esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Alternate Bearing and Thinning Performance  
Excessive yields in minimal pruning systems have been reported to cause severe losses in yield in the subsequent vintage (2018, 2019). [PERSON] et al. (2019) concluded that excessive yield, and an insufficient effective leaf area during bud induction and differentiation in the previous season, contribute to low bud fruitfulness. Therefore, crop thinning in SMPH is strongly recommended to avoid losses and extreme variability in fruit quality, caused by excessive yield and seasonal yield fluctuations (alternate bearing), which occurs especially in the first two seasons after conversion from VSP to SMPH (2018, 2019, 2019, 2019). In previous studies, [PERSON] et al. (2019) observed cultivar-dependent alternate bearing effects in SMPH. While, high coefficients of variation (CV) for yield were determined with the fungus-tolerant cv. Regent and cv. Rondo within the first three years after conversion from VSP to SMPH ([PERSON] et al. _unpublished_), cv. Riesling was less affected by alternate bearing. Moreover, the trials revealed that the general effect of alternate bearing is less pronounced within cv. Riesling trained in SMPH than trained in VSP, confirming the results in the present study. In contrast, data obtained from a study on SMPH by [PERSON] et al. (2019) indicates more pronounced alternate bearing with cv. Pinot blanc trained in SMPH compared to VSP, which matches the results for cv. Regent and cv. Rondo of [PERSON] et al. (2019). Both investigations demonstrated that mechanical bunch thinning in the seasons after conversion to SMPH is an appropriate strategy to minimize quality losses through alternate bearing.  
Nevertheless, in the present study, all thinning strategies successfully and efficiently reduced yield, accelerated ripening and increased TSS, confirming the commonly known yield/quality relationship. Mechanical shoot thinning with Darwin-rotor is a new yield reduction strategy for SMPH and was first investigated for viticulture in the present study. Our trials demonstrated good performance of SMPH ST, in terms of yield reduction. However, since the application is conducted long before fruit set, it is difficult to predict the desired thinning extent and crop level. Moreover, it has to be considered that early shoot thinning with SMPH ST led to compensation effects, which might increase the risk of bunch rot.
# Non-Destructive Testing Applications for Steel Bridges
## 5 Complementary to NDT Methods
### Global Structural Response Testing (GSR)  
The global structural response testing can also be considered a complementary NDT method for monitoring steel bridges. For example, vibration and modal techniques are implemented for evaluating the condition of elements through the mechanical properties reflected in their dynamic behavior. Defects in the structures affect the structural responses, including modal frequencies and modal shapes. As such, any change in the structural response can be a symptom of a defect. It should be noted that global techniques are applicable where the structural damage is significant, as the modal parameters are not sufficiently sensitive to identify minor structural damage and their locations [28,135]. The following discusses the application of the vibration method for the detection of fractures in steel bridges. Figure 40: Inch-worm transformation of ARA robot on a steel bridge inspection test [107]. Figure 38: Hybrid robot prototype capable of walking and flying for steel bridge inspections [128]. Figure 39: Wall-climbing robot with drone platform [134]. Damage detection through the determination of changes in the dynamic characteristics is one of the most widely used global response methods [136; 137]. The source of excitation for vibration testing can be ambient (traffic or wind) or manual (loading and shakers). [PERSON] and [PERSON] [138; 139] have developed a laser-based noncontact vibration technique for continuous or periodic bridge monitoring and damage detection through the changes in the natural frequencies of a bridge to address cost, traffic interruption, and safety issues associated with conventional bridge vibration monitoring. In this technique, a laser vibrometer is used to capture the bridge vibration under normal operation without interruption to traffic and without the need for direct access to the bridge. The results can be compared to measurements in previous period or finite element analysis for the indication of damage if a certain threshold is surpassed. The feasibility of using self-powered wireless sensors for monitoring steel bridges has also been investigated [140]. [PERSON] and [PERSON] (2021) studied the modal analysis method and demonstrated its applicability for damage detection in steel girder bridges. Their results showed that modal sensitivities are indicative enough for detecting certain expected damages in simple and continuous span bridges, where the frequency change method may not be adequately sensitive [141]. Based on the above review, Tables 2 and 3 are developed, summarizing the applications, advantages, and disadvantages of various NDT methods for the inspection of steel bridges.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 2 Materials and Methods
### Near-Infrared Modeling Methods
#### 2.4.3 Limit of Detection (\(Lod\))  
The Limit of Detection (\(LOD\)) refers to the minimum concentration or quantity of a target substance that can be reliably detected at a specified confidence level [34]. As a critical metric for assessing the sensitivity of an analytical method, the \(LOD\) ensures the quality and reliability of experimental data, particularly when dealing with complex datasets and multivariate regression models [35]. To calculate the \(LOD\) rigorously, we employed a prediction error-based approach in conjunction with cross-validation to enhance the robustness of our results. The specific formula used is as follows:  
\[LOD=t_{\alpha,n-p}.\frac{RMSEP}{slope} \tag{1}\]  
where  
\(t_{\alpha,n-p}\) represents the critical value of Student's t-distribution at a confidence level \(\alpha\), with \(n-p\) degrees of freedom (\(n\): total number of samples; \(p\): number of latent variables).  
