esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architectural Model
### Solutions for Monitoring a Plastic Injection Molding Production Line
#### 3.1.2 Intervention Request System  
The intervention request system aims to digitally manage the execution of requests and the call to the operator. The roles requested could be different based on the skills assigned to the different operators; the system allows the request to be made to the type of operator required, after which it will carry out an assessment of the availability of the required figure and then send a call. The main advantage of the digital and automatic management of this process lies in the possibility of keeping track of all the requests made and of the distribution of the workload among the various operators. In the absence of a digital intervention request system, the most commonly used tool is a walkie-talkie, which allows one to call the appropriate operator. Although this system is particularly effective for communication purposes, it does not automatically keep track of the information exchanged and the requests made.  
A digital system for managing requests for intervention has the disadvantage of being more expensive than a simpler system, such as the use of walkie-talkies, and of being less flexible to changes in the process. Furthermore, the system must be designed effectively to ensure the correct sending and receiving of requests for intervention and therefore correct communication between the parties.  
In order to achieve this goal, various technological solutions can be implemented. Concerning the application context described in Section 3.1, a simple push-button panel, interconnected with the system, could be used, which allows for a request for the intervention of a specific figure to be made using the various push-buttons. The requests are mainly made from the control and assembly station, which notices any malfunctions, qualitative defects, or other management needs to be overcome. Then, using a simple push-button panel, the line operator can effectively make the necessary request. A more complex digital device could also be prepared, such as a tablet or notebook/personal computer, but for this purpose, a simple push-button panel can suffice. On the other hand, for the operator to receive the call, a tablet or smartphone could be used, which, if interconnected to the system, could guarantee the correct reception of the intervention request. The sheets of the data required from the intervention request system are reported in Table 2.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 4 Medium Access Control (MAC)  
Next, we move to the Data-Link layer and specifically to the medium access control (MAC) mechanism, which highly affects the performance of data gathering protocols, as it influences several performance aspects, such as reliability, latency, channel utilization, power utilization (which impact the lifespan of a sensor in particular and the network in general), etc. Even though many access protocols were suggested over the years for shared-channel networks, wired and wireless, due to their unique properties and requirements, extensive research has been conducted on MAC protocols targeted to WSN. Many WSN MAC protocols were designed to comply with various traffic patterns. Accordingly, in the sequel, we provide a short WSN-MAC protocol overview for ones that can support yet are not necessarily particular to data-gathering. We review a fraction of the vast literature on the topic and the numerous MAC protocols devised over the years. Figure 3 depicts a schematic partition of the papers discussed throughout this section according to the main MAC classes.  
Energy consumption is one of the main concerns of WSN, and as previously explained, it needs to be considered in the design of protocols and algorithms in all the layers of the protocol stack. The highest energy consumer of a sensor or an IoT device is its transceiver, which consumes energy regardless of whether it is transmitting or only awake listening to ongoing traffic (e.g., [75]). One of the more prevalent solutions to save power is to embrace a duty-cycle mechanism in which the device is asleep most of the time (its transceiver is in low power mode), and is awake for transmitting or receiving data only a small fraction of the time. Another important aspect of wireless sensor networks (WSN) is channel utilization, that is, in a typical WSN, when multiple devices are trying to send reports simultaneously, air time is a crucial network resource. Particularly when the network is  
Figure 3: Schematic description highlighting the main MAC classes covered in the section.  
dense with multiple devices in the same neighborhood, issues such as coordinating between the users to avoid collisions and preventing users from occupying the channel for long time intervals are fundamental. Note that such issues can highly affect the performance in a dense network topology even when reports are not frequent. Accordingly, utilizing the channel (air time) efficiently is a crucial component for any such system's operation and performance. These two highly vital aspects justify the significance given to the design of medium access control (MAC) protocols that are particular to WSNs. Numerous protocols have been suggested to cope with different WSN objectives and demands (e.g., [76; 77]). This paper focuses on MAC protocols for WSNs, particularly on duty-cycled-based ones.
## 4 Conclusions  
The principal findings of this study can be summarized as follows:  
1. Moisture content affects shear strength differently depending on gradation. At air-dried moisture content (\(w=0.91\%\)), the original gradation, ESM, and SGM exhibit similar strengths, with weak scaling effects. Under natural (\(w=2.45\%\)) and saturated (\(w=7.66\%\)) conditions, scaled specimens show significant strength reductions.
2. For a shear box of 350 mm \(\times\) 250 mm, original gradation specimens with \(d_{\rm max}=60\) mm exhibit significantly higher shear strength in laboratory tests than in situ results, with cohesion (\(c\)) and internal friction angle (\(\phi\)) also overestimated. This suggests that using specimens with \(d_{\rm max}\leq W/5\) may lead to overestimated strength values, necessitating caution in engineering applications. For specimens scaled to \(d_{\rm max}\leq W/10\), the shear strength parameters are closer to those from in situ direct shear tests, suggesting that such scaling better reflects field conditions.
