esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithm and Validation
#### 3.1.1 Weighted Values  
Table 4 shows the weighted values of the three independent variables, calculated using Equation (1).  
Based on the results obtained in Table 4, the following equations can be used to calculate the weighted value (\(WV_{x}\)) of any other country (\(i\)), once the GDP (Equation (5)), population (Equation (6)) and RES (Equation (7)) are known.  
\[WV_{a_{i}}=\frac{Va_{i}}{12,349.34+Va_{i}} \tag{5}\]  
\[WV_{b_{i}}=\frac{Vb_{i}}{443.46+Vb_{i}} \tag{6}\]  
\[WV_{c_{i}}=\frac{Vc_{i}}{730.87+Vc_{i}} \tag{7}\]  
where:  
* \(Va_{i}\) is the GDP in billion euros of the additional country (\(i\));
* \(Vb_{i}\) is the population in millions of people of the additional country (\(i\));  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Member State (\(i\))**} & \multicolumn{2}{c}{**GHGs**} & \multicolumn{2}{c}{**Independent Factors**} \\ \cline{3-5}  & **(Mt CO\({}_{2}\) eq.)** & \(Va_{i}\) **(B EUR)** & \(Vb_{i}\) **(M People)** & \(Vc_{i}\) **(\%)** \\ \hline
1 & Austria & 81.3 & 346.8 & 8.6 & 33.2 \\
2 & Belgium & 122.9 & 418.9 & 11.2 & 7.8 \\
3 & Bulgaria & 60.1 & 48.5 & 7.2 & 29.9 \\
4 & Croatia & 25.7 & 47.7 & 4.2 & 36.4 \\
5 & Cyprus & 9.6 & 19.8 & 0.9 & 26.3 \\
6 & Czechia & 130.6 & 181.5 & 10.6 & 19.0 \\
7 & Denmark & 54.0 & 276.8 & 5.7 & 39.7 \\
8 & Estonia & 19.3 & 21.4 & 1.3 & 48.9 \\
9 & Finland & 61.1 & 215.0 & 5.5 & 51.9 \\
10 & France & 475.7 & 2204.6 & 66.2 & 19.1 \\
11 & Germany & 899.4 & 3037.7 & 81.5 & 13.5 \\
12 & Greece & 101.7 & 184.4 & 10.9 & 26.4 \\
13 & Hungary & 63.3 & 117.0 & 9.9 & 20.3 \\
14 & Ireland & 62.1 & 252.4 & 4.7 & 5.8 \\
15 & Italy & 457.2 & 1676.1 & 60.0 & 18.2 \\
16 & Latvia & 11.3 & 24.8 & 2.0 & 51.2 \\
17 & Lithuania & 20.8 & 38.8 & 2.9 & 41.8 \\
18 & Luxembourg & 12.1 & 53.6 & 0.6 & 7.1 \\
19 & Malta & 2.8 & 10.0 & 0.4 & 16.7 \\
20 & Netherlands & 200.9 & 708.6 & 16.9 & 5.2 \\
21 & Poland & 402.9 & 437.6 & 38.0 & 38.6 \\
22 & Portugal & 68.8 & 186.3 & 10.4 & 26.0 \\
23 & Romania & 118.8 & 168.1 & 19.8 & 25.8 \\
24 & Slovakia & 41.7 & 80.4 & 5.4 & 11.2 \\
25 & Slovenia & 17.8 & 40.7 & 2.1 & 33.4 \\
26 & Spain & 345.5 & 1103.9 & 46.7 & 15.4 \\
27 & Sweden & 56.9 & 448.0 & 9.8 & 62.3 \\ \hline \multicolumn{5}{c}{Tot} & Total & 12,349.34 & 443.46 & 730.87 \\ \hline \hline \end{tabular} \(\pi\): GDP; \(k\): population; \(c\): RES; \(i\): member state.  
\end{table}
Table 3: Mean values of the data collected from Eurostat between 2010 and 2020.  
