esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Building CFD Model Design Process Based on BIM
## 2 Literature Review  
Conventional CFD analysis tools did not support BIM model files as input format, therefore integrating BIM and CFD was impossible. To address this issue, various studies utilize the BIM model by converting geometry data into OBJ and SAT formats, which can be input in CFD tools [24; 25; 26; 27]. However, this requires a process for converting BIM models into a separate format and leads to limitations, such as the omission of many attribute data included in BIM models [28]. In addition to integrating geometry data, many recent studies focused on integrating the attribute data in BIM into CFD models. [PERSON] et al. [29] used BIM files in gbXML format for CFD analysis. [PERSON] et al. [19] applied BIM models to design BES and CFD models, and used these to analyze the energy performance of buildings. It used BIM models to extract the geometry, material type, and the computation domain of the target to design CFD models. [PERSON] et al. [28] designed an FDS (Fire Dynamics Simulator) model by using the geometric and attribute data in BIM models. The geometry model was designed by using PyroSim, an FDS preprocessing program, and the attribute data was entered into the FDS model by developing a module for integrating material data.  
As described above, many studies have been conducted to couple the attribute data in BIM models to CFD, but the optimization of CFD models still depends on manual work. In previous studies, the user had to modify the geometry of the BIM model by using separate software for CFD analysis, which takes a considerable amount of time. Automatic CFD model design using BIM and the optimization process must be carried out at the same time to couple BIM for CFD. In this regard, [PERSON] et al. [7] developed a technology to simplify the geometry of BIM models through algorithms to convert them into geometry modelssuitable for CFD models. They used the minimum feature size to simplify the geometry by hiding objects smaller than this feature size or by merging them with large objects. However, the shapes could only be simplified into boxes. Therefore, volume errors increased in the case of simplifying circular or cylinder-shaped objects, and resulted in analysis results different from the actual results. In addition, it did not cover the integration of attribute data and grid optimization. As described above, previous studies on the integration of BIM and CFD only focused on individual processes, such as integrating geometric data, integrating attribute data, or geometry simplification. Research on developing complete BIM-CFD integration technologies considering all of these processes has not been carried out yet.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Random Uncertainty Sources  
The Monte-Carlo Simulation method was used to generate random values for viscosity, bulk modulus, time skew, polling time, and noise level. The purpose was to observe the probabilistic effect of uncertainties on leak detectability. The hypothesis is that a pipeline system with a low \(R\) value is more tolerant of uncertainties compared to a system with a high \(R\) value. Here, the baseline was taken as the case without any error in viscosity or bulk modulus, 1 s polling time, no time skew, and with 1% Gaussian noise. For cases experiencing a flow decrease transient, the baseline detection time was 31.1 min for the low \(R\) system and 33.1 min for the high \(R\) system. In Figure 10a, a histogram is plotted that shows the percentage of delay in the detection time compared to the baseline for the 500 cases, for the low \(R\) and high \(R\) system, respectively. It can be seen that the presence of random uncertainties caused leak detection to be delayed in all cases. For the high \(R\) system, the mean value of the percentage delay was 46.5%, with a standard deviation of 24.8%, and the median was 40.8%. For the low \(R\) system, the mean value was 31.8%, with a standard deviation of 17.2%, and the median was 27.3%. This supports the previous finding that leak detection is generally less affected by uncertainties in the low \(R\) systems. It is interesting to see that the distribution for the high \(R\) system is clearly bimodal with peaks at approximately 25% and 65% in Figure 10b. The data centered around the peaks at 25% and 65% are associated with the cases with 1% and 2% Gaussian noise, respectively. Clearly, the higher the noise level in the \"measured\" data, the more leak detectability is impacted by uncertainties. This was less clear in the low \(R\) system, but a bimodal distribution still exists with peaks at approximately 15% and 55%. There are very few data points at values greater than 60-70%, again supporting the hypothesis that the low \(R\) systems tolerate uncertainties better than the high \(R\) systems.  
Figure 11 summarizes the results for the cases corresponding to a steady state operation. In the low \(R\) system, the baseline detection time was 40.2 min, and 285 of the 500 cases were detectable. The mean value of the percentage delay for the detectable cases was 21.3%, with a standard deviation of 13.5% and a median of 19.6% for all detected tests. In the high \(R\) system, the baseline detection time was 41.4 min, and only 247 of the 500 cases were detectable. The mean value was 26.7%, with a standard deviation of 14% and median of 22.7%. For the cases experiencing a flow increase transient, all 500 cases became undetectable in either the low \(R\) or high \(R\) system.  
Figure 10: Delayed leak detection time for the 500 cases with random uncertainties under flow decrease conditions in systems where: (**a**) \(R=0.49\); and (**b**) \(R=2.20\).