3. Scaled specimens (\(d_{\rm max}\leq W/10\)) demonstrate distinct differences in shear strength depending on the scaling method. ESM specimens show internal friction angles (\(\phi\)) close to in situ values but overestimated cohesion (\(c\)), while SGM specimens exhibit slightly lower \(\phi\) and higher \(c\). Overall, SGM results align most closely with in situ tests in peak strength and shear strength parameters, providing more reliable data for engineering design by better reflecting the mechanical behavior of field soil.  
Given the expense and time constraints associated with in situ direct shear tests, laboratory direct shear tests remain the primary method for evaluating CGS shear strength. The accuracy of shear strength parameters derived from laboratory tests can be significantly influenced by different scaling methods under varying moisture conditions. Through the comparison of in situ and laboratory results, this study offers valuable insights and guidance for achieving accurate CGS shear strength measurements in laboratory settings. It is important to note that the scaling methods discussed in this paper are limited to two commonly used approaches, and the particle size range considered is confined to the gradation of the Jilangtan platform CGS. Future work will concentrate on refining these methods by broadening the particle size range and considering additional scaling methods.  
[PERSON] wrote the first draft of the paper and carried out the laboratory large-scale direct shear test. [PERSON] and [PERSON] did the in situ direct shear test. [PERSON] checked and improved the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors' gratitude goes to the reviewers for their helpful comments and constructive suggestions in improving this paper.  
The authors declare no conflicts of interest.
## 4 Test Results and Discussion
### Chemical Weathering Indices
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Site**} & \multirow{2}{*}{\begin{tabular}{c} **Weathering** \\ **Grade** \\ \end{tabular} } & \multicolumn{5}{c}{**Chemical Weathering Index**} \\ \cline{3-8}  & & **VR** & **MWPI** & **CIA** & **I\({}_{\text{mob}}\)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} A \\ site \\ \end{tabular} } & VI & 3.53 & 6.93 & 66.09 & 0.35 \\ \cline{2-8}  & IV and V & 2.37 & 8.59 & 59.01 & 0.17 \\ \cline{2-8}  & III & 1.55 & 12.53 & 54.78 & 0.01 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} G \\ site \\ \end{tabular} } & VI & 3.79 & 5.41 & 66.09 & 0.62 \\ \cline{2-8}  & IV and V & 1.77 & 10.35 & 55.27 & 0.19 \\ \cline{2-8}  & III & 1.29 & 12.73 & 50.43 & 0.00 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} S \\ site \\ \end{tabular} } & VI & 1.99 & 9.88 & 57.03 & 0.24 \\ \cline{2-8}  & IV and V & 1.65 & 10.76 & 52.53 & 0.13 \\ \cline{1-1} \cline{2-8}  & III & 1.32 & 11.80 & 50.60 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average chemical weathering index. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Site**} & \multirow{2}{*}{\begin{tabular}{c} **Weathering** \\ **Grade** \\ \end{tabular} } & \multicolumn{5}{c}{**Weight Percentage of Major Oxide**} \\ \cline{3-13}  & & **SiO2** & **Al2O3** & **TiO2** & **Fe2O3** & **MgO** & **CaO** & **Na2O** & **K2O** & **MnO** & **P2O5** & **Loss on Ignition** \\ \hline \multirow{3}{*}{\begin{tabular}{c} A \\ site \\ \end{tabular} } & VI & 67.24 & 16.81 & 0.62 & 3.84 & 0.51 & 0.65 & 2.08 & 3.71 & 0.05 & 0.24 & 4.22 \\ \cline{2-13}  & IV and V & 68.51 & 15.75 & 0.57 & 3.48 & 0.64 & 0.83 & 3.28 & 3.76 & 0.05 & 0.20 & 2.91 \\ \cline{2-13}  & III & 62.58 & 15.81 & 0.54 & 3.98 & 1.89 & 2.63 & 2.2 & 4.29 & 0.05 & 0.16 & 5.85 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} G \\ site \\ \end{tabular} } & VI & 72.82 & 11.58 & 0.33 & 6.58 & 0.80 & 0.46 & 0.74 & 3.59 & 0.36 & 0.08 & 2.65 \\ \cline{2-13}  & IV and V & 68.09 & 15.66 & 0.43 & 3.78 & 1.06 & 1.70 & 3.40 & 3.75 & 0.06 & 0.18 & 1.83 \\ \cline{2-13}  & III & 66.89 & 15.94 & 0.45 & 3.69 & 1.40 & 3.32 & 3.52 & 3.55 & 0.06 & 0.17 & 0.82 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} S \\ site \\ \end{tabular} } & VI & 66.45 & 16.17 & 0.56 & 3.69 & 0.88 & 1.58 & 3.16 & 3.82 & 0.06 & 0.19 & 3.46 \\ \cline{2-13}  & IV and V & 67.89 & 15.40 & 0.50 & 3.22 & 0.82 & 1.74 & 4.08 & 3.74 & 0.04 & 0.17 & 2.38 \\ \cline{1-1} \cline{2-13}  & III & 67.54 & 15.10 & 0.52 & 3.51 & 1.15 & 2.84 & 3.72 & 3.23 & 0.05 & 0.15 & 2.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average weight percentage of major oxide. Figure 7: Cont.
