esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 1 Introduction
These power references are conveyed to the inverter control structure through the proposed LoRaWAN communication structure. The effects of different LoRa spreading factors (SFs) and transmission power levels are analyzed in detail. * A communication structure has been proposed for the control and management of remote residential microgrids in remote areas. This system combines several communication protocols for best performance. To cut a long story short, in this study the authors investigated the feasibility and benefits of utilizing LPWAN communication systems in MG management. The problem has been analyzed from both control systems engineering and communication science viewpoints, therefore, various aspects from different technology branches were investigated and considered. In the author's humble opinion, based on the aforementioned points, this paper can empower the existing literature and assist other researchers in the field. The remainder of the paper is organized as follows. Section 2 gives a brief overview of some selected LPWAN technologies. A more detailed summary of LoRa and LoRaWAN characteristics and specifications is provided in Section 3. The fundamentals of communication dependency of MG systems are explained in Section 4. The arguments of this section aim to rationalize and justify the implementation of LoRaWAN in an MG control structure. Section 5 presents the implementation of LoRaWAN for smart inverters and the description is accompanied by experimental results. A LoRaWAN/MQTT hybrid communication structure for a specific case study of residential MGs in remote areas is proposed and explained in Section 6. Final remarks and conclusions are presented in Section 7.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 4 Conclusions  
In this paper, a satellite network to provide Internet services with the QoS support was addressed. In particular, we proposed a practical overall design of the MF-TDMA satellite network with the QoS support. To derive the major factor to be considered in the QoS policy, we defined the performance metrics for the user experience. In the various environments, by varying each factor such as the service type, the traffic load, the burstiness of the traffic, the number of users, and the resource allocation method in the link layer, the network performance for the user experience in the application layer was evaluated in the satellite network with the proposed overall design. Through the performance evaluation, it was shown that the factors such as the traffic load and the burstiness should be commonly considered in the QoS policy for delay-tolerant and delay-sensitive services in the satellite network. Results also showed that the estimation of the required resource to provide the QoS should be considered for the service using RBDC. It was shown that the small data size can reduce the QoS performance in the satellite network.  
In satellite communication with its inherit characteristics such as the long propagation delay, it has not been easy to guarantee the QoS due to the resource allocation time, the large super-frame size, and the imperfect estimation of the required resource. Therefore, it is pointed out that the derived factors of this paper should be addressed in the QoS policy and the technique such as the admission control and estimation of required resource to enhance the QoS performance in the satellite network.  
[PERSON] proposed the main idea, performed and evaluated the simulation experiments, and wrote the paper. [PERSON] designed the validation methods and contributed to the discussion of this research.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 VIIRS Satellite Data  
### What Is VIIRS?  
VIIRS is a cross-track scanning radiometer with 22 spectral bands covering the visible/infrared spectrum from 0.412 to 12.05 \(\upmu\)m. The design and concept of VIIRS operations combine aspects from several legacy instruments, including the NOAA's Advanced Very High Resolution Radiometer (AVHRR), NASA's MODIS, Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and the Department of Defense's Operational Linescan System (OLS) sensors [19]. It has a wider swath (~3000 km) than MODIS, which allows a global sample of the Earth everywhere every day. It flies in a Sun-synchronous near-circular ascending polar orbit 829 km above the Earth with the local equator-crossing time at 13:30.  
VIIRS has three types of bands: imagery bands (I-bands), moderate resolution bands (M-bands), and the day-night band [6]. The M-bands (total 16 bands) have 0.742 km \(\times\) 0.776 km nadir resolution and 1.60 km \(\times\) 1.58 km at the edge of scan. Other bands are used to create the VIIRS Cloud Mask (VCM), which is used as input to aerosol algorithms, as well as in internal tests to characterize environmental conditions. Most of M-bands are used to derive the aerosol parameters. Specifically, M1 (0.412 \(\upmu\)m), M2 (0.445 \(\upmu\)m), M3 (0.488 \(\upmu\)m), M5 (0.672 \(\upmu\)m), and M11 (2.25 \(\upmu\)m) bands are used over land; and M5, M6 (0.746 \(\upmu\)m), M7 (0.865 \(\upmu\)m), M8 (1.24 \(\upmu\)m), M10 (1.61 \(\upmu\)m), and M11 are used over ocean. A detailed description of the VIIRS bands is shown in [19].