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 2 The Improved Model
### GRNN Model  
GRNN is an improvement of radial basis function networks and probabilistic neural networks and has been applied in a variety of fields, such as time series prediction, system identification, and adaptive control. The basic network architecture of GRNN with multi-input and multi-output is shown in Figure 1[29].  
Figure 1 shows GRNN contains four layers, including input layer, pattern layer, summation layer, and output layer. The input layer provides the measurement variables to all of the neurons in the pattern layer, each pattern neuron represents a training pattern, and the output of each pattern neuron is a measure of the distance of inputs based on the stored pattern. The summation layer consists of two summation neuron types: One type computes the summation of the weighted outputs of the pattern layer, the other type calculates the un-weighted outputs of the pattern neurons. Finally, the output layer performs the normalization step to compute the GRNN predicted value of the output variable.  
As shown in Figure 1, \(X=[x_{1},\,x_{2},\,\ldots,\,x_{k}]\) is the input vector of the network and \(\Phi_{i}^{X}\) is the training sample vector of the i-th neuron in the pattern layer. \(\left(X-\Phi_{i}^{X}\right)^{\prime}\!\left(X-\Phi_{i}^{X}\right)\) is the squared distance between the input vector \(X\) and the i-th training sample vector \(\Phi_{i}^{X}\). \(\sigma\) is a smooth factor that controls the estimated density. \(\theta_{i}\) is the output of i-th neuron in the pattern layer. \(w_{ij}\) is the connection weight between pattern layer neuron \(i\) and summation layer neuron \(j\). The summation layer obtains the summation \(S_{j}\) of the weighted outputs of the pattern layer and the un-weighted outputs \(S_{d}\) of the pattern neurons. The output layer merely divides \(S_{j}\) by \(S_{d}\) to yield the desired estimate \(Y=[y_{1},\,y_{2},\,\ldots,\,y_{j}]\).  
The smoothing parameter \(\sigma\) plays a key role in the GRNN model. As \(\sigma\) becomes very large, the estimated density is forced to be smooth, and GRNN model ignores detail characteristics of the training sample. With the decrease of \(\sigma\), it is trying to track every characteristic of the training sample, which may lead to over-fitting with the loss of its generalization capability. Hence, a good choice of \(\sigma\) is necessary. A useful method of selecting \(\sigma\) is the holdout method with searching by experiments. For a particular value of \(\sigma\), the holdout method removes one sample at a time and constructs a network based on all of the other samples. By repeating this for each sample and storing each estimate, the mean-squared error can be measured between the sample value and the estimate. The value of \(\sigma\) generating the smallest error should be employed in the final network. A detailed procedure for calculation of \(\sigma\) is described in [27].
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 4 Analysis of Results
### Abnormal Early Warning Results
#### 4.3.1 Normal Sample Training  
The purpose of normal sample training is to achieve an accurate prediction of the network for normal samples; therefore, the dataset of normal samples is selected and randomly divided into training and test sets according to the ratio of 2:1. The input data were the data of the five time periods before the period to be predicted, and the output data were the 179-dimensional network state data of the current time period. The loss function of the proposed model consists of two parts: LSTM-based abnormal early warning and autoencoder-based abnormal detection. The training first requires the prediction results to match the real situation such that the mean square error (MSE) between the prediction results and the real data are as small as possible. Second, the prediction results must be correctly reconstructed using the autoencoder. Therefore, the prediction results are fed into the abnormal detection network, and the MSE between the reconstructed data and predicted data are calculated again. The learning rate was reduced by half every 100 epochs during training.  
Figure 10 shows the MSE of the prediction data based on the normal dataset, and the results indicate that the error of the network appears to increase periodically over time. It is observed that most of the error spikes occur at the beginning of the day, presumably because the base stations are beginning to work during these hours and their performance and service are unstable. Because the base stations are intermittently dormant to save energy, the dormant time data will be set to zero. As mentioned in Section 4.1, these dormant time  
Figure 8: The reconstruction-error distribution without the latency fitting module.  
Figure 9: The CDF of reconstruction error without the latency fitting module.  
mal samples, the prediction of some normal samples is inevitably sacrificed. In addition, the sampling density of the data selected in this study was 15 min, and for some of the abnormal samples, the abnormality may occur within 15 min of the current time period, resulting in it not yet being reflected in the previous time period, which means that these abnormal points do not have temporal characteristics, and the prediction accuracy is poor.
