esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of the Thermo-Ultrasonic Treatment on the Fish Broth Properties  
To investigate the impact of thermo-ultrasonic treatment on the fish broth properties, we compared the nutrient and structural properties of the emulsion system of the fish broths by boiling water and thermo-ultrasonic treatment. Thermo-ultrasonic treatment accelerated the rate and the amount of nutrients (water-soluble proteins, fats and total sugars) extracted from the fish heads. When the thermo-ultrasonic treatment was undertaken for 30 min, the amount of nutrients extracted was close to or greater than the boiling water treatment at 120 min. The results from the structural characterization of the emulsified system of the fish broth showed that although thermo-ultrasonic treatment for 30 min led to the formation of an obvious emulsified system of the fish head broth, the fat uplifting phenomenon still existed, and the stability of the fish head broth product was poor. However, thermo-ultrasonic treatment for a longer period of time improved the stability of the fish broth.  
Altogether, the impact mechanism of thermo-ultrasonic treatment on the fish broth properties is illustrated in Figure 8. Due to the destruction of the fish head tissue by thermal and ultrasonic effects for 30 min, greater quantities of nutrients such as proteins, fats and total sugars were extracted more rapidly into the broth to form an emulsified system (Figure 1A-C). The cavitation effect of sonication caused the fat globules in the fish broth to become smaller at 120 min, resulting in better stability of the fish broth emulsification system (Figures 3-6). On the one hand, the cavitation and mechanical breakdown of an ultrasound can cause fish head tissue to be destroyed, accelerating the dissolution of nutrients. On the other hand, continuous ultrasonic treatment can break up a larger fat globule into smaller and more uniform droplets, which leads to an increase in the emulsification stability of a fish broth. Therefore, thermo-ultrasonic processing technology  
Figure 7: The correlation analysis of substances and droplet sizes of fish broth prepared by boiling water (**A**) and thermo–ultrasonic treatment (**B**) with different treatment times (0, 30, 60, 90, 120 min).  
is an effective method that can enhance the rate and content of nutrient dissolution and also the stability of fish head broth.
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
###### Abstract  
Based on the improved three-dimensional finite element model, this paper studies the load transfer mechanism of pile-supported reinforced embankments. The model uses an elastic medium to replace the soft soil subgrade, which reduces the calculation depth of the subgrade and improves the calculation efficiency of the model. The validity of the model is proven by field test results and theoretical calculation results. By changing the cohesion, internal friction angle, elastic modulus of the embankment filler, and geogrid strength, the effects of various influencing factors on the pile-soil stress ratio, the load-sharing ratio of the soil arching effect, and the load-sharing ratio of the membrane effect was analyzed, and the sensitivity of each influencing factor was evaluated. Based on the response surface optimization test, the multiple regression equation of influencing factors and evaluation indicators was established. The interaction between the parameters was analyzed, and the optimal combination of parameters was solved. The results show the following: Increasing the cohesion, the internal friction angle, and the elastic modulus of the embankment filler can promote the soil arching effect to a certain extent. However, for reinforced embankments, a large cohesion, a large internal friction angle, and a high elastic modulus of an embankment will reduce the pile-soil differential settlement and the pile-soil stress ratio; an increase in geogrid strength has a certain promoting effect on the pile-soil stress ratio. When the geogrid strength reaches 120 kN/m, the pile-soil stress ratio tends to be stable; the tested regression model can accurately reflect the changes in the relationship between the influencing factors and the response values, and it fits the actual situation well. Numerical simulation results show that the optimized pile-soil stress ratio increases by 13.4%.  
pile-supported embankment; response surface analysis; load transfer +
Footnote †: journal: Applied Sciences
## 3 Experimental Validation  
In this Section, the ML algorithms described in Section 2 are validated using experimental data from the literature. Section 3.1 focuses on the validation on SLM AlSi10 Mg data, whereas Section 3.2 focuses on the validation of SLM Ti6 Al4V literature data.  