\(Vc_{i}\) is the RES in percentage of the additional country (\(i\));
* \(WV_{a_{i}}\) is the weighting factor corresponding to the GDP of the additional country (\(i\));
* \(WV_{b_{i}}\) is the weighting factor corresponding to the population of the additional country (\(i\));
* \(WV_{c_{i}}\) is the weighting factor corresponding to the RES of the additional country (\(i\));
* \(WV_{x_{i}}\) is the weighting value of each variable \(x\) for the analysed MS \"\(i\)\".
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 3 Circuit Implementation
### Active Balun and Down-Conversion Mixer  
Figure 8 shows an active balun with ESD (electrostatic discharge) protection structures and a simplified active down-conversion mixer. For clarity, only the in-phase part of the quadrature mixer is shown in Figure 8. The quadrature part of the mixer was connected to ports P1 and P2.  
Active balun (for L1/E1 and L5/E5 bands) perform triple functionality in GNSS AFE. They ensure a 50 \(\Omega\) input matching, provide additional signal amplification and supply a differential RF signal to the down-conversion mixer. By using a center-tapped differential inductor to generate a differential signal at the output of the balun, the phase shift reaches 180\({}^{\circ}\) with high accuracy and the gain at each output is almost identical. The simulated PVT (process, voltage and temperature) variations of the phase and gain variation between outputs do not exceed 5\({}^{\circ}\) and 1 dB, respectively. The use of active baluns made it possible to reduce the noise requirements of the mixers. Figure 9; Figure 10 show the post-layout simulation results for the reflection coefficient (S11), voltage gain and noise figure of the L1/E1 and the L5/E5 balun, respectively. The L1/E1 and L5/E5 noise figure and the mixer conversion gain for the balun and mixer combined are presented in Figures 11 and 12, respectively. The parameters of the L1/E1 balun are: NF = 4.9 dB, \(G_{\rm V}\) = 15.1 dB, \(S_{11}\) = \(-\)37.3 dB and \(I_{\rm DD}\) = 0.54 mA. The parameters of the L5/E5 balun are: NF = 4.6 dB, \(G_{\rm V}\) = 16.7 dB, \(S_{11}\) = \(-\)21.9 dB and \(I_{\rm DD}\) = 0.64 mA. The parameters of the mixer for L1/E1 band are: NF = 15.5 dB, \(G_{\rm MIX}\) = 10.4 dB and \(I_{\rm DD}\) = 0.48 mA. The parameters of the mixer for the L5/E5 band are: NF = 15.2 dB, \(G_{\rm MIX}\) = 10.3 dB and \(I_{\rm DD}\) = 0.48 mA. The combined parameters for the L1/E1 band are: NF = 8.2 dB, \(G_{\rm CONV}\) = 25.0 dB and \(I_{\rm DD}\) = 1.02 mA. The combined parameters for the L5/E5 band are: NF = 7.3 dB, \(G_{\rm CONV}\) = 26.5 dB and \(I_{\rm DD}\) = 1.12 mA.  
Figure 8: Active balun with ESD (electrostatic discharge) protection and a simplified in-phase mixer.  
Figure 11: Conversion gain and noise figure characteristics of the L1/E1 band active balun and mixer.  
Figure 10: S11, voltage gain and noise figure characteristics of the L5/E5 band active balun.  
Figure 9: S11, voltage gain and noise figure characteristics of the L1/E1 band active balun.
# Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks
## 4 Performance Evaluation
Managing the interference power is crucial for maintaining the reliability, capacity, and quality of service in 5G networks. The lower the interference power, the more reliable the signal would be. With our proposed DQN architecture method, it can be seen in Figure 7 that interference power is very low compared to the other existing methods [1] by controlling phase shifters. Thus, lower interference power indicates better communication between \(BS\) and \(U\). In our proposed DQN-based architecture, we also reduced the served power in each step compared to [1], as we can see in Figure 8. To control the beam forming and provide the best service with less power is highly cost-effective. In our proposed method, we make sure by taking sub-optimal action that at each step that the serving power from the base station is low while providing good service. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & \begin{tabular}{l} **Existing** \\ **DQN**[1] \\ \end{tabular} & \
\begin{tabular}{l} **Existing** \\ **Q-Table**[1] \\ \end{tabular} &
\begin{tabular}{l} **Proposed** \\ **DQN** \\ \end{tabular} \\ \hline
**Minimum Serve Power** & 28.005 & 23.707 & 8.51 \\ Maximum Serve Power & 46.32 & 47.34 & 17.60 \\ Average Serve Power & 41.4 & 35.5 & 15.73 \\ Minimum Interference Power & 27.09 & 22.61 & 0.78 \\ Maximum Interference Power & 42.2 & 45.8 & 7.9 \\ Average Interference Power & 38.84 & 34.12 & 5.42 \\ Minimum SINR & 0.77 & 1.70 & 5.15 \\ Maximum SINR & 27.04 & 25.44 & 29.18 \\ Average SINR & 9.37 & 11.32 & 16.44 \\ Average Sum-Rate Capacity & 3.07 & 3.45 & 3.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison table between proposed DQN, existing DQN, and existing Q-Table. Figure 6: SINR at each successful step.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 3 Analysis of Simulation Results
### Environment Configuration and Parameter Settings
The propagation of acoustics in shallow water is mainly the repeated interaction with the sea surface and seabed. Figure 10a-f shows the difference in acoustic transmission loss in shallow water when the transmitting transducer is located at different depths. Figure 8: One of the multiple CIRs used in communication system simulation. Figure 10: _Cont._  
Figure 9: In the environment configuration, CIRs change with the changing the spatial position of the transmitting end and the receiving end. (**a**) Multiple CIRs are generated by changing the depth of the transmitting transducer. (**b**) Multiple CIRs are generated by changing the depth of the receiving hydrophone. (**c**) Multiple CIRs are generated by changing the horizontal distance.
## 3 Analysis of Original Research Articles
### Ensemble Learning Techniques
(2019) used ensemble regressors with meteorological data, applying feature engineering and Bayesian optimization for hyperparameter tuning; they found that Random Forest Regressor was the best-performing model, achieving an R\({}^{2}\) of 0.864, an EV of 0.865, and the lowest MAE, MSE, and MSLE among the ensemble approaches. [PERSON] et al. (2018) developed a hybrid ML-based model combining K-means clustering and classification (KNN, RF) to predict corn futures price states based on weather and macroeconomic factors, improving forecasting accuracy over benchmark models. [PERSON] et al. (2019) proposed an ABC-based (Artificial Bee Colony Algorithm) ensemble strategy combining denoising techniques (SSA, EMD, VMD) with models like ARIMA, SVR, LSTM, RNN, and GRU. The semi-heterogeneous ABC ensemble achieved MAPE reductions of 53.3% (corn) and 50% (soybeans).
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods
### RNA Extraction and sRNA Enrichment  
Total RNA was extracted from the _N. benthamiana_ plants at 28 dpi with pEuMV-YP:Krt18. Briefly, leaf tissues (100 mg) were macerated with 1 mL TRIzol (Invitrogen, Waltham, MA, USA) and purified with a Direct-zol(tm) RNA MiniPrep Plus kit (Zymo Research Corp., Irvine, CA, USA) according to the manufacturer's instructions. The sRNA enrichment was performed using the protocol of [PERSON] et al. (2007) [26] as follows: total RNA, previously dissolved in 250 \(\mu\)L of TE buffer (10 mM Tris-Cl pH 7.5, 1 mM EDTA), was heated at 65 \({}^{\circ}\)C for 5 min and mixed with 10% polyethylene glycol 8000 (PEG 8000) and 1 M NaCl, followed by incubation for 30 min at 4 \({}^{\circ}\)C and centrifuged at 15,000\(\times\)\(g\) for 15 min. This step allows the precipitation of high-molecular-weight RNA, leaving low-molecular-weight RNA in the supernatant. Finally, the supernatant (transferred to a new tube) was precipitated with two volumes of 100% ethanol and one-tenth volume of 3 M sodium acetate pH 5.2 and centrifuged at 15,000\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C. The sRNA was dissolved in 25 \(\mu\)L of RNase-free water.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### SDAG Computational Model Analysis  
In the SDAG model, a computation consists of a series of thread-level tasks (instances). The dependencies between these tasks are represented using a DAG. The creation of tasks is data driven, each processor executes the task for which the data dependency has been satisfied, and communication between processors is done via a FIFO queue in a shared memory area. Figure 5 of the SDAG model shows that the cost function of an instance thus constructed is as follows.  
