esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of the Supercritical Carbon Dioxide Re-compression Brayton Cycle
## 3 Results and Discussion
### Exergy Destruction in Different Components  
The total exergy destruction of the \(sCO_{2}\) re-compression Brayton cycle increased with an increase in \(T_{7}\), as shown in Figure 6. One of the reasons for this is the higher entropy generation at higher \(T_{7}\) values causing comparatively higher exergy destruction in the turbine. Figure 7 presents the exergy destruction in different components of the \(sCO_{2}\) cycle at \(T_{7}=700\)\({}^{\circ}\)C and \(x=0.74\). It can be observed that the gas cooler shows the maximum exergy destruction because a lot of exergy is destroyed when the \(sCO_{2}\) is cooled down from State 10 to State 1. Figure 8 shows that when the outlet pressure of the compressor is high, the exergy destruction is also comparatively high. Moreover, in the summer months, the exergy destruction was high because of high ambient and water temperatures. Figure 9 shows that as the compressor inlet temperature is kept to a lower value (near the critical point), the exergy destruction is minimized. The minimum exergy destruction of 390 kW was observed in January at a compressor inlet temperature of 35 \({}^{\circ}\)C and a compressor outlet pressure of 24 MPa.  
Figure 5: Exergy efficiency (\(\eta_{ex}\)) and irreversibility ratio (\(IR\)) of the \(sCO_{2}\) cycle at a turbine inlet temperature (\(T_{7}\)) of 700 \({}^{\circ}\)C.  
Figure 6: Total exergy destruction vs. turbine inlet temperature (\(T_{7}\)).
# Compact Planar Super-Wideband Monopole Antenna with Four Notched Bands
## 1 Introduction
A dual band-notched SWB antenna was presented in Reference [23], where a U-shaped parasitic element, a T-shaped stub, and a U-shaped slot were introduced in the ground plane and the radiating patch to notch Wi-MAX and satellite communication-based X-band. In Reference [24], a triple band-notched SWB antenna with U-shaped DGS and two open arc-shaped slots was reported to eliminate WLAN and X-band. A monopole SWB antenna with three notch bands was investigated in Reference [25], where elliptical and rectangular split-ring resonators (SRRs) were introduced with varactor diode to notch WLAN, Wi-MAX, and X-band. A guitar-shaped resonating patch and chamfered rectangular ground plane SWB antenna was presented in Reference [26], where RF PIN diodes based reconfigurable band-stop filters were used to eliminate Wi-MAX, WLAN, and downlink satellite system bands. However, the band-notched SWB monopole antennas available in the literature are relatively large in size with the complicated ground surface embedded with parasitic elements and stubs. Also, the reported SWB antenna structures possess one, two, or three notch band characteristics, and SWB antenna with quad-band elimination characteristics is hardly reported. In this paper, a low-profile compact-sized SWB antenna with quad notched-band behavior is presented. The proposed SWB antenna consists of a circular monopole radiator and a rectangular-shaped ground plane. A super-wide impedance bandwidth is achieved by integrating a triangular-shaped tapered feed line with a circular patch. In order to reject WLAN (5.5 GHz) and satellite communication X-band (7.5 GHz) from the SWB antenna, two inverted U-shaped elements are introduced in the radiating patch. The Wi-MAX (3.5 GHz) and amateur radio (10.5 GHz) bands are notched by introducing a meandered slot and an SRR in the monopole radiator, respectively. The measured and simulated results confirm the quadruple notched-band behavior of the antenna. The center frequency of the notched-band can be easily tuned by changing the length of the implanted slot. The presented SWB antenna is easy to design and can be easily integrated with other RF devices due to its compact and simple geometry. The antenna offers high gain, super-wide bandwidth, and omni-directional radiation characteristics. It covers various modern communication application bands and can be used for spectrum sensing in cognitive radio systems.
