esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 3 Relationship between Robot Steering Radius and Obstacle Position  
To understand the relationship between the robot's motion trajectory and obstacles, a plane motion analysis diagram is shown in Figure 4. \(R_{m}\) represents the maximum radius of the arc traversed by the robot during steering. Similarly, \(R_{s}\) denotes the minimum radius of the arc traversed by the robot during steering. The minimum space required for the robot to travel was a plane composed of two arcs, namely \(R_{m}\) and \(R_{s}\), and the distance between the two arcs was \(\Delta R\). Therefore, if the roadway width is greater than \(\Delta R\), the robot can successfully avoid obstacles without colliding with them. From Figure 5, the following equation is obtained:  
\[\left\{\begin{array}{l}\dfrac{R_{s}+B_{0}+B}{R_{s}+B_{0}}=\dfrac{v_{L}(1-i_{ L})}{v_{R}(1-i_{R})}=k\\ R_{m}^{2}=\left(R_{s}+2B_{0}+B\right)^{2}+\left(L/2+L_{b}+s\right)^{2}\end{array}\right. \tag{8}\]  
where \(k\) is the ratio of the speed of the left track to the right track. \(s\) is the offset distance of the robot's center of gravity. \(B_{0}\) is the distance from the center of the track to the outside of the swing arm. Therefore, the calculation formula of \(R_{s}\) and \(R_{m}\) is as follows:  
\[\left\{\begin{array}{l}R_{s}=\dfrac{B_{0}}{k-1}-B\geq 0\\ R_{m}=\sqrt{\left(\dfrac{B}{k-1}+B_{0}+B\right)^{2}+\left(L/2+L_{b}+s\right)^ {2}}\end{array}\right. \tag{9}\]  
Assuming that the swing arm of the robot is unfolded when it performs a steering motion, the robot must maintain a certain distance from the obstacles to realize steering. According to the relationship between robot structure and size, \(\alpha\), \(\theta\) and \(R_{1}\) can be obtained as follows:  
\[\left\{\begin{array}{l}L_{1}=R_{1}\cos(\theta-\alpha)\\ R_{1}=\sqrt{\left(L+2L_{b}\right)^{2}+\left(B+2B_{0}\right)^{2}}/2\\ \tan\theta=\dfrac{L+2L_{b}}{B+2B_{0}}\end{array}\right. \tag{10}\]  
where \(L_{1}\) is the distance between the center of the robot and the side obstacles, \(\alpha\) is the yaw angle, and \(\theta\) is the structural angle, \(R_{1}\) is the structural radius of the robot center steering.  
Figure 3: Analysis of tracked robot steering radius.  
61.3\({}^{\circ}\), as shown at point B of Figure 5b. When \(L_{1}\) > 683 mm, the robot can turn freely and is not restricted by obstacles.
###### Abstract  
Understanding the impacts and extent of both climate change and human activities on ecosystems is crucial to sustainable development. With low anti-interference ability, arid and semi-arid ecosystems are particularly sensitive to disturbances from both climate change and human activities. We investigated how and to what extent climate variation and human activities influenced major indicators that are related to ecosystem functions and conditions in the past decades in Xinjang, a typical arid and semi-arid region in China. We analyzed the changing trends of evapotranspiration (ET), gross primary productivity (GPP) and leaf area index (LAI) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite product and the Breathing Earth System Simulator (BESS) model in Xinjiang for different climate zones. We separated and quantified the contributions of climate forcing and human activities on the trends of the studied ecosystem indicators using the residual analysis method for different climate zones in Xinjiang. The results show that GPP and LAI increased and ET decreased from 2001 to 2015 in Xinjiang. Factors that dominate the changes in ecosystem indicators vary considerably across different climate zones. Precipitation plays a positive role in impacting vegetation indicators in arid and hyper-arid zones and temperature has a negative correlation with both GPP and LAI in hyper-arid zones in Xinjiang. Results based on residual analysis indicate that human activities could account for over 72% of variation in the changes in each ecosystem indicator. Human activities have large impacts on each vegetation indicator change in hyper-arid and arid zones and their relative contribution has a mean value of 79%. This study quantifies the roles of climate forcing and human activities in the changes in ecosystem indicators across different climate zones, suggesting that human activities largely influence ecosystem processes in the arid and semi-arid regions of Xinjiang in China.  
