esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Description  
#### 2.2.1 MODIS GPP Algorithm  
MOD17A2 is calculated using a light use efficiency model with the one-leaf upscaling strategy based on the radiation conversion efficiency concept of [PERSON] [9] as follows [19, 20, 69]:  
\[GPP=\varepsilon_{max}\times f(VPD)\times f(T_{air})\times PAR\times fPAR \tag{1}\]  
where \(PAR\) is the incident photosynthetically active radiation--its value is assumed to be 45% of the incident shortwave radiation [19]; \(\varepsilon_{max}\) is the maximum light use efficiency, which depends on the plant functional types; \(f(VPD)\) and \(f(T_{air})\) are the scalars of vapor pressure deficit and air temperature, respectively, both of which are ranged from 0 to 1 to downscale the maximum light use efficiency of the canopy to the actual value. The scales were calculated as follows [8, 13, 15]:  
\[f(VPD)=\frac{VPD_{max}-VPD}{VPD_{max}-VPD_{min}} \tag{2}\]  
\[f(T_{air})=\frac{T_{air}-T_{MINmin}}{T_{MINmax}-T_{MINmin}} \tag{3}\]where \(VPD_{max}\) and \(T_{MINmin}\) are daily maximum VPD and daily minimum temperature at which the light use efficiency equals 0, and \(VPD_{min}\) and \(T_{MINmax}\) are daily minimum VPD and daily minimum temperature at which the light use efficiency is maximum. We replaced the daily minimum temperature in the temperature function [8,13,15] by EC-observed half-hourly temperature (\(T_{air}\)) to calculate GPP on half-hourly time scale. \(fPAR\) is the fraction of \(PAR\) being absorbed by the canopy, which was estimated using the Beer's law [21,70] as follows:  
\[fPAR=1-e^{\cdot k\cdot LAI} \tag{4}\]  
where \(k\) is the light extinction coefficient, which is set to 0.5 [15,70,71], and \(LAI\) is the green area index.  
The soil moisture scalar (\(\beta_{i}\)) is another factor that impacts the light use efficiency of vegetation [16,17,29,30,72]. We added this factor to Equation (1) in another simulation as follows [37]:  
\[\beta_{i}=\sum_{i=1}^{15}w_{i}r_{i} \tag{5}\]  
where \(w_{i}\) is the plant-wilting factor for soil layer \(i\), and \(r_{i}\) is the fraction of roots in soil layer \(i\). The soil profile is divided into fifteen layers, for which the depth of the layer increases exponentially with the soil layer number [37]. The factor was calculated using DLM, because the soil moisture measurements were not provided by all site-years we selected and the values were simulated well by DLM [33]. More details can be found in the Appendix and [PERSON] _et al._[37]
## 1 Introduction
Building materials, as well as design, would be one of the important factors to lower the carbon dioxide emissions during the construction phase. Likewise, optimal design of a building with high-strength building materials would result in superior performance in terms of carbon dioxide emissions and structural reliability [26]. Therefore, selecting the structural design and building materials would make it possible to reduce carbon dioxide emissions during the construction phase. As the efficient use of land and urbanisation has become prevalent in recent years, it is common to construct high-rise buildings in urban areas around the world. Slabs in a building not only provide occupants with a flat surface, but also transfer various loads, such as service and permanent loads, through beams, girders, and columns, to the ground. Furthermore, the self-weight of slabs in high-rise buildings would significantly grow as the height of a building increased. Given the significant role of slabs in a building, the main focus of research regarding slab systems is to corroborate the structural performance and reliability, such as the flexural, shear, and seismic performance [37; 38; 39; 40]. Compared to studies on the structural performance of slabs, studies that evaluate the environmental impacts of slab systems are relatively scarce. For example, [PERSON] et al. [41] examined the carbon dioxide emissions from reinforced concrete slab of different thickness. The authors analysed the relationships amongst the distance between columns, the thickness of slabs, and the carbon dioxide emissions. However, this research only dealt with the conventional reinforced concrete slab system, rather than newly developed slab systems. In order to reduce the self-weight of slab systems, the flat plate slab and voided slab systems are applied to buildings for enhanced serviceability and ease of construction. Although the application of flat plate slab systems and the voided slab systems is prevalent, there is little research to compare and verify their environmental performance. The purpose of this research is to assess the environmental performance of three different slab systems (i.e., the ordinary reinforced concrete slab, flat plate slab, and voided slab systems) during the construction phase (see Figure 1). In this study, the process-based life cycle assessment (LCA) method was applied, in order to evaluate their level of performance. The comparison amongst the three slab systems was carried out for the following three stages: building material production before transportation to the construction site; transportation of the building materials to the construction site; and utilisation of construction equipment during the construction and installation. The analysed data were further discussed to illustrate the respective environmental performance of the ordinary reinforced concrete slab, flat plate slab, and voided slab systems during the construction phase.