## 2 Materials and Methods
### Meteorological Forcing Datasets  
The spatially gridded meteorological dataset was obtained from the China Meteorological Forcing Dataset (CMFD) provided by the National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)), accessed on 19 April 2022). This dataset was created by integrating remote sensing products, reanalysis datasets, and in situ station data, and it is one of the most widely used climate datasets in China. This dataset has a temporal resolution of three hours and a spatial resolution of 0.1 degrees. In addition, the dataset includes seven near-surface meteorological elements such as 2 m air temperature (Ta) with a unit of K, surface pressure (P) with a unit of Pa, specific humidity (q) with a unit of kg kg\({}^{-1}\), 10 m wind speed (Us) with a unit of m s\({}^{-1}\), downward shortwave radiation (Rs) with a unit of W m\({}^{-2}\), downward longwave radiation (RI) with a unit of W m\({}^{-2}\), and the precipitation rate with a unit of mm hr\({}^{-1}\). In this study, a long-term daily minimum temperature dataset in the Third Pole was obtained from the 3-hourly near-surface temperature elements. The frost days were based on a per-pixel basis in this dataset and were identified through  
Figure 1: The study area of the Tibetan Plateau.  
the frost day estimation methods from a previous study at both the growing season and seasonal scales. Additionally, monthly datasets of air temperature, precipitation, and downward shortwave radiation were utilized to analyze how the frost days and climate factors respond differently to vegetation growth on the Third Pole.
# Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency
## 3 Results
### Inducted and Supplied Power  
The power inducted in the batch (\(P_{B}\)) is shown in Figure 8. It is directly proportional to \(f\) and counter-proportional to \(\alpha\) and \(z_{B}\). The magnitude of impact of all those variables  
Figure 7: The relationships between \(F_{z}\) and \(z_{B}\) for \(f\) = 30 kHz and different \(\alpha\). On the left side of each table, a cross-section of the inductor is shown (according to Figure 2) to visualise the batch position.  
on \(P_{B}\) is similar. The maximum power inducted in the batch was obtained for \(f=35\) kHz, \(\alpha=\frac{211}{32}\) and \(z_{B}=0.01\) m.  
In Figure 9, a total power supplied to the system (\(P_{T}\)) is presented. The \(\alpha\) angle and current frequency \(f\) have a most noticeable impact on \(P_{T}\), while the position of the batch \(z_{B}\) seems to be less important. If the \(\alpha\) is smaller, the total length of wire is also shorter, which implicates a lower \(P_{T}\) supplied, and higher \(f\) increases the power, while \(z_{B}\) seems to have no significant impact on it. Although levitation does not occur, the maximum \(P_{T}\) was achieved for \(f=35\) kHz, \(\alpha=\frac{611}{32}\).  
Figure 8: Total power inducted on the batch (\(P_{B}\)) \(W\) for different values of the frequency (\(f\)), different positions of the batch (\(z_{B}\)), and different values of the angle (\(\alpha\)). The darker color corresponds to higher value.  
The power ratio (9) is shown in Figure 10. It is used to describe the efficiency of the melting process:  
\[\eta_{\rm{EL}}=\frac{P_{\rm{B}}}{P_{\rm{T}}} \tag{9}\]  
* The maximum efficiency (Figure 10) where levitation occurs (Figure 6) was obtained for \(f=15\) kHz, \(\alpha=\frac{271}{32}\), and \(z_{\rm{B}}=0.025\) m.
* For each \(f\), the melting process was found to be more efficient if \(\alpha\) is low and the position of the batch is close to bottom wire.  
Figure 9: Total power supplied to the coil \((P_{T})\)\(W\), for different values of the frequency \((f)\), different positions of the batch \((z_{B})\), and different values of the angle \((\alpha)\). The darker color corresponds to higher value.
## 2 Materials and Methods
### CIE1976 Color Space  
In order to express colors quantitatively, the International Commission on Illumination (CIE) developed the CIELAB (or CIE1976 L*a*b*) uniform space in 1976 [26]. L*, a*, b*, C* (chroma), and h (hue angle) are the color variables. The parameters a* (from negative green to positive red) and b* (from negative blue to positive yellow) are the chromaticity components, which range from 120 to +120. L* is the luminance component, which ranges from 0 to 100 (from black to white). C* and h are calculated from a* and b*:  
\[\mathrm{C}^{*}=\sqrt{\mathrm{a}^{*2}+\mathrm{b}^{*2}} \tag{1}\]  
\[\mathrm{h}^{\circ}=\mathrm{arctan}\frac{\mathrm{b}^{*}}{\mathrm{a}^{*}} \tag{2}\]  
The Euclidean distance between two points in the CIELAB color space, as measured by a CIE standard observer using the CIE standard light source D65, represents the color difference \(\Delta\mathrm{E}^{*}\) of an item of the same size perceived in the same neutral environment. \(\Delta\mathrm{E}^{*}\) is given by  
\[\Delta\mathrm{E}^{*}=\sqrt{(\Delta\mathrm{L}^{*})^{2}+(\Delta\mathrm{a}^{*}) ^{2}+(\Delta\mathrm{b}^{*})^{2}} \tag{3}\]  
where  
\[\Delta\mathrm{L}^{*}=\mathrm{L}^{*}_{\mathrm{PM}}-\mathrm{L}^{*}_{\mathrm{CM}} \tag{4}\]  
\[\Delta\mathrm{a}^{*}=\mathrm{a}^{*}_{\mathrm{PM}}-\mathrm{a}^{*}_{\mathrm{CM}} \tag{5}\]  
\[\Delta\mathrm{b}^{*}=\mathrm{b}^{*}_{\mathrm{PM}}-\mathrm{b}^{*}_{\mathrm{CM}} \tag{6}\]  
PM: photography measurement, CM: colorimeter measurement.  
