esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Rockburst Research: A Comprehensive Review
## 3 Technologies and Methods for Rockburst Research
### In Situ Monitoring  
The most significant advantage of in situ monitoring is that it can obtain relevant parameters directly and in real time, truly reflecting the development mechanism and evolution law of rockburst, to timely evaluate and predict the risk of rockburst. In situ monitoring methods mainly include microseismic (MS) monitoring [63], microgravity [64], infrared thermal imaging [65], electromagnetic radiation [66], acoustic emission (AE) [67] and the photoelastic method [68]. Compared with other methods, acoustic emission and microseismic monitoring technology can be used to detect the occurrence of failure in early stages, before complete failure and destructive deformation of structure, which is more suitable for the process of rockburst.  
Microseismic monitoring is a record of the temporal and spatial characteristics of microseismic events. Microseismic events are low-energy seismic events related to sudden inelastic deformation, which radiates detectable seismic waves. Both AE and MS can detect seismic waves. The main difference between the two signals is that the seismic motion frequency of the AE signal is higher than that of the MS signal, which is shown in Figure 4a. However, the acoustic emission signal is usually very weak, so it is difficult to collect accurate data in the noisy environment of the project site [67]. Moreover, due to the existence of high-frequency components, an acoustic emission signal attenuates very fast, so an acoustic emission sensor can only cover a small volume of rock. Microseismic monitoring is more suitable for rockburst microseismic event monitoring on account of its advantages of real-time monitoring, large regional detection range, large data scale and no destructive impact on production. A typical mine microseismic monitoring system is shown in Figure 4b.  
At present, microseismic monitoring technology is mainly used for rockburst early warning. Through analysis of micro fracture signals, the occurrence time and location of microseismic events are obtained, and then microsource characteristic parameters, such as number of microseismic events, microseismic energy, apparent volume and apparent stress, are obtained, which can be used for rockburst prediction.  
The accuracy of the microseismic source location will affect the application effect of microseismic monitoring [69]. The key factors for the accuracy of the microseismic source location are sensor arrangements [71], noise reduction in microseismic waveform [72,73], identification and classification of microseismic waveform [74,75], arrival time picking of microseismic waveform [76], the wave velocity model [77,78] and the source location method [79,80]. The flow chart of microseismic signal recognition is shown in Figure 4c.  
The accuracy of microseismic positioning can basically meet the requirements of mines. The problem needed to be solved urgently in microseismic monitoring is how to obtain useful laws from large amounts of data.
## 3 Results and Analysis
### Functionality at Sector Scale and Community Scale
However, the variation among scenarios that accounts for functional interdependencies with different matrices is comparatively smaller. This emphasizes the need to consider the functional interdependencies among various building sectors when assessing community functionality. The evaluation of immediately post-disaster functionality in this paper can directly reflect the community's ability to resist flood hazards. In addition, according to the results presented, the residential sector has the greatest impact on community resilience. This  
Figure 8: The overall variation of each Sector in a community. Figure 9: Sensitivity analysis of community-level functionality in the four different models. may be due to the larger number of buildings in the residential sector than in other sectors, which contributes more to the community-level resilience. On the other hand, some residential buildings have no significant damage to their frameworks. However, due to the loss function of the infrastructure they are dependent on, their occupancy cannot be successfully provided, which can lead to additional population dislocation. These all suggest that generally, attention should be given to the residential buildings when incorporating mitigation during a hazard and reducing their dependence on other sectors should be more effective. Comparing these findings with prior studies provides further context. The community functionality threshold of 0.87 at 1.57 m flood depth aligns with [6], who noted significant functionality loss in wood-frame residential buildings at similar depths, confirming the validity of our fragility parameters. Extending the analysis to flood depths beyond 1.57 m (e.g., 3 m) shows functionality dropping below 0.50, underscoring the need for robust mitigation at higher intensities. However, our focus on intersectoral dependencies--evidenced by residential impacts cascading to business (0.50) and education (0.55)--extends their building-centric analysis, echoing [8]'s earthquake resilience work where residential dominance also emerged, though with different hazard metrics. This study further diverges from qualitative approaches like [29], who broadly categorized functionality states, by offering precise, quantitative residual functionality estimates. This precision highlights a novel contribution: a flood-specific framework integrating building fragility with community-wide interdependencies. These comparisons not only validate our results against the existing literature but also underscore the added value of a systems-based approach for flood resilience assessment.
