esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This work investigated the effect of magnesium ion concentration on the formation of mineral scale in circulating cooling water under alternating electric field treatment (AEFT) by using a self-made power plant circulating cooling water simulator. In this experiment, the circulating cooling water used was artificial hard water. Three experimental groups with different magnesium ion concentrations and three corresponding control groups were studied. Scale inhibition rate, fouling resistance, calcium ion concentration, crystal phase, and crystal morphology were discussed. Results showed that 4 mmol/L magnesium chloride promoted the formation of scale on the copper tube wall, and the average scale inhibition rate was \(-\)107.86%. When the magnesium chloride concentration was 6 mmol/L, the situation was reversed, and the average scale inhibition rate reached 59.11%. The changes in calcium ion concentration supported the scale inhibition rate. Scanning electron microscope (SEM) photos showed the change in the composition of calcium carbonate crystals. The results showed that the effect of AEFT on fouling resistance is nonlinearly related to the concentration of magnesium ions in circulating cooling water.  
alternating electric field; adherent scale; non-adherent scale; circulating cooling water; magnesium ion concentration; calcium carbonate +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Single-Scale Mathematical Morphology  
The theory of mathematical morphology is an effective method for signal processing, which aims to suppress noise and extract significant features by directly applying pre-defined SE on time domain signals. The mathematical morphological filter primarily consists of four morphological operators (dilation operator, erosion operator, closing operator, and opening operator), defined as follows:  
\[\text{Dilation}(x_{i}\oplus s_{j}):y_{i}=\max(x_{i-j}+s_{j}) \tag{1}\]  
\[\text{Erosion}(x_{i}\Theta s_{j}):y_{i}=\min(x_{i+j}-s_{j}) \tag{2}\]  
\[\text{Opening}(x_{i}\circ s_{j}):y_{i}=x_{i}\Theta s_{j}\oplus s_{j} \tag{3}\]  
\[\text{Closing}(x_{i}\bullet s_{j}):y_{i}=x_{i}\oplus s_{j}\Theta s_{j} \tag{4}\]where \(x_{i}\) is the input signal, \(y_{i}\) is the output signal, and \(s_{j}\) represents the discrete function of the structural element, here equivalent to the length of the morphological kernel. Based on the properties of these four basic morphological operators, the new morphological operators are defined as follows:  
\[F_{\text{CD}}(x):y=x\bullet s\oplus s \tag{5}\]  
\[F_{\text{OE}}(x):y=x\circ s\Theta s \tag{6}\]  
where \(F_{\text{CD}}\) is a cascade combination of closed operator and expansion operator, which can enhance the extraction of positive pulse. \(F_{\text{OE}}\) is a cascade combination of the open operator and the corrosive operator, which can enhance the extraction of negative pulses. Combining the filtering performance of the above two operators, a new enhanced top-hat transformation operator, EAVGH, is defined as follows:  
\[\begin{split}\text{EAVGH}(x)=& x-\frac{F_{\text{CD}}(x)+F_{ \text{OE}}(x)}{2}\\ =& x-\frac{x\bullet s\oplus s+x\circ s\Theta s}{2} \end{split} \tag{7}\]  
The EAVGH operator can effectively enhance the characteristics of positive and negative pulses in fault signals, demonstrating excellent noise processing capabilities.
## 2 Materials and Methods
### Life Cycle Inventory (LCI)  
Data for the life cycle inventory were collected from three different sources. First, technical literature and patent documents were used for the simulation of biomass pretreatment, conditioning of the pretreated biomass, and hydrolysis processes. Also, energy and material outputs were obtained from the computational simulation performed with Aspen plus V10, a chemical process simulation software. Finally, auxiliary incomes, raw material information, and starch production processes were selected from the Ecoinvent 2.2 database. All these data were implemented in the LCA software GaBi V7.3. Particular processes and considerations are detailed below.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Pulley Internal Thermal Analysis
#### 2.4.1 Pulley Mathematical Algorithm
In particular, this group includes \(N_{f}\), \(L_{f}\), \(W_{f}\), \(H_{f}\), \(A_{0}\), \(A_{1}\), \(A_{2}\), \(A_{3}\), \(A_{fc}\), \(W_{1}\), \(W_{2}\), \(W_{3}\), \(R_{o}\), \(R_{1}\), \(R_{2}\), \(R_{i}\) and the material related parameters \(\Lambda_{pn}\). The second group includes the operation related parameters \(h_{pn}\), \(\omega_{pn}\) and \(T_{pcn}\). \(h_{pn}\) is a function of \(\omega_{pn}\), which is determined experimentally. In this case, \(\Phi_{pn}\) is a function of only two parameters \(\omega_{pn}\)Rand \(T_{pcn}\) for a specific pulley, because the parameters in the first group are determined by the type of pulley and not the operating condition. In this work, a pulley thermal coefficient \(\lambda_{pn}\) is introduced to represent the ability of heat dissipation of the \(n\)th pulley in the belt drive system, which is the ratio of \(\Phi_{pn}\) to \(T_{pcn}\). And then each investigated pulley has its own thermal coefficient \(\lambda_{p}\). The physical meaning of \(\lambda_{pn}\) is when \(T_{pcn}\) remains same,  
