esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Robot-Assisted Untact Swab Sampling System for Upper Respiratory Disease
## 1 Introduction
At present, specimen sampling from the upper respiratory tract is mainly performed through both oropharyngeal (OP) and nasopharyngeal (NP) swab sampling. In the case of OP swab sampling, samples are collected by rubbing cotton swabs several times on the posterior pharyngeal wall and palatine tonsil. In the case of NP swab sampling, the examiner inserts a flexible swab into the nostril parallel to the palate until resistance due to contact with the posterior nasopharyngealnx wall is felt. Once the swab reaches the posterior nasopharyngeal wall, it must be gently rubbed against the tissue to collect sufficient samples [13]. Although robotic systems for respiratory specimen sampling have not previously been actively studied, a robotic system for OP swabing, which was jointly developed by the Guangzhou Institute of Respiratory Health and the Shenyang Institute of Automation under the Chinese Academy of Sciences has been introduced in [14]. This robotic system consists of a snake-shaped mechanical arm with a binocular endoscope, wireless transmission equipment, and a human-computer interaction terminal. The mechanical arm can be used to collect throat swabs on the basis of high-definition 3D anatomical scenes viewed through the endoscope. In this paper, we focus on a master-slave robotic system for remote NP swab sampling. Specifically, an optimized design of the slave robot and master devices for remote swab sampling and experimental results obtained by using the realized robotic system are introduced. The developed robotic system for sample collection consists of slave and master subsystems. Section 2 presents the details of these subsystems. The experimental results presented in Section 3 show the tracking accuracy of the slave robot under manipulation by the master devices, the results of force sensing, and the sample collection results achieved in NP swab tests with a head phantom model.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 3 Generalized Random Petri Net Model for Plant Pest and Disease Spread Diagnosis
### The GSPN Model for Plant Pest and Disease Spread Evolution  
Based on the plant pest and disease early warning system diagram shown in Figure 1, define the places, transitions, and directed arcs. Following Step 1 as mentioned earlier, construct the GSPN model for plant pest and disease spread evolution, as shown in Figure 2.  
As shown in the figure above, the model contains 12 places and 22 transitions. The specific meanings of each place and transition are provided in Table 4.  
According to Step 2, the plant pest and disease spread evolution process is modeled and run using PIPEv5.2.0 to determine the reachability marking set of the Petri net. The initial marking of the GSPN is assumed to be \(\mathrm{M}_{1}=(1,1,1,1,1,0,0,0,0,0,0)\), indicating that there is one token in places \(\mathrm{P}_{1},\mathrm{P}_{2},\mathrm{P}_{3},\mathrm{P}_{4}\) and \(\mathrm{P}_{5}\). By applying different transitions, the corresponding reachability set can be obtained.  
Figure 2: The GSPN model for plant pest and disease spread evolution.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Places** & **Meaning of Places** & **Transitions** & **Meaning of Transitions** \\ \hline P1 & Water and Fertilizer Data & t1 & Information Filtering \\ P2 & Temperature Data & t2 & Root Symptom Observation \\ P3 & Humidity Data & t3 & Stem Symptom Observation \\ P4 & Light Data & t4 & Leaf Symptom Observation \\ P5 & pH Value & t5 & Flower Symptom Observation \\ P6 & Data Integration & t6 & Fruit Symptom Observation \\ P7 & Infection Severity Assessment & t7 & Young Shoot Symptom Observation \\ P8 & Invasion Severity Assessment & t8 & Bud Symptom Observation \\ P9 & Control Method Decision & t9 & Mild Infection \\  & Implementation of Control & & Moderate Infection \\ P10 & Measures & t10 & Severe Infection \\ P11 & Data Monitoring & t11 & Reaching Treatment Standards \\ P12 & End of Treatment & t12 & Environmental Control \\  & & t13 & Nutritional Management \\  & & t15 & Physical Methods \\  & & t16 & Chemical Methods \\  & & t17 & Biological Control \\  & & t18 & Field Cleanliness and Standard \\  & & & Cultivation \\  & & t19 & Recovery of Health \\  & & t20 & Plant Death \\  & & t21 & Treatment Effectiveness Assessment \\  & & t22 & Data Feedback \\ \hline \end{tabular}
\begin{tabular}{c} M\({}_{1}=(1,1,1,1,1,0,0,0,0,0,0,0,0)\) \\ M\({}_{2}=(0,0,0,0,0,1,0,0,0,0,0,0)\) \\ M\({}_{3}=(0,0,0,0,0,0,1,0,0,0,0,0,0)\) \\ M\({}_{4}=(0,0,
## 3 Service Delivery in 5G-Enabled D2D Communications Network
### System Model  
5G requires an efficient service system, access and delivery models to support its objectives, which can be achieved by integrating other architectures and services with 5G. We adopt a system model that consists of Cloud Radio Access Network (C-RAN) [35], CCN [34] and D2D communications [7] as suggested in [15]:  
* C-RAN is adopted for the centralization of base stations, resources virtualization and edge service deployment.
