esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Real Field Deployment of Systems Based on DAS+PRS
### System Evaluation in a Field Deployed System  
Once the energy detection threshold for each point of the surveilled zone has been set, a procedure to estimate the real performance of the system in real field deployment is necessary.  
The most objective evaluation procedure in field is commonly referred to as _blind field tests_, since the ideal approach is doing the tests without any prior knowledge about what is happening in any location of the surveillance zone. These blind field tests consist in carrying out some activities at certain locations along the surveillance area at certain times within a given time interval (spanning from a few hours to days). The DAS+PRS is being run during all the time interval, and their results are final compared against the ground truth of the blind field test activities. To do so, these activities must be properly labeled (as described in Section 3.4.1 with respect to the database generation).  
As an example, in [25] the authors describe their blind field tests procedures: two round of blind field tests were carried out in different locations, times, and days. The performance of the real field deployment system was found to be affected to a great extent due to several issues, which typically do not arise in off-line system evaluations:  
* There may be defects in the correct labeling of the blind field test activities, which in some cases is not as precise as required to have meaningful comparisons with the PRS output results.  
* There may be events for which no model exists in the system. These events will generate a classification error, which must also be considered to properly assess the real system performance.  
Finally, the blind field test evaluation procedures will face the problem of the statistical significance of the results. In most cases, the number of tests that can be performed will be much lower than the total amount of tests conducted during the database generation process. Therefore, the reported results in the off-line evaluation will have a higher statistical significance than those from the blind field tests. This must be taken into account to design a broad enough blind field test campaign.
## 3 Reliability Block Diagram (RBD)  
RBD is a technique for evaluating the reliability of a whole system. All the components of a system are represented graphically, and their reliability determines the overall system's reliability. A block indicates whether a component is working or not, so the reliability depends on the configurations of the components [41]. A series configuration, a parallel configuration, and a combined (hybrid) configuration are the most commonly used configuration types, as shown in Figures 5-7, respectively [5]. Whenever a component fails in a series configuration, the entire system fails. Thus, the least reliable component is the one that has the most impact on the overall reliability. Therefore, the reliability of a system in series is lower than the reliability of the least reliable component.  
In a series system, reliability is defined as the product of the reliability of the components that make it up (series reliability = R1 \(\times\) R2 \(\times\) R3), where R1, R2, and R3 represent the working reliability of components 1, 2, and 3, respectively [43]. For a parallel system to function properly, at least one unit must function.  
In a parallel configuration, the system reliability is largely affected by the item characterized by the highest reliability trend, where the system reliability is derived from the complement of the product of the reliability of each component (parallel reliability = 1 - ((1 - R1) \(\times\) (1 - R2) \(\times\) (1 - R3))) [43].  
Figure 5: RBD organized in series.  
Figure 6: RBD organized in parallel.  
In a hybrid configuration, combining blocks creates other formats: series-parallel, parallel-series, bridge, and k-out-of-n. In order to solve the reliability problem, the parallel blocks must first be solved, and then the series blocks (hybrid reliability = R3 \(\times\) (1 - ((1 - R1) \(\times\) (1 - R2)))).
## 5 Results and Discussion
### Model Comparison and Test  
#### 5.3.1 Model Comparison  
The model is a hybrid model derived from the least squares support vector machine (LSSVM) algorithm and the genetic algorithm (GA) optimization algorithm. Therefore, to verify the accuracy of the model's predictions, it is necessary to compare the model's results to those of similar algorithms to determine if the combined model demonstrates better fitting and generalization abilities.  
The least squares support vector machine (LSSVM) model is optimized using optimization algorithms such as the improved sine cosine algorithm (ISSA), grey wolf optimization (GWO), and particle swarm optimization (PSO). Three combined prediction models, ISSA-LSSVM, PSO-LSSVM, and GWO-LSSVM, are constructed for comparison with the GA-LSSVM model. The training and test set data with the same distribution ratio are imported for training and prediction. Subsequently, the PM2.5 and PM10 concentrations are tested separately. The sparrow algorithm is iterated 40 times, the gray wolf algorithm is iterated 20 times, and the particle swarm algorithm is iterated 50 times. The prediction results of the three models are depicted in Figure 12.  
Figure 12: Comparison of the model to predict the effect of PM2.5 and PM10.  
The most representative evaluation metrics \(R^{2}\), \(RMSE\), and \(STD\) are chosen to compare the model accuracy, as shown in Table 5.  
