esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Uncertainties and Future Directions  
The present study recognizes the uncertainties related to the satellite remotely sensed datasets MODIS and MISR applied to run GPP-based LUE models such as EVI, NDVI, FAPAR and PAR [7]. Several previous studies reported that these optical satellite sensors need improvement in calibration, geometry, orbital drift and spectral coverage [26; 29]. Uncertainties may also be from the quality of MODIS-based LC data used to adjust model parameters' robustness. Generally, the employed GPP-based LUE models were estimated based on the function of temperature and water stress factors; the uncertainties related to these factors may have also caused uncertainties in modelling processes.  
Therefore, uncertainties related to the LUE models inputs may have produced sub-optimal performance or mismatching problems in model parameters adjustment and may in some ways slightly affect the modelled GPP results. According to [PERSON] et al. 2020 and 2021 [13; 14], these uncertainties might be prevented when better cloud screening, composite techniques, full spectral coverage, higher resolution and field-based datasets are available to the public in future research. However, this study may provide the insightful information about the effects of climate change and ecosystem dynamics on GPP variations in the tropical and sub-tropical climate zones.
## 2 Overview of System Configuration  
For the investigated case, the main circuit of the off-board charger comprised of a full-bridge resonant LLC converter and a three-phase voltage source PWM rectifier is interfaced with the synchronous grid through LC filter, as indicated in Figure 1.  
The fast charger control strategy is split into two major parts, which are the pre-stage VSM control and post-stage double closed-loop control.  
* VSM control not only ensures inertia and damping emulation and frequency support as well as voltage control, but also make the off-board charger provide the constant-current fast charging service for the EV's power battery.
* Double closed-loop control is in charge of stabilizing the DC bus voltage \(U_{\text{dc}}\).  
Figure 1 only shows an overview of the overall control scheme, so the implementation of these control functions is described in detail in the following sections.
## 2 Materials and Methods
### Data Set
#### 2.1.3 Procedure Protocol  
Coronary angiography (Siemens Artis Zee, Forchheim, Germany) was performed after iso-centering in posterior-anterior and lateral planes, via a transradial (preferred) or trans-femoral approach. In all cases, a 6 French diagnostic catheter was used after intracoronary injection of glyceryl trinitrate according to routine practice in the hospital, with manual contrast injection and cine acquisition at a frame rate of 15 frames/second. OCT imaging was performed using a frequency-domain OCT systems (St. Jude Medical/Abbott, St. Paul, MN, USA). The fiber probe was pulled back at a constant speed and cross-sectional images were generated with a spacing of 0.2 mm.  
The acquisition of physiological data for FFR calculation was performed according to conventional practice [48] with a commercially available FFR measurement system (PressureWire Aeris; St. Jude Medical, Minneapolis, MN, USA). The 0.014 coronary wire with a pressure tip was advanced until the pressure sensor passed the orifice of the guiding catheter. Transcatheter aortic and intracoronary pressure tracings were equalized. Subsequently, the guidewire was advanced into the respective coronary artery until the pressure sensor passed the index lesion. Hyperemia was induced by the administration of adenosine either intravenously at a constant rate of 140 \(\upmu\)g/kg/min, or as an intracoronary bolus (100 \(\upmu\)g for the right and 200 \(\upmu\)g for the left coronary artery); the pressure recording was started, and the FFR was determined. A total of 102 coronary lesions in 80 patients underwent FFR analysis. This invasively measured FFR represents the ground truth that is used during the training of the deep neural networks, as described in the following.
# 2-D Seismic Response Analysis of a Slope in the Tyrrhenian Area (Italy)
## 3 Site Response Analysis
### Numerical Analyses  
To evaluate morphological and stratigraphic effects, 1-D and 2-D equivalent-linear site response analyses were performed. Several techniques were proposed for the assessment of these effects; by comparing the results from experimental estimates of local site amplification effects and numerical analyses, it was demonstrated that 1-D numerical modeling can underestimate the amplification of ground motion and cannot account for resonant frequencies every time [35]. In these cases, the use of 2-D numerical methods is almost essential to obtain a more realistic estimate of the seismic response [36].  
