esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tennis Ball Detection Using YOLOv5  
One of the most important parts of a tennis ball collection robot is object (tennis ball) detection. In this study, the challenge was to use an algorithm that worked in different environments with various illuminations. Consequently, using RGB or HSV protocols and arranging the intensity range was not a trusted method for tennis ball detection. That is why a YOLOv5 algorithm was used in this study for tennis ball detection since it is a very powerful and accurate method.  
Figure 3: Robot components connection diagram.  
In this section, we first discuss the network's overall architecture. Then, we introduce the specifics of our updated classifier and the dataset's evaluation criteria. This study employed a YOLOv5 object identification network and a multi-layer perceptron (MLP) neural network to classify tennis balls. Since May 2020, YOLO has made a significant breakthrough in its industry, which it has dominated for years. Two revised and improved versions of YOLO were released in quick succession. The first was [PERSON] and [PERSON]'s YOLOv4 [19], and the second was [PERSON]'s newly published YOLOv5. This new version was received with some criticism, but the v5 model has displayed a substantial increase in performance compared with its predecessors. YOLOv5 has a number of technical benefits. The most acclaimed change is the adoption of Python as the programming language instead of C, which was utilized in earlier versions. This considerably facilitates IoT device installation and integration. Furthermore, the PyTorch community is bigger than the Darknet community, suggesting that PyTorch gets more contributions and has a greater potential for future expansion.  
The YOLOv5 network is made up of three main components:  
1. The backbone, which consists of a CNN layer that combines image properties at multiple scales.
2. The neck, which is a layer collection that is used to gather visual attributes and pass them to prediction.
3. The head, which collects neck parameters and performs localization and classification.  
As shown in Figure 5, all object detection designs share a property, which is that the input image characteristics are compressed by a feature extractor (backbone) and then transferred to the object detector (including the detection neck and detection head).  
The detection neck (or neck) serves as a feature aggregator, combining and mixing the features created in the backbone to prepare for the next step in the detection head (or head). The head is in charge of detections as well as the classification and localization of each bounding box. The two-stage detector does these two jobs separately and then aggregates their findings (sparse detection), whereas the one-stage detector performs them simultaneously (dense detection). YOLO is a single-stage detector (You Only Look Once). Tennis ball images in various backgrounds and illuminations were utilized in this study to train the YOLO network.  
Figure 4: Developed tennis ball collection robot.
# Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures
## 2 Basic Concepts  
### Microservice Architecture  
The microservice architecture is recognized as an application development strategy that involves breaking down applications into a series of loosely interconnected components. Its growing popularity can be attributed to the ease it brings to continuous delivery and its ability to enhance the scalability of applications [22]. Within this architectural framework, each component operates independently, managing a distinct function of the application. Communication between these components is facilitated through clearly established, lightweight protocols, typically using APIs. A key advantage of the microservice architecture is the autonomy of its components, which grants development teams the flexibility to update and deploy individual services without impacting the overall application.  
This approach leads to a more streamlined development process and greater agility in software development [23].  
While microservices offer numerous benefits, they also pose challenges in debugging. With an architecture comprising multiple heterogeneous components, performance monitoring tools must take this diversity into account. Typically, instrumentation is carried out on each component using tracing libraries specific to the component's environment. To gain a comprehensive view of the system's health, it is crucial to use distributed tracers. A benefit of microservice architectures is that the instrumentation phase can be treated as an application update, allowing for its flexible integration into the application deployment pipelines. The health of microservices can also be monitored using logs, but the cost of this strategy becomes prohibitively high when the application consists of multiple nodes. The challenge lies not only in interpreting the inter-causalities among nodes but also in managing the large volume of data, which quickly becomes unmanageable. Tracing remains the best way to address this problem, as it enables understanding the system's operation as a whole and, if necessary, identifying bottlenecks.
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
###### Abstract  
During data sharing and exchange of building projects, the particular business task generally requires a part of the complete model. This paper adopted XML schema to develop a generic language to extract the partial model from an Industry Foundation Classes (IFC) model based on the proposed Selection Set (called PMESS). In this method, the Selection Set was used to integrate users' requirements, which could be mapped into IFC data. To ensure the validity of the generated partial IFC models in syntax and semantics, seven rules--including three basic rules for a valid IFC file, three extraction rules based on the Selection Set, and a processing rule for redundant information--were defined. Through defining PMESS-based configuration files, the required data can be extracted and formed as a partial IFC model. Compared with the existing methods, the proposed PMESS method can flexibly extract the user-defined required information. In addition, these PMESS-based configuration files can be stored as templates and reused in other tasks, which prevents duplicated work for defining extraction requirements. Finally, a practical project was used to illustrate the utility of the proposed method.  