## 3 Simulated Phased Array with P-CRLH Phase Shifters  
A four-element linear patch antenna array operating at the design frequency of 5.5 GHz is simulated in ADS 2016 Momentum. The inter-element center-to-center spacing is \(\lambda/2\) with the substrate having a thickness of 1.27 mm, \(\epsilon_{\tau}=2.2\) and \(\tan\delta=0.002\). The schematic of the 1 \(\times\) 4 array with directly excited and integrated P-CRLH phase shifters is shown in Figure 8. The mutual coupling between the adjacent antenna elements for directly excited and P-CRLH-excited phase shifters are shown in Figure 9. The maximum mutual coupling is \(-13.5\) dB at the design frequency for both the direct- and P-CRLH-excited array. From (5.8-6) GHz, the mutual coupling for the directly excited array is significantly less than the P-CRLH-excited array. A ladder resonator [34] can be used to reduce the mutual coupling effects in the patch antenna array structure at the resonant frequency. Next, the P-CRLH phase shifters designed in Sections 2.1 and 2.2 will be integrated with the array to test their performance for main beam scanning applications.  
Figure 6: Simulated S parameter results of the proposed unit cell and cascaded unit cells P-CRLH phase shifter with MRSs embedded on the symmetrical stubs using \(-90^{\circ}\) unit cell phase shift.  
Figure 7: Simulated phase response results of the proposed unit cell and cascaded unit cells P-CRLH phase shifter with MRSs embedded on the symmetrical stubs for \(-90^{\circ}\) phase shift.
## 4 Results
### Model Training  
In order to train the model, IMS dataset no. 2 was used. It has a total of 984 run-to-failure samples, corresponding to a motor operation of 6 days and 20 h. Accelerations were collected at a frequency of 20 kHz and outer race bearing failure was detected at the end of the test. This motor works at a frequency of 33.33 Hz and has a load of 26.69 kN. The bearing used was Rexnord ZA-2115.  
Firstly, HHT was used to preprocess the vibration signal. Then FFT was computed on the envelope of the first IMF for each sample to extract the amplitudes of the bins corresponding to 2X, 3X, and 4X BPFO. The motor used in the IMS dataset has a BPFO of 236 Hz, so the frequencies corresponding to 2X, 3X, and 4X are 472, 708, and 944 Hz, respectively. Before fusing these features into the HI curve, it is necessary to normalize them separately using min-max normalization. After that, the fusing of the features is carried out using the SAE network. Figure 5 shows the normalized features and the HI curve obtained.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Samples** & **Total Time** & **Bearing Type** &
\begin{tabular}{c} **Shaft** \\ **Frequency** \\ \end{tabular} & **Load** & **BPFO** & **Used in** \\ \hline IMS dataset no. 2 & 984 & 6 d 20 h & \multirow{2}{*}{Rexnord ZA-2115} & \multirow{2}{*}{33.33 Hz} & \multirow{2}{*}{26.69 kN} & \multirow{2}{*}{236 Hz} & Training \\ IMS dataset no. 3 & 4448 & 31 d 10 h & & & & \\ \hline FEMTO Bearing1\_3 & 2376 & 6 h 36 m & \multirow{2}{*}{NSK 6804-DD} & \multirow{2}{*}{30 Hz} & \multirow{2}{*}{4 kN} & \multirow{2}{*}{168.34 Hz} & \multirow{2}{*}{Testing} \\ FEMTO Bearing1\_4 & 1429 & 3 h 58 m & & & & \\ FEMTO Bearing1\_7 & 2260 & 6 h 16 m & & & & \\ \hline XJTU-SY Bearing2\_5 & 339 & 5 h 39 m & \multirow{2}{*}{LDK UER204} & \multirow{2}{*}{37.5 Hz} & \multirow{2}{*}{11 kN} & \multirow{2}{*}{115.61 Hz} \\ XJTU-SY Bearing3\_1 & 2538 & 42 h 18 m & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset information.  
