esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result
### Quantitative Analysis  
To quantitatively test whether there are significant differences in quality between couplets generated by the four methods, we conducted an ANOVA. We distributed 70 online questionnaires, which contained six of the same questions but with different couplets. The initial task was written as follows: \"Please refer to the personalized needs and rank the couplets in descending order of quality\". Since each set of couplets is generated using four different methods, corresponding scores of 4, 3, 2, and 1 were assigned (where 4 represents the highest quality and 1 the lowest).  
A total of 57 questionnaires were collected, with 50 valid responses in the final analysis after screening. The result is shown in Table 3. In the table, the row means the couplet and the average score, the column means generating methods. For the others, we mark 'base model' as M1, 'base model + ours' as M2, 'DeepSeek-V3' as M3, and 'GPT-4-turbo' as M4. For the couplets, we identify the generated couplets as CE1, CE2, CI1, CI2, CE11, and CE12 in the order of each line in Figures 5 and 6.  
The significance level is set at 0.05. We first conducted a homogeneity of variance test, which yielded a significance level of 0.583 (\(p>0.05\)). This result indicates that the data satisfy the assumption of homogeneity of variance, allowing for the application of one-way analysis of variance (ANOVA).  
The ANOVA results demonstrated a highly significant difference among groups (F = 12.261, \(p<0.001\)), suggesting that the quality of couplets generated by the four methods (M1-M4) differs significantly.  
Further pairwise comparisons using the LSD post hoc test revealed that method M2 exhibited statistically significant differences compared to method M1 (\(p<0.001\)), method M3 (\(p=0.001\)), and method M4 (\(p<0.001\)), all of which were below the 0.05 threshold. These findings indicate that method M2 significantly outperforms the other three methods (M1, M3, and M4) in terms of couplet quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **M1** & **M2** & **M3** & **M4** \\ \hline CE1 & 2.52 & **2.72** & 2.36 & 2.4 \\ CE2 & 2.4 & **2.78** & 2.42 & 2.4 \\ CI1 & 2.2 & **2.76** & 2.6 & 2.44 \\ CI2 & 2.48 & **2.64** & 2.46 & 2.44 \\ CE11 & 2.26 & **2.7** & 2.4 & 2.64 \\ CE12 & 2.4 & **2.58** & 2.56 & 2.46 \\ \hline \hline \end{tabular} The bolded data indicates the highest score in this group.  
\end{table}
Table 3: Statistical result.
###### Abstract  
Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.  
automatic train operation system (ATO); radial basis function neural network (RBFNN); adaptive terminal sliding mode control (ATSMC); fault-tolerant control (FTC); tracking error +
Footnote †: journal: Applied system  
12 July 2021  
Accepted: 2 August 2021  
Submitted: 5 August 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods  
### Data Description and Preprocessing  
The power generation data of the Canadian solar, 5.3 kW, polysilicon, stationary photovoltaic plant was obtained from the Australian (DKASC) photovoltaic center as the experimental data for this study. The data span from 1 January 2020 to 31 December 2022 with a time interval of 5 min, and 288 sets of data values can be collected per day. The dataset includes timestamp, Active_Energy_Delivered_Received, Current_Phase_Average, Active_Power, Performance_Ratio, Wind_Speed, Weather_Temperature_Celsius, Weather_Relative_Humidity, Global_Horizontal_Radiation, Diffuse_Horizontal_Radiation, Wind_-Direction, Weather_Daily_Rainfall, Radiation_Global_Tilted, Radiation_Diffuse_Tilted, etc.  
Through observation data and data detection, the data power data at right are 0 or negative values, and there were vacant values in some rows of data. Therefore, these data values were deleted in this study and the timestamp and empty columns weed_speed that have little relevance to the experiment are deleted. Finally, 130,941 sets of data were left, and the dataset was divided into a training set (104,752 sets of data) and a test set (26,179 sets of data) at a ratio of 8:2.  
The data information of the power time series training set and test set is shown in Figure 1. The corresponding statistical information, including mean, minimum, maximum, standard deviation, skewness, and kurtosis are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mean** & **Minimum** & **Maximum** & **Standard Deviation** & **Skewness** & **Kurtosis** \\ \hline
2.40 & \(3.33\times 10^{-5}\) & 5.47 & 1.51 & \(-0.16\) & \(-1.39\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data statistical information.
