esa-sceva/satcom-chunk-collection huggingface dataset:
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 5 Use Cases
### Use Case 2: Root Cause Analysis for Service Problems  
Understanding the service quality problem often requires definition of \"poor service quality events\" that occurred in a test cycle (Figure 2). We define four events that reflect problems during any test cycle. The first one is called a \"radio provisioning problem\". It happens when all the services are not functional. The second and third problems are \"Web  
Figure 7: Recommended RSRP signal strength levels by service (web or video) and by device used (Redmi or Samsung).  
problem\" and \"Download problem\". These two problems appear during a test cycle when the web and download services do not work and also, in the same test cycle, the video service that is more demanding in bitrate and more sensitive to delay is working. We pay attention here to the application buffer as it may help video streaming to keep working even if there is some temporary disconnection or an interference happens. We call this last situation the \"servers problem\" because the problem will instead come from the web and downloading servers. Table 7 lists these problems.  
To dive into the analysis, these problem events are examined using two different modes to find the number of occurrences based (i) on number of trace cycles or (ii) on geographic area sector.
## 2 Data and Methods  
### Data Selection and Processing  
The moment tensor solution catalog of earthquakes in Yunnan, ranging in time from 2000 to 2014 [31], is used to select events near the epicenter of the M\({}_{\rm S}\) 5 Yingjiang earthquake (between 24.5-25.5\({}^{\circ}\) N and 97.5-98.5\({}^{\circ}\) E). The moment tensor data used in this paper are based on waveforms with good signal-to-noise ratios and derived from stations with good distance and azimuthal coverage by [PERSON] et al. [31]. Based on these criteria, they were successful in estimating moment tensors for 1833 of the 4428 events that they examined.  
Figure 2 shows that the seismic activity in this area shows the characteristics of a cluster. To obtain better inversion results, the event closer to the main earthquake was selected. A total of 187 focal mechanisms were selected and used for the regional stress field inversion (Figure 2).  
According to the standard classification of focal mechanism solutions in a related study [32], among the 187 earthquakes collected for the study area, 8 were classified as purely normal, 2 as reverse, 100 as strike-slip, and 77 as oblique (\"other\" in Figure 3). The triangle diagram (Figure 3) of focal mechanisms can directly show the bias of seismic data collected in the Yingjiang area. The results show that the Yingjiang area mainly features pure strike-slip earthquakes and earthquakes with strike-slip components (Figure 3). [PERSON] et al. [33] considered that Yingjiang is located in the southwestern Yunnan block, and the inner part of the Yingjiang area is dominated by NE-SW compression, which results in local faults being prone to slip along strike.
## 9 Conclusions  
The EFSOS system through a series of laboratory, lake, and field tests, has demonstrated its feasibility and reliability. The RECAS sonde has shown remarkable performance in terms of penetration rate, borehole diameter, and its unique \"silk pay out/in\" function. Likewise, the RCUPS system has proven its stable operation over an extended test period. It is worth noting that the reliability of the armored cable is of great significance, which is directly related to the power, signal transmission, and recoverability of the RECAS sonde. Although measures such as the installation of a circulating water channel on the drum and the implementation of temperature-feedback control have been taken, more reliable cables and protection methods are required. The successful Antarctica field test provides valuable insights and data for the improvement of subglacial lake sampling technology. The optimization of the RECAS structure and its compatibility with other scientific loads holds great promise. It is expected that with continuous research and development, the EFSOS system, especially the RECAS sonde, will play an even more prominent role in polar subglacial lake exploration, contributing to a deeper understanding of the subglacial environment and its ecological systems.  
**Author Contributions:** Writing--original draft preparation, Z.D. and X.F.; methodology, Y.S., Y.L. ([PERSON]), H.Y., D.W., N.Z., Z.C., X.Y., R.W. and P.T.; software, Z.W., Y.L. ([PERSON]) and S.P.; investigation, T.W., J.X., L.X., C.A., Y.C., Y.W., X.W., X.N. and W.W.; data curation, D.G.; writing--review and editing, X.F., D.G. and B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Key Research and Development Project of the Ministry of Science and Technology of China (Project No. 2023 YFC2812602, 2016 YFC1400302) and the National Science Foundation of China (project No. 41941005).  
