esa-sceva/satcom-chunk-collection huggingface dataset:
# Protein Dynamics in F-like Bacterial Conjugation
## 4 Mating Pair Stabilization Proteins and the Dynamics of their Structural Ensemble  
Mating pair stabilization (Mps) occurs after pilus retraction and Mpf events, yet these genes are not often seen outside of the F-like plasmid family; stabilization of contact between donor and recipient cells is essential for the formation of the mating pore and therefore essential for conjugation by F-like T4 SS [11; 102]. Other T4 SS families have alternative methods for stabilizing a mating pair, such as the production of aggregation proteins, or compensation by the production and recipient-contacting of many pili in IncI plasmids [11; 103; 104]. The process of Mps for F-like T4 SS provides properties of resistance to SDS and shear forces within a mating pair [105]. Unlike other major steps of conjugation, such as pilus assembly and retraction events, Mps is thought to be much simpler as it involves only two proteins, TraN and TraG [106]. These two proteins are predicted to span the entirety of the gram-negative bacterial cellular envelope; TraG contains an IM bound NTD and a C-terminal periplasmic domain that is hypothesized to contact TraN bound in the OM, which has loops extending into the extracellular matrix (ECM) [11]. This interaction has been deemed critical for the formation of the mating pore, as both proteins are essential for DNA transfer to occur. Despite limited knowledge of their high-resolution structures, the interaction between TraN and TraG is predicted to involve an induced fit in which the highly dynamic CTD of TraG conforms to interact with the well-structured TraN, perhaps with aid from other periplasmic T4 SS components.
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 3 Results
### Instance Segmentation Experiment  
#### 3.2.1 Evaluation Metrics  
The metric used to evaluate the instance segmentation was the average precision (_AP_), as defined in Equation (8). It sets the precision as the horizontal axis and the recall as the vertical axis to obtain the \(P(r)\) curve. The definitions of P and R are shown in Equations (9) and (10). The Intersection of Union (_IoU_) represents the ratio of the intersection and union between the bounding box or mask of the prediction and the ground truth, as defined in Equation (11). Here, \(A\) and \(B\) denote the bounding box or mask of the prediction and ground truth. _IoU_\(\geq\) 0.5 indicates a true positive detection (_TP_); _IoU_\(<\) 0.5 indicates a false positive (_FP_). When the ground truth is missed, the prediction is a false negative (_FN_). These equations can be found in [5,8].  
\[AP=\int_{0}^{1}P(r)dr \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]\[Recall =\frac{TP}{TP+FN} \tag{10}\] \[IoU =\frac{|A\cap B|}{|A\cup B|} \tag{11}\]
# Near-Real-Time Global Thermospheric Density Variations Unveiled by Starlink Ephemeris
###### Abstract  
Previous efforts to retrieve thermospheric density using satellite payloads have been limited to a small number of satellites equipped with GNSS (Global Navigation Satellite System) receivers and accelerometers. These satellites are confined to a few orbital planes, and analysis can only be conducted after the data are processed and updated, resulting in sparse and delayed thermospheric density datasets. In recent years, the Starlink constellation, developed and deployed by SpaceX, has emerged as the world's largest low Earth orbit (LEO) satellite constellation, with over 6000 satellites in operations as of October 2024. Through the strategic use of multiple orbital shells featuring various inclinations and altitudes, Starlink ensures continuous near-global coverage. Due to extensive coverage and frequent maneuvers, SpaceX has publicly released predicted ephemeris data for all Starlink satellites since May 2021, with updates approximately every 8 h. With the ephemeris data of Starlink satellites, we first apply a maneuver detection algorithm based on mean orbital elements to analyze their maneuvering behavior. The results indicate that Starlink satellites exhibit more frequent maneuvers during thermospheric disturbances. Then, we calculate the mechanical energy loss caused by non-conservative forces (primarily atmospheric drag) through precise dynamical models. The results demonstrate that, despite certain limitations in Starlink ephemeris data, the calculated mechanical energy loss still effectively captures thermospheric density variations during both quiet and disturbed geomagnetic periods. This finding is supported by comparisons with Swarm-B data, revealing that SpaceX incorporates the latest space environment conditions into its orbit extrapolation models during each ephemeris update. With a maximum lag of only 8 h, this approach enables near-real-time monitoring of thermospheric density variations using Starlink ephemeris.  
