esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 3 Experimental Evaluation and Results  
### The Ultrasound Video Datasets  
An _ex vivo_ ultrasound video dataset and _in vivo_ ultrasound video dataset were utilized to perform the experimental evaluations. The _ex vivo_ dataset consists of seven ultrasound videos captured during manual needle insertion into freshly excised, store-bought bovine muscle specimens. Ultrasound imaging was performed using a Sonix Touch scanner (Ultrasonix Inc., Richmond, BC, Canada) equipped with an L14-5/38 transducer. During needle insertion, the ultrasound transducer was manipulated to keep the needle in the imaging plane. Each video is composed of ten to fifteen ultrasound B-mode image frames. The size of each image frame is 470 x 470 pixels, with a pixel resolution of 0.087 mm x 0.087 mm. The gold standard location of the needle in each image frame was determined by segmenting the needle manually.  
The _in vivo_ dataset is composed of seven ultrasound videos recorded during ultrasound-guided fine needle aspiration biopsy of thyroid nodules. Ultrasound imaging was performed at the Ultrasound Section, Jordanian Royal Medical Services, Amman, Jordan using a Logiq E9 scanner (GE Healthcare, Chicago, IL, USA) equipped with an ML6-15 transducer. The seven videos were recorded when the angle of needle insertion was between 30\({}^{\circ}\) and 40\({}^{\circ}\) with respect to the skin and the frame rate was set to 20 frames per second. Each video is composed of 15 to 20 ultrasound B-mode image frames. The size of each image frame is 400 x 400 pixels, with a pixel resolution of 0.125 mm x 0.125 mm. An experienced radiologist ([PERSON]) manually outlined the needle in each ultrasound image frame to obtain the gold standard needle segmentation.
## 2 Digital Twin Technology for V2G
### Integration with Smart Grid Infrastructure  
The integration of EV digital twins with smart grid infrastructure creates opportunities for coordinated energy management that benefits both individual vehicle owners and the broader electrical system. This integration requires sophisticated communication protocols and standardized interfaces to enable seamless data exchange between vehicle systems and grid management platforms.  
Distributed energy resource management enables electric vehicles to participate in grid services such as frequency regulation, voltage support, and peak load management. Digital twins provide the necessary real-time modelling capabilities to predict vehicle availability, energy capacity, and optimal participation strategies for different grid services. This participation can provide economic benefits to vehicle owners while contributing to grid stability and renewable energy integration.  
Demand response optimization utilizes digital twin predictions to coordinate charging activities across vehicle fleets, minimizing grid stress during peak demand periods while ensuring vehicle availability for transportation needs. Advanced optimization algorithms consider factors such as time-of-use electricity pricing, renewable energy availability, and individual user preferences to develop optimal charging strategies.  
Renewable energy integration is facilitated through digital twin platforms that can predict renewable energy generation patterns and coordinate vehicle charging to maximize utilization of clean energy resources. This capability is particularly valuable for integrating intermittent renewable sources such as solar and wind power into the transportation sector. The role of AI in advancing smart, sustainable, and secure energy systems has positioned digital twins as fundamental components for next-generation grid management [37]. In addition, researchers are exploring advanced energy management strategies within electricity markets, leveraging game-theoretical methods as discussed by [PERSON] et al. [38] to model strategic interactions between EVs and the grid, optimizing energy flow and grid stability.  
The digital twin framework, therefore, can have a broad applicability since it provides the necessary modelling and prediction capabilities to enable these advanced integration scenarios while maintaining system reliability and user satisfaction.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 6 Effects of Carbon-Based NPs on Medicinal and Aromatic Plants
### Graphene Oxide
#### 6.3.2 Attenuation of Abiotic Stress  
The addition of 800 \(\upmu\)g/L nanosized GO to _Plantago major_ L. leaf-derived calli grown on the 1/2 MS medium, in which drought stress conditions were induced by PEG, caused a 78.5% decrease in relative growth rate and 48.2% reduction of osmotic potential value, while dry matter and H\({}_{2}\)O\({}_{2}\) contents increased by 35.1% and 54.2%, respectively. Under normal water availability in the presence of 800 \(\upmu\)g/L nanosized GO, a pronounced increase of phenolic and flavonoid contents by 40.9% and 35.3%, respectively, compared to the control, was observed, while proline content showed a considerable decrease by 26.9% [307].