## 6 Comparison of Models in RSCAD and PowerFactory
### Event Comparison in RSCAD and PowerFactory  
The 66 kV offshore network model in RSCAD and PowerFactory are compared for a three-phase line to ground fault in the middle of the cable. A fault is applied at 0.5 s with a clearing time of 140 ms. The resulting voltage and current waveforms are as shown in Figures 13-15 respectively. As can be observed in Figure 13, the voltage measured at PCC drops and the generation is stopped during the fault period. The voltage profile is the same in RSCAD and PowerFactory models during the pre-fault, fault and post fault conditions as can be observed. The drop in voltage in both the graphs is the same due to equal impedance in both the models.  
Due to the unavoidable modelling differences in the software packages, currents are measured at PCC bus for the RSCAD model and at FSC bus for the PowerFactory model. However, this does not affect the magnitude as the currents are measured in per unit. The currents in both the models have a similar profile, as summarized for the d-axis in Figure 14 and q-axis in Figure 15. The active and reactive currents generated by the WG have the same set points in both the models during the pre-fault condition, confirming that the power flow in both the models is similar. As the parameters for DVC is same for both the models, reactive power injection in RSCAD model during the time of fault is achieved similar to the PowerFactory model. The transients occurring during the time of fault at 0.5 s can be damped by controlling the parameters of the washout filters, as seen in Section 4.1.1.  
Spike in the profiles at the time of fault clearance at 0.64 s is due to fast dynamics of PLL in both software packages. It is observed that the currents in RSCAD model have slight transients throughout the simulation. This is due to the detailed model representation of GSC in RSCAD when compared to a simplified representation in PowerFactory. Therefore, it can be concluded that the RSCAD model with the implemented DVC provides similar results as the benchmark PowerFactory model. Moreover, the results from RSCAD model are more detailed and hence can be used as a base model for reference as it provides a better representation of the real-world operation.  
Figure 13: Voltage measured at PCC for a three phase fault in the middle of the cable in RSCAD and PowerFactory.
## 3 Network and Energy Models and Assumptions
### The Energy Model  
The same radio-energy model as stated in [29] is used, which is described briefly as follows. The schematic of the model is presented in Figure 1. This model considers the transmission energy in two parts. The first part is the amplification energy (propagation loss) that depends on the number of bits, the distance from the receiver and the acceptable bit-error rate. The propagation loss is proportional to \(d^{2}\)  
\[E_{Tx}(L,d)=\left\{\begin{array}{ll}L.E_{e}\ +\ L.\ \varepsilon_{fs}.d^{2}&,d\leq d_{0}\\ L.E_{e}\ +\ L.\ \varepsilon_{mp}.d^{4}&,d>d_{0}\end{array}\right. \tag{5}\]  
\[E_{Rx}(L)=\ E_{Rx-e}\ (L)=L.\ E_{e} \tag{6}\]  
where \(E_{Tx}\) is transmitter energy consumption, \(E_{Rx}\) is the receiver energy consumption, \(E_{Tx-e}\) and \(E_{Rx-e}\) are the electronic processing energy consumption and \(E_{Tx-amp}\) is the amplifier energy consumption.
## 4 Conclusions and Future Trends  
The review article focused on the extraction methods for PPCPs. Although these extraction methods are clearly dominated by offline solid-phase extraction using cartridges, there are significant knowledge gaps in accurately understanding the extraction mechanisms for PPCPs, including some metabolite and/or degradation products. Many of the most recent and innovative methods are based on the combination of sorbents with different chemical-physical properties either in the same cartridge, in parallel, or even in series. These modifications are considered to be small steps, but nevertheless, they represent a great advance by improving the extraction of a group of compounds with very different polarities. Multiresidue methods able to cover more than 100 compounds are already a reality. Rapid, lower cost, and eco-friendly sample preparation techniques are urgently needed. Automated, online  
Figure 5: Percentage of studies according to the number of PPCPs analyzed.  
preconcentration, and clean-up steps prior to the instrument analysis would be the future of the technique. Solvent-free microextraction methods are the trend of extractions such as DLLME. However, these techniques are rarely used, as they work especially well with non-polar PPCPs, but most are polar.  
