esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 3 Preliminaries
### Smart Black Box Design  
The SBB aims to record high-quality high-value data through value-driven data compression and prioritized data recording. At each time step, one data frame is observed and collected. Based on event detectors, a scalar frame value \(v_{t}\in[0,1]\) is computed for each frame. The data frame is then appended to a buffer, which caches seconds or minutes of data. The process of buffering data frames is managed by a deterministic Mealy machine (DMM) which uses the new data value, data similarity, and the current buffer size to determine when to end the current buffer and start a new one [13]. After the DMM terminates, local buffer optimization (LBO) is used to determine the optimal compression  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\# of Frames** & **Data Size (GB)** & **Anomaly-Focused** & **\# of Anomalous Videos** \\ \hline KITTI & 7481 & (15 fps) & 12 & No & N/A \\ Cityscapes & 24,999 & (17 fps) & 55 & No & N/A \\ BDD100K & 120,000,000 & (30 fps) & \(\sim\)1800 & No & N/A \\ \hline A3D & 128,174 & (10 fps) & 15 & Yes & 1500 \\ DADA & 648,476 & (30 fps) & 53 & Yes & 2000 \\ DoTA & 732,932 & (10 fps) & 57 & Yes & 4677 \\ \hline \hline \end{tabular}
\end{table}
Table 2: High-quality driving video datasets. DoTA, Detection of Traffic Anomaly.  
factor \(d_{t}\in[0,1]\), called the LBO decision, for each frame in the buffer. A Gaussian data value filter can be applied over the buffered data to smooth the estimated data value. The buffered data are then compressed according to the LBO decisions and stored in long-term storage. After on-board storage is full, a priority-queue discards the lowest value buffers to make space for higher value buffers.
## 3 Methods and Materials
### Image Processing Backend
\(S\) represents a displacement vector sequence and is calculated as,  
\[S=(\Delta P_{t},\Delta P_{t+1},\ldots,\Delta P_{t+L-1}),\]  
where \(L\) is the length of the trajectory and \(\Delta P_{t}=(P_{t+1}-P_{t})\). The resulting vector \(S\) is normalized as follows:  
\[S^{\prime}=\frac{(\Delta P_{t},\Delta P_{t+L-1})}{\sum\limits_{t=t}^{t+L-1} \left\|\Delta P_{t}\right\|}\]  
Given a skeleton pose sequence \(S^{\prime}\) with \(N\) frames, defined as \(S^{\prime}=F_{t}\), where \(t=1,2,3,\ldots,N\). Let \(p_{tj}\) and \(p_{tk}\) be the 3D positions of the \(j\)-th and \(k\)-th joints in \(F_{t}\). Then, the inter-joint distance \(IJD_{jk}^{t}\) between \(p_{tj}\) and \(p_{tk}\) at time sample \(t\) is calculated as  
\[IJD_{jk}^{t}=||\mathbf{p}_{j}^{t}-\mathbf{p}_{k}^{t}||_{2},(t=1,2,3,\ldots,N)\]  
The angle of the human joint and the angle variation can be employed as deep spatial characteristics in an action-detection job based on bone data. When combined with the 2D \((x,y)\) coordinates provided in the human-skeleton dataset, the length of the bone and the angle between the bones are easily calculated. We can simply depict the location of connections in the human skeleton using these variables. It is important to compute the angle created by its two bones. The length of each bone and the angles between the joints are estimated on the basis of the coordinates of each node in the human-bone dataset and its physical relationship. \[\theta_{i}=\arccos\left[\frac{\left(x_{pred(i)}-x_{i}\right)\times\left(x_{ succ(i)}-x_{i}\right)+\left(y_{pred(i)}-y_{i}\right)\times\left(y_{succ(i)}-y_{i} \right)}{\sqrt{\left(x_{pred(i)}-x_{i}\right)^{2}+\left(y_{pred(i)}-y_{i} \right)^{2}}\times\sqrt{\left(x_{succ(i)}-x_{i}\right)^{2}+\left(y_{succ(i)} -y_{i}\right)^{2}}}\right]\]  
where \(x_{i}\) and \(y_{i}\) denote the abscissa and ordinate of the node \(i\), \(succ(i)\) is the successor node of node \(i\), \(pred(i)\) is the predecessor node of node \(i\), and \(\theta_{i}\) represents the angle on the node \(i\). A length is calculated as follows:  
