esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Performance Analyses of 5G Sharing Voice Solution
## 5 Performance Analysis
### SA Sharing VoNR Performance Analysis
#### 5.4.2 Handover from VoNR to VoLTE  
Experiments were performed on Inter-RAT-based VoNR to VoLTE handover according to Section 4.3 5G network coverage discontinuity scenario. The test results are shown in Table 4.  
The average setup delay is 1032 ms, which is much shorter than the EPS fallback setup delay. VoNR to VoLTE can achieve smooth handover. For the 5G-to-4G handover delay, the control plane delays are better than EPS fallback.  
According to Figures 6 and 8 and Equations (4), (5) and (7), it can be seen that during the handover process from VoNR to VoLTE, there is less interoperation between the UE and the gNodeB. In Equation (7), the coefficient of \(\delta\) is 2, indicating that the UE has only two communications with the gNodeB in the whole process. In the process of EPS fallback, whether it is based on handover or redirection, it can be seen from Equations (4) and (5) that the coefficient of \(\delta\) is over 10, indicating that the UE communicates with the gNodeB over 10 times in the whole process. Therefore, theoretically, the handover delay from VoNR to VoLTE should be shorter than the handover or redirection delay of EPS fallback. The test results in this chapter also fully confirm the results of the theoretical analysis in Section 4.  
Based on the field network verifications, the following conclusions can be drawn: In the 5G NSA sharing VoLTE service scenario, the non-back-to-home voice solution is consistent with the theoretical analysis and has a smaller call setup delay of about 2.2 s. The handover-based EPS fallback has a shorter call setup delay of about 0.8 s compared with the redirection, but since the fallback is more complex than the handover process in VoLTE,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Call Success Rate** & **Call Setup Delay (Average)** & **Control Plane Time Delay** & **MOS (Average)** \\ \hline
100\% & 1032 ms & 19.36 ms & 4.27 \\ \hline \end{tabular}
\end{table}
Table 4: Average Delay for VoNR to VoLTE Handover.  
Figure 17: VoNR and EPS Fallback test results.  
the call setup delay is 0.9 s higher than the VoLTE solution. VoNR has the best network setup delay of about 2.0 s according to field experiments. Therefore, voice service needs to evolve to VoNR as soon as possible to bring a better network experience to users.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 4 Network Indicator System Construction
### Node Load Rate  
The node load rate is defined as the ratio of the total traffic cache and forwarded packets to the node's actual cache capacity. In scenarios without packet loss at the node, a lower load rate indicates that tasks are being forwarded to other nodes, thereby increasing node utilization and enhancing overall network performance. However, during network topology changes or disruptions, the cache of the node increases, leading to a rise in the node load rate as some tasks may not be promptly forwarded in time. Therefore, this paper introduces the average node load rate to assess the performance of the IoV mesh network.  
\[S\_node=\frac{\sum\frac{\mu_{i}+L_{i}}{C_{i}}}{N} \tag{8}\]  
The above equation is the average load rate of the nodes in the network, \(\mu_{i}+L_{i}\) denotes the sum of the traffic cache and the number of forwarded packets of the vehicle node \(i\), and \(L_{i}\) is the cache length at the current moment after updating according to Section 3.3. \(C_{i}\) indicates the actual caching capacity of the vehicle node \(i\), and \(N\) represents the total number of nodes in the network.
