esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Rayleigh Instability  
The phase field LB model was validated by simulating the Rayleigh instability problem with different density ratios. The Rayleigh instability is defined when a denser fluid with density, \(\rho_{\rm I}\), is placed over a less dense fluid with density, \(\rho_{\rm g}\), in the domain. Here, the domain size was set as L*4L, where L is the number of nodes in the \(x\)-direction. The dimensionless At number, which represents the density ratio between phases, was defined as \(\rm At=\frac{\left(\rho_{1}-\rho_{g}\right)}{\left(\rho_{1}+\rho_{g}\right)}\), the characteristic velocity was assigned as \(\rm U=\sqrt{gL}\), and the Reynold number was \(\rm Re=\frac{UL}{\
u}\). Periodic boundary conditions were imposed on the left and right boundaries while the wall boundary condition with bounce back was applied to the bottom and top boundaries. With 10% initial perturbation, the initial interface shape was \(\rm y=2L+0.1 Lcos\left(\frac{2\uppi L}{\uppi L}\right)\)[41].  
For the first case, the domain size was selected as 128 \(\times\) 512 and the Reynolds number was \(\rm Re=256\). The At number was set as At = 0.5, which corresponds to a density ratio of three. A characteristic velocity of U = 0.04 in the LB unit was adopted, which makes the kinematic viscosity, \(\rm\upsilon=0.02\). All the results are reported in nondimensional time, which was normalized by \(\sqrt{\frac{L}{g}}\). Figure 3 shows the location of the interface during the simulation. The results are in good agreement with the benchmark solution reported in Ref. [46].  
Figure 2: The flow chart of numerical model.  
To validate our results quantitively, the spike tip and bubble front locations are shown in Figure 4. These locations are in excellent agreement with the results reported by [PERSON] et al. [37].  
The model was also validated for the same problem, but with a density ratio of 1000, which corresponds to At = 0.999. The Reynolds number was kept at Re = 256. The evolution of the interface is shown in Figure 5, which is consistent with the results of Figure 6 in Ref [41]. The results also show the dependency of the interface shape with the density ratio. For a small density ratio, the tendency of mixing between the two fluids was large. While for a larger density ratio, a more coherent structure between the two fluids was observed.  
Figure 4: Time evolution of spike and bubble positions in Figure 3 and comparison with [PERSON] et al. [37] results. Axis variables are non-dimensional.  
Figure 3: Interface evolution during simulation of the Rayleigh instability problem for At = 0.5 and Re = 256. Dimensionless time from left to right = 0, 2, 3, 4, 5.  
_Appl. Sci._**2019**, \(9\), 57  
Figure 5: Interface evolution of Rayleigh instability for a density ratio of 1000 (At = 0.999) and Re = 256. Dimensionless time from left to right is 0, 0.5, 1, 1.25.  
Figure 6: Results after 30,000 iterations for different values of G\({}_{1}\) and G\({}_{2}\) based on the values in Table 1 at T = 0.9T\({}_{\alpha}\). (**a**) case 1, (**b**) case 2, (**c**) case 3, (**d**) case 4, (**e**) case 5, (**f**) case 6. (blue: gas, red: liquid).
## 4 Discussion: Model for G-QMS in SoS
### G-QMS in SoS Model, Key Aspects
#### 4.4.2 Subcontractor's Management  
In Figure 2, an independent category [8.8] with a Si\({}_{\text{Total}}\) = 5.43 was identified for this aspect. Managing subcontractors is a critical aspect in managing complex systems, especially in SoS projects that particularly involve those providing CSs. Correspondingly, quality management for assuring the integrated systems compliance becomes crucial. Historically, the largest industrial companies, which heavily depend on subcontractors for their products, have developed this area of quality management. With this regard, ISO9001 initially served as a means to standardize the quality expectations for subcontractors, aiming to replace the inspection activity of the organizations themselves with a complied subcontractors' QMS. For example, this is also how the AS9100 standard began, which significantly expanded upon ISO9001's requirements concerning subcontracting by placing a strong emphasis on the management of subcontractors and suppliers, including a chain-down of requirements (Section 8.4, AS9100:2016 [9]). This emphasis is driven by the modular nature of industries that rely on subcontractors with established a recognized QMS with a consistently acceptable quality level, typically certified to one or more QMS standards.  
