esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Virtual Environments to Assess the Quality of Public Spaces
## 1 Introduction  
This article focuses on the research of a practical application that combines the human perspective with virtual reality technology to assess and understand public spaces for the purpose of enhancing their design and functionality. By public spaces we mean two types of urban realm: sidewalks--which are often contiguous to a road network and mixed with cycling infrastructure--and public places--which include gathering places and parks.  
User experience and perception assessment involves capturing and analyzing how users perceive and experience public spaces; it includes factors such as aesthetics, safety, accessibility, and overall satisfaction. A human-centric approach considers the experiences, perceptions, and needs of individuals who use or interact with public spaces. It is traditionally conducted through surveys with an on-street questionnaire, which asks people walking and spending time on a street about how they perceive the street. It is a more reliable method since the participants are inside the physical environment but it can be costly and time-consuming.  
It can also be done through the exploitation of street view imagery, such as Google Street View, which can be panoramic, or through the use of more personalized videos orstill image acquisitions of the scene to be assessed. These types of methods of public space auditing cannot be done for prospective projects not yet constructed, but their methodology can be useful to assess future designs based on 2D drawings or VR setups. The utilization of VR technology suggests a forward-looking and technologically advanced approach to public space assessment. This can provide a more immersive and interactive experience for participants compared to traditional assessment methods.  
The study may involve collecting data on user interactions and preferences within the virtual environment. Analysis of this data, through algorithms leading to high-level indicators, can contribute to insights into how people respond to different elements of public spaces. Findings from such assessments could have implications for the design and planning of public spaces, influencing decisions related to architecture, landscaping, and overall urban design. Indeed, the use of VR for public space assessment allows for potential applications in pre-occupancy evaluations, allowing planners and designers to anticipate the impact of proposed designs before physical implementation.
## 1 Introduction
The authors in [19; 20] considered resource active management in ADN planning. Additionally, ESSs offer new opportunities in ADN and can be utilized in active network management in order to improve the decision making on the network expansion planning [21; 22]. However, as an important active management method, network reconfiguration is ignored in the existing research. To solve the above research gaps, a robust planning model considering network reconfiguration and \(DG\) permeability is proposed in this paper. Significantly, distribution network reconfiguration means to change the network topology by changing the section switch and tie switch of the network, so that the distribution network can achieve the best power flow distribution under the constraints of the system, so as to achieve a balanced load, meet the requirements of the new load, and increase the voltage stability, the purpose of minimizing network loss [23]. Under the active management mode, the distribution network has multiple active management measures. As an important active management measure to optimize power flow and reduce network loss, network reconfiguration should be considered in the distributed generation planning stage. The essence of distribution network reconfiguration is to change the topology of the distribution system according to the load change, and to solve the optimal combination of the opening and closing states of the tie switch and the section switch under certain constraints. Therefore, the distribution network reconfiguration is a \"0-1\" combination of tie switch and section switch states. Therefore, the specific structure of the switch-gear is not be introduced. Therefore, the main contributions of this paper are as follows:  
1. The \"wind-light-load\" uncertain sets are developed to describe the uncertainty of wind speed, light intensity and loads. Moreover, the \(DG\) permeability indicator is introduced in the planning model. 2. Network reconfiguration, as an important operation means of active distribution, is considered in the planning model to optimize the location and capacity of \(DG\). The planning scheme dispatches the existing resources in the active distribution network to meet the load demand, thus avoiding the redundancy of planning resources as much as possible. 3. The two-level optimization is utilized to decompose the planning model. At the same time, a linearized AC power flow model is utilized to calculate the power flow. Therefore, the planning level and operating level are mixed integer optimization models. The remainder of this paper is organized as follows. The framework for \(DG\) planning is proposed in Section 2. The wind-light-load uncertain sets are developed in Section 3. Then, the robust programming model for \(DG\) planning is represented in Section 4. Section 5 verifies the effectiveness of the proposed model and demonstrates the simulation results. Conclusions are given in Section 6.
