \(N_{\text{ov},1}\) & 0 & 5 & 2 & 2 \\ \(N_{\text{ov},2}\) & 0 & 1 & 1 & 1 \\ \(N_{\text{ov},3}\) & 2 & 5 & 4 & 5 \\ \(N_{\text{ov},4}\) & 5 & 5 & 5 & 5 \\ \(N_{\text{ov},5}\) & 1 & 4 & 4 & 4 \\ \(N_{\text{ov},WWTP}\) & 7 & 6 & 6 & 6 \\ \(T_{\text{ov},1}\) & 0 & 0.0604 & 1.4750 & 0.8292 \\ \(T_{\text{ov},2}\) & 0 & 0.0250 & 0.0125 & 0.0125 \\ \(T_{\text{ov},3}\) & 0.0396 & 0.0729 & 0.0583 & 0.0583 \\ \(T_{\text{ov},4}\) & 0.1271 & 0.1333 & 0.1292 & 0.1250 \\ \(T_{\text{ov},5}\) & 0.0187 & 0.0813 & 0.0688 & 0.0688 \\ \(T_{\text{ov},WWTP}\) & 0.4562 & 0.7708 & 0.7250 & 0.7333 \\ \(V_{\text{ov},1}\) & 0 & \(6.9554\times 10^{3}\) & \(5.6633\times 10^{4}\) & \(4.0860\times 10^{4}\) \\ \(V_{\text{ov},2}\) & 0 & 385.1159 & 150.8754 & 214.8527 \\ \(V_{\text{ov},3}\) & \(1.1834\times 10^{3}\) & \(2.5109\times 10^{3}\) & \(2.0415\times 10^{3}\) & \(2.2327\times 10^{3}\) \\ \(V_{\text{ov},4}\) & \(1.1874\times 10^{4}\) & \(1.8044\times 10^{4}\) & \(2.7861\times 10^{4}\) & \(2.4317\times 10^{4}\) \\ \(V_{\text{ov},5}\) & \(3.2433\times 10^{3}\) & \(1.9535\times 10^{4}\) & \(1.7412\times 10^{4}\) & \(1.7102\times 10^{4}\) \\ \(V_{\text{ov},WWTP}\) & \(7.1094\times 10^{4}\) & \(219.6231\) & \(222.5543\) & \(213.9156\times 10^{3}\) \\ \(V_{\text{ov}}\) & \(8.7395\times 10^{4}\) & \(4.0695\times 10^{4}\) & \(4.7688\times 10^{4}\) & \(4.4080\times 10^{4}\) \\ \(Q_{WWTP}\) & \(3.7001\times 10^{4}\) & \(4.1641\times 10^{4}\) & \(4.0370\times 10^{4}\) & \(4.1286\times 10^{4}\) \\ \(G_{u}\) & 61.6680 & 69.4022 & 67.2835 & 68.8092 \\ \(S\) & - & \(6.3711\times 10^{10}\) & \(6.4402\times 10^{10}\) & \(1.0547\times 10^{11}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: System performance in every case: fault in tank 1 level sensor (scenario 3). Figure 16: \(Q\) statistic and threshold calculated in gate fault situation (scenario 3).
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 2 Related Work  
### Drone-Based Inventory Taking  
Although the use of drones for inventory taking in warehouses has been hailed as a major commercial breakthrough, existing commercial and academic solutions are still in their infancy. Commercial solutions such as DroneScan [15] rely on human drone operators, thereby still requiring expensive manual interventions. In contrast, fully autonomous solutions such as introduced in [3; 4; 5] use UWB for autonomous drone navigation, but have been demonstrated only in small areas with cabled infrastructure. Finally, drone based inventory-taking solutions such as proposed in [16] use highly accurate but also very expensive motion capturing cameras to localize the drone. As such, to a large extent, the vision of autonomous drone inventory taking is hindered due to the lack of affordable, scalable and easy to install localization solutions that can be installed in large warehouses without requiring significant infrastructure investments such as new cabling.