Starlink; ephemeris; maneuver detection; mechanical energy loss; thermospheric density; geomagnetic storm 2025
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 4 Observations and Derivations  
### Major Industrial Challenges  
With the boom of IoT, industrialists face challenges of the protection and privacy of their data, as traditional security methods cannot be applied in IoT. Additionally, the increase in electronic gadgets makes scalability a challenge. Therefore, a flexible and scalable architecture should be proposed. Real-time monitoring and storage space is also a great hurdle faced by industrialists [37]. IIoT deals with complicated physical machinery having industrial sensors and relevant software. The challenge is to provide real-time connectivity between humans and machines, which is possible by providing a presentation layer for error-free systems.  
It thus requires a connection of the IoT device with IP. IP plays a key role in exchanging information to the devices to which it is connected. IoT is used at the enterprise level and each enterprise comprises of different infrastructures and builds the different architectures of an IoT system. The major challenge here is that no such repetition can occur in a specific industry, as IoT devices are solely based on enterprise operation. The major challenge it involves is making the data actionable from machines to software [48]. The traditional electricity supply chain is changing from uni-directional to bi-directional, but to further utilize the full efficacy, IoT would need to work with the existing smart grid and data analysis.  
Along with the different advantages of IoT in every field, it also becomes a challenge, especially for industries in third world countries. To implement IoT 4.0 and 5.0 with IoT in third-world countries, finding good quality hardware devices, sensors, and IoT devices is difficult. Moreover, adopting IoT in the industry requires a huge cost. That is why industries do not opt for the Industrial IoT and automation, without thinking that these technologies are great cost-savers after the adoption of IoT. One of the most important challenges is the skilled persons required, who know about implementing IoT with complex automation techniques and real-time data handling on a large industrial scale [49].  
If manufacturing plants use IoT with blockchain technology to ensure security and privacy, then by using blockchain technology, they have to face high costs and long-term issues to create a block of transactions.
## 3 Results
### Geoelectric Resistivity Survey  
A total of 18 vertical electrical soundings (VES) were surveyed at the investigated site, as displayed in Figure 6, utilizing an electrode arrangement named Schlumberger with an extreme half electrode distance (AB/2) reaching 100 m. A digital, signal-enhancement resistivity meter (SYSCAL-R2) was utilized to collect the field data of the resistivity survey. The resistivity data were measured every sixth of a logarithmic decade to ensure a detailed investigation and interpretation accuracy. Figure 9: Seismic velocity contour map of (**a**) surface layer and (**b**) second layer. Investigating the behavior of the gathered plotted curves of resistivity data introduce a preliminary impression of the apparent resistivity distribution through the investigated site, which displayed two types of QH and KH resistivity curves through the investigated site and considered a straight reference on the approximate resistivity homogeneity through the study area, as shown in Figure 11.  
