esa-sceva/satcom-chunk-collection huggingface dataset:
# P System with Fractional Reduction
## 4 Fractional Simplification P System
### P System for Fraction Simplification in the Division Algorithm  
#### 4.2.1 Definition of the P System for Rolling Division  
The definition of the P System based on the division algorithm is as follows (3):  
\[\varPi_{2}=\left(V,O,H,\mu,\omega_{M_{1}},\omega_{A_{1}},\ldots,\omega_{A_{t}}, R_{M_{1}},R_{A_{1}},\ldots,R_{A_{t}},i_{0}\right), \tag{3}\]  
Among them,  
(1) \(V=\{m,n,c,t,CreateSubMem,Num,a,b,g,x,y,d,e,h,z,o,i,l,u,k,f,w,v\}\);  
(2) The definitions of \(H\) and \(\mu\) are similar to those in Section 4.1.1, so this section does not provide detailed explanations;  
(3) \(\omega_{M_{1}}=m^{x}n^{y}ct\), \(\omega_{A_{1}}=cCreateSubMem\), and \(x\) and \(y\) represent the numerator and denominator of fractions, respectively;  
(4) The \(R_{M_{1}},R_{A_{1}},\ldots,R_{A_{t}}\) evolution rule finite set will be explained in Section 4.2.2;  
(5) \(i_{0}=M_{1}\) indicates that when the entire System stops, the final result can be obtained in the membrane \(M_{1}\).  
Figure 5 depicts the initial layout of the division algorithm P System: membrane \(M_{1}\) is mainly used to compare the magnitudes of the numerator \(x\) and the denominator \(y\). The larger and smaller values are passed to object \(a\) and object \(b\) in membrane \(A_{1}\), respectively, and after obtaining the maximum convention \(x\) from membrane \(A_{1}\) and the dynamically generated class A membrane, the numerator and denominator are divided by \(x\), respectively, saving the final result of the operation \(k/l\). Membrane \(A_{1}\) is used to perform the division algorithm operation and calculate the value of the maximum convention, \(x\). With the exception of membrane \(M_{1}\) and membrane \(A_{1}\), all other required membrane structures are created dynamically during the reduction process based on the actual situation of the partition, and the rules within the newly created membrane are identical to those within membrane \(A_{1}\). In Figure 5, objects \(m\) and \(n\) are used to represent the numerator and denominator, respectively; \(x\) and \(y\) are the numbers of objects \(m\) and \(n\). Object \(c\) is used to trigger the execution of the rules in the membrane, and \(CreateSubMem\) determines whether new membranes need to be generated according to different conditions. Membrane \(A_{1}\) and other newly created submembranes are used to calculate the parameters in the rules. These newly created membranes are nested in a layer: membrane \(A_{2}\) is created within membrane \(A_{1}\), membrane \(A_{3}\) is created within membrane \(A_{2}\), and finally, membrane \(A_{t}\) is created within membrane \(A_{t-1}\).
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Coordination Constraints of OCRs
#### 2.3.3. Calculating the Min-Max Confidence Interval of Fault Currents in the DN
For the ring-type/meshed DN, it is necessary to calculate the downstream and upstream fault-current division coefficients \(k_{i-downstream\_r}\) and \(k_{i-upstream\_r}\) of the \(i\)-th DG regarding to the \(r\)-th relay, as referred to Equation (35). The \(k_{i-downstream\_r}\) coefficient is selected in case the \(r\)-th relay is used to detect the fault at its upstream direction, whereas the \(k_{i-upstream\_r}\) coefficient is selected only if the \(r\)-th relay is designed to detect the fault at its downstream direction. In general, a \([K]\) coefficient matrix can be determined to show the fault-current contribution of \(N_{DG}\) DGs to \(M_{OCPR}\) relays (noted that the number of OCRs equals to the number of buses in the DN) as expressed in Equation (36). \[[K]=\left[ \begin{array}{ccc}k_{11}&k_{1i}&k_{1N_{DG}}\\ \cdots&\cdots&\cdots\\ k_{r1}&k_{ri}&k_{rN_{DG}}\\ \cdots&\cdots&\cdots\\ k_{M_{OCPR}1}&k_{M_{OCPR}i}&k_{M_{OCPR}N_{DG}}\end{array} \right]\text{ with }\left\{ \begin{array}{l}i=1\ldots N_{DG}\\ r=1\ldots M_{OCPR}=1\ldots N\\ \end{array} \right. \tag{36}\]  
where the \(k_{ri}\) element of \([K]\) matrix can be \(k_{ri}^{min}\), \(k_{ri}^{max}\), 1, \(k_{i-downstream\_r}\), or \(k_{i-upstream\_r}\) according to the type of DGs (e.g., IBDG or RBDG), the location of DGs, the location of OCRs, and the topology of DN (e.g., radial topology or ring topology). In addition, the \([K]\) matrix can also be applied to the unbalanced faults in the distribution system.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