* CCN is adopted for content distribution and caching.
* D2D communications is adopted for direct communication, content dissemination and data offloading.  
C-RAN centralizes the baseband resources to a single baseband unit called Baseband Unit (BBU) pool, connecting to the remote radio heads transceivers combing the function of the base station. In addition, the interface between BBU and radio heads has been changed from circuit fronthaul to packet fronthaul. BBU is divided into the distributed unit responsible for physical layer and real-time MAC layer process and the centralized unit responsible for upper layer computations process [35]. The UE connects to ngRAN, Security Anchor Function (SEAF) or Access and Mobility Function (AMF) to access the network. The 5 GC consists of various network functions such as Session Management Function (SMF), Authentication Server Function (AUSF) and Unified Data Management (UDM) [36] residing in the Home Network (HN).  
The system model in Figure 1 consists of the following entities:  
* UE: Transmitter and receiver.
* Next generation NodeB (gNB): Base station connecting UE to the network.
* HN: UE registers to and receives services from it.  
The CCN protocol can be embedded into the UE, BBU pool, edge routers and 5 GC [15], or the control and user plane enhancement can be implemented to enable services such as CCN as 5 GC network functions and extend the interfaces to support CCN-protocol data unit sessions [37]. To access services, the UE must be registered with MNO/SP and subscribed to those services, whereby the UE can request to access the network, perform primary authentication with HN and then request to access services depending on the service level agreement [10].  
After a successful primary authentication, a secondary authentication and service authorization may be required between the UE and SP, to authorize the UE to access third party services, cache and share data with other UEs [38; 39]. MNO/SP is responsible for services subscription, authorizing service access and content retrieval. MNO controls the user's access to the network, connection establishment, resource allocation and security management. MNO/SP might also want to hide their visibility or deny the UE from accessing their services. There is an inter-operator agreement between different networks to provide inter-operator roaming services.
# Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
## 4 Results and Discussion
### Discussion
#### 4.4.1 Comparative Analysis of the Proposed Technique with Existing Research  
Figure 13 is showing comparative results of the proposed technique with the existing research. The comparison is based on the evaluation metric of overall accuracy. It may be observed that the accuracy of linear regression-based classification [23] over the Weizmann dataset is 61.7%, while it is 60.1% for subspace discriminant analysis [23]. The proposed technique achieved an overall accuracy of 91.4%, and it is highest among all of its counterparts. The results of multiclass SVMs [23] are 91.2% accurate while KNN based classification [23] was 86.1% correct. [PERSON] et al. [21] achieved 85.2% accuracy using sparse spatio-temporal features and unsupervised learning based classification through spatio-temporal words [46] attained an overall accuracy of 90.0%. Again, the spatial-temporal features were used by [PERSON] et al. [20], but, based on 3D gradients, it remained 84.3% accurate. It may also be observed that the Gaussian mixture model-based technique [18] could attain 91.11% accuracy and the same research using the nearest neighbor classifier [18] remained 87.78% accurate. Even the latest research [19] achieved an accuracy of 89.41%.
## 3 Theoretical Background of the SRM
### TSFs  
To minimize the torque ripple, the commonly used TSFs can be classified as linear, sinusoidal, exponential, and cubic and their details have been reported in [19; 24]. Through the torque control with a TSF in the region where it shares the torque with one or more phases (called the commutation or overlap region), the total torque reference \(T_{ref}\) is divided into separate torque reference \(T_{ref(k)}\) for each phase. According to the characteristics of the torque-current-position, the \(T_{ref(k)}\) is converted into the phase current reference \(i_{k}\) with respect to the rotor position information.  