Figure 5: Normalized features and HI curve. (**a**) Normalized features. (**b**) HI Curve.  
The BiLSTM model was trained on 70% of the randomly selected data set. After training, the rest of the dataset was used to forecast the HI curve in order to compare it with the actual HI curve. RUL prediction results with the training dataset are shown in Figure 6. The vertical dashed red line corresponds to the instant at which the prediction is made. In turn, the green line corresponds to the actual RUL, while the dashed black line corresponds to the predicted RUL. The error obtained with the developed model in IMS dataset no. 2 is 1.0869%.
# Design and Evaluation of a Shaping and Pruning Machine for Dwarf and Densely Planted Jujube Trees
## 2 Materials and Methods
### Design of Pruning Device
#### 2.3.1 Design of the Structure  
According to the growth information on dwarf and densely planted jujube trees and the shaping and pruning requirements for canopies, the designed height of the side pruning assembly is 1600 mm, and the length of the top pruning assembly is 1200 mm. The cutters of the pruning device are tungsten carbide-tipped circular saw blades. The side pruning assembly uses 12 circular saw blades, the top pruning assembly uses four, and the adjacent two circular saw blades are staggered vertically. The rotating cutter shaft is driven by the hydraulic motor when the pruning device operates, and then, the circular saw blades on the cutter shaft are driven to rotate. The other circular saw blades are rotated by the synchronous belt. To ensure steady force on jujube branches in the process of sawing and pruning, a supporting rods are installed on the sawing side of the pruning component. Additionally, the pruning machine positions the jujube branches with the supporting rods during pruning to support the sawing operation. In the pruning operation, the branches  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Corresponding power (\(kW\)) & \(\geq\)45 \\ Rotation speed of cutter (r/min) & 1700–2300 \\ Adjustment range of pruning height (mm) & 600–2200 \\ Adjustment range of pruning width (mm) & 800–1400 \\ Size of pruning device (Length \(\times\) width \(\times\) height) (mm) & 1700 \(\times\) 500 \(\times\) 1800 \\ Weight of overall machine (kg) & 450 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main technical parameters of shaping and pruning machine for jujube trees.  
are positioned by the supporting rods to facilitate the sawing operation. The structure of the pruning device is shown in Figure 3.
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 2 Be\({}_{12}\)Ti Neutron Multiplier  
### Beryllium Resources  
Beryllium is currently produced from two minerals: betrrandite (Be\({}_{4}\)Si2O\({}_{7}\)(OH)\({}_{2}\)) and beryl (Be\({}_{3}\)Al\({}_{2}\)Si\({}_{6}\)O\({}_{18}\)) [5]. Bertrandite, which contains about 15 wt% beryllium, is the principal beryllium mineral mined in the United States. Beryl, which can contain up to 5 weight percent beryllium, is the main beryllium mineral mined in the rest of the world. Beryl ore typically contains from 2 to 4 weight percent beryllium. Countries with active industrial beryl mining operations include Brazil, China, Madagascar, Mozambique, Nigeria, Portugal, Rwanda and Uganda [5].  
The United States, Kazakhstan and China are the only countries known to process beryllium ores and concentrates into beryllium products [7]. Materion Corporation, Parkland Blvd., USA is the only producer of beryllium ore in the United States. The JSC Ulba Metallurgical Plant of Kazakhstan produces beryllium hydroxide from stockpiled concentrate previously extracted from the Ermakovskoe beryllium deposit in Russia [7]. The concentrate stockpile total accumulated by 1992; it was estimated that the Ulba Metallurgical Plant produced about 90 tons of beryllium in 2015 contained in beryllium-copper alloys, beryllium oxide ceramics, and beryllium metal. China's Shuikoushan Non-Ferrous Metals Group Co., Ltd. mines beryllium ores to produce beryllium products in various forms at its refinery in Songbai, Hunan Province. The plant has an estimated production capacity of 150 metric tons per year of beryllium oxide (36 weight percent beryllium. The main beryllium world suppliers are listed in Table 1. In fact, the Materion (170 tons/year) and Ulba Plant (90 tons/year) are the major potential Be suppliers for the HCPB DEMO blankets.  