## 3 Design of an Unconventionally Shaped Wideband DRA for IoT Communications  
The process of designing the antenna has been carried out with two goals in mind. The first was to obtain a DRA exhibiting a wideband behavior that allowed it to be suitable for IoT communications. For this reason, the operating frequency range was chosen to be between 2.4 GHz and 3.8 GHz, thus enabling applications in both the ISM band and in the forthcoming 5G sub-6 GHz band. The second goal was to miniaturize the overall structure as much as possible while preserving good radiating performance.  
In order to match these needs, a complex design composed by two rectangular elements of increasing size, stacked up one on the other, has been studied as a base to achieve a wideband behavior. Indeed, it is well known how this kind of DRA composed of different substrates can fulfill this purpose [24]. Moreover, to achieve a LP DRA design, while enlarging the bandwidth, the technique of using a metallic top load onto the upper part of the resonator was adopted, as suggested in [19; 25]. Finally, a rounded fillet has been applied to the edges at the center of the resonator, in order to increase the gain and slightly increase the bandwidth, by smoothing the transition of the electromagnetic field from one element to the other of the stack.  
A comparison between the simulated S\({}_{11}\) curves of the three previously discussed designs--the one with two stacked elements, the one with the addition of the metallic top load, and the final one with the further addition of the rounded fillet to the central edges--is shown in Figure 2.  
As for the feeding method, a microstrip feed slot has been centered at the bottom of the dielectric resonator. This technique was chosen both to easily excite the TE\({}^{Z}\)\({}_{111}\) mode in the resonator, so producing a field oriented in the normal direction of the antenna main surface, as well as for easiness of fabrication. Its dimensions are 37 \(\times\) 6 mm\({}^{2}\), while the distance between the end of the microstrip and the nearest edge of the slot is 8 mm. If we consider the slot without the DR on top of it, it exhibits a good S\({}_{11}\) matching at both 4.1 GHz and 6.3 GHz.  
Due to the complexity of the antenna design, an accurate analytical analysis of the resonant frequencies was not possible, so a deep simulation campaign has been carried out to optimize it, using the full-wave software CST Microwave Studio. A graphical overview of the final design can be observed in Figure 3, where both a rendered view of the antenna as well as a technical drawing reporting all the tuned dimensions are depicted. As can be seen, the rectangular elements of the DRA have been considered to have a square base, so as to simplify the optimization procedure by maintaining the ratio between the base sides as constant and equal to 1. Moreover, the low profile is achieved with a ratio between the height and the maximum width of the DRA, which resulted as 1:4.  
Figure 2: Trend of the keyword “3D printed antennas” occurrences of the last ten years in the Google Scholar database.
## 3 Establishment of Core Indicator System
### Initial Indicator System
#### 3.1.2 Systematic Clustering  
In this paper, the systematic clustering method was used to classify the 41 indicators initially screened to achieve the secondary screening of the initial indicators. In this paper, the clustering of the preliminary screening indicators was realized by using IBM SPSS Statistics 26 software, and the indicators were classified into five categories, namely, very important, relatively important, generally important, not too important and unimportant. Principle implementation: Forty-one samples were divided into five classes, each sample was a class of its own, and then each time the two classes with the smallest distance were merged and after the merger, the distance between classes was recalculated, and this process continued until all the samples were grouped into one class. This process was drawn into a cluster diagram, which can be easily classified with reference to the cluster diagram. In this paper, SPSS was parameterized as follows: Since the indicators were grouped into five categories and the range of the clustering scheme was set to 5, the clustering method used intergroup linkage, which is capable of handling large-scale datasets and different types of data, i.e., the interclass distance is the average of the squared Euclidean distances between two data points in two classes and is normalized using the Z-Score. Icicle plots were obtained as shown in Figure 3.  
The figure above shows the cluster analysis, with the horizontal coordinate being the name of the indicator and the vertical coordinate being the number of divisible categories. Each sample name corresponds to a blue long bar, and each sample long bar has the same length. There is also a blue long bar sandwiched between every two sample long bars, the length of which indicates the similarity of the two samples. The icicle diagram should be analyzed from the lowest end, with the classes separated from each other by a white gap. If there is no white gap between two indicators, it means that these two indicators are one class. The clustering results obtained are shown in Table 2.  