## Data and Methods
### Field Data
#### 3.1.2 Climatic Variables  
Air temperature (\({}^{\circ}\)C) and relative humidity (%) at 2 m were monitored using MP100A Temperature and Relative Humidity Probe (Rotronic) in Demokeya, with Vaisala HMP45C probes (Vaisala Oyi, Helsinki, Finland) in Wankama, and Campbell CS215 (Campbell Scientific, Logan, UT, USA) in Dahra. Precipitation (mm) was recorded using ARG100 tipping buckets (Campbell Scientific, UT, USA) at all three sites. Volumetric soil moisture was recorded using CS616 water content reflectometers (Campbell Scientific) in Demokeya and Wankama, and HH2 probe (Delta T Devices) in Dahra. These parameters were recorded every 10-30 s and averaged for every 30 min. Measurement depths varied with site and were dependent on the field campaign. Volumetric soil moisture was summed from top of soil to 50 cm depth in Dahra and Wankama, and to 60 cm depth in Demokeya in order to compute plant-available water (see Section 3.3.1).
## 5 Result and Discussion  
As described in Section 3, the three benchmark BSL datasets have been used to evaluate the proposed method's performance and conduct the experiments. In Section 5.1, we have described the experimental setup, and in Sections 5.3-5.5 described the performance evaluation of 38 BdSL, KU-BdSL and Ishara-Lipi datasets consequently.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Layer No.** & **Layer Name** & **Input Shape** & **Output Shape** & **Param** \\ \hline
1 & Conv2d\_1 & 124 \(\times\) 124 \(\times\) 3 & 124 \(\times\) 124 \(\times\) 96 & 2688 \\ \hline
2 & Dropout\_1 & 124 \(\times\) 124 \(\times\) 96 & 124 \(\times\) 124 \(\times\) 96 & 0 \\ \hline
3 & Conv2d\_2 & 124 \(\times\) 124 \(\times\) 96 & 124 \(\times\) 124 \(\times\) 96 & 83,040 \\ \hline
4 & Conv2d\_3 & 124 \(\times\) 124 \(\times\) 96 & 62 \(\times\) 62 \(\times\) 96 & 83,040 \\ \hline
5 & Dropout\_2 & 62 \(\times\) 62 \(\times\) 96 & 62 \(\times\) 62 \(\times\) 96 & 0 \\ \hline
6 & Max Pooling 2d\_1 & 62 \(\times\) 62 \(\times\) 96 & 31 \(\times\) 31 \(\times\) 192 & 0 \\ \hline
7 & Conv2d\_4 & 31 \(\times\) 31 \(\times\) 192 & 31 \(\times\) 31 \(\times\) 192 & 166,080 \\ \hline
9 & Conv2d\_5 & 31 \(\times\) 31 \(\times\) 192 & 31 \(\times\) 31 \(\times\) 192 & 331,968 \\ \hline
10 & Conv2d\_6 & 31 \(\times\) 31 \(\times\) 192 & 16 \(\times\) 16 \(\times\) 192 & 331,968 \\ \hline
11 & Dropout\_3 & 16 \(\times\) 16 \(\times\) 192 & 16 \(\times\) 16 \(\times\) 192 & 0 \\ \hline
12 & Max Pooling 2d\_2 & 16 \(\times\) 16 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 192 & 0 \\ \hline
13 & Conv2d\_7 & 8 \(\times\) 8 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 192 & 331,968 \\ \hline
14 & Activation (Relu) & 8 \(\times\) 8 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 192 & 0 \\ \hline
15 & Conv2d\_8 & 8 \(\times\) 8 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 192 & 37,056 \\ \hline
16 & Activation (Relu) & 8 \(\times\) 8 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 192 & 0 \\ \hline
17 & Conv2d\_9 & 8 \(\times\) 8 \(\times\) 192 & 8 \(\times\) 8 \(\times\) 38 & 7334 \\ \hline
18 & Batch Normalization & 8 \(\times\) 8 \(\times\) 38 & 8 \(\times\) 8 \(\times\) 38 & 152 \\ \hline
19 & Global Average Pooling 2D & 8 \(\times\) 8 \(\times\) 38 & 38 & 0 \\ \hline
20 & Activation (Softmax) & 38 & 38 & 0 \\ \hline \multicolumn{5}{c}{Total params: 1,375,294} \\ \multicolumn{5}{c}{Trainable params: 1,375,218} \\ \multicolumn{5}{c}{Non-trainable params: 76} \\ \end{tabular}
\end{table}
Table 2: Details Specification of the BenSignNet model.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 4 Experimental Results
