esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Binary Classification in Non-Invasive Load Monitoring
###### Abstract  
The increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of information features and for the identification of operating electrical devices. A set of potential identification features was obtained from high-frequency measurements covering 12 types of electrical consumers and consisted of 218 features. From these, an identification vector was selected via the mRMR (minimum redundancy maximum relevance) method, which searches for features that are maximally correlated with the class and are as little correlated with each other as possible. Identification was realized by building a hybrid classifier using binary classifiers built from artificial neural networks and decision trees. The Accuracy, Precision, Recall, and F1 metrics were used to assess the quality of identification. The obtained values of the identification quality indicators confirm that it is possible to replace multiclass classification in NILM with binary classification without losing its quality. The use of binary classifiers allows for the identification of new devices without the need to change the classifier configuration.  
Keywords:feature selection; artificial intelligence; neural networks; decision trees +
Footnote †: journal: Applied sciences
## 1 Introduction  
Solar simulators are often used for the indoor characterisation of photovoltaic cells [1; 2; 3]. This is due to the varying outdoor conditions [4]. Solar simulators are either pulse or steady-state simulators [5; 6], and are classified in terms of their spectral match, spatial uniformity and temporal instability [5; 7; 8]. The properties mentioned above for any solar simulator must fall into class A, B, C or no class. A control system with dual operation modes was designed and built for the xenon (Xe) arc lamp solar simulator, to regulate the light intensity as well as to measure and store data. Control systems are widely used in the areas of research and industry [9]. The responsive control system designed in this study employs a modular approach [10], with a programmable logic control (PLC) module to maintain the light intensity of the steady-state solar simulator. The PLC allows the exchange of information among the other components that make up the control system, through a supervisory control and data acquisition (SCADA) tool, installed on a personal computer (PC), where inputs are made. At the same time, the outputs (irradiance, cell surface temperature, current, voltage and power) are measured and stored. In this paper, a control system capable of dynamically controlling the simulator's light intensity, on-siteas well as remotely, is presented. This system was designed and built at the Fort Hare Institute of Technology, as part of this study. The control system incorporates a SCADA, which starts the lamp, and processes, stores and visualises data from the solar simulator.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis
### Quasi-Static Acceleration Analysis  
The design load is determined for the following conditions, taking into account the launch vehicle's mechanical configuration and certain safety factors: 3 g for lateral direction overload and 10 g for vertical direction overload. With the conditions listed above, a quasi-static acceleration analysis is performed in three directions of the separation mechanism. In the quasi-static acceleration analysis, the maximum deformation can result in physical interferences between the components, and the stress induced by the acceleration loads is calculated as the von Mises stress. The maximum stress and deformation of the separation mechanism appear on the bottom plate and push board. Figure 8 illustrates the deformation  
Figure 6: Schematic diagram of the door cover opening and locking mechanism.  
and stress contours. The maximum deformation and maximum stress values, as well as their locations, are summarized in Table 2.  
Figure 7: Finite element analysis model.  
The bottom plate and push board are made of the aluminum alloy 2A12, and the dynamic parameters are shown in Table 3, which has a 410 MPa failure stress. The structural safety margin is calculated using the safety coefficient of 2.0. As illustrated in Table 2, the simulation results satisfy the design requirements.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Sleep/Wake-Up Schemes  
Communications in WSN accounts for most of the energy consumed by network nodes, quite higher than that used for sensing and preprocessing the data they must send. Therefore, designing energy-efficient (EE) communication protocols (MAC and routing) is key to the proper functioning of the underlying application.  
In any case, to choose/design the right energy-aware communication protocols, other factors such as topology and the functioning application itself must be taken into account. For instance, in WSNs deployed for critical missions (perimeter surveillance, tsunami/earthquake early detection, fire control, etc.) data must be transmitted immediately, so sensors with new information should have the highest priority to access the channel, avoiding whichever delay and contention to do so. Same way, the limited transmission range and the distance to the sink determine if a sensor must also function as a relay to help others or must lean on other itself.
## 3 Case Study
### Chatter Force and Frequency Measurement  
Using the framework shown in Figure 10, where the boring bar is mounted with 163 mm of overhang, cutting forces and accelerations were measured by means of a Kistler 9257A dynamometer and a tri-axial accelerometer (PCB model 356A32), acquired by an LMS SCADAS SCM202V acquisition system. Spindle speed and feed per revolution were set at 700 rpm and 0.2 mm/rev, respectively, while radial depth of cut was gradually increased until chatter condition was detected. For given values of spindle speed and feed per revolution, the machining was found to be unstable with a radial depth of cut of 1.5 mm. Measured cutting force along the three directions in the time and in frequency are shown in Figure 11a,b, respectively. Maximum force amplitude is around 3800 N along cutting velocity direction. Chatter frequency is around 463 Hz, while correspondent cutting force amplitude on cutting force direction is 430 N (Figure 11b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Workpiece Material**} & \multicolumn{3}{c}{**Chemical Composition**} & \multicolumn{2}{c}{**Hardness**} \\ \cline{2-5}  & **C\%** & **Si\%** & **Mn\%** & **Cr\%** & **(HB)** \\ \hline NCE46 & 2.00 & 0.25 & 0.35 & 11.5 & 240 \\ \hline \end{tabular}
\end{table}
Table 2: K100 (DIN 1.2080) properties.  