_RMSEP_ (Root Mean Square Error of Prediction) quantifies the predictive accuracy of the model on an external validation set. Slope denotes the regression coefficient of the PLSR calibration curve, reflecting the linear relationship between the target concentration and the spectral response.  
This formulation ensures statistical rigor in determining the detection threshold while aligning with industrial requirements for practical applicability.
# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 3 Research Methodology
### Lexicon Construction
#### 3.1.2 Constructing a Lexicon Using the Trained Data
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Lexicon** & **Setting** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline NileULex & Average without Applying Any Rule & 40.27\% & 40.27\% & 40.27\% & 40.27\% \\ senti-lexicon & & 40.27\% & 40.27\% & 40.27\% & 40.27\% \\ ArSentiCOVID & & 76.87\% & 76.87\% & 76.87\% & 76.87\% \\ NileULex & Average with Applying Negation Rules & 55.57\% & 55.57\% & 55.57\% & 55.57\% \\ senti-lexicon & & 55.46\% & 55.46\% & 55.46\% & 55.46\% \\ ArSentiCOVID & & 81.00\% & 81.00\% & 81.00\% & 81.00\% \\ NileULex & Average with Applying Emoji Rule & 40.27\% & 40.27\% & 40.27\% & 40.27\% \\ senti-lexicon & & 40.27\% & 40.27\% & 40.27\% & 40.27\% \\ ArSentiCOVID & & 79.00\% & 79.00\% & 79.00\% & 79.00\% \\ NileULex & Average with Applying Negation and Emoji Rules & 55.57\% & 55.57\% & 55.57\% & 55.57\% \\ senti-lexicon & & 55.46\% & 55.46\% & 55.46\% & 55.46\% \\ ArSentiCOVID & & 83.00\% & 83.00\% & 83.00\% & 83.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results obtained with or without using negation rules. Following that, we performed a comparative evaluation of lexicon-based features by contrasting the efficacy of different feature sets. Each of the eight machine learning classifiers (KNN, SVM, GNB, MNB, BNB, LR, RF, and DT) was conducted individually to study the impact and significance of each feature on sentiment classification. Mainly, we were trying to figure out which features in the Arabic ArSentiCOVID lexicon are most important for improving Arabic SA. This was achieved by employing eight machine learning classifiers to conduct several experiments on each feature and feature set individually. Table 12 displays the overall performance of the eight classifiers on the Arabic SA task when each feature (bigram, trigram, and four-gram) is used. The SVM classifier achieved the best performance (89.01%) when the bigram feature is used, and the same result is obtained when the trigram feature is used, but the accuracy is reduced when the fourgram feature is used (86.26%). Table 13 compares the performance of the eight classifiers on the Arabic SA task when each feature (F1-F4) from sentiment-based feature set is used independently and when all of these features are used collectively. When using the feature F1 (the frequency of positive words), the SVM achieve the highest performance. When applying the F2 (the frequency of negative words) is used, the two classifiers SVM and LR achieve the highest performance. When using the feature F3 (the frequency of neutral words), the SVM achieve the highest performance. When all features are combined as in F4, the accuracy is increased by 2%. Based on these results, it was determined that the sentiment-based features are suitable for sentiment classification when used individually or in combination.
# A Survey of Vehicular VLC Methodologies
## 7 Vehicular VLC Research Trends and Future Directions
### Lessons Learned  
The concept of vehicular VLC is a crucial enabler for ITSs to improve road safety, traffic flow, and fuel consumption efficiency. Realistic channel models are considered the first step in investigating the performance of vehicular VLC systems.  