The experimental data for the AlSi10 Mg and the Ti6 Al4V alloy have been digitized with the software Engauge from images in the original papers, if not available in tabular form. For the analysis, literature papers, where the defect sizes and the process parameters described in Section 2.2 were available, have been considered. Since the focus of the proposed approaches are the critical defects, i.e., those controlling the fatigue response and from which the crack initiates, defects at the origin of the fatigue failures after experimental tests have been considered. The risk-volume of dog-bone specimens subjected to conventional fatigue axial tests has been computed analytically by considering the gage section and the gage length. The risk-volume of hourglass specimens subjected to conventional axial fatigue tests or of specimens subjected to ultrasonic fatigue tests has been computed through Finite Element Analyses (FEAs), starting from the specimen geometry reported in the original paper. For the validation of the LEVD ML approach, the location and scale parameters of the original datasets and for a given set of process parameters have been estimated by applying the Maximum Likelihood Principle and by considering the LEVD truncated to 0 \(\upmu\)m, i.e., by implying that defects cannot have a negative size, according to [31]. The experimental probabilities for the defect sizes contained in each dataset have been computed with [PERSON]'s approximation for Median Ranks [32].  
In the following analyses, the number of folds has been selected depending on the datasets and on the number of available data, to have enough data for an effective training and a reliable validation. Accordingly, for the Probability ML the number of folds has been selected to have at least 24 data for the validation, whereas for the LEVD ML the number of folds has been selected to have at least two datasets within each validation fold for the majority number of folds (in particular, due to the number of available datasets, three folds for the Ti6 Al4V alloy have one dataset for the validation). The split ratio between training and validation data is given by the reciprocal of the number of folds.  
For the sake of clarity, in the following figures for the validation of the Probability ML algorithm, \"ML predicted\" refers to the probabilities predicted with the Probability ML, whereas \"Experimental\" refers to the experimental probability. In the figure for the validation of the LEVD ML, \"Experimental fit\" and \"ML fit\" refers to the LEVD estimated experimentally and with the developed ML algorithm, respectively.  
Figure 4: k-fold cross validation procedure: example with 5 folds.
## 4 Analysis
### Sensitivity and Uncertainty Analyses of HOE Variables and ERL
#### 4.2.2 Uncertainty Analysis of HOE and ERL
* For Case#4 ERL values for the performance solution with HOEs for 5% and 95% bounds are 4.15 \(\times\) 10\({}^{-5}\) and 4.30 \(\times\) 10\({}^{-5}\), respectively. ERL values for the DTS solution for 5% and 95% bounds are 3.83 \(\times\) 10\({}^{-5}\) and 4.02 \(\times\) 10\({}^{-5}\), respectively. Similarly, ERL values for the performance solution for 5% and 95% bounds are 2.92 \(\times\) 10\({}^{-5}\) and 3.03 \(\times\) 10\({}^{-5}\), respectively. From the average value, the ERL for the performance solution with HOEs is higher by 7% as compared to the DTS solution and by about 42% as compared to the performance solution. The results also indicate that uncertainties associated with the ERL point estimates are small. At the same time, the low standard deviations as shown in Table 7 signifies that the data points are closely distributed around the mean values. Figure 12: Cumulative Probability Distribution for Case #1 to #—DTS, Performance & Performance with HOE. Detailed results are summarized below:  
* For the DTS solution, the ERL value is highest for Case #2 followed by Case #4, #1 and #3 in descending order. * For the performance solutions, the ERL value is highest for Case #2 followed by Case #1, #4 and #3 in descending order. * When HOEs are considered, the ERL value is highest for Case #2 followed by Case #1, #4 and #3 in descending order. * The average across different cases shows that the performance solution gives the lowest ERL with an average value of 3.02 \(\times\) 10\({}^{-5}\) whereas for DTS solution it is 3.48 \(\times\) 10\({}^{-5}\). When HOEs are considered in the analysis, the ERL increases to 4.07 \(\times\) 10\({}^{-5}\), considering it is average value across different cases. Thus, across different cases, HOEs can increase the ERL value by as much as 42% compared to the performance solution. Further, the performance solution gives a lower value of ERL by much as 33% as compared to DTS solution.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 5 Results and Discussions