With regard to G-QMS in Sectors of SoS, a _QMS\({}_{\text{SoP}}\)_ entity responsible for managing the intricate quality processes for subcontractors and suppliers on a global scale, including the expert knowledge and the required resources, is typically placed within the G-QMS of G-Org. of SoS model [2]. The G-QMS in SoS model has been supported by this quality infrastructure through a dual work interface, primarily interacting with _approved_ QMSvalidated by QMS\({}_{\text{S\&P}}\) and meeting the higher standards set by the G-QMS of G-Org. of SoS. The QMS\({}_{\text{S\&P}}\) requirements from the QMS\({}_{\text{C\&S}}\)s, are often beyond the QMS standards requirements and are determined according to the CS classification. However, the SoS project typically involves a client who often mandates several CS subcontractors and even engages with them directly through contracts. In such scenarios, the G-QMS in SoS is required to work with the QMS\({}_{\text{C\&S}}\) of the dictated CS, and despite having the support of QMS\({}_{\text{S\&P}}\) infrastructures, this situation presents numerous challenges.
## 5 Deep-Penetrating Geochemical Result
### Chemical Mobile Forms of Gold in Soil  
In this exploratory study, the gold fractionation of 18 soil samples from three locations--Fengxiangshugou (FXS), Mahuanggou (MHG), and Kaziya (KZY)--was conducted using the seven-step sequential extraction method. The ratios of gold content in different fractions to the total mobile gold content are illustrated in Figure 10.  
Residual and strongly organically bound forms of gold are predominant in the soil, while water-soluble, exchangeable, and carbonate-bound forms are in smaller proportions. With the exception of samples from KZY, the primary chemical form of gold in the remaining soil samples is the strongly organically bound form. This form involves the central heavy metal ion surrounded by organic functional groups as ligands, or it combines with sulfur ions to generate water-insoluble compounds [72]. Materials in the strongly organically bound form can undergo degradation of their contained organic molecules under oxidative conditions, leading to the leaching of some metal ions and resulting in anomalies in the soil or environmental impacts [73].  
Figure 9: Line chart of soil heat-released mercury anomaly: (**a**) soil heat-released mercury anomaly of Fengxiangshugou; (**b**) soil heat-released mercury anomaly of Mahuanggou.  
A comparison between the strongly organically bound form of gold and the anomalous gold content is presented in Figure 11 for this experiment. Along the FXS profile, distinct anomalies are observed in samples within the FXS-3 faulted zone and above the potassium feldspar granite intrusion at FXS-8 (Figure 11a). However, unlike the heat-released mercury anomalies, the strongly organically bound form of gold does not exhibit anomalies directly above the fault. Both methods exhibit particularly pronounced indications for the potassium feldspar granite intrusion, with anomaly contrast surpassing gold content.  
The anomalies of the strongly organically bound gold along the MHG profile do not align with the heat-released mercury anomalies or the total gold anomalies, indicating a shift in peak positions (Figure 11b). It is hypothesized that the formation of this anomaly might be influenced by the vein morphology and the topography of the cover layer, both of which could lead to the displacement of mineralization-induced anomalies.  
Figure 11: Line chart of the strong organic bound form (SOBF) of Au and Au content in soil: (a) anomaly of SOBF of Au in Fengxiangshugou; (b) anomaly of SOBF of Au in Mahuanggou.  
Figure 10: Chemical Mobile Forms distribution of gold in soil samples.  
The KZY samples consist of loose soil adjacent to the exploration trench, with the primary chemical form of gold being the residual form. Residual forms of metals generally exist within primary minerals, secondary minerals, silicates, and other lattice structures. These forms are stable in nature, not easily released in surface environments, remain stable in soils, are poorly absorbed by plants, and exhibit limited migration capabilities.