Among them, the first category is very important, containing 25 indicators; the second category is relatively important, containing one indicator on whether or not network problems have been encountered; the third category is of general importance, containing one indicator on the number of times of disconnection from the network; the fourth category is of less importance, containing three indicators on complaints about tariffs, whether or not to care for the users and complaints about home broadband; and the fifth category is of little importance, containing 11 indicators. After systematic clustering analysis, this paper carries out preliminary clustering for the initial screening of indicators for voice service user ratings and analyzes the clustering results obtained from Figure 3 and SPSS, retaining the indicators of the three classes of very important, relatively important and generally important, i.e., retaining the 28 core indicators, as shown in Table 3.
# Structural Design, Simulation and Experiment of Quadruped Robot
## 2 Structure Designs
### Topological Structure of the Quadruped Robot  
While there is a variety of topology forms for quadruped robot design, the 8, 12 or 16 degrees of freedom (DOF) designs are commonly used. The 8-DOF structure contains four hip joints and four knee joints, with all joint axis parallel to each other. The 8-DOF robot can move forwards or backwards very fast and is relatively easy to control, without the transverse swing freedom of the hip joint. However, the 8-DOF quadruped robot cannot achieve transverse movement and its steering capability is rather limited, leading to poor motion performance. The 16-DOF quadruped robot, on the other hand, has more joints for dexterous locomotion, but it is also more difficult to control. In light of this, the 12-DOF design, with three rotational joints for each leg, is used for our design.  
In terms of leg configurations, the inner knee-elbow, outer knee-elbow, full-knee and full-elbow are most popular. The first three leg configurations have a completely symmetrical leg structure, so the robot is more stable, while the last two have more space for movement and are easier to control. Therefore, the full-knee configuration is chosen in this paper. The simplified structural topology is shown below in Figure 1, where the hip joint has both lateral swing and longitudinal swing freedoms, with another swing freedom of the knee joint.  
Figure 1: Structure topology of the 12-DOF quadruped robot design (front view).
## 4 Results and Discussion
### Fluctuating Wind Characteristics
#### 4.2.3 Turbulent Integral Scale
When the oncoming wind direction is \(180^{\circ}\), the turbulent integral scale is basically unchanged with the variation in wind speed. When the oncoming wind is ABL inflow, due to the blocking effects of roughness elements, wedges, and terrain, the distribution of turbulent integral scales at the main beam is complex, and the variation with oncoming wind speed is inapparent. \[\frac{fS_{u}(f,z)}{\sigma_{u}^{2}}=\frac{4 fL_{u}}{\left[1+70.8\left(\frac{fL_ {u}}{U}\right)^{2}\right]^{5/6}} \tag{6}\]  
where \(S_{u}\), \(\left<\!f,\!z\right>\) is the fluctuating wind spectrum at a height of \(z\), \(L_{u}\) is the longitudinal turbulent integral length scale, \(\sigma_{u}\) is the standard deviations of the longitudinal components of the fluctuating wind speed, and \(U\) is the mean wind speed. Figure 40: Turbulent integral scale of the northeast tower under ABL inflow with different wind speeds and directions. Figure 39: Turbulent integral scale of the northeast tower under uniform inflow with different wind speeds and directions.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 5 Experimental Results
### Experiments with Constant Irradiance  
In these experiments, the irradiance and temperature remained almost constant. They were useful to see the behavior of the controllers in stable conditions. In the first case, the P&O controller was observed with an irradiance of 1000 W/m\({}^{2}\) and a cell temperature of 59 \({}^{\circ}\)C (see Figure 18). In the first instant, a jump in power could be observed. This was due to the fact that, during the periods without measurement between experiments, the duty cycle was maintained at 0.1 for protection. When the measurements started, the controllers  
Figure 17: Simulation of the SMC controller in response to a step change in irradiance. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Measured PV voltage and reference PV voltage. (**e**) Duty cycle.  
were initialized in a duty cycle of 0.5 to start looking for the MPP from that moment. The jump in the duty cycle from steady state to initialization is what caused the first abrupt jump. Then, the controller started its search for the MPP and reached that point after 30 s. This time can be reduced by increasing the step size of the controller, but doing so leads to larger oscillations around the MPP, which reduces its efficiency. Therefore, it was necessary to reach a compromise between the speed and the oscillations of the controller. The control continued at the MPP with slight oscillations around it, as was expected in this controller. The PV power reached a maximum of 265 W during the experiment. The reduction with respect to the nominal power of the PV module was due to the high temperature in addition to the defects in the panel, as well as due to there being no ideal conductors or accumulated dirt, among other reasons.  