Figure 7: Pulley outer surfaces. the higher \(\lambda_{pn}\) is, the larger heat flux dissipates from pulley surfaces. In this thermal analysis, this coefficient can reduce the complexity of the thermal calculation. \[\xi_{n}P_{fn}=\lambda_{pn}\Big{(}T_{pcn}-T_{a}\Big{)} \tag{24}\]  
The mathematical algorithm illustrates the changes in \(\lambda_{pn}\) for various parameters \(\omega_{pn}\) and \(T_{pcn}\). When a pulley operates at a constant rotating speed, the severity of the turbulent flow and Reynolds number remains the same, and \(h_{pn}\) remains constant. In this case, \(\Phi_{pn}\) is directly proportion to \(T_{pcn}\) for a pulley, and \(\lambda_{pn}\) does not change. However, if the pulley rotation becomes faster, the severity of the turbulent flow and Reynolds number increase dramatically [34], and \(h_{pn}\) increases. Thus, \(\lambda_{pn}\) changes for different values of \(\omega_{pn}\), and \(\lambda_{pn}\) is a function of \(\omega_{pn}\) for the \(n\)th pulley in the system. This function is defined as \(S_{n}(\omega\text{R}_{pn})\). Therefore, Equation (24) becomes  
\[\xi_{n}P_{fn}=S_{n}(\omega_{pn})\Big{(}T_{pcn}-T_{a}\Big{)} \tag{25}\]  
No analytical method can be used to analyze the turbulence flow. Thus, in this work, a numerical method is used to calculate the data points of \(\lambda_{pn}\) at different \(\omega_{pn}\) from 2000 RPM to 6000 RPM in intervals of 1000 RPM, and \(S_{n}(\omega_{pn})\) is determined by the curve-fitting of these points. Moreover, the numerical simulations are also used to provide a baseline temperature database for the thermal model to output the thermal distributions of the pulleys considered. This aspect is discussed in more detail in Section 2.4.2.
# Emerging Technologies for Automation in Environmental Sensing: Review
## 4 Challenges and Solutions in Implementing Automation  
The successful implementation of automation in environmental sensing and monitoring is challenged by various obstacles that must be addressed. It is essential to ensure that automated sensors and monitoring systems can collect data accurately and precisely. Factors like changes in environmental conditions, sensor drift, and calibration issues can affect the accuracy of measurements, so regular calibration and maintenance are necessary. Bringing together data from different sources, such as remote sensors, satellite images, and ground-based stations, is difficult due to differences in data formats, resolution, and quality. Effective strategies for data integration are needed to create a unified picture of environmental conditions. The upfront and ongoing costs of automation technologies can be too high for many organizations, especially in areas with limited resources. Moreover, identifying cost-effective solutions that maintain data quality without compromise is essential. Keeping automated sensors and systems running smoothly requires regular upkeep, including sensor calibration, software updates, and hardware fixes. Managing maintenance costs while minimizing downtime is a significant challenge. Deploying automated monitoring systems in remote or hazardous areas can be tricky. Access to power, network connections, and infrastructure for data storage and transmission are fundamental considerations. Scaling up automated monitoring to cover large areas or handle more data can strain existing resources and infrastructure. Ensuring the system can scale while still performing well and remaining reliable is a complex task. To overcome these challenges, experts from various fields like engineering, data science, environmental science, and economics need to collaborate. Working together,
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.1 Impact of the Common Reference Clock  
The core of our solution is the perfect distribution of common reference clock. Let us consider if the clock distribution is not perfect then in such a case our solution is likely to be unsuccessful to give the desired output. The playout time provided by the sender is according to the common reference clock. The receiver calculates the playout time for the data packets using sender's \"packet sent time\" value. If clocks of receivers and senders mismatch, a receiver can play stream asynchronously.  
Let us consider the two simple cases to elaborate the possible problem.  
1. If the receiver's clock is behind the sender's clock, it can play the stream late as if it is a delayed stream. If the difference has a larger value then the receiver may run out of buffer space to buffer the packets for a long time.
2. If the receiver's clock is ahead of the sender's clock then data packet's playout time value obtained by the receiver may be the value that has already been elapsed.