\[T_{ins}=h_{ins}+w_{ins}+L \tag{10}\]  
where \(h_{ins}\) is the time interval between the first FIFO queue at the input port of instance \(ins\) satisfying Equation (9) and the last FIFO queue satisfying Equation (9). \(w_{ins}\) is the computation time of instance \(ins\) and \(L\) represents the shared memory FIFO queue operation time (including enqueue, dequeue and \"pass lock\" time). So in the SDAG model, if there are \(s\) instances, the total execution time \(T_{SDAG}\) is:  
Figure 5: SDAG model overview.  
\[T_{SDAG}=\sum_{i=0}^{s-1}h_{i}+\sum_{i=0}^{s-1}w_{i}+sL \tag{11}\]  
SDAG belongs to the shared memory model of computing and adopts the data-driven idea of creating tasks in data flow, placing the communication of tasks in a limited shared memory space. To save shared memory space, we specify that different instances of the same actor share a FIFO queue. This is reflected in tasks that compute the same data differently, which is very common in data flow tasks. To ensure data consistency, we have added a \"pass lock\" mechanism so that the current FIFO queue can only be operated by an instance that can open the \"pass lock\". Instances are created sequentially, and if the execution environment is homogeneous with multi-core processors, then the processor computation times for instances of the same actor are the same, so that most instances finish sequentially. The biggest advantage of the SDAG model is that it does not depend on a specific hardware platform and can be implemented on all common hardware platforms.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 4 Novel Peptides Derived from Enzymatic Hydrolysis of Fish Discards
### Antioxidative Peptides  
One of the crucial roles of bioactive peptides is their antioxidant activity, and this can be attributed to specific biological functions such as scavenging free radicals, the inhibition of lipid peroxidation, and metal ion chelation [85]. Novel bioactive peptides from fish hydrolysates obtained from fish byproducts have been widely studied, as in the work of [PERSON] et al. [86], in which three novel peptides were isolated from path (_Pangasius sutchi_) myofibrillar protein hydrolysates. After purification and testing for their antioxidative properties, the peptides exhibited the highest antioxidant activity. In terms of muscle protein hydrolysates, [PERSON] et al. [87] identified novel antioxidant peptides from mackerel (_Somber japonicus_) muscle protein hydrolysates. Furthermore, the authors characterized the peptide ALSTWTLQLGSTSFSASPM as having the highest DPPH scavenging activity, and the LGTLLEIIAIPI peptide to have the highest SOD-like activity.  
[PERSON] et al. [68] investigated the novel antioxidant peptides of gelatin skin hydrolysates from tilapia (_Oreochromis niloticus_). The authors showed that the amino acid sequences of peptides Glu-Gly-Leu and Tyr-Gly-Asp-Glu-Tyr were found to have high hydroxyl radical scavenging activities and suggested that using properase E enzyme could yield these antioxidant peptides. Alternatively, [PERSON] et al. [88] investigated the valorization of tuna processing waste biomass and observed that the four novel antioxidant peptides, identified as Tyr-Glu-Asn-Gly-Gly, Glu-Gly-Tyr-Pro-Trp-Asn, Tyr-Ile-Val-Tyr-Pro-Gly, and Trp-Gly-Asp-Ala-Gly-Gly-Tyr, exhibited good scavenging activity against hydroxyl radicals.  
In a research study, a novel heptapeptide from mackerel byproduct hydrolysates was identified by [PERSON] et al. [89], who characterized the heptapeptide TCGGQR with high antioxidant activities and potential functional fertilizer properties. It is interesting to note that fish byproducts obtained from the fish processing industry have high potential as major sources of peptides with antioxidant properties, and the valorization of these discards is a necessary step to reduce the waste in this industry.