The 1-D analyses were carried out in three vertical profiles extracted from the 2-D cross-section, located in this way: (1) the first behind the crest of the slope, (2) the second at the crest of the slope, (3) the third at a point downstream sufficiently far from the foot of the slope.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil** & \(\alpha\) & \(\beta\) & \(\eta\) & \(\lambda\) \\ \hline Debris & 65 & 1.15 & 34.8 & 1.9 \\ Brown Clay & 20 & 0.87 & 19 & 2.3 \\ Dark Gray Clay & 7.5 & 0.897 & 90 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soil constants for Caronia area.  
The 1-D analyses were performed by the EERA (Equivalent-Linear Earthquake Site Response Analysis) code [37] that permits performance of frequency domain analyses for equivalent linear stratified sub-soils. The same elaborations were also carried out through the QUAKE/W code to obtain a calibration of the two codes and be able to proceed subsequently with the 2-D analyses. A comparison of results from these two different codes is a good way of validating the formulation and implementation of the work. The EERA model was simulated in QUAKE/W as a column of elements where the base of the column is fixed. This has the effect of the earthquake motion being applied at the column base. The sides of the column are fixed in the vertical direction, which ensures that all the motion is in the horizontal direction only, an assumption inherent in the EERA 1-D formulation.  
The 2-D analyses were performed by the QUAKE/W [38] code based on a finite element formulation with a direct integration scheme in the time domain. \"Direct integration\" means no transformation of the equations into the frequency domain is required. Then, the motion equations are solved directly using a finite-difference time-stepping procedure.  
Figure 12 shows the cross-section used for the finite element analyses; the three-surface points monitored by 1-D and 2-D analyses were also indicated in the same figure.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion
### Performance Analysis
#### 5.3.2 Example Program of the JAFFE Dataset in the Original Scenarios  
Figure 11 shows an example program evolved by ITACIE-GP for the JAFFE dataset in the original scenarios, along with its two visualization examples. This program achieves an accuracy of 92.85% on the training set and can reach an accuracy of 89.04% on the test set.  
Figure 11: _Cont._  
In the left branch of the example program, two different image fusion operations are executed to generate new image region rich in information. Subsequently, based on these new image region, pixel features and LBP features are extracted and concatenated to obtain fused features. In the feature construction layer, new features are further constructed by combining the information transmitted from the downsampling layer. In the right branch of the example program, an image fusion operation is conducted, resulting in the creation of a new image region. By extracting pixel and SIFT features from these new image regions, fused features are obtained. In the feature construction layer, new features will be further constructed, achieved by integrating the information transmitted from the downsampling layer. Ultimately, by concatenating the features from both the left and right branches, and during this process, the handcrafted features generated in the feature construction process are passed to the feature concatenation layer, thereby assisting in the classification decision.  
Overall, through in-depth analysis of two example programs, it is demonstrated that the solutions generated by ITACIE-GP are not only easy to interpret but also exhibit excellent performance in terms of their generalization ability and classification accuracy, thanks to their program structure based on information transmission. Moreover, the ITACIE-GP method can effectively construct features according to the needs of different classification tasks, thereby ensuring effective classification decisions even in occlusion or other complex scenarios.
## 5 Conclusions  
To determine the coplanar and branch FCG rates of normal rail, head hardened rail, and wheel steel, fatigue tests were conducted under non-proportional mixed mode I/III loading cycles that simulated the RCF conditions. SEM observations and FEA were also performed to investigate the FCG behavior. The results can be summarized as follows.  
1. In RP and WT, a coplanar growth was obtained when \(\delta\) was smaller than or equal to 90\({}^{\circ}\). The growth rates were relatively well correlated when plotted against \(\Delta K_{\text{s}}\) defined by Equation (6). The highest coplanar FCG rate was observed in WT, followed by RP and then RF.  
2. In the RP case, the branch FCG occurred at \(\delta=120^{\circ}\). The growth rates were plotted against \(\Delta K_{\text{eq}}\) defined by Equation (10), which considers the \(\Delta K_{\text{III}}\) attenuation due to the crack face contact, giving a good correlation.
3. Based on the fracture surface observations by SEM and the FEA results, the growth of long coplanar cracks was assumed to be driven mainly by mode III loading.
4. The FEA results showed that RF, which is a high-tensile steel, had smaller CTODs during the loading cycles compared with RP. Therefore, contact was likely to occur between the crack faces owing to the surface irregularities, causing crack branching in RF even under the same conditions.
