esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Plasmid Presence and Antibiotic Resistance Genes
#### 3.6.5 NRPS and PKS Gene Clusters
Therefore, isolating natural products associated with the Pam BGC and elucidating the enzymatic mechanisms involved in scaffold assembly could significantly contribute to the generation of new MraY-targeted peptidyl nucleoside antibiotics through combinatorial biosynthesis. BGC Region 34 Derived from _Lentzea_ sp. JNUCC 0626  
In BGC region 34, we identified several genes involved in phenazine biosynthesis. As depicted in Figure S22, the core region contains homologs of PhzD, PhzE, PhzF, PhzG, and PhzM, which are centrally located. Additionally, upstream of the phenazine BGC, a Type I PKS gene is present, suggesting the potential for complex biosynthetic processes within this cluster. A detailed comparison of the phenazine gene cluster in region 34 with the _Streptomyces anulatus_ phenazine gene cluster shows high sequence similarity, as outlined  
Figure 8: Genetic organization of the aromatic PKS gene cluster from BGC region 24 in JNUCC 0626. This region comprises genes with the highest homology to the trioxacarcin BGC, along with confirmed homology to other Type II PKS genes, such as those in the nogalamycin cluster. For detailed information, please refer to Table S31. Figure 9: Organization of a putative Pam BGC from _Lentzea_ sp. JNUCC 0626 with the respective homologous genes from _S. coeruleophilus_ layered onto it. The original relative orientation of individual genes (indicated by the direction of the arrow) has been maintained. in Table S33 [81]. This comparison provides important insights into the evolutionary conservation and functional similarities between these clusters. Phenazine compounds are heterocyclic molecules containing nitrogen, known for their potent biological activities and significant potential in both agriculture and pharmaceuticals. Among these, phenazine-1-carboxylic acid (PCA) is one of the most widely studied compounds due to its antimicrobial properties [82]. Based on recent findings, it is plausible that _Lentzea_ sp. JNUCC 0626 utilizes chorismic acid as the starting material, with the coordinated actions of PhzA/B, PhzD, PhzE, PhzF, and PhzG facilitating the production of PCA. Furthermore, PhzM is likely involved in the subsequent methylation step, leading to the formation of 5-methyl-PCA [83]. These findings suggest that the phenazine biosynthetic pathway in _Lentzea_ sp. JNUCC 0626 may hold significant promise for the development of novel natural products. Future research aimed at elucidating this pathway in greater detail could unlock new opportunities for biotechnological applications, particularly in the areas of sustainable agriculture and drug discovery.
## 3 Experiments and Results
### Dataset Description
#### 3.2.2 DeepGlobe Road Dataset  
Originating from a globally renowned remote sensing imagery competition [40], the DeepGlobe road extraction dataset offers high-resolution satellite imagery from various countries and regions, capturing diverse geographical and climatic profiles--from tropical rainforests and deserts to mountain ranges. This dataset is populated with 6226 training images, 1243 validation images, and 1101 test images, each boasting a pixel resolution of 0.5 m. Such resolution elucidates intricate road structures. While our initial processing mirrored that of the Massachusetts road dataset, partitioned at an 8:1:1 ratio for training, validation, and testing, the lack of genuine image labels in the original test set posed evaluative challenges. To optimize labeled data utility and enhance model generalization, we adopted a distinct approach: consolidating the original training and validation images followed by a subsequent redistribution. Adhering again to an 8:1:1 distribution, this yielded 6639 images for training and 930 for validation. Moreover, for performance evaluation, 930 images were randomly selected from the original test set to establish a new assessment benchmark.  
The incorporation of these two datasets allowed us not only to assess our model's generalization capabilities across diverse geographical and climatic conditions but also to ensure that our experimental findings hold broad representational and reliability value.