## 4 Technologies for Nanocellulose Production
### From lignocellulosic Biomass to Cellulose  
The inevitable step in the conversion of LB into any value-added product is its pretreatment owing to the intricacy of the lignocellulosic structure, as mentioned in Section 2. An efficient pretreatment regimen may help in breaking down the chemical and physical barriers that prevent the fractionation of LB without producing any inhibitors [11; 51]. The lignocellulosic structure is completely disintegrated as a result of the pretreatment of the biomass, which encourages an extensive cleaving of chemical bonds connecting the polymers within the lignin-carbohydrate complex (LCC). This results in an increased accessibility of enzymes to the polysaccharides [11]. Chiefly, an ideal pretreatment method  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Properties** & **Cellulose Nanocrystals** & **Cellulose Nanofibrils** & **Bacterial Nanocellulose** & **Reference** \\ \hline Diameter & 5–70 nm & 1–100 nm & 20 to 100 nm & [33; 35; 41] \\  & 100–250 nm (plant cellulose) and & & & \\  & 100 nm-several micrometres & 500–2000 nm; can reach up & & \\  & (cellulose sources from tunicates & to \(\upmu\)m & up to 100 \(\upmu\)m & [17; 33; 41] \\  & or algae) & & & \\ Cellulose & Crystalline & & Crystalline and & [17; 33; 41] \\ Tensile strength & \(\sim\)7500 MPa & \(\sim\)3 GPa & 200–300 MPa & [33; 42; 48] \\ Young’s modulus & \(\sim\)110 GPa & \(\sim\)136 GPa & 15–35 GPa & [42; 48] \\  & Presence of lignin, hemicellulose, & Presence of lignin, & & \\  & pectin and wax & hemicellulose, pectin & No impurities & [17; 46; 47; 48] \\  & & and wax & & \\ \hline \end{tabular}
\end{table}
Table 1: Summary of various properties of different types of nanocellulose.  
should be: (1) suitable for various kinds of LB, (2) cost and energy effective, (3) not produce toxic or inhibitory compounds, (4) produce highly digestible pretreated solid and (5) have high reaction efficiency [52]. The pretreatment of biomass can be achieved through various physical (ultrasound, microwave), chemical (ionic liquids, deep eutectic solvents, acids, alkali), physicochemical and biological techniques [20; 51; 53; 54]. Ideally, combinatorial pretreatment approaches are also necessary to improve the porosity of the fiber matrices for pretreatment chemicals to permeate more easily, as well as remove any undesirable non-cellulosic components without changing the crystalline components of the biopolymer [9].
## 3 Results
### Parametrisation
#### 3.4.3 Data Augmentation  
Data augmentation holds a notable role in CNN training for classification tasks. It provides CNNs with augmented data to increase their performance. CNNs benefit from data augmentation because they can learn to ignore geometrical and positional features that introduce variations among objects of the same class. In addition, CNNs learn to ignore noisy representations and focus on discovering significant features.  
Strong augmenting in medical imaging is avoided because it generates unrealistic representations, which may cause underfitting. In the experiment, width shift, height shift, rotation, shear, and horizontal flip are applied incrementally. Table 8 summarises the performance of ParaNet+ under different data augmentation settings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **ACC** & **SEN** & **SPE** & **PPV** & **NPV** & **F1** \\ \hline Transfer learning & & & & & & \\ (zero Freedom) & 0.6620 & 0.6711 & 0.6414 & 0.8097 & 0.4618 & 0.7339 \\
1-layer Freedom & 0.9306 & 0.9711 & 0.8384 & 0.9318 & 0.9274 & 0.9510 \\
2-layer Freedom & 0.9861 & 0.9889 & 0.9798 & 0.9911 & 0.9749 & 0.9900 \\
4-layer Freedom & 0.8843 & 0.8556 & 0.9495 & 0.9747 & 0.7431 & 0.9112 \\
6-layer Freedom & 0.7623 & 0.7578 & 0.7727 & 0.8834 & 0.5840 & 0.8158 \\
8-layer Freedom & 0.7423 & 0.7822 & 0.6515 & 0.8361 & 0.5683 & 0.8083 \\ Training from scratch & & & & & & \\ (19-layer Freedom) & 0.6790 & 0.6844 & 0.6667 & 0.8235 & 0.4818 & 0.7476 \\ \hline \hline \end{tabular}  
* ACC: accuracy; SENS: sensitivity; SPE: specificity; PPV: positive predictive value; NPV: negative predictive value; F1: F1 score.  