**Data Availability Statement:** The data presented in this study are available on reasonable request from the corresponding author.  
**Acknowledgments:** We thank the teachers, engineers, and postgraduate students of the EFSOS project team for their hard work in testing the sonde and solving various problems.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 6 Discussion
### BN Parameter Issue  
One of the problems when dealing with a Bayesian Network (BN) is to define its parameters (the probability tables). Generally, these probabilities are defined by an expert of the system. However, this expertise may be incomplete or impossible to achieve in some domains and/or systems. In this case, it is necessary to use a learning method for the BN parameters [12]. There are different learning methods for BN parameters in the context of complete data [13] or incomplete data [14]. In the real application like the evaluation of autonomous system health status, the expert databases are often incomplete. The missing data can be provided by sensor failures, inaccurate measurements of sensors data, etc. In this case, it is possible to learn the BN parameters from the databases using the Expectation and Maximization (EM) algorithm [38]. As this algorithm can consider the structure of the BN, the learning phase can be manageable. In [39], we show that we can reduce the timing in the learning phase if the structure of the BN is known in advance.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & & \multicolumn{2}{c}{**Resource**} & \multicolumn{2}{c}{**Latency**} & \multicolumn{2}{c}{**Speed-Up**} & \multicolumn{1}{c}{\multirow{2}{*}{**Cono.** **(**uJ**)**}} \\ \cline{3-3} \cline{5-10}  & & & **BRAM** & **DSP** & **LUT** & **FF** & \multicolumn{1}{c}{**(cycles)**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{4}{*}{2 Bayesian networks (54 nodes)} & \multirow{2}{*}{2 Actions} & All in HW & 14\% & 14\% & 23\% & 12\% & 310 & 5.96 & 6.33 \\ \cline{3-3} \cline{5-10}  & & Mixed & 14\% & 6\% & 20\% & 11\% & 528 & 3.50 & 10.50 \\ \cline{3-3} \cline{5-10}  & & All in HW & 14\% & 24\% & 32\% & 16\% & 355 & 7.33 & 7.56 \\ \cline{3-3} \cline{5-10}  & & Mixed & 14\% & 6\% & 20\% & 11\% & 1328 & 1.96 & 26.42 \\ \hline \multirow{4}{*}{10 Bayesian networks (270 nodes)} & \multirow{2}{*}{2 Actions} & All in HW & 61\% & 41\% & 78\% & 34\% & 6001 & 11.12 & 170.42 \\ \cline{3-3} \cline{5-10}  & & Mixed & 60\% & 6\% & 44\% & 24\% & 60,370 & 1.10 & 1521.32 \\ \cline{3-3} \cline{5-10}  & & All in HW & 80\% & 60\% & 80\% & 40\% & 11025 & 21.3 & 330.84 \\ \cline{3-3} \cline{5-10}  & & Mixed & 60\% & 6\% & 44\% & 23\% & 232,475 & 1.02 & 5835.12 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Resource and performance of the DN implementation by varying the number of Bayesian networks and actions.
