esa-sceva/satcom-chunk-collection huggingface dataset:
The scale of the turbidite reservoirs is significantly smaller than that of the delta front reservoirs, but the difference between oil well production and core oil-bearing reservoirs is large, which is reflected in the net gross ratio (total oil layer thickness/sand body thickness) of the sand body, oil abundance, and oil well productivity. The statistical results show that the net gross ratio of turbidite sand reservoirs in 66.7% of the same oil wells is markedly better than that of delta sedimentary reservoirs, while the influencing factors of reservoir oil-bearing differences are still unclear. Previous studies have investigated the heterogeneity of reservoirs in the delta sedimentary system of Es\({}_{3}\)[3] on the southern slope of Linnan Subsag [32], but the differences in the microscopic pore structure characteristics of reservoirs under the control of different sedimentary systems in the area have not been compared. In addition, in-depth studies have been carried out only on the sedimentation, diagenesis, and fluid evolution of reservoirs in the Jiangjiadian area of the Shahejie Formation in Linnan Subsag. However, few studies have investigated the sedimentary factors, diagenesis, and formation mechanism of effective reservoirs in Es\({}_{3}\)\({}^{3}\) of the tight sandstones in the study area. In addition, the influence of sedimentation and diagenesis on the microscopic heterogeneity of reservoirs is not clear [33; 34; 35; 36; 37; 38]. Therefore, we explored whether there are differences in the microscopic pore structure characteristics of turbidite reservoirs and delta front reservoirs in Es\({}_{3}\)\({}^{3}\) in the Linnan Subsag, which affects the oil-bearing properties of tight reservoirs. In this study, HPMI, NMR, cast thin sections, scanning electron microscopy (SEM), and other methods are used to identify the pore type, geometry, and size distribution, fractal theory is used to characterize the pore-throat connectivity, and the cathode luminescence method is used to determine the type and stage of reservoir cementation. To clarify the differences in the microscopic pore structure of the two sets of sedimentary reservoirs, the sedimentary factors and diagenesis of the different sedimentary systems are discussed in detail, and their influences on the microporous pore structure are compared and summarized, providing an in-depth understanding of the heterogeneous formation process of the two types of sedimentary reservoirs. Finally, a sedimentary-diagenetic model suitable for the microscopic heterogeneity of reservoirs in the study area is established. This study deepens the theoretical research on the microscopic pore structure characteristics and influencing factors of tight sandstone reservoirs and can provide guidance for the evaluation, exploration, and development of tight oil sweet spots.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
###### Abstract  
Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.  
energy-efficient; energy harvesting; precision agriculture; wireless communication technology; WSN +
Footnote †: journal: Sensors
## 5 Results and Discussion
### Development of a Graphical User Interface (GUI)  
With the objective of enhancing the applicability of the established ERTR-BO model for the prediction of flyrock in real-world field scenarios, an attempt was made to develop a simple graphical user interface (GUI), which is depicted in Figure 16. The creation of this GUI involved the application of the 'Tkinter' Python library to provide a platform for the users to input the predictor variables and subsequently obtain the predicted flyrock velocity [103]. As depicted in the GUI illustration, the process commenced with the user inputting values for input parameters, including burden, stemming length to blasthole length ratio, stemming length to burden ratio, burden to blasthole diameter ratio, energy factor, and rock density. The flyrock launch velocity was then automatically calculated utilizing the optimized ERTR-BO model and presented in the predicted VF section. This predicted velocity could then be employed to carry out a simulation exercise for forecasting  
Figure 15: SHAP summary plots showing relative feature importance in the ERTR-BO model: (**a**) dot plot, (**b**) bar plot, (**c**) force plot showing local explanation for a datapoint in the test dataset.  
the flight trajectories and determining the potential maximum range of a probable flyrock occurrence, as demonstrated in the following sub-section.