Two contour maps of apparent resistivity at electrode spacings (AB/2) equal to 1 and 30 m were constructed for analyzing the areal distribution of apparent resistivity at different levels, which can perform a preliminary impression of the apparent resistivity at  
Figure 11: Acquired vertical electrical soundings throughout the study area. Figure 10: Samples of GPR records: (**a**) GPR-01 and (**b**) GPR-02. the picked level according to the selected electrode spacing, as shown in Figure 12. The apparent resistivity values at the spacing (1 and 70 m) introduces the same distribution of apparent resistivity throughout the study area, which was characterized by the low range at the southwestern sector of the investigated area. Quantitatively, the gathered geoelectrical resistivity data were interpreted in two parallel procedures: (1) a primary model of resistivity was generated based upon the Master Curves approach, and (2) the primary model was entered into a software iterative package to model and calculate the final earth resistivity sounding. Firstly, a preliminary model is assumed for each geoelectric resistivity sounding curve utilizing the approach of Master Curves [39] and the geological background. The resulting initial model is composed of true resistivity and thickness values. Secondly, the approach of computerized iteration is applied to define the primary attained model as considered in the starting step. The software iterative package \"Resist\" [40] is executed to optimize the final model of resistivity. The justification of the calculated end resistivity model is governed by computing the Root Mean Square Error (RMS Error) among the observed and computed resistivity values, which has a range between 1.7-6.3%, as presented in Figure 13.  
Figure 12: Apparent resistivity contour maps: (a) AB/2 = 1 m and (b) AB/2 = 30 m.  
The common subsurface geoelectrical resistivity model of the investigated site can be represented by two main distinctive resistivity layers: (1) a surface overburden layer with true resistivity values ranging between 397.7-6039.2 Ohm.m.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 2 System Model and Problem Formulation
### Optimization of \(\{P_{k}\}\) with Fixed \(\{\rho_{k}\}\) and \(\{Q_{k}\}\)
There might be two cases:  
* **ID Dominated Case** happened when \(P_{\text{ID}}^{*}\geq P_{\text{EH}}^{*}\), indicating RN needs more energy to achieve the target BER than that of EH. In such a case, \(\{P_{k}^{*}\}\) can be directly applied as the final solution in current stage, while the RNs may harvest amount of energy that more than needed. We then can adaptively adjusting \(\{\rho_{k}\}\) and \(\{Q_{k}\}\) to re-balance the portion of energy assigned to EH and ID in the following step. The purpose is to leave less power for EH since the EH constraint is the weak one now and can be easily satisfied. While contrarily for ID function more power can be provided to help achieve its target. With the new setup of \(\{\rho_{k}\}\) and \(\{Q_{k}\}\) one can perform the power allocation iteratively to further reduce the total required transmit power. * **EH Dominated Case** occurs, on the other hand, when \(P_{\text{ID}}^{*}<P_{\text{EH}}^{*}\). In such situation, \(\{P_{k}^{*}\}\) is no longer the feasible solution to satisfy the EH constraint since \(\sum_{k=1}^{K}P_{k}^{*}<P_{\text{EH}}^{*}\). In addition, it is impossible to get the exact power allocation profile of individual RN solely based on (13), who gives only the boundary of total transmit power. Nevertheless, we can proportionally increase \(\{P_{k}^{*}\}\) to make it meet the EH requirement as \(\tilde{P}_{k}^{*}=\eta P_{k}^{*}\),\(\forall k\), where \[\eta=\frac{P_{\text{EH}}^{*}}{\sum_{k=1}^{K}P_{k}^{*}}\] (32) is the proportional enlargement factor. By such operation, we keep the initial signal to interference ratio of each RN with \(\tilde{P}_{k}^{*}\) remains the same as that with \(P_{k}^{*}\). Apparently it guarantees the BER performance of RNs by scarifying more power than actually needs. Nevertheless, similar to that in **ID Dominated Case**, we can dynamically change \(\{\rho_{k}\}\) and \(\{Q_{k}\}\) to weaken the EH constraint, so that the total transmitter power might be further reduced.
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 5 Conclusions  
In this paper, we propose a VSP-IMUD-based reception scheme by exploiting the interactions among multiple wireless signals. By virtually combining multiple signal components, we can obtain an equivalent structured signal, and then employ IMUD to decode this effective signal based on its spatial characteristics. Before applying IMUD, we must detect and mitigate channel ambiguity for each signal component to ensure the uniqueness of the equivalent signal. Ultimately, we select the signal with the highest channel gain to decode a referential symbol. With this referential symbol, the Rx can output the final symbol set. Compared to other existing MUD methods, such as ICom/SIC-ZF, VSP-IMUD can effectively avoid the error propagation problem by reducing the number of SIC applications, thereby achieving improved BER performance.  
