esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment and Methods
### Prediction Method  
#### 2.3.1 Random Forest  
Random forest (RF) is one of the most widely used algorithms in ensemble learning. It is an ensemble learner with multiple decision trees. It establishes regression and classification CART trees and analyzes regression and classification problems by calculating the optimal bifurcation nodes without pruning [46]. The Bootstrap autonomous sampling method was used to randomly select M training sample sets with the same size as the original sample set and construct CART trees for M training sample sets [47]. The tree is split and grown by counting the amount of information each feature contains. The decision result of each decision tree is synthesized by majority voting method, and the classification result is output. For regression problem, the final result is obtained by means of average treatment of each decision result.  
When the sample data is randomly sampled, about one-third of the data is not selected. This is called out-of-bag (OOB) data [48]. Since OOB data do not participate in the construction of decision tree in the random forest model, OOB error estimation is an error estimation method that can replace the test set. The out-of-bag data error is the unbiased estimation of the test data set error. OOB data can be used to test the generalization ability of the model and analyze the relative importance of the input indexes to the predicted results of the model [34]. The algorithm flow framework is shown in Figure 5.  
In the training stage, noise interference is performed on input indicators based on OOB data. The eigenvalues of input indicators are randomly changed to calculate the importance of input indicators (the influence of input on output). By randomly changing the value of the indicator variable F in each tree of OOB sample, the sum of the average error of the prediction accuracy of OOB sample before and after the change of the input indicator is calculated to obtain the importance of the input indicator. The importance of each input indicator is obtained from the mean of all trees [48]. The calculation formula is as follows:  
\[FI^{(t)}(f)=\frac{\sum_{x_{i}\in\mathcal{G}^{(t)}}\left(\frac{\sum_{x_{i}\in \mathcal{G}^{(t)}}I\left(\int_{i}=c_{i,\pi f}^{(t)}\right)}{|\beta^{(t)}|}- \frac{\sum_{x_{i}\in\mathcal{G}^{(t)}}I\left(\int_{i}=c_{i,\pi f}^{(t)}\right) }{|\beta^{(t)}|}\right)}{T} \tag{1}\]  
where, \(\beta^{(t)}\) is the OOB sample of the corresponding decision tree, and \(t\) represents the number of the tree (1, 2,..., 7), \(T\) represents the total number of trees, \(c_{i}^{(t)}\) and \(c_{i,\pi f}^{(t)}\)  
Figure 5: Flow chart of RF algorithm.  
represent the predicted value of each sample in the tree before and after changing variables respectively. \(x_{i}\) indicates that the sample value \(l_{j}\) is a real label. \(I\) represents the importance function based on \(l_{j}\) value, \(i\) represents the number of samples per leaf in the tree, and \(j\) represents the number of samples per tree in the forest.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 6 Numerical Experiments
### Sensitivity Analysis
#### 6.4.2 Comparison of Different Vehicle-Restricted Zones and No-Fly Zones  
Four examples from Section 6.3 are selected to compare and analyze three different vehicle-restricted zones and no-fly zones cases. Case 1 is consistent with this paper; case 2 does not set any vehicle-restricted zones and no-fly zones; and case 3 does not use detour strategies [33], with other conditions consistent with case 1. The HGAVNS-LM algorithm proposed in this paper is used to solve each example, and the results are summarized in Table 5. The metrics in Table 5 have the same meaning as those in Table 2. The analysis shows that the delivery cost deviation between case 1 and case 2 ranges from \(-4.9\%\) to \(-1.9\%\), with an average deviation of \(-3.5\%\), and both vehicle and drone delivery distances are reduced. The average delivery cost deviation between case 1 and case 3 is \(10.1\%\), with a deviation range from \(5.5\%\) to \(15.8\%\), and both vehicle and drone delivery distances increase, especially in the C08 example, where the number of vehicle routes increases by 1 and the number of drone routes increases by 2. Based on the analysis above, it can be concluded that the presence of vehicle-restricted zones and no-fly zones increases delivery costs and distances, affecting the delivery plan. Meanwhile, the detour strategy proposed in this paper is more effective in solving the TDEVDRF-RZ-NFZ problem compared to the plan proposed in reference [33]. These research findings will help companies optimize delivery plans and bring economic benefits.
