esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### 2.2. Field Experiment  
#### 2.2.1 Bacterial Culture, Soil Analysis, Field Site, and Seed Inoculation  
After isolation and characterization, the bacterial strain MR-15 was used to inoculate the seeds of two wheat varieties. For the inoculum preparation, the bacterial colony was cultured in Luria Broth-based liquid medium in an Erlenmeyer flask (500 mL) and incubated in an orbital shaker for 24 h at 32 \({}^{\circ}\)C and 120 rpm until a concentration of 108 colony-forming units (CFU) mL\({}^{-1}\) was reached. The bacterium was then harvested by centrifugation at 8000 rpm for 10 min, and the pellet was washed with sterile distilled water. Further dilution after centrifugation to a concentration of 107 CFU mL\({}^{-1}\) was performed for the inoculation of seeds.  
The experiment was conducted between November 25 (temperature ranges between 25-27 \({}^{\circ}\)C) and April 25 in the experimental field located in BARI (Barani Agriculture Research Institute), Chakwal, Pakistan. The soil was classified as typical sandy clay loam in texture. The detailed physiochemical analysis of the soil is given in Table 1. The seeds of two _Triticum aestivum_ varieties, Sehar-06 (V1) and GA-2002 (V2), were sterilized by using ethanol (70%) for 2 min and then sodium hypochlorite (5%) for 5 min and subsequently washed with distilled water three times. The inoculum was applied to sterilized seeds; control seeds were divided into two categories. Non-primed seeds (NP) were sown directly into the soil, but hydro-primed seeds (HP) were sown after soaking the seeds in water for 1 h. Nine seeds were sown in each block. The field experiment was designed as a Randomized Complete Block Design (RCBD) with three replicates. To maintain the stress conditions, the soil field capacity was determined, and according to the treatment, water was applied. One group was irrigated at 60% field capacity, while the second group was irrigated at normal field capacity (100%).
## 3 Experimental Validation of the Hydroelasticity Theory  
### Experimental Setup  
A 1/50 scaled segmented model was designed and built according to the full-scale ship. The main dimensions of the ship and the model are listed in Table 1. The model is divided into seven parts longitudinally and cuts are provided at stations number 2, 4, 6, 8, 10 and 12. Steel backbones with variational cross-section are used to connect the segments and sectional loads on the backbone are measured at the six cut sections. The backbones are designed according to the principle of stiffness similarity. A self-propelling mechanical system is designed at the stern region of the model to achieve the expected speed of the ship in waves.  
The tests were conducted in the ship model towing tank of Harbin Engineering University, which has a dimension of 108 m long by 7 m wide by 3.5 m deep. A tow  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Symbol** & **Full Scale** & **Small Model** \\ \hline Scale & \(\lambda\) & 1:1 & 1:50 \\ Overall length (m) & \(L_{OA}\) & 312 & 6.24 \\ Waterline length (m) & \(L\) & 292 & 5.84 \\ Moulded breadth (m) & \(B\) & 39.5 & 0.79 \\ Depth (m) & \(D\) & 25.5 & 0.51 \\ Draft (m) & \(T\) & 10.1 & 0.202 \\ Block coefficient & \(C_{B}\) & 0.616 & 0.616 \\ Displacement (t) & \(\Delta\) & 71,875 & 0.575 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main dimension of the ship.  
along the tank is used to serve as the platform of translational movement system. A computerized flat-type wave generator that can generate both regular and irregular waves is located at the end side of tank. The incident waves will be absorbed by a damping beach located at the other side of tank to prevent wave reflection. A wave probe is positioned near the wave generator to monitor and record the generated waves. Wave-induced ship motions are measured by an in-house developed, four-DOF contact-type seakeeping apparatus, which is installed on the towing carriage and connected to the ship model by its two heave sticks. The ship model and tank facilities are shown in Figure 3.