5. When \(\delta\) increased, \(\Delta\sigma_{\text{max}}\) with respect to \(\Delta\tau_{\text{max}}\) also increased, and the \(\Delta\sigma_{\text{max}}\) plane turned toward the branch direction. Therefore, it can be concluded that the cracks tend to branch when \(\delta\) increases.
6. The comparison of the fracture surfaces, branching conditions, and coplanar FCG rates data under mixed mode I/III loading and those under mixed mode I/II loading [25] indicated that the coplanar crack growth mechanisms in these two loading cases were similar regardless of whether the main driving force was in-plane or out-of-plane shear.  
Conceptualization, M.A.; Methodology, M.A. and A.K.; Software, M.A.; Validation, M.A. and A.K.; Formal Analysis, M.A.; Investigation, M.A. and A.K.; Writing-Original Draft Preparation, M.A.; Writing-Review & Editing, M.A.; Supervision, M.A.  
This research was performed as part of the research and development programme for the future of railways, entitled 'Creation of rail damage/ballast track deterioration models and evaluation of maintenance work saving technologies', and was funded by the Railway Technical Research Institute.  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# A Perforated Plate with Stepwise Apertures for Low Frequency Sound Absorption
## 3 Results and Discuss
### Sound Absorption of PPSA Absorfer with Varied Hole Spacing, Aperture and Plate Thickness  
The theoretical absorption coefficients of the single PPSA absorber with varied hole spacing, aperture and plate thickness are marked with the color bar in Figure 7a-d. The parameters of PPSA are listed in Table 2 and hole spacing b = 30 mm, air cavity D = 80 mm. In Figure 7a, when other parameters remain unchanged, with the increase of hole spacing b, the effective sound absorption is distributed in the lower frequency range, and the maximum sound absorption coefficient reaches 1 and then decreases. In Figure 7b, when aperture \(d_{1}\) increases, the resonance shifts to higher frequencies and becomes weak, and the maximum sound absorption coefficient is significantly reduced. That is, when the perforation ratio is constant, the PPSA with a smaller aperture \(d_{1}\) is more conducive to sound absorption in the low frequency range. The effect of aperture \(d_{2}\) on the sound absorption is similar to that of aperture \(d_{1}\), so no explanation is given here. In Figure 7c, as the thickness \(t_{1}\) increases, the resonance occurs in the lower frequency, but the maximum sound absorption coefficient becomes lower. This is because the small aperture with a large thickness causes excessive damping. In Figure 7d, when the thickness \(t_{2}\) increases, the resonance also moves to lower frequencies, however, the maximum absorption coefficient of almost 1 is perfect in the whole range of \(t_{2}\). This is because for the large aperture, the sound mass changes obviously as the thickness increases, while the sound resistance changes relatively slightly. Therefore, proper parameters should be considered to obtain an effective PPSA absorber for low frequency sound absorption.  
Figure 7: The sound absorption coefficients of PPSA absorber with varied structure parameters. (**a**) Hole spacing b; (**b**) Hole aperture \(d_{1}\); (**c**) Plate thickness \(t_{1}\) of the small aperture; (**d**) Plate thickness \(t_{2}\) of the large aperture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **PPSA** & **Single PP1** & **Single PP2** \\ \hline \(d(d_{1}/d_{2})\,(\text{mm})\) & 2/4 & 4 & 4 \\ \(t(t_{1}/t_{2})\,(\text{mm})\) & 2 & 7 & 2 \\ \(b\) (mm) & 39.6 & 39.6 & 39.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for PPSA and single PP absorbers with the same hole spacing.
## 3 Results
### Soils
#### 3.2.2 Soil Enzymatic Activities  
Soil enzymatic activities at the end of the experiment are presented in Table 5.  
In the avocado tree, the four enzymatic activities studied were significantly higher in the soils under waste than in the bare soil, with lower differences for the phosphatase activity. Soil dehydrogenase and urease activities were higher under GA.  