## 6 Experiments on Benchmark Functions
### Ablation Experiment  
From Section 4, it can be seen that MIARO introduces the three strategies. Therefore, to appraise how various strategies affect algorithm performance, MIARO is compared with the ARO algorithms based on a single strategy. LARO, GARO, and AARO denote the ARO algorithms, combining Levy flight, the gold sine mechanism, and Archimedean spiral mutation, respectively. In this part of the experiments, a population size of 30 is established, with a maximum of 1000 iterations. Table 4 furnishes the average(avg) and standard deviation(std) of various ARO algorithms, executed independently across 30 trials on the 23 classical functions, which are provided, along with the average rankings in the last row. Based on the ranking outcomes, it is evident that MIARO has the highest average ranking followed by LARO, AARO, GARO, and ARO, which indicates that MIARO outperforms the other algorithms, and the algorithms that add a single strategy, LARO, GARO, and AARO, outperform ARO. In addition, GARO has a higher accuracy for most of the unimodal functions, which is attributed to the golden sines mechanism that augments the algorithm's ability to perform local searches and improve its convergence outcomes. LARO performs well on many multimodal functions thanks to the Levy flight that supports global exploration and raises the chance of escaping local optima. AARO shows stable performance in most functions due to the Archimedean spiral mutations that increase population diversity. \begin{table}
\begin{tabular}{l l l l l} \hline
**ID** & **Function** & **Dim** & **Range** & **Optimum** \\ \hline F1 & \(f_{1}(x)=\sum_{i=1}^{n}x_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\ F2 & \(f_{2}(x)=\sum_{i=1}^{n}[x_{i}]+\prod_{i=1}^{n}[x_{i}]\) & 30  
Furthermore, to determine if the results obtained from MIARO are significantly superior to those from the other algorithms, a hypothesis test is conducted with the Wilcoxon rank-sum method at a critical level of \(\alpha\), where \"+\", \"-\" and \"=\" signify that MIARO is higher-performing, lower-performing, and equivalent to the comparison algorithms. The statistical results in Table 5 suggest that most of the \(p\)-values obtained when comparing MIARO with other algorithms are far below 0.05, while the statistics in the last rows are 18/0/5, 19/0/4, 17/0/6, and 13/1/9, respectively.
# Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study
## 2 Related Work
The field of automatic code-smell detection continues to evolve with an ever-changing list of code smells and languages to cover. It is common to identify code smells in monolithic systems using code-analysis. For instance, tools such as SpotBugs [37], FindBugs [38], CheckStyle [39], or PMD [40] can detect code patterns that resemble a code smell. [PERSON] et al. [41] recently analyzed 24 code smells detection tools. While the tools correctly mapped the code smells in an application, they are limited to a single codebase, and so they become antiquated as modern software development tends towards microservice architectures. While extensive research has been done to define and detect code smells in a monolithic application, little has been done for distributed systems [42]. It would be possible for a developer to run code-smell detection on each of the individual modules, but this does not address any code smells specific to microservice architecture. In a distributed environment, in particular microservices, there have been multiple code smells identified. In one study [43], these smells include improper module interaction, modules with too many responsibilities, or a misunderstanding of the microservice architecture. Code smells can be specific to a certain application perspective, including the _communication perspective_, or in the _development and design process_ of the application. These smells can be detected manually, which usually requires assessing the application and a basic understanding of the system, but this demands considerable effort from the developers. With code analysis instruments, smells can be discovered almost instantly and automatically with no previous knowledge of the system required. However, we are aware that no tool can detect the code anomalies that can exist between discrete modules of a microservice application.
