esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis
### The Influence of the Number of Groups on the Coverage Rate  
As can be seen from the previous experiments, when the robot's coverage distance \(d_{cover}\) is set as 6, various performance measures are relatively good. Therefore, under the condition that \(d_{cover}\) is set as 6 and other parameters are the same, we also explored the influence of the number of packets on the coverage. Figure 21 shows the change of average coverage over time in 30 experiments when the number of grouping \(K\) is 1, 3, 4, 6, 8, and 10, respectively.  
As shown in Figure 21, when the number of groups \(K\) is less than 6, the coverage curve of the robot decreases gradually with the increase of the number of groups, and the coverage rate can reach 100%. When the number of groups \(K\) varies from 6 to 10, the time when the robot's coverage curve reaches a plateau increases, and the coverage rate decreases. It can be concluded from the experiment that the increase of the number of robot groups can improve the coverage speed of swarm robotic system to a certain extent, but when the number of groups is too much, the conflicts between different groups will be more and more, which leads to the decrease of coverage rate and the slowing down of coverage speed.
## 2 Quasi-Static Experiment
### Experimental Results and Discussion
#### 2.3.3 Seismic Performance of the PC Specimen  
In order to study the influence of the connector on the PC specimen, the seismic performance of the PC specimen needs to be analyzed and compared with that of the RC specimen. Figures 10-13 and Table 3 show the force-displacement curves, the secant stiffness curves calculated by Equation (2), the equivalent viscous damping coefficient curves calculated by Equation (3), and the displacement ductility coefficients calculated by Equation (4). We can see that: (1) the hysteresis loop of the PC specimen is more plump than that of the RC specimen (see Figure 10); (2) after the maximal load is reached, the load of the PC specimen drops more slowly than that of the RC specimen (see Figure 11); (3) the stiffness of the PC specimen is almost the same as that of the RC specimen (see Figure 12); and (4) the ductility coefficient (see Table 3) and the equivalent viscous damping coefficient (see Figure 13) of the PC specimen are larger than those of the RC specimen. Meanwhile, the ductility coefficient and the equivalent viscous damping coefficient of the PC specimen are greater than 4 and 0.1, respectively. These are measured by [PERSON] [46], since the limits are not specified in the Code for Seismic Test of Buildings [45].  
\[K=\frac{F}{\Delta} \tag{2}\]  
where \(K\) is the secant stiffness; \(F\) is the peak load for the first loading cycle; and \(\Delta\) is the peak displacement for the first loading cycle.  
\[h_{e}=\frac{1}{2\pi}\frac{S_{DAB}+S_{BCD}}{S_{OBE}+S_{ODF}} \tag{3}\]  
where \(h_{e}\) is the equivalent viscous damping coefficient; \(S_{DAB}+S_{BCD}\) is the dissipated energy in a loading cycle on the hysteresis curve; and \(S_{OBE}+S_{ODF}\) is the energy absorbed by a linear elastic body equivalent to the specimen, defined as the areas surrounded by the triangles \(OBE\) and \(ODF\).  
\[\mu=\frac{\Delta_{u}}{\Delta_{y}} \tag{4}\]  
where \(\mu\) is the ductility coefficient; \(\Delta_{\mu}\) is the ultimate displacement; and \(\Delta_{y}\) is the yield displacement.  
Figure 9: Connector strain curves.  
Figure 11: Skeleton curves.  
Figure 12: Stiffness curves.  
Figure 10: Hysteretic curves.  
In summary, the seismic performance of the PC specimen is close to that of the RC specimen, which implies that the connector has little influence on the seismic performance of the PC specimen.
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 2 Literature Review  
At the beginning of the 1980s, inland terminals were mentioned as being an object that could serve as a solution for container storage congestion at seaports, accelerating intermodal transport and reducing total transport costs [6]. Inland terminals for international shipping are becoming extremely important worldwide. Numerous studies have documented the advantages and benefits of intermodal transport implementation in reducing transport costs between ports and inland terminals or final users [7; 8; 9; 10; 11; 12; 13; 14; 15]. Studies on inland terminals have generally focused either on the problem of where to locate inland terminals, or on the problem of connecting inland terminals with seaports for efficient intermodal transport [16]. Numerous quantitative models have been proposed for optimizing the position of inland terminals within transport networks [17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Similarly, several models of intermodal transport involving seaports and inland terminals have been described [27; 28; 29; 30; 31; 32; 33; 34].  