e 25 June 2022  
10 August 2022  
12 August 2022  
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# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 4 Radio Telescope Measurements with Illuminators of Opportunity  
Typically, terrestrial broadcasting networks, including television and radio, comprise a distributed array of multiple transmitters. This configuration enables the simultaneous utilization of several transmitters as radar illumination sources above the receiver. Transmitting antennas are typically oriented towards the ground to maximize the power directed at the intended terrestrial targets. However, this design choice can present challenges for illuminating satellites, as considerable effort has been made to minimize outward radiation, with the main elevation sidelobes exhibiting power levels that are substantially lower, up to 15 dB, compared with the main beam [74]. Currently, there are at least two prominent fa  
Figure 13: Map showing the layout of the MeerKAT radio telescope array. The receptors, labeled M000 to M063, are distributed across the site based on a two-dimensional Gaussian configuration [70].  
cilities, the Murchison Widefield Array (MWA) located in Australia and part of the LOFAR telescope in Poland, which are actively working on developing and utilizing passive radar systems for space debris monitoring. These passive radar configurations leverage existing sources of illumination, such as FM and DVB signals, to detect and track space debris, thereby providing a cost-effective and expandable solution for this growing challenge. The MWA, like the MeerKAT radar, is part of the SKA project, and a detailed discussion is provided in Section 4.1. Measurement results from the LOFAR telescope are presented and discussed in detail in Section 4.2.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 2 An Overview on Multi-Objective Optimization and Multi-Objective Evolutionary Computation
### Multi-Objective Evolutionary Computation Algorithm  
There are many types of multi-objective evolutionary computation algorithms. They can be classified according to certain characteristics. In the existing research, the algorithms based on differential evolution are reviewed, and the method of classification is proposed [42]. In our paper, from the view of different MOEA search strategies, we can classify them as follows:  
1. Decomposition-based MOEA algorithms.
2. Dominant relationship-based MOEA algorithms.
3. Evaluation index-based MOEA algorithms.  
To obtain a deeper understanding of the MOEA search process, we present the basic flow of Pareto-based MOEA, as shown in Figure 1. First, population \(P\) is initialized, and an evolutionary computation algorithm, such as a decomposition-based multi-objective evolutionary algorithm, is selected to perform evolutionary operations on \(P\) to obtain the new population \(R\). Second, the optimal solution set of \(P\cup R\) is constructed, and the size of the optimal solution set is set to \(N\). If the size of the current optimal solution set \(N\) is inconsistent with the size of \(N\), the size of \(N\) needs adjustment, and the adjusted \(N\) needs to meet the requirements of distribution. It determines whether the algorithm termination conditions have been met. The individuals in \(N\) will be copied to the population \(P\) to continue in the next round of evolution, otherwise, it will stop. The iteration count of the algorithm is generally used to control its execution.  
In MOEA, maintaining and incorporating the optimal solution set from the preceding generation into the evolution process of the current generation is a critical requirement for convergence [59]. This allows the evolutionary population's optimal solution set to converge to the real Pareto front, ultimately leading to a satisfactory evolutionary outcome.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 5 The Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM  
### The Proposed Method  
To begin with, a modification of MFO is utilized to enable adaptive selection of VMD parameters, thereby avoiding the influence of manually set parameters on VMD decomposition effect. The decomposed IMFs are screened using the EWCS index. Given that the energy levels of individual components of the vibration signal's IMF differ based on the rolling bearing's distinct movement states, the energy attributes of these components are isolated to develop a feature matrix. Lastly, the energy eigenmatrix is input into DE-OSELM in order to perform fault classification using the principles outlined in reference [37]. Figure 7 shows the flow chart for our proposed method.  
The specific process is listed as follows:  
1. The improved MFO was used to optimize \(K\) and \(\alpha\) in VMD parameters, and the optimal parameter combination \((k_{0},a_{0})\) was obtained.