Figure 19 shows the graphical results of the SMC controller under a constant irradiance scenario. In this case, the irradiance has a value of 1008 W/m\({}^{2}\), and the temperature matches with the previous one, i.e., 59 \({}^{\circ}\)C. Again, there was a jump from the steady state to the initial condition in the duty cycle at the beginning of the experiment. The time to reach the MPP was about 30 s, which is very similar to the P&O case. Similarly, increasing the controller pitch can reduce this time, albeit at the cost of higher oscillations. The PV panel power reached 265 W, as with the P&O, showing that both controllers completed the task of achieving the MPP in a reasonable time under the constant irradiance scenario.  
Figure 18: The experiment results of the constant irradiance and temperature conditions for the P&O controller. **(a)** Cell temperature. **(b)** Solar irradiance. **(c)** PV power. **(d)** Duty cycle.
# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
## 4 Results
### Discussion  
The case study has a rather limited number of parameters, allowing for the development of a simple sorting algorithm based on statistical methods. This algorithm is compared with a more advanced and complex method based on neural networks. As highlighted in the previously paragraphs, both methods are capable of distinguishing between healthy and unhealthy hazelnuts. The simplicity of the algorithm, referred to as 'classic', and its relatively low computational effort are evident. However, this simplicity comes at the cost of a system with less adaptability and greater susceptibility to variations in the measurement system. Calibration is required for each change in the measurement configuration.  
On the other hand, the sorting system based on the use of neural networks is more flexible and has greater potential. In this case, nine transmission values acquired for each hazelnut represent the input of the network. For this initial test, a 2-by-2 grid with a total of four neurons was employed. The neural network successfully formed a homogeneous cluster, encompassing most of the healthy hazelnuts, and a non-homogeneous cluster (highlighted by darker colors), associated with rotten, shriveled, and empty hazelnuts. The increased development effort and computational complexity are undoubtedly outweighed in the long run by the flexibility and potential of the method.  
For example, the ability to distinguish between different types of unhealthy hazelnuts and the ability to search for specific types of defects, particularly those within the clusters, makes the system attractive for more in-depth studies and developments.  
If a more rudimentary and cost-effective system is desired, capable of simply selecting the 'good' hazelnuts or eliminating the 'bad' ones, the system based on the traditional algorithm may still be of interest.
## 3 Results and Discussion
depletions due to the travel ionospheric disturbances, F-layer disturbances, and reduced density owing to the spread of ions by the Fountain effect. Moreover, attributing the TEC depletion characteristics to the prevailing EPBs is supported by a strong agreement with the high-intensity scintillation indices in our study. It is important to mention that the shallow sTEC depletions correspond to the weak or no-scintillation intensity in all the above PRNs and constellations, emphasizing the weakening of the EPB after the local midnight periods that is in agreement with the  
Figure 16: (**a**) The S4 (blue) and \(\sigma_{\varphi}\) (orange) of Signal 3 for all the available PRNs showing the TEC depletions in the GPS, Galileo, and BeiDou constellations between 1500 UT and 1800 UT on the 23 April 2023 geomagnetic storm day. (**b**) The PRNs of the GAGAN and NavIC in constellation having severe scintillations (S4 (blue) and \(\sigma_{\varphi}\) (orange)) between 1500 UT and 1800 UT on the 23 April 2023 geomagnetic storm day. previous studies [91]. The upward E \(\times\) B drift is a crucial factor in the investigation of ionospheric scintillations because it triggers plasma density anomalies, which produce greater scintillations at far low-latitude locations than at the magnetic equator [9]. The present study at a low-latitude location enabled us to emphasize the strong relationship of the TEC depletion and scintillation indices which are altogether associated with the EPBs and are responsible for degrading the performances or even causing loss of locks in satellite-based services.
## 5 Experimental Results
### Imbalance Learning Performance Comparative Analysis
#### 5.4.2 F-Measure  
For F-measure results presented in Table 4, we can observe that, the best average of F-measure is still achieved by margin based bagging. The achieved improvement of our algorithm is about 6% (data set _Hayer-roth_) compared to UnderBagging and about **10%** (data set _Pendigit_) with respect to SMOTEBagging. Moreover, unsupervised margins slightly outperform supervised margins in our method. In addition, for the binary data _Wilt_ and multi-class data _Page-blocks_ which is with the imbalance ratio of up 175, all the improved bagging methods lose effectiveness. This means that imbalance classification algorithms still face great challenges in avoiding hurting the accuracy of majority class when increasing the accuracy of minority class in the case of very high imbalance rate.