## 5 Numerical Examples
### Experimental Runs Results
#### 5.2.1 Experiment 1  
A five-step tree search was performed using BasicPOMCP and ARDESPOT solvers for the continuous model described above (Tables 6 and 7). With the same initial state of the pipeline, the two solvers took maintenance action based on the succeeding states that each randomly landed on. The observation considered the reading variation of the ILI method and gave the reading obtained as the percent deterioration. The current state and action taken determined the reward (cost) in each step of maintenance service. The discounted aggregate of these costs defined the total reward of the campaign.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(s\) & \(a\) & \(s^{\prime}\) & \(o\) (\%) & \(r\) \\ \hline
0.051 & Do-nothing & 0.649 & 66 & \(-\)200 \\
0.649 & Replace & 0.051 & 0 & \(-\)2250 \\
0.051 & Do-nothing & 0.488 & 57 & \(-\)200 \\
0.488 & Do-nothing & 0.717 & 80 & \(-\)800 \\
0.717 & Replace & 0.052 & 15 & \(-\)2250 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Example of CSPOMDP model run result using POMCP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{_Do-Nothing_} & \multicolumn{6}{c}{_Inhibitor_} \\ \hline \multirow{2}{*}{**State (s)**} & \multicolumn{3}{c}{**State (s)**} & \multicolumn{6}{c}{**State (s)**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & 0.01 & 0 & 0 & 0 & 0 & 0.15 & 0 & 0 & 0 & 0 \\
2 & 0.598 & 0.524 & 0 & 0 & 0 & 0.85 & 0.75 & 0 & 0 & 0 \\
3 & 0.299 & 0.342 & 0.536 & 0 & 0 & 0 & 0.25 & 0.536 & 0 & 0 \\
4 & 0.016 & 0.019 & 0.032 & 0 & 0 & 0 & 0 & 0.032 & 0 & 0 \\
5 & 0.077 & 0.115 & 0.432 & 1 & 1 & 0 & 0 & 0.432 & 1 & 1 \\ \hline \multicolumn{10}{c}{_Pegging_} & \multicolumn{6}{c}{_Replace_} \\ \hline \multicolumn{10}{c}{**State (s)**} & \multicolumn{6}{c}{**State (s)**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & 0.01 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 \\
2 & 0.598 & 0.524 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
3 & 0.299 & 0.342 & 0.7 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
4 & 0.016 & 0.019 & 0.3 & 0.75 & 0 & 0 & 0 & 0 & 0 & 0 \\
5 & 0.077 & 0.115 & 0 & 0.25 & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Discrete state transition probability.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 5 Conclusions  
In this work, a multimodal AI-based fusion framework for reliable identification and detection of gases is developed. We considered four classes (2 individual gases, alcohol vapor obtained from perfume and smoke from incense sticks, 1 as mixture of these gases and 1 no gas) for data collection using sensors, namely, thermal camera for capturing the thermal signature of the gases and array of gas sensors (7 numbers) for detection of specific gases. The data collected is unique and has 5200 samples of both Thermal Images and Gas Sensor Sequence of vector size (1 \(\times\) 7) Sensors. Both these modalities were fused using Early and Late Fusion Techniques. In summary, the contribution of this work is in bringing in innovative engineering tools for solving a real world problem by developing a more reliable gas detection method involving two modalities and fusing them. The multimodal model outperforms the individual models by supporting or opposing the individual modalities. In case if one modality fails, the other modality can work alone until repair takes place. This is essential in high-risk applications such as leak detection in chemical plants, identification of explosives, etc. The proposed architecture is based on the deep learning frameworks and hence require large number of data samples for appropriate training of the network. The complex data samples involving different combinations of multiple gases in data sample will lead to the robust training of the network. Also, to have efficient and effective operation, a dedicated hardware processing module is essential. The future course of action will focus on the collection of datasets comprising of multiple gases and their combinations in different environmental conditions.  
Conceptualization, P.N. and R.W.; methodology, P.N. and R.W.; software, S.M. and P.C.; validation, P.N., R.W., S.M., and P.C.; data curation, S.M. and P.C.; writing--original draft preparation, P.N., R.W., S.M., and P.C.; writing--review and editing, P.N., R.W., K.K., and G.G.; supervision, R.W. and K.K.; funding acquisition, P.N. and R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Symbiosis International (Deemed University) under the grant Minor Research Project Grant 2017-18 void letter number SIU/SCRI/MRPAPPROVAL//2018/1769.  
Not applicable.  
Figure 9: Confusion matrices (**a**) CNN, (**b**) LSTM, (**c**) Early fusion, (**d**) Max Fusion, and (**e**) Average Fusion predictions over a test set of 1280 samples.
# A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
## 2 Materials and Methods
### Model Evaluation  
#### 2.4.1 Evaluation of Instance Segmentation Model  
The loss of the instance segmentation model is used to measure the inconsistency between the actual and predicted values, and the model performance is improved as the loss decreases. The loss involves five aspects of calculation, including bounding box loss, segmentation loss, classification loss, and distribution focal loss. This study adopted the precision, recall, and AP of the COCO dataset to evaluate the segmentation performance of the YOLOv8-seg model, and AP values included AP@[0.50] and AP@[0.50:0.95] of the mask image. AP@[0.50] was calculated by fixing the intersection over union (IoU) at 0.5 and AP@[0.50:0.95] was the average precision when the IoU ranged from 0.5 to 0.95 with an interval of 0.05. In addition, this study adopted the accuracy (A) of the category to evaluate the classification performance of the model. The predicted category of a single wheat spike depended on the mode of the predicted categories of all the corresponding spikelets.
