esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
proposed a method for modeling a closed-circuit cable-pulley-driven robotic system with a hysteresis cable tension model and a cable-pulley network friction model. The Hysteresis Cable Tension Model captures the cable response, including elasticity, internal friction, material damping, and hysteresis. The modeling method was validated on the RAVEN II robotic surgery platform [25; 26]. [PERSON] et al. proposed a joint torque disturbance observer (JTDO)-based method for estimating the clamping force of the end effector of a cable-driven surgical robot. The JTDO Variation proposed the cable tension between the actual cable tension and the estimated cable tension using a Particle Swarm Optimization Back-Propagation Neural Network [27]. [PERSON] et al. proposed a grasping force sensing method based on a denoising Auto Encoder Neural Network (dAENN), which uses encoder and motor current readings as the input for the dAENN, to learn the nonlinear mapping between the compact features and the grasping force labels [28]. [PERSON] proposed an improved scheme based on a fed neural network and a prediction model, to eliminate the coupled motion of the end effector and reduce position tracking error [29]. Based on the dynamic model of the cable-pulley system, [PERSON] et al. used the motor encoder and motor torque information as the observed values of the Unscented Kalman filter to jointly estimate the grasping force and grasper position of slender cable-driven surgical instruments [30]. [PERSON] et al. used an improved cable-pulley model to compensate for the recoil of minimally invasive surgical instruments and estimated the distal force of the end effector, but did not consider the transition state of the rope pulley drive, and its static assumption of the guide pulley on the transmission path Thus, it does not match the actual situation of most surgical instruments [31; 32]. In summary, for the cable-driven mechanism with a simple transmission path, the cable-pulley transmission model has higher transmission efficiency than the tendon-sheath transmission model. When using the cable-pulley model to analyze the force and position transmission characteristics, few existing studies quantitatively analyze the force and position transmission characteristics of the cable-driven system in the transition state. Therefore, this paper will establish a force and position transfer model for the cable-pulley system considering the transition stage, and realize the efficient calculation of the movement hysteresis and output torque of surgical instruments based on this model.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 2 Mathematical Preliminaries: SVS Indices
### Potential Indices  
Potential indices, \(PI_{v}\) and \(PI_{t}\), are determined according to NERC/WECC standards. These two indices are similar to \(SI_{v}\) and \(SI_{t}\), which are used for calculating voltage violation and its time, respectively. Their mathematical formulas are given in Equations (8) and (9):  
\[PI_{v}(t_{f})=\sum_{i=1}^{N}\sum_{t=t_{cl}}^{t_{f}}\frac{1}{2m}(\frac{v_{i}(t )-v_{i,0}}{\Delta v_{\max}})^{2m} \tag{8}\]  
\[PI_{t}(t_{f})=\sum_{i=1}^{N}\sum_{t=t_{cl}}^{t_{f}}\frac{1}{2m}(\frac{\tau_{ ti}}{\tau_{\max}})^{2m} \tag{9}\]  
In (8), \(t_{f}\) is the time taken for SVS assessment and \(\Delta v_{\max}\) is the maximum acceptable voltage dip. In (9), \(\tau_{it}\) is the time of voltage dip continuance in bus \(i\), and \(\tau_{\max}\) is the maximum time which is considered for unacceptable voltage variations. The parameter \(m\) is considered for taking difference between cases with small voltage dip in more buses and cases with intense voltage dip in a limited number of buses, which is called hiding effect. Usually, \(m\) is taken equal to 1.  