Most of the environmental studies carried out so far have two aspects, (i) analytical including validation of the methods that mainly improves the accuracy in the quantification and the elimination of the matrix effects, and (ii) the environmental aspect in which the whole cycle of the water is covered, including the identification of the sources of these compounds to the environment, the efficiency in the elimination, and the influence of environmental factors such as seasonality. All these studies have contributed to an important advance of knowledge about the distribution and hazards of PPCPs. A gap detected in these studies is the lack of knowledge about the mixtures of PPCPs found in the environment and on the different metabolites and/or degradation products that can be present. It is expected that, in the near future, there will be an increase in knowledge in these fields.  
Conceptualization, D.S. and Y.P.; resources, D.S.; writing--original draft preparation, D.S.; writing--review and editing, Y.P.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science, Innovation and Universities and the ERDF (European Regional Development Fund) through the project CICLIC--subproject WETANPACK (RTI2018-097158-B-C31) and by the Generalitat Valenciana through the project ANTROPOCEN@ (PROMETEO/2018/155) and [PERSON] was funded by Generalitat Valenciana through a [PERSON] grant: \"GRISOLIAP/2018/102, Ref CPI-18-118.\"  
The authors declare no conflict of interest.
## 6 Result and Analysis
### Multi-User Full-Duplex Visible Light Communication System Performance Analysis
It can also be seen that as the number of the users increased, the data rate decreased; this is because more users share the same amount of bandwidth. From this figure, one can conclude that the proposed system is capable of handling the user and smart device data, and provide acceptable performance that meets the requirements of an average smart home.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 4 Evaluation Setup
### Baseline Configuration  
As we have seen in the presentation of the problem of nested congestion control, the actual distribution of delay is highly likely to have a significant influence on the performance of nested congestion control. We therefore needed to perform a detailed evaluation using a realistic range of RTT values. To create realistic scenarios, we measured actual RTT values to establish benchmarks for detailed evaluation. Over WiFi, we measured a baseline RTT of 32.6 ms to a national server and 83.8 ms to an international server. Doing the same over cellular access resulted in an RTT of 75.1 ms to a national and 84.0 ms to an international server. Based on this, we chose the default RTT values as presented in Table 1. The default RTT corresponded roughly to the RTT values received when pinging local and national servers, with the tunnel proxy assumed to be located halfway between the server and the user. We then performed evaluations on a range of RTT values, as presented in greater detail in Section 5. Table 1 also presents other parameters for the default configuration used in our Mininet-based emulation.  
The MAC buffer size in Table 1 refers to those buffers in each path that act as bottleneck buffers, where packets queue up when the tunneling system is transmitting packets at a rate higher than what the bottleneck can deliver (i.e., the WiFi or cellular link); the size of these buffers were set to the BDP produced when multiplying the path throughput with an RTT of 100 ms--meaning that the maximum queuing delay was also 100 ms. When an extra-large or extra-small buffer was used, the size was changed to 50% or 200% of the default size, respectively. The size of the scheduler buffer was three times the size of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Server link RTT & 20 ms \\ Path 1 RTT & 20 ms \\ Path 2 RTT & 40 ms \\ Path 1 and 2 bandwidth & 50 Mbps \\ Path 1 and 2 MAC buffer & 500 pkt \\ Path 1 and 2 queuing discipline & FIFO \\ Path 1 and 2 drop rate & 0\% \\ Scheduler & CPF \\ Scheduler buffer & 1500 pkt \\ Reordering & Adaptive \\ Reorder buffer & 1500 pkt \\ Moving average weight & 95\% \\ Added margin & 10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default Mininet configuration.  
bottleneck buffer. When estimating the RTT at the receiving side, we used an exponentially weighted moving average, with each new value determining 95% of the estimation. We also padded the reorder wait limit with 5 ms, and thereby erred on the side of solving packet scrambling at the expense of the E2E delay.