## 4 Results and Discussion
### Adaptability Test  
In the adaptability test, the cutting frequency of the cutter unit was fixed at 16.7 Hz and the rotating speed of the rotary base was fixed at 13.5 r/min. Each plant was trimmed by two rotations of the knife system. The results of the test are shown in Table 5.  
It can be seen from Table 5 that all five plants were successfully trimmed by the designed form trimming machine, and the profile of the knife system and the profile of the trimmed plants matched well. As the calculations show, the overall similarity was above 93%. This confirms that the designed form trimming machine could adapt to different shrub plants.  
However, this similarity level was lower than the anticipated performance. As a preliminary analysis, the reason would be the differences in the branch densities and strengths. The plants _Aglaia odorata_, _Murraya paniculate_, and _Camellia oleifera_, which resulted in the smallest similarities in the test, had thinner, softer branches and the trimming process was not as smooth as what was recorded in trimming the other plants. A different rotating speed of the rotary base would be more appropriate for those plants. The correlation between trimming performance and the branch density and strength can be taken as future studies to further improve the performance of the trimming machine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotating Speed (t/min)** & **Trimming Circle** & **Time Consumed (s)** & **Similarity (\%)** \\ \hline
7.5 & 2 & 16 & 96.38 \\
10.5 & 2 & 11.43 & 96.18 \\
13.5 & 2 & 8.89 & 96.45 \\
16.5 & 3 & 10.91 & 96.01 \\
19.5 & 4 & 12.31 & 97.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Trimming performance under different rotating speeds of the rotary base.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plant** & _Aglaia odorata_ & _Murraya exotica_ & _Camellia oleifera_ & _Osmanthus frugrans_ & _Radernachera sinica_ \\ \hline Form before & & & & & \\ trimming & & & & & \\ Form after trimming & & & & & \\ Similarity (\%) & 94.87 & 93.79 & 94.32 & 94.95 & 95.49 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the adaptability test.
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
## 4 Performance Analysis and Comparisons
### Complexity Analyses  
Table 5 compares the computational complexity of BP, MS, NMS, SCMS, and DT-SCMS algorithms in a single iteration where \(N\) represents the encoding length, \(K\) represents the effective information length, and \(W\) is the column weight of the check matrix.  
It can be seen that the improved algorithm based on the MS algorithm, on the basis of the BP algorithm, reduces a large number of multiplication and division operations, which will greatly reduce the calculation complexity in the implementation. The DT-SCMS algorithm proposed in this paper adds a small amount of multiplication and division operations to the variable node message processing. During implementation, the adjustment factors of thresholds can be set to 1/2, 1/4, 1/8, etc., which can be converted into simple shift and addition operations to obtain corresponding results. In general, the DT-SCMS algorithm improves the performance of the decoding algorithm and reduces the number of iterations with a small amount of computational complexity.
## 3 EQPD-MAC Protocol Development
### Castalia Implementation and Simulation
#### 3.4.1 Implementation Challenges  
To implement priority in the proposed MAC protocol, several functions had to be modified in Application, MAC, and Network layer, which is the most difficult part because in Castalia, it is necessary for a new protocol to derive from predefined base/virtual classes. These virtual classes have already established the methods and structure to implement new protocols that a developer may and should utilize. These Virtual classes are namely: VirtualApplication, VirtualMAC and VirtualRouting. So, priority had to be defined inside each of the virtual classes.  
EQPD-MAC had to be tested with some of the routing protocols available in Castalia in order to support the multi-hop features of the data packets so that the proposed MAC protocol could achieve the maximum energy efficiency possible. To incorporate the multi-hop feature in the proposed MAC protocol, several functions in the AODV had to be modified as well. This is because AODV only keeps track of the source ID and destination ID. When the neighboring node receives the data packets, it checks the source and destination. After the implementation of the priority into AODV, the neighboring nodes can also check the priority field. In this way the higher priority data packets can be sent much quicker.