# A Review on Flavonoids as Anti-_Helicobacter pylori_ Agents
## 4 Conclusions  
This review provides an overview of the current landscape regarding _Helicobacter pylori_ infection, including the pathogenesis, current medical interventions, associated challenges,  
Figure 4: Flavonoid pharmacological pathways against _H. pylori_. Schematic representation of the potential effect of flavonoids on cells and _H. pylori_ infection. Created in BioRender. [PERSON] (2025) [[https://BioRender.com/z25s605](https://BioRender.com/z25s605)]([https://BioRender.com/z25s605](https://BioRender.com/z25s605)), accessed on 18 March 2025.  
and potential genomic markers for future therapeutic targeting. Flavonoids demonstrate significant potential as therapeutic agents against _H. pylori_, targeting various mechanisms such as urease inhibition, membrane disruption, enzyme interference, and modulation of virulence factors. Quercetin, rutin, baicalin, hesperetin, chrysin, astragalain, and myricetin have shown antimicrobial and anti-inflammatory properties, with quercetin, rutin, and myricetin enhancing antibiotic efficacy. From a treatment perspective, flavonoids offer a compelling complementary approach to traditional antibiotic therapies. The synergistic effects observed when flavonoids are co-administered with antibiotics provide an avenue to enhance therapeutic efficacy and combat antibiotic resistance.  
Additionally, developing flavonoid-based formulations, including flavonoid-rich extracts, nanoformulations, and co-crystallisation strategies, holds promise for improving bioavailability and targeting capabilities. Flavonoids are derived from natural sources, so their incorporation into dietary regimens or as nutraceuticalials could provide a safe and accessible means of managing _H. pylori_ infection. Future research should focus on optimising flavonoid formulations, understanding their precise mechanisms of action, and validating their efficacy through clinical trials.  
A.T.: Methodology, Writing--original draft, Writing--review and editing. K.C.S.: Methodology, Writing--original draft, Writing--review and editing. F.B.: Conceptualization, Methodology, Supervision, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
[PERSON] received a scholarship from PRINT (CAPES-PRINT--88887807/2023-00--Brazil); [PERSON] is a CNPq fellowship-honoured researcher (305520/2022-9--CNPq, Brazil).  
Not applicable.  
The data are presented in the article. Any other questions can be made to the corresponding author.  
The authors thank Trinity College Dublin, CNPq, and CAPES.  
The authors declare no conflicts of interest.
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture
### Wireless Gateway  
It is thus far a fact that traditional IoT lacks uniform standardization in communication protocols and sensing technologies, in addition to constraints on computational power, memory, and other resources. To address these issues, [PERSON] et al. [78] propose an IoT architecture consisting of a Wireless Gateway (WG). This gateway aims at entertaining issues of heterogeneity, manageability, and bridging legacy intranet to endpoint networks. It consists of three layers: (1) a sensing layer, containing M2M devices and endpoints, (2) a gateway API layer, and (3) an application layer. The outstanding feature of this system isthat it is capable of interacting with non-smart devices over Modbus in addition to being highly scalable and generic to be compliant with future standards as well as providing an incremental methodology for adding new services.
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Secure Aggregation Algorithms  
Although federated learning does not disclose local data, sensitive information may still leak during model exchanges. Secure aggregation algorithms integrate privacy-preserving mechanisms with aggregation to ensure the confidentiality of model information. This section introduces some of the commonly used secure aggregation algorithms.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 3 Composition of ECMs from Various Tissues
### Pulmonary ECM  
The pulmonary ECM contains a dynamic array of components, such as fibrous proteins, glycoproteins, glycosaminoglycans and proteoglycans [70]. A wide range of cell types also promote and maintain tissue architecture, including fibroblasts, smooth muscle cells and epithelial cells [71]. Within the pulmonary system, two fundamental structures exist in the form of basement membranes and interstitial matrices [72]. The basement membrane predominantly contains components such as collagen, laminins, chondro sulfate proteoglycans and fibronectin. Conversely, the interstitial matrix hosts a meshwork of components such as elastin, versican, fibronectin, vitronectin and decorin [71]. Amidst the molecular complexity of the pulmonary ECM, collagen is the most abundant contributor to the protein matrisome. Fibrillar collagens contribute to the overall structure and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Example** & **Gene Symbol** & **Glycosaminoglycan Chain** \\ \hline \multirow{2}{*}{Cell surface} & Betaglycan & _TGFBR3_ & Chondroitin sulfate/Heparan sulfate \\  & Syndecan & _SDC_ & Heparan sulfate \\ \hline \multirow{2}{*}{Pericellular} & Agrin & _AGRN_ & Heparan sulfate \\  & Collagen XV & _COL15A1_ & Chondroitin sulfate/Heparan sulfate \\ \hline \multirow{2}{*}{Extracellular} & Biglycan & _BGN_ & Chondroitin sulfate \\  & Decorin & _DCN_ & Dermatan sulfate \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._integrity of the lung, with collagen subtypes I and III promoting structural integrity in the alveolar walls and subtype IV being mainly located in basement membranes. Elastic fibres are responsible for providing an element of stretch and recoil to the lungs [72].