## 5 Experiments
### Real-Time Electricity Price Prediction Results
#### 5.3.2. Avoid Costs  
Normally, we use avoided costs to evaluate electricity price strategies and benefits, which express that power suppliers can avoid or partially avoid power supply resources and costs.  
\[B_{se}=\frac{C-C_{d}}{\Delta W} \tag{15}\]  
Figure 10: Prediction accuracy contrast diagram.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{\begin{tabular}{c} **Actual** \\ **Value** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **SAMD-** \\ **SVM** \\ **Predicted** \\ **Value** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **SAMD-** \\ **SVM Error** \\ **(\%)** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Neural** \\ **Network** \\ **Predicted** \\ **Value** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Neural** \\ **Network** \\ **Error (\%)** \\ \end{tabular} } \\ \hline
01:00 & 17.506 & 18.925 & 3.32 & 16.716 & 4.51 \\
02:00 & 15.42 & 14.919 & 3.25 & 14.695 & 4.70 \\
03:00 & 15.142 & 14.786 & 2.33 & 14.486 & 4.33 \\
04:00 & 18.632 & 18.134 & 2.67 & 18.025 & 3.26 \\
05:00 & 18.642 & 18.411 & 1.24 & 17.892 & 4.02 \\
06:00 & 21.098 & 20.489 & 2.89 & 20.410 & 3.26 \\
07:00 & 21.162 & 21.331 & 0.75 & 20.836 & 3.75 \\
08:00 & 21.062 & 20.879 & 0.87 & 20.482 & 2.72 \\
09:00 & 21.263 & 20.842 & 1.98 & 20.756 & 2.38 \\
10:00 & 20.280 & 20.226 & 0.27 & 19.827 & 2.23 \\
11:00 & 20.534 & 19.894 & 3.12 & 19.827 & 3.44 \\
12:00 & 21.2908 & 20.435 & 4.02 & 20.647 & 3.02 \\ \hline Average error & & 2.45 & & 3.54 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electrolyte price prediction comparison.  
\(C\) and \(C_{d}\) represent the original power supply cost and cost after real-time electricity implementation, respectively. \(\Delta W\) represents power saving, and the results are shown in Table 4.
###### Abstract  
This paper investigates a circle formation control problem for multi-agent systems with directed topologies via pull-based distributed event-triggered control principles. Firstly, for scenarios of continuous communication, a pull-based distributed event-triggered principle is proposed. It is proved that if the communication topology is irreducible and has a directed spanning tree, the event-triggered coupling continuous communication can lead multiple agents to form a desired circle formation. Then, the results are extended to discontinuous communication scenarios, where all the agents use a model of their neighborhoods to generate self-triggered instants without monitoring continuously, update the local controller here, and if necessary, local broadcast information based on the adopted control inputs to neighboring agents. In addition, Zeno behavior can be excluded during the whole process. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed circle formation control methods.  
circle formation control; multi-agent systems; directed topology; pull-based event-triggered; self-triggered; discontinuous communication +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Proximate Composition  
The proximate compositions of the flower and thorn parts of _E. billardieri_ and _E. planum_ are presented in Table 1. No significant differences were observed in moisture content (6.7-8.1%) across all samples (\(p>0.05\)). The ash content of EPF (9.96%) was slightly higher than that of EBF (9.01%). Notably, EBT exhibited the lowest ash content at 4.94% (\(p<0.05\)).  