## 5 Experimental Results
### The KDD99 Dataset  
The KDD99 dataset comprises 41 features, excluding the labels, and five classes: DoS, Normal, remote-to-local, user-to-root, and probe classes. The 10.0% variant of this dataset consisted of 494,021 records in the training subset and 311,029 records in the testing subset. Each of the 41 features is the result of a connection and has a label that specifies the status of the connection records, whether they are normal records or a specific type of attack [50].  
The KDD99 Cup dataset is the most suitable, and many researchers have used the reference dataset for ID experiments as a benchmark to validate their results. In this study, a 10% of the KDD99 dataset was used to train and test the proposed IDS. The KDD99 dataset comprises network packets, each of which is called a record. The records in this dataset contain information on 41 network features [50].
## 1 Introduction
Under the optimization of PSO, the accuracy of PNN and SVM fault diagnosis classification results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of PNN and SVM in fault diagnosis technology of aero-piston engine control system. It provides a truly effective method for fault diagnosis of aviation piston engine control systems.
## 2 Twelve-Pole System Nonlinear Model
\(\frac{c_{0}}{l_{0}}k_{1}\), \(d=\frac{c_{0}\xi}{l_{0}}k_{2}\), \(f=\frac{\varepsilon}{c_{0}}\), \(\Omega=\frac{\omega}{\zeta}\), \(\zeta=\sqrt{4\mu_{0}l_{0}^{2}N^{2}Acos(\varphi)/mc_{0}^{3}}\), with omitting the asterisks for simplicity, we have  
\[\ddot{x}+2\mu\dot{x}+\omega^{2}x-(\alpha_{1}x^{3}+\alpha_{2}xy^{2}+\alpha_{3} x^{2}\dot{x}+\alpha_{4}\dot{x}y^{2}+\alpha_{5}xy^{2}+\alpha_{6}x\dot{x}^{2} \tag{8}\] \[+\alpha_{7}xy\dot{y})=\Omega^{2}f\cos(\Omega t)\] \[\ddot{y}+2\mu\dot{y}+\omega^{2}y-(\alpha_{1}y^{3}+\alpha_{2}yx^{2 }+\alpha_{3}y^{2}\dot{y}+\alpha_{4}\dot{y}x^{2}+\alpha_{5}yx^{2}+\alpha_{6}yy^ {2}\] \[+\alpha_{7}yx\dot{x})=\Omega^{2}f\sin(\Omega t) \tag{9}\]  
The normalized nonlinear dynamical system that is given by Equations (8) and (9) describes the relative oscillations (\(x\) and \(y\)) of the twelve-pole system in \(X\) and \(Y\) directions with respect to the nominal air gap size \(c_{0}\), where the dimensionless coefficients \(\mu,\ \omega,\ \alpha_{j}\) (\(j=1,2,\ldots,7\)) are given in Appendix B. To investigate the performance of the proposed control method, bifurcations behaviors, and stability conditions of the twelve-pole system, asymptotic analysis is employed to obtain the autonomous amplitude-phase equations and the corresponding nonlinear algebraic system of Equations (8) and (9) as given in the following section.
## 2 Related Works
### Machining Process Monitoring  
Many studies aimed at improving the accuracy of part machining based on the monitoring of a machining process and adjusting certain process parameters have been performed. [PERSON] et al. [7] proposed a deformation error compensation method based on deformation monitoring. [PERSON] et al. [8] presented an online deformation prediction method based on applying recurrent neural networks to the monitored deformation data. [PERSON] et al. [9] proposed an allowance compensation approach based on an online measuring method for adaptive compensation of cutting depth in the finishing stage of machining. [PERSON] et al. [10] offered a method to improve part surface quality by monitoring the distance between the tool and workpiece. In the realm of additive manufacturing, which is being used more frequently in aerospace manufacturing industry [11], [PERSON] et al. [12] proposed an online deformation prediction method based on temperature monitoring for laser melting additive manufacturing. As the clamping force data [3] during a machining process reflects the potential deformation, [PERSON] et al. [13] developed an intelligent fixture for aero-engine casings, which is able to monitor the clamping force on the lower end face and automatically adjust the clamping status. [PERSON] et al. [14; 15; 16; 17] proposed several methods to control the machining deformation for aerospace structural parts by means of pre-deformation and adjustingthe machining sequence. Based on monitoring the deformation force, [PERSON] et al. [3] proposed a data-driven deformation prediction method that can online-predict the machining deformation.  