## 3 Results and Discussion
### Stiffening Effects
#### 3.5.2 Micromechanics Modelling Based on Laboratory Testing  
The results show that these models reasonably predict the stiffening potential of fillers (Figures 9 and 10). Moreover, most models show an accurate stiffening effect at lower concentrations with the exception of the Chong model. This model underestimates the stiffening at lower concentrations and overestimates the stiffening effect at higher concentrations. The Maron-Pierce model under-estimates the stiffening potential for granite mastic at higher concentrations. The deviations are noticeably more apparent beyond the 30% filler volume fraction. The advantage of models is the reasonable prediction of maximum packing as well as the stiffening potential of fillers.  
Figure 7: Shift factor plots of mastics.  
Figure 8: Stiffening ratios for limestone and granite.  
Figure 10: Micromechanics models for granite.  
Figure 9: Micromechanics models for limestone mastic.  
The curve of the measured value of stiffening ratio is not clearly visible in Figures 9 and 10. This curve extends up to 0.29% filler volume content, and it is overlapped by the curves of different models. The stiffening ratios of different models are close to each other at lower volume fractions of fillers.  
[PERSON] et al. also studied some of the micromechanics-based models for mastics, and according to them, these models are not sensitive to DSR results and underestimate the stiffening of mineral fillers [46]. It is difficult to precisely model the mastic data with any single model at higher filler content due to the presence of complex stiffening effects beyond volume filling. The presence of physicochemical interactions between the filler and bitumen as well as the adsorption layer around the particles increase the effective volume and maximum packing. These subsequently affect the stiffening potential of fillers.
## 3 DCS Scheme Families
### Double Rulings  
Considering **information brokerage** as one of the prime concerns, the authors in [28] propose a **Double Rulings** scheme storing data replica at a curve instead of one or multiple isolated sensors. In this approach, information hints can be left along the trail when data travels from source to a rendezvous node at no extra communication cost. Hence, discovering relevant data also becomes easier for the consumer. The scheme provides greater flexibility to design a network's retrieval strategy subject to the current network load and energy level. The paper presented a number of possible retrieval mechanism such as GHT retrieval, distance-sensitive retrieval, aggregated retrieval and double rulings retrieval. The flexibility in retrieval curves removes the possibility of having bottlenecks around the rendezvous node since the retrieval curve may not necessarily visit the rendezvous node. A local recovery scheme is achieved by storing data on the boundary when the sensors in a certain region are destroyed. Compared to SR-GHT, the double rulings scheme improves data robustness by imposing much lower communication costs for replication through the organization of replicas along a closed curve. The paper also proposed a new routing technique referred to as greedy routing on a curve, which adds extra complexity when implemented. Furthermore, flat replica in sensor nodes along the replication curve deplete the storage capacity of the network quicker than other schemes.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 2 Background Information
The second is the flexurals created due to wave mode conversion [36]; as these signals exhibit the same characteristics of excitation waveform, they can lead to the detection of false alarms. The third category is the non-coherent white noise, which is not correlated to the excitation sequence. This category is the main one which can be removed using generalized filtering techniques, as their characteristic is completely different to the excitation sequence.
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 4 The Galileo HAS User Algorithm for HAS Initial Service
### Main Blocks of User Algorithm  
The algorithm consists of two main blocks (Figure 1), including their input and output interfaces. The first main block is preprocessing of observation, navigation and correction data (PPO), which includes, e.g., the determination of the standard PVT solution and the forming of satellite-satellite single differences. The second main block includes the actual High Accuracy Service PVT solution (HAS) with an extended Kalman filter and partial integer ambiguity resolution.  
Additional details on this algorithm are provided in a previous paper [1].
