esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The effect of the mineralogy of aggregates was evaluated in regards to concrete performance characteristics such as the CTE, compressive strength, and microstructure, especially near the ITZ.  
The following conclusions are drawn from the analyses of the experimental investigation conducted in this study.  
* Based on the proposed technology which the line scanning analyses (SEM and EDX) of ITZ regions, it is feasible to establish a ITZ database for the different types of aggregate used in concrete with sensitive analyses. The ITZ database which can be established by this proposed technology may help engineers to identify potential problem of micro-cracking due to the different types of aggregate.
* With the proposed technology, the ITZ and CTE database of a wide range of aggregates such as lightweight aggregate (porous limestone, expanded shale, clay and slate, etc.) and recycled concrete aggregate (RCA) can be established and characterized thus structural and field engineers can be aware of the probability of nano- and micro-cracks due to the aggregate types.
* The type of fine aggregate and its volume were found to have a significant effect on CTE, which increased with the Si volume content in the fine aggregate. Natural sand had approximately twice the Si content of manufactured sand in petrographic analyses, and the CTE of natural sand concrete specimens was thus always higher than that of manufactured sand specimens. Thus, it is recommendable to determine the volume content of Si in aggregates before engineer decides a concrete mix proportion.
* The concrete specimens with dolomite coarse aggregate had a higher CTE than specimens with granite, but the influence of the coarse aggregate type on the concrete CTE appears to be relatively small.
* For the concrete specimens with higher CTE, the micro-crack density in the ITZ tended to be higher. This is attributed to the early-age thermal cracking. Specimens with higher CTE are expected to be more susceptible to thermal stress.
* Higher CTE appears to result in lower compressive strength, suggesting that specimens with natural sand would have higher CTE values but lower compressive strength than manufactured sand specimens.
* From SEM images, the manufactured sand has a more angular shape and rougher surface than the natural sand. This angular shape provides better interlocking and the rough surface texture develops a greater mechanical bond with the cement paste so that higher compressive strength can be achieved.
# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC  
Considering the low density of the RF signal, it is crucial to efficiently utilize the harvested energy of limited amount. Many power management strategies have been proposed in recent  
Figure 11: Power conversion efficiency of the circuits designed by [PERSON] [48], [PERSON] [50], [PERSON] [49], [PERSON] [3], and [PERSON] [51].  
Figure 12: Combined power conversion efficiency of two different energy harvesters (recreated from Figure 20 in [3]).  
years. Those strategies dynamically adjust the transmission power of wireless communications based on the energy harvesting rate and traffic load. As discussed in the following subsections, an appropriately designed power management policy can significantly improve the throughput and reduce the transmission latency of an RF-EHWC device.
## 4 Simulation Results  
This paper proposes WT-ANFIS-HFPSO as the short-term forecasting model of a solar power system in Malacca, Malaysia. The proposed model is different from the traditional forecasting model as it is a perfect combination of the following parts: (1) a WT to remove the noises in the panel temperature data, tilted radiation data, global radiation data and solar power data; (2) an ANFIS as the forecasting tool of future solar power data; and (3) HFPSO to optimize the premise parameters of the ANFIS model. The results are analyzed according to three conditions of days, namely, sunny days, partially cloudy days and cloudy days. Sunny days referred to the days with the highest average daily value of solar radiation while cloudy days are days with the lowest average daily value of solar radiation. Three days that have been specified for each condition of the day are depicted in Table 3. The comparative performance analysis is shown and discussed under these three conditions of days which involves the analysis of the five performance metrics (the MAE, RMSE, NRMSE, WIA, and LCE) for every forecasting strategy. Note that this work displays the results for every day in the time step of 1 h.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 2 Background and System Model