## 4 Antimicrobial Agents
### Antibiofilm Peptides
However, this impact on biofilm cells was slower when compared to the planktonic cells [113]. Similarly, another synthesized peptide, RN3(5-17P22-36) was acquired from the cationic proteins of eosinophil granules. It was also capable of causing cell lysis by membrane disruption in biofilms. There was a 2-3-fold reduction in membrane depolarization in the biofilm cells as compared with the planktonic cells at the same dose of AMP [114]. Another peptide, known as S4 (1-16) M4 Ka, which is a derivative of dermaseptin S4 also causes the flux of membrane lipids, bacterial dispersal, thus leading to a reduction in _P. aeruginosa_ biofilm [115]. Bacterios such as nukacin ISK-1, lactic Q, and nisin A also destroyed the membrane potential of _S. aureus_ biofilm cells and caused the ATP flux from the cells [116]. Several AMPs also target biofilm EPS components. AMPs such as P1 could destroy biofilm EPS formed by _Streptococcus mutans_ that resulted in decreased biofilm formation on polystyrene and saliva-coated hydroxyapatite [119]. Another AMP, hepcidin 20, was able to decrease EPS load and affect the biofilm structure of _S. epidermidis_ by inhibiting PIA [117]. Similarly, Human \(\beta\) -defensin 3 (HBD-3) induced downregulation of ica operon genes, such as _icaA_ and _icaD_, and at the same time upregulated _icaR_ genes of _S. epidermidis_ thus decreasing PIA production resulting in biofilm inhibition [118]. As all of these results suggest antibiofilm peptides can control biofilms using several different kinds of mechanisms, these peptides can be further classified into several sub-groups as suggested by [PERSON] et al. [28]. A few antibiofilm peptides have even been used in treating biofilms in animals and agriculture. Since antibiofilm peptides have a complex mechanism of action, improving our understanding of these mechanisms in biologically relevant situations appears to be important for determining structure-function associations and finally optimizing synthetic antibiofilm peptides for improved antibiofilm potential [120]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Biofilans** & **AMP** & **Amino Acid Sequence** & **MW (g/mol)** & \ \begin{tabular}{c} **No. of** \\ **Resolutes** \\ \end{tabular}  & **Mode of Action** & **References** \\ \hline \multirow{8}{*}{_P. aeruginosa_} & 1L-37 &
\begin{tabular}{c} LLGDPFRKSKS
## 2 Mechanism Introduction and Modeling Analysis
### Design of Key Components
#### 2.2.2 Seedling Cylinder  
A seedling picking cylinder and seedling dropping cylinder are the main components of seedling caching and seedling launching, and their sizes are the key to the seedling dropping process and the volume of the whole machine. To avoid sticking seedlings, blocking the cylinder and intermittent seedling dropping during seedling drop, and to reduce the machine volume and the distance of the horizontal dispersing pneumatic seedling picking and launching device, the size of the seedling cylinder should be as small as possible to meet the requirements of free seedling dropping. Therefore, to make the device symmetrical, the overall width of the device should be related to the planting spacing. The main dimensions of the seedling separating cylinder and seedling dropping cylinder (Figure 7) should satisfy the following relation:  
\[\left\{\begin{array}{l}10L_{2}=2D_{1}\\ L_{2}=L_{4}=L_{5}=0.8L_{1}\\ L_{3}=2L_{2}\\ L_{5}>1.2H_{2}\\ L_{6}=0.8D_{2}\\ H_{3}=H_{5}=H_{1}\\ H_{4}=L_{4}\end{array}\right. \tag{1}\]  
where \(D_{1}\) is the required row spacing for planting operations (200 mm); \(D_{2}\) is the planting ditch width (40 mm); \(L_{1}\) is the average maximum leaf expansion amplitude of pot seedlings (rounded to 100 mm); \(L_{2}\) is the transverse length of the single-chamber opening of the seedling cylinder (40 mm); \(L_{3}\) is the longitudinal length of the seedling cylinder compartment opening (80 mm); \(L_{4}\) is the minimum longitudinal length of the seedling separator (40 mm); \(L_{5}\) is the longitudinal length of the bottom opening of the seedling cylinder (40 mm); \(L_{6}\) is the transverse length of the bottom opening of the seedling cylinder (40 mm); \(H_{1}\) is the average height of the pot seedlings (including the pot body), rounded to 150 mm; \(H_{2}\) is the average height of the pot seedlings with a hole height of 33 mm; \(H_{3}\) is the distance between the top of the seedling cylinder and the plane \(U_{1}\) where the horizontal state of the seedling plate is located, 150 mm; and \(H_{4}\) is the plane \(U_{2}\) in the rotating area of the seedling carrying plate, which is an unsafe area. If the potted seedling falls through the area, the seedling carrying plate rotates and there is a risk of seedling jamming, this being 40 mm. \(H_{5}\) is the height of the safe area for the dropping of the pot seedlings in the seedling cylinder, 150 mm, and \(H_{6}\) is the height of the vertical area of the dropping mouth, 50 mm.  