# A Review of Research on Dropwise Condensation Heat Transfer
## 4 Methods, Problems, and Solutions for Promoting Dropwise Condensation
### Methods for Promoting Dropwise Condensation
#### 4.1.2 Micro-Nano Structure Surface
For example, [PERSON] [55] fabricated an array of carbon nanotube films on the surface of quartz glass sheets by the high-temperature pyrolysis method, with the contact angle with water as high \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanostructure** & **Reference** & **Methods** & **Substrate** & \begin{tabular}{c} **Deposition** \\ **Materials** \\ \end{tabular} & **Contact Angle** \\ \hline \hline \multirow{8}{*}{\begin{tabular}{c} Nanowires \\ (nanoneedles) \\ \end{tabular} } & [55] & Pyrolysis & Quartz glass & \begin{tabular}{c} Carbon \\ nanotube \\ \end{tabular} & \(161^{\circ}\) \\ \cline{2-6}  & [57] & Chemical & Cotton & ZnO & \(156^{\circ}\) \\ \cline{2-6}  & [58] & Spray & Copper & \begin{tabular}{c} carbon \\ nanotubes \\ \end{tabular} & \(162^{\circ}\) \\ \cline{2-6}  & [69] & Chemical & — & \begin{tabular}{c} SiO\({}_{2}\)-coated \\ SiC \\ \end{tabular} & \(153^{\circ}\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Nanocones \\ \end{tabular} } & [60] & \begin{tabular}{c} Electro- \\ deposition \\ \end{tabular} & Copper & Ni & \(153.6^{\circ}\) \\ \cline{2-6}  & [61] & Oxygen & PTFE & Polystyrene & \(144^{\circ}\) \\ \hline \multirow{4}{*}{Nanorods} & [57] & Chemical & Cotton & ZnO & \(167.5^{\circ}\) \\ \cline{2-6}  & [63] & Chemical & Glass and & ZnO-a-Gs & \(120.8\) \\ \cline{2-6}  & [70] & \begin{tabular}{c} Electro- \\ deposition \\ \end{tabular} & Zinc & ZnO & \(167\) \\ \hline \multirow{4}{*}{Nanosheet} & [64] & \begin{tabular}{c} Electro- \\ deposition \\ \end{tabular} & Copper & C\({}_{36}\)H\({}_{90}\)CuO\({}_{4}\) & \(130.5^{\circ}\) \\ \cline{2-6}  & \begin{tabular}{c} Hydrothermal \\ \end{tabular} & Copper & CuO & \(124.7^{\circ}\) \\ \cline{2-6}  & [65] & Hydrothermal & Copper & CuO & \(172^{\circ}\) \\ \cline{2-6}  & [66] & Chemical & Fluorine doped & SnO\({}_{2}\) & \(125^{\circ}\) \\ \cline{2-6}  & [9] & \begin{tabular}{c} Chemical \\ vapor \\ deposited \\ (CVD) \\ \end{tabular} & Copper & \
\begin{tabular}{c} Nano \\ graphene \\ particles \\ \end{tabular} & \(87^{\circ}\) \\ \cline{2-6}  & [67] & Spray & Glass & ZnO & \(154^{\circ}\) \\ \cline{2-6}  & [68] & Direct melt & Ceramic &
\begin{tabular}{c} Rare Earth \\ Oxide \\ \end{tabular} & \(100^{\circ}\)–\(120^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Typical micro-nano structures. as 161\({}^{\circ}\) and the contact angle after fluorosilane treatment as 172\({}^{\circ}\). However, the surface hydrophobicity obtained by [PERSON] [56] using the same method of the copper substrate was only 134\({}^{\circ}\). Many scholars focused on how to achieve dropwise condensation during the process of promoting dropwise condensation by surface modification but neglected another question: can dropwise condensation enhance heat transfer? What are the main reasons that dropwise condensation enhances heat transfer?
## 2 Materials, Methods, and Experiment Procedure  
The workpiece material examined during this study was the AZ91D (MgAl9 Zn1) magnesium alloy. Milling operations were realised on an AVIA VMC 800 HS milling centre. Three different types of HSK-A63 toolholders were tested: ER, shrinkfit, and hydraulic toolholders. The unbalance of the holder-tool set was verified on a CIMAT RT 610 balancing machine according to the ISO 21940-11 standard [37].  
A view of the toolholders with the mounted cutting tool is shown in Figure 1.  
The research was performed at variable milling parameters including a cutting speed \(v_{c}=400\)-\(1000\) m/min and feed per tooth \(f_{z}=0.05\)-\(0.30\) mm/tooth, while the axial depth of cut \(a_{p}=6\) mm and the radial depth of cut \(a_{e}=25\) mm were constant. This parameter selection was determined by the milling centre spindle speed, experience in magnesium alloys machining, and the generated Stability Lobe Diagrams. A carbide end mill with TiAlN coating was selected for the realisation of the milling tests. The tool had a diameter of 16 mm and two cutting edges. The machining process was conducted through a trochoidal strategy. Siemens NX 10 software was used to generate the toolpaths and the trochoidal pitch was set at 15%. A view of the generated trochoidal toolp  
Figure 1: View of ER, shrinkfit, and hydraulic toolholders with mounted cutting tool.  
During machining tests, vibration measurements were taken in the feed direction. The vibration sensors used were the optoNCDT LD1605-2 laser sensor (displacement measurements) and the PCB 352B10 accelerometer (acceleration measurements). Vibration sensors are characterised by their high universality and ease of application. The accelerometer was fixed directly in the centre of the front surface of the machined workpiece. The laser beam of the displacement sensor was also directed at the same front surface of the workpiece. This sensor was positioned at a distance of less than 2 mm from the workpiece. CutPro 9 software was used to generate SLDs based on the signal recorded with the modal hammer.  