### Parameter and Environment Settings  
We use Matlab R2016a to conduct simulations for the proposed algorithm, SBS-Cell-Enlarged, and SBS-Random, in order to assess their respective performance results. Specifically, the size of the two-layer cellular network under consideration is set at 1000 m \(\times\) 1000 m. A model of grid deployment suggested by [PERSON] et al. is adopted [28], with the field designed such that 50 m \(\times\) 50 m squares are situated over a 20 \(\times\) 20 grid consisting of 400 squares in total. The region for an SBS deployment is represented by a square, while the MBS itself is situated at the very center of the grid. The given SBSs are randomly distributed over the grid. The number of coverage holes is set at 10 (recall that each of their approximate centers has one SBS that is normally switched off). Moreover, users are assumed to move about the grid in random directions at a sustained velocity of 10 km/h. The period for the execution of the algorithm is set at 1 minute. Referring to the aforementioned study [15] by [PERSON] et al., \(\text{P}\text{m}_{\text{basic}}\), \(\text{P}\text{m}_{\text{max}}\_{t}\), \(\text{P}_{\text{s}_{\text{basic}}}\), \(\text{P}_{\text{s}_{\text{t}}}^{\text{(high)}}\), and \(\text{P}_{\text{s}_{\text{t}}}^{\text{(low)}}\) are set at 130 W, 20 W, 4.8 W, 0.05 W, and 0.025 W, respectively. Additionally, \(\text{P}_{\text{rz}}\), \(\text{N}_{0}\), \(\text{B}_{\text{m}}\), \(\text{B}_{\text{s}}\), and \(\text{C}_{\text{ave}}\) are set at \(-73\) dBm, \(-150\) dBm, 10 MHz, 2 MHz, and 2 Mbps, respectively. The simulation time is set at 1 h. Furthermore, the physical-layer channel model in the current study is viewed as consisting of path loss, while the underlying path-loss model, utilized in the aforementioned study [20] by [PERSON], was also chosen for the current study.  
For the sake of completeness, the effects of the number of users and the number of SBSs on the three aspects of performance (i.e., the outage ratio, total power consumption, and average transmission rate) are examined in sequence, using the three methods. The corresponding results are included in Sections 5.2 and 5.3, respectively, and it should be noted that, for the former, the number of users is the variable, with the number of SBSs being set at 310, while for the latter, the number of SBSs is the variable, with the number of users being set at 240.
## 3 Workflow Description
### Mesh Generation
#### 3.3.1 Automated Configuration of the _blockMeshDict_  
An underlying grid is required as input to use _snappyHexMesh_, which will then be refined using an octree-based algorithm. The input mesh is here created by the OpenFOAM utility _blockMesh_, which splits a bounding box of the fluid domain into a Cartesian grid. The parameters for this utility are specified in the blockMeshDict file located in the system directory. This configuration file defines the coordinates of the bounding box vertices and the number of subdivisions along each edge. To automatically obtain the coordinates of the box, T-WorkFlow uses the numpy-stl Python library [16]. This library extracts the minimum and maximum values along each Cartesian axis from the provided.stl files. The quality of the final mesh generated by _snappyHexMesh_ is significantly influenced by the \"splitting strategy\" used by _blockMesh_. A basic condition is to try to keep the aspect ratio of the hexahedral cells as close to unity as possible; ideally, the grid should be made of cubes. To achieve this, the length of each edge of the bounding box must be divisible by a single factor, obtaining an integer result. For this purpose, the coordinates of the vertices of the box are adjusted appropriately. In T-WorkFlow, this common divisor is set to 30% of the inlet diameter, a value automatically captured from the inlet.stl files using the numpy-stl library. Our numerical experiments have shown that this value balances the computational efficiency of _snappyHexMesh_ and the quality of the resulting mesh. This methodology helped standardize the mesh generation process and ensure a consistent grid quality across various geometries.
## 3 Methods
### Calculation of Vertical Ground Deformation Residuals  
The obtained predicted values were imported back into the GIS project for further spatial analysis. A comparison was made between the predicted uplift values and the actual satellite-measured uplift values for each point. The residuals were measured as the differences between the expected (predicted) values and the observed (measured) values of z. Negative values of the residuals mean that the measured values are greater than the expected values of vertical ground deformation, while positive values of the residuals mean that the measured values are lower than the expected values. The calculated residuals were then mapped to visualize the spatial distribution of discrepancies between the measured and predicted uplift values that show second-order ground deformation vertical patterns.  
The spatial representation of these residuals provides valuable insights into the deformation patterns and allows for a better understanding of the areas experiencing significant differences in uplift or subsidence with reference to a simple radial circular model. By identifying these discrepancies, it is possible to refine models and improve the accuracy of future predictions.