###### Abstract  
Soil moisture (SM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often hindered by the vegetation layer and soil roughness. Most SM inversion algorithms require in situ SM data for a calibration to eliminate these two disturbing factors, while collecting in situ data is a project that consumes a lot of manpower and resources. This paper aims to tentatively develop an inversion algorithm for retrieving SM in the absence of in situ SM in areas covered by winter wheat vegetation. Based on the analysis of the data set simulated by the Michigan Microwave Canopy Scattering (MIMICS) model, an improved ratio model is proposed to remove the effect of the vegetation layer. Through the parameterization of the advanced integral equation model (AIEM), the effect of the soil roughness on the inversion of soil moisture is eliminated. The spatial distribution of SM in winter wheat fields is obtained using the Sentinel-1 SAR and Sentinel-2 images. The comparison results between the inverted SM and the in situ measured data reveal a good correlation (R = 0.85, RMSE = 0.032 cm\({}^{3}\)-cm\({}^{-3}\)), and the result confirms that the algorithm developed only based on theoretical models can also effectively monitor the spatial changes of SM over winter wheat fields.  
soil moisture; Sentinel-2 image; Sentinel-1 SAR; winter wheat +
Footnote †: journal: Applied Sciences  
17 October 2022  
Accepted: 23 November 2022  
Published: 25 November 2022  
**Published:** **Notic:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 1 Introduction
Over the past two decades, using learning management systems has attracted increasing interest from researchers around the globe. Previous research results brought us many different instruments and models for measuring IS success, its shortcomings and benefits in learning. However, it must be acknowledged that previous studies have yet to consider the impact of end-user agility on the widespread use and success of the system. COVID-19 did not ask; it disrupted the existing work environment, due to which the only survival pre-condition was fully transferring to IS and e-learning. Consequently, the authors of this paper sought to investigate how unpredictable changes and disruptions affect the learning environment and whether workforce agility affects the success of the IS in order to cover this gap. Namely, empirical research was conducted on a sample comprising teaching staff from two European universities: 381 respondents from the University of Novi Sad (UNS), Faculty of Technical Sciences, Serbia and 149 respondents from the Polytechnic Institute of Castelo Branco (PICB), the School of Technology, Portugal. IS and information and communication technology play a crucial role in developing agility related to speed and flexibility in responding to change [14]. Consequently, the results of this research provide a solid basis for enriching the previously tested and confirmed UTAUT model with factors that address workforce agility and its effect on overall IS success. In addition, the research results provide more extensive evidence and findings for scholars and practitioners that could support measuring IS success in various contextual settings, emphasizing the importance of people's responses to changes in the work environment. Eventually, the main contribution of this research is the proposed Extended Information Systems Success Measurement Model (EISSMM), created by combining factors from the UTAUT model and the Theory of Workforce Agility, offering a road map for future research. The rest of this paper is structured as follows. The upcoming section reviews the literature on the taxonomy of IS success and the role of workforce agility in IS success. Section 3 presents the research framework and hypothesis, followed by an explanation of the research instrument, data collection process, and sample demographics in Section 4. In Section 5, statistical data and results are presented, and Section 6 discusses results, followed by a conclusion with limitations and directions for future research.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 2 Related Work
(3) Priority-based resource allocation algorithm: While some studies have explored resource allocation algorithms that prioritize specific service packets [24; 25], no research has yet addressed the prioritization of new communication types, such as platoon communication, in 5G networks. Furthermore, existing priority-based algorithms often struggle to ensure adequate communication for general vehicles while prioritizing specific services. In particular, platoon vehicles frequently transmit packets at shorter intervals than other vehicles, which leads to higher resource consumption [9; 10]. This issue becomes more significant in congested vehicular networks, where general vehicles may experience considerable delays due to resource re-allocation failures and prolonged waiting times [26; 27]. Additionally, similar to challenges seen in remote driving applications, the communication delay in 5G networks remains an obstacle in ensuring real-time communication for platooning, especially under high vehicle density [28]. While 5G networks aim to address latency issues, certain Key Performance Indicators (KPIs), such as ultra-reliable low-latency communication (URLLC), have yet to be fully implemented in commercial networks. This gap in performance is expected to be addressed by 6G networks, which will offer even lower latency and enhanced reliability, thereby supporting applications like platooning more effectively.