The signals registered during machining were analysed according to the following indicators: peak-to-peak and root mean square (RMS). The value of the peak-to-peak indicator was determined as the difference between the maximum and minimum values of the recorded vibrations signal. The RMS values of the vibration displacement and acceleration were determined according to Equations (1) and (2).  
\[x_{RMS}=\sqrt{\frac{1}{T}\int_{0}^{T}x^{2}(t)dt} \tag{1}\]  
\[a_{RMS}=\sqrt{\frac{1}{T}\int_{0}^{T}a^{2}(t)dt} \tag{2}\]  
where \(x(t)\)--displacement; \(a(t)\)--acceleration; and \(T\)--time.  
The Composite Multiscale Entropy (CMSE) algorithm, which enables a detailed insight into its concept and computational steps, was described extensively in [38]. It is an enhanced solution to other methods based on the entropy phenomenon used so far. The complete calculation procedure was carried out in Matlab R2019b software.
## 6 Experimental Results
### Comparative Analysis
#### 6.2.1 Analysis of Energy Consumption  
The energy consumption in the SDWSN is defined as the amount of energy spent by the WSN nodes for performing sensing in the environment. Mathematically, the difference among the initial and consumed energy reveals the energy consumption of the WSN node. The formulation is provided below:  
\[\text{Ene}=\text{Tot}^{\text{ene}}-\text{Con}^{\text{ene}} \tag{39}\]  
where \(\text{Tot}^{\text{ene}}\) is the total energy and \(\text{Con}^{\text{ene}}\) is the consumed energy by the WSN nodes.  
Figure 6 represents the comparison of the energy consumption rate for the SDWSN nodes among the proposed MTS-SDWSN and existing works SEMRP-WSN and EDASS, respectively. The graphical inference reveals that the energy consumption gradually increases with an increase in SDWSN nodes. Intuitively, the proposed work gains lesser energy consumption than the existing works. The reason for such lesser energy consumption is the utilized clustering and cluster management methods, respectively. More especially, the clustering is performed using the DWTMB algorithm, which ensures precise and robust clusters, whereas in the worst case, we also perform cluster management by performing cluster splitting and merging based on the density and mobility of the SDWSN nodes. In disparity, the existing works SEMRP-WSN and EDASS lack in performing cluster the WSN nodes, which results in higher energy consumption as all the sensor nodes perform transmission and reception to a longer range. On the whole, the inference results reveal that the proposed work gains lesser energy consumption than the existing models.  
The quantitative analysis from the graph shows that the proposed MTS-SDWSN gains energy consumption of 23 J when the number of SDWSN nodes reaches 100, whereas for the same SDWSN nodes the energy consumption rate of SEMRP-WSN and EDASS gains with 40 J and 47 J, respectively, which is 17 J-24 J higher than our proposed model.  
\begin{table}
\begin{tabular}{c c} \hline
**Simulation Parameter** & **Description** \\ \hline No. of cloud server & 1 \\ \hline Packet size & \(2^{8}\) bytes \\ \hline Transmission rate of packets & 2 packets/sec \\ \hline MAC protocol & IEEE 802.11p \\ \hline Data transmission rate & 3 Mbps \\ \hline Area for simulation & \(1000\times 1000\) \\ \hline Simulation time & 300 s \\ \hline Routing protocol for SDN & Open flow \\ \hline Transmission range of nodes & 25 m to 50 m \\ \hline Size of payload & \(2^{8}\) bytes \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 4 Results
### Evaluation of Simulated Typhoon Wind Speed
On 6 July 2016, at 22:00, the maximum wind radius simulated by the empirical parameterized typhoon model Jelesnianski was found to be closer to the SMAP data compared to the Holland model. The Holland model tends to overestimate the high wind speed regions of the typhoon. Regarding the WRF simulations, the use of FNL data for model driving resulted in stronger wind speeds compared to simulations driven by ERA5 data. This demonstrates that the choice of driving data significantly influences the wind speed simulations in WRF and using more accurate and realistic driving data can enhance the performance of wind speed predictions in the WRF model.
# Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS
## 5 Discussion
### Challenges  
The results of this research also highlighted numerous challenges that forensic examiners might face, particularly the disparities in the quantity of retrieved data available on different smartphones and operating systems. Moreover, it was found that the time between the data population and the device acquisition could have an impact on how many data could be retrieved. During this investigation, we used two different iOS devices, an iPhone SE and the iPhone X. At first, we used the iPhone SE with iOS version 14.0.1. After the data population, the image acquisition process involved Cellebrite UFED 4 PC and Physical Analyzer acquiring the advanced logical image. Upon further examination and analysis of the created image, no significant data were found related to the SimpliSafe application and the activities carried out. The time between the data population and the data acquisition was more than a few days. The initial data population was conducted on 19 August 2022, and the acquisition was performed on 24 August 2022. This could be a possible explanation for the missing data. The second data population was performed on an iPhone X with iOS 14.2 (18B92). The process for the data population was very similar to the first, with some minor variations. The main difference was the phone and the time between the data population and the acquisition, which was performed on the same day. The second method generated more results. Due to negligent relevant findings from the first acquisition of the iPhone SE, only the findings recovered from the iPhone X were reported in this research in Section 4.2 (iOS Findings). The data population process was the same for both devices, leading us to believe that the time between population and acquisition might be essential to the investigation. This hypothesis, however, needs to be tested further to prove the exact reason for the variation in the quantity of recoverable data.  
During the data population phase, the SimpliSafe system needed to be unlinked from the Android account (email address) before it could be used with the Apple account. For this to happen, SimpliSafe requires customers to contact customer service to have them unlink the system on their end, a time-consuming process. Despite the inconvenience, this step adds another layer of protection for customers, since a stolen or second-hand preconfigured SimpliSafe system cannot be linked to a new account without having prior information about the previous owner.
## 4 Physical Features and Response Characteristics
### Harvesting Physical Features from the Network
The information gathering process plays a vital role in the autonomous framework. This does however incur additional overhead to the network. Depending on how dynamic the network must be to change, this overhead can be more or less significant. Once all the information from the network is gathered, the network manager can extract all physical features necessary to complete the models and calculate expected performance levels for each solution. Matching the correct solution to the application preferences, described in Section 6, is then necessary. Figure 5: Information gathering process instigated by network manager. (**a**) The network manager sends a message to the root of the network instigating the information gathering process; (**b**) The root relays this message to all nodes in the network; (**c**) Each node in the network sends out a beacon requesting information from its neighbours; (**d**) The neighbouring nodes send a reply and a neighbour list is generated with physical link information included; (**e**) Each node in the network sends its neighbour list back to the root of the network and this information is then passed to the network manager; (**f**) The network manager maintains a virtual topology with the network information from which it can extract the physical features.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### End-to-End Speech Synthesis
#### 4.6.3 Speech Synthesis Based on Convolutional Neural Networks (CNNs)  
Although the Tacotron-based end-to-end system has achieved promising performance recently, it still has a drawback that there are many recurrent units. This kind of structure makes it quite costly to train the model and it is also infeasible for researchers without high-performance machines to conduct further research on it. To address this problem, a lot of works have been proposed. The authors in [69] proposed a deep convolutional network with guided attention which can be trained much faster than the RNN-based state-of-the-art neural system. Different from the WaveNet model, which utilized the fully-convolutional structure as a kind of vocoder or a back-end, Ref. [69] is rather a fond-end (and most of back-end processing) that can synthesize a spectrogram. The authors in [75] used CNN-based architecture for capturing long-term dependencies of singing voice and applied parallel computation to accelerate the model train and acoustic feature generation processes. The authors in [76] proposed a novel, fully-convolutional character-to-spectrogram architecture, namely DeepVoice 3, for speech synthesis, which enables fully parallel computation to make the training process faster than that of using recurrent units.
# Do Heavy Vehicles Always Have a Negative Effect on Traffic Flow?
## 4 Speed Changes According to Traffic Volume and Heavy Vehicles Ratios
### Changes According to the Proportion of Heavy Vehicles  
The changes in speed based on the change in the ratio of the heavy vehicles were analyzed for the traffic flows with similar traffic volumes (or traffic flow rates). The speed variation is expressed as the difference between the maximum speed, minimum speed, average speed, and standard deviation of the speed with the legend shown in Figure 8. Using this legend, Figures 9-14 show speed variations according to the weight of heavy vehicles for the traffic flows with similar traffic volumes. It is known that in the case of the traffic flows with similar traffic volumes, converted traffic volume increases due to the existing research results and the PCE when the proportion of heavy vehicles increases, resulting in a drop in the service level of traffic flow. Decreasing service levels of traffic flow lead to decreasing average speed and increasing density. Given these characteristics, Figures 9-14 show a downward curve or line as the slope decreases toward the right side of the graph.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\% of HV** & **Regression Model (First-Order Perimeter)** & \(\mathbf{R}^{2}\) & **Number of Datasets** \\ \hline
0 & \(v=-1.137q+109.34\) & 0.932 & 117,338 \\
15 & \(v=-0.012q+102.39\) & 0.927 & 46,734 \\
30 & \(v=-0.971q+97.66\) & 0.793 & 8590 \\
50 & \(v=-0.155q+94.46\) & 0.233 & 4433 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Relationship of flow rate–speed by the proportion of heavy vehicles.  