## 1 Introduction
A group of capacitors is used to shift charge among batteries. A single switched capacitor requires only one capacitor to balance cells. A double-tiered switched capacitor balancing method is a derivation of the switched capacitor but it uses two capacitors for energy shuttling. The advantage is that the second capacitor reduces the balancing time. Figure 1b shows a switched capacitor balancing circuit. The transformer/flyback balancing method is an active balancing method and removes energy from one cell to another cell [11; 12; 13; 15; 26; 27; 28]. It may have a smaller balancing/executing time. It also has disadvantages, such as high cost and magnetic losses. Figure 1c shows the multi secondary  
Figure 1: **(a)** Switched resistor balancing method; **(b)** switched capacitor balancing method; **(c)** multi secondary winding transformer balancing method. winding transformer balancing method. If all transistors are always switched at the secondary side, the leakage impedance determines the equalizing current for a voltage difference. It is not clear if all secondary voltages are equal due to the non-homogeneous field close to the air gap on the flyback transformer. If only some selected transistors are driven, more precise control is possible, but this requires much more circuitry. In this paper, a high-voltage detecting, equalizing, and monitoring system is proposed. To achieve that, a light-emitting diode (LED) band gap is used as a voltage reference to inform the user if any cell is at its high voltage. A smart-monitoring display (using a liquid crystal display (LCD)) shows the user whether one cell is high or all cells are high. This provides a signal to the microcontroller to turn on/off the charger if all cells are high as well. Furthermore, a Bluetooth module is designed to command the microcontroller to turn on/off the charger via voice/text message by using a smartphone. A notable characteristic of the system is its ability to carry a very-low current; as a result, the system does not contribute significantly to the self-discharge of the battery and does not require complex control as compared to the existing passive methods [21; 22; 29].
## 4 Experimental Validation and Result Analysis
### Processing of Experimental Data
#### 4.1.2 Sensitive Feature Selection Results  
Considering Bearing1-3 as an example, features are then extracted using the method presented in Section 2.2. Seven time-domain, six frequency-domain, and eight time-frequency domain features are finally extracted; the 21 features are illustrated in Figure 9. These features each have their own advantages. For example, the time-domain features of P\({}_{1}\)-P\({}_{6}\) are characterized by changes in signal waveform over time. The frequency-domain features of P\({}_{7}\)-P\({}_{14}\) represent the distribution of different signal characteristics in the frequency spectrum, allowing for the study of signal curve characteristics from the spectrum. The time-frequency domain features of P\({}_{15}\)-P\({}_{21}\) can capture the instantaneous characteristics of signals, reflecting fault information of bearing vibration signals from different perspectives. According to reference [44], the performance of bearings changes over time. When predicting bearing life, the focus is primarily on long-term trends. Predictability represents the accuracy of the model, and a model with strong robustness can provide more stable predictions of bearing life. The weights for each indicator are 0.5, 0.35, 0.05, and 0.1, respectively. The weight scores for each feature are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Monotonieity** & **Trendability** & **Prognosability** & **Robustness** & \(\mathbf{J}\) \\ \hline P1 & 0.020 & 0.003 & 0.323 & 0.417 & 0.07 \\ P2 & 0.075 & 0.324 & 0.454 & 0.955 & 0.27 \\ P3 & 0.018 & 0.008 & 0.240 & 0.462 & 0.07 \\ P4 & 0.020 & 0.022 & 0.567 & 0.831 & 0.13 \\ P5 & 0.034 & 0.230 & 0.460 & 0.858 & 0.21 \\ P6 & 0.075 Figure 9: Feature maps.  
The fitted data of weighted averages for each indicator are arranged in descending order, as shown in Figure 10. It can be seen that when the weighted average is almost 0.23 (as indicated by the position of the red dashed line in Figure 10), the curve shows a significant turning trend. This is consistent with the result obtained in [16]. For sensitive feature selection, features with weighted averages (\(J\)) greater than 0.23 are selected.  
