esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Milk, Retentate, and IF Chemical-Physical Composition
\begin{table}
\begin{tabular}{l c c} \hline  & **IF1** & **IF2** \\  & **(_n = 6_)** & **(_n = 6_)** \\ \hline Retentate \({}^{*}\) (\%) & 31 \(\pm\) 6 & 9 \(\pm\) 1 \\ Whole milk (\%) & 23 \(\pm\) 1 & 52 \(\pm\) 1 \\ Skim milk (\%) & 3 \(\pm\) 1 & 13 \(\pm\) 1 \\ Lactose powder (\%) & 8.1 \(\pm\) 0.3 & 4.8 \(\pm\) 0.2 \\ Water (\%) & 34 \(\pm\) 5 & 21 \(\pm\) 2 \\ \hline \end{tabular} \({}^{\rm a}\) Retentate from ultrafiltration of skimmed whey.  
\end{table} 
Table 2: Relative incidence of the ingredients utilized in the preparation of the liquid milk-derived formulations IF1 and IF2, to be used in the production of first stage (from 0 to 6 months of age) and follow-on (from 6 to 12 months of age) infant formulae, respectively (mean \(\pm\) SD). Values of pH and dry matter did not differ between the two formulations, while all the other parameters were significantly different from each other (\(p<0.05\)). It is worth remembering that, in designing the final FM, IF1 and IF2 are meant to be the only sources of proteins, while the content of fat, lactose and other components must be adjusted by adding other ingredients. As reported in Table 3, the qualitative changes in the protein fraction in IF1 and IF2 reflected those that occur naturally in breast milk during the growth period of the new-born [2]. As expected, IF1 had a significantly lower casein level than IF2 (1.3% and 2.8%, respectively; \(p<0.05\)), while the whey protein value was higher in IF1 compared to IF2 (2.6% and 1.6%, respectively; \(p<0.05\)). This aspect is consistent with the use of a larger amount of retentate to obtain IF1, whereas more milk (whole and skim) was used to obtain IF2 (Table 2). Therefore, the CN:WP ratio (0.49 and 1.67, respectively in IF1 and IF2) was close to the predetermined target (30:70 in IF1 and 60:40 in IF2). Whey proteins, particularly \(\beta\)-lactoglobulin and \(\alpha\)-lactoglobulin, are most useful in the first few months of the neonate's growth. Since whey proteins remain soluble in the stomach, they are digested more quickly, pass more rapidly to the intestines, and are readily used in the building of muscle mass. Caseins, on the other hand, form a coagulated mass during gastric digestion and thus remain longer in the stomach; they are absorbed slowly, making them more suitable for a later stage of growth [22; 23]. Table 3 also shows the fat, lactose, and ash content of the two formulations. IF2 showed about twice the fat content of IF1 (3.1 vs. 1.5; \(p<0.05\)). Lactose content was significantly higher in IF1 than in IF2 (10.16% and 7.88%, respectively; \(p<0.05\)), while ash percentage was lower in IF1 compared to IF2 (0.46% and 0.69%, respectively; \(p<0.05\)).
# A New Approach to Image-Based Estimation of Food Volume
## 2 Method  
The system has been designed as a client-server solution. The smartphone acts as the client and runs the semi-automatic part of the system. Building on our previous work, we complete the processing cycle addressing all of the main practical and theoretical issues related with food volume estimation.  
In the system described in this paper, as a first step, the user is asked to take a short video of the food. Then, a subset of the frames is automatically extracted from the video to provide relevant views of the food from all sides. Next, the user marks some food and non-food parts in one of the selected frames of the video. A mobile application that we developed facilitates this part (see Figure 1). The segmentation process, which is performed on the server side, is seeded by the user's marks. The segmented images are then fed into a customized image-based process that builds a 3D model of the food items. This model is finally used to calculate the volume of the food. A small checkerboard is used as the size and ground reference for the modeling process.
## 3 Design and Implementation
### Applying Dynamic Object Detection While Moving to the Target Position  
One of the other goals we set was to apply Algorithm 1 during the execution of active SLAM, in order to avoid dynamic objects during the execution of the movement to the target position. As the total detection time was about 4 s, optimization was necessary. During the optimization, some auxiliary processes were removed, which were used to visually check the functionality of the entire process. In addition, a more powerful computing unit was provided, which resulted in a faster process. As a result, the response time averaged 0.3 s where the value interval was <0.20;0.65>. After these results,  
there was a need to properly place Algorithm 1 in an ongoing active SLAM. Detection of dynamic objects was implemented in the part where the robot was moving and reading its odometric data. The result of this implementation can be seen in Figure 18, where a combination of ASLAM and object detection was used. Detected objects are marked in green on the map. The measurement was performed at a time when the corridor was quite busy.  