## 4 Results  
### Landslide Potential Controlling Factors  
#### 4.1.1 Static Factors  
Topography is a causative factor of landslides and plays a controlling role in their occurrence. Elevation is a fundamental aspect of analyzing landslides. In Taxkorgan, 98% of slides occurred below the snow line (H < 3400 m), while 95.7% of debris flows occurred above the snow line (4200-5400 m) (Figure 4a). The slope affects the sedimentation and accumulation of loose debris. Steep slopes are more prone to landslides. Slides and debrisflow mainly occurred on slopes ranging from 10 to 60\({}^{\circ}\) (Figure 4b). The potential energy of slides and debris flows is determined by their relative relief, which ranges from 0 to 695 m/km\({}^{2}\) (Figure 4c). However, the slides and debris flows were primarily concentrated in locations with a relative relief of less than 100 m/km\({}^{2}\). The aspect affects hydrological processes and meteorological events, which in turn result in landslides (Figure 4d). The TRI shows the degree of regional ground relief. Landslides in the study area tended to occur in high/mid-rough terrain, where the TRI ranged from 7.1 to 21.1 (Figure 4e). Taskorgan, which crosses the Pamirs and the Karakorum, has a complex landform type, with landslides primarily occurring in glacial erosion and denudation landscapes, valleys, and moraines in the alpine and subalpine zones (Figure 4f).  
Figure 4: Distribution of landslides in the topography and landform.  
Lithology can reflect rock weathering and erosion while also influencing landforms. Figure 5a displays the distribution of rock hardness, revealing that slides were more distributed in softer rock regions and debris flows in hard rock. Figure 5b depicts the water yield property of Taxkorgan, which has a scarcity of groundwater, particularly fissure and pore water. Landslides were statistically more common in fissure water (water production of 1 L/s). The LS factor is an important element in estimating regional erosion. The TWI might reflect the soil's moisture content. Larger valley depths may expand the slope area, providing materials and pathways for landslides. River erosion can destabilize hillsides, resulting in landslides. Landslides in the region are statistically dispersed in places with an LS factor of less than 19.8, a TWI of less than 14.9, valley depths ranging from 159.1 to 641.4 m, and river distances of less than 69.1 m (Figure 5c-f).  
Figure 5: Distribution of landslides in geology and hydrology conditions.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
###### Abstract  
Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.  
t 1 2020 10 356 10.3390/app10010356
## 2 The Proposed SAR ADC
### SAR Logic  
SAR ADCs implement a successive approximation process using the SAR control logic, which continuously determines the digital output of each bit based on the output of the comparator and generates control signals to drive the switching circuitry of the capacitor array. Normally, the SAR logic has the second largest overall share of power consumption in SAR ADCs, after DAC capacitor arrays, so power optimization of the SAR logic is also necessary. The conventional SAR logic mainly consists of ring counters and shift registers, which require at least 2N flip-flops with a large number of MOS tubes and, thus, consume large amounts of power.  
Some papers [20; 21; 22; 23] used the dynamic SAR logic, which greatly reduces the complexity of digital circuits and avoids the use of a large number of transistors compared with the conventional SAR logic, thus greatly reducing power consumption and increasing speed. Figure 10 shows that the dynamic SAR logic consists of bit-slice circuits placed one after the other, where bit-slice circuits have the function of shifting and storing the comparison results.  
Figure 10: Dynamic SAR logic.  