Figure 2: The test-piece device. The tsavorite sample was placed on top of the background. The spectrophotometer gathered the color signals, and the color information was then sent to a computer for analysis.
## 2 Materials and Methods
### Downscaling Model
#### 2.2.3 Training Strategy  
There is always a trade-off between model complexity and data size. The larger spatial and temporal coverage of the data used for training, the more complex the target function \(f\) becomes. As this makes it more difficult to learn, we simplified the learning task by spatially and temporally splitting the data while maintaining a reasonable data size, and fitting separate models on each of the subsets. Spatially, the data were grouped into four regions: 1. Afghanistan; 2. United Arab Emirates and Qatar; 3. Saudi Arabia; and 4. Iraq and Kuwait. Temporally, the data were divided approximately equally into four seasons that have 91, 91, 91 and 92 days, respectively. In order to produce temporally continuous downscaled predictions, a 45-day overlap was added to each season as shown in Figure 4.  
Figure 3: Neural Network structure of the transferred model. The notation LSTM: 8s represents a LSTM layer with 8 nodes and return sequence. The notation of Building Block:8 represents a building block with 8 nodes. The light yellow block represents the dropout layer with dropout rate 0.5.  
The model in Equation (1) illustrates the prediction for the forward temporal direction; that is, to predict the future with historical observations. We also trained a backward prediction model with a slight variation of the same model format, but using future observations to predict historical data (Figure 4). Training this way allowed downscaling in both directions, forward and backward in time, which was needed for our application where we aimed to downscale before and after the 2-year training period. Consequently, 32 models (4 regions\(\times\) 4 seasons \(\times\) 2 directions) were fitted on all combinations of region, season and direction. Within each subset of data, the data were composed of the same seasons from two years (2005 and 2006), as shown in Figure 4. The two years of data were evenly divided into 10 parts and the last 10% of the data were used as test set. The validation set was the fourth 10% of data. The remaining 80 % was used as the training set.
## 3 The AgroTRACE Infrastructure
### Data Mapping System  
The information, which is produced in the platform and the above four subsystems, is standardized via GS1 standards, with the aim of sharing information with other systems. Hence, the information can act as reference knowledge (benchmarking) via its extensive dissemination (knowledge diffusion).
## 3 Methodology
### Model Evaluation  
The extracted features are fed into the two ensemble classifier algorithms, and the performance of the classifiers is evaluated using the metrics listed in Table 4. Here, the true positives (TP) indicates the data points, which are appropriately labeled as faults, whereas the false positives (FP) wrongly labeled as faults to the normal data points. On the other hand, the data correctly labeled as normal is called the true negatives (TN), and when faulty data is mistakenly labeled as normal, is known as false negatives (FN).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(Accuracy=\frac{TP+TN}{TP+TP+TN+TN}\) & (23) \\ \(Precision=\frac{TP}{TP+TP}\) & (24) \\ \(Sensitivity=\frac{TP+TN}{TP+TN}\) & (25) \\ \(Specificity=\frac{TP+TN}{TP+TN}\) & (26) \\ \(F1=2\times\frac{Precision=Recall}{Precision=Recall}\) & (27) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation parameters.  
Figure 11: (a) Random forest and (b) XGBoost algorithm architectures.  
