esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 ENO Controller Design
### Controller Gain Tuning
#### 5.1.2 Controller Gain  
Controller gain tuning was performed by observation of GPS fix interval and battery SOC. \(K_{P}\), \(K_{L}\), and \(K_{D}\) parameters were tuned manually, where one gain parameter was tuned at a time. Two sets of PID gains were used, one for battery SOC to a set point, and another for reducing fluctuation in data collection rate once the set point was reached. Initial values for the first set of gains were selected based on a constant PVC model of 10 mA: \(K_{P1}\) was 0.3, \(K_{D1}\) was 500, and \(K_{I1}\) 0 because the SOC and GPS fix intervals became unstable for non-zero gain values. Additional tuning was performed on the first set of gains after battery capacity was increased from 100 \(mA-hr\) to 800 \(mA-hr\) and a variable PVC model was implemented. \(K_{P1}\) was assigned a value of 0.3 and \(K_{D1}\) was 2000.  
Values for the second set of PID gains were selected to account for fluctuation in GPS fix interval caused by a variable PVC model. \(K_{P2}\) was assigned a value of 0.1, \(K_{D2}\) was assigned 2000, and \(K_{I2}\) was 0. Table 5 shows the results from tuning for a gain scheduled system. The first set of gains minimized variation in GPS fix interval, and the other drove battery SOC to a reference point. Gain values denoted with a subscript of 1 refer to the gains used for \(\epsilon(k)\) greater than 10%, and a subscript of 2 represents gain values used that corresponding to \(\epsilon(k)\) less than 10%.
# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 4 Results
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Precision (95\% CI)**} & \multicolumn{4}{c}{**Recall (95\% CI)**} \\ \cline{2-11}  & **NN** & **SVM** & **KNN1** & **KNN10** & **Disc An.** & **NN** & **SVM** & **KNN1** & **KNN10** & **Disc An.** \\ \hline \multirow{2}{*}{Baseline} & 82.7 & 89.3 & 81.1 & 85.4 & 85.5 & 82.7 & 89.6 & 81.1 & 83.9 & 84.8 \\  & (81.5-84.0) & (88.0-91.5) & (80.0-82.5) & (84.0-86.7) & (84.1-86.9) & (81.5-83.9) & (88.5-91.4) & (80.0-82.3) & (83.0-85.0) & (83.2-85.9) \\ \multirow{2}{*}{KW} & 87.6 & 90.9 & 87.5 & 88.5 & 86.6 & 87.7 & 90.9 & 87.2 & 88.5 & 86.4 \\  & (85.3-89.7) & (99.2-92.7) & (85.0-89.8) & (86.0-90.8) & (85.0-88.1) & (85.3-89.9) & (99.0-92.9) & (84.5-89.6) & (86.2-90.4) & (84.2-88.0) \\ \multirow{2}{*}{KS} & 86.3 & 88.5 & 86.7 & 89.7 & 87.3 & 86.4 & 88.5 & 86.8 & 89.3 & 86.8 \\  & (83.7-88.5) & (86.4-90.3) & (84.2-88.9) & (87.5-91.6) & (85.0-89.3) & (84.0-88.6) & (86.5-90.6) & (84.3-88.8) & (87.2-91.0) & (84.3-88.7) \\ \multirow{2}{*}{Fr} & 88.2 & 89.7 & 85.5 & 90.1 & 87.9 & 88.1 & 89.7 & 85.6 & 89.7 & 87.6 \\  & (85.5-90.4) & (87.6-91.5) & (82.5-88.1) & (87.7-92.0) & (85.2-90.1) & (85.3-90.3) & (87.4-91.8) & (82.8-87.9) & (87.2-91.7) & (84.8-90.0) \\ \multirow{2}{*}{SFS} & 89.8 & 88.8 & 88.9 & 87.0 & 86.9 & 89.7 & 88.8 & 88.8 & 86.8 & 86.8 \\  & (87.3-92.1) & (86.2-91.0) & (86.3-91.1) & (84.4-89.2) & (84.3-89.0) & (87.0-92.0) & (86.0-91.2) & (86.0-91.2) & (84.0-89.3) & (84.0-89.0) \\ \multirow{2}{*}{SBS} & 88.0 & 91.4 & 84.9 & 89.7 & 85.2 & 88.1 & 91.4 & 85.2 & 89.3 & 84.8 \\  & (85.2-90.4) & (89.2-93.2) & (82.0-87.6) & (87.4-91.9) & (82.5-87.7) & (85.2-90.6) & (89.1-93.4) & (82.3-87.8) & (87.0-91.6) & (82.1-87.2) \\ \hline \end{tabular}
\end{table} 
Table 4: Precision and _recall_ rates with respect to feature selection method, including confidence intervals [in %]. Figure 6: Classification _recall_ vs. feature-selection method.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 7 TDoA in SFN  
The proposed algorithm for calculating the desynchronization of DVB-T transmitters working in an SFN is shown in Figure 5.  
