esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 4 DSLMS Adaptive Filter
This module adapts to signal variations and noise characteristics by precisely adjusting the filter coefficients, ensuring high output accuracy and stability. Figure 7 represents a schematic diagram of the RTL structure of the DSLMS algorithm after generating the synthesized Verilog code, representing the implementation of Equations (10)-(12), respectively. Figure 7: Schematic diagram of the filter RTL structure. fir_filter_inst is the filtering module, error_n_inst is the error calculation module, mu_update_inst is the step update module, and w_update_inst is the weight update module. data_in[15:0] denotes the input signal, data_ref[15:0] denotes the desired signal, clk_i denotes the clock signal, rst_n_i denotes the reset signal (active, low), error_o[15:0] denotes the error signal, mu3 denotes the step factor mu of Equation (12), and coef1 to coef8 denote the weight update; coef1 denotes the initial weights, which are set to one. data_o[15:0] denotes the final filtered output. Since the RTL view of each module is too cumbersome, the design schematic is used to show them individually. Figure 8 shows FilterError calculation module and Figure 9 shows Weight update module. Figure 10 shows Error calculation module. Figure 11 shows Step update module.
## 3 System Model and Working Mechanism  
In order to provide services for mobile users and IoT users in remote areas, the space-air-ground integrated network is investigated in this paper, and its network architecture is shown in Figure 1. It utilizes an aerial network composed of HAP drones to collect traffic generated by various IoT users, thus providing stable and large-scale access services for areas without ground communication infrastructures. Via a drone-LEO link and multiple LEO-LEO links, the collected traffic is then relayed to the LEO satellite connected to a ground station to achieve traffic backhaul. Finally, the ground station downloads the traffic via the LEO-ground link and transmits it back to the terrestrial traffic center for processing via optical fibers. Ground devices access HAP drones through the C-band. HAP drones are directly connected to LEO satellites through the Ka-band to achieve high-rate traffic backhaul [26].
# Indoor Air Pollution Increases the Risk of Lung Cancer
###### Abstract  
(1) Background: Cooking and burning increase are important sources of indoor air pollutants. No studies have provided biological evidence of air pollutants in the lungs to support this association. Analysis of pleural fluid may be used to measure the internal exposure dose of air pollutants in the lung. The objective of this study was to provide biological evidence of indoor air pollutants and estimate their risk of lung cancer. (2) Methods: We analyzed 14 common air pollutants in the pleural fluid of 39 cases of lung adenocarcinoma and 40 nonmalignant controls by gas chromatography-mass spectrometry. (3) Results: When we excluded the current smokers and adjusted for age, the adjusted odds ratios (ORs) were 2.22 (95% confidence interval CI = 0.77-6.44) for habitual cooking at home and 3.05 (95% CI = 1.06-8.84) for indoor incense burning. In females, the adjusted ORs were 5.39 (95% CI = 1.11-26.20) for habitual cooking at home and 6.01 (95% CI = 1.14-31.66) for indoor incense burning. In pleural fluid, the most important exposure biomarkers for lung cancer were naphthalene, ethylbenzene, and o-xylene. (4) Conclusions: Habitual cooking and indoor incense burning increased the risk of lung adenocarcinoma.  