The world annual Be production presented in Table 1 is significantly below the total needs for the future HCPB DEMO. Because of its chemical properties, beryllium metal is required in many industrial applications. A wide variety of products made from beryllium are used in aerospace, computer, defense, medical, nuclear and telecommunications applications. In the USA, a high-purity beryllium metal is classified as a strategic and critical material because of its vital importance for national security [7]. Therefore, it is very unlikely that the beryllium demands for the HCPB DEMO exceeding its annual world production can be covered without a long-term buying strategy. The appearance of such an important consumer as the DEMO on the global market could very likely be a driving force for the Be price increase. Hence the economical factor has to be accounted for when developing a strategy for the beryllium handling within the EUROfusion DEMO project.
## 2 Terrain Model
### Initial Geometry
#### 2.1.2 Height Modifier  
The modified height position of any point in a geometric layer is related to its distance from the fault edge. The geometry is modified for any point, where \(s>0\). To normalise and limit this modification to the interval from \([0,1]\in\mathbb{R}\), the distance is also divided by the fault radius. The height modifier (\(\Delta h\)) is calculated for each \(p\in G\):  
\[\Delta h=\left\{\begin{array}{ll}\sqrt{s}/r,&\mbox{if}\quad s>0\\ 0,&\mbox{in other cases}\end{array}\right. \tag{3}\]  
The height modifier reaches their maximum limits (value 1.0) for points that positions are identical to \(p_{o}\) and decreases with the increasing distance. Minimum (value 0.0) is reached when the point is on the fault edge or outside it due to the fact that \(s\) is not a linear function and \(\Delta h\) does not change its value in a linear way. This property was visualised in Figure 7. In the next phase, \(\Delta h\) is added to the value in the geometric layer of point \(G(p)\):  
\[G^{\prime}(p)=G(p)+\Delta h \tag{4}\]  
The result of geometry modification by the CFF algorithm is a fBs and for further use it should be normalised to the interval \(0\leqslant G(p)\leqslant 1\), where \(G(p)\in\mathbb{R}\).  
Figure 6: Graphical interpretation of the circular fault properties.  
Figure 7: Graph of the height modifier function.
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 5 Results and Analysis
### Results Based on GRU Model  
The datasets above were divided into training datasets and test datasets by a proportion of 3:7 and then fed into GRU model. The network parameters for the training process were as follows: the Adam optimizer was used to update the weights of the network during the training process; the learning rate was set to 0.001; the time step of the GRU model was set to 3 (3 frames as a time step as input).  
In this work, we used the raw temperature datasets, i.e., R1 and R2, and the respective principal component datasets, i.e., M1 and M2, to train the GRU network. A normalized confusion matrix was applied to assess the performance of the GRU model. In true labels and predicted labels, the number 0 represented the sound areas; the numbers 1, 1.5, and 2 corresponded to the defect depths of 1 mm, 1.5 mm, and 2 mm, respectively. The values on the diagonal of the matrix represented the recall rates of the defect depth recognition, where the recall rates were defined as the proportion of true positive examples that were correctly predicted as positive in the test datasets [25]. As shown in Figure 12, the datasets M1 and M2 obtained a relatively high accuracy in the recognition of the four different defect depths. Under the two diameters conditions, the recognition rate of each defect depth was exceeding 90%. In Figure 13, the recall rates of R1 and R2 were not equal to those of M1 and M2, except the defects with a depth of 1 mm. One possible reason is that the infrared camera was affected by environment noise; this in turn meant that the raw temperature datasets R1 and R2 contained redundant information that contributed to the performance of defect depth recognition, and this is considered undesirable. A summary table that recorded the recall rates regarding the four kinds of datasets is seen in Table 3. The datasets M1 and M2 had higher recall rates in defect depth recognition than datasets R1 and R2 on the whole due to the former eliminating redundant information and strengthening the features of the defect depth. The proposed method did not perform well in the recognition of particular depth defect (e.g., 1 mm); a possible reason is that the samples from the transition region between the defect area and sound area caused the error of recognition.  
Figure 11: The result processed by PCA method: PCT1, PCT2, and PCT3.  
Figure 12: The normalized confusion matrix of test datasets from (**a**) M1; (**b**) M2.
## 3 Processes to Eliminate B-O LID
### Annealing Incorporating Minority Carrier Injection  
Rather than reducing the defect precursor concentration, anneals that incorporate minority carrier injection can lead to an electrical neutralisation of the formed defects and the recovery of the carrier lifetime. The role of illuminated annealing processes at elevated temperatures for the permanent deactivation of B-O defects was first identified by [PERSON] et al. in 2006 [83; 84]. Importantly, it was observed that the lifetime recovery that occurs with illuminated annealing is stable with subsequent exposure to illumination at typical solar panel working conditions. This recovery is typically termed'regeneration' or 'passivation'. With the identification of an additional stable high-lifetime state, a three-state model was developed to describe the B-O defect system [83]. This model has formed the basis for numerous studies investigating the kinetics of the B-O defect system [10; 12; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95].  