Protein content was significantly higher (\(p<0.05\)) in EPF (11.2%) and EPT (10.4%) compared to EBF (8.3%) and EBT (4.5%). [PERSON] et al. (2017) reported that the protein contents in the aerial parts of _E. martiimum_ and _E. campestre_ were 13.97 and 10.77 g/100 g, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameters (\%)**} & \multicolumn{2}{c}{_E. billardieri_} & \multicolumn{2}{c}{_E. planum_} \\ \cline{2-5}  & **EBT** & **EBF** & **EPT** & **EPF** \\ \hline Dry matter & 93.2 \(\pm\) 1.0 a & 93.3 \(\pm\) 0.0 a & 92.0 \(\pm\) 0.1 a & 93.1 \(\pm\) 0.2 a \\ Moisture & 6.8 \(\pm\) 1.0 a & 6.7 \(\pm\) 0.0 a & 8.1 \(\pm\) 0.1 a & 7.0 \(\pm\) 0.2 a \\ Ash & 4.9 \(\pm\) 0.4 c & 9.0 \(\pm\) 0.0 b & 10.3 \(\pm\) 0.17 a & 10.0 \(\pm\) 0.1 a \\ Protein & 4.5 \(\pm\) 0.2 d & 8.3 \(\pm\) 0.0 c & 10.4 \(\pm\) 0.1 b & 11.2 \(\pm\) 0.0 a \\ Fat & 0.5 \(\pm\) 0.1 b & 1.8 \(\pm\) 0.2 a & 2.0 \(\pm\) 0.0 a & 1.8 \(\pm\) 0.1 a \\ Carbohydrates & 83.3 \(\pm\) 0.4 a & 71.9 \(\pm\) 0.1 b & 71.5 \(\pm\) 0.1 b & 70.1 \(\pm\) 0.2 c \\ Crude fiber (\%) & 50.9 \(\pm\) 3.6 a & 34.5 \(\pm\) 1.6 b & 25.0 \(\pm\) 2.0 c & 22.0 \(\pm\) 3.1 d \\ Neutral detergent fiber (\%) & 71.8 \(\pm\) 0.9 a & 51.5 \(\pm\) 2.4 b & 44.5 \(\pm\) 0.0 c & 45.0 \(\pm\) 0.8 c \\ Acid detergent fiber (\%) & 48.8 \(\pm\) 0.5 a & 35.7 \(\pm\) 0.3 b & 29.7 \(\pm\) 0.0 c & 29.7 \(\pm\) 0.9 c \\ \hline \hline \end{tabular}  
* Values are mean \(\pm\) standard deviation (n = 3). Different lowercase letters in the same row are significantly different (\(p<0.05\)); EBF: _E. billardieri_ flowers; EBT: _E. billardieri_ thors; EPF: _E. planum_ thorns.  
\end{table}
Table 1: Analysis of the proximate compositions of the aerial parts of two studied plants *.  
Furthermore, EBF, EPF, and EPT did not exhibit statistically significant differences in fat content (_p_ < 0.05). However, the EBT had the lowest fat content (_p_ < 0.05).  
Table 1 also presents data on crude fiber, NDF, and ADF, which are important parameters for assessing total cell wall and lignocellulosic content. The EBF exhibited slightly lower levels of crude fiber, NDF, and ADF compared to EPF. Notably, the EBT displayed the highest concentrations of crude fiber (50.9%), NDF (71.8%), and ADF (48.8%) among the analyzed samples. This increased content may be related to the higher presence of thorns in the _E. billardieri_ variety. Thorny structures may comprise a significant portion of the cell wall, which plays a critical role in providing strength and rigidity to the plants. Previous research has shown that these components constitute a substantial fraction of a plant's dry weight, primarily consisting of cellulose, hemicellulose, and lignin [18].
## 4 Experimental Results  
We implemented the SAC program with Python 3. The knowledge-base training data and the chat score log data are both configured in YAML (yet another multicolumn layout) format. Moreover, YAML is a data-serialization language readable by both humans and machines, which makes it generically suitable for our task. For the trigram module's CWS processing, we set the text encoding to UTF-8 to accommodate Chinese texts. In our approach, we used the double blind assessment method to verify the correctness of the proposed SAC method. The weight factors \(f_{A}\), \(f_{B}\), and \(f_{C}\) for SAC's FBC assessment score computation were originally assigned the values 0.6, 0.25, and 0.15, respectively, according to the directives of the doctors. Later on, the values of \(f_{A}\), \(f_{B}\), and \(f_{C}\) were slightly adjusted after we received the doctors' assessment score of each user. Other model hyper-parameter settings for the training were as default in ChatterBot python library. Due to data size limitation, we trained the SAC with the entire FBC Q&A medical data availed by the research sponsor CGMH.  