indoor air pollutants; bioaccumulation; combustion sources; pleural fluid; exposure assessment; machine learning +
Footnote †: journal: Journal of Environmental Research and Public Health
# Tuning of Multivariable Model Predictive Control for Industrial Tasks
## 3 MPC Tuning Procedure
It is recommended not to choose too large values of \(K_{n}\) as this may result in dangerously large values of the manipulated variables. v. The value of the parameter \(K_{n}\) is chosen for which the performance indices are the best. 2. The best trajectory (6) is found, i.e., its parameter \(a_{n}\) is determined. i. The selected parameters \(m_{n}\) and \(K_{n}\) are used. ii. The performance indices are calculated from the simulations of the MPC algorithm for a few values of the parameter \(a_{n}\). It is recommended to perform simulations starting from relatively low values of the parameter, e.g., \(a_{n}=1\), and slowly increase it until it is clear that any further increment results in no improvement of control quality. iii. The value of the parameter \(a_{n}\) is chosen for which the performance indices are the best. 2. Tuning is repeated for the consecutive manipulated variables, for \(n=1,\ldots,n_{y}\), i.e., the algorithm goes to Step 1. Having completed the above procedure, the values of the coefficients \(\psi_{1,n},\ldots,\psi_{N,n}\) are calculated from Equation (6) for the found parameters \(a_{n}\), \(K_{n}\) and \(m_{n}\), for all \(n=1,\ldots,n_{y}\). The flowchart of the tuning procedure is depicted in Figure 1. Let us define the control error for the sampling instant \(k\) and process output \(n\): \[e_{n}(k)=y_{n}^{\text{SP}}(k)-y_{n}(k)\] (7) Although different performance criteria may be used to assess control accuracy, in this work, we use the multicriteria approach based on the following three indicators [36; 39]: * the Sum of Squared Errors (SSE\({}_{n}\)) of the control error \(e_{n}\), * the Huber standard deviation \(\sigma_{n}\) of the control error \(e_{n}\), * the rational entropy \(H_{n}\) of the control error \(e_{n}\). The above indicators are calculated for all \(n=1,\ldots,n_{y}\) controlled variables. In addition to the Gauss function (6), the application of other functions such as bell-shaped, triangular and trapezoidal ones was studied in [34]. The tuning procedure is similar, i.e., we do not optimise all parameters at the same time, but the influence of the consecutive parameters is analysed, while the best ones are chosen. It turns out that the proposed Gauss function results in the best control quality. This conclusion is based on the authors' experiences with various MPC control algorithms, applied to a few Single-Input Single-Output (SISO) and MIMO processes. Figure 1: The flowchart of the tuning procedure.
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 4 Simulation Experiments
Additionally, dual-head nodes are relatively simple to maintain and upgrade, reducing the complexity and cost of long-term operations. By integrating with Automatic Identification Systems (AISs), dual-head nodes enable real-time transmission and sharing of vessel position and status information, thereby improving the efficiency and safety of maritime communications. Despite the technical feasibility and superiority of the HDTA algorithm, encouraging more ships to actively participate in crowdsourcing tasks remains a challenge in practical applications. Increasing vessel acceptance of HDTA and ensuring data privacy and security are key to promoting the technology.
###### Abstract  
The Bayesian model updating approach (BMUA) benefits from identifying the most probable values of structural parameters and providing uncertainty quantification. However, the traditional BMUA is often used to update stiffness only with the assumption of well-known mass, which allows unidentifiable cases induced by the coupling effect of mass and stiffness to be circumvented and may not be optimal for structures experiencing damages in both mass and stiffness. In this paper, the new BMUA tailored to estimating both mass and stiffness is presented by using two measurement states (original and modified systems). A new eigenequation with a stiffness-modified system is formulated to address the coupling effect of mass and stiffness. The posterior function is treated using an asymptotic approximation method, giving the new objective functions with stiffness modification. Analytical formulations of modal parameters and structural parameters are then derived by a linear optimization method. In addition, the covariance matrix of uncertain parameters is determined by the inverse of the Hessian matrix of the objective function. The performance of the proposed BMUA is evaluated through two numerical examples in this study; a probabilistic damage estimation is also implemented. The results show the proposed BMUA is superior to the traditional one in mass and stiffness updating.  
Bayesian model updating approach; coupling effect; modified system; stiffness modification; probabilistic damage estimation 202111, 10615. [[https://doi.org/10.3390/app112210615](https://doi.org/10.3390/app112210615)]([https://doi.org/10.3390/app112210615](https://doi.org/10.3390/app112210615))
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 4 Bacteria
### Rickettsella Species  
_Ricketsiella_ is a Gram-negative bacterial genus, of which many different species infect coleopteran, dipteran, orthopteran and lepidopteran insects [19]. _Ricketsiella_ is an intracellular pathogen that primarily targets the fat body. It proliferates within cytoplasmic vacuoles, eventually lysing the cell and moving into the hemolymph, which often gives the abdomen of infected larvae a bluish coloration. The bacteria weakens the larvae over time, making them sluggish, but death typically takes over a month to occur [19]. _Ricketsiella popillae_ is the most entomopathogenic species to scarabs and was isolated from June chater (_A. solstitialis_) in Germany [63], the New Zealand grass grub (_C. zealandana_) [64], in New Zealand, _Odontria_ species [64], and African black beetle (_Heteronychus arator_) [65] populations in New Zealand. Various _Ricketsiella_ species also infect grasshoppers, aphids, wireworms, and ticks.  