\[|L_{1}|=\sqrt{\left(x_{1}-x_{2}\right)^{2}+\left(y_{1}-y_{2}\right)^{2}}\]  
where \(|L_{1}|\) denotes the length of the bone between the nodes, and its subscript represents the label of the first bone. The deep-space feature matrix is obtained by extracting the angle between bone length and bone, as well as the angles and lengths between all bones in a single frame.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 1 Introduction
However, there are applications for which the TDMA (Time Division Multiple Access) based on duty cycles is more efficient [24]. We have analyzed the advantages of the existing works and have been inspired by them in our proposal of a modification of the basic ZigBee protocol. ZigBee was selected because of its small control overhead compared to other similar technologies, such as BLE. We have combined the duty-cycle-based approach with prioritization at the central node of ZigBee star-topology communication. The goal was to increase energy efficiency of periodic sending of end-node sensor data to the central node by reduction of simultaneous access to the medium and resulting retransmissions. This way, more important data are preferred over less important, and they are not lost in case of a traffic congestion. The solution is designed for the star WSN network topology with one central device (an Internet gateway, also called an IoT gateway) and several end sensory devices, which can use various data priorities (e.g., multiple sensors in a single end device with different data). The proposed method is called EEMIP (Energy-Efficient Method using Intervals and Prioritization). We intend to use the method for health-monitoring applications, but the method can be used for other applications as well. The article is structured as follows. In this section, we have summarized motivation and similar studies that deal with the reduction of energy in communication. In Section 2, a background regarding ZigBee technology is given. Section 3 contains proposal of a new method for energy-efficient communication targeting ZigBee protocol. In Section 4, the experimental results are described and discussed. The last section concludes the article.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 6 Further Discussion
### Adopting a Routing Registry  
One major concern in BGP inter-domain routing is the exploration and impersonation of the ownership of IP prefixes for ASes. Hijacking, misconfiguration, and errors [46] can lead to the disconnection of portions of the Internet (black hole routing), Man-In-The-Middle attacks (MITM), Distributed Denial of Service (DDoS), and other security issues. These issues occur because the control plane of BGP does not provide security mechanisms for discovering what an AS owns and is allowed to advertise for a given prefix.  
To be reachable on the Internet, an AS has to inform the prefixes its network is configured and passes those values to its ISP (or its Peers). Often, this is done basically by signed contracts between ASes during the establishment of their AS relationships, and an agreement of which Routing Registry (RR) is used. An Internet Routing Registry (IRR) is a database on ASNs and IP routing prefixes. A registry in an IRR is used for configuring BGP routers to avoid security issues between ASes [47].  
When a RR is adopted, it is possible to build filters based on the registries of that database. Although there are some global IRRs (for example, ARIN's IRR [47]), often the providers run their Routing Registries. The problem of using a global Internet Routing Registry is that the current efforts do not provide extensive, updated, and reliable databases of most of the ASes, and hence the registries usually do not reflect the reality of the ASes relationships and the ownership of their prefixes.