Inasmuch as the performance outcome of text-based dialog models are popularly evaluated with a variety of computer-based metrics such as in [37], etc., we however subjected the performance of the SAC to expert human evaluators. According to [PERSON] et al. [22], human physicians are the traditional benchmark for treating patients over many centuries. Thus, their keen evaluation and approval of a medical chatbot's performance is crucial. For this reason, the SAC model's scoring mechanism (S\({}_{1}\)) was fine-tuned to meet the doctors' scoring standard (denoted as \(S_{2}\)). The doctors used 135 anonymized patients' FBC Q&A screening data to evaluate the symptomatic assessment scoring capabilities of the SAC. The subsequent approval of SAC for clinical trial by IRB of Chang Gung Medical Foundation reflects the satisfaction of the evaluators with SAC's high accuracy and stable performance outcome.  
Figure 6: An overview of the SAC dialog system architecture. Herein, \(e_{A}\) represents the user’s input answer embedding, whereas \(e_{Q}\) represents the agent’s output question embedding.
## 5 Camera Placement based on HybVOR Approach
### Generating Voronoi Diagram from Buildings
#### 5.1.2 Providing a Better Observability for Buildings Facades and Entrances  
In order to have a better observability, objects in the environment have to be as a large as possible in the image or the video [6]. To achieve this goal, it is necessary to minimize the \"Foreshortening effect\". This effect impacts the quantity of information that may be extracted from an observation [1]. The foreshortening effect appears when the angle between the camera's view and the object becomes increasingly smaller. Accordingly, the projection of the object on the camera's image plan will decrease and the monitored object will appear shorter on the image [6]. Figure 7 highlights the impact of the foreshortening effect.  
In the case of monitoring a built environment, and taking into consideration the possible minimum number of cameras, the placement of a camera closer to a building than the facing one will increase the foreshortening effect when the camera is directed to the farthest building. The appearance of the shortening effect may be caused by the large distance of the farthest building compared to the nearest one, especially if the angle between the camera's view and the facade of the farthest building to be monitored is small. Therefore, deploying cameras on Voronoi edges will optimize foreshortening effects in both sides, and hence, provide better observability for building facades and entrances.
## 3 Results
### Statistical Test of EEG Switching Rate
#### 3.3.1 ANOVA Test  
In this section, the one-way ANOVA test was given to analyse the difference of inter-hemispheric switching in three groups.  
1. Test of data normality and variance homogeneity In order to perform the ANOVA test, two conditions need to be satisfied: (1) data normality and (2) variance homogeneity. Thus, we reported the test results of these two conditions for Groups and Sleep Stages, respectively. For normality test, \(p\)-value is the probability of observing a test statistic as extreme as, or more extreme than, the observed value under the null hypothesis. Small values of \(p\) cast doubt on the validity of the null hypothesis. Also, h = 1 indicates the rejection of the null hypothesis, i.e., the data in vector x comes from a distribution in the normal family, while h = 0 means a failure to reject the null hypothesis. Our experiments reported that the switching rate for both Groups and Sleep Stages are normally distributed, with h = 0 and \(p>0.05\) for both considerations. Besides,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Wake** & **Light Sleep** & **Deep Sleep** & **REM Sleep** \\ \hline Group A & 0.2497 \(\pm\) 0.0041 & 0.3009 \(\pm\) 0.0041 & 0.2316 \(\pm\) 0.0198 & 0.3537 \(\pm\) 0.0077 \\ Group B & 0.2093 \(\pm\) 0.0024 & 0.2319 \(\pm\) 0.0035 & 0.1785 \(\pm\) 0.0178 & 0.3293 \(\pm\) 0.0086 \\ Group C & 0.1505 \(\pm\) 0.0049 & 0.1800 \(\pm\) 0.0081 & 0.1541 \(\pm\) 0.0232 & 0.2267 \(\pm\) 0.0180 \\ \hline \end{tabular}
\end{table}
Table 2: Mean Values \(\pm\) Variance of Switching Rate in Three Groups.  