###### Abstract  
Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.  
model predictive torque control; switching table; weight coefficients; two-dimensional switching table; permanent magnet synchronous motor +
Footnote †: journal: Applied Sciences
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 2 Related Work
The scheme has two main operations: (a) the original data packets should be encoded before being sent from the source cluster and they should be re-encoded before being sent from the transitional cluster, where both encoding and re-encoding are performed using random network coding; and (b) in each hop, several sensor nodes with a certain probability of temporarily failing constitute a cluster to cooperatively send data packets. At the cost of acceptable delay, NCCC can achieve reliable performance. Although this scheme can achieve better reliability, the transmission delay is longer. The major challenge of the schemes described above is balancing the transmission delay and network transmission reliability. When adopting a complex scheme, the network lifetime will be damaged due to the larger energy consumption. However, if the algorithm is simple, the network reliability cannot be guaranteed. Due to the disadvantage of these schemes, this paper uses the remaining energy of the nodes to increase the transmission power. In this way, the network reliability can be improved while preserving the network lifetime.
## 3 Methodology and Results
### Sensors  
The sensors used on the smart pipe demonstrator were off-the-shelf components based on MEMS technology chosen to represent the variety of data that are available. The types of sensors used include piezoelectric transducers, force sensors, light detection circuits, two-axis accelerometers and randomly distributed temperature sensors. As these commercial sensors were not designed specifically for integration into buried infrastructure, there are advantages in developing and miniaturising these sensors. The issues and results arising from a preliminary investigation into miniaturisation is discussed in Section 3.5.  
The results from several of these sensors are shown below, however further information can be found in [29]. An example of the data obtained from the piezoelectric transducer, force transducer and dual-axis accelerometer within the smart pipe demonstrator unit as it is subjected to various types of excitation is shown in Figures 7 to 9. The additional load due to the backfill is registered by the force transducer as shown in Figure 8(b).  
In addition, the light and temperature sensors provided some promising results. Figure 10 shows the variations in detected light intensity by the LDR in air and water, when the Smart Pig went past and when the LDR was obstructed.  
Note how the attenuation of the light in water has caused the signal to drop significantly suggesting that changes in intensity may also identify air entrainment as well as suspended solids in the water. The trace related to the pig passing is of interest as it shows the change of light intensity as the wire connected to the pig obstructs the light to different degrees as it moves along the pipe.  
Figure 11 shows the temperature data collected from the various temperature sensors along the pipe when exposed to different conditions. Not only does it show that all but one (sensor 5) of the sensors survived the burial and were providing useful data after burial, it also shows that they did react to the pipe being flooded by warm water after it was buried. It also indicates that the variations along the pipe were small and that the temperature varied only small amounts once buried.  
Figure 10: Variations in detected light intensity by the LDR under various conditions.  
Figure 9: The response of the dual-axis accelerometer as a car is revved whilst parked on top of the buried pipe **(a)** x-axis, **(b)** y-axis.  
The data outputs from the demonstrator unit indicated that even very inexpensive sensors can provide useful data. In particular, the piezoelectric transducers incorporated into a flange on the demonstrator unit showed significant sensitivity at less than $1 US per unit, and can potentially provide data on external and/or internal stress changes on the pipe, while also being used to harvest power from say vehicles moving along the road above the pipe.
## 3 Case Study
### Evaluation Criteria  
In order to analyze and evaluate the forecasting performance of the proposed model, five commonly used evaluation indexes including mean absolute error (MAE), mean relative percentage error (MRPE), root mean square error (RMSE), root mean square relative error (RMSRE) and equal coefficient (EC) were used in the study [26; 29]. Their specific definitions are given by:  
\[MAE = \frac{1}{n}\sum\limits_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{15}\]  
\[MRPE = \frac{1}{n}\sum\limits_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{16}\]  
\[RMSE = \sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i} \right)^{2}} \tag{17}\]  
\[RMSRE = \sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(\frac{y_{i}-\hat{y}_{i} }{y_{i}}\right)^{2}} \tag{18}\]  
\[EC = 1-\frac{\sqrt{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i} \right)^{2}}}{\sqrt{\sum\limits_{i=1}^{n}\left(y_{i}\right)^{2}}+\sqrt{\sum \limits_{i=1}^{n}\left(\hat{y}_{i}\right)^{2}}} \tag{19}\]  
The smaller values of MAE, MRPE, RMSE, and RMSRE indicate the higher accuracy. The closer to one the EC value is, the higher accuracy the prediction is.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 2 Paper Overview
### Related Work
In [22], the authors propose a cross-layer optimized geographic node-disjoint multipath routing algorithm. The proposed routing algorithm achieved shorter average path length without causing much energy consumption, and a considerable increase of the network sleep rate was achieved. In [23], inspired by the structure composed of edge nodes around which there is no routing void, an efficient bypassing void routing protocol based on virtual coordinates was proposed in this paper. The proposed protocol achieved higher delivery ratio, shorter path length, less control packet overhead and energy consumption.