## 2 Materials and Methods
### Data Collection and Processing
#### 2.3.2 Remote Sensing Surveys of Vegetation Cover  
On the plots at the hillslope scale, in 2016, a scheduled drone flight was carried out. The UAV used was a quadcopter md4-1000 (Microdrones Inc., Kreuztal, Germany) with a RGB SONY ILCE-5100 digital camera (Sony Corporation, Tokyo, Japan) on board. The sensor of the SONY ILCE-5100 camera was a complementary metal oxide semiconductor (CMOS) Exmor(r) type APS-C (23.5 \(\times\) 15.6 mm) with pixel size of 4 \(\times\) 4 \(\mu\)m. The image size was 6000 \(\times\) 4000 (columns and rows) and its focal length was 20 mm. Flight planning was performed for a flight altitude of 120 m obtaining a ground sample distance (GSD) of 0.015 m (Figure 4). LANDSAT8 images of the same date were collected over the plots at the catchment scale (Figure 4). _VARI_ values were calculated using UAV and LANDSAT8 images, both from 2016. LANDSAT image was subjected to atmospheric correction and radiometric calibration [49] without using LEDAPS approach. _VARI_ was calculated for each pixel, which identifies the vegetation in the part of the visible spectrum (ideal for RGB images). _VARI_ is calculated as follows [24]:  
\[VARI\ =\frac{Green\ -\ Red\ }{Green\ +\ Red\ -\ Blue} \tag{1}\]  
Once calculated, _VARI_ was then classified, splitting the entire range of data into five classes, to which a \"rank\" was given for both image sources (satellite and UAV) (Table 2). Then, possible correlations between the _VARI_ values and the vegetation cover measured in the plots in 2016 at the two spatial scales were found. Using the _VARI_ values, the effects of the land restoration measures on the vegetation cover in comparison with the burned and not treated, as well as to the unburned land, were evaluated.
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
## 3 The Proposed Approach: MSRBF-MCS
### Multiple Predictions Launched by Different Shape Parameters  
The value of shape parameter \(c\) has a significant influence on the surrogate model [13]. Hence, RBF models with widely varying prediction results can be obtained by setting different values of \(c\):  
\[c=[c_{1},c_{2},\dots,c_{n_{c}}] \tag{8}\]  
where \(c\) is the vector of shape parameters, and \(n_{c}\) is the number of shape parameters. Assuming that there are \(n_{ini}\) sample pairs in the initial DoE. Then \(n_{c}\) RBF models for multiple prediction can be generated according to \(c\) and DoE.  
In order to reduce the global prediction error of the surrogate model, the shape parameter \(c\) needs to be optimized with the objective of minimum \(PRESS\)[25, 26, 27]. Similarly, the following steps are designed to reduce the global prediction error of the metamodel proposed in this paper. First, a k-fold cross-validation of each RBF model is performed to calculate the \(PRESS_{i}\) of the \(i\)-th RBF model \(\hat{g}_{i}(\mathbf{X})\). Second, the weight coefficient \(q_{i}\) of \(\hat{g}_{i}(\mathbf{X})\) is calculated accordingly:  
\[q_{i}=\frac{PRESS_{i}{}^{-1}}{\sum\limits_{i=1}^{n_{c}}PRESS_{i}{}^{-1}},i=1,2,\dots,n_{c} \tag{9}\]  
Apparently, \(q_{i}\) satisfies the following:  
\[\sum\limits_{i=1}^{n_{c}}q_{i}=1 \tag{10}\]  
Finally, the weighted mean and variance can be calculated as follows:  
\[mean^{w}=\sum\limits_{i=1}^{n_{c}}q_{i}\hat{g}_{i}(\mathbf{X}) \tag{11}\]  
\[sigma^{w}=\sqrt{\sum\limits_{i=1}^{n_{c}}\left(\hat{g}_{i}(\mathbf{X})-mean^{w} \right)^{2}} \tag{12}\]  
\(mean^{w}\) is regarded as the predicted value at sample point \(\mathbf{X}\) of the metamodel:  
\[\hat{g}(\mathbf{X})=mean^{w} \tag{13}\]