After the successful implementation of Algorithm 1 during ASLAM execution, it was necessary to determine the extent to which the detection of dynamic objects would interfere with the activity performed during ASLAM. In this step, the conditions under which the robot would stop and calculate a new path had to be determined. As mentioned above, from each identified object, we obtained information about its geometric center in global coordinates using relation, Equation (11).  
\[x_{center}\ =\ \frac{x_{min}\ +\ x_{max}}{2}, \tag{11}\]  
where \(x_{min}\) and \(x_{max}\) are the boundary coordinates of the object. Alternatively, the y coordinate is calculated. Based on these coordinates, the distance from the planned trajectory is calculated. If the object is located at a distance of 0.5 m from the planned trajectory, the robot is stopped and calculates the new trajectory. In order to determine the new trajectory, which means that the identified object is in the place where the robot has already passed, it is necessary to update the path that it still has to take, based on the measurement. The whole implementation process is shown in Figure 19. It should be added here that this whole procedure was performed only when the robot was moving. One of the things to consider was the possibility of stopping the object at the robot's target. In this case, the area around the destination was clear and a new navigation destination point was selected.  
Figure 18: ASLAM execution and detection of dynamic objects in space.
# Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin
## 6 Conclusions  
In this paper, a high-quality virtual workshop is constructed, and a workshop fault monitoring model is established based on neural network. Moreover, the dynamic schedul  
Figure 10: Complete rescheduling Gantt charts under non-digital twin with 12 \(\times\) 6 scale.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Processing End Time (min)** & **Energy Consumption (kW-h)** & **Time Deviation** & **Machine Deviation** \\ \hline Without digital twin & 207 & 371 & 458 & 17 \\ Based on digital twin & 200 & 362 & 266 & 14 \\ Difference & \(-3.4\%\) & \(-2.4\%\) & \(-41.9\%\) & \(-17.6\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Scheduling results.  
ing of workshop production schedule based on digital twin is studied and an example is analyzed. The main conclusions of this paper are as follows:  
1. The shop floor scan was completed with Autodesk Recap software. A high-fidelity digital twin workshop was built with the Unreal Engine 4 program. On this basis, A middleware system management node is constructed, which can realize production line equipment data collection, status display, RFID reading and writing control, etc.
2. A neural network-based fault monitoring model was constructed, and a dynamic scheduling strategy of workshop production based on digital twin is constructed with fault perturbation as the rescheduling trigger mechanism.
3. Taking an aerospace plant as an example, it is demonstrated that the scheduling scheme based on digital twin reduces processing end time by 3.4%, energy consumption by 2.4%, time deviation by 41.9% and machine deviation by 17.6% compared with the scheduling scheme obtained without digital twin technology.  
The shop floor dynamic scheduling method based on the digital twin proposed in this paper can realize more foresighted and proactive shop floor dynamic scheduling, which is of great significance to the practical application of job shop dynamic scheduling research.  
