esa-sceva/satcom-chunk-collection huggingface dataset:
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied
### Multilayer Perceptron  
Multilayer perceptron neural networks (MLP) are widely used for satellite images classification [18] and for burned area mapping applications [19]. These networks have an input layer, one or more hidden layers and an output layer [20]. They are fitted using the back-propagation algorithm, which consists of minimizing the output mean squared error of the network using a gradient descent algorithm [19; 21]. In general, MLPs have poor performance when the optimization algorithm falls into local minimum and due to the overfitting phenomenon [18]. However, it is possible to improve the optimization algorithm performance through heuristic search techniques [21].
# Generalized Paxos Made Byzantine
## 2 Background and Overview
### Byzantine Fault Tolerant Replication  
The Byzantine Generals Problem is defined as a set of Byzantine generals that are capped in the outskirts of an enemy city and have to coordinate an attack. Each general can either decide to attack or retreat and there may be \(f\) traitors among the generals that try to prevent the loyal generals from agreeing on the same action. The problem is solved if every loyal general agrees on what action to take [20]. Like the traitorous generals, a process that suffers a Byzantine fault may display an arbitrary behaviour and, in case of multiple Byzantine faults, an adversary may even coordinate multiple faulty replicas in an attack.  
Practical Byzantine Fault Tolerance (PBFT)  
PBFT is a protocol that solves consensus while tolerating up to \(f\) Byzantine faults [9]. The system moves through configurations called _views_ in which one replica is the primary and the remaining replicas are the backups. The safety property of the algorithm requires that operations be totally ordered. The protocol starts when a client sends a request for an operation to the primary, which in turn assigns a sequence number to the request and multicasts a _pre-prepare_ message to the backups. This message contains the timestamp, the digest of the client's message, the view and the actual request. If a backup replica accepts the pre-prepare message, after verifying that the view number and timestamp are correct, it multicasts a _prepare_ message and adds both messages to its log. The prepare message is similar to the pre-prepare message except that it does not contain the client's request message. Both of these phases ensure that the requested operation is totally ordered at every correct replica (note that the two phases described informally are not necessary for safety as demonstrated by [21]). The protocol's safety property requires that the replicated service must satisfy linearizability and, therefore, operations must be totally ordered. After receiving \(2f\) prepare messages, a replicamulticasts a _commit_ message and commits the message to its log when it has received \(2f\) commit messages from other replicas. The liveness property requires that clients must eventually receive replies to their requests, provided that there are at most \(\lfloor\frac{N-1}{3}\rfloor\) faults and the transmission time does not increase continuously. This property represents a weak liveness condition but one that is enough to circumvent the FLP impossibility result [17]. A Byzantine leader may try to prevent progress by omitting pre-prepare messages when it receives operation requests from clients, but backups can trigger new views after waiting for a certain period of time. The description of the Byzantine Generalized Paxos protocol presented in this paper largely follows from the PBFT protocol.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Performance Analysis of Direction-Finding via the Fusion Algorithm
#### 4.2.2 Analysis of the Horizontal Angle Measurement Precision of the Sound Source  
The measurement precisions of the horizontal angle for the X0Y, X0Z, and Y0Z planes and the three-plane array were simulated, compared, and analyzed. The result is shown in Figure 3 for an array element spacing \(a\) of 1 m, a sound velocity \(c\) of 340 m/s, a time-delay estimation error value \(\sigma_{\tau}\) of 1 \(\upmu\)s, a horizontal angle of 45\({}^{\circ}\), and an arbitrary planar elevation angle ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\).  
Figure 3 shows that when the elevation angle \(\theta\) is between 0-80\({}^{\circ}\), the horizontal angle estimation error \(\sigma_{\varphi}\) changes little. However, when the elevation angle \(\theta\) is at an extreme angle of 80\({}^{\circ}\) to 90\({}^{\circ}\), the error measured in the Y0Z plane obviously increases with elevation angle \(\theta\). The error measured in the X0Z plane also rises sharply with increasing elevation angle \(\theta\), reaching approximately 0.1\({}^{\circ}\), which is second only to that of the Y0Z plane. The errors for the X0Y plane and the three-plane array are  
Figure 3: Comparison and analysis of the horizontal angle estimation error of the sound source caused by an elevation angle change.  
