esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Design
### Model Performance Evaluation
#### 4.2.2 Daily Predictive Model  
1. Average Prediction Error  
The prediction error, also called residual, is used to measure how well the model predicts the response value. The average error of a sequence \(\{y_{1},y_{2},\cdots,y_{T}\}\) obtained from the observed value over T times, with corresponding predicted values \(\{\hat{y}_{1},\hat{y}_{2},\cdots,\hat{y}_{T}\}\), is computed as:  
\[\text{Error}\ =\ \frac{|(\hat{y}_{1}-y_{1})|+|(\hat{y}_{2}-y_{2})|+\cdots+|( \hat{y}_{T}-y_{T})|}{T} \tag{23}\]  
The smaller the mean residual is, the more accurate the model is.  
2. Root Mean Squared Deviation (RMSD)  
The root mean square deviation is the standard deviation of the residuals, which estimates how spread out these prediction errors are. In other words, RMSD is a measure of how far from the estimated value of crashes to the true observed number of car accidents are. The RMSD of predicted values \(\hat{y}_{t}\) at time \(t\) of a dependent variable \(y_{t}\), with the independent variables observed over T times, is computed for T different predictions as the square root of the mean of the squares of the deviations:  
\[\sqrt{\frac{\sum_{t=1}^{T}(\hat{y}_{t}-y_{t})^{2}}{T}}. \tag{24}\]  
The smaller RMSD means more accurate predicted values.
# Deformation Analysis of Large-Scale Rock Slopes Considering the Effect of Microseismic Events
## 4 Numerical Simulation of the Rock Slope
### Discussion  
Figure 15 shows the deformation comparison of rock slopes under different approaches during excavation process. The \(\Delta\)Mzjc-3 monitoring point of the multipoint extensometer was selected to record the excavation processes data, both on the feedback analysis model and the model without importing the MS data. Excavation data at elevations from 650 m to 550 m during the MS monitoring period between 1 March 2016 and 31 December 2016, and the corresponding results of the multipoint extensometer, were selected. It was found that the displacement of the numerical simulation considering MS damage was slightly less than the displacement of the field monitoring. This finding might be attributed without considering the effect of the columnar basalt in the numerical simulation. Compared with the model without considering MS damage, the displacement of the slope considering the MS damage effect was closer to the field monitoring data. However, the increment was not obvious, being only approximately several millimeters. Some interpretations might be that the selected research period was short and that the MS events in the selected period had a small source radius and were distributed. The monitoring displacement of the field multipoint extensometer in the early stage grew faster than that of numerical simulation. This result was mainly because the multipoint extensometer was installed on 14 December 2014 and the displacement during the early stage of installation would clearly grow.  
Figure 14: Shear rate of the rock slope considering the damaging effect of the MS event during excavation processes. Excavation to elevation (**a**) 800 m, (**b**) 720 m, (**c**) 680 m, (**d**) 650 m, and (**e**) 550 m.  
Figure 15: Deformation comparison of the rock slope using different approaches during excavation processes.
## 2 Bimodal Information Collection System
### Micro-Bio-Radar Module  
After the suspected targets are detected by multispectral technology, the bio-radar module will play another critical role in re-identifying the human nature of these suspected targets, which is referred to _stage 2_ in Figure 3 during the entire search process. Just as the working principle illustrated by Figure 3, after the suspected human target is detected, the bio-radar module will be triggered and accurately released around the target. Based on the radar Doppler principle [4], the physiological activity information of the suspected target can be obtained. Our group has successfully realized the detection of human respiration and body movement in the field environment [29]. So, taking physiological motion features (respiration rate or heartbeat rate) as a reference, accurate re-identification of surviving human targets will be possible.  
The bio-radars, which were thrown out to remote sensing the respiratory in our study is JC122-3.3 UA6 module. It is a 24 GHz continuous wave (CW) micro-radar and its effective detection range is 10 m. The hardware of this system mainly includes a radar sensor for human chest breathing detection [4], STM32 single-chip microcomputer, and LoRa module for initial data A/D convert and data sending.
# Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes
### Fault Diagnosis
The hidden Markov model is a probabilistic model of time series, which is used to describe the process of randomly generating an unobservable state random sequence from a hidden Markov chain, and then generating an observation from each state to generate an observation random sequence. HMM is determined by the initial probability distribution, state transition probability distribution, and observation probability distribution. Since the hidden Markov model is a parametric model, when HMM is applied to the field of fault diagnosis, if the basic parameters of the model are determined, the observed data can be used to describe the transfer process of the internal operating state of the air brake tube or to identify the operating state. Compared with the literature [21], the condition of using the hidden Markov model to diagnose the fault in the air duct is that the model is reliable, but the engineering system is often more complex in the production environment, resulting in the model may be inaccurate, which affects the diagnosis effect. Therefore, a hit rate comparison experiment of two fault diagnosis methods is designed for the same set of data. The traditional hidden Markov method is compared with the fault diagnosis method of a high-speed train air brake tube based on the model space proposed in this paper. The results are shown in Table 9.  
Through the comparison table of diagnostic results, it can be seen that the diagnostic hit rate in the known methods is above 89%, but the hit rate in the new fault in the hidden \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Normal** & **Leakage Fault** & **Blocking Fault** & **Compressor** & **Unknown Fault** & **Diagnostic** \\  & **State** & **State** & **State** & **Fault State** & **State** & **Rate** \\ \hline Normal State & 49 & 0 & 0 & 1 & 0 & 98\% \\ Leakage Fault State & 0 & 44 & 6 & 0 & 0 & 88\% \\ Compressive Fault State & 2 & 0 & 0 & 48 & 0 & 96\% \\ Blocking Fault State & 0 & 8 & 42 & 0 & 0 & 84\% \\ Unknown Fault State & 0 & 0 & 0 & 1 & 5 & 83\% \\ Average Diagnostic Rate & & & & 89.8\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical results in air brake pipe fault diagnosis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Model Space Method** & **HMM Method** \\ \hline  & Normal & 98\% & 98\% \\  & Blocking Fault & 88\% & 84\% \\ Existing Fault Type & Compressor Fault & 96\% & 96\% \\  & Leakage Fault & 84\% & 80\% \\  & Average Diagnostic Rate & 91.5\% & 89.5\% \\ Unknown Fault & 83\% & 66.67\% \\  & Average Diagnostic Rate & 89.8\% & 84.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative results in fault diagnosis based on the model space and the HMM. Markov model is lower, and the overall fault diagnosis in the model space diagnosis method has a slightly higher hit rate than the hidden Markov model. Compared with the fault diagnosis method based on the model, the model space method can improve the fault diagnosis rate to a certain extent and has the characteristics of identifying new faults.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 4 System Model
### Sensing Model  
IoT nodes carry out channel sensing based on energy detection through its incorporated Received Signal Strength Indicator (RSSI). Two types of sensing are performed by the sensor nodes; the first one is for identifying the WLAN inactive period, and the second one is for channel access control. Sensing measurement is based on sensor node's parameter called the _maximum sensitivity level_ (\(\psi\)). It is defined as the minimum signal SNR that can be detected by a sensor node. Sensing procedure within a finite time can be modeled as probabilistic energy detection, which is characterized by the parameters, such as the probability of missed detection \(p_{MD}\) (signal is not detected) and the probability of false alarm \(p_{FA}\) (when the sensing detects the signal while the channel is idle).  