Significant differences were found in the soils under the cherimoya tree, especially in dehydrogenase and urease activities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **pH** & **EC (dS m\({}^{-1}\))** & **BD (kg dm\({}^{-3}\))** & **OC (\%)** & **N (\%)** & **C/N** \\ \hline AA & 6.7 (0.2) a & 0.4 (0.0) a & 0.5 (0.2) ab & 9.8 (0.4) c & 0.7 (0.0) e & 14.5 (0.5) a \\ GA & 7.2 (0.2) bc & 1.3 (0.0) e & 0.4 (0.1) a & 7.8 (1.9) b & 0.5 (0.1) d & 13.5 (2.1) a \\ SA & 7.1 (0.0) bc & 0.5 (0.0) b & 0.8 (0.1) bc & 1.4 (0.5) a & 0.2 (0.0) b & 10.3 (4.0) a \\ CC & 7.8 (0.1) e & 1.9 (0.1) f & 0.7 (0.1) abc & 2.4 (1.0) a & 0.2 (0.0) bc & 11.2 (3.8) a \\ GC & 7.6 (0.1) de & 0.5 (0.0) b & 0.8 (0.1) c & 2.3 (1.3) a & 0.1 (0.0) a & 29.0 (22.9) a \\ SC & 7.0 (0.1) b & 0.7 (0.0) c & 0.7 (0.1) abc & 2.2 (0.5) a & 0.2 (0.0) b & 14.0 (0.6) a \\ MM & 7.3 (0.1) c & 0.6 (0.0) b & 0.7 (0.1) abc & 3.1 (1.6) a & 0.2 (0.0) b & 13.9 (0.8) a \\ GM & 7.4 (0.1) cd & 0.4 (0.0) a & 0.7 (0.1) abc & 2.7 (0.3) a & 0.2 (0.0) bc & 15.0 (2.5) a \\ SM & 7.3 (0.0) c & 0.8 (0.0) d & 0.6 (0.1) abc & 3.2 (0.8) a & 0.3 (0.0) c & 12.9 (3.0) a \\ \hline \hline \end{tabular} Note: EC—electrical conductivity; BD—bulk density; OC—organic carbon. AA—avocado pruning waste; CC—chernoya pruning waste; MM—mango pruning waste; G—urban garden waste; GA—urban garden waste under avocado tree; GC—urban garden waste under cherimoya tree; GM—urban garden waste under mango tree; SA—soil without added pruning waste under the avocado tree; SC—soil without added pruning waste under the avocado tree; SC—soil without added pruning waste under the cherimoya tree; SM—soil without added pruning under the mango tree. Data not sharing a common letter are statistically different among crops (Tukey’s test, \(p<0.05\)); standard deviation in brackets.  
\end{table}
Table 4: Mean values of the soil properties analyzed at the end of the experiment.  
However, under the mango tree, no significant differences were noted in the soil enzymatic activities between the soils under waste and the bare soils except for a higher urease activity under garden waste (GM).
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 2 Principles of Power Flow in Power Systems
### MT-HVDC Systems Power Flow
#### 2.2.4 DC Side of MT-HVDC Systems  
When the current flowing through the DC-bus capacitor is zero, \(I_{DCi}^{p}=I_{Li}^{p}\) and \(I_{DCi}^{n}=I_{Li}^{n}\), where \(I_{Li}^{p}\) and \(I_{Li}^{n}\) are the current of the positive and negative DC link connected to the \(i\)th/ converter station.  
The injected current can be determined by the conductance matrix of the DC grid. The DC current injection at the positive and negative poles of the DC bus is as follows:  
\[\left[\begin{array}{c}-l_{DC1}^{p}\\ \vdots\\ -l_{DCi}^{p}\\ -l_{DCi}^{p}\\ -l_{DC1}^{n}\\ \vdots\\ -l_{DCi}^{m}\\ \vdots\\ -l_{DCi}^{m}\\ \vdots\\ \vdots\\ -l_{DCN}^{n}\\ \end{array}\right]=\left[\begin{array}{ccccccccc}G_{pp11}&\cdots&G_{pp1i}& \cdots&G_{pp1N}&G_{pn11}&\cdots&G_{pn1i}&\cdots&G_{pn1N}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ G_{pp11}&\cdots&G_{ppii}&\cdots&G_{ppiN}&G_{pn11}&\cdots&G_{pnii}&\cdots&G_{pniN} \\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ G_{ppN1}&\cdots&G_{ppNi}&\cdots&G_{ppNN}&G_{pnN1}&\cdots&G_{pnN1}&\cdots&G_{pnNN }\\ G_{np11}&\cdots&G_{np1i}&\cdots&G_{np1N}&G_{nn11}&\cdots&G_{nn1i}&\cdots&G_{np1N} \\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ G_{npi1}&\cdots&G_{npii}&\cdots&G_{npiN}&G_{nn11}&\cdots&G_{mnii}&\cdots&G_{ npiN}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ G_{npN1}&\cdots&G_{npNi}&\cdots&G_{npNN}&G_{nnN1}&\cdots&G_{nnNi}&\cdots&G_{ npNN}\\ \end{array}\right] \tag{23}\]  
Hence, the injected current at the \(i\)th bus in the DC side is as follows:  
\[I_{DCi}^{p}=-\sum_{j=1}^{N}(G_{ppji}V_{DCj}^{p}+G_{pnji}V_{DCj}^{n}) \tag{24}\]  
\[I_{DCi}^{n}=-\sum_{j=1}^{N}(G_{npji}V_{DCj}^{p}+G_{nnji}V_{DCj}^{n}) \tag{25}\]  
where \(N\) represents the total number of buses in the DC grid. In addition, \(G_{ppji}\), \(G_{mnji}\), \(G_{pnji}\), and \(G_{npji}\) are the conductance of the positive and negative DC link between nodes \(i\) and \(j\), respectively.
