esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multilevel Inverters (MLIs)
### Classification of Multilevel Inverters
#### 4.2.2 Capacitor-Clamped (Flying Capacitor)  
This is another type of multilevel inverter [123; 124]; it is like the diode-clamped inverter as in [125; 126] and uses capacitors to clamp the voltage of the unit instead of diodes [123]. The flying capacitor (_FLC_) converter was introduced in the 1990s by [124; 127] as another modification of the multilevel inverter topology. Its fabrication involved the serial connection of capacitors' clamped switching cell, thereby allowing the transfer of limited voltages to the electrical devices through the capacitors. It differs from the diode-clamped MLI by having capacitors and not diodes; the work of the capacitors is to separate the _DC_ supply voltages. However, _VDC_ represents the voltage across each switch and capacitor. An inverter of m level flying cap needs (2\({}^{m-2}\)) switches, with (\({}^{m-1}\)) number of capacitors [113].  
The _DC_ side of this topology has a ladder structure capacitor, and the voltage passing through each neighboring capacitive branch differs. This voltage difference is considered to determine the size of the output voltage phase, which is equivalent to _VDC_. In contrast to the diode-clamped multilevel converter, the major benefit of the flying capacitor inverter is that it has redundancies for internal voltage levels. In other words, to obtain a similar output voltage, there is a need to combine two or more different valid switches. Furthermore, the flying capacitor topology has phase redundancy, while only a line redundancy is observed in the diode-clamped inverters. This is an important feature because it is the basis for using capacitive branch voltage balancing control techniques. The output voltage level determines the number of redundant states; in this kind of _MLC_, a balance in the voltage of the capacitors must be maintained. This can be achieved by using the appropriate sequences of regulation so that the internal capacitors recharge and discharge evenly over time. Nonetheless, there are some risks, as follows [25; 128]:  
* Voltage level control is difficult for all capacitors; it is also a complex task to pre-charge all the condensers at the same voltage level.
* The efficiency of switching is low.
* In a diode-clamped _MLI_, several capacitors are used, and these are mostly expensive and bulkier compared to the clamping diodes. A flying capacitor multilevel topology is shown below in Figure 32.
## 5 Discussion  
In this section, in order to examine the relative performance of the compressed air foam compared to the other fire-extinguishing agents, a synthetic surfactant type of foam developed and applied in Korea was assessed. Table 4 summarizes the general material properties for the synthetic surfactant foam of 3%. These contents satisfied limited conditions regulated in the Korean specification [10]. In this study, the discharging performance was not investigated because the fire-extinguishing agent through the Korean approved process was used. A specimen for which the mixing ratio of the synthetic surfactant foam of 3% and compressed air was 1:4 was used in the experiment. The discharged foam was released at a speed of 200 L/min. The experimental results are shown in Figure 10.  
Figure 10: Measured results of the specimen with an air-to-aqueous foam solution of 1:4 and the synthetic surfactant foam of 3% for the flame temperature and the heat flux in accordance with the measured distance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\
\begin{tabular}{c} **Generation** \\ **Zone (sec)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Combustion** \\ **Zone (sec)** \\ \end{tabular} } & \multicolumn{2}{c}{**Flame Temperature**} & \multicolumn{2}{c}{**Heat Flux**} \\ \cline{3-6}  & & & **Suppression** & **Extinction** & **Suppression** & **Extinction** \\  & & & **Zone (sec)** & **Zone (sec)** & **Zone (sec)** & **Zone (sec)** \\ \hline Specim I & 6 & 66 & 50 & 38 & 20 & 8 \\ Specim II & 9 & 59 & 33 & 25 & 16 & 8 \\ Specim III & 7 & 60 & 53 & 36 & 23 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the fire-extinguishing performances.  
In the measured result of the thermocouple, the flame is ignited in 106 s and is maintained to 207 s such that the total fire suppression time is 101 s. If the preliminary combustion time of 60 s is excluded from the total fire suppression time, the net fire suppression time is 41 s. On the other hand, from the results of the intensity of the heat flux, the flame is first caught at 106 s and is eliminated at 191 s. It was concluded that the net fire suppression time is 25 s.  