Energy detection of a signal depends on the estimation of energy decision threshold \(\gamma\), which is a function of the probability of false alarm \(p_{FA}\) and sensing time \(t_{s}\) and is calculated for a certain value of the probability of false alarm. Probability of false alarm \(p_{FA}\) is expressed by the following equation [40]:  
\[p_{FA}(t_{s};\gamma)=Q\left((\gamma-\sigma_{N}^{2})/\sigma_{N}^{2}\sqrt{2/(f_{ s}t_{s})}\right), \tag{7}\]  
where \(\sigma_{N}^{2}\) is noise power, \(f_{s}\) is sampling frequency and \(t_{s}\) is sensing time. Energy decision threshold \(\gamma\) is given as follows:  
\[\gamma(p_{FA})=max\left\{\psi,\sigma_{N}^{2}\left[1+\sqrt{(2/(f_{s}t_{s})}Q^{ -1}(p_{FA})\right]\right\}. \tag{8}\]  
Sensor nodes determine the received signal power level and compare it with the energy decision threshold \(\gamma\) to decide about the presence of the signal. Probability of missed detection \(p_{MD}\) depends on the received signal power \(P_{Rx}(d)\) and distance to the transmitter \(d\):  
\[P_{Rx}(d)=(P_{WLAN})(P_{Lo})(d^{-\eta}), \tag{9}\]  
where \(P_{WLAN}\) is WLAN transmit power, \(P_{Lo}\) is attenuation at a reference distance, and \(\eta\) is the path loss exponent. With known received signal power, we can determine the probability of missed detection, which is expressed as follows [40]:  
\[p_{MD}(t_{s},d;\gamma(p_{FA}))=1-Q\left(\frac{\gamma(p_{FA})-(\sigma_{N}^{2}+P _{Rx}(d))}{\sigma_{N}^{2}\sqrt{(2/(f_{s}t_{s})}}\right). \tag{10}\]Complement of probability of missed detection is probability of detection, i.e., (\(p_{D}=1-p_{MD}\)), which is given as follows:  
\[p_{D}(t_{s},d;\gamma(p_{FA}))=Q\left(\frac{\gamma(p_{FA})-(\sigma_{N}^{2}+P_{Rx} (d))}{\sigma_{N}^{2}\sqrt{(2/(f_{s}t_{s})}}\right). \tag{11}\]
## 3 Discussion
SPOP negatively regulates GLI activity by enhancing the proteasomal degradation of GLI proteins [50]. In line with these data, we observed that chemical inhibition of the proteasome machinery at least partially reversed the repressive effect of CSNK1D drugs on GLI protein expression levels (data not shown). Further biochemical and molecular studies will be required to precisely dissect the mechanism of action of CSNK1D on HH--GLI, and a careful evaluation and comparison of distinct and pharmacologically validated small-molecule inhibitors will finally reveal the therapeutic potential of CSNK1D targeting. To address the oncogenic role of the CSNK1D-GLI axis in stem-like TICs, we performed 3D growth assays to monitor clonal spheroid growth, which is considered a hallmark of TICs [60]. Genetic inhibition of CSNK1D was sufficient to abrogate colony formation in Ewing sarcoma cells. Pharmacological targeting of CSNK1D by CK1D008 and SR-3029 selectively decreased colony formation at concentrations sufficient to abrogate HH pathway activity, while not affecting cell proliferation in planar 2D culture settings. This suggests that CSNK1D-GLI is preferentially required for the expansion of TIC rather than non-CSCs. In agreement with this notion, we show that genetic inhibition of _CSNK1D_ diminishes the engraftment and tumor initiation capacity of GLI-dependent A673 Ewing sarcoma cells in immunodeficient mice. The tumor-initiating role of CSNK1D is further supported by our findings that pharmacological inhibition of CSNK1D abrogates the tumor initiation and in vivo engraftment capacity of GLI-dependent PANC1 cells [60]. In summary, we identified CSNK1D as a novel positive regulator of oncogenic GLI transcription factors in TIC. This study provides a basis for the development and use of selective CSNK1D inhibitors to abrogate HH--GLI signaling in SMOi-sensitive and SMOi-resistant settings, which is an important step towards the development of novel oncology drugs targeting GLI transcription factors to eliminate highly malignant cancer stem cells.