## 5 WST Applications
### Cold Chain Monitoring and Traceability
Also these tags take around five seconds to transfer recorded temperature values over the RFID interface [45]. A high data rate is required according to a normal flow of goods in a warehouse. Figure 2: Proposal of WSN inside refrigerated trucks [57]. Environmental temperature can differs from each other depending in the location of the logger, packing material, or heat dissipation of the product [98,99]. Semi-passive loggers can be also used to measure, not just the walls of the vehicle, but also inside the boxes [46]. There are available commercial solutions for monitoring containers and trucks, but they do not bring complete information about the cargo, because they typically measure in a single or very limited number of points [14]. [PERSON] and [PERSON] proposed sensor fusion for the development of smart containers in order to improve security, gathering data from several sources in order to trigger the alarms. Containers may incorporate a variety of sensors to detect, identify, log and communicate what happens during their journeys around the world [100]. [PERSON] _et al_. presented a system for intelligent containers combining wireless sensor networks and RFID [36]. Such devices can be placed in transport vehicles in order to monitor the on-the-go environment and can be the basis for distributed systems, enabling environment sensing together with data processing [14]. In the intermodal transportation, the performance of radio waves inside metal enclosed areas was studied. [PERSON] and [PERSON] tested a RF device in a metal cargo container and demonstrated that it is possible to communicate with the outside world [101]. [PERSON] _et al_. focuses on the 3-D mapping of RFID signal strength inside a 12 m (40') refrigerated marine container. Three different types of radio frequency configurations were tested: 2.4 GHz, 915 MHz and 433 MHz. The main goal was to find a frequency and configuration that would allow real time reading of the temperature in a shipment of perishable products using RFID. The results showed a significantly higher performance at the 433 MHz level [102]. Appropriated monitoring requires an increasing number of measurements to be performed in food logistics. Specialized WST monitoring devices promise to revolutionize the shipping and handling of a wide range of perishable products giving suppliers and distributors continuous and accurate readings throughout the distribution process. Precise, frequent and automated readings, interpreted by software and coordinated with existing and planned product inventories, should translate into more intelligent goods management and fewer rejected shipments. They could be used to remedy the cause of the problem. But even if a direct access to the means of transport is not possible, online notifications offer new opportunities for improve transport planning. If fixed delivery commitments require ordering of a replacement, the time of information is very crucial.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Satellite Resource Pool Architecture
This is why we proposed the pooled model, as shown in Figure 2c. For users in the target region, several satellites can be equivalent to a large satellite with a common resource pool, and all satellite resources of the resource pool can be used. It is worth noting that the tasks of the satellite are not necessarily sent by the ground station, but also generated by the satellite itself. However, traditional satellites, especially corresponding to the isolated mode, are generally transmitted by the ground station. Therefore, other models make the same assumptions. Based on this, the satellite in independent utilization mode is equivalent to the classical \(M/M/1\) queuing system. In cooperative mode, tasks are released to a satellite through a channel, and then the satellite services the tasks. In this mode, when a task comes, if there are idle satellites, the task will be distributed to them for execution. In the pooled mode, \(k\) satellites build a unified computing cluster, which is equivalent to a server with larger resources and faster service. The notations frequently used in this paper are summarized in Table 1.  
Figure 2: Modes of satellite resource sharing showing (**a**) isolated, (**b**) cooperative, and (**c**) pooled.