Figure 4: SFN (single frequency network) signal propagation. Assuming the geometry of two receivers and two transmitters, there are four different signals to consider in cross-correlation analysis.  
All the stages of the algorithm can be described in the following way: in the first step, after acquiring raw data from radar sensors and applying initial signal conditioning (e.g., decimation, pre-filtration, windowing), data from each pair of sensors are cross-correlated. Then, based on a priori knowledge of the localization of emitters, the maxima of the cross-correlation functions are identified. The peaks corresponding to the possible locations of emitters are marked as TDoA peaks. The rest of the peaks are classified as ghost peaks. The next step is to calculate the emitters' precise localization using measured TDoA peaks. After this step, 3D positions of all emitters are determined. Based on the estimated Tx's positions and known Rx position, the values of ghost peaks are estimated. Finally, through the comparison of the calculated and measured values of the ghost peaks, the estimated value of the desynchronization between emitters can be found. In the next section, the validation of the proposed algorithm using real measurements is presented.
## 2 Materials and Methods
### Vulnerability Impact Estimation
* \(impact(vul)\), which corresponds to the impact of the vulnerability, i.e., a measure of the adverse effects that the exploitation of the vulnerability by attackers may have on the platform. The value of \(impact(vul)\) is assigned by human experts, after careful review of the application code. If the current security issue \(S_{i}\) is associated to weaknesses \(W(S_{i})=CWE_{1},CWE_{2},\ldots CWE_{n}\), then the impact estimate of \(S_{i}\), which will be denoted as \(IE(S_{i})\), is computed, as shown in Equation (1):  
\[IE(S_{i})=\max_{cwe\in W(S_{i})}\frac{1}{|Vuls(cwe)|}*\sum_{vul\in Vuls(cwe)} impact(vul) \tag{1}\]  
where \(Vuls(cwe)=v\in VulDB:cwe\in weak(v)\), i.e., the set of all vulnerabilities in \(VulDB\) that are rooted to the particular CWE. Effectively, for each weakness that is associated with the current security issue, the average impact values of all known and human expert-assessed vulnerabilities rooted to the particular weakness are calculated, and finally the maximum of these values is used as the impact assessment for the security issue under the premise that attackers may pursue the weakness path that will result in the maximum possible damage to the system.
## 3 Dynamic Load Balancing Strategy in Cloud Computing  
Master-slave architecture is a mature architecture with a single master server or job tracker and several slave servers, which has been widely used in cloud computing like Google's MapReduce and Hadoop. Figure 1 shows the typical scenario of network topology of virtual resources in cloud computing, which is based on the master-slave architecture and the cloud platform discussed in this paper.  
In master-slave architecture, a job is firstly submitted to a master node by the user. Then the job is divided into several executable tasks in the master node and the generated tasks are distributed to different slave nodes. After that, the tasks are executed in the slave nodes separately with the guidance of the master node, and the results are returned to the master node. Finally, the distributed results are combined together in the master node and sent to the requesting user. Furthermore, the master node is responsible for monitoring the whole steps and re-executing the failed tasks.  
During this process, the uneven distribution of tasks may cause that some slave nodes are in light load conditions while others are in heavy load conditions. In this case, load balancing operation ought to be carried out dynamically for the cloud platform in order to keep the platform stable and operating efficiently. By analyzing the cloud computing platform, there are several characteristics in common with ACO, such as cloud nodes being analogous to food locations, data repositories to nests, and load allocation to foraging activity.  
In our approach, the main procedure of load balancing with ACO consists of two steps before load balancing execution as below.  
Figure 1: Network topology of virtual resources in cloud computing.  
(1) Ant generation. Check the cloud platform periodically, and generate ants if and only if there are existing overload nodes or underload nodes; and  
(2) To find target nodes. According to searching rules, the ant is looking for the target nodes which meet the conditions of load balancing in its surrounding area. The target node is also called the candidate node for load balancing.  
For fast convergence, we employ two diverse ants in search of the slave nodes. At the same time, we leverage max-min rules to trigger ant generation, so as to improve the efficiency of the algorithm.
