esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Performance Analysis of the CNN in this Work  
Traditional classifiers such as RF, KNN, and SVM can achieve good results when solving simple or completely constrained functions, but have limited capabilities of modeling for complex signals. However, the deep CNN has powerful capabilities of feature extraction and capabilities of modeling for complex signals [30]. In order to verify the classification performance of the CNN designed in this work, we compared its accuracy with traditional classifiers using the acoustic dataset published on the Internet. The Google Audio Set [31], an open environmental sound dataset, is the largest sound dataset with the most abundant sound categories. We selected three kinds of acoustic events (shots, screams, and car horns) from this dataset, and the number of sound samples for each category was 950. We divided them as 70% training, and 30% test set for each category to make an experiment of classification, and the results are as shown in Table 4.  
As can be seen from Table 4, for each category, the method of CNN presents a higher accuracy than the other classification methods. On average, the accuracy of CNN is 13.7%, 14.4%, and 18.4% higher than that of RF, KNN, and SVM, which presents that the excellent ability of CNN designed in this work makes it well suited to the recognition of acoustic events. In real-life scenarios, it is inevitable that the sounds of interest are superimposed to a significant level of background sounds, thus it might be negative to the recognition. It is necessary for us to verify the performance of the CNN in real-life applications, as explained in the following chapters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Gunshots** & **Screams** & **Car Horns** \\ \hline RF & 83\% & 78.5\% & 77\% \\ KNN & 79\% & 56.3\% & 86.2\% \\ SVM & 80.7\% & 70.7\% & 73.1\% \\ CNN & 97.3\% & 88.5\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of three kinds of sounds with four classification methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node Number** & **The Number of Data Received on the Server** & **Missing Rate** \\ \hline
1 & 2997 & 0.10\% \\
2 & 2995 & 0.16\% \\
3 & 2998 & 0.07\% \\
4 & 2997 & 0.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of data received on the server.  
TensorFlow, an open and stable source, is used to construct the CNN in the proposed system. In order to verify the reliability and efficiency of CNN, we performed the feature extraction and recognition with 10,000 samples of acoustic sounds and calculated the consuming time. The results are as shown in Table 5.  
As can be seen in Table 5, the average time for feature extraction is 182 ms and the average time for recognition is 0.23 ms. Most importantly, the feature extraction is an offline procedure and the recognition is online, thus we consider that 0.23 ms is enough for online conduction with high efficiency.
## 3 Experiment and Analysis
### Experimental Parameter Setting  
To visually showcase the efficacy of the proposed methodologies in this study, the reliability and proficiency of the network were ensured through comparative experiments involving several widely employed semantic segmentation models. The network optimization was performed using the stochastic gradient descent algorithm with a momentum parameter (Momentum) of 0.9. The initial learning rate (Learning Rate) was set to 0.001. During the training of the model, we used the freeze training strategy commonly used in deep learning. It is used to fix the parameters of certain layers in the early stages of model training and then fine-tune them in subsequent stages. To accelerate the model training process, the network was subjected to frozen training for a specified number of epochs (Epoch), which was set to 50. The batch size (Batchsize) was set to 8, and the total number of steps was set to 100. For the unfreezing part, a batch size of 4 was used. Considering the potential extreme situations during the capture of tilapia fish in real environments, where there might be significant differences in pixel values for different parts (e.g., only capturing a specific body part), a combination loss function of Focal Loss [28] and Dice Loss [29] was employed in this experiment to further enhance the model's prediction ability.
# Constitutive Characteristics of Rock Damage under Freeze-Thaw Cycles
## 3 Preparation of Samples and Their Associated Physical Characteristics
### Rock Mass Loss Rate and P-Wave Velocity  
Ultrasonic wave speed, as an effective means of non-destructive rock testing, can effectively explore the development of the interior of the rock, according to the ultrasonic wave speed difference, in order to judge the integrity of the interior of the rock, as shown in Equation (1).  
\[K_{m}=\frac{m_{s1}-m_{s2}}{m_{s1}}\times 100\% \tag{1}\]  
Figure 2: Relation of saturation density and porosity of rocks with the number of FTCs after freeze–thaw test: (**a**) Porosity; (**b**) Saturation density.  