# Approaches to the Functionalization of Organosilicon Dendrones Based on Limonene
## 2 Experimental
### Material and Methods
_Tris(heptamethylsilylpropyl)silyl-1,2-epoxy-limonene (LimOx-G1.STMS6)_  
This substance was obtained similarly to _Lim-G\({}_{0.5}\)TMS\({}_{2}\)_O from 2.0 g (0.0021 mol) _Lim-G\({}_{1.5}\)TMS\({}_{6}\)_, 0.54 g (0.0031 mol) m-CPBA, 0.35 g (0.0042 mol) NaHCO\({}_{3}\), and 80 mL DCM. \(\phi\) = 83%. _Epoxidized dendrimer of the 2.5 generation (LimOx-G2.5 TMS8)_  
Synthesized similarly to _Lim-G\({}_{0.5}\)TMS\({}_{2}\)_O from 2.0 g (0.0014 mol) _Lim-G\({}_{2.5}\)TMS\({}_{8}\)_, 0.37 g (0.0021 mol) m-CPBA, 0.24 g (0.0028 mol) NaHCO\({}_{3}\), and 76 mL DCM. \(\phi\) = 80%. _Epoxidized dendrimer of the first generation (LimOx-G1 Bu4)_  
Synthesized similarly to _Lim-G\({}_{0.5}\)TMS\({}_{2}\)_O from 2.0 g (0.0035 mol) Lim-G\({}_{1}\)Bu\({}_{4}\), 0.89 g (0.0052 mol) m-CPBA, 0.58 g (0.0069 mol) NaHCO\({}_{3}\), and 90 mL DCM. \(\phi\) = 82%. _Azide-functionalized dendrimer of the first generation (LimN3-G1 Bu4)_  
To a stirred mixture of 0.50 g (0.84 mmol) of _Lim-G\({}_{1}\)Bu\({}_{4}\)_O, 0.045 g (0.84 mmol) of NH\({}_{4}\)Cl and 0.11 g (0.0017 mol) of sodium azide, 3.5 mL dry methanol was added. RM was refluxed for 5 h. After that, the product was washed under standard conditions. Organic layer was dried over Na\({}_{2}\)SO\({}_{4}\). Solvent was removed by evaporation under reduced pressure. \(\phi\) = 88%. _Hydroxy-functionalized dendrimer of the first generation (LimOH-G1 Bu4)_  
First, 0.038 g (0.0010 mol) of LiAlH\({}_{4}\) was added to a 5% solution of _Lim-G\({}_{1}\)Bu\({}_{4}\)_O (0.5 g, 0.84 mmol) in anhydrous THF, and the mixture was refluxed for 5 h under argon. Then, CH\({}_{3}\)COOH was added dropwise and the product was extracted with diethyl ether and washed with distilled water three times. Organic layer was dried over Na\({}_{2}\)SO\({}_{4}\). Solvent was evaporated. \(\phi\) = 86%. _Propargylate-functionalized dendrimer of the first generation (LimC\(\equiv\)C-G1 Bu4)_First, 0.5 g (0.84 mmol) of _Lim-G\({}_{1}Bu_{4}O\)_ and 0.071 g (0.0010 mol) of propiolic acid were mixed and stirred at 70 \({}^{\circ}\)C for 6 h. After reaction completed, the product was washed under standard conditions. Organic layer was dried over Na\({}_{2}\)SO\({}_{4}\). Solvent was distilled off. \(\phi\) = 90%.
## Results and Discussion
### Effect of Microchannel Shape
The results summarized in Table 5 reveal that, among all five microchannels with different shapes, the circular one had the smallest contact area, which is consistent with the statement of isoperimetric inequality. The results also suggest that the triangular shape of the microchannel has the highest overall volume flow rate value. These results, also plotted in Figure 13, confirm that there is a direct relationship between the contact area and the overall volume flow rate. Overall, the results indicate that, as the number of sides of a polygonal shape decreases, the volume flow rate increases.