### Capacity Results for Gaussian Improper Signals
Figure 2: Improper constellations generated according to (16) for different values of \(\kappa\) and \(\phi\) when \(X\) is proper Quadrature Phase Shift Keying (QPSK). Note that by doing this, only \(M\) points of the lattice \(\Lambda(\phi,\kappa)+\mathbf{H}(\phi,\kappa)(\frac{1}{2},\frac{1}{2})^{T}\) are enclosed, and their average energy is \((M-1)/6\). Since translation of a lattice does not affect its minimum distance between signal points, \(d_{min}\), we conclude that the minimum distance of the unnormalized family of constellations \(\mathcal{A}(\phi,\kappa,M)\) (19) is given by the minimum distance of the lattice \(\Lambda(\phi,\kappa)\). For a given \(\kappa\), the optimal \(\phi\) that maximizes \(d_{min}(\Lambda(\phi,\kappa))\) can be obtained as:  
\[\phi^{*}(\kappa)=\arg\max_{\phi\in[0,\pi/2]}d_{min}(\Lambda(\phi,\kappa)). \tag{20}\]  
This problem will be solved in Section 4. Taking into account that \(\phi^{*}(\kappa)\) does not depend on the scaling of the lattice \(\Lambda(\phi,\kappa)\), we arrive at the following proposition:  
Proposition 1: _The angle \(\phi^{*}\) that maximizes the minimum distance of the family constellations \(\mu\times\mathcal{A}(\phi,\kappa,M)\), where \(\mu\) is an arbitrary normalization factor, only depends on the circularity coefficient \(\kappa\) and is given by (20), where \(\Lambda(\phi,\kappa)\) in (18) is the lattice that results from the \(\mathbf{H}(\phi,\kappa)\)-transformation of the integer lattice \(Z^{2}\)._
## 1 Introduction
Chips are liable to break spontaneously at the weakest points along their lengths, or against the cutting tool, or against the workpiece surface [30], see Figure 1a-c. The second and third scenarios can damage the tool or surface quality, respectively. This makes it critical to control the chip evacuation process, which can be satisfactorily achieved using proper chip breaker geometry to bend the generated chip sufficiently so that it breaks on its own. Figure 1: Chip breaking methods (**a**) breaks on its own; (**b**) breaks against the tool; (**c**) breaks against workpiece. Researchers have studied different methodologies to optimize chip breakers design, experimentally [16] and combining experimentation and simulation [14; 31], but most of them restricted their work to non-PCD insert tools. On the other hand, the authors previously worked on developing chip breakers for PCD inserts welded into milling-tools [14]. One of the main issues was the implementation of a simulation tool to model the processing of PCD by a ns-pulsed laser. However, the study was focused on a milling case, and while milling processes generate discontinuous chips, the study did not address the chip evacuation problem. When turning ductile materials, long continuous chips are generated and they tend to wrap around the part. Accordingly, the motivation for this research study was to carry out a systematic methodology to design customized chip breakers that enable a controlled removal of the chips from the working zone. The proposed approach utilizes finite element method (FEM), due to its high ability to accurately simulate the chipping process [32], taking into account the machining process parameters and the material to be machined. Following this introduction section, there is a description of the methodology used, based on a finite element (FE) study, to design a customized chip-breaker. After that, the case study of the design a chip breaker for a PCD turning insert is presented, which shows the design process in a practical way. Next, the chip breaker was manufactured by means of laser technologies and real machining tests were conducted. Finally, there is an assessment of the results and, afterwards, conclusions are drawn.
## 4 Current Research Status of Energy Management Techniques
### Deterministic Rule-Based Control Strategy  
Owing to their simplicity, high computational efficiency and robustness to vehicle uncertainty, deterministic rule-based energy strategies are the most extensively used strategy in hydraulic vehicles.  
In order to enhance energy utilization and keep the deviations of the SOC within a high-efficiency range, based on Ref. [48], [PERSON] et al. [116] studied a logic threshold technique and optimization algorithm for the key parameters. [PERSON] al. [117] presented a rule-based control method for a recovery braking system. [PERSON] et al. [41] suggested a rule-based energy management method to enable the real-time control of the hydraulic vehicle. Moreover, a parameter-match method based on dynamic programming was provided to improve vehicle performance.  