## 2 CFD Analysis  
### Analysed Airfoils of the Morphing Wing  
Depending on the current flight condition the aerofoil of an aircraft wing has to meet different requirements. In cruise, a low drag is required together with a lift coefficient that corresponds to aircraft weight at cruise velocity. For take-off and landing, a maximum lift coefficient is required in order to reduce take-off/landing velocity contributing to shorten required runway length as well as to reduce perceived noise on the ground. Increasing the lift coefficient is possible by increasing the chord and camber of an aerofoil, but for achieving low drag at cruise, lower values of chord and camber are desired. The MLE allows us to switch between an aerofoil with high chord and camber, and another aerofoil with lower drag at cruise. The aerofoils and flight conditions considered here are for a regional turboprop aircraft with a capacity of approx. 40 passengers, 26 m span, a max. take-off weight of 20 t, and a cruise speed of 180 m/s. This kind of plane typically does not use high lift devices at the leading edge up to now.  
To confirm the aerodynamic effect of the MLE, four aerofoil profiles having different LE shapes were analysed, comparison see Figure 2. Aerofoil S4 is the target aerofoil for cruise, aerofoil S5 is the one for take-off and landing with increased chord and increased camber. Additionally, it was analysed whether intermediate aerofoils, which occur due to the implemented actuation system during the morphing process, are aerodynamically feasible. A monotonic change of lift and drag is required during the shape transition. For this purpose, the two intermediate baselines, called BL and 050 between S4 and S5, have been considered. Suffix HL implies that each profile has a deployed trailing edge (TE) flap for analyses in the high-lift condition. For CFD analysis in this condition, the original single Fowler flap of the prototype wing, which the MLE development is based on, was used.  
Figure 2: LE (leading edge) comparison of profiles S4 HL, BLHL, 050 HL, and S5 HL [28].
## 4 The Proposed Routing Protocol: HPA-SR
### Energy Consumption Model  
In this paper, we used an energy model to calculate the energy consumption for sending and receiving packet over a link [42; 43]. As we can see in Figure 6, the energy model will be used for the HPA-SR to calculate the residual energy required by the sender and receiver to send a number of packets. Therefore, a node can choose the next node to send data so that the residual energy of the sender and receiver is greater than the energy threshold, thereby extending the life of the route. We consider that all nodes are equipped with IEEE 802.11a 11 Mbps network interface card, whose electric currents are 280 mA and 330 mA in reception mode and transmission mode, respectively, and the electric potential is 5 V [44]. The remaining energy \(ER_{i}\) at node \(i\)-th can be formulated as:  
\[ER_{i}=ER_{pi}-E_{\text{mode}}\quad[\text{joule}], \tag{14}\]  
where \(ER_{pi}\) is the current remaining energy of the node \(i\)-th, and \(E_{\text{mode}}\) is energy consumption model that has transmit or receive modes. The energy consumption model when node \(i\)-th transmits and receives data packet p is expressed mathematically as follows.  
\[\text{E}_{\text{Tx}}(i,\text{p})=\text{I}_{\text{tx}}\times V\times t_{\text{ p}}\quad[\text{joule}], \tag{15}\]  
\[\text{E}_{\text{Rx}}(i,\text{p})=\text{I}_{\text{rx}}\times V\times t_{\text{ p}}\quad[\text{joule}] \tag{16}\]  
where \(\text{I}_{\text{tx}}\) and \(\text{I}_{\text{rx}}\) represent the electric currents of transmission and reception, respectively. \(V\) is the electric potential, and \(t_{\text{p}}\) represents the time taken to transmit the packet p (in seconds).  
Figure 6: The basic concepts of the energy consumption model for the HPA-SR.
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
## 4 Results and Discussion
### Comparison of \(E_{\text{vc}}\) in Filling Layer and \(E_{\text{vib}}\)  
For the filling section with a high-strength underlying layer, the underlying layer will affect the value of the continuous compaction control index on the quality of the current roller layer, which will lead to a higher value of measurement results. In order to eliminate this influence, \(E_{\text{vc}}\) of each filling layer in the test field was calculated with simulation results of the database, and the variation law of the compaction space variability at the filling-excavation junction is analyzed from two aspects of the overall vibration modulus (\(E_{\text{vib}}\)) and the vibration modulus of the filling layer (\(E_{\text{vc}}\)).  