Another way for preventing hiding effect is using \(PI_{v\max}\), which is computed from the following equation:  
\[PI_{v\max}(t_{f})=\max\left\{\left|v_{i,t}-v_{i,0}\right|\left|t_{cl}<t<t_{f}, i=1,..,N_{b}\right\}\right. \tag{10}\]  
With summation of these three indices, the potential index (\(PI\)) is calculated for each contingency as follows:  
\[PI=PI_{v}+PI_{t}+PI_{v\max} \tag{11}\]
## 1 Introduction
To overcome the above issue, this study proposes a novel approach that performs the Extract Class refactoring based on the similarities in terms of clients between the methods of the class in question. The proposed approach can be more beneficial than the traditional refactoring techniques that consider the internal view of the class when performing the extract class refactoring. It identifies the different responsibilities of a class based on the usage of its clients. The intuition behind this is that if there are different sets of methods in the class that are used by different sets of clients than the class has different responsibilities from the perspective of its clients [6; 7; 8]. In addition, the proposed approach supports the adherence to the _Interface Segregation Principle_ (ISP), which is an important object-oriented design principle that states \"no client should be forced to depend on methods it does not use\" [2]. Our proposed approach is not meant to replace the existing approaches of the Extract Class refactoring but to complement them because considering the structural and semantic relationships between the methods of class can be useful in many cases of the Extract Class refactoring. Figure 1: The class Circle and its clients. The paper extends a previous short paper [9] that only presented an initial overview of the proposed approach without an empirical evaluation. In this paper, however, we present and extensively discuss the proposed approach for the Extract Class refactoring. In addition, we conduct an extensive empirical experimentation to evaluate the proposed approach based on real classes selected from two open source systems. The rest of the paper is organized as follows. In Section 2, we present and discuss related work. Section 3 presents the proposed approach. In Section 4, we present the empirical evaluation of the proposed approach. Section 5 gives the conclusion and future work.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.4 Spatial Characteristics  
The model estimated results showed that, compared with rural areas, crashes that occurred in towns played a significant role in injury severity outcomes. Interestingly, the town indicator had a complex effect on the two collision modes over the four periods. For example, the effects on angle and non-angle collisions were the same in \(2012\)-\(2013\), as well as in \(2014\)-\(2015\). There was a stable negative correlation between the town indicator and severe injuries during the two periods. However, opposite effects were observed for angle and non-angle collisions in \(2016\)-\(2017\). Angle collisions in town had a lower probability of severe injury and a higher probability of no injury and minor injury, as opposed to non-angle collisions. One probable explanation is that, compared to drivers in rural areas, town drivers with higher levels of education adhere to roundabout driving rules and are more modest to each other when approaching or departing roundabouts, hence preventing severe angle collisions. From the perspective of all analyzed periods, the town indicator was temporally unstable, which may be related to the town's nature [21]. Towns have more vehicles and better traffic management than rural areas. Orderly traffic management in towns may reduce the risk of severe injury. However, due to volatile elements such as traffic flow in different periods, the effects still demonstrate temporal instability.  
Another indicator, merging area, was found to be associated with a decrease in the probability of severe injury (\(-0.0086\)) and an increase in the probability of minor injury (0.0039) only for the 2016-2017 angle collision model, which may be attributed to the slower speed when driving in the merging area.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 3 Results
### Optimum Frequency for a Given Distance  
In this work, the frequency with less attenuation is referred to as the optimum frequency. This frequency is valuable as carrier frequency for a system that must optimize energy consumption. Figure 10 shows that there is a frequency with minimum attenuation for each distance. In other words, there is a local minimum for each distance, which means that lower and higher frequencies than this one will present higher attenuation at that distance. This optimum frequency value reduces as the distance between antennas increases, that is, it is shifted towards lower frequencies. This effect is also presented in simulation results for further distances (Figure 13), where optimum frequencies start with a value of nearly 40 KHz at 3 m and reaches 2 KHz at 26 m. These results lead to a compromise  
Figure 11: Path loss (dB) comparison up to 8 m (10 KHz–100 KHz).  
Figure 12: Path loss (dB) comparison up to 13 m (10 KHz–100 KHz).  
when designing the communication system of the sensor network, either more bandwidth or longer distances between nodes.  
In addition, in Figure 14, minimum attenuation for the previously mentioned frequencies is shown. This value ranges from 67 dB at 3 m to 154 dB at 30 m, but it is important to remember that in this work attenuation includes all losses of the testbed and path loss of the link. Taking into account these results, if nodes transmit 35 dBm of power and their sensitivity is around \(-120\) dBm, achieving communications over 30 m is feasible varying the optimum frequency.
## 4 CO2 Emission Calculation for a Tunnel Project
### CO\({}_{2}\) Emission Calculation of the Main Equipment  
4.1 The Process of Calculating CO\({}_{2}\) Emission for the Main Equipment is Illustrated for the Wheel Loader Used for Earthwork Below  
Step 1: Getting the earthwork quantity  
The authors used Revit to draw the excavation scope to get earthwork quantity; the volume from the Revit is 748.4 m\({}^{3}\), as shown in Figure 8.  
Step 2: Calculating the CO\({}_{2}\) emission of the wheel loader  
Referring to the national standard estimating reference by the Korea Institute of Construction Technology (KICT), and assuming the necessary data, which is listed in Table 4, the authors can calculate the fuel consumption for wheel loader.  
Productivity per hour (Wheel loader): Q = 3600\(\cdot\)q\(\cdot\)k\(\cdot\)f\(\cdot\)e/(m\(\cdot\)1 + t1 + t2) (m\({}^{3}\)/h)  
Figure 6: Making a CO\({}_{2}\) emission computing formula.  
Figure 8: Tunnel excavation scope.  
Figure 7: The materials quantity and CO\({}_{2}\) emission.  