Therefore, as the measured result of the heat flux of the flame is shorter than that of the AFFF, it was confirmed that the synthetic surfactant foam is better for subduing an actual fire. However, the measured result for the thermocouple under the same discharge condition showed that the fire suppression time of the AFFF of 3% is seven s faster. It was determined that flowing and aqueous film phenomena arise when the manufactured foam covers the surface of the oil, affecting the fire-extinguishing performance.
# The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends
## 5 Conclusions  
In this paper, we conducted a study in Southwest China (SWC), which experienced severe drought events during 2009-2012, to investigate if drought events affect long-term vegetation trends. We used LAI, EVI, and GPP to characterize the growth of vegetation, and explored the impact of drought events on long-term vegetation trends. The drought events in 2009-2012 did cause synchronous obvious damage to vegetation. By the end of the drought events in 2012, the vegetation greening trend showed a certain degree of decline, compared to the period before the droughts. However, on studying the latest records, it was noted that the vegetation quickly recovered after the droughts, and the long-term trends of vegetation indicators have basically returned to the level before the droughts. In addition, an unexpected phenomenon was discovered--that drying trends coexist with greening.  
Partial correlation analysis shows that climate warming is one of the driving factors for vegetation greening in SWC. In addition, human activities in this area are extensive, which act as another potential driver of greening. The consumption of pesticides, fertilizers, and agricultural machinery has gradually increased, followed by yield and output value. The rapid growth and continuous forestry projects have gradually increased forest area in SWC. Satellite-based land cover changes and residual trend methods have also been confirmed at this point. In other words, climate warming and human activities (especially land management) are the main drivers for the rapid restoration of vegetation after severe drought. As a consequence, there is ongoing greening in Southwest China, despite severe droughts in 2009-2012 and long-term drying trends. However, one thing to pay attention to is whether the vegetation in SWC will change from green to brown due to the continuous drying trend.  
Conceptualization, methodology, writing--original draft preparation, X.C. and T.C.; data curation, visualization, J.C., R.G., and M.G.; writing--review and editing, X.W. and Y.X. supervision, validation, Q.Y., S.Z., and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (No. 2017 YFB0504000) and the National Natural Science Foundation of China (No. 42130506, 31570464
## 4 Discussion
### Possible Technical Solutions
#### 4.1.1 Performance Adjustment
The total average power consumed by the locomotives in the tunnel, denoted as \(P_{MT}\) (kW), is calculated as  
\[P_{MT}=N_{L}\;P_{M} \tag{3}\]  
Figure 4: Percentage of the days with different NO\({}_{2}\) average concentrations. The energy consumed by the locomotives in a day, denoted as \(E_{L}\) (kWh/day), is given through the following equation:  
\[E_{L}=P_{MT}\ t_{L} \tag{4}\]  
If the locomotive's NO2 emission rate is \(\epsilon_{NO2}\) (g/kWh), and the NO2 density is \(d_{NO2}\) (kg/m\({}^{3}\)), then the daily volume of NO2 emitted by the locomotives, denoted as \(V_{NO2}\) (m\({}^{3}\)/day), can be calculated as  
\[V_{NO2}=\frac{E_{L}\ e_{NO2}}{1000\ d_{NO2}} \tag{5}\]  
For a uniform operation, the ring installation is distributed throughout the day, and this volume of NO2 will dilute into the incoming airflow over the course of the day. The average NO2 flow rate introduced into the tunnel, denoted as \(q\) (m\({}^{3}\)/s), is calculated as  
\[q_{NO2}=\frac{V_{NO2}}{24\times 3600} \tag{6}\]  
If the clean air supply from the ventilation system is denoted as \(Q\) (m\({}^{3}\)/s), the average volume-based NO2 concentration within the tunnel, denoted as \(C_{NO2}\) (ppm), is calculated as  
\[C_{NO2}=10^{6}\ \frac{q_{NO2}}{Q} \tag{7}\]  
The theoretical curve of the flow rate as a function of the tunnel length is determined based on the fan's characteristic curve and tunnel data. Assuming a ventilation pipe with a 1.6 m diameter, the flow rate \(Q\) (m\({}^{3}\)/s) can be calculated as follows:  
\[Q=Q_{min}+(Q_{max}-Q_{min})\ exp(-c\ L) \tag{8}\]  
The typical values for an installation with a 250 kW fan and a 1.6 m diameter pipe with minimal leaks are \(Q_{max}\) = 55 m\({}^{3}\)/s, \(Q_{min}\) = 25 m\({}^{3}\)/s, and \(c\) = 0.0004. These calculations reproduced the actual situation, where a maximum advancing rate of 18 rings/day ensured that NO2 concentrations remained below 3 ppm, complying with the current legislation. Only a slight decrease in the advancing rate would be necessary for tunnel lengths beyond 5500 m (Figure 5). However, conforming to the new directive's requirement without making modifications to tunnel facilities presents significant challenges. As the tunnel length exceeds 1000 m, maintaining an average performance of 18 rings/day would necessitate the operation of two locomotives. Upon reaching a tunnel length of 6500 m, the performance would need to be gradually reduced to 3 rings/day to meet the OELV (Figure 6). Implementing such performance reductions could result in potential economic losses, rendering this approach less feasible in practice. However, if real conditions lead to an advancement rate of less than 3 rings/day (e.g., due to difficult ground excavation), the tunnel could still be mined using the existing facilities and work schemes.