A power-following control method was studied by inserting some engine working points in the optimal line [118], as shown in Figure 21. Through the test bench, the results suggested that the control design could enhance energy efficiency by more than 24%. The power-split hydraulic hybrid vehicle characteristics can be expanded to deploy in other vehicles while designing related hydraulic hybrid vehicles [119].  
Figure 20: Control strategy classification of hydraulic hybrid vehicle.  
Aimed at investigating the energy-saving potential of a series of hydraulic hybrid systems, [PERSON] et al. [120] devised a rule-based tunable energy approach to the trade-off between energy consumption and the dynamic performance of the wheel loader. The results revealed that the series HHWL had fuel savings of up to 18.9%. A rule-based dynamic optimal management approach was provided to address the issue of short battery life [87].
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 4 Effect of Confining Pressure on Jointed Coal Mass Under Different Joint Distributions
### Failure Modes and Mechanism of Coal Mass Under Different Confining Pressures
A large number of microscopic fissures were still developed in the model in addition to the main fissures, as illustrated in Figure 11. The shear-stress-dominated single-shear rupture plane mode was more pronounced in the intact coal mass models at high confining pressures (4 MPa, 8 MPa), as shown in Figure 11,d. The high confining pressure resulted in a very small aperture of the rupture plane in the model, indicating that the high confining pressure was able to maintain the integrity of the coal mass even when the microscopic fissures were extremely developed in the model (the total number of microscopic fissures in the coal mass was more than 7000 for a larger fissure degree). Figure 11: _Cont_. In summary, both the joint distribution and confining pressure exert influences on the failure mode of coal masses. However, the angle of the main fissure of the coal mass changed more significantly when the confining pressure increased, and it dominated the failure mode of the coal mass. Taking the coal mass model with \(K_{\rm Y}=8.26\) m\({}^{-1}\) as an example, there were many splitting failures inside the model under a low confining pressure. With the increase in the confining pressure, the development rate of the microscopic fissures in the coal mass slowed down. Under high confining pressure conditions, the impact of internal joints is further diminished, the proportion of matrix fissures increases significantly, and the orientation of the primary fissures is further altered, mirroring the failure modes observed in intact coal masses. The variation in the angles of the main fissure under different confining pressures is displayed in Figure 12.  
Figure 11: The failure mode under different fissure degrees and confining pressures.
###### Abstract  
Volatile organic compounds (VOCs) are monitored in numerous fields using several commercially-available sampling options. Sorbent-based sampling techniques, such as solid-phase microextraction (SPME), provide pre-concentration and focusing of VOCs prior to gas chromatography-mass spectrometry (GC-MS) analysis. This study investigated the dynamics of SPME Arrow, which exhibits an increased sorbent phase volume and improved durability compared to traditional SPME fibers. A volatile reference mixture (VRM) and saturated alkanes mix (SAM) were used to investigate optimal parameters for microbiological VOC profiling in combination with GC-MS analysis. Fiber type, extraction time, desorption time, carryover, and reproducibility were characterized, in addition to a comparison with traditional SPME fibers. The developed method was then applied to longitudinal monitoring of _Bacillus subtilis_ cultures, which represents a ubiquitous microbe in medical, forensic, and agricultural applications. The carbon wide range/polydimethylsiloxane (CWR/PDMS) fiber was found to be optimal for the range of expected VOCs in microbiological profiling, and a statistically significant increase in the majority of VOCs monitored was observed. _B. subtilis_ cultures released a total of 25 VOCs of interest, across three different temporal trend categories (produced, consumed, and equilibrated). This work will assist in providing foundational data for the use of SPME Arrow in future microbiological applications.  