# Personalized Federated Multi-Task Learning over Wireless Fading Channels
## 2 System Model and Problem Formulation
### Personalized Federated Multi-Task Learning (PF-MTL)  
We consider a PFL setting with \(N\) clients, in which client \(i\) has its own local dataset \(D_{i}=\{(\mathbf{x}_{j}^{(i)},y_{j}^{(i)})\}_{j=1}^{n_{i}}\) where \(n_{i}\) is the size of the local dataset, and \(T_{i}\) is the task of client \(i\), \(i\in[N]\). The system model consists of a global representation network \(q_{\omega}:\mathbb{R}^{d}\rightarrow\mathbb{R}^{d^{\prime}}\) which is a function parameterized by \(\omega\in\mathcal{W}\) and maps data points to a lower space of size \(d^{\prime}\). All clients share the same global representation network which is synchronized across clients with global aggregation. Client-specific heads \(q_{h^{(i)}}:\mathbb{R}^{d^{\prime}}\rightarrow\mathcal{Y}\) are functions parameterized by \(h^{(i)}\in\mathcal{H}\) for all clients \(i\in[N]\) and map from the low dimensional representation space to the label space \(\mathcal{Y}\). The system model is shown in Figure 1. Then, the local model for the \(i\)th client is the composition of the \(i\)th client's global representation model \(q_{\omega}\) and the personalized model \(q_{h^{(i)}}\),  
\[q_{i}(\cdot)=(q_{h^{(i)}}\circ q_{\omega})(\cdot) \tag{2}\]  
The local loss for the \(i\)th client is represented as  
\[F^{(i)}(h^{(i)},\omega)=F^{(i)}(q_{i}(\cdot))=F^{(i)}((q_{h^{(i)}}\circ q_{ \omega})(\cdot)) \tag{3}\]  
Through alternating minimization, the clients and the centralized server aim to learn a set of global parameters \(\omega\) together, while each client \(i\) learns its own set of client-specific parameters \(h^{(i)}\) locally. Specifically, client \(i\) performs \(\tau_{h}\) local gradient based updates to optimize \(h^{(i)}\), \(i\in[N]\), while the global network parameters at client \(i\), i.e., \(\omega^{(i)}\), are frozen. Thereafter, client \(i\) performs \(\tau_{\omega}\) local updates to optimize the global shared network parameters, while the parameters corresponding to the client-specific head are frozen. Then, the global shared network parameters \(\{\omega^{(i)}\}_{i=1}^{N}\) are aggregated at the PS to obtain a common \(\omega\). Thus, the problem is \[\min_{\omega\in\mathcal{W}}\frac{1}{N}\sum_{i=1}^{N}p^{(i)}\min_{h^{(i)}\in \mathcal{H}}F^{(i)}(h^{(i)},\omega) \tag{4}\]  
_FedRep_[7] investigates this framework with \(p^{(i)}=1,i\in[N]\).
###### Abstract  
Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 \({}^{\circ}\)C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m\({}^{2}\) aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R\({}^{2}\) coefficients ranging from 0.879 to 0.997 with the majority exceeding an R\({}^{2}\) of 0.95, all with \(p\)-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.  
urban temperature patterns; urban morphology; LiDAR; mobile sensors; random forest
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 4 Emerging Biodegradation Technologies
### Bioremediation
#### 4.3.2 Biopile  
Biopiling, also branded as bioheaps, biocells, or biomounds, is an ex-situ bioremediation technique commonly used to address a wide variety of petrochemical pollutants in soils and sediments. This entails building piles of contaminated soils or dried sediments and encouraging aerobic microbial populations to degrade the material by fostering ideal or nearly ideal growth conditions inside the pile [65; 69]. This includes aeration, adjustment of pH and humidity levels, nitrogen and phosphorus addition, and introduction of heat. As a result of these optimal conditions for growth, the increase in microbes' activity disintegrates bioavailable pollutants. Biopile innovation is effective for removing difficult-to-desorb natural impurities or moderately biodegradable natural constituents. Nonetheless, the biopile system has its drawbacks, such as conserving plenty of space, viable engineering, installation and repair, non-availability of power supply in isolated zones, and reduced microbial activity resulting in heat generation [57; 65]. Likewise, constituents such as volatiles are dissipated during the process, and it is limited for hydrocarbon concentrations exceeding 50,000 ppm.