Specific indicates the rate of correct detection of the true negative class, whereas the sensitivity measures the effectiveness for the model to detect events in the positive class. Though type I errors are measured by sensitivity and type II errors by precision, precision and sensitivity typically are stated pairwise. Finally, the F1 score provides the harmonic mean of precision and sensitivity. In addition, the receiver operation characteristics (ROC) curve is presented to evaluate classifier performance.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
These approaches, though, have shortcomings that can include the following:  
* The lack of hazard and risk databases that are connected to exact materials, elements or construction processes and correspond to local legislation, which differs from country to country. If they do exist, they are hardly readable by a computer or applicable to the BIM-based code-checking without manual database adjustment. The issues associated with BIM-based code-checking are detailed in [59]. * The lack of open BIM solutions based on IFC or BCF (BIM Collaboration Format) formats. The methods mentioned above were mostly run on one program (e.g., Autodesk Revit). Every BIM engineer uses a variety software, so these solutions are not applicable for every user or region. * The lack of solutions based on CDE or BIM coordination software. * The lack of HS BIM libraries. Despite the progress in BIM applications, plug-ins and algorithms, there is still a lack of commercially available specialized HS BIM software. A few companies are developing HS BIM software for safety equipment [60; 61; 62] or hazard sign [63; 64] modeling, but they are still missing functions for hazard detection, risk calculation, modeling automatization or basic options settings (e.g., English language). There is also software for BIM rule-checking, such as Solibri [65], or several systems for national regulation compliance-checking, but these are not intended for HS construction hazard detection. Some of these can be customized by users, but this requires the creation of a computer-readable database from existing HS regulations [59]. To analyze the current state of the use of BIM in hazard detection and the adaptation of available technology and software to solve this issue in Slovak construction companies, the research team decided to develop a questionnaire. The second purpose of the survey was to analyze the current state of technologies and processes used for transfer of information about hazards to the on-site person responsible for HS (e.g., project manager, HS coordinator). The last goal was to record and analyze requirements and recommendations from respondents, which could be used in further research, or in application and software development. The aim of the research is to use the collected data to develop an efficient method for HS hazard detection and transference of the information about the hazards to the on-site person responsible (e.g., project manager, HS coordinator) as quickly as possible. The higher efficiency and digitization that comes with these processes also has a direct impact on economic and environmental sustainability. Hazard prediction can lead to improved construction planning that avoids actions connected with unplanned preventions or accidents. Fewer HS incidents means healthier people, and that directly enhances the gross domestic product (GDP). In addition, less paperwork, which is still common in HS control processes, and higher computer operations efficiency will reduce the carbon footprint.
## 3 Methodology
### Multi-Sensor IF Estimation Algorithm
#### 3.1.1 Finding out Highest Energy Time-Instant  
The energy with in a short time \(t+\Delta T\) to \(t-\Delta T\) is found for the \(k\)-th sensor where \(k=1,2,\ldots,M\), as:  
\[\hat{e}_{k}(t)=\int_{t+\Delta T}^{t-\Delta T}|x_{k}(\tau)|^{2}d\tau, \tag{6}\]  
The short time energy is averaged across \(M\) sensors to find \(e_{\text{avg}}(t)\) as:  
\[e_{\text{avg}}(t)=\frac{1}{M}\sum_{k=1}^{M}\hat{e}_{k}(t), \tag{7}\]  
The highest energy time-instant is at the location where \(e_{\text{avg}}(t)\) is maximum:  
\[t_{0}=\underset{t}{\text{argmax}}\ e_{\text{avg}}(t), \tag{8}\]  
The obtained \(t_{0}\) will be used to compute the windowed Fourier transform of the signals received at all the \(M\) sensors for searching the location of the highest energy frequency bin.  
Figure 3: The illustration of the proposed direction of arrival (DOA) estimation algorithm.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### Route Selection  
After the sink receives the first route request, it sets a timer. All route requests that arrive at the sink after the timer runs out are classified as out-of-date and are discarded. This is because it can be relatively judged that the late arrival of such packets is an indication of the inferior quality of the correspondingroutes; thus, they are immediately dropped. In selecting the final route, two major metrics are considered, namely the hop count and the channel switching count. These metrics are formulated into a route cost function, \(R_{i}^{cf}\), that assigns half the weight of the hop metric to the channel switching metric in order to prefer routes that offer less channel switching,  
\[R_{i}^{cf}=\omega_{h}\cdot W_{h}+\frac{\omega_{s}}{2}\cdot W_{s} \tag{10}\]  
where \(\omega_{h}\) and \(\omega_{s}\) are the assigned weights for the hop count and the channel switching counts along the route, respectively. While \(W_{h}\) and \(W_{s}\) represent the collected values of hop counts and the channel switching count, respectively. The route with the lowest cost is chosen as the final route, and all nodes along this route are referred to as LMR nodes.