2. The VMD with optimized parameters was used to process the collected signals, and \(K\) IMFs were decomposed.
3. The EWCS index was used to screen \(K\) IMFs, eliminate the false IMFs, extract the energy features of effective IMFs, and form the feature vector matrix.
4. The energy feature matrix is normalized and input into DE-OSELM for training and fault classification.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Characterization of Canopy-Level Photosynthetic Efficiency
#### 2.3.3 Rapid Light Curve and Standardized ETR  
Because light intensity influences electron transport rates and can fluctuate throughout the day or even within the same measurement period for a given plot, rapid light response curves were generated from diurnal data to provide a standardized measure of maximum electron transport rate for each plot. ETR and PAR values were used to generate rapid light curves (RLCs) using the method proposed by [54]. To quantitatively analyze RLCs, RLCs were fitted using an empirical model (Equation (6)) [55].  
\[ETR=mETR\times(1-e^{-\alpha\times PAR/mETR}), \tag{6}\]  
where \(mETR\) was the photosynthetic capacity at saturating light by which all reaction centers were hypothetically closed, and \(\alpha\) was the initial slope of the RLCs before the onset of saturation; i.e., the slope of the light-limiting region of RLCs.  
RLCs were generated and modeled at two levels: the group level and the plot level. RLCs at the group level were calculated using all data points (\(N=48\)) of a group (a genotype-treatment combination), and RLC parameters were compared to evaluate differences in canopy-level photosynthetic efficiency among the four groups. In order to test the statistical significance of these differences, RLCs at the plot level were calculated using data points (\(n=6\)) of individual plots in each group. Consequently, an individual group had 8 replicates for conducting statistical analyses.  
While \(mETR\) can be used to compare differences of photosynthetic efficiency between groups, standardized ETR (sETR) values were calculated by setting PAR equal to 1500 \(\upmu\) mol/m\({}^{2}\)/s, which is a common value in the study area and is generally considered a saturating light intensity for cotton [56].
## 6 Conclusions  
This study took Chongqing as a case study, and used meteorological data, and MODIS atmospheric and land parameters to develop a GLOPEM-CEVSA model to estimate NPP (net primary productivity), and then analyzed the spatial and temporal distribution of NPP for different vegetation types. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. The annual NPP in the study area was approximately 534 gC/m\({}^{2}\)a from 2001 to 2011, and showed a gradually decreasing trend from northeast to southwest. The average NPP of forest, scrub, cropland, grassland, urban land, and bare land was 412.29, 554.83, 347.93, 362.40, 203.32 and 110.68 gC/m\({}^{2}\)a, respectively, and the NPP showed differences among different land cover types.  
The GLOPEM-CEVSA model not only improved the accuracy of PAR and NPP estimates by using remote sensing parameters instead of coefficient transfer, but also integrated MODIS land parameters to drive the model. The growth rate of vegetation was mainly determined by precipitation and temperature factors. NPP in the karst region was significantly correlated with precipitation (R\({}^{2}\) = 0.571) and temperature (R\({}^{2}\) = 0.924) at monthly temporal scales and showed stronger sensitivity to interannual variation in temperature. Although the study showed promising results for remote sensing based vegetation NPP estimation, there are limitations to the accuracy of fPAR inversion using MODIS data; therefore, further work is needed to improve the estimation accuracy. In addition, spatio-temporal patterns of NPP and their relationships with climate factors still need further research in the Karst area. [PERSON] et al. [41] have already studied the relationship among precipitation, relative humidity, mean temperature and vegetation EVI in Karst rocky area. However, there are many other meteorological factors, such as how the growing season maximum and minimum temperatures affect NPP. Therefore,the vegetation response to climate change is complex, and deserves more detailed and deeper inquiry in future research.  
This study was supported by the National Key Research and Development Program of China (2016 YFC0803004), the National Natural Science Foundation of China (41301501), and Youth Innovation Promotion Association of CAS (2015129).  