Building Information Modeling (BIM); Industry Foundation Classes (IFC); partial model extraction; query language; selection set +
Footnote †: journal: Applied Sciences
## 4 Evaluation and Benchmarking
### wM-Bus Connectivity Evaluation
We can also see that the numbers we recorded seemed quite similar to the ones presented in [23], depending on the exact installation characteristics. Another important metric we wanted to evaluate was the effects of human presence in the vicinity of the installation. Figure 9 shows the average RSSI measurement for the packets received from a single smart meter and by its assigned Mox node. As we can seem, these values did not present any huge deviation throughout the day, allowing us to be sure that there were no daily events that may periodically affect our communication. The same behaviour was presented in all devices of our installation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Device** & \
\begin{tabular}{c} **Expected** \\ **Packets** \\ \end{tabular} &
\begin{tabular}{c} **Received** \\ **Packets** \\ \end{tabular} & **(\%)** \\ \hline
1 & 1 & 40,701 & 3059 & 65 \\
1 & 2 & 40,701 & 3321 & 70 \\
1 & 3 & 40,701 & 3021 & 64 \\ \hline
2 & 1 & 40,701 & 1997 & 42 \\
2 & 2 & 40,701 & 2017 & 43 \\ \hline
3 & 1 & 40,701 & 2632 & 56 \\
3 & 2 & 40,701 & 2628 & 56 \\ \hline
4 & 1 & 40,701 & 3092 & 66 \\
4 & 2 & 40,701 & 3140 & 67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of received packets based on the manufacturer’s setting for hourly broadcasts. Figure 8: wM-Bus smart meter devices with unstable RSSI 2 (Location 4).
###### Abstract
[20; 21] studied that the influence of two blending methods on NO\({}_{x}\) emission and burnout characteristics of blended coals (bituminous coal and sub-bituminous coal) in the co-firing process using both experimental and simulation approaches. The results showed that the combustion characteristics of the in-furnace units were better than those of the out-furnace case. [PERSON] et al. [22] investigated that the NO\({}_{x}\) emission and burnout ratio of co-firing the bituminous coal and lignite, and indicated that the concentration of NO\({}_{x}\) increased with the moisture content in lignite. [PERSON] et al. [23] explored the influences of excess air ratio and side over fire air (SOFA) arrangement on the temperature distribution, NO\({}_{x}\) emissions and burnout characteristics of blended coal. They revealed that the fuel type exhibited certain influence on the temperature distributions and NO\({}_{x}\) emissions during the co-combustion process. Most of the previous studies involved the co-firing behaviors of bituminous coal and other kinds of coals, which are difficult to guide in pulverized-coal fired boilers. The exploration of co-firing bituminous coal and semi-coke was firstly conducted in a 300 MW tangentially-fired boiler in our previous study [24]. The study suggested that semi-coke can be co-fired with coal in utility boilers. However, to our knowledge, no study has been conducted on the blends of semi-coke and lean coal in supercritical or ultra-supercritical wall-fired utility boilers. The influences of blending ratio and blending method of semi-coke on combustion performance and NO\({}_{x}\) emission on such wall-fired boilers are still unclear. More efforts remain necessary to optimize the air distribution during the co-firing process to improve the combustion performance of blends. More research should be performed to find the optimal operation mode of co-firing lean coal and semi-coke in wall-fired boilers. Because of the high cost of obtaining relevant data through in-situ tests, the retrofitting and optimizations of utility boilers using solid fuels can be guided by the numerical approach. In this paper, the combustion and NO\({}_{x}\) generation characteristics of co-firing lean coal and semi-coke in a 600 MW supercritical wall-fired boiler were numerically studied. After the model validation according to some data from in-situ tests, the influences of semi-coke blending ratio, injection strategies of semi-coke, blending method, excess air ratio in primary combustion zone, and over fire air (OFA) arrangement on the NO\({}_{x}\) generation and boiler operation performance of co-firing lean coal and semi-coke were investigated and some strategies for the operation optimization were further discussed. The present study can provide guidance for practical application of blending semi-coke in wall-fired boilers and promote clean and efficient usage of semi-coke, which is of benefit for coal grading utilization.