## 4 Discussion
### Strengths and Limitations
Previous studies in the YRB mainly focused on the variation of annual ET, while we further analyzed the characteristics of monthly ET variation and its influencing factors. Also, this study explored the correlation between ET and individual climatic factors in the YRB and found that it exhibited a complex pattern in response to elevation at the monthly scale. The main contribution of this study is to provide new ideas for further research considering the driving mechanisms of ET changes at the monthly scale, and the response relationship with elevation that should not be neglected. PML_V2 ET, ERA5, and GLDAS-2.1 datasets were used in this study, and the ET changes were attributed using correlation analysis and normalized multiple linear regression. However, there may be some limitations and uncertainties that should be taken into account in future research. First of all, there are significant differences in the estimation of ET by different models [60]; however, we only studied the impact of climate change on the ET changes in the PML_V2 dataset in this study. In order to quantify the uncertainty, further comparisons between individual results from different ET datasets may be required. In addition, the nearest-neighbor interpolation method was used to uniformly resample to 500 m, which will inevitably bring uncertainty to the results. Future studies can use meteorological data with higher spatial resolution to improve the research accuracy of the contribution of ET changes. Finally, the normalized multiple linear regression method was used to attribute the ET changes, and the regression coefficients of different climatic factors reflect the degree of their influence on ET. Different data sources may have different sensitivity and amplitude to the changes in climatic factors, which may bring uncertainty to the separation results. ET itself is a complex physical process, and many factors need to be considered. In addition, the large altitude span and complex landform of the YRB increase the difficulty of investigating the driving mechanism of climatic factors affecting ET changes. In this study, only five climatic factors, namely air temperature, precipitation, solar radiation, specific humidity, and wind speed, were selected as the main climatic drivers to drive the analysis of ET changes in the YRB, and the influence of human activities on ET changes was not considered. Future studies can combine more meteorological data, topographic data, land use type data, etc., and comprehensively explore and analyze the interaction between meteorology and land cover types to conduct further research on ET influencing factors in the YRB and reduce the uncertainty of the above relationships and then develop a model to predict the future \"spatiotemporal variation in ET\" with respect to average climate conditions; this will be crucial for climate change research and water management in the context of global warming.
## 5 Ride Performance Analysis
### Result Analysis
#### 5.2.3 Effect of the Initial Gas Volume  
Since the sectional area of the piston is constant, different initial gas volumes mean different pneumatic chamber lengths. The EF value is plotted in Figure 15a for different lengths of the pneumatic chamber, in which the diameter and length of the pipe are 6 mm and 0.5 m, respectively. There exists an optimal length that minimizes the EF value. When the length varies between 50 mm and 80 mm, the increase of the pneumatic chamber length results in a lower EF value, which indicates a softer suspension system can be obtained by increasing the nitrogen volume. However, on the left side of 50 mm and the right side of 85 mm, the pneumatic chamber length significantly influences the EF value.  
The bounce acceleration of the red curve in Figure 15b indicates that the EF value of Test 1 is mainly caused by the peak acceleration value at 12.21 s. It also can be seen from Figure 16a that when the vehicle traverses the trapezoidal barrier with a 40 mm pneumatic chamber length, a positive suspension breakdown (the collision between piston and cylinder) occurs at the fourth cylinder. When the third wheel passes through the obstacle, the center of gravity of the vehicle comes to the right side of the barrier, and the balance is instantly altered. During the forward tilt of the body, the fourth load wheel will serve as the only fulcrum to carry the entire weight of the vehicle, thereby triggering a positive impact with an acceleration of 4 m/s\({}^{2}\).  
On the contrary, when the vehicle traverses the trapezoidal barrier with a 110 mm pneumatic chamber length, the negative suspension breakdown occurs as shown in Figure 16b. Due to the large-stroke compression when the front wheels contact with the ground under pitching motion, the pedrail under the fifth and sixth load wheels become slack, thus the collision between the piston and the bottom of the cylinder will happen. Moreover, the clockwise pitch angular acceleration of the vehicle body also aggravates the negative impact of the suspension.  