During the sampling phase, the Reset is low, and both OUTP and OUTN are high, so the Valid output through the AND gate is also high; then, Pi and Ni in all bit-slice circuits are reset to \"0\". When the Reset increases to indicate the end of the sampling phase, D of the first dynamic logic increases, causing CLK1 to decrease. At this point, if OUTP > OUTN, P1 increases, and N1 remains low. In contrast, if OUTP < OUTN, P1 remains low, and N1 increases. When Valid decreases, P1 and N1 of the first comparison output are latched, and Q of the initial bit-slice circuit increases to indicate that the conversion is complete. The Q of the previous bit-slice circuit is used as the D of the next bit-slice circuit, causing it to repeat the operation of the first bit-slice circuit until all 10 bits of data have been saved.
## 2 Related Work
### Mobile Sensor Networks  
Recently, many studies have considered applications of MSN where mobile sensors are equipped with sensing, computing and communication capabilities, and powerful batteries for complex processing tasks. For example, a network of distributed mobile sensors can be developed as a solution for searching immobile human victims, following gradients of temperature, investigating dangerous area [1; 2; 3].  
One distinct advantage of mobile nodes comes with their possibly controlled mobility, which has been extensively studied to address research challenges in MSNs [13; 14; 17; 18; 19]. For example, in [13; 14], controllable mobility was used to enhance the coverage in distributed wireless sensor networks. Initially, MSN nodes are placed at random positions which do not always lead to the effective coverage. Then, controllable mobility is used to move sensors to optimal positions such that the maximum coverage is achieved. The authors in [13; 18] studied the use of mobile robots for target exploration and localization.  
In [19], controllable mobility of mobile robots, which can pick, carry and unload sensor nodes has been used to assist sensor replacement in a large scale static sensor network. When failed sensors are detected, the mobile robots first move to the positions of the failed nodes, then unload and replace failed sensors.  
Mobile robot networks where all nodes are robotic nodes have also been studied for decomposition, allocation, search&rescue, and planetary exploration which are traditionally performed by human teams [20; 21]. The search and rescue operations can be executed using a system of cooperative multiple mobile robots which are deployed in teams or groups [20]. The multiple mobile robots perform distributed sensing, monitoring or surveillance in a large area, and pass the collected data to a command center for further analysis through cooperative communication. In [21], a distributed cooperative communication algorithm was proposed for _ad hoc_ networked mobile robots.  
Our work differs from those works in that the objective of RoCoMAR is to improve the performance of routing protocols for MSN using controllable mobility of robotic nodes rather than coverage maximization, exploration and localization.
# Energies of Mechanical Fractional-Order Elements: Causal Concept and Kernel Effects
## 4 New Examples with the Riemann-Liouville Integral as a Force-Displacement Relationship
### Power-Law Force Ramp  
This example is a generalization of the examples provided by [PERSON] and [PERSON] [6]; we now apply the aforementioned causal formulation of the force-displacement relationship. Let the force be \(F(t)=t^{m}\) with \(m>0\). For \(m=0\), we obtain the unit step force input \(m=1\), which corresponds to the linear ramp.  
With \(F(t)=t^{m}\), the displacement is  
\[x(t)=\frac{1}{k_{\lambda}}\frac{1}{\Gamma(\lambda)}\int\limits_{0}^{t}\frac{ \tau^{m}}{(t-\tau)^{1  
For \(m=0\), we have \(W_{power-law}(m=0)=\frac{1}{k_{\lambda}}\frac{\Gamma(1)}{\Gamma(\lambda+1)}t^{\lambda}\), which coincides with (15). For \(m=1\), the result (38) reduces to (20), namely, \(W_{power-law}(m=1)=\frac{1}{k_{\lambda}}\frac{1}{\Gamma(\lambda+1)}\frac{t^{2 +\lambda}}{(2+\lambda)}\).  
For \(\lambda=0\), we have \(\frac{d\tau(t)}{dt}=\frac{1}{k_{\lambda}}mt^{m-1}\), which is a time dependent rate. In addition, for \(m=1\) we obtain the result mentioned in Remark 3, i.e., a constant rate of displacement.  