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### Support Vector Regression Forecasting Model  
Support vector network is a branch of machine learning that analyzes data with respect to operational learning techniques. Support vector regression (SVR) uses the principles of support vector machine (SVM), except the fact that SVR adjusts the prediction function with the threshold error. SVR tries to minimize the generalization error so as to achieve generalized performance. On the contrary, most of the other regression techniques try to decrease the observed error between the forecasted value and the original value. SVR is the most common application of SVM. SVR can be applied for time series prediction, financial forecasting, estimation of challenging engineering tasks, etc. SVR is classified into linear, polynomial, and rbf kernel. Due to its high accuracy, the rbf kernel model is considered in this paper.  
In this paper, \(\varepsilon\)-SVR was implemented, and the value of \(\varepsilon\) was set as 0.2. The regularization parameter 'C' and the gamma '\(\gamma\)' were defined through grid search.  
The train test split function was used, with random classification of 70% of data as train set and using the remaining 30% as test set. The train set was fitted using rbf kernel, and forecasting of the data was achieved. The forecasted data was compared with the test set to verify the result, and mean square error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) [35; 36] indices were calculated. The following steps were implemented for the SVR model forecasting, as also shown in Figure 3:  
**Step 1:**: Create Testing and training datasets with the train_test_split function. Here, we divided it into 70% training dataset, 30% testing dataset.
**Step 2:**: Build the support vector regression model using SVR function for RBF kernel with appropriate parameters for the training dataset.
**Step 3:**: Forecast the consumption values for the testing dataset.
**Step 4:**: Plot the actual and forecasted values of the testing dataset.
**Step 5:**: Calculate MSE, MAE, and RMSE.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 6 Performance Evaluation
### Sensors  
The I3 Mote integrates several low-power sensors including ambient light, magnetic sensor, humidity, pressure, accelerometer, magnetometer, object temperature and ambient temperature.  
The current drained by the acquisition block can reasonably be approximated by a weighted average of the sleep and the active state of the sensors over the period \(T\), according to Equation (2) (see [26] for a detailed description):  
\[I_{\text{SENSOR}}=\frac{I_{\text{ACTIVE}}\cdot T_{\text{S}}+I_{\text{SLEEP}} \cdot(T-T_{\text{S}})}{T}. \tag{2}\]  
Table 4 summarizes the consumption of the different sensors, both in the active state \(I_{\text{ACTIVE}}\) and in the low-power state \(I_{\text{SLEEP}}\), along with the typical time to read a sample \(T_{\text{S}}\). Based on these values, Figure 10 shows the combined consumption of the sensor block for different sampling periods \(T\), obtained from Equation (2).  
\begin{table}
\begin{tabular}{l c c c l} \hline \hline
**Sensor** & \(\mathbf{I_{ACTIVE}\) [\(\mu\)A]} & \(\mathbf{I_{SLEEP}\) [\(\mu\)A]} & \(\mathbf{T_{S}\) [ms]} & **Configuration** \\ \hline TMP007 & 270 & 2 & 260 & \\ HDC1080 & 190 & 0.1 & 6.2 & Temperature Sample 11 bit; Humidity 8 bit \\ OP3001 & 3.7 & 0.4 & 100 & Full Scale Range Lux \\ BMP208 & 720 & 0.1 & 5.5 & 1 Pressure + 1 Temperature Samples \\ LIS2H12 & 180 & 5 & 10 & ODR=100 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 4: On-board sensors power consumption.  
Figure 9: Power consumption as a function of the number of child nodes. On the left, the figure shows a possible configuration in which node 2, in addition to its own traffic, retransmits the traffic of 3 additional children nodes. The figure on the right shows the increment in energy consumption caused by the additional children.