## 3 Results and Discussion
### Case 2: Experimental Analysis of QoS Guarantee  
#### 3.3.1 User Satisfaction  
On the surface of the AUDR, PLS and FTPA appear better than NOMA FL and OFDMA FL. However, the purpose of this paper is QoS-guaranteed, and Figure 3 cannot reveal the defects of PLS and FTPA for two reasons:  
Figure 4: Comparisons of the AUDR in three dimensions with varied users and QoS expectations of 30 Mbps and 60 Mbps.  
Figure 3: Comparisons of the average user data rate (AUDR) in two dimensions without quality-of-service (QoS) features. The labels FL, FTPA, NOMA, OFDMA, and PLS are fuzzy logic, fractional transmit power allocation, non-orthogonal multiple access, orthogonal frequency division multiple access, and power law strategy, respectively.  
* The tradeoff between the AUDR and QoS guarantee exists in VLC networks. Only AUDR cannot reflect the performance of QoS provision.
* The measurement of AUDR is one-sided, which is not enough to indicate the degree of users' QoS satisfaction. To discuss the QoS guarantee, user satisfaction should be considered to comprehensively evaluate the performance of FL in QoS provision.  
We obtained Figure 5 to indicate the defects of PLS and FTPA, which are unable to meet QoS demands. References [21; 28] show that the ratio of the AUDR to QoS requirement is related to user satisfaction. Hence, the high performance of AUDR is not enough to provide full consideration for demands of all users. Although Figure 3 shows the high AUDR in PLS and FTPA, Figure 5 indicates that PLS and FTPA are not effective for QoS guarantee.  
In Figure 6, we use the indicator of user satisfaction for integrated assessment of both the user data rate and the QoS guarantee of fairness. First, Figure 6 indicates the exceptional variety of PLS CDF, caused by the strategy of PLS and leading to the polarization of both the power allocation and the guaranteed QoS data rate. Second, when \(K=4\), the user satisfaction of NOMA FL was better than the user satisfaction of the other methods in \(K=9\). The situations in \(K=4\) were better than those of \(K=9\) because \(K=4\) is smaller than the critical value of the ultra-dense user number and is not overloaded for resource allocation. Third, the maximum performance gap in \(K=4\) was greater than that in \(K=9\). The user satisfaction gap between FTPA and NOMA FL decreased when the number of users grew. Under the same QoS expectation, a growing number of users may cause limited resource allocation which may result in the deficiency of the QoS guarantee. Finally, in terms of static and dynamic user priority, NOMA FL outperformed PLS and FTPA, and OFDMA FL also outperformed NOMA PLS. This means that our FL system is effective for modeling user priority and achieving the QoS guarantee. In regard to NOMA and OMA, NOMA FL was better than OFDMA FL, which means that NOMA improves the user data rate and QoS guarantee.  
Figure 5: Comparisons of user satisfaction with the QoS expectation at 20 Mbps.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
###### Abstract
[17] analyzed eight administrative districts in southwest China on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) products and found that WUE tended to decrease therein and was positively correlated with temperature but negatively correlated with precipitation. In summary, although there are some results on WUE in southwest China, the spatio-temporal variations and drivers are somewhat different because the study area, data sources, and methods are not fully consistent. Therefore, further studies are warranted to identify factors influencing spatio-temporal variations to WUE in karst landforms. The objective of this study is to understand the spatial and temporal variation of WUE and the driving factors in the karst region of southwest China. In this study, three provincial administrative regions of China (i.e., Yunnan, Guizhou, and Guangxi), which are characterized by widely distributed and relatively continuous karst landforms, were considered the study area. The Penman-Monteith-Leuning V2 (PML-V2) product with higher spatial resolution and accuracy was chosen to calculate WUE, and more robust methods, such as partial correlation analysis and random forest model, were used to study the spatio-temporal variations of WUE and explore the influence of temperature, precipitation, and enhanced vegetation index (EVI) on ecosystem WUE changes. The results of this study will help to clarify the factors driving carbon and water cycles in karst regions and provide important theoretical support for ecological restoration.