Unlike dark thermal processes that tend only to reduce the B-O defect concentration but not completely eliminate B-O defects, illuminated annealing demonstrated the ability to completely overcome B-O related LID provided that temperatures below 230 \({}^{\circ}\)C were used [86; 95; 96]. This breakthrough offered new hope for industrial p-type solar cells fabricated on boron-doped Cz silicon wafers to overcome the LID that had plagued the industry, without the typical Pareto trade-offs that occur when using alternative silicon materials. However, the time scales for the permanent recovery were in the order of minutes to hours in the early studies and hence too slow for commercial production.  
Although this paper discusses the role of illumination in the permanent deactivation of B-O defects, the reaction has been demonstrated to be driven by excess minority carriers [86; 90; 91; 97; 98] and can also be induced by the application of a forward bias [83; 84; 86; 98]. However, for rapid processes, the currents required would generate significant variations in carrier concentrations throughout the devices and thus are not the preferred approach. The following section discusses progress in the fundamental understanding of illuminated B-O LID mitigation processes over the last ten years that have led to the development of rapid processes for LID mitigation.
## 2 Materials and Methods
### Mechanical Properties Tests  
The compressive strength (\(f_{c}\)) has been obtained on 150 mm cubic samples (EN 12390-1) per mix at ages of 7, 28 and 91 days. The test specimens have been tested using a load application rate of 0.6 MPa/s in a servo-hydraulic press of 3000 kN capacity and in accordance with EN 12390-3. The ultrasonic pulse velocity test has been performed on the specimens intended for the compressive strength test, prior to testing the compressive strength and just after their surface is dry. The measurement has been carried out according to EN 12504-4 with the transducers in direct transmission placed in collinear directions between two parallel faces with Vaseline on the contact surface between transducers and the concrete surface. The pulse velocity is calculated as the \(\frac{length\ of\ specimen}{pulse\ travel\ time}\) ratio. The compressive modulus of elasticity (E) has been determined in a servo-hydraulic press of 250 kN capacity, on three cylindrical specimens with 150 mm and 300 mm in length per mix, after 28 days of curing in a humidity chamber. The upper and lower faces of the test specimens have been levelled before the test and a compressor/extensometer equipped with high precision displacement transducers is used to measure the micro-deformation. Four loading/unloading cycles have been used, applying an initial stress of 1 MPa (17.6 kN) and a load application speed of 0.5 MPa/s (8.8 kN), using a maximum load of \(f_{c}\)/3 according to LNEC E-397. The splitting tensile strength has been determined on the three specimens used in the modulus of elasticity test, for all mixes. A servo-hydraulic press of 3000 kN capacity and a load rate of 0.05 MPa/s (3.5 kN/s) was used, according to EN 12390-6.  
Figure 2: Recycled aggregate concrete (RAC) manufacturing process.
###### Abstract  
Aiming to solve the problem of the low path-tracking accuracy of mobile robots in agricultural environments, the authors of this paper propose a path-tracking method for agricultural machinery based on variable look-ahead distance. A kinematic model of the four wheel independent steering-four wheel independent drive (4 WIS-4 WID) structure based on pure pursuit was constructed to obtain the functional equation of the current position and the four-wheel steering angle. The fuzzy controller, which takes the lateral deviation and heading deviation as input and the look-ahead distance in a pure pursuit model as output, was designed to obtain the look-ahead distance that changes dynamically with the deviation of mobile agricultural machinery. The path-tracking performance of 4 WIS-4 WID agricultural machinery in three scenarios (1 m, \(-90^{\circ}\); 1 m, \(0^{\circ}\); and 0 m, \(90^{\circ}\)) with different initial deviations was tested using a pure pursuit model based on a variable look-ahead distance. The obtained field test results showed an average deviation of 19.7 cm, an average tracking time of 5.1 s, an average stability distance of 203.9 cm, and a steady state deviation of 3.1 cm. The results showed that the proposed method presents a significant path-tracking performance advantage over a fixed look-ahead distance pure tracking model and can be a reference for high-quality path-tracking methods in automatic navigation research.  
look-ahead distance; pure pursuit model; fuzzy control; path tracking Article