Despite its well-documented occurrence in natural systems, little research has been conducted on _Ricketsiella_ as a biocontrol option for insect control. As a result, little is known about its infectivity or virulence. Natural epizootics involving _Ricketsiella_, however, have been documented in a New Zealand population of manuka beetles (_Pyronota setosa_ and _P. festiva_) [66; 67] and a German population of cockdafers (_Melolontha_ species) [68]. _Rickettsiella_ infections have also been documented in Japanese beetle and May beetle (_Phylophage anxia_, and _P. ephilida_) larvae in Pennsylvania [69], Japanese beetle, European chafer, and Asiatic garden beetle in Connecticut [70], and masked chafer in California [71]. Although these instances show potential for _Rickettsiella_ as a biocontrol agent for scarab pests, more work is needed to determine its efficacy.
## 3 Experimental Results
### Experimental Results for LGC
Figure 7: Fusion results on 12 December 2004, generated by different models on the LGC dataset. (**a**) Ground truth. (**b**) STARFM. (**c**) FSDAF. (**d**) Fit-FC. (**e**) EDCSTFN. (**f**) GAN-STFM. (**g**) MLFF-GAN. (**h**) STF-Trans. (**i**) CTSTFM. (**j**) Proposed. Table 2 summarizes the performance metrics for each method applied to the LGC dataset, with the best values highlighted in bold. The results clearly demonstrate that the proposed method achieves optimal performance across most metrics. Notably, in the last three bands, the proposed method significantly outperforms the others, indicating its superior performance even in scenarios involving substantial changes.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 3 Results
### Investigation of Factors Affecting the Cell Concentrations  
As stated above, serum was selected as the storage medium, while neomycin sulfate, chloramphenicol, sodium azide, and glycerol were added to increase the viscosity, limit bacterial and fungal infections, and ensure the stability of blood cells as well as reduce the hemolysis. Five factors were selected for initial screening, including serum, neomycin sulfate, chloramphenicol, sodium azide, and glycerol using a Plackett-Burman matrix (Table S2).  
Factors with a positive and high level of influence were selected as affecting factors (Table 2). Results showed all three blood cells (human RBCs, pseudo-leucocytes, pseudo-platelets) were affected mostly and significantly by the three factors serum (%), neomycin sulfate (g/L), and glycerol (%) (\(p<0.1\)). Results of screening of influencing factors showed that the dilution ratio of serum and blood cells, the concentration of antibiotic neomycin sulfate, and the ratio of glycerol were the most influencing factors on the stability of cell concentrations. Therefore, the serum, neomycin sulfate, and glycerol were chosen to expand the range of concentrations for the optimization design using RSM-CCD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Notation**} & \multicolumn{2}{c}{**Pseudo-Leucocytes**} & \multicolumn{2}{c}{**Pseudo-Platelets**} \\ \cline{2-5}  & **Before Fixing** & **After Fixing** & **Before Fixing** & **After Fixing** \\ \hline WBC (10\({}^{9}\) cells/L) & 1280 \(\pm\) 2.65 & 1290 \(\pm\) 2.15 & 16.50 \(\pm\) 0.50 & 2.70 \(\pm\) 0.06 \\ RBC (10\({}^{12}\) cells/L) & 1.24 \(\pm\) 0.02 & 0.02 \(\pm\) 0.01 & 0.95 \(\pm\) 0.04 & 0.09 \(\pm\) 0.02 \\ HGB (mmol/L) & 5.20 \(\pm\) 0.21 & 0.00 & 8.16 \(\pm\) 0.02 & 0.00 \\ MCV (fL) & 155.00 \(\pm\) 2.5 & 0.00 & 0.12 \(\pm\) 0.01 & 0.00 \\ PLT (10\({}^{9}\) cells/L) & 0.00 & 0.00 & 2220 \(\pm\) 3.15 & 2420 \(\pm\) 1.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of hemoglobin and MCV before and after fixing RBCs in geese and goats.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 3 Mitigating the ARP-Poisoning Attack  
Since the ARP-poisoning attack is the precursor to several broadcast-based LAN attacks, in this subsection, we review the current development in this topic. Recall that ARP-poisoning is a type of attack in which a malicious actor sends falsified ARP messages over a local area network. This results in the linking of an attacker's MAC address with the IP address of a legitimate computer or server on the network. Once the attacker's MAC address is connected to an authentic IP address, the attacker will begin receiving any data that are intended for that IP address.  
Solutions to ARP-poisoning can broadly fall into two categories:  
1. Algorithm-based solution: Here, solutions are mostly implemented within a client-server architecture. An agent running on either a client or the server monitors for ARP-poison attacks and provides solutions.