In order to facilitate the comparative analysis of prediction results, the Taylor diagram(Figure 13) is used to illustrate the relationship between the root mean square error (\(RMSE\)), the sample standard deviation (\(STD\)), and the correlation coefficient (\(R^{2}\)), allowing for a comprehensive evaluation of the errors. The scatter points in the Taylor plot represent various prediction models. The x-axis represents the standard deviation (\(STD\)), the radial line represents the correlation coefficient (\(R^{2}\)), and the dotted line represents the root mean square error (\(RMSE\)). According to Figure 13 and Table 5, the optimization results of the three comparison algorithms are not significantly different. Compared with the LSSVM model, the \(RMSE\) of the PSO-LSSVM model is reduced by 32% for PM2.5 and 29% for PM10, respectively. Additionally, the \(R^{2}\) is increased by 67% for PM2.5 and 74% for PM10, respectively. The \(RMSE\) of the GWO-LSSVM model is reduced by 31% for PM2.5 and 43% for PM10, respectively. Additionally, the \(R^{2}\) is increased by 68% for PM2.5 and 84% for PM10, respectively. The \(RMSE\) of the ISSA-LSSVM model is reduced by 19% for PM2.5 and 41% for PM10, respectively. Additionally, the \(R^{2}\) is increased by 58% for PM2.5 and 79% for PM10, respectively. The comprehensive analysis shows that the model's performance is ranked as follows: GA-LSSVM > GWO-LSSVM > PSO-LSSVM > ISSA-LSSVM > LSSVM. Therefore, the LSSVM model optimized by the GA algorithm obviously demonstrates a good prediction effect with low error and high fitting.
## 2 The CSES Satellite and Its Primary Application in EQ Prediction Practice
### Application of CSES Data
On the one hand, the right orbit where the anomaly occurred was specified as 18111_1 (orbit_suborbit), and it was formed on 8 May 2021, 14 days prior to the Maduo event. Figure 7 displays the corresponding variations of (a) the He\({}^{+}\) and O\({}^{+}\) densities and (b) the electron density automatically recorded by the system software. An automatic search method utilized by [PERSON] and [PERSON] [13] was also performed on these perturbations, and the corresponding peak occurred at 37.5\({}^{\circ}\) N and 104.8\({}^{\circ}\) E with an enhancement of 41.6% for O\({}^{+}\) density and 22.2% for electron density, and a persistent distance of 322 km and 525 km, respectively. The changed percentage of an ionospheric parameter can be calculated by using the value corresponding to the peak time, subtracting the background value, and then dividing the background value. This peak was 680 km away from the epicenter of the Maduo event. We also examined its revisiting orbits 18187_1 and 18263_1  
Figure 6: Variations in the Kp index (**a**) from 27 April to 26 May 2021 and (**b**) from 10 December 2021 to 10 January 2022. The red star stands for the earthquake occurring time. near the Maduo EQ and found no obvious disturbances of related ionospheric parameters as shown in Figures 4c and 5c.
## 2 Background
### SDN Security Issues  
As the controller in the SDN paradigm has a global network view, SDN networks are considered more secure than traditional networks. In particular, in SDN networks, all network elements are capable of collecting and reporting traffic information and statistics. This is considered the big difference from traditional distributed networks, which require several steps (sometimes not possible) to partially infer the state of the network and where only a few network elements are logging traffic information and statistics.  
However, the SDN paradigm is still a novelty in the world of industrial networks, as they have not yet matured resilience to attacks [33]. In fact, each component and level of the SDN architecture is prone to attacks. It is possible to identify four attack vectors [34] (i.e., system layers subject to threats that can be exploited by attackers). The first vector of attack is the trust between controllers and applications. Controllers usually work on some form of Linux-based operating system, so they do not have predefined mechanisms to establish trust. The second attack vector is the presence of physical devices, which can lead to malware infection at runtime. The third vector is the lack of adequate authentication, authorization, and accounting mechanisms. The last attack vector is constituted by the vulnerabilities of the controllers. The number of weak points of the controllers is unfortunately high, and above all, they are all of a fair severity.  
In general, the main security issue of the control plane is DoS and DDoS attacks that can turn the SDN controller into a single point of failure, thus taking the network down. Network resource visibility is an important characteristic in the SDN paradigm, but the visibility must be hidden to all applications that do not need controller functionalities. However, the number of security issues in the SDN paradigm is expected to grow in the future due to the adoption of the SDN paradigm in the industrial context. To take full advantage of the SDN paradigm in future industrial networks, it will be very crucial to face all security issues so that countermeasures can be implemented in an efficient way. Consequently, in the following subsections, we propose an overview of the most relevant security issues that involve all the SDN stacks: (1) the application plane; (2) the control plane; and (3) the data plane. A list of the most common security issues in the SDN paradigm is presented in Table 1[35].