# Emotion Recognition Method for Call/Contact Centre Systems
## 5 Proposed Approach  
Figures 3 and 4 propose a new approach to solve the problem of recognizing client and agent emotions in text and voice channels of CC systems. The proposed method has been integrated with various functional blocks used in conversation analysis to form a fully complementary hybrid environment. We can distinguish three basic components to the solution: TRANSCRIPTOR (a transcription module for calls originating from CC voice channels), TEXT ANALYZER and VOICE ANALYZER. The data from the text channel (CHAT TALKS) is routed directly to the TEXT ANALYZER module for further processing. In turn, the data coming from the voice channel (VOICE TALKS) goes simultaneously to the modules: VOICE ANALYZER and TRANSCRIPTOR. The TRANSCRIPTOR MODULE performs automatic transcription of conducted conversations according to the method described in paper [53]. Conversation transcripts are directed to the TEXT ANALYZER module, while audio signals go to the module that is designed for audio analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**MFCC** & **Anger** & **Sadness** & **Happiness** & **Fear** & **Neutral** \\ \hline F0 & \(-\)0.070 & \(-\)0.002 & 0.118 & 0.035 & \(-\)0.081 \\ JITTER & 0.157 & \(-\)0.132 & 0.075 & \(-\)0.087 & \(-\)0.013 \\ SHIMMER & 0.289 & \(-\)0.178 & \(-\)0.001 & \(-\)0.093 & \(-\)0.016 \\ MFCC0 & 0.335 & \(-\)0.290 & 0.003 & 0.079 & \(-\)0.128 \\ MFCC1 & 0.218 & 0.117 & \(-\)0.144 & 0.036 & \(-\)0.227 \\ MFCC2 & \(-\)0.304 & 0.061 & 0.065 & 0.133 & 0.044 \\ MFCC3 & 0.151 & \(-\)0.089 & \(-\)0.006 & \(-\)0.014 & \(-\)0.042 \\ MFCC4 & 0.241 & \(-\)0.021 & \(-\)0.059 & \(-\)0.083 & \(-\)0.078 \\ MFCC5 & \(-\)0.338 & 0.110 & 0.054 & 0.024 & 0.150 \\ MFCC6 & \(-\)0.154 & 0.029 & 0.011 & \(-\)0.034 & 0.148 \\ MFCC7 & 0.002 & \(-\)0.103 & 0.083 & \(-\)0.106 & 0.123 \\ MFCC8 & \(-\)0.324 & 0.027 & 0.150 & \(-\)0.033 & 0.181 \\ MFCC9 & \(-\)0.182 & 0.090 & 0.019 & \(-\)0.024 & 0.096 \\ MFCC10 & \(-\)0.011 & \(-\)0.108 & 0.028 & 0.016 & 0.075 \\ MFCC11 & \(-\)0.178 & \(-\)0.135 & 0.151 & \(-\)0.020 & 0.182 \\ MFCC12 & \(-\)0.000 & \(-\)0.013 & 0.073 & \(-\)0.146 & 0.086 \\ \hline \end{tabular}
\end{table}
Table 8: Values of Pearson’s correlation coefficients.  
The modules that analyse CHAT TALKS and VOICE TALKS provide the results of classifying emotional states according to the emotion classification for machine detection of affect-tinged conversational contents in voice and text channels of contact centre systems. A high level flowchart of our method is presented in Figure 3, with a detailed conception presented in Figure 4.  
Figure 4: Method for emotion recognition in text and voice channels of CC systems.  
Figure 3: High level flowchart for emotion recognition method in text and voice channels.
###### Abstract  
We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone \({}^{18}\)F-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm\({}^{-1}\)) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm\({}^{-1}\)), air and bone (0.151 cm\({}^{-1}\)) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23-26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland-Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution.  
PET; TOF reconstruction; attenuation correction +
Footnote †: journal: Medical Physics  
Article
# Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks +
## 3 Downlink Coverage Probability Calculation  
In this section we study the downlink coverage probability. When evaluating channel performance, a typical user in the area is associated with a drone-BS when the SINR is greater than a predefined threshold. According to [3], the received power at the user \(j\) from its drone-BS is given by:  
\[p_{r}^{ji}=p_{t}-PL\big{(}h,d_{ji}\big{)}-\vartheta \tag{7}\]  
where \(\boldsymbol{p_{t}}\) is the power transmission and \(\vartheta\) is the shadow fading with Gaussian random variable in dB for LOS and NLOS environments. The SINR ratio is given by the following formula:  
\[SINR=\frac{p_{r}^{ji}}{\sum_{i^{\prime}\in D,\;i^{\prime}\
eq i}\;p_{r}^{i^{ \prime}j}+N} \tag{8}\]  
\[\mathbb{P}[SINR>SINR_{thr}]=\mathbb{P}\left[\frac{p_{r}^{ji}}{\sum_{i^{\prime} \in D,\;i^{\prime}\
eq i}\;p_{r}^{i^{\prime}}+N}\geq SINR_{thr}\right] \tag{9}\]  
**Lemma 1**.: _In the drone-BS network, the downlink coverage probability for a ground user located at a distance \(\boldsymbol{d_{ji}}\) from drone-BS can be expressed as:_  
\[p_{c}=Q\Bigg{(}\frac{\Big{(}PL\big{(}h,d_{ji}\big{)}\text{+}SINR_{thr}\Big{(} \sum_{i^{\prime}\in D,\;i^{\prime}\
eq i}p_{r}^{i^{\prime}}+N\Big{)}-p_{t}+ \mu\Big{)}}{\sigma}\Bigg{)} \tag{10}\]  
_where \(Q(.)\) is the \(Q\) function._  
Figure 1: System Model.  