## 3 Operational Principles
From the condition of the minimum free energy, we can define one new function, \(h(\theta)=\frac{\mu^{2}}{\cos^{2}\theta\ \left[1+\beta\sin^{2}\theta\right]}-\frac{1}{ \left[1+\gamma\sin^{2}\theta\right]\gamma}\), which is used to describe the relationship between \(\theta_{\text{max}}\) and the applied voltage (V) [25],  
\[\int_{0}^{\theta_{max}}\frac{\sqrt{\left(1+k\ \sin^{2}\theta\right)}}{\sqrt{ \left[h(\theta_{\text{max}})-h(\theta)\right]}}\frac{1}{\left[1+\gamma\ \sin^{2}\theta\right]}\ d\theta=\frac{\pi}{V_{T}}\ \frac{V_{RMS}}{2}. \tag{1}\]  
In Equation (1), \(\theta\) is the tilt angle of the LC molecule and \(\gamma=(\varepsilon_{//}-\varepsilon_{\bot})/\varepsilon_{\bot}\) is the dielectric anisotropy. \(k\) = \((k_{3}-k_{1})/k_{1}\) and \(\beta\) = \((k_{3}-k_{2})/k_{2}\) is the elastic anisotropy. Equation (1) can be rewritten to find \(\theta\) = \(\theta_{max}\) as follows:  
\[\int_{0}^{\theta_{max}}\frac{\sqrt{\left(1+k\ \sin^{2}\theta\right)}}{\sqrt{ \left[h(\theta_{\text{max}})-h(\theta)\right]}}\frac{1}{\left[1+\gamma\ \sin^{2}\theta\right]}\ d\theta=\int_{0}^{d/2}\frac{\pi}{V_{T}}E\ dz, \tag{2}\]  
where \(E\) is the electric field experienced by the LC molecules in the TN-LC phase shifter. After finding \(\theta_{\text{max}}\) at each value of the applied voltage using Equation (1), the effective birefringence experienced by the THz wave transmitting through the LC cell can be calculated as follows [2; 5; 6; 8; 9; 10]:  
\[\Delta n_{eff\text{-}Max}=(\frac{cos^{2}\theta_{max}}{n_{0}^{2}}+\frac{sin^{2 }\theta_{max}}{n_{e}^{2}})^{-\frac{1}{2}}-n_{0}, \tag{3}\]  
where \(n_{\text{e}}\) and \(n_{\text{o}}\) are extraordinary and ordinary indices of refraction of the LC, respectively. The values of \(n_{\text{e}}\) and \(n_{\text{o}}\) of the LCs used in this work at THz frequencies will be presented in a later section of this paper. The phase shift (\(\delta\)) experienced by the THz wave propagated through a TN-LC cell due to the effective birefringence is then given by the following:  
\[\delta(\text{V})=2\pi\cdot f\cdot d\cdot\Delta n_{\text{eff\text{-}Max}}/c, \tag{4}\]  
where \(d,f,\) and \(c\) are the thickness of the TN-LC cell, frequency, and speed of propagation of the THz wave in vacuum, respectively. The use of Equation (4) is valid, because we employ a thick LC cell, the thickness being of the order of the THz wave's wavelength. If information about losses suffered by the transmitted THz wave is also of interest, an analysis based on the Jones matrix formalism taking into account the extinction coefficients of the components used in the device (e.g., the LC) should be employed because the dichroism affects the polarization state.