## 5 Further Research Opportunities of the SDN-Enabled Smart Grid
To solve this problem, multiple SDN controllers need to be distributed to the network and HES. In the case of multiple SDN controllers, all of them need to share the common management policy of the network. For example, the elastic SDN controller distribution (ElastiCon) algorithm can be utilized for a large-scale smart grid network [43]. In this algorithm, the SDN controllers are classified as a master and slaves. The master SDN controller can be located in the HES, and slaves are inserted at the substations. The master SDN controller assigns and distributes the slave SDN controllers to the network depending on the link capacity of the switch or router. Further, the SDN network functions (application) are distributed to the multiple slaves. During the migration process between the master and sub-controllers, at least one controller is activated to avoid a service suspension problem. The experimental results show that the network congestion, end-to-end control message delay, flow request-response time problem can be solved by using an elastic controller distribution system. * Interoperable design between IEC 61850 and SDN protocols: In traditional layered networking architecture, communication strategies, such as routing policy, QoS policy and MAC parameters, may not dynamically adapt to applications and services. However, an SDN-enabled smart grid network can be optimized for specific smart grid services by using IEC 61850 standard profiles. The IEC 61850 protocol is the de facto application standard of the smart grid [12]. The information model of the smart grid substation is defined in IEC 61850-7, and the configuration language is also defined in IEC 61850-6. The IEC 61850 application defines the power-monitoring interval, transmission type (IEC 61850 Manufacturing Message Specification or multicast) and service priorities. The IEC 61850 uses the XML (or JSON) data format, which can be decoded using SDN applications because the SDN API basically uses the XML and JSON formats to exchange control messages between the SDN controller and clients. Thus, the service profile of the IEC 61850 can be simply identified and utilized for the network configuration. By using an interoperable management architecture between IEC 61850 and SDN, the service capability of the SDN-enabled smart grid can be extended from the network to smart grid application management. * Interoperable controller system: Interoperability among different SDN architectures needs to be considered for SDN-enabled smart grid systems [45]. This is because SDN is implemented by a vendor-neutral strategy, depending on the capability and business goal of the service domain, without the need for any international agreement. In particular, northbound API and SDN controller systems need to be standardized to realize simplified communication and the development of smart grid SDN applications.
# Transcriptomic Analysis of the Candidate Genes Related to Aroma Formation in _Osmanthus fragrans_
## 5 Conclusions  
The dynamic transcriptome sequencing analysis of \"RiXiangGui\" at different flowering stages was performed using Illumina RNA-Seq technology. Based on the DEG analysis and previous research onthe patterns of the release of floral compounds, several candidate genes related to monoterenes, sesquiterpenes, and phenylpropanol biosynthesis were obtained. Furthermore, a series of TFs were identified and theqRT-PCR analysis indicated that _OfMYB1_, _OfMYB6_, _OfWRKY1_, and _OfWRKY6_ participate in terpenoid biosynthesis. These results are helpful for exploring the molecular mechanism of floral scent formation in _O. fragrans_, and supplies important gene resources for the floral scent breeding of ornamental plants.  
**Supplementary Materials:** The following are available online. Table S1. The summary of transcriptome sequencing data and transcriptome assembly; Figure S1. The length distribution of assembled _O. fragrans_ unigenes. All the Illumina reads for each flowering stage were combined together with 136,611 transcripts obtained. The horizontal and vertical axes showed the size and the number of transcripts, respectively; Figure S2. The characteristics of homology search of unigenes against the Nr database; Figure S3. The Eu Karyotic Orthologous Groups (KOG) classifications in _O. fragrans_. A total of 136,611 sequences with KOG classifications within the 25 categories were shown; Figure S4. The summary of the KEGG pathways involved in the _O. fragrans_ flower transcriptome. A: Metabolism; B: Genetic Information Process; C: Environment Systems; D: Cellular Process; E: Organismal Systems; Figure S5. A heat map plot of nine clusters displaying the relative expression levels of centroids; Table S2. The primers used for qRT-PCR; Table S3. The primers of TFs used for qRT-PCR.  
**Author Contributions:** C.X. and L.W. designed the experiments; X.-L.Y., H.-Y.L. and W.-J.D. performed the experiment. X.-L.Y., H.-Y.L. and Y.-Z.Y. drafted the manuscript. L.-G.W. and Y.-Z.Y. revised the manuscript. W.-J.D., C.X., T.-T.S. and G.-W.C. analyzed the data. All authors read and approved the final manuscript.  
**Acknowledgments:** This work was supported by The Project of Key Research and Development Plan (Modern Agriculture) in Jiang Su (BE2017375), The Selection and Breeding of Excellent Tree Species and Effective Cultivation Techniques (CX(16)1005), the Project of Osmanthus National Germplasm Bank, and the Doctorate Fellowship Foundation of Nanjing Forestry University, and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
## 2 Data and Methods
### Research Methods  
#### 2.3.1 Classifying the Multifunctional Types of Land Use  
The land system is composed of economic, social, and ecological subsystems [28], and the land is rendered whole with comprehensive functions via the organic coupling of each subsystem [29]. This study is based on the economic, social, and environmental dimensions of sustainable development, combined with the idea of a national \"production-living-ecology (PLE),\" based on the optimization of land use, by subdividing the total functions (TF) for which land is used as PLE sub-functions (Table 1): production functions (referred to as PF), living functions (LF), and ecological functions (EF). Production functions are based on the key needs for human survival. Land provides agricultural products to guarantee the capacity of non-agricultural economic output and transportation. These functions are, therefore, measured as the three aspects of agricultural production, economic growth, and transportation security [30]. Living functions are the ability of the land to meet the needs for human development [31, 32], which are mainly reflected by four aspects: employment support, social security, cultural leisure, and the residential home. Ecological functions are related to the high-quality production and living needs of human beings. This function is evaluated from the three dimensions of maintaining ecological balance, providing resources, and keeping the environment clean. This study, therefore, divides the function types of land use into three levels from top to bottom: total function, sub-function, and single function. The number of functions at each level is 1, 3, and 10, respectively.  