## 3 Parameterization Schemes for Fiber Orientation
### Continuous Parameterization
Conclusively, [PERSON] and [PERSON] [89] demonstrated that the fiber orientation coincides with the principal stress/strain fields for relatively weak shear and some strong shear types of anisotropic materials. Recently [90], a strain-based method framework has been utilized to optimize laminate topology and fiber orientation for various in-plane and out-of-plane loading conditions. Furthermore, the CFO methods are highly dependent on the initial fiber configuration. As drawn out here [91], these approaches will fail for shear'strong' type materials due to repeated global minimum solutions. Nevertheless, the shortcomings of these methods encouraged the formulation of the energy-based method introduced by [PERSON] and [PERSON] [92; 93]. This method uses an inclusion cell to estimate the strain and stress fields' dependency on the fiber orientation by introducing an approximate energy factor. Yet, the  
Figure 3: Finite elements are considered design variables in DTO: (**a**) Continuous material orientation; (**b**) Discrete material orientation. dependence of energy factors on the traction stress, material properties, and direction of the inclusion cell and its surroundings makes it challenging to formulate the framework for 3D and complex loading problems. Following the principles of the energy-based method, [PERSON] et al. [94] proposed a hybrid stress-strain method by weighting the optimality condition of the mean compliance in the stress and strain form. Numerical examples demonstrate their method on weak and strong shear materials and extension to 3D problems. The assumption regarding the elemental strain and stress field invariant to the neighboring elemental orientation is considered; however, it may restrict the solution of 3D problems and result in a suboptimal solution. An alternative is employing curvilinear parameterization schemes that define fiber paths as the graphs of analytical function, which guarantee the continuity of the fiber angle and have a small number of design variables [95; 96; 97]. Nevertheless, the restrictive design search space will limit the tailoring of the fiber path, thus deteriorating the stability of the optimization problem [19] and the quality of the optimized solution. In addition, the parameterization schemes can follow equidistant iso-contours of a level set function to represent curvilinear fiber paths [98; 99], naturally ensuring fiber continuity and often being parallel to the neighboring fiber paths. Furthermore, the optimization result becomes highly dependent on the initial configuration, and local solutions often appear [100].
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.1 Routing Service Framework  
A cross-layered modular based QoS-aware routing service framework proposed in [32] aims to provide priority-based routing services and user specific QoS support. The QoS metrics used to determine the routes are: user specific QoS requirements, wireless channel status, priority level of the data packets and willingness of the sensor nodes to behave as a router. The main functions of this framework are: QoS-aware route establishment and maintenance, prioritized packet routing, Application Programming Interfaces (APIs), feedback on network condition to the user application(s) and finally adaptive network traffic balancing. As shown in Figure 3, redrawn from [32], the architecture of this routing service framework has four modules: Application Programming Interfaces (APIs) module, routing service module, packet queuing and schedule module and system information repository module.  
The APIs module of [32] acts as an interface between the user application and the routing service module. The four sub-modules of the APIs module are: QoS metrics selection, packet sending/receiving, packet priority level setting and admission control and service level control. The  
Figure 3: Architecture of QoS-aware routing service framework [32].  
QoS metrics selection sub-module includes end-to-end delay, delivery ratio and power consumption. The packet sending/receiving sub-module is responsible for receiving the sensed data from the user application and sending it to sink node or any other node. The packet priority level setting sub-module is responsible for setting the priority level of the received data packets. Finally, the admission control and service level sub-module control returns feedback on the network conditions to the user application. The second module (routing service module) is responsible for constructing and maintaining the routing table with the help of the receiving neighbor's status information. All data packets, including both the control and data packets, are categorized into eight different priority levels, where the control packets have more priority as compared to the data packets. The node's buffer will reach a pre-assigned threshold value if the sensor node is not able to access the wireless channel due to network congestion. In such cases the packet queuing and scheduling module will inform the user application to reduce service level and willingness level to be a router. The System Information Repository module maintains two tables: link state table and willingness table. The link state table provides the link state of each node, including link quality, end-to-end delay, communication bandwidth, and average packet delivery ratio, while the willingness table contains the information for each node to behave as a router.
# Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment
## 1 Introduction
Therefore, considering that the communication ranges of the robots are generally limited [31], the communication distance constraint is used to solve the problem of inaccurate Voronoi partitioning and robot collision due to limited communication distance. By using multi-robot formations in a MATLAB simulation, we demonstrate that the proposed method can effectively solve the non-convex environment limited communication distance of robot localization in the uncertainty of a division, ensuring that each robot is in a safe area, even when the robot's center is located at the edge of an adjacent BUVC. Thus, the collision avoidance performance and the robustness of a BUVC method in the process of multi-robot coverage are further improved. The structure of the remainder of this article is as follows. In Section 2, the principle of a CVT and some problems occurring in practical applications are described. In Section 3, a BUVC partition with localization uncertainty and robot radius parameters is proposed to solve these problems. Thereafter, a constrained Voronoi partition and a communication distance constraint are proposed for a non-convex environment and the communication distance constraint, respectively. In Section 4, corresponding simulation results are presented to verify the effectiveness of the proposed algorithm. Sections 5 and 6 present the distributed control strategy and numerical simulations employed in this study, respectively. Section 7 discusses the BUVC experiments using TurtleBot3 robots. Conclusions and future prospects are presented in Section 8.
# Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
## 2 Motivation and Outline of Method
However, we believe that the local repository could also include bitmap images, or even diagrams produced with general drawing tools (perhaps prior to the adoption of a specific CASE tool by the organization). It could be the case, also, that the organization's policy for information dissemination among its departments does not consider the automatic sharing of information, but only considers sharing documents in generic human readable formats such as PDF or HTML; in those cases metadata will not be enough for effective retrieval and our tool will be most useful. Our work is limited to bitmap images (or, more specifically, pixmap images, i.e., spatially mapped arrays of colored pixels), either uncompressed bitmaps such as BMP or compressed formats such as GIF, JPG/JPEG, PNG or TIFF, with a typical resolution for web images of 72-96 dots per inch. We do not consider, instead, vector graphics. The work is also limited to UML static diagrams (classes, components) which are mainly made of straight lines and rectangles, leaving diagrams that use rounded forms (such as use case diagrams, activity diagrams and statecharts) for future research and development. Since the tool is tuned to detect static diagrams, other diagrams that use mainly straight lines and rectangles (such as sequence diagrams) will produce a certain number of false positives. More specifically, our hypothesis is that an automatic rule-based classifier obtained through machine learning algorithms can emulate the experts' classification of images as static UML diagrams. The method we have followed can be roughly described as follows. First, we obtained a sample of nearly 19,000 images from the web (exactly 18,899 items) that resulted from queries in Google Images involving the terms \"uml diagram\"; then a team of experts manually classified the images as UML static diagrams (Yes/No). The reader is referred to our previous conference paper [9] for the details regarding the criteria followed in this manual classification. Second, we analyzed the main graphical characteristics of images that represent diagrams, such as grayscale histogram, color histogram, elementary geometric forms detected with image recognition techniques (especially rectangles), and so on. Third, using machine learning techniques (rule induction), we selected a combination of characteristics that maximize the effectiveness of the automatic classification. Fourth, we implemented the rules in a tool that is freely available for the public and that can be used to classify new images. The rest of this article explains these steps in detail.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 3 Methodology
### Landslide Detection Based on SSD Model  
We select the current mainstream SSD model for landslide detection and compare it with the transfer learning method. The SSD model is currently the most widely used deep learning algorithm in the target detection. In order to improve the accuracy of the results of different feature maps, the model makes the prediction using the feature pyramid prediction mode. The default box with multiple ratios is set. The model combines both speed and accuracy, with faster detection than Faster-R-cnn and higher accuracy than YOLO. At the same time, the end-to-end training method was adopted, and the classification results were very accurate, even for the pictures with relatively low resolution.  
Figure 5: DOM production of the study area.  
The objective function of the SSD algorithm is based on the Multibox objective detector. The loss function consists of the location loss and confidence loss [27].  
\[L(x,c,l,g)=\frac{1}{N}\big{(}L_{conf}(x,c)+\alpha L_{loc}(x,l,g)\big{)} \tag{1}\]  
where \(x\) = {1, 0} represents whether the target exists; \(c\) represents confidence; \(l\) is the prediction box; \(g\) represents the ground-truth box; \(N\) represents the number of prediction boxes that match the real box; \(\alpha\) is the weight coefficient.  