Figure 8a,b show that the difference in \(E_{\text{vib}}\) in two test strips is evident from the first layer to the fourth layer, and it has no obvious difference after four layers (0-2 m of horizontal direction is the test zone for the 60 MPa underlying layer, and 2-4 m of horizontal direction is the test zone for the 80 MPa underlying layer). However, the difference in \(E_{\text{vc}}\) in two test strips is not obvious in the first four layers. It can be inferred that the roller  
Figure 7: Variation in degree of irregularity of compaction with roller passes under different working conditions.  
variability at the filling-excavation junction actually comes from the underlying layer rather than the roller layer.  
Figure 8: Cont.  
Figure 8c,d present the variation law of \(E_{vib}\) and \(E_{vc}\), respectively, in three test strips (4\(-\)6 m of the horizontal direction is the test zone for the 93 MPa underlying layer) from the fourth layer to the sixth layer. \(E_{vib}\) of the test zone with the 93 MPa underlying layer is much higher than that of the other two test zones, but \(E_{vc}\) of the roller compaction layer with the 93 MPa underlying layer is not different so much from the other two test zones. Moreover, the difference in \(E_{vib}\) between the test zone with 93 MPa and the other two test zones gradually decreases with the filling proceeds, which indicates that the influence of the high-strength underlying layer gradually weakens with the increasing number of filling layers. Comparatively, \(E_{vc}\) of the filling layer is stable at the same level as that of the other two test zones from the beginning to the end.
## 3 Materials and Methods
### Fishtail Probe Depth (\(L_{d}\)) Optimization  
A fish-tail-shaped probe was attached at the far end of the microstrip line and inserted into the waveguide WR-28. A 50 \(\Omega\) excitation was applied at the near end. The fish-tail probe played an important role in impedance matching. The width and depth of the triangular-shaped probe were varied so that excellent impedance matching was achieved over the entire Ka-band. The depth of the fish-tail probe was varied in a step size of 0.20 mm in the direction of the marked arrow, as shown in Figure 5. Four depth increments from the substrate end were implemented, as signified in Figure 5 by the different colored tags. Corresponding insertion loss and return loss family plots are presented in Figure 6a,b. All variation effects on the absolute BW, RL, and IL are given in Table 4. It was observed that the lowest RL, highest \(-\)10 dB absolute bandwidth (BW), and minimum IL was obtained at a resonance frequency close to 30 GHz and with a fish-tail depth, L\({}_{\text{D}}\), equal to 2.5 mm. Therefore, the maximum energy transfer and minimum IL occurred at a design frequency with a 2.5 mm distance from the substrate end. It was concluded that an increase in the fish-tail depth from the substrate end or waveguide broadside wall resulted in a reduction in reflections and improved the transmission. For distances of L\({}_{\text{D}}\) equal to 1.9 mm/2.1 mm/2.3 mm the IL values were below \(-\)0.50 dB. An excellent IL above 0.50 dB was obtained with a fish-tail depth of 0.4 mm from the flat microstrip end over an ultra-wide bandwidth of 23.52-45.0 GHz.  
Figure 5: Fish-tail depth variations with a step size of 0.2 mm from the substrate end.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Slot Height,** & **Min.** & **RL** & **Resonance** & **–10 dB** \\
**H\({}_{\text{Slot}}\) (mm)** & **IL** & **(dB)** & **Frequency** & **Absolute** \\  & **(dB)** & **(dB)** & **f\({}_{\text{r}}\) (GHz)** & **BW (f\({}_{\text{L}}\)–f\({}_{\text{H}}\))** \\ \hline
1.5 h= 0.1905 & \(-\)0.48 & \(-\)11.51 & 27.35 & (24.98–29.98) \\
2 h = 0.254  
Figure 6: Effect of fishtail depth variations on (**a**) insertion loss, S\({}_{21}\) (dB), (**b**) return loss, S\({}_{11}\) (dB).