## 4 Discussion
### Lift and Drag of the Investigated Commercial 4-Fin (T:80/A:150) Configuration
#### 4.2.2 Comparing the 3- and 4-Fin Configurations  
Lift: The maxima of the lift coefficients of the _FCS Accelerator_ 3-fin and 4-fin configurations differ by 6.8%. The maxima of the lift coefficients of the 3- and 4-fin configurations of the _FCS k2.1_ fin differ by 3.2%.  
Drag: The maximum difference of 23% between both drag coefficients of the _FCS Accelerator_ fin configurations occurs at an AoA of 35\({}^{\circ}\), whereas the maximum difference of both _FCS k2.1_ fin configurations occurs at an AoA of 40\({}^{\circ}\).  
Lift-to-Drag: The lift-to-drag ratios of the 3- and 4-fin configurations using the _FCS Accelerator_ fin differ by 2.6%, while the 3- and 4-fin configurations using the _FCS k2.1_ fins differ by 15%. The maximum lift-to-drag ratios using the _FCS Accelerator_ fins are approx. 1.7 times higher for the 3-fin and approx. 2.0 times higher for the 4-fin configuration compared to that using the _FCS k2.1_ fins.  
Figure 18: Lift-to-drag ratio versus the angle of attack. The solid lines present the commercial 3- and 4-fin configurations with the _FCS Accelerator_ fins of this study and [11]. The dashed lines present the results of [13] with 3- and 4-fin configurations with plugged _FCS k2.1_ fins.
# Applications of Plant Polymer-Based Solid Foams: Current Trends in the Food Industry
## 2 Food Industry Applications
### Plant Polymer-Based Foams as Edible Materials
#### 2.1.2 Potato Protein  
Potato protein obtained as a by-product of starch production has several potential applications in the food industry. The most abundant protein fraction is patatin, which has shown good foaming properties, as demonstrated by [PERSON] et al. [23]. The foaming capacity of patatin samples tested by [PERSON] ranged from \(\sim\)0.8-1.8 L/L, with the highest overrun value at pH 3 and reduced values at pH 5 and 7, respectively. Higher foam overrun at pH 3 is likely related to the unfolding of patatin at pH levels lower than 4.5. Foam stability of different potato protein fractions displayed a wide range of values. The relative foam stability was lowest at pH 3, ranging from 18-78% of the initial foam, while 67-80% was seen at pH 5 and 7. Descended stability value was especially pronounced for the patatin fraction, though the reason behind this is not known. It is suggested that phenolic compounds present in these fractions can alter the hydrophobic character of proteins. Thus, patatin fractions may show different stability values due to modifications in surface activity, foam height stability, and liquid drainage [23].  
[PERSON], [PERSON], [PERSON], and [PERSON] [24] examined the use of patatin as a foaming agent and pectin as a foam stabilizer to produce a snack from dried raspberry puree foam by freeze drying. They also compared the effects of using microwaves during freeze drying on the structure and on the storage behavior of the raspberry puree foam. The results showed that raspberry puree has a better storage stability under the foam structure during the long-term storage period at 37 \({}^{\circ}\)C. It was expected that the open porous structure could have increased the deterioration of the bioactives due to the higher surface area. However, by the end of the study, [PERSON] et al. hypothesized that hydrocolloids and potato protein produced a protective barrier as a dried lamella in the foam structure around the pores which resembles a glassy membrane structure and slows down the deterioration. The microwave-assisted freeze-drying process did not affect raspberry puree foam negatively during storage, as compared to the traditional freeze-drying technology used as a control. For example, there was no significant difference (\(p>0.05\)) in the color among all samples by the end of storage. Furthermore, water activity was below 0.6 for all samples during the entire storage period; thus, microbiological stability was ensured.
## 3 Result and Discussion  
### Effect of Nozzle Type  
To analyze the effect of the nozzle design, underwater laser-cutting experiments were performed using three types of nozzles. The other laser-cutting process parameters are shown in Table 2. The feed rate (V\({}_{c}\)) is the moving speed of the laser-cutting head. The negative focus position of the beam (F\({}_{b}\)) value means that the laser beam focus is located below the top surface of the specimen.  
The top and bottom kerf widths were measured, and the cut surface was evaluated to check the cutting quality. In most thermal-cutting processes, striation lines can be observed on the cut surface due to the assist gas flow and molten metal flow.  