To better understand the calculation process, consider the following situation. A hauling activity requires a volume of 748.4 m\({}^{3}\) of earthwork. A wheel loader with a bucket capacity of 1.72 m\({}^{3}\) loads the earth into a dump truck. Authors suppose that the bucket coefficient is 100%. That is, the loader has an output of 1.72 cubic meters of the earth per cycle. Meanwhile, it has a total cycle time for the activity, as follows: m \(\times\) L + t1 + t2 = 1.8(s/m) \(\times\) 8.0(m) + 6(s) + 14(s) = 34.4 s, where m (second per meter) is the reciprocal of the velocity of the wheel loader, L (meter) is the average distance, and t1 (second) and t2 (second) are load time and idle time, respectively. Cycles of equipment per hour are calculated as 3600(s)/34.40(s) = 104.65 cycles per hour. The soil conversion factor (f) and production efficiency (e) are assumed as 77% and 60%, respectively. Such values are applied to Equation (1), resulting in the working hours of the loader for the hauling activity [4].  
So, the hours of the work are: 748.4/(1.72 \(\times\) 104.65 \(\times\) 0.77 \(\times\) 0.60) = 9 h.  
The fuel consumption is: 9.8 L/h \(\times\) 9 h = 88.2 L  
Referring to the LCI DB, the CO\({}_{2}\) emission factor is 2.6 kg/L, The CO\({}_{2}\) emission of the wheel loader is: 2.6 kg/L \(\times\)88.2 L = 229.32 kg
## 4 Results, Discussions and Achievements
### Cumulative Distribution Function of RMSE  
CDF is a non decreasing function and in this subsection, it is applied to specify the persistence and failure times of optimization techniques in respect with Monte Carlo runs. CDF is the part of probability density function and it is presented in the form of probability. Figure 13 shows that FPA performs very well as compared to PSO in respect with RMSE. In case of FPA, RMSE has been occurred around zero at 80% of MC runs at 0 dB, \(-5\) dB and \(-10\) dB while RMSE is exceeded up to 150 at 25% of MC runs of PSO algorithm.  
In case of nine targets estimation, again FPA performs superbly. The value of RMSE is near to zero at 50% of MC runs while it exceed up to 1500 in the scenario of PSO at 0 dB, \(-5\) dB and \(-10\) dB as depicted in Figure 14.  
In case of eleven targets estimation, the performance of FPA is remarkable. In this case, the RMSE value is near to 50 at 50% of MC runs in FPA algorithm while it exceed 1000 in the scenario of PSO at 0 dB, \(-5\) dB and \(-10\) dB as shown in Figure 15.
## 3 Results and Discussion
### Microstructure of the Deposited Coatings  
Figure 4 illustrates a comparison between the GC coating and the WC-Co-Cr reference coating in cross-section. It is distinctly apparent that the thickness of the reference coating is substantially greater than that of the GC coating. Additionally, the WC-Co-Cr layer reveals a distinctly more homogeneous microstructure. This suggests that the deposition efficiency of the reference coating is considerably superior in comparison with that of the GC coating.  
Both coatings shown in Figure 4a,b are dense and do not delaminate when subjected to metallographically induced stresses. Therefore, they could be used in subsequent planned investigations. The black particles located at the interface between the coatings and the substrate originate from the substrate blasting (Al2O3).  
Figure 4: SEM/BSD micrographs in cross-section: (**a**) GC coating; (**b**) WC-Co-Cr reference coating.  
Figure 3: XRD diffractograms of the reference powder WC-Co-Cr and the GC powder.  
Some microstructural features, in the context of microporosity or microcracks, were observed when the WC-Co-Cr coating was investigated at higher magnification (Figure 5). Furthermore, Figure 5 depicts the homogeneous distribution of the WC particles (light gray) in the metallic matrix (Co and Cr). Examination of the GC coating in cross-section at higher magnification revealed a heterogeneous microstructure in terms of both the chemical composition and the particle size, which is attributed to the initial powder material. Figure 6 provides clearer evidence of the presence of WC particles (light gray).  
The EDX mapping shown in Figure 7 provides a clear picture of the distribution of the particles with different chemical composition, and thus the ratio of the cermet fraction (light gray) to the fraction of other metallic oxides (several dark gray scales).  
At this point, both coatings examined were analyzed using XRD in order to better compare them with each other.  
Figure 5: SEM/BSD micrographs of the WC-Co-Cr coating in cross-section at higher magnification: (a) \(500\times\); (b) \(2000\times\).  
Figure 6: SEM/BSD micrographs of the GC coating in cross-section at higher magnification: (a) \(500\times\); (b) \(2000\times\).  