2. Hardware-based solution: Here, network hardware like switches and routers run specialized software that can detect and mitigate ARP-poisoning.  
Figure 1: Taxonomy of attacks due to IPv4 broadcasting. MITM, Man-In-The-Middle.  
In this section, we analyse the strengths and weaknesses of each class of solutions.
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
## 4 Experiments
### Results Analysis of Different Optimization Algorithms  
In this section, we analyze the optimization effect of Equation (9) with different algorithms and \(\text{L2-Norm}\big{(}\frac{\mathbf{g}}{\|\mathbf{g}\|_{1}}-\frac{\mathbf{w}}{ \|\mathbf{w}\|_{1}}\big{)}\) as the evaluation index. For traditional methods, we choose interior point method and sequential quadratic programming (SQP) for comparison; for heuristic algorithm, we choose particle swarm optimization (PSO) and simulated annealing algorithm (SA) for comparison.  
From the results in Table 14, we can see that for a single system, the optimization effect between traditional algorithms is basically the same, and the optimization effect between heuristic algorithms is basically the same, but the heuristic algorithms are generally better than the traditional methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multicolumn{2}{c}{**DBCAMS**} & \multicolumn{2}{c}{**DBCAMS-MMD**} & \multicolumn{2}{c}{**DBCAMS-RN**} \\ \cline{2-7}  & **Database** & **Contribution** & **Database** & **Contribution** & **Database** & **Contribution** \\ \hline
1 & Route & **0.215** & Station & 0.609 & Route & 0.279 \\
2 & Travel & **0.205** & Train & 0.068 & Travel & 0.195 \\
3 & Station & **0.112** & Security & 0.062 & Food-map & 0.053 \\
4 & Order & **0.055** & Food-map & 0.054 & User & 0.050 \\
5 & Train & **0.043** & Config & 0.042 & Consign & 0.046 \\
6 & Food-map & **0.042** & Assurance & 0.027 & Order & 0.045 \\
7 & Price & **0.038** & Order & 0.026 & Food-order & 0.043 \\
8 & Food-order & **0.036** & Contact & 0.022 & Config & 0.042 \\
9 & Consign-price & **0.036** & Price & 0.019 & Train-food & 0.041 \\
10 & Contact & **0.035** & Food-order & 0.017 & Price & 0.041 \\
11 & Config & **0.034** & Consign-price & 0.015 & Train & 0.039 \\
12 & Train-food & **0.033** & Train-food & 0.012 & Consign-price & 0.029 \\
13 & User & **0.033** & User & 0.011 & Station & 0.028 \\
14 & Security & **0.030** & Route & 0.007 & Contact & 0.026 \\
15 & Consign & **0.030** & Travel & 0.005 & Assurance & 0.024 \\
16 & Assurance & **0.024** & Consign & 0.002 & Security & 0.019 \\ \hline
**L2-Norm** & & 0.3558 & & 0.4458 & & 0.4276 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The main results of TrainTicket. The best result of L2-Norm is underlined, and the databases’ contributions of the corresponding method are in bold (\((\alpha,\beta,\gamma,\delta)=(2,4,3,1)\)).
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 5 Conclusions  
(1) The NACA airfoil with deformable covering effectively hinders the re-entrant jet, and it improves the flow field around the covering and suppresses the development of the low-pressure area.  
(2) With the increase in the deformation degree of the deformable airfoil, the cavitation occurrence area is concentrated to the surface of the covering, and the influence range of different degrees of deformation on the cavitation is reduced by 30% to 80%, and the degree of cavitation is reduced by 70%, indicating that this method effectively suppresses the development of cavitation.  
(3) The optimal maximum deformation of the deformable covering is between 3 mm and 6 mm, which can hinder the development of cavitation and reduce the erosion of cavitation on the wall under abnormal working conditions, ensuring that the normal operation of machinery does not produce negative effects such as vibration and noise.  
Conceptualization, W.S. and Z.X.; methodology, Z.S.; software, Z.S.; validation, W.S., Z.X. and Y.Y.; formal analysis, Q.Z.; investigation, Q.Z.; resources, W.S.; data curation, L.T.; writing--original draft preparation, Z.S.; writing--review and editing, Z.S.; visualization, Q.Z.; supervision, Z.X.; project administration, L.T.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China (no. 2019 YFB 2005300), National High-Tech Ship Scientific Research Project of China (no. MITI [2019] 360), National Natural Science Foundation of China (No. 51979138), National Natural Science Foundation of China (No. 52109106), General project of natural science research in colleges and universities of Jiangsu Province (No. 19 KJB470029), and Jiangsu Water Conservancy Science and Technology Project (No. 2019038).  