\end{table}
Table 6: Classification results of ParaNet+ under multiple learning scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **ACC** & **SEN** & **SPE** & **PPV** & **NPV** & **F1** \\ \hline Adam & 0.9861 & 0.9889 & 0.9798 & 0.9911 & 0.9749 & 0.9900 \\ Adagrad & 0.6960 & 1.0000 & 0.0051 & 0.6955 & 1.0000 & 0.8204 \\ AdDelta & 0.6975 & 0.9933 & 0.0253 & 0.6984 & 0.6250 & 0.8202 \\ Nadam & 0.8148 & 0.9800 & 0.4394 & 0.7989 & 0.9063 & 0.8802 \\ RMSProp & 0.9074 & 0.9356 & 0.8434 & 0.9314 & 0.8520 & 0.9335 \\ \hline \hline \end{tabular}  
* ACC: accuracy; SENS: sensitivity; SPE: specificity; PPV: positive predictive value; NPV: negative predictive value; F1: F1 score.  
\end{table}
Table 7: Classification results of ParaNet+ under different optimisation settings.  
It is observed that the best accuracy is obtained when combining width and height shifts, rotations, and horizontal flips.
## 4 Results and Discussion
### Stability Analysis of Mining Slope
#### 4.3.1 Non-Uniform Deformation  
The non-uniform deformation of the overlying strata and surface caused by mining is often the internal factor that causes large surface cracks, uplift, caving, and even landslides. In this study, formula (9) was applied to calculate the horizontal deformation of M06-(M02, M03, M04) and M01-(M02, M03, M04). In the advancing direction of the working face, an analysis of the displacement of the monitoring points found that the movement speed at M01 was slower than that at M02, M03, and M04, which caused a stretching of the ground area. Combined with the results of a topographic analysis, it was apparent that ground movement at M02, M03, and M04 received an influence due the downhill direction of the slope body, while M01 did not, which was one of the reasons for the uneven movement speed. The horizontal movement at M06 was in the opposite direction to that at M02, M03, and M04, resulting in an extrusion deformation dominating the area. Monitoring point M06 was located at the bottom of the slope, and its north-south slope body was limited. The amount of movement at M06 was therefore small, which resulted in the strain being concentrated in an area between M06 and (M02, M03, and M04), where the surface was raised.  
\[\varepsilon(t)=1000\times\frac{\sqrt{\left(X_{t,i}-X_{t,j}\right)^{2}+\left(Y_{ t,i}-Y_{t,j}\right)^{2}+\left(Z_{t,i}-Z_{t,j}\right)^{2}}}{D_{0,i-j}}(mm/m)\]  
where \(D_{0,i-j}\) is the distance between the monitoring points \(i\) and \(j\) at 0 days of data acquisition.  
\((X_{t,i},Y_{t,i},Z_{t,i})\) and \((X_{t,j},Y_{t,j},Z_{t,j})\) are the displacements of \(X\), \(Y\), and \(Z\) of point \(i\) and \(j\) at moment \(t\).  
Figure 10 shows that with the continuous advancement of the working face, the extrusion deformation at M06 and (M02, M03, and M04) increased. The deformation rate of the shape of the ground surface decreased over time, with the maximum compression being more than 11 mm/m. The largest deformation occurred at M03-M06, which highlights the effect of the ground slope on the slope body. Figure 10b shows that the tensile strength of M01 and (M02, M03, and M04) increased continuously, the deformation rate decreased slowly, and the maximum deformation reached 17 mm/m. The rock formations in this area generally consist of hard rock types. When the deformation value reached 2-3 mm/m, cracks, extrusion uplift, and caving occurred on the ground. Although mining has caused large-scale deformation of the ground surface in this area, the deformation speed gradually decreased during this study. After 70 days of monitoring, the deformation rate decreased and the associated risk of high-steep slope failure decreased. The area with an increasing deformation velocity was always located in the influence zone ahead of the working face (i.e., surface non-uniform deformation was mainly concentrated in this area). However, the deformation rate and the non-uniform deformation in the influence zone behind the working face after mining began to decrease, and disturbance of the slope body decreased.