**Proof.**  
\[\begin{split} p_{c}=\mathbb{P}\Bigg{[}\frac{p_{r}^{i\mu}}{\sum_{ \varphi\in D,\ \tau\
eq i,\ \mu_{r}^{i\prime}+N}}\geq SINR_{thr}\Bigg{]}\\ =\mathbb{P}\Bigg{[}\ p_{r}^{i\mu}\geq\ SINR_{thr}\left(\sum_{ \tau^{\prime}\in D,\ \tau\
eq i}p_{r}^{i\prime}+N\right)\Bigg{]}\\ =\mathbb{P}\Bigg{[}p_{t}-PL\big{(}h,d_{ji}\big{)}-\vartheta\geq \ SINR_{thr}\left(\sum_{\hat{\tau}^{\prime}\in D,\ \tau\
eq i}p_{r}^{i\prime}+N\right)\Bigg{]}\\ =\mathbb{P}\ \Bigg{[}\ \vartheta\leq p_{t}-PL\big{(}h,d_{ji} \big{)}-\ SINR_{thr}\left(\sum_{\hat{\tau}^{\prime}\in D,\ \tau\
eq i}p_{r}^{i\prime}+N\right)\Bigg{]}\\ Q\left(\frac{\left(\frac{PL\big{(}h\big{(}h,d_{ji}\big{)}+ SINR_{thr}\left(\sum_{\hat{\tau}^{\prime}\in D,\ \tau\
eq i}p_{r}^{i\prime}+N\right)-p_{t}+\mu\big{)}}{\sigma}\right)}{\sigma} \right)\end{split} \tag{11}\]  
where \(\vartheta\sim N\big{(}\mu_{LOS},\ \sigma_{LOS}^{2}\big{)}\) and \(\vartheta\sim N\big{(}\mu_{NLOS},\ \sigma_{NLOS}^{2}\big{)}\) represent the shadow fading with normal distribution, in dB, for LOS and NLOS cases, respectively. However, the Complementary Cumulative Distribution Function (CCDF) of a Gaussian random variable as show in Equation (12) below gives the downlink coverage probability for LOS environment.  
\[p_{c}=Q\Bigg{(}\frac{\left(PL_{LOS}P_{LOS}+SINR_{thr}\Big{(}\sum_{\hat{\tau}^{ \prime}\in D,\ \tau\
eq i}p_{r}^{i\prime}+N\Big{)}-p_{t}+\mu_{ LOS}\right)}{\sigma_{LOS}}\ \Bigg{)} \tag{12}\]
Therefore, we may explore the potential influences of PRI on the SIF-GPP relationship at diurnal and seasonal scales using the continuous observations. In addition, the information from reflectance-based PRI and SIF can be used to estimate GPP at global scales in the future thanks to the launch of the European Space Agency's (ESA) Fluorescence Explorer (FLEX) mission [11; 25]. Although the fact that the combination of SIF, PRI, and structure information has been proven to be beneficial for quantifying GPP based on airborne or satellite observations, the potential impact of PRI on the relationship of GPP to SIF for C3 and C4 crops has yet to be investigated based on tower-based measurements. According to the key hypotheses and the theoretical foundation proposed by [PERSON] et al. [18], the usefulness of combining the information of NPQ reflected by PRI with \(SIF_{t}\) to improve GPP estimation was examined for soybean (a C3 crop) and corn (a C4 crop) crops based on tower-based measurements in this study. The specific issues to be addressed in detail including: (1) How might PRI affect the \(SIF_{t}\)-GPP relationships? (2) How might these relationships differ for C3 (soybean) and C4 (corn) crops? (3) Is it possible to increase the accuracy of the GPP estimation by combining PRI and \(SIF_{t}\)?