* Earlier results have focused on indoor channel modeling, which does not apply to vehicular VLC systems with inherently different characteristics. For example, earlier works assumed the ideal Lambertian model for vehicular light sources, which fails to match the illumination characteristics of automotive headlights, taillights, traffic lights, and streetlights, with their asymmetrical intensity distributions. Any modification of the antenna pattern will significantly affect the communication performance.
* The effects of road reflectance, road type, weather conditions, the orientation of the user/vehicle equipment and infrastructure, receiver aperture size, and the sunlight might strongly affect the link performance of vehicular VLC systems. Also, vehicular mobility can result in LoS blockage, and thus, assisting technologies such as relaying techniques and RISs should be utilized. It is paramount to investigate the effects of these factors on traffic safety based on realistic channel models.
# The Boundary Element Method in Acoustics: A Survey
## 2 Acoustic Model
### The Wave Equation and the Helmholtz Equation  
The acoustic field is assumed to be present in the domain of a homogeneous isotropic fluid and it is modelled by the linear wave equation,  
\[\
abla^{2}\Psi(\mathbf{p},t)=\frac{1}{c^{2}}\ \ \frac{\partial^{2}}{\partial t^{2}} \ \Psi(\mathbf{p},t), \tag{1}\]  
where \(\Psi(\mathbf{p},t)\) is the scalar time-dependent velocity potential related to the time-dependent particle velocity \(V(\mathbf{p},t)\) by \(V(\mathbf{p},t)=\
abla\Psi(\mathbf{p},t)\) and \(c\) is the propagation velocity (\(\mathbf{p}\) and \(t\) are the spatial and time variables). The time-dependent sound pressure \(Q(\mathbf{p},\ t)\) is given in terms of the velocity potential by \(Q(\mathbf{p},t)=-\rho\frac{\partial}{\partial t}\ \Psi(\mathbf{p},t)\) where \(\rho\) is the density of the acoustic medium.  
The time-dependent velocity potential \(\Psi(\mathbf{p},t)\) can be reduced to a sum of components each of the form  
\[\Psi(\mathbf{p},t)=Re\Big{\{}\varphi(\mathbf{p})\ e^{-i\omega t}\Big{\}}, \tag{2}\]  
where \(\omega\) is the angular frequency (\(\omega=2\pi\
u\), where \(\
u\) is the frequency in hertz) and \(\varphi(\mathbf{p})\) is the (time-independent) velocity potential. The substitution of the above expression into the wave equation reduces it to the Helmholtz (reduced wave) equation:  
\[\
abla^{2}\varphi(\mathbf{p})+\ k^{2}\varphi(\mathbf{p})=0, \tag{3}\]  
where \(k^{2}=\frac{\omega^{2}}{c^{2}}\) and \(k\) is the wavenumber. The complex-valued function \(\varphi\) relates the magnitude and phase of the potential field.  
Similarly, the components of the particle velocity have the form \(V(\mathbf{p},t)=Re\Big{\{}\
abla\varphi(\mathbf{p})\ e^{-i\omega t}\Big{\}}\). Often the boundary normal velocity \(v(\mathbf{p})\) is given as a condition or is required and this is defined as follows,  
\[v(\mathbf{p})=\
abla\varphi(\mathbf{p})\cdot\boldsymbol{n}_{p}=\frac{ \partial\varphi(\mathbf{p})}{\partial n_{p}}, \tag{4}\]  
where \(\boldsymbol{n}_{p}\) is the unit normal to the boundary at \(\mathbf{p}\).