For \(t\geq 1\), the displacement is  
\[x(t)=\frac{1}{k_{\lambda}}\frac{1}{\Gamma(\lambda)}\left[\int_{0}^{1}\frac{ \tau^{m}}{\left(t-\tau\right)^{1-\lambda}}d\tau+\int_{1}^{t}\frac{0}{\left(t- \tau\right)^{1-\lambda}}d\tau\right],\quad t\geq 1 \tag{39}\]  
and \(\frac{dx(t)}{dt}\) is presented by (37).  
If the force is removed for \(t>0\), then the conditions describing the energy recovery are those related to Equation (25), slightly changed for the case in which \(m\
eq 1\).  
The energy plots corresponding to given values of \(m\) and various \(\lambda\) are shown in Figure 4, while Figure 5 conversely shows the effects of various \(m\) when \(\lambda\) is fixed.  
**Remark 9**.: _Here, we refer to an approach summarized in the appendix of [6] concerning the following fractional (Riemann-Liouville) derivative developed by induction:_  
\[\frac{1}{\Gamma(1-\lambda)}\frac{d}{dt}\int_{0}^{t}\frac{\tau^{m}}{\left(t- \tau\right)^{\lambda}}d\tau=\frac{mt^{m-\lambda}}{\Gamma(m+1-\lambda)} \tag{40}\]  
_from the non-causal Definition (1)_  
As demonstrated in this example, fractional integration following the causal construction of (3) allows for much easier operations.  
Figure 4: Work (energy) of a fractional Maxwell element for the causal Formulation (3), singular power-law memory, and a power-law ramp force, following the results (38): (**a**) \(m=1\); (**b**) \(m=2\); (**c**) \(m=3\) ; (**d**) \(m=5\).
### Related Work  
#### 1.3.1. ECG Signal Quality Assessment Overview  
ECG SQA is crucial for improving accuracy in automated analysis systems. Traditional systems often give incorrect measurements due to noisy signals, leading to high false alarm rates. A signal quality index (SQI) approach can be used alongside noise reduction strategies to assess the reliability of ECG signals in noisy conditions. SQIs can be based on signal processing techniques, machine learning (ML), and/or decision rules. In previous work, the ECG signal quality has been categorized into five groups (excellent, good, adequate, poor, and unacceptable), three groups (acceptable, indeterminate, and unacceptable), or two groups (acceptable and unacceptable), with the two-group classification being the most used [7].  
[PERSON] et al. [8] used three datasets (PhysioNet/CinC Challenge 2011, MIT-BIH Arrhythmia Database, and MIMIC II Database (Table 1)) to assess the quality of an ECG for normal and abnormal rhythms. Their study used various SQIs, including the relative power in the QRS complex (pSQI); the relative power at the baseline (basSQI); the skewness of the distribution (sSQI); the kurtosis of the distribution (kSQI); the percentage of matching beats detected by the eplimited and wqrs algorithms (bSQI) [9]; the ratio of the number of beats detected by the eplimited and wqrs algorithms (rSQI); and the ratio of the sum of the eigenvalues associated with five principal components to the sum of all the eigenvalues (pcaSQI). A Support Vector Machine classifier was used for classification, achieving 99.0% accuracy (Ac), 98.5% sensitivity (Se), and 99.4% specificity (Sp).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
## 3 Results and Discussion
### Model Validation Using ROC Curve  
The receiver operating characteristic (ROC) curve was employed to assess the performance of the landslide susceptibility maps obtained from the three ML models. The ROC curve is a critical tool for comparing the sensitivity and specificity of binary classification models, thereby facilitating the selection of the most suitable model based on the trade-off between the true-positive rate (TPR) and the false-positive rate (FPR).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** & **MCC** & **Kappa** \\ \hline RF & 0.879 & 0.862 & 0.909 & 0.846 & 0.885 & 0.758 & 0.757 \\ LG & 0.800 & 0.792 & 0.826 & 0.771 & 0.809 & 0.599 & 0.598 \\ XGBoost & 0.867 & 0.852 & 0.896 & 0.837 & 0.874 & 0.735 & 0.734 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance metrics for models.