## 3 The Mathematical Model for RHF Formulation Scheme Optimization
### Constraints  
This paper requires that the formulation scheme satisfies the following two constraints.  
1. To ensure the effectiveness of the formulation scheme, the sum of the proportions for the internal bins must equal 1. This constraint guarantees the consistency of the sum of the proportion values in each blending scheme, thereby maintaining balance in the production process, i.e. \[\sum_{i=2}^{10}b_{i}=1\] (8)
2. The proportion of each bin needs to be kept within a reasonable range, i.e. \[\left\{\begin{aligned} b_{i}=0& off\\ b_{imin}\leq b_{i}\leq b_{imax}& on\end{aligned}\right.\] (9) where \(\text{b}_{i}=0\) indicates that the bin is not in use, while \(\text{b}_{\text{imax}}\) and \(\text{b}_{\text{imin}}\) are the upper limit and lower limit of the allowed proportion range for the corresponding bin. If \(\text{b}_{i}\) exceeds the maximum flow limit \(\text{b}_{\text{imax}}\), the feeders and belts in the blending system will be unable to output material flow rates beyond this upper limit. Conversely, if \(\text{b}_{i}\) falls below the minimum flow limit \(\text{b}_{\text{imin}}\), the feeders and belts may operate at their lowest flow rate, thereby increasing flow control errors, wasting operational resources, and leading to a decline in production efficiency.  
In summary, the multi-objective optimization problem for the RHF formulation scheme addressed in this paper is a continuous multi-objective optimization problem with four optimization objectives and two constraints, which is formulated as the following minimization problem:  
\[\text{min}f(B)=(f_{1}(B),f_{2}(B),f_{3}(B),f_{4}(B)) \tag{10}\]  
\[s.t.\left\{\begin{aligned} \sum\limits_{i=2}^{10}b_{i}=1\\ b_{i}=0\text{ or }b_{imin}\leq b_{i}\leq b_{imax}\end{aligned}\right. \tag{11}\]
## 3 Results and Discussion
### Analysis of Disturbance Temporal Characteristics  
Based on the inter-annual TVDI variation information, disturbance years for the pixel dataset from 2015 to 2021 were extracted, and the disturbed pixels exhibited a clustered distribution within the same year (Figure 10a), indicating that the locations of these pixels were significantly affected by open-pit mining activities in a concentrated manner. Spatially, the disturbance years align closely with the mining direction of the open-pit mine. The largest disturbed area occurred in 2016, likely due to the stripping of overburden, which destroyed the aquifer, leading to pit flooding and subsequent changes in SM. Since 2016, mining activities expanded southeastward and northwestward, gradually shifting towards the northwest, with the disturbance range continuously increasing spatially. This is consistent with the satellite imagery changes from the time series (Figure 10b), further demonstrating the feasibility of extracting mining disturbance based on the TVDI. As the open-pit mine progressed, TVDI values significantly decreased, likely due to the moisture accumulation effect in localized regions within the mining pits, further increasing SM.  
Figure 9: TVDI-CFS variation between the Xiwan open-pit mine and open-pit mine 2.  
The disturbance pixels for each year were statistically analyzed, resulting in Table 2. Temporally, the disturbed area initially increased, decreased, and eventually stabilized at a smaller size. This suggests that mining activities have entered a relatively static phase, and the disturbance area affecting the original ecosystem has stabilized. However, this study focused solely on SM without considering interactions with other environmental factors, such as vegetation regrowth, changes in soil structure, or alterations in local microclimatic conditions. Future studies should adopt a more integrated approach by incorporating these variables to provide a more comprehensive understanding of the environmental transformations induced by mining.  
Additionally, the spatial resolution of Landsat imagery may limit the detection of subtle SM variations, particularly in ecologically heterogeneous landscapes. Employing higher resolution remote sensing platforms, such as Sentinel-2 or unmanned aerial vehicles (UAVs), would allow for a more detailed analysis of spatial patterns of SM disturbance. Future work should also explore ecosystem rehabilitation and restoration, particularly focusing on understanding the recovery trajectories of SM and vegetation following the cessation of mining activities. This would offer valuable insights into ecosystem resilience and inform the development of sustainable land management strategies for post-mining landscapes.