## 1 Introduction
In aviation communication networks, the calculation time required to estimate the location of the counterpart aircraft is very important. The monopulse technique calculates the estimated location based on the monopulse ratio curve, which is the synthesis of the sum and difference patterns formed via the monopulse comparator or excited signal weights. Not only can the sum and difference patterns be generated through simple signal synthesis, but based on the monopulse ratio curve, the location can also be estimated using a simple equation. Therefore, this method takes little time to estimate the location of the communication counterpart. However, given the beam shape of a general array antenna, the linear region of a monopulse ratio curve is limited. This phenomenon not only limits the location estimation range, but also increases the location estimation error. In addition, it increases the number of beams that must be formed for the location estimation to occur. To solve the above-mentioned problems, techniques such as the optimization of the design element values of the monopulse receiver and artificially expanding the beam width of the antenna have been studied. However, one disadvantage of using the monopulse receiver design element optimization technique is that it requires that a separate monopulse receiver is installed in addition to the active-phased array antenna system. Artificial antenna beamwidth expansion is the most representative method of expanding the linear region. However, its main disadvantage is that it cannot ideally solve the problem of linear region limitations. In this paper, we studied a technique for the fast and accurate estimation of a communication counterpart's location based on the monopulse technique in an aircraft equipped with an active phased array antenna system to build an aviation communication network. A configuration of an aircraft-mounted antenna system suitable for establishing an aviation communication network was presented. Moreover, based on the presented antenna system, we studied a method for improving the linear characteristics of the monopulse ratio curve and more accurately estimating the location of a communication counterpart. The details of the active phased array antenna system configuration for an aviation communication network, the monopulse ratio curve improvement method, and the performance verification of the proposed method through simulation are described in the second chapter.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 3 Metrics for Evaluating 5G Network Energy Efficiency  
Recently, the main standardization organizations, such as the 3 rd Generation Partnership Project (3 GPP), the ETSI and the International Telecommunication Union-Telecommunications Standardization Sector (ITU-T), accepted two types of the EE metrics as the key performance indicators (KPIs) for expressing the EE of mobile networks (MNs).  
The first metric represents the data EE metric of MN expressed in bit/J, and it is defined as the amount of data transmitted per Joule of energy. For known mobile network area, \(A\), data \(EE\) is expressed as follows:  
\[EE_{DA}=\frac{DV_{A}}{EC_{A}}\ [\text{bit/J}] \tag{1}\]  
According to Equation (1), the data EE metric (\(EE_{DA}\)) is calculated as the ratio between the overall DV (\(DV_{A}\)) transferred by the analyzed MN area and energy (\(EC_{A}\)) needed by MN equipment installed in this area for transferring these DVs.  
Another standardized EE metric expressed in m\({}^{2}\)/J represents the coverage EE of the MN area, and it is defined as the unit area which can be covered with the 5G wireless signal, using Joule of consumed energy. For known mobile network area, \(A\), the coverage EE is expressed as follows:  
\[EE_{CA}=\frac{S_{A}}{EC_{A}}\ \Big{[}\text{m}^{2}/J\Big{]} \tag{2}\]  
Based on Equation (2), the coverage EE metric (\(EE_{CA}\)) is calculated as the ratio between the overall size of the area (\(S_{A}\)) covered with the wireless signal of the analyzed MN and the total energy (\(EC_{A}\)) consumed by the equipment allocated in the analyzed MN area. The total EC (\(EC_{A}\)) of the analyzed MN area can include the sum of the energies consumed by the RAN equipment (BSs), transmission equipment (e.g., wired or wireless backhauling equipment and radio controllers) and all ancillary equipment (e.g., air-conditioning, power backup, etc.).  
These two standardized metrics are used to effectively analyze the deployment and operation performance of MN in terms of EE. They can be used for the assessment of MN EE performance in the case of various deployment and operation strategies of different BS types and device density classes [1; 2]. As different deployment and operation strategies for a new generation of MN can be implemented in practice, the EE assessment of mobile networks by using standardized metrics becomes crucial for MNOs when assessing approaches related to the deployment and operation of new generations of mobile networks.
## 1 Introduction
We compare the two networks with their learned features in terms of classification performance of the cat sounds. We try to classify the cat sounds using five different machine learning algorithms, and finally ensemble their predictions to make the performance more robust. Each of these five algorithms is powerful for classification problems and needs less labeled data for training because of the small number of parameters. The cat sounds in each class have different nature, which mainly varies in frequency bands. To make our classification more efficient, we also modify the conventional global average pooling for dimension reduction, and name it frequency division average pooling (FDAP). The FDAP first divides each feature map in different frequency bands and take the average value in each band. For the classification we exploit five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect to the following factors; the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional global average pooling (GAP) or FDAP, the five different machine learning algorithms, and an ensemble of them. As we expected, the FDAP features with the data increased using augmentation produces the best accuracy in the ensemble approach. Also both learned features from pre-trained CNN and unsupervised CDBN produce successful results in the experiment. Therefore, we have obtained with the combination of all those positive factors the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score. This paper is organized in the following order. Section 2 describes the domestic cat sound dataset preparation. Section 3 is an overview of data pre-processing, CNN and CDBN network feature extraction, and classification of the extracted features. The results with discussion are included in Section 4. Finally, the conclusion and future works are mentioned in Section 5.