Figure 7: Key dimension analysis of seedling cylinder.
# High Spatial and Temporal Resolution Bistatic Wind Lidar
## 3 Validation and Characterisation
### PTB bistatic Lidar/LDA in Wind Tunnel  
In 2018, the PTB bistatic lidar was characterised in the wind tunnel at PTB's Competence Center for Wind Energy (CCW). Being specifically constructed for the validation of the PTB bistatic lidar, the wind tunnel is mounted on a platform at a height of 8 m, allowing the PTB bistatic lidar trailer to be positioned immediately below it to sense its velocity field.  
It was shown that the wind tunnel provides a well-defined, homogeneous velocity field suitable for calibrations. The wind tunnel shows a turbulence level of less than 0.35% for flow speeds ranging from 2 m \(\cdot\) s\({}^{-1}\) to 30 m \(\cdot\) s\({}^{-1}\). At distances from the nozzle below 375 mm, where the measurements take place, it shows deviations of the flow velocity below 0.15% \(\cdot\) dm \({}^{-1}\)[23].  
A laser Doppler anemometer (LDA) was used as a reference standard, as LDA devices have measurement uncertainties below 0.2% [13], which is lower than the measurement uncertainties achievable with cup anemometers (roughly between \(\pm\)0.7% and \(\pm\)1%). Figure 10 shows the deviation of the PTB bistatic lidar from the LDA. These first validation measurements of wind speeds ranging from 4 m \(\cdot\) s\({}^{-1}\) to 16 m \(\cdot\) s\({}^{-1}\) showed an average deviation of 0.37% between the LDA and the PTB high-resolution bistatic lidar [24; 25].  
Figure 8: Results of the 2016 comparative measurement between the PTB bistatic lidar and the top anemometer and between the monostatic lidar (WindCube) and the top anemometer in _undisturbed flow_ at a measurement height of 135 m, evaluated according to IEC 61400-12-1 using 10 min intervals. For bins in which at least 10 values were obtained, the PTB bistatic lidar’s deviation is less than 0.5%, which is well within the cup anemometer’s measurement uncertainty (roughly between \(\pm 1\%\) at \(4\,\mathrm{m}\cdot\mathrm{s}^{-1}\) and \(\pm 0.7\%\) at \(13\,\mathrm{m}\cdot\mathrm{s}^{-1}\)). Under the same conditions, the monostatic lidar shows deviations up to \(-1.5\%\).  
Figure 7: Results of the 2016 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on a met mast boom at a height of 100 m in _disturbed flow_ at a measurement height of 100 m, evaluated according to IEC 61400-12-1 using 1 s intervals. The PTB bistatic lidar exhibits absolute deviations below 1% over a large range of wind speeds from \(6\,\mathrm{m}\cdot\mathrm{s}^{-1}\) to \(12\,\mathrm{m}\cdot\mathrm{s}^{-1}\). The PTB bistatic lidar’s deviation only increases significantly beyond 1% at very low or very high wind speeds. Extensive investigations and mathematical simulations showed that this is due to the different dynamic responses of the sensing devices employed, and also due to the limited correlation of wind velocities sampled at slightly different locations.