The small size of cross-linking GE fibers is capable of mimicking the ECM, which structurally and biochemically supplies nearby cells [25]. [PERSON] et al. stated that high GE content integrated with the three-dimensional composite structure could effectually support the osteogenic differentiation of adipose-derived stem cells [27]. The electrospun fibers produced from a blend of GE and PCL have been proven in various tissue engineering studies to exhibit favorable mechanical strength and biocompatibility [16; 28]. However, due to the distinct physicochemical properties of PCL and GE, their blend tends to undergo phase separation, compromising the electrospinning process and forming inhomogeneous structures with suboptimal blending [29]. Notably, a 3D sandwich-structured prototype (GPP), which integrates PCL electrospun fibers with GE electrospun nanofibers using a lamination technique, has been demonstrated to be a promising material. The PCL layer enhances the mechanical properties of the material without compromising its biocompatibility, while on the other hand, the surface GE layer achieves a degree of water resistance through in-situ crosslinking and retains the hydrophilicity of nanofibers [17; 30]. The ideal bio-substitute for periodontal surgery should have certain characteristics, including biocompatibility, biodegradability, proper malleability, simplifying surgical manipulation, and facilitating cell proliferation, adhesion, and differentiation [10; 15; 31]. The remarkable tissue regeneration ability of the gingiva renders it an attractive tissue for cell isolation, both for therapeutic applications and research purposes. hG-MSCs can be readily isolated from gingival tissue possessing a range of functions, encompassing the regulation of oral tissue homeostasis and regeneration, and the modulation of inflammatory responses [32; 33; 34]. In the above sense, exploring the response of hG-MSCs to GPP, VSCM, and TCP holds significant promise not only for applications in regenerative medicine, but also for the development of 3D cell culture models focusing on complex cell-matrix interactions. A previous study from our research group has validated the integrity of the material's specific structure along with its biocompatibility [35]. However, the potential of GPP for oral soft tissue regeneration has not been well investigated. Especially, the interaction with hG-MSCs, which are vital for gingival tissue regeneration, has yet to be studied. Our study aims to investigate the interaction of GPP with hG-MSCs in vitro and to compare the biological properties of GPP with those of the aforementioned VSCM.
# Numerical Investigation of the Pile-Soil Interaction Problem under Dynamic Loads
## 3 Finite Element Analysis  
The two-dimensional Plaxis 2022 2D finite element program was used in the numerical modeling of the study by [PERSON] et al. [13]. The Plaxis program is a finite element program that uses advanced soil models to solve deformation and stability problems and allows for the simulation of static and dynamic loading conditions. The most important advantage of the program is that it allows for the most realistic modeling of soil and loading conditions and is user-friendly. When deciding to use the Plaxis 2D program, which performs nonlinear analysis, in this study, the modeling capabilities of the program were taken into account. These include many aspects such as using advanced ground models, simulating earthquakes in the time domain, and creating dynamic boundary conditions. In addition, the compatibility of studies in the literature regarding such analyses was also taken into account. Considering the studies in the literature, the Plaxis 2D program has been used many studies [31; 32; 33; 34; 35; 36]. Additionally, it is stated in the Plaxis manual (2023) that such dy  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Standard** & **Value** \\ \hline Specific gravity, Gs & ASTM D854 [26] & 2.65 \\ Maximum void ratio, \(e_{max}\) & ASTM D4254 [27] & 0.767 \\ Minimum void ratio, \(e_{min}\) & ASTM D4253 [28] & 0.49 \\ Effective particle size, \(D_{10}\) (mm) & ASTM D6913 [29] & 0.68 \\ Average particle size, \(D_{50}\) (mm) & ASTM D6913 [29] & 0.80 \\ Coefficient of uniformity, Cu & ASTM D6913 [29] & 1.221 \\ Coefficient of curvature, C\({}_{c}\) & ASTM D6913 [29] & 0.97 \\ Relative density, D\({}_{t}\) (\%) & ASTM D4254 [27] & 85 \\ Peak friction angle, \(\phi_{\rm p}\) (\({}^{\circ}\)) & ASTM D7181 [30] & 37.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of sand soil used in the experiments [13].  
Figure 1: Schematic view of experimental set-up [13].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Plastic limit, PL (\%) & 30 \\ Liquid limit, LL (\%) & 63 \\ Plasticity index, \(PI\) (\%) & 33 \\ Specific gravity, Gs & 2.60 \\ Slope of critical state line (CSL) in q-p\({}^{\prime}\) plane & 0.90 \\ Slope of an unload-reload line, (\(\kappa\)) & 0.039 \\ Intercept of CSI. at p\({}^{\prime}\) = 1 kPa (\(\Gamma\)) & 3.31 \\ Slope of normal consolidation line (\(\lambda\)) & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of Speswhite kaolin clay [11].  
namic analyses can be performed with the Plaxis 2D program, and single piles can be modeled under plane strain conditions. The Plaxis 2D program was used in the analysis for reasons such as its suitability for use in the dynamic analysis of single foundations under plane strain conditions, its ability to select advanced soil models, and its ability to perform analysis in the time domain. In this section, the numerical modeling and verification of experimental results are presented under subheadings.