## 5 Simulation  
In order to verify the effectiveness of the proposed algorithm, it was tested with numerical simulation and real ISAIL m-D data. Simultaneously, compared with the use of singular-spectrum analysis extraction micro-Doppler features [16]. Simulation 1, the ISAIL data was simulated on the helicopter miniaturization model. The simulation parameters are shown in Table 1.  
The imaging results are shown in Figure 5. Figure 5a shows the helicopter model used in the simulation. Figure 5b shows the result of distance pulse pressure. It is obvious that the micro-Doppler generated by the main rotor interferes with the echo signal of the helicopter body. Figure 5c is the results of imaging with S-transformation without separation of micro-Doppler information. Since the window function of the S-transform has amplitude weighting and the weighting value is 0 at zero frequency, there is a separate line at the zero frequency in the figure. However, it can still be seen that the helicopter body imaging results are significantly disturbed. Figure 5d shows the result of imaging the helicopter main body after separating the micro-Doppler information by the singular-spectrum analysis [16] and Figure 5e is by the RANSAC method. It can be seen from the figure that the separated target features using the RANSAC algorithm are more obvious.  
Simulation 2, the micro-Doppler feature extraction algorithm based on the RANSAC algorithm is verified by the real data of an Ann26 aircraft. The imaging results are shown in Figure 6. Figure 6a is an S-transform imaging result with significant micro-Doppler interference between the distance units 118-142. Figure 6b shows the separated micro-Doppler component. In Figure 6c, the data processed by the singular-spectrum analysis [16] is shown and Figure 6d is the data processed by the RANSAC algorithm. It all separates the rigid body portion from the micro-Doppler portion such as a propeller. However, it can be seen that Figure 6c lacks one wing of the aircraft and the features of Figure 6d are more detailed. Therefore, the RANSAC algorithm is much better than the singular-spectrum analysis.  
The above simulation experiments and the real data test show that the RANSAC based micro-Doppler feature extraction algorithm is effective for the processing of ISAIL target imaging and is much better than the singular-spectrum analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Variable** & **Value** & **Parameter/Variable** & **Value** \\ \hline Center wavelength & \(10.6\times 10^{-6}\) m & Target size & 2.5 m \(\times\) 2.5 m \\ Transmitting bandwidth & \(20\times 10^{9}\) Hz & Velocity & 100 m/s \\ Pulse repetition frequency & \(7\times 10^{3}\) Hz & Main rotor speed & 10 r/s \\ Coherent accumulation time & 0.1 s & Route angle relative to radar ray & 30\({}^{\circ}\) \\ Transmitting width & 1 \(\upmu\)s & Imaging distance & 10 Km \\ Range sampling points & 620 & Cross-range sampling points & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Simulation of the helicopter model.
## 3 Methodology
### Ensemble Learning
#### 3.4.1 Random Forest  
The Random forest (RF) algorithm is a collection of decision tree, where each tree is individually trained on an arbitrarily selected independent training dataset. Here, the respective input dataset for each tree is sampled separately, and the distribution rate is the same for all trees. RF not only performs well in classification and regression, but also shows outstanding performance in variable selection. The trees are obtained from the combination of datasets with bootstrap subsampling and various subsets of features for splitting at every node. The nature of each tree is unique and possesses low bias when mature. Also, low correlation is obtained based on selection of random subsets of features for the individual trees. Finally, after ensemble of all the trees, the RF results in low bias and low variance for the model. Bootstrap aggregating from bagging is designed to increase the stability and accuracy for individual trees in RF [48]. For decision making, the class which receives majority vote from the trees is selected in classification problem. On the other hand, the average of the predicted values from all decision trees is considered in regression models. The RF can also overcome the overfitting problem, which is one of the main concerns in a decision tree algorithm. RF uses a bagging technique, where each time a random subset of feature is used to train a single decision tree and it aggregates the result of a number of decision trees to determine the final output. Thus, RF is less prone to overfitting. In addition, the parameter's tuning also helps RF to overcome the overfitting problem, which also applied in this study by using a GridSearch technique. The diversity of the tree in the RF is controlled by the number of features. A higher number of features ensures the most highly correlated trees with the cost of high computational power, whereas a lower number of features results in a lack of correlation. The parameters for implementing the RF are number of trees, number of features in every split, maximum depth, and number of sample leaf nodes. Generally, a high number of trees is mandatory for acquiring a steady state solution for both the classification and regression problems. The RF model consists of a splitting process performed by dividing the single node into two or more nodes; whereby the majority voting process decides the final output of the model, as is illustrated in Figure 10(a).