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods  
### Study Area  
The TP is located in southwestern China (Figure 1) between 73\({}^{\circ}\)-104\({}^{\circ}\) E and 26\({}^{\circ}\)-40\({}^{\circ}\)N. It is the highest plateau on Earth. The extent of the TP is approximately 2.5 million km\({}^{2}\), with an average elevation of \(>\)4000 m above mean sea level. Because of the complex topography and extremely high elevation, the precipitation on the TP exhibits substantial spatiotemporal variability [26]. Spatially, much of the precipitation occurs in the central and southern parts of the plateau, with little in the western and northern parts. Temporally, the amount of rainfall differs seasonally. Spring and summer receive approximately 90% of the annual precipitation because of the effects of the Indian monsoon, which brings substantial water vapor from the sea. In winter, the westerlies influence the western part of the plateau, resulting in little rainfall. The TP contains the upstream reaches of the five major Asian rivers, including the Yellow, Brahmaputra, Ganges, Indus and Yangtze Rivers [27,28], which supply water resources for more than 1.4 billion people [29].
## 4 Discussion
Our results seem to be more closely aligned to those of [PERSON] et al. [28], who also did not see any difference in HTs over the EHF range for ears with or without SOAEs. In general, it seems that factors like ear side, gender, and SOAE presence exert only minor effects on both DPOAEs and HTs in the ST and EHF ranges. Such effects may be more detectable in bigger datasets. Nevertheless, it seems that these effects are probably not important when measuring routine DPOAEs and EHFs in clinical practice. Some limitations of our study should be mentioned. Although the numbers in our study group were quite large compared to other OAE studies, a greater number of significant relations may emerge if the numbers were increased. There is also the important issue of calibration, which becomes increasingly difficult for frequencies over 8 kHz due to the generation of standing waves in the ear canal. In our work, this seemed to be the case for EHF DPOAEs at 12 kHz, where DPOAEs appeared to be present even though the HT was around 60 dB HL--a level which should effectively rule out DPOAEs (since DPOAEs are usually present only for HTs better than around 40 dB HL). Therefore, the most likely explanation for recording DPOAE-like responses at 12 kHz is that standing waves are present at this frequency, caused by reflections within the ear canal. The side-effect is that it becomes difficult to tell whether the measurements represent true OAEs or artifacts (see [46]). This problem can be overcome with advanced methods, but these are beyond the capabilities of a commercial OAE system. Employing denser frequency points for DPOAEs may help, as here, in order to speed up measurements, fewer points were chosen. Nevertheless, Figure 3 does show that, at 12 kHz, the better the HL, the higher the DPOAEs--so despite some possible contamination by artifacts, it does seem that some real responses were detected. Furthermore, it seems that at 10 and 16 kHz DPOAEs were not contaminated by standing waves, as the responses declined in line with hearing loss. In Figure 3, the DPOAEs for G16 responses were well above the noise level, although for G8 and Gim they approached this level. Finally, it should be mentioned that there are still unsolved standardization problems involving different OAE equipment and analysis methods, meaning that each may produce a different result (e.g., [47]). This may explain differences between our findings and those of other studies, which have looked at the effect of SOAEs on HTs and DPOAEs. Despite this note of uncertainty, our study confirms the results of earlier works that have shown that HTs and DPOAEs in the EHF band seem to show promise for identifying early signs of hearing loss [17; 48; 49].
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 6 Conclusions  
In this paper, we proposed E-MQTT, a novel mechanism addressing the end-to-end communication problem inherent in MQTT. From the experimental results, although E-MQTT has a slightly larger packet size than MQTT, its distinctive response simplification strategy and reduced number of exchanged packets contributed to shorter end-to-end delay and less energy consumption. The advantages gained from these performance improvements far outweigh the minor difference in packet size.  
The current E-MQTT still has limitations and needs to improve further. Firstly, we developed E-MQTT based on MQTT version 3.1.1, which is not the most recent one. Secondly, although we combined the publish-subscribe model with the request-response pattern, the response message is just a simple acknowledgement without carrying additional information. Lastly, E-MQTT is an improvement of MQTT QoS 2 but not the other QoS levels (0 and 1). As future research, we plan to resolve the current limitations of E-MQTT. We will redesign E-MQTT according to the specifications of MQTT version 5.0, which is the latest one to accommodate new features. Using the new features of MQTT version 5.0, we will extend the combination of the publish-subscribe model and request-response patterns so that the response message can contain additional information. Furthermore, we will improve E-MQTT such that it can improve the other QoS levels 0 and 1 of MQTT in the request-response pattern communication.  
Conceptualization, M.L.; methodology, M.L. and Y.L.; software, Y.I. and M.L.; validation, Y.I. and M.L.; formal analysis, Y.I.; investigation, Y.I. and M.L.; resources, M.L.; data curation, Y.I.; writing--original draft preparation, Y.I.; writing--review and editing, M.L.; visualization, Y.I.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1F1A1047032).  