Few studies have examined how to optimize container location assignment at inland terminals, in contrast to the extensive literature on this question at seaports.
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 2 Models and Methods
### Simulation Method
#### 2.3.2 The Sampling Position  
In this study, the number of sensors on the robotic fish can be increased as much as needed since the pressure sensors are virtual. We use the simulation experiments to determine the appropriate position of sensors. Limited by computational resources, time costs, experimental costs and the size of real sensors, we choose 40 sensors to complete our work. The kinematic pressure caused by fluid disturbance is different at the different surface position. Therefore, we set a series of pressure sensors on different sampling positions on the robotic fish surface. The \(x\) coordinates of sensors are \(\{xi|xi=0.05\ L\times i,0\leq i\leq 20\), where \(L\) is the length of robotic fish\(\}\). The sensor at the robotic fish head is named as \(S_{h}(i=0)\), and sensor \(S_{t}\) is located at the robotic fish tail \((i=20)\). Two pressure sensors are set symmetrically on both sides of the body at position \(x\), which are called pair sensors. Pair sensors are coded from sensor 1 to sensor 19, defined as \(S_{i}\), \(1\leq i\leq 19\). \(S_{iL}\) is located on the left side of robotic fish body, and \(S_{iR}\) is on the right side. The sensor positions are shown in Figure 2. In this work, all sensors are pressure sensors and we calculate the kinematic pressure by the mean values in a swimming cycle. The kinematic pressures on pair sensors are recorded as \(p_{iR}\) and \(p_{iL}\)\((1\leq i\leq 19)\). As for the head and tail, the kinematic pressure are recorded as \(p_{h}\) and \(p_{t}\).
## 3 System Model and Data Preprocessing  
### System Model  
The semi-supervised-learning-based AMC system model is shown in Figure 2. At the transmitter side, the input signal \(i(t)\) is modulated to obtain a modulated signal \(s(t)\). The modulated signal \(s(t)\) is affected by noise and channel fading during transmission. At the receiver side, the received signal \(x(t)\) can be expressed as:  
\[x(t)=s(t)*h(t)+n(t) \tag{1}\]  
where \(h(t)\) denotes the multi-path channel and \(*\) stands for the convolution operation. \(n(t)\) denotes additive noise. The additive noise can be broadly classified into four categories according to its source, namely radio interference, industrial noise, atmospheric interference, and internal noise. The implementation of semi-supervised AMC typically comprises three stages. Initially, we deploy receivers to receive signals from wireless devices, ideally performing data preprocessing on received signals. Here, processing involves scrutinizing and processing the acquired signals to derive an excellent representation of the signals (time-frequency images, constellation images, or more complex features). The objective is to construct more precise, robust, and comprehensive models. Subsequently, supervised and unsupervised losses are effectively constructed, independently adhering to the proposed semi-supervised algorithm. The supervised loss is derived by contrasting the predicted values of labeled samples with their authentic labels. The unsupervised loss is obtained by training on unlabeled signal samples. Specifically, two distinct levels of perturbation are implemented for unlabeled samples. Thereafter, two different probability distributions are generated by the model. The unsupervised loss is calculated by assessing these two probability distributions. Both the supervised and unsupervised losses collaboratively update the model parameters. Lastly, time-frequency signal images for the test are fed into the trained model. The model is capable of achieving the segregation of twenty modulation schemes.
## 2 Problem Description
### Medium-Level Planning
#### 2.2.3 Ward Is the Bottleneck Resource (R*)  
In the integrated planning of the operating room with the ICU and ward, when the percentage utilization of beds in the ward is maximized, the ward becomes the bottleneck. Figure 5 explains the procedure for multi-level planning when the ward becomes the system's bottleneck. In such a case, the capacity information of the ward beds is provided through the rope mechanism to the ICU and operating rooms, and some portion of the capacity allocation of the operating room becomes fixed. The operating room allocation of patients is then performed based on this capacity constraint of ward beds. The objective function given by Equation (30) is aimed at maximizing the number of patients that require a bed in the ward.  