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 2 Theory and System Design
### Theory of laser relay station networking
The input signal in any direction can be output in any direction after the address routing selection, and the specific configuration is selected according to the transceiver address, or it can be set during the human-computer interaction of the repeater; here, the address routing scheme in six directions is given. The address routing process is completed through a switch array--each switch has a different state, the input signal can choose a different path for output, and the specific implementation process is implemented in the FPGA. Each switch is composed of two two-choice data selectors; when the control terminal Select is high, the output end out_a corresponds to the input end in_a, and the output end out_b corresponds to the input end in_b; when the control terminal Select is low, the output end out_a corresponds to the input end in_b, and the output end out_b corresponds to the input end in_a, meaning that each switch has two working states. Each switch in the switch array with different states is combined with each other, and the signal output of any path can be realized. The output end in each direction corresponds to an address number. The signal input end in each direction will carry a piece of address information. The address router will configure the switch array according to the address information, so as to output the input signals in different directions along the specified direction. If the sent data do not carry address information, we can configure the input and output paths in human-computer interaction, and manually select to output the input signal in the specified direction. Figure 5: Internal structure diagram of the laser relay station. Figure 6: Principle of address routing. (**a**) Address routing structure. (**b**) Switch structure.
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 2 Algorithm for Automatic Detection of Unevenness in Ballast
### Detection of Sleepers  
We used the intensity image generated by the laser triangulation system as an input. The unevenness of the ballast is measured relative to the surface of each sleeper. On this basis, the algorithm should detect each sleeper as well as its mask. Therefore, it provides the classes of the image objects and the location of the image objects in the form of bounding boxes. Additionally, it should provide labels for every pixel belonging to the objects. Each pixel is labelled according to the object class within which it is enclosed. This process is called instance segmentation and simultaneously solves the problem of object detection as well as semantic segmentation (pixel labelling). According to [27; 28], instance segmentation algorithms can be divided into four groups:  
* Classification of mask proposals (modest segmentation accuracy, slow and difficult to optimize training, slow testing, not suitable for real-time applications);
* Detection followed by segmentation (relatively simple to train, better generalization, relatively faster, good segmentation accuracy);
* Labelling pixels followed by clustering (relatively simpler techniques, lesser segmentation accuracy, intense computation necessitating high computational power, not suited for real-time applications);
* Dense sliding window methods (modest segmentation accuracy, use complex algorithms, difficult to train and optimize, not suitable for real-time applications).  
Due to its advantages, detection followed by segmentation was selected. Two networks--Mask R-CNN and YOLACT--belonging to this group were tested in the article. Mask R-CNN and YOLACT are a two-stage and a one-stage instance segmentation algorithm, respectively. Mask R-CNN as a two-stage algorithm is more accurate and slower than YOLACT.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Multi-Hop Network Performance  
Two scenarios (random node placement and REM-guided placement) were tested to evaluate the impact of REM-based optimization. The objective was to assess packet delivery reliability and data loss:  
* Without REM: In the first setup, nodes were positioned randomly, resulting in a packet loss of 39.6%. Out of 500 data packets sent, only 302 were successfully received while 198 were lost. This high packet loss was attributed to sub-optimal node placement, where certain relay nodes were located in low-signal areas causing data transmission interruptions.
* With REM: Using a REM, signal strengths were analyzed throughout the environment to identify optimal positions for each node based on calculated coverage gaps and interference zones. Node placement was adjusted based on RSSI values, ensuring each node was within an effective signal range. Table 1 summarizes the packet  
Figure 10: Node placement in the multi-hop network, detailing the following (**a**) source node, (**b**) relay node 1, (**c**) relay node 2, and (**d**) gateway nodes’ interactions with REM.  