Figure 4 shows an illustration of a TSF. The \(k\)th phase torque reference denoted as \(T_{ref(k)}\) is calculated as:  
\[T_{ref(k)}(\theta)=\left\{\begin{array}{ccc}0&\theta_{u}\leq\theta\leq \theta_{on}\\ T_{ref}\times f_{rise}(\theta)&\theta_{on}<\theta<\theta_{on}+\theta_{ov}\\ T_{ref}&\theta_{on}+\theta_{ov}\leq\theta\leq\theta_{off}-\theta_{ov}\\ T_{ref}\times f_{fall}(\theta)&\theta_{off}-\theta_{ov}<\theta<\theta_{off}\\ 0&\theta_{off}\leq\theta\leq\theta_{al}\end{array}\right. \tag{5}\]  
where \(\theta_{u}\), \(\theta_{on}\), \(\theta_{ov}\), \(\theta_{off}\) and \(\theta_{al}\) are the unaligned, turn-on, overlap, turn-off and aligned angles, respectively; \(f_{rise}(\theta)\) and represents the ascending area and \(f_{fall}(\theta)\) represents the descending area in the TSF. In the commutation region, \(f_{rise}(\theta)\) must increase from 0 to 1 and \(f_{fall}(\theta)\) must decrease from 1 to 0. For any TSF, the function \(f_{fall}(\theta)\) that is relevant to \(f_{rise}(\theta)\) can be computed as follows:  
\[f_{fall}(\theta)=1-f_{rise}(\theta+\theta_{on}+\theta_{ov}-\theta_{off}) \tag{6}\]  
The angle \(\theta_{p}=2\pi/N_{r}\) denotes the rotor pole pitch, where \(N_{r}\) is the number of rotor poles. And \(\theta_{d}=2\pi/(mN_{r})\) denotes the angular displacement between the neighboring phases. In the case of a normal SRM commutation, these angles have the following relationships:  
\[\theta_{al}-\theta_{u}=\theta_{p}/2 \tag{7}\]  
\[\theta_{ov}=\theta_{off}-\theta_{on}-\theta_{d}\leq\theta_{p}/2-\theta_{d} \tag{8}\]  
Thus, we can get \(\theta_{p}=90^{\circ}\), \(\theta_{d}=30^{\circ}\), and \(\theta_{ov}\leq 15^{\circ}\) from Equations (7) and (8) in the three-phase 6/4 SRM. For the four-phase 8/6 SRM, we have \(\theta_{p}=60^{\circ}\), \(\theta_{d}=15^{\circ}\), and \(\theta_{ov}\leq 15^{\circ}\).  
Figure 4: Illustration of a torque sharing function (TSF).
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 4 LQA-AODV Routing Protocol
### Packet Forwarding Across 2-hop Clusters  
Due to limited transmission capacity of tactical MANETs, the number of hops for data transmission must be limited. Usually, the maximum number of hops from a sending node is allowed to be at most 4 hops away. Therefore, we extend AODV protocol to incorporate a hierarchical cluster-based routing. Hierarchical MANETs with clusters assign each node to one of the following functions: gateway node (GN), cluster member (CM), and cluster head (CH). The CHs should always be able to reach nodes in other clusters via their respective CHs or via GNs. The CM nodes within the same cluster can communicate either via their CH, or they communicate directly in a peer-to-peer (P2P) manner with possibly up to 2-hops. In our proposed protocol, we assume energy efficient clustering of nodes where CHs are selected to have the largest remaining energy level. In addition, the CHs need to have sufficient connectivity to nearby nodes, so there is a trade-off between CH connectivity and its residual energy. If either the energy level drops below a threshold or the number of known neighbors decreases, a CH rediscovery mechanism is initiated by the current CH to possibly find its replacement.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 3 Mathematic Model
### Solving of the MWP-DLP Model  
#### 3.2.1 Basic Data  
In the MWP-DLP model, the known basic data include the actual daily gas load on day \(i\)\(C_{i}\); the average temperature on day \(i\)\(T_{AVG}\); the average temperature on day \(i-1\)\(T_{AVG,i-1}\); the wind speed on day \(i\)\(WS_{i}\); and the solar radiation value on day \(i\)\(SR_{i}\).  
Training samples were selected for 71 groups of daily gas loads (excluding weekends and holidays) from November 15 of the first year to March 15 of the second year in Wuhan City. Some basic data are shown in Table 1.