[PERSON] contributed to the main design of the proposed method, which includes the proposal of the main concept, application scenario of the method, and design of the signal processing procedure and receiver structure. [PERSON] collaborated with [PERSON] on the design of the method and completed the simulation, result analysis, and manuscript organization. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Science and Technology Research Project of Henan Province under Grant 252102211120.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### High-Precision Dynamic Model  
In order to satisfy the high-precision requirements for the long-term operation of the lunar navigation system, high-precision dynamic modeling research on the lunar navigation constellation needs to be carried out in the future. High-precision modeling and characteristic analysis are the keys to ensuring the accurate implementation of engineering missions. Dynamic modeling such as third-body gravity, solar radiation pressure and the albedo effect, and jet unloading caused by the Earth and sun should be studied. Orbit stability and other conditions should be analyzed. For functional requirements of communication, navigation and positioning, timing, information services, scientific detection, and expanded applications of the comprehensive constellation, focusing on achieving full-month communication coverage and supporting subsequent unmanned and manned lunar exploration and other mission requirements, the Earth-Moon integrated constellation system track plan design and plan iteration optimization should be developed. [PERSON] et al. investigated trajectory corrections for lunar flyby transfers to Sun-Earth/Moon libration point orbits (LPOs) with continuous thrusts using an ephemeris model [104]. [PERSON] et al. investigated stretching directions in cislunar space to apply to departures and transfer design [105].  
At present, a set of high-precision dynamic models have been established for the lunar probe, but the long-term forecast is still inaccurate. Accurate modeling of solar radiation pressure and the albedo effect, the jet unloading dynamic model, etc. are key links in providing long-term orbit recursion accuracy. However, at present, there are few relevant studies, and research needs to be carried out. For the configuration of the communication constellation, the design considerations are complex, the constraints are many, and the optimization is difficult. There is currently no definite conclusion, and optimization design work still needs to be carried out.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 4 Traffic Optimization Algorithm
### The Load-Balanced Topic-Connected Overlay Algorithm
Then, we find the widest path from each destination vertex (SubInt) to any source vertex (PubInt) (Lines 6-27); this procedure will generate several disjoint and acyclic subgraphs. Finally, we merge these subgraphs into a minimum spanning tree (also known as a minimum overlay network) (Lines 28-29). For the latter case, we first figure out the vertices that are not in the father topic's overlay network (denoted as \(G_{t,father}\)), but meanwhile in its own publishlist or subscribelist (Lines 31-34). Then, we add these vertices to \(G_{t,father}\) to form a connected spanning tree adopting the Kruskal algorithm (Lines 35-37). Algorithm 3 depicts the routing fine-tuning operations for physical topology change. In fact, any changes on the subscription topology or physical topology would trigger a routing update. For the first case, when a node \(v\) adds to the subscribelist for topic \(t\), the routing update algorithm first checks the overlay \(G_{t}\); if \(v\
ot\in G_{t}\), it finds the shortest and widest path from \(v\) to \(G_{t}\) and adds this path to \(G_{t}\). On the contrary, if a node \(v\) unsubscribes from topic \(t\), it first checks whether \(v\) is a leaf node in \(G_{t}\) or not. If \(v\) is a leaf node, it will remove node \(v\), as well as the edge attached to \(v\). Otherwise, \(v\) is a relay node, and there is nothing to do. There is no difficulty in performing this update, so this algorithm is omitted in this paper. For the latter, as any link can suffer from a failure, the traffic flowing through the invalid link originally should take detours via other links. When a new link comes into operation, it should share the traffic of other links to balance the load as much as possible. To avoid traffic vibrating, this process moves mildly. It just depend on subsequent adjustment. Algorithm 3 carries out routing fine-tuning when the link state changes. Note that the routing update induced by node failure is included in Algorithm 3, because this situation equals multiple links' failure. ```