The location loss function is defined as follows:  
\[L_{loc}(x,l,g)=\sum_{i\in Pos}^{N}\sum_{m\in\{cx,cy,m,h\}}^{N}x_{ij}^{p}smooth_ {L1}(l_{i}^{m}-g_{j}^{m}) \tag{2}\]  
where \(\xi_{j}^{cx}=\frac{(\xi_{j}^{cx}-d_{i}^{cx})}{d_{i}^{p}},\xi_{j}^{cy}=\frac{( \xi_{j}^{cy}-d_{i}^{cx})}{d_{i}^{p}}\delta_{j}^{cy}=log(\frac{\xi_{j}^{cy}}{d_{ j}^{p}}),\xi_{j}^{h}=log(\frac{\xi_{j}^{h}}{d_{j}^{p}})\); if \(x_{ij}^{p}\) = 1, the \(i\) prediction box matches with the \(j\) real box of class \(p\), if \(x_{ij}^{p}\) = 0, does not match.  
The confidence loss is defined as follows:  
\[L_{conf}(x,c)=-\sum_{i\in Pos}^{N}x_{ij}^{p}log(\xi_{i}^{p})-\sum_{i\in Neg}^{ N}log(\xi_{i}^{0}) \tag{3}\]  
where \(\xi_{i}^{p}=\frac{exp(\xi_{i}^{p})}{\sum_{p}^{p}exp(\xi_{i}^{p})},\xi_{i}^{p}\) is the probability that the \(i\) prediction-box matches with the class \(p\).
## 4 Conclusions
The length of the linker region is crucial for light-gated cyclase activity of all the BLUF; cyclases were shown to be similar except in _Leptonema illini_, where the length of the linker region was surprisingly very short, Figure S2: LgAC-ATP interaction analysis. (**A**) A 3-D model of the cyclase domain of LgAC docked with the ATP as the ligand (encircled). (**B**) Interaction between ATP and amino acid residues forming the ATP binding pocket within the cyclase domain (Inset). (**C**) 2-D interaction image depicting the amino acid residues and the nature of interaction involved in the binding of ATP. The green, yellow, and violet dashed lines represent conventional hydrogen bonding, attractive charge-based bonding, and pi-alkyl bonding, respectively, Figure S3: (**A**) Growth curve of _E. coli_ expressing recombinant LgAC in the dark and BL. Growth was estimated at 600 nm (**B**) Biofilm formation by _E. coli_ carrying LgAC under dark and BL. The biofilm formation of _E. coli_ was estimated at 550 nm. Data are presented as mean \(\pm\) SD (\(n=3\)). Statistical comparisons were performed using ANOVA using SFSS version 16.0. Asterisks (*) represents the level of significance difference as compared to control: *\(p<0.05\), **\(p<0.01\), ***\(p<0.001\) and \"ns\", not significant. Positive control was _E. coli_ cells supplemented with 0.1% glucose and negative control was untransformed _E. coli_ cells only. [PERSON] and [PERSON] have executed and analyzed proteomics experiments. [PERSON] and A.M. have performed bioinformatic analysis and LgAC expression. [PERSON] had done functional characterization of LgAC. All authors have written relevant section of the manuscript. [PERSON] had conceptualized, outlined experiments, analyzed data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript. [PERSON] is financially supported by University Grant Commission (UGC)-[PERSON] Postdoctoral Fellowship, India. [PERSON] is funded by University Grant Commission (UGC) non-NET fellowship. [PERSON], [PERSON] and [PERSON] are thankful to Council of Scientific and Industrial Research (CSIR), government of India and Department of Biotechnology (DBT), government of India for providing fellowship(s). [PERSON] is thankful to Department of Science and Technology (DST)-Science and Engineering Research Board (SERB)-India [SERB(EEG)/2018/000781], University with potential of Excellence (UPE)-II Jawaharlal Nehru University (JNU) and Department of Science and Technology (DST)-Promotion of University Research and Scientific Excellence (PURSE) Jawaharlal Nehru University (JNU) for the research grant. We are also thankful to [PERSON] and [PERSON] for valuable editing of the manuscript. The authors have no conflict of interest.