## 5 Workshop Results and Analysis
### Dot Voting Activity Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **All Samples [_n = 27_] & \multicolumn{2}{c}{**Residents [_n = 18_]} & \multicolumn{2}{c}{**Specialists [_n = 9_]} \\ \hline  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline Nonspecific Acceptance & 3.370 & 0.742 & 3.222 & 0.808 & 3.667 & 0.500 \\ Performance & 3.222 & 0.847 & 3.056 & 0.938 & 3.556 & 0.527 \\ Expectancy & 3.519 & 1.087 & 3.278 & 1.127 & 4.000 & 0.866 \\ Ageism/Prejudice & 1.963 & 1.224 & 2.278 & 1.364 & 1.333 & 0.500 \\ Loss of Independence & 2.704 & 1.382 & 3.056 & 1.305 & 2.000 & 1.323 \\ Technology Anxiety & 2.481 & 1.252 & 2.778 & 1.353 & 1.889 & 0.782 \\ Privacy Concerns & 3.259 & 1.095 & 3.111 & 1.183 & 3.556 & 0.882 \\ Compatibility & 2.333 & 1.240 & 2.611 & 1.335 & 1.778 & 0.833 \\ Access to Support & 2.963 & 1.126 & 2.667 & 0.970 & 3.556 & 1.236 \\ Subjective Norm & 3.185 & 1.001 & 2.833 & 0.924 & 3.889 & 0.782 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics for CIREI questionnaire responses. Privacy concerns are a significant barrier, particularly among residents, who are wary of disclosing personal information to technology providers. Both groups also recognise the importance of social influence, with the likelihood of adopting technology increasing if it becomes more mainstream. Compatibility with existing devices and access to support are viewed positively, suggesting that with the right assurances and assistance, the adoption of smart home technologies among pre-frail older adults could be successful.
# Numerical Simulations of Novel Conning Designs for Future Super-Large Wind Turbines
## 1 Introduction
[PERSON] [20] utilized the VM method to investigate the influence of different blade tip configurations such as sweep, bending and winglets, and found that the whole blade was influenced by the curved part. Additionally, it was found that upwind prebending yielded increased power compared to the downwind configuration, which agreed with research presented in [15; 18; 19]. Further study is needed to understand the nonlinear behavior related to blade bending, as noted by [PERSON] [20]. [PERSON] et al. [21] utilized the VM method to optimize rotor blades and found that the bedrock blade tip had an aerodynamic influence on the whole blade, and that this could not be accounted for using the traditional BEM method. Lastly, wind tunnel experiments could be conducted to explore these rotor concepts [22; 23; 24]. Due to their complexity, experiments to date have only investigated overall performance, such as thrust and torque, rather than span-wise force distribution. Therefore, dedicated CFD simulations are indispensable, especially on full-scale wind turbine rotors with 3D body-fitted meshes. Prebending has a continuously changing slope or cone angle, so it is hard to quantify its effects. Conning is the basis of prebending, and conning designs are suitable for parametric studies. In a previously published paper [25], the authors simulated different up/downwind conning and presented a preliminary aerodynamic analysis. The present paper will analyze the aerodynamic performance of different conning effects, such as inflow velocity decomposition and angle of attack analysis. Additionally, this paper will cover novel cones with three segments, which is a simplification and standardization of the new concepts shown in Figure 1.  
The paper is organized as follows. In Section 2, the configurations of the cones and the employed numerical methods are presented. Results and discussions are given in Section 3. Finally, conclusions are drawn in Section 4.
## 3 Results and Discussion
### Reducing Sugar (Maltose and Glucose)  
The end products of amylase (maltose) and invertase (glucose) were evaluated in fresh samples and those dried at 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C (Figure 4a,b). The results revealed that, for all three varieties, the maltose and glucose contents varied significantly (_p_ < 0.05) under different drying temperatures. The samples dried at 40 \({}^{\circ}\)C contained a maximum amount of maltose and glucose in the ranges (25.96-44.45, 22.06-27.51 g 100 g\({}^{-1}\) d.w.) compared to the fresh samples (15.49-23.92, 9.77-14.10 g 100 g\({}^{-1}\) d.w.) and those dried at 60 \({}^{\circ}\)C (19.18-39.80, 17.27-20.65 g 100 g\({}^{-1}\) d.w.) and 80 \({}^{\circ}\)C (16.40-25.57, 12.92-17.20 g 100 g\({}^{-1}\) d.w.). These findings may be due to the fact that, during drying at 40 \({}^{\circ}\)C, the amylase and invertase remained active for a longer period of time, and exhibited their maximum activity, compared to drying at 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. Related results were also discussed in our previous study during heat incubation trials of amylase and invertase, where we found the maximum amylase and invertase activity at 40 \({}^{\circ}\)C [2]. However, it has also been documented in the literature that continuous generation and accumulation of end products have a negative influence on enzyme activity by disturbing the equilibrium needed to maintain their naturalstructure and can lead to enzyme degradation [3; 33; 43; 44]. Consequently, the mango slabs dried at 40 \({}^{\circ}\)C preserved the least amylase and invertase units.  