### Intra-Dataset Testing
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **MAE** & **RMSE** & **MER** & \(r\) \\ \hline POS [6] & 8.35 & 10.00 & 9.85\% & \(-0.24\) \\ CHROM [5] & 8.20 & 9.92 & 9.17\% & 0.27 \\ [PERSON] et al. [4] & 3.34 & 6.46 & - & 0.66 \\ [PERSON] et al. [18] & 5.06 & 10.16 & - & 0.34 \\ RhythmNet [13] & 3.91 & 7.72 & 4.06\% & 0.93 \\ [PERSON] et al. [14] & 3.88 & 6.23 & - & - \\ PhysNet [12] & 3.00 & 10.05 & 3.52\% & 0.75 \\ RADIANT [16] & 2.91 & 4.52 & - & - \\ SS-Net-RGB & 2.06 & 2.86 & 3.40\% & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 3: Estimation of HR on the UBFC-rPPG dataset by the proposed method and several SOTA methods. Figure 10: (**a**) The Bland–Altman plot demonstrating the agreement of the ground truth HRgt and the predicted HRpred for the SS-Net in the MR-NIRP dataset. The lines present the mean and 95% limits of agreement. (**b**) The scatter plot comparing the ground truth HRgt and the predicted HRpred by the SS-Net in the MR-NIRP dataset.
# Damage Assessment of Shear Wall Structures in an Earthquake-Blast Disaster Chain
## 2 Methodology  
### Framework of Damage Evaluation  
Although some studies have successfully achieved damage evaluation of shear wall structures under individual earthquake excitation [15; 16] or explosion loads [17; 18], there are so far limited efforts on developing analytical approaches for damage evaluation of shear wall structures in an earthquake-blast disaster chain. In this paper, an analytical method is developed for the damage evaluation of shear wall structures that experiences an earthquake-explosion disaster chain. The results of this paper should help the community to better understand the damage modes of such structures, especially in the earthquake-explosion disaster chain.  
The entire framework for damage assessment of shear wall structures in an earthquake-blast disaster chain is shown in Figure 1.  
1. The finite element (FE) models of shear wall structures were established.
2. A series of ground motion records, based on the site conditions of structure and the epicentral distance, was used for the first phase analysis.
3. A series of blast loads at different locations with the same TNT equivalence were used for the second-phase analysis.
4. Random combination ground motion records with explosion loads to generate scenarios.
5. A two-stage analysis method was used for response and damage analysis of shear wall structures in an earthquake-blast disaster chain, as shown in Figure 1. The first stage was the non-linear time-history (NLTHA) for ground motion records, which was followed by blast loads without any chance for recuperation or reparation of the shear wall structures. Thus, two-stage accumulated damage effects of earthquake loading and blast loading were considered.
6. The results from the two-stage damage analysis were used for deriving the damage index (\(\xi_{DR}\)).
7. Determine the damage states (DSs).
8. Finally, as presented in Figure 1, the damage evaluation was then developed based on the damage index and the damage states.
# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 5 Conclusions and Recommendations
### Applications and Development Recommendations
#### 5.2.3 Implement Big Data Analytics-Based E-Commerce Precision Marketing Optimization Strategies  
Utilize big data analytics and multimodal learning technologies to base decisions and actions on precise, real-time data insights. Use multimodal learning models to deeply analyze customer data on the cross-border e-commerce platform. Segment customers based on behavior patterns, purchase history, and social media activities to uncover unique needs and preferences, supporting customized marketing strategies. Develop and deploy personalized recommendation systems by combining users' purchase histories and browsing behaviors. Implement machine learning algorithms such as collaborative filtering and content-based recommendation systems to improve the relevance and accuracy of the recommendations.  