## 3 Actual Platform Benefits
### Self-Service Analytic  
As described in this paper, the healthcare platform combines and standardizes data across different source systems to provide actionable insights in a single platform. The platform integrates data from different sources, such as claims data, cost data, financial data, clinical data, and other patient data. With self-service analytics, the organization increases the number of users accessing the analytic component, improving data visibility and providing actionable insights to improve patient outcomes.
## 5 Experiment and Results
### Comparison of Result with Recent Feature-Extraction Methods
In the case of the deep learning method based on LSTM, incorporating data from 10 subjects and encompassing 10 different gestures, our approach exhibited substantial performance enhancements of 8.59%. It is important to highlight that deep learning methodologies have been increasingly adopted in EMG pattern recognition, as they aim to automatically extract more informative features and identify EMG patterns end-to-end, despite the associated increase in computational complexity. Furthermore, with regard to the machine learning with sequential floating feature selection approach, which involved data from 21 subjects encompassing 17 gestures, our method demonstrated a performance improvement of 1.87%. The SPD-manifold achieved 84.85% with 10 subjects and 11 gestures. However, it is worth noting that the feature extraction process in this method is relatively complex and can be time-consuming, potentially presenting challenges for real-time recognition in practical scenarios.
## 4 Experiments and Results  
### Results of the PSI  
The data processing begins with a stack of single-look complex images (SLC). Based on the single reference image mode, the image acquired on 27 July 2009 is selected as a reference image, and the others are registered with it to form interferograms. The terrain phase is simulated by a 30 m resolution SRTM DEM, and 38 single-look differential interferograms are finally obtained.  
The purpose of this experiment is to monitor the deformation of the building, so it is necessary to accurately determine the permanent scatterers on typical buildings in the study area and ensure a certain density to establish a three-dimensional model. In this experiment, the coherent coefficient and amplitude deviation indexes are used to detect PSs. A small threshold is used first to extract the PS candidate points, which can filter out certain regions, such as watery areas, vegetation, and other areas with low-coherence, and improve the accuracy of the second selection. See Figure 4, where the green points are detected PSs, which are placed on an intensity image. The number of PS points finally extracted is 108,139, the point density is about 18,023 PS/km\({}^{2}\), and the main region is well covered. The red dot on the road is the reference point for unwrapping, with little effects on the PSI results.  
Figure 3: Flowchart of high-resolution permanent scatterer interferometry (PSI) deformation monitoring.  
The final PSI results are shown in Figure 5, where Figure 5a is the absolute elevation of the building, ranging from 0 m to 250 m. The height estimated by the PSI is consistent with the height of the actual building model. In the deformation rate map (Figure 5b), the deformation rates, fitted by linear and nonlinear deformation, are mainly between \(-10\) mm/year and 10 mm/year. Most of the buildings tend to be stable, but we also observed some signs of deformation. The most obvious deformation occurred in the Shenzhen International Convention and Exhibition Center, the top of which has a large area uplift trend. We suspect that this trend is related to thermal expansion, and we will analyze it later.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 1 Introduction
In this environment, increasing the integration time is usually an effective way to achieve high sensitivity [19]. In addition, non-coherent and differential integration methods are the traditional methods to attenuate the effect of noise [20; 21]. However, navigation data bit sign transitions limit the duration of the correlation between the local code and the received signal [22]. A semi-bit method suppresses the data bit sign transition problem by considering that if a segment of the signal is divided into two parts, one half does not necessarily contain a data bit transition [23]. Besides, software-defined receivers are widely studied because of their convenience and the fact that they only need to process digital signals and do not require hardware support. Referring to the upcoming European Student Moon Orbiter mission, [PERSON] et al. [24] discussed the advantages of embedding an airborne GNSS receiver and investigated such a possibility, considering GPS and Galileo signals with an acquisition threshold of 35 dB-Hz. [PERSON] et al. [25] developed a proof-of-concept receiver called WeakHEO, the simulations of which showed it to be capable of capturing and tracking GPS L1 C/A signals down to 15 dB-Hz. Designed for lunar missions, the HiSGR [26] can handle signals down to 15 dB-Hz, and it uses an all-bit approach to increase the sensitivity to take full advantage