## 2 Architecture of Dima System
### Hardware Layer
#### 2.2.5 Remote Data Concentrator  
Remote Data Concentrator (RDC) connects sensors and actuators through ADCN with CPMs. RDC is usually placed near sensors and actuators so that fewer cables are needed. It translates signals between different protocols such as Controller Area Network (CAN) and Inter-Integrated Circuit (I2C) of peripherals.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 3 DHT-IT2 FLC Design
### TI-FLC Design  
1. The MF of T1 FLC is set as shown in Figure 6. For two inputs e, \(\Delta\)e, and one output k, the five language variables NB, N, Z, P, PB are set. The NB language value of k is set to a constant of 0 instead of an interval, representing the disconnection of the integrator.
2. The rule base setting.  
Before setting up the fuzzy rule base, the judgment mechanism of type switching should be analyzed. Through a large number of previous experiments, it is found that when e is large, the access integrator can easily lead to system divergence. When \(\Delta\)e is large, the system error will change rapidly; at this time, the interruption of integrator should be handled carefully, otherwise the system divergence will easily occur. When e is near  
Figure 5: The DHT-IT2 FLC structure.  
Figure 6: The T1 FLC membership function (MF): (**a**) MF of ‘e’; (**b**) MF of ‘\(\Delta\)e’; (**c**) MF of ‘k’.  
the steady-state range, the access of the integrator should be avoided as much as possible; because there is noise in the actual system, its \(\Delta\)e is irregular. In this case, it is easy to connect the integrator and cause the system to deviate from the balanced position again. When e is in P and N state, it is the best time to connect the integrator, which can achieve a significant correction effect under the premise of system stability. Finally, the mechanism for determining type switching is shown in Figure 7, when e and \(\Delta\)e are in six states in the figure:  
* State 1: e is P, \(\Delta\)e is PB, and e will continue to increase. At this time, the integrator should be connected to intervene to make the output k value of FLC be PB so that the system can quickly return to the reference value.
* State 2: e is PB, \(\Delta\)e is P. To prevent integral saturation, disconnect the integrator.
* State 3: e is PB, \(\Delta\)e is Z. The system is about to approach the equilibrium position, and the integrator should be connected to speed up the response.
* State 4: e is PB, and \(\Delta\)e is N. The system approaches the equilibrium position at a faster speed. At this time, the integrator can be properly connected, but the gain cannot be too large.
* State 5: e is P and \(\Delta\)e is NB. It is safer to connect to the integrator in this situation.
* State 6: e is Z. As mentioned earlier, the integrator should be disconnected to prevent the system from deviating from the equilibrium position.
* Similarly, the same judgment mechanism is used when e is N and NB. Therefore, set the fuzzy rules of T1 FLC as shown in Table 1.  
Figure 7: The judgment mechanism of type switching.  
The defuzzification uses the COG method:  
\[y_{COG}=\frac{\sum_{l=1}^{M}y_{l}\mu_{A}^{l}(y_{l})}{\sum_{l=1}^{M}\mu_{A}^{l}(y_{ l})} \tag{23}\]
## 5 Discussion of Results and Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Input Data** & **ML/DL Methods** & **Classification Problem** & **Reference Standard** & **Results** \\ \hline \multicolumn{6}{c}{Quantitative Data and Clinical Data from Polar Maps} \\ \hline [PERSON] et al. [48] & Quantitative data & Boosted ensemble learning & Normal/Abnormal & Coronary Angiography & \begin{tabular}{c} Stress TPD: Accuracy 0.88, AUC \\ 0.94, Specificity 093, Sensitivity 0.81 \\ \end{tabular} \\ \hline [PERSON] et al. [45], 2020 & Clinical and quantitative data & \begin{tabular}{c} Ensemble LogitBoost \\ algorithm (ML) \\ \end{tabular} & Normal/Abnormal & Coronary Angiography & Accuracy 0.72, AUC 0.79 \\ \hline \multirow{4}{*}{[PERSON] et al. [47], 2019} & \multirow{4}{*}{\begin{tabular}{c} Clinical