Evaluate the effectiveness of different recommendation models through A/B testing to select the optimal model for comprehensive deployment. Implement dynamic pricing strategies and intelligent promotional activities by developing demand-based dynamic pricing models. Use regression analysis and machine learning to predict price sensitivity and adjust prices according to market supply and demand. Analyze the impact of promotional activities on different customer groups, customizing targeted promotions based on historical data and consumer behavior patterns. Additionally, use big data analytics to optimize the timing and content of promotional activities, ensuring that marketing efforts reach the most interested customer segments directly.  
Integrate cross-channel marketing efforts by combining multimodal data analysis to unify online and offline marketing channels, ensuring consistency in brand messaging and marketing activities across all touchpoints. Use cross-channel tracking tools to monitor and analyze the performance of marketing campaigns, and adjust strategies in real time to maximize return on investment. Establish a real-time feedback mechanism to continuously monitor the effectiveness of marketing activities and customer feedback, allowing for ongoing adjustments and optimization of marketing strategies to stay aligned with market trends and consumer expectations.  
By implementing precision marketing strategies, cross-border e-commerce enterprises can more effectively meet the diverse needs of global consumers, enhance customer satisfaction, and strengthen market competitiveness.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 6 Example Analysis
### Effect of Buried Depth Ratio  
When the shallow-tunnel burial depth of the slope section \(H=20\) m, rectangular tunnel height \(h=10\) m, span \(b=10\) m, bulk density of surrounding rock \(\gamma=20\) kN/m\({}^{3}\), uniaxial tensile strength \(\sigma_{\rm t}=30\) kPa, cohesion of surrounding rock \(c_{0}=10\) kPa, angle of internal friction in the surrounding rock \(\varphi=18^{\circ}\), slope surface inclination \(\delta=18^{\circ}\), ratio of horizontal support force to vertical support force of surrounding rock \(K=1.0\), nonlinear coefficients \(m=1.0\), \(1.1\), \(1.2\), \(1.4\), the slope top load is \(p=100\) kPa, so that the buried depth ratio (the ratio of the buried depth of a shallow tunnel to the height of the tunnel) \(H/h=1.0\)-\(2.0\), and the variations in the peripheral rock pressures and damage modes of the shallow tunnel are shown in Figures 6 and 7.  
As is shown in Figure 6, with the increase in the depth of burial ratio, the perimeter rock pressure increases significantly. \(H/h\) changes from \(1.0\) to \(2.0\) when \(m=1.0\), and the perimeter rock pressure increases to a minimum of \(29\) kPa; when \(m=1.4\), the perimeter rock pressure increases to a maximum of \(54\) kPa; when \(m\) changes from \(1.0\) to \(1.4\) and \(H/h=1.0\), the perimeter rock pressure increases to a minimum of \(112\) kPa; and when \(H/h=2.0\), the perimeter rock pressure increases to a maximum of \(136\) kPa, which indicates that the larger the burial depth ratio, the more significantly it is affected by the nonlinear coefficient. It can be seen that, on the basis of the determined tunnel cross-section size, adjustment of the geotechnical nonlinear coefficient and other methods of increasing the stability of the tunnel surrounding rock can be used to reduce the shallow-tunnel depth or grouting reinforcement. It is shown from Figure 7 that when the burial depth ratio increases, the damage of the shallow-buried tunnel is shifted toward the shallower side of the slope. Therefore, in engineering practice, reinforcement measures can be taken in advance for the geotechnical body on the shallower side of the burial depth to prevent the potential danger caused by the offset.  
Figure 6: Influence of burial depth ratio on surrounding rock pressure.  
Figure 7: Influence of burial depth ratio on failure mode of surrounding rock (\(m=1.2\)).