## 3. Results and Discussion
### Experimental Results
#### 3.1.2 Pachymetry  
The mean pachymetry value obtained for the initially applied pressure was 885 \(\upmu\)m (\(\sigma=158\)\(\upmu\)m). Figure 2 shows that the pachymetry value lowered for both increasing and decreasing pressures. This behavior was not found in those studies that employed apical displacement as a corneal deformation measure, where the pachymetry was calculated with the corneal incompressibility and curvature radii (determined as previously explained). This was why those authors obtained lower corneal thickness values when the pressure increased and higher values when the applied pressure dropped.  
The data pachymetry found in experiments using the Scheimpflug technology indicated two different behaviors. [PERSON] et al. (2014) [27] reported narrowing pachymetry variations for both increasing and decreasing pressure values, which is similar to what we found. [PERSON] et al. [26; 28] revealed a different behavior. Changes in pachymetry were significantly less marked, with a slight increase in pachymetry when the pressure dropped, and the pachymetry had even higher values than the initial ones. Although this behavior is not completely clear, it might be related to the previous treatment to which the corneal samples were submitted. Further experiments must be carried out on this subject.
###### Abstract  
Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.  
Keywords:yield measurements; point Big Data; uncertainty expression; interactive 3D visualization; ISO 19157 +
Footnote †: journal: Applied sciences
## 4 Discussion
### Practical Applications and Future Perspectives  
Reliable field tests are essential for measuring physical fitness in school-aged children, as they provide accurate and meaningful data without the need for expensive equipment or specialized training [24; 25]. Accurately measuring physical fitness is crucial for monitoring health, detecting potential health issues, or identifying delays in the physical development of children [50]. In the school context, physical attributes can be evaluated regularly, taking advantage of their broad accessibility and the availability of new, cost-effective equipment for accurate measurement. Precisely, the Fitnessmeter mobile application offers a practical, affordable and reliable tool for assessing COD performance in various contexts. Its portability and ease of use make it suitable for sports coaches, physical education teachers in schools and healthcare professionals monitoring rehabilitation progress. By bridging the gap between professional-grade systems and general users, Fitnessmeter has the potential to enhance athletic development, track inter-limb asymmetries and promote accessibility in sports performance assessment across both amateur and professional settings.
## 3 Experimental Results
### Critical Speed Rising  
Another feature of the proposed approach, which improves safety, is demonstrated in Figure 11. Here, with identical environment parameters, the vehicle without prediction, moving at a speed above critical, loses control and crashes, while the model with prediction successfully finishes the race. The reason for this results from differences in trajectories. As it was shown in previous studies [6], the vehicles' critical speed during the turn is proportional to \(\sqrt{R}\). The model with prediction starts the turn earlier, which results in increased \(R\), and as a result, increased critical speed. In target quality function, this should lead to improvements in the parameter, corresponding to the car stability--second addend in Equation (10). This is covered in the Discussions Section in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **\#Road** \\ **Condition** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Overall** \\ **Friction**\(\mu\) \\ \end{tabular} } & \multicolumn{3}{c}{**0.85 of Critical**} & \multicolumn{3}{c}{**0.9 of Critical**} & \multicolumn{3}{c}{**0.95 of Critical**} \\ \cline{3-11}  & & PD & PPD & GP-RMEP & PD & PPD & GP-RMEP & PD & PPD & GP-RMEP \\ \hline
1 & 0.5 & 14.72 & 13.71 & 14.18 & 14.56 & 13.57 & 12.81 & 14.44 & 13.35 & 13.52 \\
2 & 0.3 & 18.47 & 17.59 & 17.18 & 18.3 & 17.89 & 17.19 & 18.19 & 17.1 & 16.9 \\
3 & 0.1 & 37.51 & 34.58 & 33.14 & 35.59 & 32.17 & 31.42 & 34.31 & 30.48 & 30.16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Times needed to return to the lane center, second.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **\#Road** \\ **Condition** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Overall** \\ **Friction**\(\mu\) \\ \end{tabular} } & \multicolumn{3}{c}{**0.85 of Critical**} & \multicolumn{3}{c}{**0.9 of Critical**} & \multicolumn{3}{c}{**0.95 of Critical**} \\ \cline{3-11}  & & PD & PPD & GP-RMEP & PD & PPD & GP-RMEP & PD & PPD & GP-RMEP \\ \hline
1 & 0.5 & 685 & 298 & 373 & 711 & 334 & 318 & 843 & 364 & 385 \\
2 & 0.3 & 1693 & 383 & 374 & 1801 & 417 & 399 & 1854 & 458 & 413 \\
3 & 0.1 & 1659 & 408 & 381 & 1717 & 432 & 420 & 1782 & 471 & 461 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Steering controllers target quality function for each parameter combination.