## 2 Materials and Methods
### Extracellular Lytic Enzyme Activity  
The yeast isolates were evaluated for their ability to produce lytic enzymes, including proteases, chitinases, glucanases, cellulase, starch-degrading amylases, pectinase, and lipases. A 10 \(\mu\)L suspension of each yeast culture (\(\pm\)1 \(\times\) 10\({}^{8}\) cells/mL) was spotted onto agar plates containing specific substrates for each enzyme assay (Figure 1). The plates were incubated at 28 \({}^{\circ}\)C for 4-7 days, after which enzymatic activity was assessed. Each treatment was conducted in triplicate. Enzyme activity was indicated by clear halos surrounding the yeast colonies (Figure 1), and was recorded as either (--) for no activity or (+) for activity.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Yeast Code** & **Species Name** & **Starch** & **Celluse** & ** Protease** & **Glucosidase** & **Chitinase** & **Pectinase** & **Lipase** & \(\beta\)**-13** \\ \hline Y6 & _Aurchosiadium melanogaster_ & +* & + & + & + & + & + & + & + \\ Y11 & _Dehrapomyces instenti_ & – & – & – & – & – & + & – \\ Y17 & _Hatseniegron oxidendalis_ & – & – & – & – & – & – & + & – \\ Y24 & _Avergnean gillermothi_ & – & – & – & – & – & – & + & – \\ Y35 & _Rhodzottaamericis_ & – & + & – & – & – & – & + & + \\ Y39 & _At_ & _–_ & – & – & – & – & + & – \\ Y63 & _Submers pyridine_ & + & + & + & – & – & – & + & + \\ Y64 & _Pichia lawyer_ & – & – & – & – & – & – & + & – \\ Y65 & _[PERSON]_ & – & – & + & – & – & – & + & + \\ Y74 & _Torulaspora dellurchi_ & – & – & – & – & – & – & + & + \\ Y75 & _Saccharomyces cerevisiae_ & – & – & – & – & – & – & + & – \\ Y83 & _Breatanomyces trasiliensis_ & – & – & – & – & – & – & + & – \\ Y84 & _[PERSON]_ & – & – & – & – & – & + & – \\ Y88 & _[PERSON]_ & + & + & + & – & – & – & + & + \\ Y89 & _Zyugoslav helicineus_ & + & + & + & – & – & – & + & + \\ Y91 & _Zyugoslav revati_ & – & – & – & – & – & – & + & – \\ Y92 & _[PERSON]. [PERSON]_ & – & – & – & – & – & – & + & – \\ Y93 & _Z. microellipsoales_ & – & – & – & – & – & – & + & – \\ Y95 & _[PERSON]. [PERSON]_ & – & – & – & – & – & – & + & – \\ Y96 & _Z. fiorntini_ & – & – & – & – & – & – & + & – \\ Y97 & _Z. bisporas_ & – & – & – & – & – & – & + & – \\ Y102 & _Starmerella magazine_ & – & – & – & – & – & – & + & – \\ Y103 & _Saccharomyces cerevisiae_ & – & – & – & – & – & – & + & – \\ \hline \hline \end{tabular}
* (–) no enzyme activity, (+) enzyme activity.  
\end{table}
Table 1: Yeasts screened for the production of lytic enzymes.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 1 Introduction
In the obstacle-present environment, OTPP guarantees that all the unknown nodes can receive three location information (packets) from a MAN and the number of beacon points of MAN in OTTP is less than in Z-curve. 2. OTPP uses a rectangle as the boundary of each of the obstacles; any convex polygon is transformed into a rectangle, and two path trajectories for MAN are proposed. 3. In the obstacle-present environment, this study is the first to propose a static path planning method to optimize the number of beacon points. OTPP optimizes the number of beacon points, thereby reducing the number of broadcast times of MAN and receiving packet times of unknown nodes to facilitate the reduction in power consumption. Figure 1: Network architecture of MANAL. 4. OTPP performs better than Z-curve because it uses less total number of beacon points and is thus more suitable in an obstacle-present environment. Compared to the Z-curve, OTPP can reduce localization error and improve localization coverage. 5. Online path planning methods cannot guarantee that all the unknown sensors in the sensing area can be positioned. Compared to them, our approach, OTPP, does not only guarantee full coverage of sensor localization but also has scalability and flexibility to adapt to environmental changes by adopting the divide-and-conquer method. The rest of this paper is organized as follows: Section 2 reviews the related works and summarizes the existing path planning method for MANAL. Section 3 explains the problems for existing path planning in the obstacle-present environment. Section 4 presents the OTPP approach. Section 5 provides an analysis of the performance of the Z-curve, and OTPP through simulations, and Section 6 contains our conclusions and future works.