## 1 Introduction
The RRT software includes the option to add a visual cue to the reading exercises, which highlights portions of the text which has to be read in synchrony with the rhythmic base. Such a setting was originally introduced to facilitate the synchronization task during reading and it was meant to be turned off by the trainer as soon as the participant was able to accurately coordinate their reading to the beat without any visual aid. The personalized use of the visual cue setting in RRT made it impossible to control for the specific effect of the visual component of the intervention in the previous RRT clinical trials. Temporal anticipation can, indeed, be generated not only by auditory stimuli, such as the beats of a metronome or a music piece, but also by visual stimuli. Selective attention to a spatial location has been shown to enhance perception and facilitate response times to attended visual stimuli compared to unattended visual locations [28]. Several authors have investigated the relationship between temporal and spatial expectancies proving an additive effect when both are matched [29]. Likewise, in rhythm task, multisensory rhythms temporally aligned (e.g., auditory and motor) can also enhance expectancies and boost human behaviour [17]. Reading, by definition, begins in the visual system with a perceptual segmentation of the letters string into its constituent graphemes and involves an accurate and rapid shifting of visuo-spatial attention. A deficit in visual attention shifting has been demonstrated to play a critical role in dyslexia [30; 31]. Although the role of auditory temporal expectancies has already been investigated in dyslexia [20], to our knowledge the combined role of visuospatial and auditory expectancy was not. Innaturalistic environments, we perceive objects through our different senses and redundant intersensory information is bound together, with cross-modal enhancement taking place at multiple levels. A striking example comes from face-to-face dialogue where audio-visual synchronized information facilitates detection of amodal features (e.g., prosody) [32], as predicted by intersensory redundancy hypothesis [33]. Based on previous literature on the additional anticipatory effect of auditory and visual rhythms, it is possible that the combination of the auditory and visual components of RRT would maximize the effect of the intervention in terms of reading improvements. The present study aimed to assess the specific contribution of the visual component of the training, namely, the presence of a visual cue supporting rhythmic synchronization. To do so, two RRT administration conditions were implemented: (a) RRT auditory and visual condition, in which a visual cue is synchronized with the rhythmic stimulation (i.e., Visual cue); (b) RRT auditory only condition, in which the visual cue was excluded (i.e., Rhythm).
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 3 System Model
### Multi-User Mobility-Aware Time Latency Computation
\(t_{rt}I_{1}\) _denotes the round-trip latency, and_ \(I_{1}\) _is an indicator function, which is expressed as:_ \[I_{1}=\left\{ \begin{array}{ll}1,&\text{if the user connects to the remote cloud}\\ 0,&\text{otherwise}\end{array} \right.\] (6)_t_\({}_{dt}\) _denotes the downtime generated by service migration, and_ \(I_{2}\) _is an indicator function, which is expressed as:_  
\[I_{2}=\left\{ \begin{array}{ll}1,&\text{if the service is migrated}\\ 0,&\text{otherwise}\end{array} \right. \tag{7}\]  
The time consumption computation method for invoking a service is presented in Definition 6. However, in the real world, users always invoke a series of services when they are moving [11]. Therefore, the time consumption for which a user invokes the entire service composition can be calculated as:  
\[Utc=\psi_{ws\in Sus}tc_{ws} \tag{8}\]  
where _Sws_ is the set of composite services and \(\psi\) is an operator that integrates the values of time consumption of invoking composite services. The integration rules include \(\sum\) representing summation, \(\prod\) representing the product, \(max\) representing the maximum, and \(min\) representing the minimum. For the ease of calculation, we assume that the composite services are in a sequential execution path, and we only use the \(\sum\) integration rule. Therefore, we can get the multi-user mobility-aware time latency computation as follows:  
\[SUtc=\sum_{1}^{U}Utc \tag{9}\]  
where \(U\) denotes the set of all users.