The effect of the nozzle was evaluated through the cut surface analysis. As shown in Figure 7, there were three kinds of striation line zones on the cut surface: the upper, middle, and lower zones. The upper zone, where the striation line appeared in a straight line, and the momentum of the assist gas was more than enough to blow away the molten metal. The middle zone, where the molten metal was blown away by assist gas, but the striation line was appeared in a curved line. The lower zone, where the flow momentum of the assist gas was not enough to blow away the molten metal. The molten metal was flowed down, mainly due to the force of gravity and the weak assist gas momentum. From the point of view of cutting quality, the longer the length of the striation line of the upper and middle zones, the better the cutting quality. If the striation line of the upper zone and the middle zone was short, it means that the assist gas flow was not good because the assist gas was not sufficiently transferred between the cut surfaces. As shown in Figure 4, most of the assist gas changed to nitrogen bubbles on the top surface, and these bubbles produce the aerosols.  
As shown in Figure 7 and Table 3, laser cutting with a supersonic nozzle showed the best cut surface quality, followed by cutting with conical and conical-flat nozzles. However, in the case of using a supersonic nozzle, the top kerf width was measured approximately 50% larger than others. For some reason, the supersonic nozzle made a wider cut kerf and the molten metal was removed well compare to others.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Laser Power (kW)** & **Feed Rate** & **Focus Position of** & **Assist Gas** & **Stand-Off Distance** & **Nozkle Types** \\  & **(V\({}_{c}\), mm/min)** & **Beam (F\({}_{b}\), mm)** & **Pressure (P\({}_{g}\), bar)** & **(D\({}_{n}\), mm)** & **Nozkle Types** \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & & & Conical \\
9 & 30 & \(-\)20 & 10 & 1 & Conical–flat \\  & & & & Supersonic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser-cutting process parameters depending on the nozzle types.  
Figure 6: Cross-sectional view of the laser-cut specimen depending on the laser beam focus position: (a) laser beam focus is positioned on the top surface of the specimen; (b) laser beam focus is positioned on the inside of the specimen.
# Improving Sustainability of Steel Roofs: Life Cycle Assessment of a Case Study Roof
## 2 Methods and Life Cycle Assessment Processes
### Goal and Scope of LCA  
The primary goal of the study was to complete the LCA of a steel roof, including ancillary items, for a case study house in New Zealand. The results were analyzed to understand the extent of the life cycle impacts of key processes. The analysis focused on the steel coil, the roll forming of roofing from a coil, and the extent of impacts from ancillary items. The functional unit was defined as the \"coverage of one square metre of the roof with steel roofing in a very high wind zone and exposure zone B under NZS3604 definitions, while also providing waterproofing in compliance with the NZ Building Code\". The timber roof trusses, ceiling level structure, services, and insulation were outside the scope of the study. The case building lifespan was assumed to be 60 years, which was recommended by IStructE [6] as the standard practise for building projects. The selected system boundaries in the study were modules A1-A3 (production stages), A4 (transportation to the site), C2-C4 (from transportation at the end-of-life stages to the disposal process), and D (potential benefits of reuse, recovery, and recycling).  
Figure 1: Life cycle assessment framework for the research.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 3 Different Underwater Applications
### Image Classification  
Image classification is an important application in image processing. It segregates objects in an underwater image based on the object of interest, which can be useful in various fields, such as for the detection of pipeline corrosion, marine salt, fish detection, of ship wrecks, mineral exploration, marine animal detection, pollution monitoring, subsea investigation, and sea floor terrain examination.  
In machine learning, the model learns a pattern in a dataset, from which prediction of a given situation of interest can be made. The learning process starts by providing a training dataset, which is fed to a designed model to establish the relationship between dependent and independent variables. Once trained, the pretrained model may then be used to predict the output given a set of test inputs.  
Machine-learning methods are generally classified as either supervised, unsupervised, semisupervised or reinforcement learnings. A supervised machine-learning method is used here for the image classification purpose. The method predicts a new set of outputs based on what has been learned from the past training datasets, with the learning process starting with the help of training data that has a set of input and target vectors. In supervised learning, it is assumed that the actual output values are known for each input pattern.  