Figure 14: Performance comparison of different pipe lengths: (**a**) EF value (**b**) Acceleration values of trapezoidal barrier traversing (**c**) Acceleration values of step barrier traversing.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 6 Research Challenges and Future Directions
### Design the Modulation Identification Method of OFDM Signal  
It can be seen from the above introduction that most of the current deep learning-based modulation identification algorithms are mainly aimed at single-carrier systems, and there is relatively little research on modulation identification algorithms for OFDM signals. Orthogonal Frequency Division Multiplexing (OFDM) is a very important transmission method in multi-carrier modulation. It is widely used in communication systems due to its good anti-interference performance and is an important method to complete large data transmissions in the future. Therefore, it is of great significance to study the modulation and identification methods of OFDM signals. However, due to the diversification of modulation types, the channels of multi-carrier systems are very complex, resulting in the identification of sub-carrier modulation of OFDM signals and the identification between OFDM signals and single-carrier signals becoming very difficult [117]. This brings great challenges to future wireless communication. Therefore, it is very necessary to strengthen the research on modulation and identification of OFDM signals.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 2 Mathematical Model of the Rolling Element Bearing
### Nonlinear Effect--Eccentricity of the Shaft-Inner Ring System  
One of the factors affecting the dynamic response of the ball bearing is the eccentricity of the rotor, on which the bearing is seated [49; 50]. In rotor-based systems, it is impossible to avoid the shaft's eccentricity due to its improper manufacturing (mass distribution) or improper coupling with the propelling motor. In the real conditions, the acceptable eccentricity level is equal to (1-6) \(\upmu\)m and affects the variable contact in the ball bearing. In Figure 2, the effect of eccentricity acting on the rotor system is presented. The gravitational acceleration (\(m_{ir}+m_{s}\))g additionally intensifies the effect of the eccentricity on the system's dynamics. Centrifugal force \(F_{s}\) of the rotor-bearing system refers to the shifted center of gravity by eccentricity, gravitational acceleration, and deformations in the x-y plane.  
Figure 1: 2-degrees of freedom (DOF) mathematical model of the rolling element bearing. The inner ring with the shaft is treated as one rotating mass of the bearing.  
Figure 2: The graphical representation of the eccentricity of the shaft-inner ring system. The gravity force and subjected loads in the \(x\)- and \(y\)-axis are acting on the rotating system [10; 19].
## 1 Introduction
The information gain ratio (IGR) is an effective method used to calculate the factor contribution for model accuracy. It provides a powerful technique to quantitatively identify and select significant causal factors for landslide susceptibility modelling. In this paper, the Wushan segment of TGRA was selected as a study area. Multicollinearity analysis and IGR were applied to select landslide causal factors. Then, three machine learning models (SVM, ANN, CART) and a multivariate statistical model (LR) were utilized to conduct landslide susceptibility modelling. Finally, the accuracy of the four models was evaluated and compared using the receiver operating characteristic (ROC) and the accuracy statistic methods. The authors hoped that it would find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA.
## 2 Related Works
### Data Collection Schemes Based on Mobile Data Collector
#### 2.1.1 Mobile Data Collectors with Random Mobility  
The concept of mobile data collector was first introduced in [3], in which MDCs are referred to as Data Mules. In this proposal, generated data is buffered at source sensors until MDCs move randomly (using a Markov model based on a two-dimensional random walk) and collect data opportunistically from sensors in their direct communication range. The MDCs then carry the collected data and forward it to a set of access points. In this case, static nodes periodically wake up and listen for advertisements from mobile node for a short time. If it does not hear any beacon message from a mobile node, it return to sleep. Otherwise it starts transferring data to the mobile node.  
An efficient Data Driven Routing Protocol (DDRP) was proposed in [22] where mobile sinks periodically broadcast beacon messages to their one-hop neighbors as they move around the network area. Each node maintains a variable called Dist2 Sink (Distance to Sink), which stores the shortest number of hops to the sink. Beyond a certain value, Dist2 Sink is equal to infinity, which means the node has no route to the sink. When a node's Dist2 Sink variable is equal to infinity, it waits for a certain amount of time to overhear about a new valid route to the sink. However, if unsuccessful within a time bound, nodes adopt a random-walk strategy until the packet finds a valid route to the sink or is timed out and dropped. The main problem with DDRP is that with each movement of the mobile sink, subsequent topological changes occur that are propagated in the entire network in the form of the overhearing mechanism.  
[PERSON] _et al._ proposed an improvement in [23] for Routing Protocol for Low Power and Lossy Networks (RPL) to efficiently support sink mobility. In case of basic RPL, mobile sink frequently broadcasts its presence which is propagated in the entire sensor field and each node in the network makes a Directed Acyclic Graph (DAG) to the sink. This promotes extensive network traffic and high energy consumption with each movement of the sink. However, in [23] the sink's topological update is restricted to a confined zone, consisting of a few hops around the sink. Hence, nodes within the confined zone can immediately send data to the sink using the known DAGs. The size of confined zones is increased for low sink mobility and decreased for high sink mobility. Nodes outside the confined zones implement on demand sink discovery. With each unsuccessful attempt, the zone size for broadcasting the route discovery request is increased. This procedure is repeated until a network wide broadcast for route discovery is initiated.