Data Availability Statement: Data on the analysis and reporting results during the study can be obtained by contacting the authors.  
The authors declare no conflict of interest.
## 3 Numerical Modeling of Concrete Beams Reinforced with SFCB
### Verification of Numerical Modeling with Experimental Results
#### 3.3.2 Comparison of Cyclic Loading Test  
The calculated and experimental hysteresis curves of the S12B97 beam are presented in Figure 14b. It can be seen that the numerical results of the beam correspond well with those obtained experimentally. Under the same displacement, the load value from the numerical simulation is slightly larger than the test result, which may be because the bond slip between SFCB and concrete is ignored in the numerical simulation. With the increase in lateral displacement, the concrete in the plastic hinge area broke off before the specimen failed. When the component is damaged, the phenomenon of fiber fracture and buckling of longitudinal bars occur. As the displacement increases, the corresponding load and stiffness decrease. The results of numerical simulation reflect this process effectively.  
Figure 14: Comparison between calculated and tested load-displacement curves of beam. (**a**) S12B43 beam; (**b**) S12B97 beam.  
A comparison between simulation and experimental results for load and displacement at feature points is shown in Table 4. Generally speaking, the numerical simulation value is in good agreement with the experimental value. However, the error of \(\Delta_{y}\) is bigger than the other results due to the reduction of the pre-yield stiffness in numerical simulation. In addition, the hysteretic curve of the experiment shows more pinch effect than the numerical simulation. Two factors might lead to these results, one is the slippage effect between the longitudinal reinforcement and concrete, the other is the shear deformation caused by concrete crack. Nevertheless, the simulation results give reasonable and accurate predictions for the experimental results. The numerical method for analyzing the seismic behavior of the concrete beam is verified. The proposed numerical method will be used to study the effects of different parameters on the seismic behavior of the beam reinforced with composite bars.
## 2 Short-Term and Long-Term Collision Probability Calculation Models
### Short-Term Collision Probability Calculation Model
#### 2.1.3 Spatial Discrete Volume Element Proximity Analysis
Given that the size of volume elements decreases along the latitudinal direction, the common method is to introduce the maximum latitude and combine the volume elements with the latitude greater than \(\varphi_{0}\) that in each altitude layer into the same volume element to deal with the volume elements near the poles of the constellation shell.
## 3 Results
### Classification on AOI  
In Figure 10, the land cover classification of the AOI is presented. Along the Tyrrhenian Sea coastline, the dominant land cover is attributed to the NPS_AL class, outlining a landscape characterized by expansive arable fields and rural settlements. The discernible features of Lake Vico and Lake Bolsena contribute to the distinctiveness of this pattern.  
Clusters of the MAA class are observed in specific regions, including the western flank of Mount Cimino and the peripheries of Lake Bolsena, showcasing a combination of vineyards and olive groves.  
The VEG class is represented by large, scattered patches, encompassing diverse vegetation formations like Mediterranean maquis, garigue, and oak groves. This botanical continuum extends inland, featuring a notable prevalence of chestnut trees and other shrub-like species. Particularly in areas with pronounced topographical gradients unsuitable for conventional agriculture, these botanical constituents thrive. Importantly, the classification reveals branching, ramified configurations indicative of riparian vegetation within the overall vegetation mosaic.  
Figure 9: Variable importance (MDI) analysis for the RF1, RF2, RF3 classifier.  
Hazelnut Groves on AOI  
The area occupied by HZ (Figure 11) forms a compact central nucleus, covering the eastern volcanic slope and the crater of Mount Cimino, with significant extensions around Lake Vico. Moving westward, the HZ extension significantly diminishes, primarily appearing as large fragmented fields, especially towards the coastal area and the city of Viterbo. After sieving, the predicted HZ area covers 22,780 hectares, of which 22,670 (99.5%) are located within the PDO area.  
Figure 11: Map of the area occupied by HZ within the AOI.  
Figure 10: Hierarchical classification result over the entire AOI. For enhanced graphical interpretation, classes were sieved using different thresholds: NPS-AL at 100 pixels, HZ at 20 pixels, VEG and MAA at 5 pixels.