Figure 6: LI distribution for different sleep states.  
since the \(p\)-value for homogeneity test is also bigger than 0.05, with \(p\) = 0.9896 for Groups test and \(p\) = 0.2204 for Sleep Stages test, it also reveals that the data satisfies variance homogeneity.
2. ANOVA test Based on the test results of data normality and variance homogeneity, the ANOVA test for three groups and four sleep stages were given in this section. First of all, the one-way ANOVA test was given to test the inter-differences among groups or sleep stages. Results showed that the \(p\)-values for both tests were 0.0012 (\(p1<0.05\)) and 0.0026 (\(p2<0.05\)), respectively, which indicates that the differences among groups or sleep stages were significant. Figure 7 illustrates the boxplot of data distributions for Figure 7a Groups and Figure 7b Sleep Stages. It also clearly reveals the inter-differences in each subfigure. Also, since we were considering two factors, i.e., groups (AHI) and sleep stages that may affect the switching rate, we also performed the two-way ANOVA to give a more comprehensive difference test for both factors. The results were given in Table 3. The \(p\)-values for both factors also agrees with the previous one-way ANOVA test, but it also reveals that there is no evidence of an interaction effect of the two.
## 7 Conclusions  
The problem of decision fusion for distributed detection under the non-ideal communication channel in WSNs is investigated in this paper. As the BER is able to characterize all uncertainties of the received data at the FC, an LRT-based fusion rule with non-ideal channel using BER is proposed. When the local detection performance indices of sensors are equal to each other, the closed-form system detection performance is derived. When the local detection performance indices of sensors are not identical, the CLT approximation and the deflection coefficient are used to describe the detection performance. Numerical results are presented to demonstrate the performance of the LRT-BER fusion rule. Furthermore, the performance of the proposed LRT-BER is compared to that of MRC, EGC, two-stage CV and LRT-CS. The proposed LRT-BER fusion rule provides the most robust detection performance compared to the mentioned fusion rules. However, the local detection performance indices and the received SNR are required for the proposed LRT-BER. The fusion rule under unknown local detection performance indices and SNR will be studied in our future work.  
Figure 8: ROC curves of LRT-BER, MRC, EGC and LRT-CS statistics with different local detection probabilities and different received SNRs by using the CLT approximation. The local false alarm probability is \(P_{fa}=0.1\). BPSK modulation and the Rayleigh fading assumption are considered.
## 3 Methodology
### Signal-to-Noise-Ratio-Based Subtractive Clustering Algorithm (SN-SC)  
The SN-SC algorithm introduces a clustering approach that integrates considerations of both local density and SNR, as outlined in Algorithm 1. In the SN-SC method, each data point is considered a potential cluster center with the maximum local density, where the density measure \(D_{i}\) is calculated as follows:  
\[D_{i}=\sum_{k=1}^{N}\exp\left(-\frac{\|x_{i}-x_{k}\|^{2}}{K_{a}\cdot r_{a}{}^{2 }}\right),i=1,2,\cdots,N. \tag{4}\]  
The SN-SC algorithm relies on the 'density' metric to identify initial cluster centers within a neighbourhood radius (\(r_{a}\)), which is defined as \(\sqrt{\frac{1}{N}\sum_{i=0}^{N-1}|x_{i}|^{2}}\). \(r_{a}\) also represents the average noise power amplitude. If the radius is too small, potential data points near the cluster center may be overlooked. Conversely, setting the radius too high increases the contribution of all potential data points, including noise, which can distort the modulated signal and affect the density radius parameter of the constellation diagram. The coefficient \(K_{a}\) represents the adjustment weighting of different types of modulated signals. Choosing an appropriate scaling factor, \(K_{a}\), enables the algorithm to attain a relatively stable density metric across the entire SNR range. Determining the optimal value of \(K_{a}\) in practical engineering often involves conducting multiple experiments.  