\[obj=\text{maximize}\sum_{p=1}^{p}Z_{pw}^{d\tau}\;\forall 0<d\leq D\;\forall\tau \tag{30}\]  
\[\sum Z_{pw}^{d\tau}=1\;\forall p\in P,0<d\leq D\;\forall\tau \tag{31}\]  
\[\sum_{p=1}^{p}\sum_{d=1}^{D}Z_{pw}^{d\tau}\leq N_{w}^{d\tau}\;\forall p\in P\;\forall\tau \tag{32}\]  
\[N_{wd\tau}^{oc}=N_{w(d-1)\tau}^{occ}-N_{w(d-1)\tau}^{disc}+N_{wd\tau}^{o}+N_{ wdt}^{itu}\;\forall i\in I\;\forall 0<d\leq D\;\forall\tau \tag{33}\]  
\[N_{wd\tau}^{occ}\leq N_{wd\tau}\;\forall i\in W_{d\tau}\;\forall 0<d\leq D\;\forall\tau \tag{34}\]  
\[t_{pw}^{disc}=t_{pw}^{d\tau}+LOS_{p}^{w}\;\forall w\in W\;\forall p\in P \tag{35}\]  
Figure 4: Multi-level integrated planning when the ICU is the bottleneck.  
Equation (31) ensures that on a given day \(d\) of the planning horizon \(\tau\), a patient can be assigned to one bed only in the ward. Equation (32) ensures that the number of patients requiring a bed in the ward is less than the total number of beds in the ward. Equation (33) provides the method to calculate the number of occupied beds in wards, where \(N_{wdt}^{disc}\) is the number of beds in wards from where patients are discharged on the day \(d\). \(N_{wdt}^{o}\) and \(N_{wdt}^{iciu}\) are the number of beds required in wards for the patients from the operating room and ICU, respectively.  
Equation (34) puts an upper bound on the wards' workload such that the number of beds occupied should be less than or equal to the total number of beds in the ward. Where \(N_{wdt}\) is the total number of beds in the ward on the day \(d\) of the planning horizon \(\tau\). \(N_{wdt}^{occ}\) is the number of occupied beds in downstream wards on \(d\) of planning horizon \(\tau\). Equation (35) provides the patient's discharge time from the ward. The information on the bed occupancy in the ward is then sent to the operating rooms and ICU to plan surgeries and to make a schedule of the ICU as per the available capacity of the ward beds.  
Figure 5: Multi-level integrated planning when the ward is the bottleneck.
# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis
### Analysis of Imbalanced Data Processing Methods
CatBoost showed distinct differences depending on the imbalanced data processing method, and XGBoost showed less sensitive results. In \"ROC Curves of 1 st Grade CatBoost\", the model using RandomOverSamplerSMOTEENN showed good performance. The SMOTE model showed a slightly higher performance in \"ROC Curves of 1 st Grade XGBoost.\" Figure 5a shows that when the false positive rate is less than 0.8, CatBoost shows high volatility in the ROC curve. In Figure 5b, XGBoost shows relatively low volatility and high dropout precision. After analyzing various imbalanced data preprocessing methods and metrics, we chose to use XGBoost with SMOTE (Model 1) and CatBoost with RandomOverSamplerSMOTEENN (Model 2). Table 13 describes the performance of each model. To test the SDP system, 46,104 data records (70% of the total 67,060 data) were used as training data, and 20956 data records (30% of the data) were used as test data. The results show that both models predict with high accuracy. The dropout precision was higher in Model 1 by 17%,  
Figure 5: ROC Curve (Freshman). 21%, and 9% for each grade, and in the case of dropout F1, it was higher by 7%, 5%, and 3%, respectively. The dropout recall was 3%, 4%, and 2% higher in Model 2 for each grade. In summary, Model 1 had high dropout precision and dropout F1, and Model 2 had high dropout recall. When high-dropout-precision Model 1 predicts students as dropout candidates using the combiner's rule in Table 7, we categorize them as high-risk and advise counselors to consult them first. When high-dropout-recall Model 2 predicts students as dropout candidates, we categorize them as low-risk. When all models categorize students as non-dropout, we exclude them from counseling.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.3 Defense against a Malicious Server  
Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, a recent study [51] proposed an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. According to the study's results, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75-85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 3 Problem Formulation