Figure 2: Comparison and analysis of the elevation angle estimation error of the sound source.  
smaller at less than 0.05\({}^{\circ}\) and are almost unaffected by changes in the elevation angle \(\theta\). In summary, when the elevation angle \(\theta\) is large, the three-plane fusion algorithm is slightly inferior to the X0Y plane approach in horizontal angle measurement precision; when the elevation angle \(\theta\) is extreme, the measurement precision of the fusion algorithm is better than both the X0Z and Y0Z plane results.  
Results are shown in Figure 4 for an array element spacing \(a\) of 1 m, a sound velocity \(c\) of 340 m/s, a time-delay estimation error value \(\sigma_{\tau}\) of 1 us, an elevation angle \(\theta\) of 75\({}^{\circ}\), and an arbitrary planar horizontal angle ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\).  
Figure 4 shows that the horizontal angle estimation error \(\sigma_{\varphi}\) measured in the X0Y plane does not influence itself. The error measured in the X0Z plane decreases with its own increase, and the error in the Y0Z plane increases with its own increase. The error measured for the three-plane array shows little change with a change in the horizontal angle \(\varphi\), and the error \(\sigma_{\varphi}\) is less than 0.001\({}^{\circ}\). In summary, the three-plane fusion algorithm has distinct advantages in horizontal angle measurement precision over a single-plane array, and the stability is significantly better than that of the X0Z and Y0Z planes.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 3 Results  
### Specificity of the Two mPCR Assays and the MPLT Assay  
First, nine sets of specific primers and DNA templates extracted from thirteen respiratory pathogens were used and the specificity of nine primers was confirmed using nine simplex PCR reactions. Then, two mPCR reactions were generated. The resultant amplicon sizes from the two mPCR reactions for the pathogens were similar to their corresponding target sequences in Table 2. The results (Figure 1) showed that these two mPCR reactions were feasible and could achieve effective amplification for nine target respiratory pathogens with no cross-reactions being observed. The MPLT assay for each target pathogen (Figure 2A,B) showed a single and specific fluorescence signal. Cross hybridization and non-specific hybridization between sequences was not observed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Organism** & **Gene Target** & **Oligonucleotide/Oligo Sequence** & **Reference** \\ \hline \multirow{3}{*}{_S. pneumoniae_} & \multirow{3}{*}{_lytA_} & Forward: ACCGCAATCTAGCAGATGAAGCA & \multirow{3}{*}{[33]} \\  & & Reverse: TCGTGGCTTTTTAATTCCAGCT & & \\  & & Probe: FAM-GCCGAAAACGCTTGTATACAGGAG-BHQ1 & & \\ \hline \multirow{3}{*}{_M. catarrhalis_} & \multirow{3}{*}{_copB_} & Forward: CGTCCGTGTGTACCGTTTTGACTTTA & \multirow{3}{*}{[34]} \\  & & Reverse: CACGCTGCCAAAAAAAACTGCCCAAAG & & \\  & & Probe: FAM-CAGCGTAACCTAATCTATGCCACTC-BHQ1 & & \\ \hline \multirow{3}{*}{_S. aureus_} & \multirow{3}{*}{_spa_} & Forward: TACATGTCGTTAAACCTGGTG & \multirow{3}{*}{[35,36]} \\  & & Reverse: TACAGTTGATACGATGAATGG & & \\  & & Probe: FAM-CGCGATCCAAGAACTTGTTGTGATAAGAAG & & \\  & & CAACCGATCCG-BHQ1 & & \\ \hline \multirow{3}{*}{_S. pyogenes_} & \multirow{3}{*}{_165 rRNA_} & Forward: GAGAGACATAACCACCATTATG & \multirow{3}{*}{[37]} \\  & & Reverse: TACGTGTACCGTACTTGGGTGT & & \\  & & Probe: FAM-CGCGATGCGCGCAGGATTACATAGCCGACCT & & \\  & & GGTCCG-BHQ1 & & \\ \hline \multirow{3}{*}{\(H. influenzae\)} & \multirow{3}{*}{_165 rRNA_} & Forward: TTGACATCCTAAGAAGACCTCAGAG & \multirow{3}{*}{[34]} \\  & & Reverse: CTTCCCCTGTATACGCCTTGTACC & & \\  & & Probe: FAM-ATGGTGTGTGTCAGCTCTGT-BHQ1 & & \\ \hline \multirow{3}{*}{_M. pneumoniae_} & \multirow{3}{*}{_P1_} & Forward: CCAACCAAAAACAACCTTCA & \multirow{3}{*}{[38]} \\  & & Reverse: ACCTTGACTGGACGCGTTA & & \\  & & Probe: FAM-TCAATCCGAAATAACGGCTGACTTCT & & \\  & & TACCACTG-BHQ1 & & \\ \hline \multirow{3}{*}{_Legionella_ spp.