```
Step  Operation Input:\(\mathbf{x},\lambda,\text{SNR}\) Output:\(\mathbf{z}\) Initialization \(cl=1,\text{Flag}=0\), \(r_{a}=\frac{p_{s}}{\text{SNR}+1}\) for\(i=1:N\) \(D_{i}=\sum_{k=1}^{N}\exp\left(-\frac{\|x_{i}-x_{k}\|^{2}}{k_{a}\cdot r_{a}}\right)\) end \(D_{cl}=\max\{\mathbf{D}\}\), \(z_{cl}=x_{cl}\) while Flag \(=0\) for each \(i\) data point \(D_{i}=D_{i}-D_{cl}\cdot\exp\left(\frac{\|x_{i}-x_{cl}\|^{2}}{k_{b}\cdot r_{a}}\right)\) \(cl=cl+1\) repeat step 3 if\(D_{cl}<\lambda D_{1}\), Flag \(=1\) return \(\mathbf{z}\)
```  
**Algorithm 1** SN-SC Algorithm  
Each data point updates its density based on the obtained center for the \(l\)th cluster (\(x_{cl}\)) as follows:  
\[D_{i}=D_{i}-D_{cl}\cdot\exp\left(\frac{\|x_{i}-x_{cl}\|^{2}}{K_{b}\cdot r_{a}} \right), \tag{5}\]  
where \(D_{cl}\) represents the density of the data point (\(x_{cl}\)), and \(K_{b}\) is a value greater than one to avoid obtaining closely spaced centers. The algorithm selects the data point with the maximum density as the new center of the cluster, denoted as \(x_{cl+1}\). The algorithm repeats the process until it meets the condition \(D_{cl+1}<\lambda D_{1}\), signaling the termination of the cluster center decision. In this condition, \(\lambda\) is a given parameter (\(0<\lambda<1\)).
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 1 Introduction
### Literature Survey  
Due to the special configuration of the induced lift aircraft, there are few studies in this aspect. There are many studies on vertical take-off/landing, including aerodynamic characteristics calculation, dynamic modeling, and flight performance. [PERSON] and [PERSON] [4; 5] studied the theoretical calculation model of the coaxial dual rotor paired lift fan, established the aerodynamic calculation model of the coaxial dual rotor based on the blade element momentum theory, and studied the optimal blade matching of the upper and lower rotors of the coaxial dual rotor through the theoretical model. [PERSON] and [PERSON] [6] established a six-degree-of-freedom nonlinear equation for lift-fan-type vertical take-off and landing UAV and carried out simulation based on the established mathematical model. Taking tilt-rotor aircraft as the research object, [PERSON] [7] modeled the longitudinal aircraft and established the aerodynamic model of the aerodynamic components of the aircraft and carried out the balancing and stability analysis of the transition stage according to the established longitudinal model. Taking tilt-rotor aircraft as the object, [PERSON] [8] studied the determination method of tilt-rotor corridor envelope, established a dynamic model, and analyzed and calculated the case according to the model. According to the characteristics of the aircraft, the control strategy of tilting rotor is studied to meet the requirements of hover, low speed flight and transition flight mode. [PERSON] [9] established the dynamic theory model of tilt-rotor aircraft and conducted simulation research on the transition corridor. These works focused on the dynamics modeling and transition corridor research of tilt-rotor aircraft and lift fan aircraft. Thus, the dynamic modeling and transition corridor of the UAVs will be investigated. [PERSON] [10] proposed an adaptive fuzzy-based global sliding mode control strategy for quadrotor UAVs in robust trajectory tracking against parameter uncertainties and external disturbances. [PERSON] [11] provided an inverse kinematic solution for the main rotor actuating mechanism of a small-scale helicopter that includes a four-point swashplate system and a Bell-Hiller mixer and the tail rotor actuating mechanism are derived using an approach that is suitable for real-time control applications.