Conceptualization, G.D.; data curation, S.G.; formal analysis, G.D.; investigation, G.D.; methodology, G.D. and S.G.; resources, X.W.; supervision, X.W.; validation, G.D., S.G. and X.W.; writing--original draft, G.D.; writing--review and editing, S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Major Project of China, grant number 2019 ZX04024001.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
It was found that SSDE increased as the CTDI\({}_{\text{vol}}\) and patient BMI increased, respectively. The results of the aforementioned studies are in accordance with our findings, where statistically significant relationships were found between each pair of the CT dose indices (\(p\)-value \(<0.05\)). As discussed in this section, several studies have explored the relationships between patient parameters and CT dose indices and between the pair indices themselves. Other studies have been proposed in the literature for other purposes related to CT dose estimation and prediction. For example, the work in [35] explored deriving SSDE from BMI. Moreover, artificial neural networks (ANNs) were used to estimate the DLP in chest CT examinations [36], while convolutional neural networks (CNNs) were used to estimate SSDE in CT medical examinations [37]. In [22], several regression predictive models were used to predict the patient's CT dose in various CT protocols. All in all, this study found that all CT dose indices correlated with BMI and weight in all CT exams with varying degrees. Age did not display any significant correlation with CT dose indices across all CT exams. All of the CT exams considered in the present study were either in the chest, abdomen, or pelvis area. Body thickness and composition in the body part mentioned were not affected by age, unlike the brain body part, which is affected by age, especially in the pediatric population. This explains the rationale behind the different age band groups to establish the diagnostic reference levels (DRLs) for pediatric CT brains. Our observation supports the recommendation of international commission on radiological protection (ICRP) Report 135 [38], which suggests DRLs to be established using weight criteria for both adult and pediatric populations. The work presented in this paper investigated the correlation between different CT dose indices and patient-related parameters. One of the limitations of this work is the limited sample size. Moreover, the fact that the dataset was retrieved from a single CT machine and from a single institute does not address the different protocol setups between the different radiology departments. One cannot neglect the fact that CT detector configuration also varies across vendors, which could also contribute to CT exposure. The pediatric population is at more risk with the increased demand for requested CT. Our dataset was exclusively investigating adults. As a future work, this study can be applied to a larger dataset captured from a population with different age groups and collected from different institutions using different CT machines with different configurations.
###### Abstract  
The failure types and ultimate loads for eight carbon-epoxy laminate specimens with a central circular hole subjected to tensile load were tested experimentally and simulated using two different progressive failure analysis (PFA) methodologies. The first model used a lamina level modeling based on the Hashin criterion and the Camanho stiffness degradation theory to predict the damage of the fiber and matrix. The second model implemented a micromechanical analysis technique coined the generalized method of cells (GMC), where the 3D Tsai-Hill failure criterion was used to govern matrix failure, and the fiber failure was dictated by the maximum stress criterion. The progressive failure methodology was implemented using the UMAT subroutine within the ABAQUS/implicit solver. Results of load versus displacement and failure types from the two different models were compared against experimental data for the open hole laminates subjected to tensile displacement load. The results obtained from the numerical simulation and experiments showed good agreement. Failure paths and accurate damage contours for the tested specimens were also predicted.  
Keywords:progressive failure analysis; composite laminates with open holes; tensile loading; generalized method of cells; multiscale analysis +
Footnote †: journal: Applied Sciences
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Visual-Based Modality  
The visual-based modality includes any state changes that a camera sensor can capture, convey meaning, and can be used to describe the user's intention to interact or present visual feedback. Figure 2 visualizes all the contexts and methods identified for the visual-based modality. A detailed review for each context is presented in the following subsections.  
There are two ways of interacting with or visually perceiving a mixed reality environment. As previously stated, an MR environment is where the real and the virtual coexist. Thus, two of the main ways of visual coexistence are [68]:  
* Optical see-through systems (OST): by displaying digital objects in a semi-transparent screen where real objects can be directly perceived through the glass.
* Video see-through systems (VST): by displaying digital objects on a screen together with real objects captured by a camera sensor, commonly used by smartphones in AR.
## 2 Co-Simulation System
### MATLAB/Simulink Model
#### 2.2.2 The Power Battery Model  
Common equivalent circuit models for power batteries encompass the Randle model and the Rint model, among others. Notably, the Rint model demonstrates superior statistical characteristics in terms of voltage error. Hence, this study is grounded on the Rint model for modeling energy storage systems. In the Rint model, the power battery model is equivalently represented as a circuit consisting of the battery's internal resistance \(R_{bat}\) in series with an open-circuit voltage source \(U_{ac}\)[33].  
The output voltage \(U_{bat}\) of the power battery is  
\[U_{bat}=U_{ac}-I_{bat}R_{bat} \tag{3}\]  
In the equation, \(U_{ac}\) is the open-circuit voltage of the power battery, \(I_{bat}\) is the current flowing through the power battery, \(R_{bat}\) is the internal resistance of the power battery, and \(U_{bat}\) is the output voltage of the power battery.  