Figure 4: (**a**) Maltose and (**b**) glucose concentrations in fresh and dried samples of varieties Sindri, SB Chaunsa, and Tommy Atkins; FS = fresh samples, D = dried samples, at temperatures 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. Different letters (a–c) above bars within the same variety represent significant differences \(p<0.05\), (\(n=6\)).  
Figure 3: Residual activity A/A\({}_{o}\) and Arrhenius plot for the energy of deactivation of (**a,b**) amylase and (**c,d**) invertase; and (**e**) water activity during drying of mango variety Sindri at 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C; air velocity 1.0 m s\({}^{-1}\) or 1.4 m s\({}^{-1}\).
## 3 Methodology
### Determination of the Properties, Parameters, Input Variables and Initial Conditions for the Model
#### 3.3.4 Kinetic Parameters of the Reactions  
The kinetic parameters for the Arrhenius equation (activation energy and frequency factor) were calculated from the kinetic constant reported in the literature at different temperatures. Ideally, a detailed study must determine the kinetic parameter for the reaction of every component of the mixture, namely it must consider the reaction of every different glyceride and ester; however, this means that the reaction kinetic model has a large number of equations [44]. Most studies characterize the reaction of every glyceride and ester with solely the kinetic constant; therefore, considering the reversible three-step mechanism, kinetic studies only determine six kinetic constants. Some researchers have even opted to represent the reaction with a one-step mechanism. Consequently, kinetic models with different mechanisms are available in the literature. However, most studies, even those that consider the same mechanism, were developed under different conditions (see Table 2). In order to correctly evaluate changes in oil supply, it is necessary to use consistent kinetic parameters. For this reason, in order to choose consistent reaction constants, a comprehensive review of different kinetics studies was undertaken in the present study and is presented in Table 2. The table shows that the research on vegetable oils conducted by [PERSON] (mustard and palm oils) and [PERSON] (sunflower oil) was developed under the same experimental conditions [42; 43]. Hence, the Arrhenius equation parameters for elementary reactions in vegetable oil transesterification were determined from the kinetic constants reported in the above studies.
## 3 Methods
### Validating the Glacial Lake Mapping Results  
By comparing with the glacial lake outlines that were manually digitized from the GF-2 images, the Sentinel-based glacial lake maps were assessed. Due to the variable nature of some glacial lake conditions and the surrounding environment, the difference in the image acquisition date between Sentinel-1A/1B and GF-2 images were no more than five days in order to ensure the reliability of the evaluation results. Commission and omission errors were calculated for all lake and land pixels together. A low percentage error in the water and land classes suggests an accurate classification. Moreover, the kappa coefficient and overall accuracy were also computed from these errors. In addition to the above detailed accuracy analyses, further validation of the robustness of the proposed method was undertaken by comparing the glacial lake maps generated using support vector machine (SVM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) classification methods.
## 1 Introduction
These non-linearity can be identified in the amplitude with the Amplitude Modulation/Amplitude Modulation (AM/AM), and in the phase with the Amplitude Modulation/Phase Modulation (AM/PM) functions [19]. In addition, those techniques, as indicated earlier, increase transmission power due to the expansion on the transmitted constellation. In order to overcome this problem, we can pre-distort the signal before the HPA like to linearize the HPA, i.e., to linearize the AM/AM and AM/PM characteristics of the HPA. In order to better understand the effect and issues with AM/AM and AM/PM distortion, please see [20] and the references therein. Artificial NNs are being successfully applied through a wide range of complex computational problems [21; 22]. Among the different NN architectures [23], Multilayer Perceptron (MLP) is the most used one. It connects a set of input data to an appropriate output's set by using a supervised training techniques. In this paper, we developed a very simple pre-distorter architecture based on two MLPs for, respectively, AM/AM and AM/PM conversions. Then, two HPA models, namely, TWTA and SSPA [24] have been tested. Finally, the implementation of the proposal on FPGA is described and analyzed showing that it is well suited to future wireless communications systems [25]. The remainder of this paper is organized as follows. Section 2 describes the NN models for HPA pre-distortion. Section 3 presents the implementation of our proposed design on FPGA. Then, results are detailed in Section 4. Finally, Section 5 summarizes the conclusion of the paper.