loss results for both REM and non-REM setups. The data demonstrate the effectiveness of REM-guided node placement in reducing packet loss from 39.6% to 0%, ensuring reliable communication across the multi-hop network. The table provides a quantitative comparison that underscores the critical role of the REM in optimizing network performance.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 5 Results
### Experimental Result
#### 5.2.2 Comparison of Communication Volume
The vertical axis of the figure represents the percentage of the communication volume and profit of the hybrid strategy to the basic CBBA. It can be seen that when \(\alpha=2\), the average scheduling profit reaches over 94.8% of the basic CBBA algorithm, while the average communication decreases to 36.1%. When \(\alpha=3\), the scheduling gain reaches more than 97.8% of the basic algorithm on average, while the communication drops to 46.2% on average. It can be seen that in similar scenarios in this experiment, when \(\alpha=3\), c-CBBA can significantly reduce communication volume while ensuring scheduling effectiveness. 3. SCS and TPS strategies can effectively reduce the sensitivity of CBBA to mission scale and constellation scale. It is evident from Figure 10a that CNP and basic CBBA communication volume increases rapidly as the mission or satellite scale rises. When the scenario scale is small, the difference in communication volume between different algorithms is small. However, as the scenario scale increases, the increase in communication volume of c-CBBA is significantly weaker than that of the basic CBBA. This suggests that c-CBBA has better adaptability to large-scale scenarios. Figure 10: Comparison of communication volume. (**a**) Comparison of communication volume among five mechanisms. The vertical axis represents the total number of times the satellite sent information to its neighbors. (**b**) Comparison of communication volume (Comm) and scheduling effectiveness (P) of hybrid strategy. The vertical axis represents the percentage of the communication volume and profit of the hybrid strategy to the basic CBBA.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 1 Introduction  
WSNs vary from typical wireless networks in several ways, including restricted memory, limited processing capabilities, a limited energy supply, and an explicit nature [1, 2]. The objective zone describes the region or area where the network hopes to provide a specified amount of coverage. This goal zone may encompass the full deployment area or just a portion of it. Identification of regions inside the objective zone where actual coverage is less than expected is the main purpose of coverage hole identification. This zone denotes the region where the network should have adequate coverage to suit the needs of the application. When compared to wireless networks, radio transmission of sensor nodes, for example, has low power and short range [3]. A coverage hole can be formed for a variety of reasons. Aside from subjective node categorization, improper network topology [4] node replacement, the ROI's potential for blockage, aggressive condition, and energy waste all contribute to coverage hole As a result, identifying such coverage holes is critical [5], since the presence of such holes can devastate a WSN's performance in termsof poor communication and unnecessary energy [6]. Data fusion is principally supplied, and scaling measures for coverage, network compactness, and SNR are established to detect coverage through sensor collaboration [7; 8]. In any case, a few explanations indicate a drop in system coverage, such as random distribution, location changes, and node energy depletion. As a result, data restriction or information loss will happen [9]. The three categories of current hole recognition algorithms include computation techniques, probabilistic approaches, and topographical strategies [10]. Figure 1 depicts the review's hierarchical chart.  
The rest of this article is divided into the following sections. The existing coverage hole methods are covered in Section 2. Section 3 provides the details of the experimental setup, Section 4 provides a discussion and comparison of the performance of various approaches, and Section 5 concludes with a summary.
# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results
### Eight-Node Measurements in a Public Cloud  
The detailed read and write latency measurements are provided in Table 2. We computed those measures for an Indy network that consists of eight nodes. We also checked if the size of the dataset stored on-chain affected the latency, and came to the same conclusion--that it does not. We can also see that increasing the arrival rate increases the average latency for both types of requests. Depending on the arrival rate, it is observable in the table that the average write latency fluctuates from 2.6 to 10.4 s per TX. The same dependency can be seen for the average read latency, which fluctuates from 0.12 to 2.49 s per TX.
## 1 Introduction
Another novel aspect is the feedback of visually measured states, that is, joint positions, of the robot to the controller. Vision-based sensing is used in other publications as well [25; 26; 27], but with a different indication, that is identifying a target position for the robot's end-effector. The actual robot movement to reach this position is then controlled based on encoder feedback, whereas in this contribution, the motion is controlled by visual feedback in real-time. In general, this contribution's work can be seen as proof-of-concept and preparation for further studies on robots with flexible links. The position of flexible bodies cannot be described solely by encoders in the joints because of the links' elastic deformation, whereas, for vision-based sensing, elasticity in the links can be managed. In this work, also the challenges coming along with vision-based sensing are pointed out. Moreover, the used semi-automatic trajectory planning algorithm is presented, which has been developed incidentally to avoid obstacles. The paper is organized along with the proposed step-by-step procedure for the vision- and MPC-based control system design. Section 2 presents the available hardware, that is, the modular manipulator and camera system, as well as the lean process control system. In Section 3, the modeling is presented, including parameter identification and the derivation of the forward and inverse dynamics. Furthermore, the semi-automatic trajectory planning workflow is shown. Section 4 is dedicated to control design. A description of the image processing to measure the system states visually and runtime analysis of the vision-based control loop is found in Section 5. Section 6 presents the results of two meaningful experimental studies to point out the advantages and restrictions of the proposed LTV MPC scheme for the rigid MBS and also show the effects of vision-based control feedback on the control performance. The contribution ends with the conclusion and outlook in the final section.