## 3 Dataset Construction
### COM Configuration and Equalizer Setting  
This work utilizes the COM 4.0 version program provided by IEEE 802.3. The configuration references the enhanced COM (eCOM) simulation setup with the USB4 Gen4 standard and incorporates the elements of the 50 GBASE-KR standard. The PAM4 configuration settings, as shown in Table 2, employ a combination of a transmitter FFE and receiver-side CTLE and DFE. This configuration includes the tap coefficients of the TX FFE and RX DFE, the adaptive range of DC gain, and the zero-pole positions of the RX CTLE. Additional parameters include the number of signal levels, denoted by \(L\). For PAM4 signals, \(L\) is set to 4. According to the recommended PAM4 setup based on USB4, the symbol rate is set to 20 GBd, and the differential peak voltage output of the transmitter is set to 0.4 V, denoted as \(A_{v}\). The USB4 Gen4 standard suggests that PAM3 should be used as the modulation method. The COM settings for PAM3 are roughly same as those of PAM4, except that the symbol rate is set to 25.6 GBd, and \(L\) is set to 3.  
Based on the adopted configuration of TX FFE + RX CTLE + RX DFE, the prediction parameters include tap coefficients of the FFE, DC gain of the CTLE, and tap coefficients of the DFE. The TX FFE and RX CTLE data are obtained by searching for FOM values throughout the design space, while the DFE coefficients are determined according to the pulse response \(h(t)\) corresponding to the optimal FOM. The SI is significantly impacted by post-cursors closer to the main cursor, while the more distant post-cursors exert minimal influence, so only the first four post-cursors of the DFE are predicted.
## 5 Analysis of the Scheme
### Analysis of Experimental Simulation  
To conduct a thorough evaluation of the proposed scheme's practical effectiveness, a series of experiments were performed in this paper. All experiments were conducted on a computer running the Win10 operating system with the following hardware configuration: Intel Core i7-8750H CPU, 2.20 GHz clock frequency, and 8.0 GB RAM. The paper utilized the PBC [46] library based on pairings and the PALISADE API. A simulation framework was built using the C++ programming language. Additionally, a blockchain virtual network based on Ganache [47] was set up, and the Remix platform provided by Ethereum was used for compiling and deploying smart contract code online. When selecting the performance index, the scheme in this paper does not change the transaction process of the blockchain network but only encrypts the data in the network by LWE-CP-ABE, which will not affect the operation efficiency of the blockchain network. Therefore, this paper only evaluates the performance index of the LWE-CP-ABE scheme.  
In this experiment, the plaintext data is set to 308B. The scheme in this paper does not need complex operations such as modulus exponentiation and bilinear mapping. Still, only the addition operation, so the time cost in each stage is better than that of the scheme in reference [7].  
As shown in Figures 4 and 5, with the continuous increase of attributes in the system, the time spent by the data owner in the setup and encryption under the blockchain in this scheme is shorter than that in the scheme of reference [7], which greatly improves the overall efficiency of the system.  
Figure 4: Time cost of Setup [8].  
As shown in Figure 6, in the key generation stage, with the continuous increase of attributes, this scheme is superior to that in reference [7], and it will be more practical in key generation with multi-user and multi-attribute sets.  
As shown in Figure 7, in the decryption stage, the scheme in this paper takes about 0-2 ms, which is far lower than the scheme in reference [7]. Therefore, it is more suitable for blockchain data sharing.  
In addition, considering the pre-quantum blockchain system, the transaction cost for executing the smart contract related to signature verification on KMS is around 1,091,035 gas, with an execution cost of 965,801 gas. The transaction cost for executing the smart contract related to the correctness of the re-encrypted results is 3,597,371 gas, with an execution cost of 3,300,467 gas. That means that we can create a multi-lateral market to provide incentive mechanisms to enhance the security of access control based on the blockchain system.  
Figure 5: Time cost of Encryption [8].  
Figure 6: Time cost of key generation [8].