### Model  
The 2E-EVRPTW-BSS is defined on a complete digraph \(G=(V,\,A)\), where the set of nodes \(V\) includes customers, satellites, and BSSs. Demands are transported from the depot to the set of customers. Denote \(q_{i}\) as the demand of the \(i\)th customer. The number of available ICVs in the first echelon is \(K_{1}\), while that of EVs in the second echelon is \(K_{2}\). The capacity of the ICVs and EVs are \(Q_{1}\) and \(Q_{2}\), respectively. Some assumptions are made for the model. Figure 1: Illustration of 2E-EVRPTW-BSS. \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline Sets & \\ \(V_{0}\) & Depot, \(V_{0}=\{0\}\). \\ \(V_{S}\) & Set of satellites, \(V_{S}=\{1,2,...,m\}\). \\ \(V_{C}\) & Set of customers, \(V_{C}=\{1,2,...,n\}\). \\ \(V_{B}\) & Set of BSs, \(V_{B}=\{1,2,...,l\}\). \\ \(V_{1}\) & \(V_{1}=V_{0}\cup V_{S}\). \\ \(V_{2}\) & \(V_{2}=V_{S}\cup V_{S}\cup V_{C}\). \\ \(V\) & \(V=V_{0}\cup V_{S}\cup V_{B}\cup V_{C}\). \\ \(K_{1}\), \(K_{2}\) & Set of the ICVs in the 1 st-echelon and set of EVs in the 2 nd-echelon. \\ \(K\) & \(K=K_{1}\cup K_{2}\). \\ Parameters & \\ \(M\) & A large positive number. \\ \(T_{\max}\) & The max running time of the proposed VNS algorithm. \\ \(Q_{1}\), \(Q_{2}\) & The capacity of the ICV and EV, respectively. \\ \(B\) & The battery capacity of the EVs. \\ \(c_{1}\), \(c_{2}\) & Cost of unit time from \(i\) to \(j\) for ICV and EV, respectively. \\ \(c_{3}\) & Battery swapping cost. \\ \(c_{f}^{1}\), \(c_{f}^{2}\) & The fixed cost of ICV and EV. \\ \([i_{i},l_{i}]\) & The time window for the deriving at node \(i\). \\ \(a\) & The electric power consumption of each EV for unit time. \\ \(q_{i}\) & The demand for node \(i\). \\ \(t_{ij}\) & Travel time from node \(i\) to node \(j\). \\ Decision and variables \\ \(t_{i}^{1}\), \(t_{i}^{2}\) & Arrival time at the node \(i\) in the first echelon and the second echelon, respectively. \\ \(t_{s}\) & Latest arrival time of ICV at the satellite \(s\). \\ \(b_{ik}^{+}\) & The remaining battery power of EV \(k\) when it arrives at node \(i\) in the 2 nd-echelon. \\ \(b_{ik}^{-}\) & The remaining battery power of EV \(k\) when it leaves node \(i\) in the 2 nd-echelon. \\ \(u_{ik}\) & The remaining demand in vehicle \(k\) at node \(i\). \\ \(x_{ijk}\) & 1 if vehicle \(k\) travels arc (\(ij\)) in the 1 st-echelon; 0 otherwise. \\ \(z_{ijk}\) & 1 if EV \(k\) travels arc (\(ij\)) from the satellite \(s\); 0 otherwise. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables and parameters of the 2E-EVRPTW-BSS. (3) Fixed cost of ICVs and EVs:  
\[f_{4}=c_{f}^{1}(\sum_{k\in K_{1}}\sum_{j\in V_{5}}x_{0 jk}) \tag{4}\]  
\[f_{5}=c_{f}^{2}(\sum_{s\in V_{5}}\sum_{k\in K_{2}}\sum_{j\in V_{C}\cup V_{B}}z_{ sjk}) \tag{5}\]  
The model is presented as follows.
## 2 Problem Definition and Related Work
### Related Work
[PERSON] et al. established a prediction model using multi-source sparse attention convolutional neural networks called MasCNN to predict students' course grades according to the features of students and courses. The prediction accuracy was up to 84.9%, and the experimental results showed that the performance of this model was better than that of LR, SVM, KNN, decision tree, RF and other models [26]. [PERSON] et al. collected 3518 undergraduate students' logs from an LMS, extracted eight counting-based learning behavioral features, and established aprediction model based on FNN to predict students' course grades, achieving a prediction accuracy of 80.47% [27]. [PERSON] et al. constructed the behavioral characteristics of learners based on VLE click streams, counted the numbers of clicks on different objects by learners in different time periods, and constructed an artificial neural network to predict learners' performance [28]. Some researchers use convolution, long-short-term memory (LSTM), and attention to aggregate sequences extracted from click logs to construct learners' behavior features [29; 30; 31]. However, these features still depend on learners' long-term logs, which cannot be used for early prediction. A comparison of related works is shown in Table 1.  