The aforementioned studies offer a wide range of solutions to machining process monitoring for different kinds of parts, especially large structural parts, with respect to deformation monitoring. However, for parts like aero-engine casings that have unique structural characteristics, the development of a more advanced deformation force monitoring systems and the associated methodologies is still necessary. That is the main objective of this paper.
# Social Internet of Vehicles for Smart Cities
## 4 Social Network Analysis in SIoVs  
Social network analysis (SNA) can be used to discover important players in a network and refers to the use of network theory to analyse social networks. Individual actors within the network, which can either be vehicles, drivers or even passengers, are represented by nodes, and the interactions or relationships among them are represented by edges [57]. Based on the interaction among entities of the network, which can be either static or dynamic, metrics, like centrality, cohesion, degree and clustering coefficient, can reveal relations among nodes, as well as groups of entities that share common habits.  
[PERSON] _et al._[58] showed that vehicles tend to show a similar behaviour and routines in terms of mobility. The mobility of vehicles can be mapped as a social network, following the same basic laws of degree distribution and distance among nodes. Applying SNA on vehicular networks can therefore improve the performance of communication protocols and services. Traditional concepts from graph theory, like centrality and clustering, can be applied in vehicular networks, as long as they incorporate their specific features, such as mobility of nodes, channel conditions and drivers' behaviour.  
The centrality of nodes plays a crucial role in information spreading in a network [59, 60]. In a similar way, central nodes can serve as good spreaders of infections [61] or as good points for building defence mechanisms [62]. Furthermore, central nodes can be elected as the clusterhead of groups that are created on the fly. A clusterhead may act as a relay node for traffic coming or destined to different clusters or as a relay node for intra-cluster communication [63].
## 3 Results and Discussion
### Performance Comparison Between Residual Attention Mechanism and Traditional Pooling Layer  
In the structural branch, a severe performance bottleneck was encountered. Reliance on ordinary pooling operations to process structural contact map data, which contain rich structural details, was found to be insufficient for effective extraction and retention of critical local features. Moreover, essential details are prone to be lost, which negatively affects the overall recognition ability and generalization performance of the model. As a result, more advanced and intelligent feature integration strategies have been explored.  
A residual attention mechanism was introduced to ensure that the most critical information for the final prediction was retained, even when feature reduction was performed, thereby preventing performance degradation caused by information loss. The effects of residual attention in the model were compared with those of traditional pooling layers on the three sub-datasets of japonica protein. Each experiment was repeated five times, and the results are shown in Table 3. The experimental results show that residual attention outperformed the other pooling layers in terms of AUPR and MCC metrics for the three sub-ontologies. In terms of Fmax and AvgPr metrics, the mean effect of the residual attention outperformed that of the other pooling layers. Only for the Fmax and AvgPr metrics of the CC sub-ontology dataset, the standard deviation of the residual attention was 0.002 and 0.003 higher than the global average pooling, respectively. In the case of the AvgRe metric, the standard deviation of the residual attention in the MF sub-ontology dataset was 0.002 higher than that of the global maximum pooling, the standard deviation of the residual attention in the BP sub-ontology dataset was 0.007 higher than that of the global average pooling, and the mean effect of the residual attention in the CC sub-ontology dataset was 0.002 lower than that of the global average pooling. When the residual attention tention mechanism was employed, a data error of less than 1% was observed in cases of performance degradation compared with other pooling layer configurations.  
This indicates that, even in the most unfavorable situations, a performance level close to that of the optimal pooling strategy can still be maintained by the residual attention mechanism, further confirming its excellence in reducing prediction bias and improving model robustness. This finding emphasizes the importance of adopting flexible and intelligent feature-processing strategies for specific data characteristics when designing deep-learning architectures.