[PERSON] conceived and designed the study; [PERSON], [PERSON] and [PERSON] assisted with the study design and the interpretation of the results; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Experiment
### Execution and Results
#### 4.4.2 Comparing Overall System Performance
Base Case  
As the first step of the whole-model simulation, setting the capacity profile to two months each for ramp-down and ramp-up and a 20% discretionary capacity, the results show a 39% improvement in lost sales (RNN over heuristics) performance and a 6% improvement in inventory (see Figures 11 and 12, and Table 2). At first glance, the results seem to live up to the promise of the forecast accuracy gains of RNN over heuristics. However, studying the graphs gives pause as it suggests that there are further improvements to be made. Focusing on the lost sales and capacity subplots and comparing the RNN and heuristics graphs, one sees that the capacity profile in the case of RNN has significantly more spikes. The lost sales in the case of RNN are also much more densely clustered compared to heuristics. This behavior results from RNN's superior ability to capture the peaks and troughs in customer demands (one can see this by comparing the production rate curves). In particular, the inability of heuristics to anticipate the troughs results in excess inventory. The inventory build-up obviates the need for sustained additional capacity in the case of heuristics--thus, the capacity availability curve is smoother. On the other hand, the much-improved forecast accuracy of RNN translates to the production rate closely chasing actual demands, thereby leading to a leaner inventory profile. An additional consequence of the better anticipation of low \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Case B vs. Case A**} \\ \cline{2-7}  & \
\begin{tabular}{c} **Metric** \\ **(in Units)** \\ \end{tabular} &
\begin{tabular}{c} **Heuristics** \\ **(H)** \\ \end{tabular} & **RNN (R)** & **R vs. H** & **Heuristics** & **RNN** \\ \hline \multirow{3}{*}{**(A) Base case: 20\% discretionary capacity,** **quick ramp-up and ramp-down (1)**} & Lost Sales & 29.06 & 17.85 & \(\mathbf{-39\%}\) & **N/A** & **N/A** \\ \cline{2-7}  & Inventory & 39.46 & 37.17 & \(\mathbf{-6\%}\) & **N/A** & **N/A** \\ \hline \multirow{3}{*}{**(B) After heuristic adjustment: 20\%** **discretionary capacity; quick ramp-up and slow ramp-down (2)**} & Lost Sales & 26.10 & 10.27 & \(\mathbf{-61\%}\) & \(\mathbf{-10.2\%}\) & \(\mathbf{-42.5\%}\) \\ \cline{2-7}  & Inventory & 39.52 & 37.54 & \(\mathbf{-5\%}\) & \(\mathbf{0.2\%}\) & \(\mathbf{1.0\%}\) \\ \hline \hline \end{tabular}  
Notes: (1) Quick ramp-up and ramp-down: Capacity ramp-up lasts two months. Capacity normalization when delivery pressure eases also lasts two months. (2) Quick ramp-up and slow ramp-down: Capacity ramp-up lasts two months. However, capacity normalization when delivery pressure eases lasts four months.  
\end{table} 
Table 2: Outcome metrics summary. to normalize too quickly during periods with \"rugged\" peaks. This suggests a simple adjustment (an increase) to the capacity ramp-down delay. Intuitively, a slower ramp-down should allow the provisioning of some buffer capacity to clear the backlog, even as the production rate continues to roughly trace the sharp turns in the demands.
## 2 Materials and Methods
### Statistics and Reproducibility  
We conducted three runs of training, each with an equal distribution of training, validation, and testing data. The patients included in each run were randomly selected, resulting in a unique set of patients for every iteration. Sensitivity (proportion of true positives correctly identified among the total number of individuals with lesions), specificity (proportion of true negatives correctly identified among the total number of individuals without lesions), accuracy (proportion of correctly identified cases [both true positives and true negatives] out of the total number of individuals [all predictions]), negative predictive value (NPV--proportion of true negatives correctly identified among all individuals who were predicted to not have lesions), and positive predictive value (PPV--proportion of true positives among all cases with a positive test result) were calculated for each test group.  