} & \multirow{3}{*}{_165 rRNA_} & Forward: AGGCTAATCTTAAAGCCCCAGCCC & \multirow{3}{*}{[34]} \\  & & Reverse: GCATGCTTAAACACATGCAAGTCGAAAC & & \\  & & Probe: FAM-CATATTCCTACGCGTTACACCCG-BHQ1 & & \\ \hline \multirow{3}{*}{_P. aeruginosa_} & \multirow{3}{*}{_165 rRNA_} & Forward: GACGGGTGAGAATAGCCTAG & \multirow{3}{*}{[34]} \\  & & Reverse: CCACTGTGTTCCTTTCCTATT & & \\ \cline{1-1}  & & Probe: FAM-AGTGGGGAGATCTCCGACCTA-BHQ1 & & \\ \hline \multirow{3}{*}{_K. pneumoniae_} & \multirow{3}{*}{_gapA_} & Forward: TGAAGATTGACCTCACCTG & \multirow{3}{*}{[34]} \\ \cline{1-1}  & & Reverse: CTTCAGAAGCCGCTTTTG
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
## 3 Results
### Reduction Effacies of PLE
#### 3.1.2 Effects of pH  
The effects of varying pH on Cr(VI) reduction efficiency is shown in Figure 2. As indicated, pH plays a crucial role on Cr(VI) using PLE. At pH 2, 97% was Cr(VI) was reduced to Cr(III) in 60 min, which decreased to 77% at pH 4. From pH 6 to 10, Cr(VI) reduction efficacies varied from 69 to 66%, which is an important finding and PLE can also be utilized to reduce Cr(VI) in an alkaline medium. The reduction of Cr(VI) under varying solution pH has been studied previously and most of the reductants have either too low or no ability to reduce Cr(VI) in alkaline media [27; 28]. In contrast, PLE could be used as a green reductant to successfully remediate Cr(VI) across a wider range of pH; acidic solution has the maximum reduction efficiency when compared to neutral and alkaline solutions.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture  
To deal with diverse IoT requirements, such as latency, energy efficiency and mobility, the fog computing paradigm has been introduced. Centralized cloud solutions are not suitable for future IoT applications, with real-time constraints and enormous volumes of data to be transported in the network. This way, fog computing architectures can provide effective ways to overcome many limitations of the existing network architectures that rely only on computing resources in the cloud and on end-user devices.
## 4 Verification of Evaluation Methods
### Verification of Simplified Method  
The beam shear forces (\(P_{b,u}\)) evaluated by the iterative and simplified methods are compared in Figure 15. Without the correction of concrete axial shortening \(\Delta^{*}_{c,ex}\) introduced in Section 3.4, the errors of the simplified method relative to the iterative one are found to be within 5%. As the beam deflection angle (\(R_{b,u}\)) is calculated directly based on the value of \(\Delta_{c,ex}\), the values of \(\Delta_{c,ex}\) evaluated by the simplified method with and without the correction are compared in Figure 16a. The corresponding values of \(R_{b,u}\) are compared in Figure 16b. It is clear that the improvement in evaluation accuracy is significant, and the maximum error for \(R_{b,u}\) is reduced from 30% to less than 5%. Correspondingly, change in the errors of \(\Delta_{c,ex}\) before and after the correction has a similar tendency. Therefore, the correction is able to ensure the evaluation accuracy of the simplified method is similar to the iterative method.