and \\ Quantitative data \\ \end{tabular} } & \begin{tabular}{c} Kernel-forward ANN \\ (multilayer perceptron) \\ \end{tabular} & \begin{tabular}{c} Absence/Presence of \\ ordinary \\ artery stenosis \(>\)50\% \\ in at least a vessel \\ \end{tabular} & Coronary Angiography & Accuracy 0.86, Specificity 1.00, Sensitivity 0.82 \\ \cline{3-6}  & & \multirow{2}{*}{\begin{tabular}{c} Feed-forward ANN \\ \end{tabular} } & \begin{tabular}{c} (2 classes) \\ Normal/Abnormal \\ angiography \\ \end{tabular} & Coronary Angiography & Accuracy 0.93, Specificity 1.00, Sensitivity 0.92 \\ \hline \multirow{2}{*}{[PERSON] et al. [55]} & Quantitative and \multicolumn{2}{c}{\begin{tabular}{c} Mountain data \\ functional data \\ \end{tabular} } & \begin{tabular}{c} SVM \\ \end{tabular} & \begin{tabular}{c} Absence/Presence \\ of coronary \\ artery stenosis \(>\)270\% \\ \end{tabular} & Coronary Angiography & \begin{tabular}{c} Stress TPD: Accuracy 0.86, \\ AUC 0.92, Specificity 0.88, \\ Sensitivity 0.84 \\ \end{tabular} \\ \hline [PERSON] et al. [49], 2010 & Quantitative polar maps & \
\begin{tabular}{c} ANN: multilayer perceptron \\ (WEXA), input layer of \\ 50 nodes, one hidden layer of \\ 5 nodes and one output node. \\ \end{tabular} &
\begin{tabular}{c} Absence/Presence \\ of coronary \\ artery stenosis \\ \end{tabular} & Coronary Angiography & AUC 0.74, Specificity 0.68, Sensitivity 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Related works in coronary artery disease classification using ML and DL techniques.
## 3 Results and Discussion
### Main Findings of This Study  
Based on the results obtained throughout the hyperparameter tuning, training, testing, and validation stages, as well as the nonparametric statistical tests, this study successfully met its primary objectives. The key findings can be summarized as follows:  
* Despite the relatively small dataset used for predicting dilution (_ELOS_) in underground mines and the low correlation of some independent variables with _ELOS_, the proposed methodology enabled the development of multiple machine learning models capable of accurately predicting _ELOS_ from the given dataset.
* The application of genetic algorithms (GA and AGA) with varying population sizes strengthened the hyperparameter tuning process, leading to models with improved generalization capabilities for unseen data.
* Incorporating machine learning models based on different paradigms and strategies provided a robust analytical framework, facilitating the assessment of the strengths and weaknesses of each model within the proposed scope.
* The performance limitations observed in certain models within this study should not be generalized, as machine learning models are highly sensitive to data characteristics and may perform differently when applied to other datasets or problem domains.
* Implementing a cross-validation procedure with 10 folds and 30 repetitions allowed for a more comprehensive analysis, ensuring that even a small dataset could be evaluated rigorously. The statistical tests confirmed the superiority of certain models, while refuting the existence of significant performance differences between others.
* The methodology emphasized the importance of a well-structured validation phase. A simple division into training and testing datasets may bias the results in favor of a specific model configuration, which, when exposed to different data partitions, may fail to maintain its performance.
* The study demonstrated that stacking metamodels built from distinct base models can be equally or even more effective than the use of individual models, as confirmed by the Nemenyi test results.
* Although AGA-ANN-1 (20) was identified as the best-performing model during the testing phase, it was not among the top models in two out of three Nemenyi test evaluations. This finding underscores the risk of relying solely on a single test dataset, particularly when dealing with small datasets, as it may lead to anomalies and models that do not consistently deliver optimal results.