of GNSS side lobe signals. [PERSON] et al. [27] designed a satellite navigation receiver architecture for future lunar exploration missions, demonstrating the feasibility of using GNSS signals integrated with an orbital filter. Most of the above-mentioned works only consider the availability of GPS and Galileo signals in lunar missions. The algorithms for detecting BDS signals in the lunar environment have not been studied in depth. This paper focuses on the application of the GNSS in the lunar environment, and in particular on the design of a receiver architecture for future lunar exploration missions. The visibility and signal strength of different GNSS satellite combinations in LLO are analyzed. With the reception thresholds of 15 and 10 dB-Hz, we analyze the acceptance performance of the lunar receiver for main and side lobe signals using GNSS combinations. The BDS B1I signal characteristics are then specifically investigated and coherent integration, non-coherent integration, differential integration, and the alternate half-bit method for capturing low power signals are briefly introduced. The simulation results of four methods for acquiring BDS signals from LLO receivers are presented. This paper is organized as follows: Section 2 describes the scenario of lunar navigation using the GNSS and the acquisition algorithms of weak GNSS signals in detail. Some results on the received signal performance and the capability of the acquisition algorithms are presented in Section 3. Section 4 discusses the effect of the side lobe signals on the navigation performance in LLO. Several conclusions are presented in Section 5.
# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 7 Conclusions  
In this paper, we reviewed the latest advances in the development and use of intelligent data analysis methods in the domain of sport training. The purpose of this systematic literature review was twofold. Firstly, we wanted to identify the main intelligent data analysis methods that can be used in different training phases, and, secondly, we wanted to determine which sports are the most supported by these methods.  
The study revealed that researchers apply various methods, including computational intelligence, conventional data mining methods, deep learning, machine learning, and some other methods. Computational intelligence algorithms have been rising in popularity in recent years, while the most used intelligent data analysis methods remain support vector machine (19 studies), artificial neural networks (14 studies), k-nearest neighbors (11 studies), and random forest (11 studies). According to this review of 109 studies, we identified that soccer (12 studies), running (11 studied), and weight lifting (10 studies) were the most researched ones. When comparing sports based on participation levels, we have found that over half of the research in this field (54.6%) can be classified as research based on sports for individuals, and that team and mixed sports represent roughly a third of the existing research. The research domain still has a lot of room for improvement, where more validation-level research is needed as well as more publicly available datasets for replicating research and allowing an improvement of methods. Since the field is relatively new, a lot of sports exist with no research in the domain of smart sports training, which offers a wide area of possibilities for research to be made.  
**Author Contributions:** Conceptualization, A.R. and I.F.J.; data curation, A.R.; literature search and review, A.R.; methodology, A.R. and I.F.J.; supervision. I.F.J.; validation: I.F.J. and A.R.; visualization, A.R.; writing, A.R. and I.F.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Javna Agencija za Raziskovalno Dejavnost RS (Research Core Funding No. P2-0057).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Limitations of the Study  
There were some limitations in this study. First, the onset of hypertension among older persons could not be determined because of the cross-sectional designed we used, which could only report on inferences. Second, the study was limited to rural communities; hence, it cannot be generalized to older persons living in different settings, except for rural settings with similar infrastructure and culture. Third, the use of 90% CI in sample calculation adds a large variation in the study and decreases power. As a result, the results of this study need to be duplicated, at the very least, in order to be more conclusive. A sample of 350 individuals marginally reaches adequacy for analysis. By using further stratification, the results can only be seen as preliminary findings based on inferences. Fourth, we could not include biochemical markers of hypertension and obesity among older persons due to budget constraints. Fifth, the current study did not assess other comorbidities (e.g., diabetes). Future studies should consider a comprehensive assessment of behavioural factors, dietary intake, biochemical variables and comorbidities to study hypertension and obesity among older persons. However, our study sheds a light on the prevalence of obesity indicators and hypertension and their relationship among older persons in the rural Vhembe District in South Africa.