###### Abstract  
Background: The assessment of giant cell arteritis (GCA) in fast-track assessment clinics (FTA) including the use of ultrasound (US) is becoming the preferred practice in specialized centers. Methods: Patients with suspected GCA referred to the FTA of the Rheumatology Department, University of Pavia, Italy, between 2016 and 2021 were included to analyze the clinical and US findings. Results: A total of 553 US examinations were performed on 347 patients. A total of 246 were female (71%), and the mean age was 73 \(\pm\) 12. Of these, 287 US on newly referred patients led to a confirmed diagnosis of GCA in 111 (39%). The sensitivity of US was 81.98% (95% CI 73.55-88.63%), and the specificity 99.43% (95% CI 96.88-99.99%). Only 4 patients required temporal artery biopsy. The most specific symptoms to inform the pre-test probability of GCA and differentiate from patients with other conditions were: jaw or tongue caluication, scalp tenderness, and bilateral visual loss. Headache was not reported in 33% of patients. Systemic symptoms were significantly more frequent in GCA (42.3%), together with combinations of cranial, systemic, and/or polymylgia rheumatic symptoms. Out of 88 patients, there were 52% with a confirmed relapse. Of these, 67% had a positive US. Conclusion: The use of FTA in clinical practice ensures an early diagnosis, avoiding invasive procedures for the patient. Our data support the increasingly recognized adjunctive role of US in the monitoring of GCA.  
ultrasound; colour duplex sonography; giant cell arteritis; vasculitis 2022
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology  
This study aims to investigate the impact of weather factors on wireless signal propagation between a GCS and a UAV. These factors are atmospheric gases, clouds and fog, and rain. The effects of these factors were evaluated using attenuation and BER as key metrics to analyze the results both qualitatively and quantitatively, using functions of several parameters, including temperature, pressure, humidity, propagation distance, path elevation angle, geographic elevation, and operating frequencies.  
Figure 1 illustrates the weather dependent characteristics of the atmosphere and related effects on propagating wireless signals.  
Weather-induced phenomena can be primarily classified into four categories: attenuation leading to signal power reduction and signal to noise ratio (SNR) degradation, path bending and elongation due to refractive index changes, dispersion due to frequency-dependent signal speed variations, and additive noise-induced SNR degradation. Assuming a relatively short distance (maximum 2 km) and the line-of-sight communication considered in this study, only attenuation and additive noise-related considerations are essential.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Rst-Flare
#### 2.3.3 Gas Flared Volumes Estimation  
A linear regression model coupling BTEs and official data on gas flared volumes is developed for assessing the amount of gas burned off by the hot sources within the studied area, on an annual basis. Gas flared volumes, with units of BCM, are calculated using Equation (3):  
\[\text{Gas flared}_{y}=\gamma^{*}\text{BTE}_{y}+\beta, \tag{3}\]  
where gas flared (assessed for the year \(y\)) is the response variable in the model (Y) and BTE the predictor variable (X); \(\gamma\) and \(\beta\) are, respectively, the regression coefficient and the Y-intercept. The latter are site-specific coefficients derived and valid for the Niger Delta region.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 2 Related Work
### Controller Placement in the Software-Defined STIN
To the best of our knowledge, this paper is the first to consider the controller placement problem from the perspective of energy-efficiency in the STIN. We focus on the optimization of the energy consumption of the control path and propose the EECP problem for the green economic development of the countries along the Belt and Road. By solving the EECP problem through the MSAP algorithm, the total energy consumption of the control path is reduced and saves energy.