In the process of classification, image features of the input images from the training set are extracted using the Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM), with these features fed into the classification algorithm for training purposes. Two supervised machine-learning algorithms: Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) have been chosen. SVM has evolved as one of the most powerful supervised machine-learning methods in classification problems and linear and nonlinear regressions, whilst KNN is a nonparametric statistical method for classification and regression problems, and has been used for pattern recognition and feature detection [29].
## 2 The Principle of Structural Reliability Analysis
### Degree of Reliability for Structure
\[P_{f}=P(Y<0)=F_{Y}(0)=\int_{-\infty}^{0}\frac{1}{\sqrt{2\pi}\sigma_{Y}}\exp[- \frac{(Y-\mu_{Y})^{2}}{2\sigma_{Y}^{2}}]\mathrm{d}Y \tag{7}\]  
The reliability index and the failure probability can be converted using Equation (8). \[\beta=-\Phi^{-1}(P_{f}) \tag{8}\]where \(\Phi^{-1}(\bullet)\) is the inverse cumulative distribution function (CDF) of the standard normal distribution. Due to the complexity of structures, it is generally difficult to define the explicit limit state function. The surrogate model method is an effective approach to address such challenges. This method uses a finite number of structural analysis results to fit an explicit expression, which approximately replaces the unknown limit state function for reliability index calculation. However, commonly used surrogate models, such as the Kriging model, PCE model, and RSM model, often struggle to handle scenarios with multiple inputs or high-dimensional problems. To overcome these limitations, a particle swarm optimization-back propagation neural network (PSO-BPNN) method for reliability analysis is proposed. This method trains a neural network using a finite number of structural analysis results, enabling it to serve as an approximate replacement for the implicit limit state function in reliability index calculations.
## 2 Materials and Methods
### Method
#### 2.3.3 Detection of CO\({}_{2}\) Correction for SIF-GPP Model  
In order to explore the impact of atmospheric CO\({}_{2}\) concentrations on estimating GPP, we constructed a SIF-CO\({}_{2}\)-GPP linear model to estimate GPP and compared it with the frequently used SIF-GPP model. When constructing the SIF-CO\({}_{2}\)-GPP model, we used the surface CO\({}_{2}\) mole fraction data of the EC flux towers. The SIF-CO\({}_{2}\)-GPP model utilizes the daily SIF/57 as the primary variable and the CO\({}_{2}\) mole fraction as the secondary variable to estimate GPP (Hereafter, GPP\({}_{\text{M}}\) represents the result of the SIF-CO\({}_{2}\)-GPP model while GPP\({}_{\text{S}}\) represents the result of the SIF-GPP model). We used the method of k-fold cross validation to compare the SIF-GPP model with the SIF-CO2-GPP model. In other words, the samples were divided into k portions. These k-1 portions were used for establishing the model and 1 portion was used for validation. Then we repeated k times of the above procedures so that all samples were used for the validation. For sites that have more than 10 samples, k is 10. Otherwise, k is 4. By comparing performances of the SIF-GPP model and the SIF-CO\({}_{2}\)-GPP model, we tried to find out whether the introduction of CO\({}_{2}\) mole fraction can improve the accuracy of estimating GPP, since the ultimate goal of both models is to estimate the regional GPP by remotely sensed proxy parameters. Here we conduct additional experiments to investigate the relationship between the surface CO\({}_{2}\) molar fraction measured by the EC flux tower and the XCO\({}_{2}\) obtained by OCO-2. We selected four flux towers with sufficient XCO\({}_{2}\) data to establish a linear relationship between surface CO\({}_{2}\) molecular weight and XCO\({}_{2}\). Since the EC flux tower and the OCO-2 footprint barely overlap, the XCO\({}_{2}\) data for OCO-2 within 10 km of the EC flux tower were averaged and compared to the surface CO\({}_{2}\) molar fraction measured in the EC flux tower. Finally, the gridded GPP\({}_{\text{S}}\) and GPP\({}_{\text{M}}\) were produced using OCO-2 products in surroundings of US-PFa on four representative days of different seasons. The coverage and spatial resolution are \(\sim\)20 \(\times\) 20 km and 1 km, respectively. Differences between areal GPP\({}_{\text{S}}\) and GPP\({}_{\text{M}}\) were demonstrated in terms of spatial and temporal scales.