Figure 8 clearly shows that secondary carbides W2C have now also formed in the reference coating during coating. Nevertheless, there are still more phases in the GC coating than in the reference. The results therefore correlate well with the previous investigations.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.3 Hybrid Models  
Hybrid approaches are increasingly common. These approaches integrate different algorithms or methodologies to improve detection accuracy and reliability by exploiting the strengths of different strategies. To benefit from different anomaly detection models, many works have studied hybrid models [8; 60; 61; 62; 63]. For example, [PERSON] et al. [62] combine the LSTM with the MPPCA model for spacecraft anomaly detection. The developed method tends to overcome the limitation of the single channel of the LSTM model and improve the detection performance. It was able to attain a precision of more than 90%. The LSTM model was also combined with the transformer in [8; 63]. Compared to the original models (transformer and LSTM), the hybrid transformer-LSTM structure achieved higher precision. It could also effectively extract both global and local features from satellite time series data. Another study [61] considered a hybrid CNN-transformer model, joining the spatial learning efficiency of CNNs with the sequence learning of transformers to extract global and local spatial information. Experiments have demonstrated that the designed model can learn the spatial-temporal correlations from multi-variate time series data and achieve better anomaly detection performance than the literature approaches. Some good results based on CNN and LSTM within a spatial-temporal Generative Adversarial Network (GAN) were obtained in [60].  
Experimental studies have shown that the hybrid method presents high aptitudes to detect anomalies in real datasets while conserving a lower false alarm rate. The current state-of-the-art methods with their corresponding benefits, limits, and experimental details can be seen in Table 1. Figure 2 presents the taxonomy of anomaly detection approaches used in spacecraft systems.  
Table 2 presents the performance metrics (precision and recall) of anomaly detection for ML/DL approaches on the SMAP/MSL datasets. We highlight the best results in bold. From this table, we can see that the GCN [30] has the highest precision, while TranAD [31] outperforms the reference methods in terms of recall.
## 5 Conclusions  
Based on the metrological forcing dataset and remote sensing datasets, this study quantitatively evaluated the frost days during the growing season and seasonal scales in the Third Pole in relation to long-term climate change and explored the relationship between vegetation changes and environmental factors during recent decades. We examined the spatial distribution patterns and temporal trends of frost days on the Third Pole from 2001 to 2018. Additionally, we estimated the spatial-temporal variations and trends of the NDVI and GOSIF during the growing season and seasonal scales on the Third Pole from 2001 to 2018. We further compared the absolute values of the correlation coefficients for each pixel, reclassified them into four groups, and subsequently obtained the spatial distribution of the controlling factors at the growing season and seasonal scales on the Third Pole from 2001 to 2018. Notably, some of the key conclusions were as follows. With rapid warming, frost days showed no significant decreasing trends from 2001 to 2018 on the Third Pole. The GOSIF data revealed a higher sensitivity of alpine vegetation to frost compared to the NDVI data. This study highlighted the importance of understanding frost events and revealed the diverse responses of NDVI and GOSIF values to frost events on the Third Pole.  
[PERSON] and [PERSON] conceived the ideas for this research. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed the research, collected and processed the data, and wrote the draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 42371386), the Joint Research Program of the Chinese Academy of Sciences and Government of Qinghai Province (Grant No. LHZX-2020-03), the Youth Innovation Promotion Association CAS to X.W. (Grant No. 2020422), and the Foundation for Distinguished Young Scholars of Gansu Province (Grant No. 22 JR5 RA046).  
All remote sensing data and metrological forcing data used in the manuscript are already publicly accessible, and we have provided the web addresses for their download in the manuscript.  
**Acknowledgments:** The authors would like to thank TPDC for providing the data and anonymous reviewers for their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Result and Discussion
### Pressure Loss  
Vortices are one of the most important sources of pressure loss. The regions with the value of total pressure loss coefficients higher than 0.30 can be considered to be the secondary vortex flow region [1]. The total pressure loss is defined by the difference between the local total pressure and the reference total pressure at the passage inlet [1].  
Total pressure loss coefficient (\(\frac{\Delta P}{q_{ref}}\)) is obtained by Equation (8) [26]:  
\[\frac{\Delta P}{q_{ref}}=\frac{P_{i}-P_{\infty}}{1/2\rho U_{\infty}^{2}} \tag{8}\]  
Figure 10 shows the total pressure loss coefficient contours of the understudied passages in different spans. From Figure 10, it is clear that the tip vortex (next to the P.s) is the most severe vortex with the highest total pressure loss coefficient value. The total pressure losses in the tip vortex core region are more than 10 times the total pressure losses in the free stream region with a total pressure loss coefficient of less than 0.2. The LR vortex near the S.s is compressed under the 0.2 span height, while the trailing edge (Tr.) vortex is located under the mid-height of the passage. The LR vortex adjacent to the suction side disappears while moving in the spanwise direction. The total pressure loss coefficient variation and the difference between the hub and shroud on the suction side are higher than the pressure side.  
Figure 10: Total pressure loss coefficient contours in various span heights in blade passages 5–6, 10–11, and 6–7 of the equal case and in blade passages 3–4, 4–5, and 9–10 of the partial case.