## Results and Discussion
### Purification of APs from MPTH
#### 3.2.2 Anion-Exchange Chromatography of MPTH-I  
As shown in Figure 2A, six fractions (MUA-1 to MUA-6) were separated from MPTH-I fraction using a DEAE-52 cellulose column. Amongst those fractions, MUA-1 and MUA-2 were eluted using DW, MUA-3 and MUA-4 were eluted using 0.1 M NaCl, MUA-5 was eluted using 0.5 M NaCl, and MUA-6 was eluted using 1.0 M NaCl. DPPH- and HO- scavenging activities of MPTH-I and six fractions were presented in Figure 2B, and the data made clear that DPPH- (76.64% \(\pm\) 3.43%) and HO-(69.39% \(\pm\) 3.58%) scavenging abilities of MUA-4 were significantly stronger than those of MPTH-I (DPPH: 65.52% \(\pm\) 3.86%; HO: 58.78% \(\pm\) 3.05%), MUA-1 (DPPH: 34.46% \(\pm\) 0.98%; HO: 28.13% \(\pm\) 2.28%), MUA-2 (DPPH: 42.65% \(\pm\) 2.16%; HO: 34.53% \(\pm\) 1.95%), MUA-3 (DPPH: 55.91% \(\pm\) 2.67%; HO: 49.52% \(\pm\) 2.83%), MUA-5 (DPPH: 57.34% \(\pm\) 3.247%; HO: 48.25% \(\pm\) 2.96%), and MUA-6 (DPPH: 55.36% \(\pm\) 3.86%; HO: 53.22% \(\pm\) 3.05%) at the concentration of 5.0 mg protein/mL (\(p\) \(<\) 0.05).  
Figure 1: DPPH- and HO- scavenging activity of MPTH and its three fractions (MPTH-I, MPTH-II, and MPTH-III) by membrane ultrafiltration at the concentration of 5.0 mg protein/mL. The data are presented as the mean \(\pm\) SD (\(n\) = 3). \({}^{a-d}\) or \({}^{A-C}\) column-wise values on radical scavenging activity with the same superscripts indicate no significant difference (\(p\) \(>\) 0.05).  
Acidic and hydrophobic amino acid residues in peptide sequences, such as aspartic acid (Asp), methionine (Met), glutamic acid (Glu), phenylalanine (Phe), leucine (Leu), and tyrosine (Tyr), can be adsorbed on the anion-exchange resins [5; 32]. In addition, hydrophobic and acidic amino acid residues will enhance the bioactivities of peptides [21; 33]. The present results suggested that the peptides in MUA-4 fraction should contain acidic and/or hydrophobic amino acid residues and those residues should be beneficial to their antioxidant activities. So, MUA-4 was selected for the following experiment.
## 3 Results and Discussion
### Discussion
#### 3.2.3 Different Speed-Up Times  
To steadily pass through the critical speed points of the winding rotor, there is another effective method where the speed-up time is shortened to make the winding rotor pass quickly through the critical speed range. Hence, this paper also discusses the influence of the speed-up time on the winding rotor dynamics, as shown in Figure 16.  
Research on the dynamic characteristics of the winding rotor under different speed-up times through the simulation, shows results as follows:  
* During the rotational speed of the winding rotor passing through the critical speed of the winding rotor, when the speed-up time is short (30 s), the action time acting on the critical speed range is short, and the response amplitude is small. When the speed-up time increases (90 s), the action time that acts on the rotor's critical speed range increases, that is, the action time that the external excitation force acts on the critical speed range increases, resulting in large amplitude and longer response time, as shown in Figure 16;
* Figure 16d shows, by shortening the speed-up time, that the acceleration amplitude can be reduced effectively, or even be crossed in some critical speed points with weak vibration. This study lays a solid foundation for providing the reference to design the working speed range and avoid the rotor working near its critical speed point. However, the winding rotor needs to install a high-power motor to shorten the speed-up time.  
Figure 15: Dynamic response of investigated point A (node 51) of different supporting stiffness and damping coefficients of the rubber O-ring under the speed-up time of 60 s. (**a**) Displacement amplitude; (**b**) velocity amplitude; (**c**) acceleration amplitude.