Figure 10: Experimental setup for chatter tests.  
Figure 9: Original boring bar.
## 4 Conclusions
* Considering the effects on the mechanical properties of stainless steel, future research directions could focus on the detailed examination of how varying additive manufacturing parameters, such as layer thickness, build orientation, and print speed, influence the microstructure and, consequently, the mechanical performance of the material. Investigating the long-term durability and fatigue resistance of 316L stainless steel in real-world applications, especially under high-stress conditions, would be valuable for understanding the material's behavior in different industrial environments. Moreover, the potential for optimizing the post-processing techniques, such as heat treatment and surface finishing, to further enhance the mechanical properties of additively manufactured stainless steel should be explored. Future research could also focus on the development of advanced simulation models that predict the material's behavior under various loading conditions, thus allowing for a more precise design of components. Another promising area of research would involve studying the impact of different alloying elements on the mechanical properties of 316L stainless steel produced via additive manufacturing. Investigating these effects could lead to the development of specialized alloys tailored for specific industrial applications, improving both performance and cost efficiency. Finally, exploring the potential for reducing material waste and improving energy efficiency during the additive manufacturing process could be an essential optimization, especially in large-scale production settings. The integration of sustainability metrics into the design and manufacturing process could further enhance the economic and environmental feasibility of using additive manufacturing to produce high-performance stainless-steel components. In conclusion, while the thermal debinding and sintering processes show potential for improving material properties, further development in post-processing techniques is necessary to address challenges such as bubble formation and to optimize mechanical performance. This research received no external funding. It was all completed within the Sakarya university laboratories. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The author declares no conflict of interest.
## 4 Simulation Analysis
### Experimental Results
#### 4.3.1 INS+LEO 3-Satellite Alternate Switching Ranging Integrated Navigation Algorithm
(**a**) Position, velocity and attitude errors. (**b**) 3D trajectory error. effect is significantly better than the comparison scenario; that is, we use a continuous real ranging value combined with an alternating real ranging value, which is better than the simple alternating scheme of real ranging value and virtual ranging value. The optimal switching time varies with the system used. Generally, a shorter switching time may be better, but frequent switching increases the power consumption in the receiver and if the switching time is longer, this situation is conducive to satellite communications; however, although the LEO system assists in correcting the INS error, the cumulative effect of the INS error worsens the navigation and positioning effect. Therefore, in actual projects, we have to compromise based on actual business (according to time task) and precision requirements.
# Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System
## 5 Subject Matter Validation
### Results  
From the interview responses it is apparent that, for significant faults, the most valuable piece of information for identifying the validity and fault type is high-definition video footage of the pantograph and overhead line from a vehicle-mounted camera. Since this is a relatively recent development (as of 2023), it can be expensive to fit this camera to every pantograph of every train. Additionally, keeping the lenses clean from carbon dust can be difficult; this system assumes all vehicles are fitted with all sensors. However, the location and asset histories alongside the event data are more useful since they allow users to easily spot trends where a specific pantograph or location is consistently producing faults.  
Information regarding faults does not only come from sensor data, but can come from many different sources; for example, drivers play a large role in reporting faults (experience makes them efficient at identifying OHLE sagging, insulators, excessive oscillations or bouncing of the OHLE) and members of the public can also call in to report issues.  
The types of faults discussed In the interviews could be separated into three categories: faults relating to the condition of the overhead line equipment, faults relating to the condition of the pantograph, and faults caused by external factors. All of the faults discussed in Table 2 can eventually result in catastrophic failure due to dewirement; avoiding this should be the primary concern of any RCM system.  
In terms of stages of cognition, all participants agreed that the overall process of Notification, Acceptance, Analysis, Clearance made sense as a fault handling process. However, multiple participants mentioned that the acceptance stage is less about identifying false alarms (which is still important) and more about the prioritisation of issues: to quote, 'Does this need to be dealt with right now or can I (or someone else) look into it in more detail later?'  
Feedback from experts was that there are two kinds of event type that the system should aim to assist with:  
1. Where a significant issue has arisen, and the train needs to be stopped or pantograph needs to be dropped immediately to prevent further damage to the system. In this case, once it is clear that the train needs to be stopped the number of clicks should be minimized;
2. Where trends in the data show a developing issue that should be investigated and resolved before it develops into a significant issue.  