## 2 Related Works
### Quality of Experience  
The standard P.10 [16] proposed by the ITU Telecommunication Standardization Sector (ITU-T) was first interpreted as QoE in Amendment 1 [17] in 2007. The QoE represents the user's subjective perception of the overall service acceptance of an application. The European Cooperation in Science and Technology (COST) proposed a more detailed white paper to explain QoE [18], which was included in ITU-T P.10 Amendment 5 in 2016 [19].  
The QoE focuses more on users' subjective experience than traditional QoS. Therefore, besides common network parameters such as throughput, delay or packet loss rate, users' expectation may also affect the QoE. However, users may have different requirements when using different network services in a mobile network. For example, authors [PERSON] and [PERSON] proposed a QoE measurement based on page response time (PRT) and video response time (VRT) [20] and summarized three key factors that affect PRT or VRT. Thesethree factors were used to quantify whether the service is good or not to meet the required QoE from users.  
The authors [PERSON] and [PERSON] [21] used the Moving Picture Quality Metric (MPQM) model to evaluate the MOS of video quality, which is based on the human spatio-temporal vision projection. The MPQM model is based on human spatio-temporal vision, and the process not only considers the effect of network parameters, but also includes the degradation of image quality caused by the compression and decompression of images.  
In [22], the authors proposed a QoS-aware flexible mobility management scheme for software-defined networking (SDN)-based mobile networks. The proposed QoS-based differential handover procedure for each class can be provided by the interaction between the forwarding plane nodes and SDN controller. The performance analysis showed that the proposed scheme could enhance flexible network resource utilization to meet the QoS requirements for each class. A handover management scheme for device-to-device (D2D) communication was proposed in [23]. The proposed approach predicted the destination eNB of D2D users based on their movements and the received signal characteristics. Therefore, both UE in a D2D pair resided in the same cell to increase throughput and reduce latency.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## 2 BLE Signal Collection Setup
### Signal Map Setup  
In this study, the indoor location is defined by the coordinates of positions in a two-dimensional space, relative to a coordinate reference point located at the bottom-left corner of the designed indoor space.  
For the signal map used in the localization models, a grid was established with 1 m spacing between 24 neighboring positions. At each position, signal measurements were taken while standing, once facing the left (180\({}^{\circ}\)) direction and once facing the right (0\({}^{\circ}\)) direction, resulting in 48 sample positions.  
Figure 3: Illustration of the gateway receivers layout in the indoor area (wall GWs and ceiling GWs).  
The BLE signal broadcasts at a frequency of 1 Hz, with signal measurements taken at each point for a duration of 3 min. All 10 gateways were used to capture signals from the 5 BLE beacons. Six test points, shown in green, were selected to measure the signal for 3 min, chosen to represent varying levels of signal reception difficulty. In total, 2700 signal data points were collected for the experimental models. This database has been shared with BLE signal researchers for further studies. Figure 4 illustrates the arrangement of signal measurement positions within the indoor space.
## 6 Solutions for Security and Trust in IoV
### Machine Learning-Based Solutions for Security
#### 6.2.1 Supervised-Learning-Based Solutions  
[PERSON] and [PERSON] describe an ML-based framework that uses fake alarm messages and positional falsification to detect vehicle misbehavior. By communicating information via a beacon, the vehicles keep each other informed about various events such as road conditions, accidents, emergency vehicles, and collision warnings. The authors of this work used beacons and information deviations from standard protocol conditions as input to ML algorithms. This study compared five different types of ML algorithms, with the decision-tree classifier achieving the greatest accuracy of 95% [135]. In [136], the authors proposed using k-nearest neighbors (kNN)and support vector machine models to detect malicious node attacks in VANET. By adapting KNN models to attack detectors, network features such as IP addresses, delays, jitters, dropped packets, and throughput are learned. The authors used an NS-3 network simulator to generate a multihop communication scenario, and the result showed 99% accuracy. In [137], the authors proposed a scheme to detect jamming attacks in a vehicular network. Recent work developed a data-centric misbehavior detection system for IoVs. This research is unique in that it combines plausibility tests with traditional supervised ML algorithms to improve detection accuracy. The authors evaluated the performance of six supervised ML algorithms using location and movement plausibility checks. With the added plausibility checks, the findings reveal a 5% and 2% gain in precision and recall, respectively [138]. In IoV networks, real-time data integrity is dependent upon the detection of data falsification. The IoT and ML are used to address vehicular network (VN) security challenges in [139]. The study addresses backdoor, DDoS, and MITM attacks and uses TON-IoT dataset ML algorithms for intrusion detection. Intrusion detection uses RF, NB, and KNN machine learning. KNN has the highest accuracy, demonstrating ML's ability to detect VN attacks. In [140], the authors proposed an IDS scheme to classify normal and malicious traffic messages in vehicle networks using eight supervised models. Their scheme demonstrated high performance. The authors in [141] proposed a Randomized Search Optimization Ensemble-based Falsification Detection Scheme (RSO-FDS) that uses Random Forest (RF) as its primary model. This scheme's efficacy in detecting falsification attacks is demonstrated by the performance evaluation and addresses the detection of falsification, which is necessary to maintain trust in the data shared between vehicles and IoV services. These studies provided a foundation for enhancing the security of vehicular networks and IoV with supervised machine learning. Future research should concentrate on addressing all of these obstacles and expanding the applicability of these techniques to situations in the real world.