Based on the ampere-hour integration method, the state of charge (\(SOC\)) of the energy storage system is calculated, as shown in Equation (4):  
\[SOC=SOC_{s}-\frac{\int n_{bt}I_{bat}dt}{Q} \tag{4}\]  
In the equation, \(SOC_{s}\) is the initial state of charge of the energy storage system, \(Q\) is the total capacity of the energy storage system, and \(n_{bt}\) is the number of batteries connected in series in the energy storage system. Other symbols are as defined in the previous context.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Sparse Jamming Estimation
#### 3.4.2 Bayesian Spare Jamming Estimation Algorithm  
As an alternative to GSJE, we propose a novel BSJE algorithm. In the previous section, we discussed that GSJE requires prior knowledge of jamming, such as the sparsity or power of jamming. Instead, we aim to develop the BSJE method in which the jamming is modeled as a Gaussian mixture and Bayesian inference is sought to estimate the jamming vector. From Equation (6), BSJE determines the posterior estimation of \(\mathbf{\mathrm{e}}\) by exploiting the assumption that \(\mathbf{\mathrm{e}}\) is a realization of a zero-mean i.i.d. Gaussian-distributed random vector of which the covariance matrix is \(\mathbf{\Gamma}=diag\big{[}\ \gamma_{1}\ \ \ \gamma_{2}\ \ \ \dots\ \ \ \gamma_{N}\ \ \big{]}\) (i.e., \(\mathbf{\mathrm{e}}\sim\mathcal{CN}(0,\mathbf{\Gamma})\)). A simple suboptimal solution is to determine the maximum a posteriori probability (MAP) estimation without exploiting \(\mathbf{\Gamma}\), i.e.,  
\[\hat{\mathbf{\mathrm{e}}}=\underset{\mathbf{\mathrm{e}}}{\text{argmax}}\ p(\mathbf{ \mathrm{e}}|\mathbf{\mathrm{b}}), \tag{8}\]  
where \(p(\mathbf{\mathrm{e}}|\mathbf{\mathrm{b}})\) is a conditional PDF. In contrast, the BSJE algorithm exploits the information about \(\mathbf{\Gamma}\) by applying SBL [24]. Let the ML estimation of \(\mathbf{\Gamma}\) be \(\hat{\mathbf{\Gamma}}\). The algorithm first determines \(\hat{\mathbf{\Gamma}}\) and then evaluates the MAP estimator for a given \(\hat{\mathbf{\Gamma}}\). That is,  
\[\hat{\mathbf{\mathrm{e}}}=\underset{\mathbf{\mathrm{e}}}{\text{argmax}}\ p(\mathbf{ \mathrm{e}}|\hat{\mathbf{\Gamma}})p(\hat{\mathbf{\Gamma}})p(\mathbf{\mathrm{b}}|\mathbf{ \mathrm{e}}), \tag{9}\]  
where  
\[\hat{\mathbf{\Gamma}} =\underset{\mathbf{\mathrm{\Gamma}}}{\text{argmax}}\ p(\mathbf{\Gamma} \bigg{|}\mathbf{\mathrm{b}}) \tag{10}\] \[=\underset{\mathbf{\mathrm{\Gamma}}}{\text{argmax}}\ p(\mathbf{\Gamma})\ p (\mathbf{\mathrm{b}}\bigg{|}\mathbf{\Gamma})\] \[=\underset{\mathbf{\mathrm{\Gamma}}}{\text{argmax}}\ p(\mathbf{\Gamma}) \int p(\mathbf{\mathrm{b}}|\mathbf{\mathrm{e}})p(\mathbf{\mathrm{e}}|\mathbf{\Gamma})d\mathbf{ \mathrm{e}}.\]  
To solve the optimization problem, the spirit of expectation-maximization (EM) is exploited; rather than directly maximizing the log-likelihood, the algorithm maximizes the \(Q\) function iteratively. The \(Q\) function is defined by  
\[Q(\mathbf{\Gamma})=E_{\mathbf{\mathrm{e}}|\mathbf{\mathrm{b}}\gamma_{2}}[\log\ p(\mathbf{ \mathrm{b}}|\mathbf{\mathrm{e}})+\log\ p(\mathbf{\mathrm{e}}|\mathbf{\Gamma})+\log\ p(\mathbf{ \Gamma})], \tag{11}\]and the solution to Equation (10) is equivalent to the maximizer of Equation (11),  
\[\hat{\mathbf{\Gamma}}=\underset{\mathbf{\Gamma}}{\operatorname{argmax}}Q(\mathbf{\Gamma}). \tag{12}\]  
Let us recall the SJE problem in Equation (6).