In general, the results of related works indicate that students' online learning behavior records contain a lot of valuable information, such as students' learning style, degree of engagement and cognitive characteristics, and they can be used to predict students' performance. Deep neural networks perform better than the classical classifiers in predicting students' performance [32]. However, the features of learning behavior used in previous studies are based on statistics rather than sequence, and cannot be used to predict students' performance at the beginning of or during the learning process. Classical classifiers cannot extract more representative hidden features from the original data. In addition, the comparison of the features of online learning activities and other types of features used to predict students' performance is absent in previous studies.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### Network Formation  
The network formation starts once the UAVs start flying. Along with task-oriented sensors, UAVs are also equipped with GPS and height sensors. These two pieces of equipment provide the 3D positional information of the UAV node. We also assume that UAVs have four discrete power levels, which correspond to transmission ranges of 400, 600, 800 and 1000 m, respectively. At the start, nodes select highest power level. Later on, nodes can adopt the power level that is most suitable according to its position and the neighbor nodes. This technique is used to save node energy.
_Applied_  
_sciences_  
Development of an Ionic Liquid-Based Ultrasonic/Microwave-Assisted Simultaneous Distillation and Extraction Method for Separation of Camptothecin, 10-Hydroxycampotthecin, Vincoside-Lactam, and Essential Oils from the Fruits of _Campottheca acuminata_ [PERSON]  
[PERSON] 1,2,3, [PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2,3, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2  
Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China; [EMAIL_ADDRESS] (C.Z.); klp14 [EMAIL_ADDRESS] (Y.Z.); klp13 [EMAIL_ADDRESS] (X.H.); [EMAIL_ADDRESS] (L.Y.); [EMAIL_ADDRESS] (Y.F.); klp15 [EMAIL_ADDRESS] (J.Z.); klp15 [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (Y.Z.)  
State Engineering Laboratory of Bio-Resource Eco-Utilization, Northeast Forestry University, Harbin 150040, China  
Collaborative Innovation Center for Development and Utilization of Forest Resources, Harbin 150040, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-451-8219-0848; Fax: +86-451-8210-2082  
Academic Editor: [PERSON]  
Received: 21 September 2016; Accepted: 10 October 2016; Published: 13 October 2016  
An ionic liquid-based ultrasonic/microwave-assisted simultaneous distillation and extraction (IL-UMASDE) method for isolating camptothecin (CPT), 10-hydroxycampotthecin (HCPT), vincoside-lactam (VCS-LT), and essential oils (EOs) from _Campottheca acuminata_ Decne fruits was developed. The important parameters were optimized using single-factor and central composite design experiments. The optimum conditions were 0.75 M 1-octyl-3-methylimidazolium ([C\({}_{\mathrm{S}}\)mim]Br) as the extraction solvent, a liquid-solid ratio of 13.7 mL/g, an extraction time of 33.2 min, a microwave power of 582 W, and a fixed ultrasonic power of 50 W. The yields of CPT, HCPT, and VCS-LT obtained under the optimum conditions were 2.463, 0.164, and 0.297 mg/g, respectively; these are 1.08-, 1.12-, and 1.04-fold higher, respectively, than those obtained by conventional 55% ethanol heat reflux extraction (HRE). The extraction time for the equilibrium yields of CPT, HCPT and VCS-LT using IL-UMASDE was 33.6%, 58.5%, 63.1%, and 66.8%, respectively, less than the corresponding times using IL-MASDE, IL-ultrasonic-assisted extraction (IL-UAE), 55% ethanol UAE and 55% ethanol HRE. The yield of EOs obtained using IL-UMASDE was 0.793 mg/g, i.e., 1.31-fold higher than that obtained by conventional hydrodistillation extraction (HDE). The components of the EOs obtained using IL-UMASDE and HDE were similar. The extraction time for the equilibrium yields of EOs using IL-UMASDE is 33.6%, 58.5%, 52.6%, and 72.3% lower than those for IL-MASDE, water-UMASDE, and HDE, respectively. Compared with other extraction methods, IL-UMASDE gave the highest yields of CPT, HCPT, VCS-LT, and EOs and also had the shortest extraction time. IL-UMASDE is a potential green and highly efficient technique for the extraction of CPT, HCPT, VCS-LT, and EOs from _Campottheca acuminata_ Decne fruits.  
ultrasonic/microwave-assisted simultaneous distillation and extraction; ionic liquid; essential oil; camptothecin; _Campottheca acuminata_