0:\(G=(V,E)C(E),L\) //\(C(E)\) is the residual bandwidth capacity matrix //\(G_{t}\) is overlay network for topic \(t\)
0:\(max(minR(E))\) //\(min(R(E))=min(R_{e,j})\),for \(\forall i,j\,e_{ij}\in E\) //R(E) is residual bandwidth matrix, equals C(E) initially. 1:if the links set is invalid
2: find the affected overlay network set GA;
3:foreach\(G_{t}\) in GA
4: merge the connected subgraphs split by failed links
5:endfor
6:else //there is newly added link
7: update the residual bandwidth matrix
8:endif
```  
**Algorithm 3** Routing fine-tuning algorithm for physical topology change.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 2 Semantic Segmentation Models for Multi-Damage Detection
### Efficient Neural Network (ENet)  
[PERSON] et al. [42] proposed an efficient lightweight deep neural network (ENet) for semantic segmentation tasks. It is a state-of-the-art model with very low parameters (0.4 million) and FLOPs. The ENet was trained and tested on three types of datasets with different numbers of classes. For the Cityscapes dataset [49], with 19 classes, and the CamVid testing dataset [50], with 11 classes, the mIoUs of ENet were reported as 58.3% and 51.3%, respectively. Figure 1a shows the ENet initial block, containing the convolutional and max-pooling layers with concatenation. The initial block was first used in the encoder part to downsample the input image resolution to half. Figure 1b depicts the ENet bottleneck module, which consists of three convolutional layers, two max-pooling layers, and a regularizer. The 1 \(\times\) 1 convolutional layer was used for the projection and expansion of the input features. The convolutional layer was dilated at different ratios to perform classification with a large receptive field. A spatial dropout [51] regularizer was placed at the end of the convolutional layers in the bottleneck module to avoid overfitting issues that could be caused by a small training dataset. The ENet architecture comprisesfive sections that contain the initial block and bottleneck modules. Several bottleneck modules were used in Sections 1-3 to further downsample the input image resolution to 1/8. Batch normalization (BN) and a parametric rectified linear unit (PReLU) were used after each convolutional layer to reduce the computational cost. The PReLU is an activation function that generalizes the conventional rectified unit by incorporating a negative slope. PReLU enhances model fitting with negligible additional computational expense and minimal overfitting risk [52]. The ENet architecture is different from other encoder-decoder models because the decoder module in ENet has fewer layers than the encoder. This was prompted by the idea that the encoder should be able to function on lower-resolution data and provide information processing and filtering in a manner similar to the original classification architectures. Sections 4 and 5 describe the decoder part for the upsampling of the feature map, and very few bottleneck modules were used, compared with the encoder.
## 2 Materials and Methods
### Satellite Data and Preprocessing
#### 2.1.3 Supplementary Data  
Land cover data: The MCD12Q1 data, which are obtained from the MODIS data by combining Terra and Aqua, have a spatial resolution of 500 m and provide five global land cover classification systems. These systems are the International Geosphere-Biosphere Programme (IGBP) classification system, the IGBP revision from the University of Maryland (UMDD) classification system, and other services for estimating LAI/FPAR, net primary productivity (NPP) and plant function type (PFT), respectively. The IGBP system has been selected and applied for data classification in this study.  
Surface reflectance data (MOD09 data): This article used the MOD09 GA product (based on Terra satellite product data), which has a spatial resolution of 500 m and provides a global daily ground-based basic reflectivity. These data are accessible from the LAADS DAAC ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))).  
The FPAR product of the Heihe research area [25]: This product was provided by the Cold and Arid Regions Science Data Center at Lanzhou ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn))). The data are based on the canopy Bidirectional Reflectance Distribution Function (BRDF) model and the LUT algorithm, with a spatial resolution of 1 km and a temporal resolution of 8 days.