Figure 1: Map of the study area.
# An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction
## 2 Materials and Methods
### Combining Models  
Using the fragmentation and stress models previously described [21; 22] within a flow simulator, it is feasible to integrate key variables and mechanisms for a more accurate estimation of secondary fragmentation in block saving mining. A summary of the methodology used to accomplish this is presented in Figure 6.  
The flow simulator used considers a probability function (\(P_{i}\) in Figure 6) to determine the probability of a higher cell descending. This function is what allows the modeling of the flow of broken material in a cellular robot. The variables of this function are the distance between cells (\(d_{i}\)) and the average size (\(d_{50}\)). When a void is introduced in the robot (due to mineral extraction), the void rises, exchanging positions with a higher cell using this function. The closer the cell is (smaller \(d\)) and the smaller the granulometry (smaller \(d_{50}\)), the greater the probability of exchange for that cell. More details on this operation can be found in [33]. As can be seen, the average size influences the probability of material flow according to this formula. This is where a fragmentation model is integrated that impacts  
Figure 6: Methodology used in this work to integrate previous modeling of stress (vertical stress) and fragmentation (secondary fragmentation) [7; 21; 22; 33; 34].  
the flow by decreasing the d\({}_{50}\) as the simulation progresses, using Equation (6). Originally, this equation considered an average stress calculated by the lithostatic load; however, as the material is broken, this value may be overestimated, which is why in this work the stress model for broken material developed in [22] is used.  
An integrated fragmentation + stress model was developed using a series of steps. First, the stress model [22] and the fragmentation [21] were merged by relating the two through vertical stress (\(\sigma_{\mathrm{v}}\)). Then, the coupling between the two models was introduced into a flow simulator based on the void diffusion rule, to replicate the physical experiments carried out by [11]. After analyzing the results obtained in this step, the next step, indicated by green dotted lines, was carried out in which shear deformation and friction were incorporated. Subsequently, a new simulation was carried out with the new model under the same conditions as the first simulation, comparing both results based on the \(R_{i}\) values obtained for the different particle sizes (d\({}_{10}\), d\({}_{20}\), d\({}_{50}\), and d\({}_{80}\)) to assess the improvement in the model's accuracy. Finally, the new model integrating the shear strain effect was applied at mine scale to evaluate the generation of fine material.
# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 2 Research Methodology
### Review Protocol
The few works in surveillance (11%) employ hybrid RRT*DRL techniques that achieve re-planning rates of 92% in dynamic environments, but they require further validation in real scenarios. This distribution reflects the current sectoral priorities, where precision agriculture leads technology adoption, while surveillance emerges as a critical area with outstanding methodological challenges.
## 3 Results and Discussion
### The Viability of ATDC5 Cells  
To evaluate the usefulness of BNC/\(\kappa\)-Car composites as a scaffold material for cartilage repair, cell viability was assessed with Presto Blue(r) test. The effect of the scaffold on the viability of ATDC5 cells is presented in Figure 8.  
As the biocompatibility of pure BNC produced by _K. xylinus_ E25 is well known [58] the significant differences in the viability of cells cultivated on the BNC control and nearly all BNC/\(\kappa\)-Car composites were promising. After 21 days of growth on various BNC/\(\kappa\)-Car composites ATDC5 cells exhibited the increased cell viability in comparison to the BNC control sample. The only exception was BNC/\(\kappa\)-Car\({}_{2.0}\), for which the ATDC5 cells viability was reduced to 88.5%. The growth of the cells on pure 2 and 4% \(\kappa\)-Car gels was also monitored (at the lower concentrations, \(\kappa\)-Car does not form stable gels). The observed high toxicity of 4% \(\kappa\)-Car was consistent with data obtained by [PERSON] et al. [59], who  
Figure 8: The viability of ATDC5 cells after 21 days of growing on the surface of BNC and BNC/\(\kappa\)-Car composites. (The data are presented as the means \(\pm\) SD from at least three independent experiments; the means that are marked with different letters are significantly different).  
reported the correlation between \(\kappa\)-Car concentration and cell viability, and showed that the growing polysaccharide concentration caused the decrease in cell viability, which depended also on the time of growth.