## 5 Proposed Hardware Architecture  
The proposed accelerator architecture is motivated by the need to improve the performance of ECC curve (BEC) operations in resource-constrained devices. ECC is a popular choice for cryptography, but it can be computationally expensive. The proposed accelerator architecture can improve the performance of ECC operations, making them more suitable for use in resource-constrained devices. The proposed accelerator architecture is designed to improve the performance of BEC operations in resource-constrained devices. The architecture is based on a number of design principles, including:  
1. Data parallelism: The architecture exploits data parallelism by performing multiple BEC operations at the same time. This can significantly improve the performance of BEC operations.
2. Pipelining: The architecture uses pipelining to overlap the execution of different BEC operations. This can further improve the performance of BEC operations.
3. Modular multiplication: The architecture uses modular multiplication to reduce the number of arithmetic operations required to perform BEC operations. This can improve the performance and energy efficiency of BEC operations.
## 4 Discussion of Prediction Results
### Simulation Results  
Time history analyses were performed for all buildings. First, the mass, stiffness, and strength parameters of MDOF models were determined according the procedure presented in Section 3.1. Subsequently, time history analyses were performed for all buildings and the damage states of were determined according to the limit state as shown in Figure 3. The results of the time history analysis using MDOF shear model and the MI-CNN model for the M6.5 earthquake scenario are shown in Figure 18. The results of the time history analysis were adopted as the benchmark results to demonstrate the accuracy of the proposed MI-CNN model. The results show that the MI-CNN prediction agrees well with the time history analysis results. Most of the buildings exhibit slight to moderate damages, and buildings near the epicenter exhibit more severe damages.  
Figure 16: Earthquake data: (**a**) ground motion attenuation functions (M6.5); (**b**) acceleration/velocity time history.  
Figure 17: Distribution of ground motion intensity measure \(Sa(0.3\mathrm{s})\) (\(\mathrm{m/s^{2}}\)): (**a**) M6.5; (**b**) M7.5.  
The prediction results for the M7.5 earthquake scenario are shown in Figure 19. The results also show a good agreement between the MI-CNN results and the time history analysis results.  
According to the results in Figures 18 and 19, the prediction error of the MI-CNN model compared to the time history benchmark model are presented in Figure 20. For the M6.5 earthquake scenario, only 151 out of 1760 buildings experienced a one-level error, while the rest of the 1609 buildings are in the same damage states as in the benchmark results; for the M7.5 earthquake scenario, 15 buildings experienced a two-level error and 420 buildings experienced a one-level error, while the rest of the 1325 buildings have the same damage states as in the time history results. The above simulation results show that the proposed MI-CNN prediction results have a good prediction accuracy.  
Figure 19: Distribution of damage states of the M7.5 earthquake scenario: (**a**) time history analysis results; (**b**) MI-CNN results.  