###### Abstract  
In order to study the adaptability of photovoltaic greenhouses to climate in tropical areas, a photovoltaic greenhouse model (photovoltaic panel coverage rate: 76.9%) was built in this study according to a 1:1 proportion. The distribution law of the indoor illuminance, temperature, and humidity were studied simultaneously in the photovoltaic greenhouse by actual measurements and simulation. The filed data are shown as follows: (1) Illuminance: in limited rain winter, the average illuminance and light transmittance were 7.02 kLux and 25.77%, respectively (10:00-16:00), but in different weather conditions during summer, the average illuminance and light transmittance were 15.47 kLux and 32.35%, respectively (9:00-16:00). (2) Temperature and humidity: the indoor temperatures of the greenhouse were between 22.1 and 29.3 \({}^{\circ}\)C in limited rain winter, with a relative small temperature difference between indoor and outdoor environments; the relative humidity values were between 69% and 97%; but in summer, the temperatures at all indoor test site were higher than outdoor sites, with an average temperature difference of 2.7 \({}^{\circ}\)C and relative humidity values between 46% and 94%. According to the simulation by Design Builder, the average light transmittances were 33.09% in winter and 37.54% in summer, the temperature difference between winter and summer was less than 1 \({}^{\circ}\)C, and the relative humidity decreased with the increase of temperature, which basically coincided with the filed data. The results of the analysis showed that the illuminance, temperature and humidity of the photovoltaic greenhouse can satisfy the production requirements of shade-enduring and neutral crops. At the same time, by comparing the illumination, temperature and humidity of the photovoltaic greenhouse with that of an ordinary greenhouse, the former had good adaptability to climate in tropical areas, which can achieve the goal of photovoltaic generation and agricultural production synchronously.  
photovoltaic greenhouse; light and thermal environment; Design Builder simulation; tropical area +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Feature-Based Approach with Machine Learning Algorithms  
A feature-based approach with three different shallow ML algorithms was used as a comparison to the CNN. The features are applied to the windows of the \(C/N_{0}\) time series and they are selected due to the specific characteristics of this context: the presence or absence of interference will, respectively, decrease or increase the \(C/N_{0}\) values. Features that provide an estimate of the values or a change in values are preferred. Ultimately, the nine features listed here were selected: minimum value, maximum value, Root Mean Square (RMS), skewness, kurtosis, standard deviation, and quantile with proportion 0.7, 0.8, and 0.9.  
Three different classifiers were used in this study: the Decision Tree (DT) algorithm, the Random Forest (RaF) algorithm, and the Extremely Randomized Tree (ERT) algorithm. The ERT algorithm, which is an evolution of tree-based ensemble methods, is described by the authors of [17]. The DT algorithm is based on the MATLAB 2023a implementation using the _templateTree_ and _fitecoc_ functions configured with the automatic hyper-parameters optimization. The RaF algorithm is based on the MATLAB 2023a implementation using the _templateTree_ and _fitensemble_ functions with the Adaptive Boosting algorithm (i.e., AdaBoost) and the automatic hyper-parameters optimization.  
Figure 1: Methodology of the proposed approach.
## 4 Determining the Optimal Number of Clusters  
In engineering applications, the termination condition of clustering usually is determined by the number of groups or the similarity threshold value. However, it is very difficult to determine the threshold value of morphology similarity and coplanarity, so the termination condition of clustering has to be determined by the number of groups. There are some cluster validity measures that are concerned with determining the optimal number of clusters and checking the quality of clustering results. In this paper, the Dunn index (DI), Davies-Bouldin index (DBI), and CS were used to determine the optimal number of clusters.
## 2 Materials and Methods
### Remote Sensing Data
#### 2.4.2 Data Pre-Processing  
The NDVI datasets underwent several pre-processing steps to prepare data for interpolation and data smoothing before seasonal parameters were identified and extracted. The pre-processing steps involved were pre-projection of the coordinate system, removal of bad data/outlier, cloud masking, rubber area masking, applying scale factor, combining both datasets using the maximum-value composite (MVC) [55] procedure on pixel-by-pixel basis and converting the data to readable file format. The outliers were removed based on pixel reliability information from MODIS. The cloud state information for each image was downloaded and used for cloud masking. The combination of datasets was conducted using the ArcGIS image analysis tool. All other steps were undertaken in RStudio software.
## 4 Discussion  
In order to obtain the simulation performance of our proposed Cooperative Data Downloading (CDD) routing scheme, we used the Satellite Tool Kit (STK) to construct the satellite network scene and jointly carried out algorithm simulation with OPNET modeler and MATLAB.