Figure 3: Examples of created heatmaps demonstrating how DL model distinguishes a pleomorphic esophageal lesion: (**a**) erosion, (**b**) protruding lesion, (**c**) varies, and (**d**) diverticula.  
Figure 2: Examples of how the algorithm estimated probability of being considered normal (N) vs. having a pleomorphic esophageal lesion (PL). Considering the one with the highest probability, each frame was classified in one of categories stated earlier. The matching evaluation provided by the three physicians, considered the gold standard, is written in the upper right rectangle.  
The final metrics for the model diagnostic performance were determined based on the median and range values of these variables. Additionally, we computed the area under the ROC curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) for each test set and calculated their average values. We chose to compute both precision-recall and conventional ROC curves to address the imbalance between normal mucosa frames (true negatives) and frames containing pleomorphic lesions (true positives), as this imbalance could potentially lead to misinterpretation when relying solely on the ROC curve.  
Furthermore, we assessed the computational performance efficiency of our ViT model in our machine by measuring the processing time for all frames within the test set  
Our statistical analysis was carried out using Sci-kit Learn v0.22.2 [17].
## 4 Results and Discussions
### System Identification Analysis  
To estimate an optimum model of system identification for the behavior of a coupled structure and controller system effects, the parameter setting should be evaluated as shown in Figure 3. This section is divided into two parts: first, the training setting which contains the evaluation of the third-floor behavior of the building with 1.80 Kg TMD mass (TMD-1.80). Second, the optimum models are evaluated and discussed using the behavior of the third floor experiments based on 2.39 kg TMD mass (TMD-2.39). This system case is used as a testing setting.
# PET/CT Radiomics in Lung Cancer: An Overview
## 4 Discussion  
Many recent studies have consistently emphasized the potential advantages of PET/CT radiomics in lung cancer and other oncological disorders. Chief among such advantages are the ability to capture information beyond the capabilities of the human eye, non-invasiveness, virtually real-time response, and full-field analysis of the lesion.  
The results available in the literature are undoubtedly promising, but they also need to be considered with care. Lack of reproducibility, for instance, is a well-known problem in radiomics, and is mostly a consequence of the absence of standardized methods and settings in all the steps of the workflow [14; 17]. A number of studies also suffer from serious limitations at the validation level: among them improper statistical analysis (e.g., lack of adjustment of the \(p\)-value for multiple tests) and/or absence of an independent validation dataset to confirm the results. This may easily lead to biased discovery rates and inflation of type-I errors, as correctly pointed out in [77]. Last but not least, publication bias naturally tends to overstate positive results against negative ones (for a discussion on this, see also [78; 79]).
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 3 Request Expectation Index Based Cache Replacement Algorithm
### RXI-Based Cache Replacement Algorithm  
In an ICN router, the chunk with the minimum \(RXI(i,j)\) denotes that the chunk \(C_{i,j}\) has the lowest expectation of being requested in the future. Therefore, when cache replacement occurs, the \(RXI(i,j)\) of each cached chunk is calculated according to the current value of \(P_{f}(i)\) and \(IRXI_{f_{i}}(j)\). Subsequently, the cached chunk with the minimum \(RXI(i,j)\) is evicted, as shown in Algorithm 2. Since the \(RXI(i,j)\) of each cached chunk is calculated when cache replacement occurs, a traversal of cache space is needed for the calculations; thus, the time complexity of RXI-based cache replacement algorithm is \(O(n)\).  
```
0:\(P_{f}(i)\), \(IRXI_{f_{i}}(j)\), \(MinRXI\), \(MinChunk\)
1:\(MinRXI\gets 1\)
2:while All cached chunk \(C_{i,j}\) in the cache space do
3:\(RXI(i,j)\gets P_{f}(i)\times IRXI_{f_{i}}(j)\)
4:if\(MinRXI>RXI(i,j)\)then
5:\(MinRXI\gets RXI(i,j)\)
6:\(MinChunk\gets C_{i,j}\)
7:endif
8:endwhile
9:Evict the chunk \(MinChunk\)
```  
**Algorithm 2** RXI-based Cache Replacement