###### Abstract
Moreover, NDVI is often claimed to misrepresent the non-linear nature of vegetation responses, and is thus regarded as less efficient to be a proxy of GPP [20; 21]. Recently, a few novel products have been tested and regarded as more robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Among these products, GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Chlorophyll Fluorescence data set)-GPP, NIRv (Near-Infrared Reflectance of terrestrial vegetation)-GPP and FluxSat (Fluxnet data fused with satellite images)-GPP, based on Solar-Induced chlorophyll Fluorescence (SIF), canopy near-infrared reflectance, eddy flux and satellite data fusion, respectively, are shown to better reflect photosynthetic activities [22; 23; 24; 25]. In this study, we have utilized these three products, and the approach of each product is distinctly different from the others. Based on a higher spatial resolution (0.05\({}^{\circ}\)), we aim to explore the direct and legacy effects of spring temperature anomalies on seasonal GPP with greater detail and accuracy. We have hypothesized that direct and legacy effects of spring warming or cooling, will either stimulate or suppress GPP in summer, autumn or across both seasons. This, in turn, depends on amplified or dampened water stress, the sensitivity of plant species to summer warmth and biological conditions of spring. The summer response of GPP can further affect the autumn phenology by advancing or delaying the senescence of plants. Therefore, the main aims are to answer the following questions: (1) What are the geographical distribution and patterns of direct and lagged responses of GPP to spring temperature anomalies? (2) How does each biome respond to these direct and lagged effects? (3) How do the dominant drivers (environmental variables, current and pre-seasonal GPP) explain summer and autumnal GPP? For environmental variables, this study only considers warming and drought-related variables, that is, temperature, soil moisture and vapor pressure deficit (VPD).
## 2 Materials and Methods
### Experimental Design  
The validation focused on above-ground biomass, LAI, SWI, root zone soil moisture, and water- and CO\({}_{2}\) fluxes. Open-loop (SURFEX run without assimilation) and LDAS (assimilation with GEOV1 LAI and SWI) runs were compared with each other with the satellite measurements (GEOV1 LAI and SWI) and with in situ data collected at the Hegyhatsai site [40] for 2008-2015. The model simulations started in January 2007. The first year is considered as a spin-up period for the model in order to reach the equilibrium state.  
In our experiments, the SURFEX model was run on a regular lat-lon grid with 8 \(\times\) 8 km resolution over a domain covering Hungary. The model was running in cycling mode; EKF assimilated the observations every 24-h and then a 24-h forecast was produced with 6 h outputs frequency. The following model outputs were evaluated: LAI (Leaf Area Index), WG2 (root-zone volumetric soil moisture content), GPP (Gross Primary Product), NEE (Net Ecosystem Exchange), and LE (Latent Heat Flux). Table 1 summarises the experiments used in this study.
# A Study on the Preference of Electric Vehicle Front Design Elements
## 2 Theoretical Background  
### Classification of Electric Vehicles  
Conventional internal combustion engine vehicles use fuel to generate power. Vehicles that are powered by electrical energy are called electric vehicles. Currently, electric vehicles include pure (battery) electric vehicles (BEVs), plug-in hybrid vehicles (PHEVs), fuel cell electric vehicles (FCEVs), and general hybrid vehicles (HEVs) [4], depending on the method of generating electrical energy [Figure 1].  
1. Pure (battery) electric vehicle (BEV): A vehicle that does not use any fuel such as gasoline and runs solely on electricity. This is a vehicle that runs on an electric motor powered by a battery, and requires fewer parts compared to an internal combustion engine vehicle.
2. Plug-in hybrid electric vehicle (PHEV): A vehicle equipped with both gasoline and electric motors. It can be driven using electricity, and when the battery runs out, it switches to a regular internal combustion engine and drives.
3. Fuel cell electric vehicle (FCEV): Like a pure electric vehicle, it operates on electricity, but the power system is not a battery. Instead, it generates electricity by chemically combining hydrogen gas in the vehicle tank and oxygen in the air.
4. Hybrid electric vehicle (HEV): A vehicle that operates by combining an internal combustion engine and an electric motor. The electric motor is charged while driving, and the gasoline engine and electric motor operate by simultaneously rotating the transmission.  
These diverse categories of EVs cater to varied consumer preferences and offer alternative solutions to conventional fossil-fuel-dependent transportation, contributing to a more sustainable automotive landscape.