## 2 Manufacturing and Measurement Techniques
### Investigative Measurement Framework
Step 6: After the measurements: (a) for profile surface texture analysis, perform leveling operation and apply the selected \(\lambda_{\text{c}}\) value from step 4 to obtain the profile waviness and roughness results; (b) for areal surface texture analysis, perform the form and noise removal operation first and apply the \(\lambda_{\text{c}}\) value from step 4 for filtering. Step 7: After filtering, waviness and roughness profile/areal topography were obtained. The resulting roughness profile/areal topography contains wavelengths less than the \(\lambda_{\text{c}}\) value, and the waviness profile/areal topography contains wavelengths larger than  
Figure 1: Investigative measurement framework. the \(\lambda_{c}\) value. This indicates that the resulting profile/areal surface topography results contain the errors majorly due to particular wavelengths in the respective roughness (wavelength errors less than \(\lambda_{c}\)) and waviness (wavelength errors greater than \(\lambda_{c}\)) profile/areal surfaces. (Note that determining the accurate size distribution of asperities might be limited due to overlapping asperities in AM components, SEM resolution, and image analysis technique. However, the user can follow any technique apart from SEM image analysis that might be more accurate and suitable to determine the size distribution of the asperities present in the surface. This technique is only efficient if the interest lies in specific types of asperities rather than multiple types of asperities. However, categorizing the asperities based on size and selecting a suitable \(\lambda_{c}\) can help understand each asperity separately.) Figure 2 shows the effect of applied \(\lambda_{c}\) and the resulting output topographies. Initially, the AM surface is measured, and a profile/topography is obtained. The resulting surface is known as an S-F surface. The S-F surface contains errors largely due to loosely attached particles, partially melted particles, balling melts, semi-welded structures, staircase effect, etc. Figure 2: Working principle of the investigative measurement framework. The suitable \(\varphi_{\text{m}}\) found from SEM image processing is selected and used to calculate and apply a high pass filter (\(\lambda_{\text{c}}\) = 2 \(\times\)\(\varphi_{\text{m}}\)). The high pass filter removes the higher wavelengths above the selected filter value from the surface. The resulting topography will have the presence of wavelengths lower than the selected filter value.
# Attenuation of DC-Link Pulsation of a Four-Wire Inverter during Phase Unbalanced Current Operation
## 3 Reduction of DC Link Current Pulsation  
It is impossible to eliminate the DC link current pulsation using passive means: authors investigated if increasing a DC link capacity would resolve the problem: in simulation, only 20-30% reduction was achieved and by means of increasing the capacity several times, making it inapplicable (Figure 10). As such, an active compensation is required.  
Dual active bridge topology is an attractive solution for this application due to its high dynamics and easy transition between charging and discharging mode [20; 21]. A control algorithm is quite similar to the one driving the four-wire inverter (Figure 3). This time; however, the DC link current is used as feedback, and the reference signal is zero (Figure 11). The proportional resonant filter is tuned for frequency of 100 Hz. Additional filters can be connected in parallel, tuned for harmonics of 100 Hz (200, 300, 400 Hz). Furthermore, any residual DC component from the signal is removed, and the result is passed directly to phase control module. A mathematical description of this algorithm is presented in Equation (4).  
Figure 10: DC current pulsation with DC link capacity 5 mF (**a**) and 25 mF (**b**) (simulation results).  
\[G_{DAB\_res\_control}=\left(\sum_{n=1}^{N}\frac{K_{nH}2\omega_{r\_nH}s}{s^{2}+2 \omega_{r\_nH}s+\omega_{o_{-}}^{2}}\frac{s}{s+\omega_{PF}}\right) \tag{5}\]  
where: \(K_{nH}\)--resonant controller gain, \(\omega_{r\_nH}\)--with resonant span parameter \(\omega_{o}\)--controller resonant pulsation, \(\omega_{PF}\)--proportional controller gain.  
The resulting system naturally damps any DC components, and amplifies components of 100 Hz frequency and its harmonics (Figure 12).  
The proposed algorithm effectively removes AC components from the DC link of the power inverter. Additionally, because of the high rejection of DC components from the control circuit it is possible to avoid overcharging or completely discharging the bank of capacitors. During laboratory tests, authors did not observe any aforementioned phenomena, and concluded, that additional voltage control loop, or overvoltage protection is not required. Additional harmonic compensators are redundant in case when power converter operates in steady-state grid-tied applications, but are highly recommended when the power inverter operates in transient states or operates in island mode with highly nonlinear loads.