## 7 Results and Discussion
### Discussion of the Calculations Results
However, as the traffic volume and diverging-emerging ratio increase, the score of Scheme 2 decreases significantly and is smaller than the remaining three schemes. Scheme 3 achieves its highest score with large traffic volumes and a significant diverging-emerging ratio. For example, traffic volumes ranging from 5280 to 6600 veh/h and a diverging-emerging ratio of 30-40%. Due to the heavy traffic volume on both the mainline and auxiliary lanes, vehicles on the auxiliary lanes cannot change lanes to the mainline. However, Scheme 3 extends the inner auxiliary lane to a certain distance behind the diverging nose. This design allows vehicles to continue moving and change lanes to the mainline after passing through the heavily congested diverging area. Thus, Scheme 3 performs better in high-traffic conditions. The score of Scheme 4 is highest only for the extreme case where the traffic volume and diverging ratio are the highest, but this is rarely the case in practice. Although the design of Scheme 4 makes it possible for vehicles to emerge into the mainline without changing lanes, it also brings some safety problems, resulting in a low comprehensive score. Therefore, Scheme 4 is less applicable. In summary, the traffic volume and diverging-emerging ratio greatly impact the choice of auxiliary lane form. Scheme 2 has the best improvement effect in the case of both low traffic volume and diverging-emerging ratio, and Scheme 3 has better performance in the case of both high traffic volume and diverging-emerging ratio. The conclusions obtained via the FAEWM provide a reliable basis for engineering design and maximize traffic efficiency and safety while simultaneously mitigating environmental pollution. For the design of compound interchange auxiliary lanes, the design form can be selected directly based on the traffic volume and the diverging-emerging ratio. However, the scheme selected in Figure 11 under various traffic combinations is based on a comprehensive score, and there may still be a disadvantage in one of the evaluation indexes. For example, a reasonable selection of auxiliary lane design forms can effectively improve pollutant emissions. Through the comparison of four solutions, it can be observed that as traffic volume increases, both Scheme 2-4 show significant improvements in CO emissions, particularly Scheme 3 and Scheme 4, with the highest improvement rate reaching 80%. Notably, while Solution 4 performs relatively poorly in terms of safety and efficiency, it exhibits substantial environmental improvement. However, selecting design solutions needs to consider multiple aspects and should not solely prioritize environmental protection at the expense of efficiency and safety improvements. In reality, as long as traffic efficiency is enhanced and traffic congestion is alleviated, it can reduce vehicle exhaust emissions.
## 1 Introduction
Simulation and experimental results show that the NSVD filter has a better ability to reduce noise and clutter as compared to SVD. With the combination of the NSVD filter and adaptive weighting methods, the contrast and quality of the speckle pattern of reconstructed images will be enhanced. The paper is organized as follows. The next section is a brief introduction to the CPWC, SVD, and NSVD, and theories of CF, GCF, and SLSC are also provided. Then, we describe simulated and experimental datasets and image quality metrics. Imaging results of different methods are illustrated in Section 4. The paper concludes with a discussion on the proposed methods and a conclusion in Sections 5 and 6, respectively.
## 3 Experimental Results  
Since the proposed method consists of detection and segmentation steps, the results of each step are presented in separated sections. In the detection step, the methodology employed to select the most appropriate configuration for the MLP is presented. In addition, a comparison with eleven vessel enhancement methods is carried out by using the area under the ROC curve (\(A_{z}\)) as comparative metric. On the segmentation step, the selection process to define a specific method that complements the proposed detection step as an integral vessel extraction approach is introduced. Moreover, this section presents the comparative analysis of the proposed coronary arteries segmentation method with respect to twelve state-of-the-art blood vessel extraction methods in terms of six binary classification metrics.  
The computational experiments were implemented on a computer with an Intel Core i7, 4 GB of RAM, and 3.50 GHz processor using the version 2016b of Matlab.