## 2 Proposed Methodology
### DCNN Models
#### 2.3.1 ResNet-50  
ResNet-50 is a residual network deep learning model proposed by [39], shown in Figure 5. It has fifty layers, including a (1) convolutional layer, (2) max pooling layer, (3) deconvolution layer, and (4) Softmax layer. It accepts input images with a size of \(227\times 227\). ResNet-50 was selected for the PQD classification task due to its superior performance and effective solution to the vanishing gradient problem.  
Figure 4: An example of a time–frequency representation of PQDs with 20 dB noise: (**a**) flicker; (**b**) flicker + harmonics; (**c**) flicker + sag; (**d**) flicker + swell; (**e**) harmonics; (**f**) impulsive transient; (**g**) interruption; (**h**) interruption + harmonics; (**i**) normal; (**j**) notch; (**k**) oscillatory transient; (**l**) sag; (**m**) sag + harmonics; (**n**) spike; (**o**) swell; (**p**) swell + harmonics.
## 4 Empirical Results
### Generating Developer Metrics
#### 4.1.1 Single Metrics  
RQ2: Can we use the activities of GitHub users as a developer metric individually?  
Developer activity on projects was handled as a metric. Activity includes all kinds of comments, code contributions, revisions, and so on. In this section, all metrics were treated individually in order to evaluate the significance of each. These metrics refer to the number of activities per project for a given developer (Table 8).  
In addition to these metrics, we calculated the optimized metrics from the values of single developer metrics. We named these metrics with the prefix 'O_'. For example, name of optimized _pr_closed_ is _O_pr_closed_. We aimed to show the contribution ratio of each user for each project. For example, [PERSON] closed 10 PRs in a projectA, and 10 in projectB. The total number of closed PRs is 100 in projectA and 1000 in projectB. Therefore, [PERSON] contributed much more to projectA (10% contribution) than to projectB (1% contribution), as his closing PR ratio in projectA is higher. We calculated the rating with Equation (8).  
\[\textit{O\_activity}_{x}=\frac{\#\_of\_activity_{x}^{user}}{\#\_of\_total\_ activity_{x}^{project}} \tag{8}\]  
For comparison purposes, we added another metric proposed by [PERSON] et. al. They scored developers and projects using _like_, _star_, _create_ activities and used textual data extracted from projects' README and source code files to find project similarities [5]. Their dataset included approximately 22,000 repositories and 1700 developers. In our dataset, the ratio of number of developers to number of projects was approximately 1:400, in [PERSON] et al.'s study it was 1:14. We also planned to use this less sparsed dataset to make a fair comparison but could not because the dataset was unshared. As we were unable to communicate with the authors, we applied a very similar rating to our dataset.
## 3 Overview of Pseudo-Dynamic Testing and Result Analysis
### Experimental Results and Analysis  
Both the test specimen with no reinforcement applied (PD-FR) and the test specimen retrofitted with the CFT-MF system (PD-CFT-MF) were subjected to pseudo-dynamic testing and then tested for any cracks and breakage. Further, the resultant load-displacement curves (restoring force), temporal hysteresis loops with respect to the displacement, and maximum seismic response were analyzed to determine and compare the seismic retrofitting effect of PD-FR and PD-CFT-MF.