microbial VOCs; SPME Arrow; gas chromatography-mass spectrometry (GC-MS); sampling optimization; _Bacillus subtilis_
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 4 Results  
### Epoch Solutions  
The system's GNSS working session starts daily at a time of 16:00:12 UTC with a 15 s sampling interval, while the reference dataset is recorded from 00:00:00 AEST at 10 min intervals. As a result, the two datasets are not aligned to the same epoch. Therefore, linear interpolation of the system's GNSS solution estimated by Equation (9) is employed to facilitate comparison at reference epochs based on (13):  
\[wl_{epoch_{ref}}=wl_{epoch_{1}}+(epoch_{ref}-epoch_{1})\frac{wl_{epoch_{2}}-wl_ {epoch_{1}}}{epoch_{2}-epoch_{1}} \tag{13}\]  
where \(wl_{epoch_{ref}}\) represents the interpolated system solution at reference data epoch \(epoch_{ref}\), \(wl_{epoch_{1}}\) and \(wl_{epoch_{2}}\) are system epoch solutions before and after \(epoch_{ref}\), respectively, \(epoch_{1}\) is the epoch before \(epoch_{ref}\), and \(epoch_{2}\) is the epoch after \(epoch_{ref}\).  
The system epoch results are categorized into original solutions (\"orig\") and high-accuracy solutions (\"high\"), the latter defined as epoch solutions with an STD < 3 cm for a given day. Figure 11(a) displays the time series of the three solutions at their respective intervals. The reference data time series, with fewer data points, shows the water surface variation trend, while \"orig\" and \"high\" have significantly more epochs and closely align with the reference data. The green segments, appearing as error bars, are discrete points clustering together. Figure 11(b) depicts the differences between reference data and \"orig\" and \"high\". The overall differences between the reference data and system results are less than 0.15 m, with the missing solution gaps due to the unavailable _uls_ data caused by a loss of data communication. The differences in the two solutions' normal distributions are shown in Figure 11(c).
## 2 Methods  
### Knowledge Distillation  
Knowledge distillation is a model compression method that is a training method based on the \"teacher-student network idea\", which introduces soft targets associated with the teacher network (complex, but with superior predictive accuracy) as part of the overall loss to induce training of the student network (lean, low complexity, better suited for inference deployment) to achieve knowledge transfer [11]. In past research, KD has often been used to complete classification tasks [12; 13].  
The task completed by [PERSON] in this paper is a regression task. Although KD has rarely been used for regression in past studies, there are some results from regression tasks performed by KD which also provide some basis for our research [9]. The structure of our KD is roughly shown in Figure 1. The teacher model is a biGRU of which the output we have softened as \(y_{T}\), and the student model is a 1D-CNN of which the output is \(y_{S}\) without any processing. After this, we put \(y_{T}\) and the similarly softened \(y_{S}\)' into a loss function, and \(y_{S}\) and the true label \(y_{true}\) into another loss function. The two are weighted by \(\alpha\) and added together to obtain the total loss, as shown in the Formula (1). Training the student model according to this total loss can make the student model learn from both the teacher label and the real label, and obtain the common information of both. \(\alpha\) controls the proportion of the student model learning from the two labels. A larger alpha indicates a greater knowledge contribution from the teacher model. In addition, it is worth noting that the loss function in the formula can use multiple parameters, such as the Euclidean distance, mean square error and cross entropy; the Euclidean distance is used in this paper because it reflects the best performance.  
\[\text{L}_{\text{KD}}=\alpha L(y_{S}^{\prime},y_{T})+(1-\alpha)L(y_{S},y_{true}) \tag{1}\]  
Therefore, under the action of KD, our student model, 1D-CNN, can learn part of the knowledge of the teacher's model, biGRU, especially the advantages of time sequence information mentioned above. In this way, the parallelized 1D-CNN can be skillfully used to greatly reduce the processing time of the equalizer. As such, we can change a heavy teacher model biGRU into a light student model 1D-CNN, which is a practical way to construct the minimalist deployment of a neural network equalizer.