## 5 Example Analysis
### Results and Analysis of Siting and Sizing  
#### 5.2.1 Original Working Condition  
The eight generators (six for use and two for standby) in the distribution network area were able to meet the load demand with the cooperation of the higher-level distribution network. The load hardly changed with time, and the power flow distribution was relatively stable, as shown in Figure 6.  
Figure 5: 10 kV distribution network structure.  
Figure 6: Power-flow distribution under original condition.  
Since the load hardly changed with time, the power flow hardly changed in 1 day when only the gas-turbine output power. The stability indicator \(F_{1}\) was 0.094, the penetration indicator was 0, the voltage fluctuation indicator \(f_{1}\) was 11.96, and the total loss in 1 day was 103.2 MWh.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.2.3 Validity Analysis of the SSECRF Algorithm
At the same time, the kappa values of the classification results are compared in Figure 7, which are all lower than the results obtained with SSECRF using the SSE feature set. In order to verify both the effectiveness of the proposed SSECRF algorithm and the effectiveness of using the emissivity information to improve the classification accuracy, the kappa coefficients of the top-six classification results [44] in the 2014 Data Fusion Contest, the published classification results obtained with other methods, and the results of the proposed algorithm are compared in Figure 7. All the experiments were undertaken under the same conditions, i.e., the same training samples and test samples. The comparison shows that the classification results of the proposed method show a competitive advantage, but the results are not as good as the results of 1 st place in the 2014 Data  
Figure 6: The subset classification results obtained by the SSECRF algorithm. The regions numbered 1, 2, 3 and 4 are the corresponding area subsets from the classification map in Figure 5.  
Fusion Contest and the algorithms proposed later. It is possible that the results of the proposed method could be improved when the misclassification of vegetation and trees is solved.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 5 Subjective Measurements
### Analysis of the Second Measurement  
Figure 13 shows that the values are centered around zero. There are no visible differences between musical genres. In the case of test sequences, the new devices appear to be rated slightly more favorably. The test sequences cluster around zero for all perceptual features, as expected.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **SS Effect** & **df Effect** & **MS Effect** & **SS Error** & **df Error** & **MS Error** & **F** & \(p\) \\ \hline Clarity & 0.516 & 2 & 0.258 & 65.930 & 129 & 0.511 & 0.505 & 0.605 \\ Dynamics & 0.317 & 2 & 0.159 & 56.057 & 129 & 0.435 & 0.365 & 0.695 \\ Brightness & 1.796 & 2 & 0.898 & 45.091 & 129 & 0.350 & 2.569 & 0.080 \\ Overall rating & 1.705 & 2 & 0.853 & 73.464 & 129 & 0.569 & 1.497 & 0.228 \\ \hline \hline \end{tabular}
\end{table}
Table 2: [PERSON]’s test for homogeneity of variance for new and used devices.  
Figure 12: Mean ratings with \(75\%\) confidence intervals for new and used audio evices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **SS Effect** & **df Effect** & **MS Effect** & **SS Error** & **df Error** & **MS Error** & **F** & \(p\) \\ \hline Clarity & 0.045 & 2 & 0.023 & 111.682 & 129 & 0.866 & 0.026 & 0.974 \\ Dynamics & 0.197 & 2 & 0.098 & 109.886 & 129 & 0.852 & 0.116 & 0.891 \\ Brightness & 1.106 & 2 & 0.553 & 87.409 & 129 & 0.678 & 0.816 & 0.444 \\ Overall rating & 0.288 & 2 & 0.144 & 137.591 & 129 & 1.067 & 0.135 & 0.874 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of variance (ANOVA) for new and used devices.  
The statistical tests shown in Tables 4 and 5 conducted for the second measurement revealed no statistically significant effects. [PERSON]'s test confirmed the assumption of homogeneity of variances, indicating that the variability within groups (new vs. conditioned devices) is consistent across all perceptual features. Additionally, the ANOVA results showed no significant differences in mean ratings between the devices.  
These findings suggest that the process of burning in the device did not lead to perceptible improvements in sound quality as evaluated by listeners. The mean values for all tested features--clarity, dynamics, brightness of sound, and overall evaluation--clustered around zero, indicating a general consistency in perception regardless of device condition. Moreover, the lack of visible differences across musical genres further supports the conclusion that the device's usage state does not significantly influence subjective auditory impressions.  
This outcome reinforces the reliability and consistency of the devices tested and suggests that any perceived differences may be more influenced by individual listener expectations or contextual factors rather than by the physical state of the equipment.