In those situations where a significant, urgent event has arisen, it may be necessary to shortcut at least some of the process to immediately determine a course of action that may involve stopping one or more trains. Therefore, participants thought it was also useful to add a means of communicating directly with drivers if necessary.  
Interviewees offered specific feedback on HMIs as follows.
## 2 Materials and Methods
### Experimental Setup  
The total 576 training images were subjectively evaluated by 10 researchers who specialize in image processing for analysis, image adjustment, and artifact reduction in the dark room display and in the same display. The random order images were individually evaluated on a scale of 1 (minimum score) to 5 (maximum score).  
A total of 114 adjusted images were assessed by our proposed method as well as by two conventional methods, which were NIQE and CQE. The subjective evaluation was also performed as the ground truth quality scores. Three intact images (traffic, cactus, and basketball) were selected from the LIVE video database [42] and processed with different combinations of three levels of colorfulness adjustment, three levels of contrast adjustment, and four levels of sharpness adjustment, which resulted in 108 adjusted images using MATLAB (R2017a, The Mathworks, Natick, MA, USA) software automatically. The examples of these test image sets are shown in Figure 7. The other six images included the original three images plus enhanced ones by an HDR-toning toolbox in Photoshop CS6. These 114 test images were not used in the training process.  
Figure 6: Diagram of the proposed method, which comprises two processes. Above side process: extracting the standard HDR-derived features. Below side process: extracting the features of the test image. These two sets of features are compared with each other to produce the final quality score of the test image.
## 3 Proposed DRFENet Construction
### Loss Function  
The mean absolute error (L1 loss, MAE) and the mean squared error (L2 loss, MSE) are commonly used loss functions in the field of images.  
The MSE is the sum of the squares of the difference between the target variable and the predicted value, so L2 loss will capture the average feature weight and a stable solution. DRFENet uses MSE to predict the residual image. Equation (8) shows the formula for finding MSE from a clean image I and a noise image K with a given image size of m \(\times\) n:  
\[\text{MSE}=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2}, \tag{8}\]  
The training dataset is rendered as \(\{X_{C},~{}X_{N}\}_{i=1}^{N}\), where \(X_{C}\) and \(X_{N}\) represent the clear image, noise image, and input noisy image, respectively. DRFENet is trained to create the noise mapping, and it then uses the noisy image minus the noise mapping to obtain a clean image, as shown in Equation (9). Letting \(y\) stand for the clear image, it is easy to derive Equation (10):  
\[y=X_{N}-F_{BTBUNet}(X_{N}), \tag{9}\]  
\[F_{BTBUNet}(X_{N})=X_{N}-y, \tag{10}\]  
where \(F_{BTBUNet}(X_{IN})\) denotes the noise mapping. In this study, the noise mapping generated in Equation (11) is used to calculate the loss function:  
\[l(\Theta)=\frac{1}{N}\sum_{n=1}^{N}(F_{BTBUNet}(X_{N})-(X_{N}-X_{C}))^{2}, \tag{11}\]  
where \(\Theta\) and \(N\) are the numbers of parameters and noisy image patches, respectively.  
To verify the effect of MAE and MSE, we conducted a control test, as shown in Table 1.
## 3 Data Prediction for Energy Saving in WSNs
### Single Prediction Scheme  
Predictions are made in one location of the network, which can be either in the sensor, BS, or Cluster Heads (CHs). The CH or BS can forecast the data that were received from the sensor node and decide when to forecast more points based on the reliability of the current predictions [30]. This architecture can be used in applications where sensors are close and present a well-defined spatio-temporal correlation. The correlation can be used to generate a model that predicts the data and check if the results are inside a confidence interval [8]. Another alternative is to make predictions in the sensor node. This case can be useful in situations where it is more expensive to obtain a sample than to predict one.
# An Effect of White Space on Traditional Chinese Text-Reading on Smartphones +
## 4 Discussion
### Page Spacing  
The result showed no significant difference in reading time between 0H and 1/2H page spacing, nor between 0H and 1/3H page spacing. However, the reading time of 1/2H page spacing was significantly longer than 1/3H page spacing. It indicated that too large page spacing is not conducive to reading performances. Oversized page spacing would decrease a display area dramatically, which would limit the number of characters presented in one screen and increase screen pages. Consequently, participants must slide the text frequently, resulting in degraded readability. Therefore, the result showed that the reading performance of moderate page spacing (i.e., 1/3H page spacing) was significantly better than that of the larger page spacing (i.e., 1/2H page spacing).  