Figure 18: Distribution of damage states of the M6.5 earthquake scenario: (**a**) time history analysis results; (**b**) MI-CNN results.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 1 Introduction  
The idea of modularity is widely discussed and analyzed in diverse fields, e.g., in computer science, in neurophysiology, in evolutionary biology, in smart manufacturing systems, etc. It is due to the fact that modularity creates system information on different levels to support Industry 4.0 or smart manufacturing in a sustainable way [1]. In relation to manufacturing systems, modularity is considered an effective approach, e.g., to minimize production costs [2], to increase flexibility of smart manufacturing systems [3], and to improve readiness for mass customization [4]. Therefore, modularity is seen as a tool that improves the productivity and efficiency of manufacturing processes. Especially in recent years, with the increasing importance of mass customization, modularity has become a popular topic in product and process development since implementation of its principles [5] helps companies in organizing complex products and processes. In addition, process modularity allows the shortening of cycle times, e.g., by organizing production as a modular consortium, the concept that was established in automotive industry since 1996 [6; 7; 8]. In this context, this study aims to explore the relation between process modularity and manufacturing lead time, and between process modularity and process complexity. The main goal of this research is focused on the following two research questions (RQs):  
**RQ 1:**_To what extent does modular process affect manufacturing lead time?_  
**RQ 2:**_How does modular process affect the process complexity?_  
This article is organized in the following manner: Firstly, the related works are briefly described in the second section. Thereafter, the methodological framework describes the steps of the proposed approach. Subsequently, the selection of relevant indicators is provided together with demonstration of their application in a simple process example. Then, theoretical representative process models and a realistic case study are introduced and used to explore the relationship between optimal modularity (OM) and MLT, and betweenOM and operational complexity of manufacturing processes. Finally, the obtained results are summarized and answers to the above-mentioned research questions are provided.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 4 Results, Analysis, and Discussion  
### Results and Analysis  
When applying the TFPF-SU method and the hydrological analysis method for slope unit subdivision, both methods were based on 8.5 m of DEM data to ensure the reliability of the comparison. We compared the two results from both qualitative and quantitative perspectives, analyzing the practicality and rationality of the TFPF-SU method. For a more intuitive comparison of the division results between the two methods during the qualitative analysis, we selected two regions from Figure 5: Case 1 is a relatively flat area, and Case 2 is a mountainous area with significant terrain fluctuations.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
###### Abstract  
The traditional satellite access selection algorithm, which is used in large-scale satellite networks, has some disadvantages, such as frequent link switching, high interrupt probability, and unable to adapt to a dynamic environment. According to the periodicity of the large-scale satellite network and the prior knowledge provided by acknowledgement packages, a dynamic domain-based adaptive access algorithm (DAA) is proposed in this paper. Firstly, this algorithm divides the large-scale satellite network into different domains according to the minimum elevation angle of the Earth station (ES) and the predictable characteristics of the trajectory of the satellite. Then, the ES selects the access satellites according to the relationship between the traffic volume and the satellites' coverage time. Finally, the ES selects the backup access satellite based on the satellites' coverage time, the traffic volume of the ES, satellite status provided by prior knowledge, and other information. When the access satellite cannot satisfy the communication demand, the ES adaptively switches the earth-satellite link to the backup access satellite. The ES first choice of access satellite does not require interaction with the satellites, reducing the consumption of communication resources. The selection strategy of backup access satellite and the concept of virtual destination address proposed in this paper can reduce the routing overhead after switching. Through theoretical analysis and simulation results in the StarLink constellation, it is proved that this paper improves the coverage time utilization of accessing satellites and reduces the switching probability compared with the traditional access algorithm, which is more suitable for ES to access large-scale satellite networks.  
large-scale satellite network; access selection algorithm; satellite access technology; resource allocation; StarLink +
Footnote †: journal: Sensors  
doi:10.3390/s22165995
# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 5 Experiment
### Experimental Results and Discussion
Following the analysis of the equipment learning process, it can be concluded that the knowledge reuse process is not a straightforward application, but a matching search for similar processing tasks to extract the knowledge that meets the similarity requirements. Analyzing the results of the experiment, we can find that: (1) During the execution of this experiment, the collection, processing and knowledge extraction of manufacturing resource information are all completed by the intelligent gateways and servers deployed at the edge. The knowledge used in the knowledge reuse stage partly uses the knowledge extracted on the spot, and the system supplements the knowledge through the cloud for the parts that are not included in the learning samples input from the edge. The entire platform can directly carry out multi-variety mixed-flow processing after learning. The successful operation of the platform fully demonstrates the rationality of the process and the effectiveness of the cloud-edge combined knowledge reuse system used in this article. (2) It can be seen from the experimental results in Figure 6 that under the same task scenario, the knowledge reuse method we proposed has high accuracy, and the quantitative index of action reduction effect reaches 95.8%. The high reduction of the action proves that the knowledge matching algorithm we proposed is reliable. (3) In the second set of experiments, the selected equipments' functions are different, but because we have adopted the task decomposition strategy in the process of knowledge extraction and reuse, more fine-grained knowledge decomposition makes it possible to learn between different tasks. The learning of action control also confirms the superiority of our proposed task decomposition strategy.