**Institutional Review Board Statement:** Not applicable.  
Figure 18: Comparison of battery usage of publisher results.
## 3 X-ray Micro-CT Scanning Test
### Equipment and Test Condition  
In CT-scanning technology, the voltage of the X-ray tube determines the photon energy of X-ray harnesses. The higher the voltage of X-ray tube, the higher the X-ray penetration. On the premise that other scanning parameters are constant, increasing the voltage of the X-ray tube can significantly increase the physical dose of X-ray and reduce the standard deviation (SD), so as to improve the image quality and image resolution.  
Figure 3 shows the X-ray micro-CT scanner (nanoVoxel-4000, Tianjin Sanying Precision Instrument, Tianjin, China) used in the present work. When scanning the carbonate rock, the resolution of the image was set to 61.75 \(\upmu\)m with the voltage of X-ray tube set to 180 kV. However, the aforementioned settings were not feasible for the sandstone sample as the obtained images were blurred. This is because the X-rays are unable to penetrate the sample due to the compaction of grains in the sandstone. Therefore, a higher resolution of imaging (30.45 \(\upmu\)m) and a higher voltage of X-ray tube (200 kV) were adopted when scanning the sandstone sample.  
The samples were scanned in the directions of X, Y, and Z, with Z being the longitudinal direction of the cylinders. The positive directions of X, Y, and Z axes follow the right-hand rule.  
As aforementioned, the resolution of the imaging in the X, Y, and Z axes, i.e., the distance between adjacent slices (cross-sectional pictures), was set to 61.75 \(\upmu\)m for the carbonate samples and 30.45 \(\upmu\)m for the sandstone sample. Scanning a single rock sample took approximately 1 h with an operating current of 150 \(\upmu\)A. A total of around 3200 cross-sectional pictures were obtained for each carbonate sample while scanning the sandstone sample generated 6430 cross-sectional pictures.
## 5 Conclusions and Future Improvements  
A novel HP-SFC protection mechanism was proposed in this manuscript, which focused on efficient network traffic rerouting in SFC when a failure occurred. HP-SFC was designed based on the segment routing technique, where SF chains were divided into segments and backup paths established for each segment using the backup SFs. Any failure in a segment was taken as a failure of the whole segment, and traffic was detoured to a pre-installed backup path from the initial point of the failed segment. The results showed that HP-SFC recovered traffic within 50 ms, which is an industry standard. These results were made possible by the segmentation technique, which reused the already established primary path, similar to local recovery, and required only three flow entry update messages to detour the traffic. Another benefit of using the segmentation technique was a more stable and consistent performance in terms of the RTT increment due to traffic detouring, as shown by the results. Moreover, for traffic steering in SFC, a new label stacking mechanism was proposed in this paper that was not limited to the protection mechanism and could also be used for other traffic engineering purposes in SFC. This mechanism labeled SFs instead of SF chains and stacked these labels in the order of SFs in a particular SF chain before adding it as the MPLS header in a packet. The results showed that it not only reduced the footprint of flow entries' usage by HP-SFC, but also solved the scaling problem with the massively increasing number of SF chains in the network. The results clearly showed that the performance of HP-SFC and other protection mechanisms was intrinsically dependent on the composition of the network topology, which is a major limitation. Currently, we are working to reduce this limitation by integrating HP-SFC with the delay- and availability-aware placement of exclusive and shared backup SFs in the network. In the next step, we will aim to make HP-SFC robust against second- and third-level failures through a multi-level labeling approach that encapsulates the information of multiple labels in a single label.  
Conceptualization, S.M.R. and H.C.; methodology, S.M.R., M.K., and H.J.; software, H.J.; validation, H.C., M.K., and S.M.R.; formal analysis, H.C. and M.K.; visualization, S.M.R.; writing--original draft preparation, S.M.R. and H.J.; writing--review and editing, M.K. and H.C.; supervision, H.C. and M.K. All authors read and agreed to the published version of the manuscript.  
This work was partly supported by the Ministry of Education, Institute of Information and Communications Technology Planning and Evaluation (IITP), and the National Research Foundation (NRF), Korea, under the GITRC support program (IITP-2021-2015-0-00742), the ICT Creative Consilience program (IITP-2021-2020-0-01821), and the mid-career support program (NRF-2020R1A2C2008447).  
Not applicable.  
Figure 8: Enterprise topology, average RTT increment of SFCs 1, 3, and 8 for local recovery, global recovery, and HP-SFC protection mechanisms. (**a**) RTT increment for SFF-SFF link failure. (**b**) RTT increment for SFF-SFF link failure.