However, the result also showed the reading performance of the smallest page spacing (i.e., 0H page spacing) was not significantly better than the moderate page spacing (i.e., 1/3H page spacing). It is possible that too small a page spacing (0H) causes overcrowded typography, not providing readers with visual relief and relaxation in reading tedious text. Accordingly, the smallest page spacing increases loadings of the reading task, degrading reading performances. On the contrary, at the same time, the smallest page spacing (i.e., 0H) does decrease participants' scrolling frequencies, promoting reading performances. This advantage dilutes its disadvantage. Therefore, the outcomes did not show the difference in reading time between 0H page spacing and 1/3H page spacing.  
In conclusion, the reading performance of the 1/3H page spacing was significantly better than that of the 1/2H page spacing and not less than that of the 0H page spacing. Hence, it seemed that 1/3H page spacing was suitable for page spacing on smartphones.  
However, the results demonstrated that 0H page spacing was rated the best, 1/3H page spacing was the next, and 1/2H page spacing was the worst. Larger page spacing may reduce more rating scores of ease, comfort, aesthetic appeal, and preferences. That is, participants felt more ease, comfort and attractiveness, and higher overall satisfaction toward larger page spacing. In short, participants believed that white space would degrade overall preference.  
The participants in this study did not have design experience. Many people without a design background considered white space wasted space and believed it should house more information or other visual elements to avoid waste [5]. Therefore, the findings agreed with the design laypeople's opinions that the white space would degrade subjective preference [5]. Conversely, design practitioners believed white space would not waste the screen space but improve visual communication and reading comprehension. They promoted the use of white space for elegance and ensuring a high-quality user experience. It seems to be an individual difference between design practitioners and laypersons in page spacing evaluation. Future studies should explore the differences between designers and laypeople.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 5 Hybrid SC Converters
### Other Traditional Converters with SC Voltage Multipliers  
As well as to the boost converter, other traditional topologies can be used to drive a diode-capacitor voltage multiplier. Figure 27a shows the traditional Cuk converter. Figure 27b shows the multiplier Cuk Converter. Figure 27c shows the traditional Single-Ended Primary-Inductor Converter or SEPIC, while Figure 27d illustrates their multiplier version. Those converters have been studied in [60; 61].  
Other configurations have been investigated based on the same principle for either a dc input [62; 63; 64; 65; 66] or an ac input [67; 68; 69].  
Other configurations have been tried with more than one input switching state, for example, with two interleaved switching stages to drive a ladder voltage multiplier [70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85]; some of those topologies resemble the series capacitor boost converter.  
Another possibility is using the quadratic boost converter to drive the voltage multiplier, as explored in [90; 91], or topologies with multiple inductors [92; 93; 94; 95], Z-source networks [96; 97], and the cascaded connection or the interconnection with sets of multipliers in different manners [98; 99; 100; 101].
## 4 Discussion  
### 2D-ERT to Assess Cd Sources in Cacao Farms  
The primary source of Cd in unpolluted soils is from parent material [21], cretaceous sedimentary rocks (i.e., farms in Muzo) [22] and shales, which are frequent rock types found in the central zone of Santander, especially in some municipalities in the southwest of the district [23]. However, the contribution of these sources to Cd release could be low, less than 0.3 mg\(\cdot\)kg\({}^{-1}\) of Cd\({}^{2+}\)[24], compared to other sources due to human activities.  
This work describes a resistivity analysis mainly due to the presence or absence of solid-state phase Cd-like material throughout the assessed pits that were geoelectrically assessed. Figure 7 shows the correlation between resistivity obtained by 2D-ERT and Cd determination at the same points in the assessed pits. There is a high correlation between resistivity and Cd in soils (R\({}^{2}\) = 0.87), which demonstrates the accuracy of this geophysical technique to study Cd content in cacao soils following calibration. Regarding land use within the studied regions where cacao grows, it is possible to assess Cd-like material in both topsoil and subsoil, using the 2D-ERT technique. According to Figure 2, one could associate Cd richness to anthropogenic sources, as the farms assessed are near to emerald and coal mines (i.e., in Muzo and San Vicente de Chucuri), and close to oil pipelines (i.e., in Arauquita) that could influence the Cd influx into cacao farms, as found in other cacao producing countries [25]. However, such assumptions may require further exploration using other techniques such as Cd\({}^{114}\) isotopic markers.  
Figure 7: Correlation between resistivity determinations and Cd concentrations obtained by the 2D-ERT and inductively coupled plasma optical emission spectrometry (ICP-OES) techniques, respectively. The coefficient of determination was high for the data collected in this survey of 27 farms. The municipality of Arauquita (green) shows the lowest resistivity and cadmium levels (below 1000 Ohm\(\cdot\)m and 0.62 mg\(\cdot\)kg\({}^{-1}\) Cd, respectively). The municipality of San Vicente de Chucuri (red) shows the highest resistivity and cadmium values in the soil (1815 Ohm\(\cdot\)m and 2.76 mg\(\cdot\)kg\({}^{-1}\) Cd, respectively). The municipality of Muzo (blue) shows the second highest values.