Figure 8: Legend of points and lines in relation figures.  
Figure 10: Relationship of ratio of heavy vehicles–speed 2 (flow rate: 200–300 veh/h/ln).  
Figure 9: Relationship of ratio of heavy vehicles–speed \(1\) (flow rate: 0–100 veh/h/ln).  
However, according to the actual data of the Seoul-Chuncheon Expressway, even though the proportion of heavy vehicles increases, the speed does not decrease significantly, but remains at a constant level. It is difficult to conclude that an increase in the proportion of heavy vehicles was not related to the decrease in service level, as speed is insensitive to changes in the service level of the road. However, it is possible to explain that the traffic flow was stabilized as the proportion of the heavy vehicles increased based on the phenomenon that the width of the speed change (the vertical line and the box size in Figures 9-14) decreased as the proportion of the heavy vehicles increased. This is similar to the argument presented by [PERSON] et al. [15], which is shown in the literature review section. In other words, even within the traffic flows with similar traffic volumes, as the proportion of heavy vehicles increases, the speed deviations among vehicles decrease, which leads to the stabilization of the traffic flow.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Product of Atmospheric Damping Factor and Cross-Section  
The calculation and measurement method of atmospheric regenerative acceleration was addressed. Equation (2) indicates that to invert the thermospheric atmospheric density, it is necessary to accurately calculate the effective cross-sectional area of the satellite and the atmospheric damping coefficient.  
The areas of the 15 panels of the SWARM-A satellite and the normal unit vectors of the panels in the instrumental coordinate system are provided in Table 3[47]. The atmospheric damping coefficient of the SWARM-A satellite can be calculated from the product of the atmospheric damping coefficient and the effective area following Equation (2), and the variation is illustrated in Figure 3 below.  
Figure 3 illustrates the temporal variation of the product of the atmospheric damping coefficient and the cross-sectional area of the SWARM-A satellite [51]. The data presented are for day 119 of 2024. It can be observed that this parameter attains a maximum value of approximately 2.586 and a minimum value of approximately 2.568 over a day, exhibiting a relative deviation of approximately 0.69%. This is primarily attributable to the disparate atmospheric temperatures on the day and night sides, which exert a profound influence on the damping coefficient. Additionally, the satellite attitude exerts an impact on the angle between the incident atmospheric molecular flow and the satellite panels, which, in turn, affects the outcomes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Panel Name** & **Panel Size** & **X** & **Y** & **Z** \\ \hline Nadir 1 & 1.540 & 0.0 & 0.0 & 1.0 \\ Nadir 2 & 1.400 & \(-\)0.19766 & 0.0 & 0.98027 \\ Nadir 3 & 1.600 & \(-\)0.13808 & 0.0 & 0.99042 \\ Solar Array +Y & 3.450 & 0.0 & 0.58779 & \(-\)0.80902 \\ Solar Array & 3.450 & 0.0 & \(-\)0.58779 & \(-\)0.8090 \\ \(-\)Y & 3.450 & 0.0 & \(-\)0.58779 & \(-\)0.8090 \\ Zenith & 0.500 & 0.0 & 0.0 & \(-\)1.0 \\ Front & 0.560 & 1.0 & 0.0 & 0.0 \\ Side Wall +Y & 0.753 & 0.0 & 1.0 & 0.0 \\ Side Wall \(-\)Y & 0.753 & 0.0 & \(-\)1.0 & 0.0 \\ Shear Panel & 0.800 & 1.0 & 0.0 & 0.0 \\ Nadir Front & & & & \\ Shear Panel & 0.800 & \(-\)1.0 & 0.0 & 0.0 \\ Nadir Back & 0.600 & 0.0 & 1.0 & 0.0 \\ Boom +Y & 0.600 & 0.0 & \(-\)1.0 & 0.0 \\ Boom Zenith & 0.600 & \(-\)0.23924 & 0.0 & \(-\)0.97096 \\ Boom Nadi & 0.600 & 0.22765 & 0.0 & 0.97374 \\ \hline \end{tabular}
\end{table}
Table 3: The 15 panel sizes and normal vectors of the SWARM-A satellite, including every panel name, panel size and the components of X, Y, and \(Z\) in the three opposite directions.