## 6 Discussion
Although roundabouts in maritime traffic management are anticipated to improve vessel movement and reduce collision risks, their actual impact on traffic efficiency remains uncertain due to the various factors that influence their performance, including peak-hour factor, the consideration of heavy vehicles (which vary depending on classification), critical gaps, and follow up times. These factors, along with others, present limitations. However, despite these challenges, the concept shows promise and demands further investigation, as evidenced by the results of this study, to refine its application and enhance its effectiveness in maritime traffic management. The following points outline the limitations. First, the applicability of the roundabout design is constrained by the specific marine traffic, geographic, and environmental conditions of the Lombok Strait, and it may not be directly transferable to other maritime regions with varying traffic, terrains, or soundings. Second, the calculation of the entry/exit and circulating flow of traffic was also considered as is, and if any, the left exit and right exit were also computed as future predictions to enhance the traffic flow. Third, the analysis relies heavily on observed traffic data and ship domain models, which may vary based on local vessel behavior, to determine the critical gap, although other well-known domains, such as Fujii and Tanaka, Coldwell, etc., can be used as parameters in the initial design. Other aspects, such as construction costs, maintenance costs, and economic implementation, were beyond the scope of this study. However, these factors are also crucial for the practical implementation of such traffic schemes and require future investigation, as addressed in similar research [54, 55]. In light of these limitations, future research should aim to expand the scope of the study to further enhance the overall understanding of the subject and contribute to the promotion of navigational safety.
## 4 Application Example
### Energy Study of the Transformation House
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dipalance**} & \multicolumn{2}{c}{**Dipalance**} \\ \cline{2-5}
**Type of Day** & \begin{tabular}{c} **Energy Losses** \\ **(kWh/year)** \\ \end{tabular} & \begin{tabular}{c} **CO2 Emissions** \\ **(kg/year)** \\ \end{tabular} & \
\begin{tabular}{c} **Energy Losses** \\ **(kWh/year)** \\ \end{tabular} &
\begin{tabular}{c} **CO2 Emissions** \\ **(kg/year)** \\ \end{tabular} \\ \hline Working days (251) & 1322.77 & 318.74 & 3737.39 & 900.63 \\ \hline Holidays (114) & 1772.70 & 427.21 & 113.92 & 27.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Annual energy losses and CO2 emissions caused by unbalanced (\(W_{cu}\)) and distorted (\(W_{ch}\)) currents in the transformer of the transformation house. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dipalance**} & \multicolumn{2}{c}{**Dipalance**} \\ \cline{2-5}
**Type of Day** & \begin{tabular}{c} **Energy Losses** \\ **(kWh/year)** \\ \end{tabular} & \begin{tabular}{c} **CO2 Emissions** \\ **(kg/year)** \\ \end{tabular} & \
\begin{tabular}{c} **Energy Losses** \\ **(kWh/year)** \\ \end{tabular} &
\begin{tabular}{c} **CO2 Emissions** \\ **(kg/year)** \\ \end{tabular} \\ \hline Working days (251) & 1322.77 & 318.74 & 3737.39 & 900.63 \\ \hline Holidays (114) & 1772.70 & 427.21 & 113.92 & 27.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Annual energy losses (\(W_{cr}\)) and CO2 emissions caused by reactive currents in the operation of the transformation house. Energy and Environmental Effects Resulting from Improving the Sustainability of the Transformation House  
The elimination of penalties for capacitive reactive energy, as well as a reduction in carbon dioxide emissions in the transformation center, can be carried out based on three actions, included in the procedure described in Section 3:  
1. Optimization of the operation of the capacitor banks;
2. Reduction of copper losses caused by imbalances and distortions;
3. Replacing the transformer with a more efficient one, with less power. The first of these actions is the one that can bring economic benefits, while the third provides, above all, energy and environmental benefits.