## 3 Results and Discussion  
### Von Mises Force (VMS)  
The VMS is a mechanical parameter used to reflect the internal stress distribution of a structure [18], which is defined as follows:  
\[\text{VMS}=\frac{1}{\sqrt{2}}\sqrt{\left(\sigma_{1}-\sigma_{2}\right)^{2}+ \left(\sigma_{2}-\sigma_{3}\right)^{2}+\left(\sigma_{3}-\sigma_{1}\right)^{2}} \tag{7}\]  
where \(\sigma_{1}\) is the first principal stress, Pa; \(\sigma_{2}\) is the second principal stress, Pa; and \(\sigma_{3}\) is the third principal stress, Pa.  
The VMS value at the plaque has an effect on plaque rupture; the larger the value, the greater the risk of plaque rupture. In order to compare the effect of the circumferential distribution of the plaque on the risk of plaque rupture, the VMS at the plaque section was taken for postprocessing, as shown in Figure 5. It can be observed from the figure that a large VMS was exhibited both in front of and behind the plaque, while the VMS was small on the plaque. Due to the different responses of the vessel wall and the plaque to the force, cracks are most likely to occur at the junction between the plaque and the vessel wall, that is, at the fibrous cap, leading to plaque rupture.  
The role of the VMS was further determined by the quantification of the VMS for one cardiac cycle, which is shown in Figure 6. It was observed that the distribution of the maximum VMS was consistent across the models, indicating that the risk of plaque rupture was the same in each model. Therefore, it can be concluded that the circumferential distribution of plaques has little effect on plaque rupture.  
Figure 5: VMS contours at the plaque at peak systole.
# The Design and Building of a Hexapod Robot with Biomimetic Legs
## 6 Comparison to Existing Hexapods  
For the sake of comparison, an overview of existing hexapod robots is conducted in this section. Since the first hexapod robot with electric drivers and a computer-controller [28] was built in 1972, a variety of hexapod robots have been constructed according to varying technologies and design principles. Several hexapod robots limited to 14 kg at maximum are listed in Table 4.  
Biobot [29], inspired to the cockroach, was a hexapod with a great speed and agility. Hamlet [30] was built to study force and position control on uneven terrain. RHex [31], designed in mechanical simplicity and having only six actuators, can achieve fast and robust forward locomotion. Sprawlita [32] was based on functional principles derived from biomechanical studies of the cockroach to achieve a fast run. [PERSON] [33] presented an incremental method for building robot control systems analogous to evolution of the organism, i.e., each one being a strict augmentation of the previous one, which control a six legged walking machine. Bill-Ant-p [34], based on ants' behavior, was developed and consisted of 18 degrees of freedom for locomotion, i.e., each leg with three actuators and one force-sensing foot. Gregor I [35] is also a bio-inspired hexapod where the locomotion control is based on the CPG and the core of its control architecture is the Cellular-Nonlinear-Network CPG chip. RiSE [36] is a hexapod robot which is able to climb on a variety of vertical surfaces. It employs arrays of miniature spines to catch on surface asperities. The hexapod robot of this article, named Hexapod-II, is an improved version of the Hexapod-I [37]. The difference between Hexapod-I and Hexapod-II lies in their biomimetic legs. The former uses four-bar linkages, while the latter uses the TJLs. The four-bar linkage can only generate a circular orbit, which leads the hexapod robot to move in a way with a larger fluctuation (Figure 18) because its step height is always one half of its step size, as two tripod gaits are alternated with a phase lag of 180\({}^{\circ}\). In contrast, the TJL generates an ellipse-like trajectory which possesses a transversal axis longer than the lateral axis, so that it can achieve energy efficient walking, as compared to the circular orbit generated by the four-bar linkage. Note that the cost of a robot is generally proportional to the number of actuators. Therefore, the cost can be roughly estimated by counting the number of actuators. Taking a glimpse at Table 4, the numbers of actuators used by other hexapods are all much higher than the number of actuators used by our robots, i.e., Hexapod-I and Hexapod-II. This is due to the novelty of our approach, which uses