The selected features are P2, P6, P13, P14, P15, and P21, as shown in Figure 11. These features demonstrate either monotonic increase or decrease. This indicates that, when the degradation increases, the vibration signal amplitude also increases with monotonically increasing features, while monotonically decreasing features describe the reduction in bearing RUL function of time. These features encompass most of the degradation information of bearings, and these sensitive prognostic features significantly contribute to mapping accurate bearing health conditions, providing a reference for precise RUL prediction.
## The Experimental Process and the FEM Model
### FBG Sensors Deployment  
During the in-site monitoring, FBG sensors were installed on PCCV cylindrical wall at an NPP in China. The top elevation of the auxiliary structure is 22 m as shown in Figure 1b. Apparently, the roof of auxiliary structure provided large space for installing FBG. As a result, the monitoring location was chosen at the mid height of the PCCV (see Figure 1a).  
On the cylindrical wall of PCCV, two kinds of FBG were installed as shown in Figure 2. The Figure 2a shows the general measuring point which consists of a vertical direction strain FBG and a circumferential direction strain FBG. In total, there were four general measuring points around the PCCV. Besides, Figure 2b shows the crack measuring point which consists of two circumferential FBG sensors which are perpendicular to the concrete crack. During installation, these two strain FBG sensors were pasted across the concrete crack. Both general measuring points and crack measuring points had a temperature-compensating FBG beside the strain FBG.  
Before installation, FBGs were divided into six groups and fusion spliced together. Here were the processes of the installation. Firstly, the concrete surface was polished to clear the dust away Secondly, the FBGs were pasted on the cylindrical wall by using No. 502 glue for primary fixing. Thirdly, epoxy resin was smeared on FBGs for reinforcement and protection. After those processes, the optic fiber connecting line was pasted on concrete surface. At last, a fiber optic connecting line was connected to Optical Sensing Interrogator sm125 (manufactured by MICRON OPTICS) for data collection. In order to distinguish those FBG in one channel, the wavelength of each FBG and the specific location was recorded after installation.  
The measuring points are located at the middle of the PCCV as shown in Figure 3. The specific locations of each FBG are summarized in Figure 3.  
Figure 1: The location of fiber Bragg grating (FBG) measuring points on the prestressed concrete containment vessel (PCCV). **(a)** Prospect of the measuring points; **(b)** Close shot of the measuring points.  
Figure 3: The measuring point locations on the PCCV and the specific location of each measure point (length unit: m).  
Figure 2: Two types FBG measuring points. **(a)** General measuring point; **(b)** Crack measuring point.
## 3 Results and Discussion
### Experiment 2: Effect of Biomass-Specific Photon Supply Rate under Nitrogen Starvation
However, if the goal is to produce a starch-rich biomass with high percentage starch content, a higher BSPSR is to be preferred. Figure 4: Time course over 96 h after N starvation expressed as average of triplicate cultures in experiment 2: (**a**) culture density expressed as absorbance at 750 nm; (**b**) culture density expressed as biomass dry weight; (**c**) starch concentration expressed as % of biomass dry weight; (**d**) starch concentration expressed as grams per litre. Figure 5: Starch maximum accumulation in _C. vulgaris_ at different biomass specific photon supply rates in experiment 2. (**a**) peak of maximum concentration in the biomass, observed 12 h within starvation; (**b**) peak of maximum starch production in the culture, expressed as g L\({}^{-1}\), and observed 24 h within starvation. It is interesting to know that starch contents higher than what was achieved in our study have been previously reported in _Chlorella_, and have been often achieved combining high light and low inoculum density, reaching up to 55% in C. _vulgaris_[18] and even 77% in a CO\({}_{2}\) adapted strain grown in a 2-stage process with high dilution [25]. In continuous chemostat growth, it has also been reported a very high effect of light and inoculum density, with the best result of 25% starch-rich biomass [19]. However, [PERSON] et al. reported in 2011 a high starch content of 44% in C. _vulgaris_ at a relatively low light intensity of 70 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)[22]. Differences in mediums used and growth vessels may be the cause of those discrepancies, nonetheless, our study contributes to a solid body of literature that identifies C. _vulgaris_ as a high starch producer, especially at higher photon supply rates.
## 4 Discussion
### Impact of Meteorological Data on MODIS GPP
Unexpectedly, the eight-day GPP estimates recalculated using the site-based meteorological observations did not result in obvious improvements (Figure 1a,b). Conversely, the annual GPP estimates recalculated using the site-based meteorological observations were not as accurate as those calculated using the GMAO meteorology reanalysis dataset (Figure 3a,b). The implication of these results is that an improvement in meteorological input data will not necessarily improve the reliability of GPP estimation by the MOD17A2H GPP algorithm, emphasizing the requirement of identifying additional sources of uncertainty.