Figure 3 shows the relationship between rock mass loss rate and longitudinal wave velocity with the number of FTCs; the values in the figure are the average values of each group of rocks under the same number of FTCs. By observing Figure 3, we can find that, with the reduction of FTC frequency, the loss of rock sample quality is also gradually reduced. However, in the first 10 FTCs, the variation range of rock mass loss rate is significantly greater than that in the subsequent FTCs (20, 30, 40). This is because the sample itself is in a certain breezy state when the damage frequency of the FTC is small, the rock sample is mainly concentrated in the surface layer, and the material spalling on the rock is the main reason for the substantial reduction of its mass. With the increase in FTC frequency, the damage was transferred to the inside of the specimen. The damage of the internal material does not leave the specimen, while the damage of the dry rock sample is 2813 m/s. The reason is that the pores of the saturated water sample are filled with water, and the wave speed in water is much larger than that in air. Second, the wave impedance of water is closer to the solid skeleton of rock than that of air. According to the elastic wave propagation theory, this will weaken the refraction and reflection of stress waves to a certain extent. The propagation path of the stress wave in the rock sample is shortened because of the reduction, which results in the velocity of the P-wave slowing down with time. The decrease in P-wave velocity is not only affected by the freeze-thaw cycle. It is also closely related to its internal structure and mineral composition. In the early FTC stage, the intercrystalline cohesion in rock is weaker than the frost heave force, and a large number of argillaceous cements in rock will significantly soften and argillify in the early FTC stage, which also has a significant impact on the P-wave velocity. The cementation between quartz particles will be weakened and the wave velocity will decrease.
## 3 Results
### Hemodynamic Characteristics
Mean vortex volume was increased in all regions (LPA: 3.55 \(\pm\) 2.04 mm\({}^{3}\) vs. 2.14 \(\pm\) 1.11 mm\({}^{3}\), \(p\) = 0.014; MPA: 19.00 \(\pm\) 8.71 mm\({}^{3}\) vs. 11.97 \(\pm\) 4.76 mm\({}^{3}\), \(p\) = 0.005; PA: 24.55 \(\pm\) 10.60 mm\({}^{3}\) vs. 15.58 \(\pm\) 5.41 mm\({}^{3}\), \(p\) = 0.004) except for the RPA (2.61 \(\pm\) 1.41 mm\({}^{3}\) vs. 2.09 \(\pm\) 1.19 mm\({}^{3}\), \(p\) = 0.175). Maximum KE attained was also lower in PHT patients (LPA: 1.1 \(\pm\) 0.7 mJ vs. 2.2 \(\pm\) 1.2 mJ, \(p\) \(<\) 0.001; MPA: 2.8 \(\pm\) 1.1 mJ vs. 4.2 \(\pm\) 1.9 mJ, \(p\) = 0.002; PA: 4.0 \(\pm\) 1.7 mJ vs. 7.3 \(\pm\) 3.3 mJ, \(p\) \(<\) 0.001; RPA: 0.7 \(\pm\) 0.5 mJ vs. 2.0 \(\pm\) 1.2 mJ, \(p\) \(<\) 0.001). Peak velocity did not differ in any region of the PA (RVOT: \(0.71\pm 0.26\) m/s vs. 0.82 \(\pm\) 0.17 m/s, \(p\) = 0.145; LPA: 0.71 \(\pm\) 0.27 m/s vs. 0.81 \(\pm\) 0.25 m/s, \(p\) = 0.210; MPA: 0.72 \(\pm\) 0.26 m/s vs. 0.84 \(\pm\) 0.22 m/s, \(p\) = 0.092; RPA: 0.69 \(\pm\) 0.31 m/s vs. 0.81 \(\pm\) 0.18 m/s, \(p\) = 0.173). Forward flow was lower in the RPA (26.08 \(\pm\) 13.12 mL vs. 33.82 \(\pm\) 12.08 mL, \(p\) = 0.042) of PHT patients but otherwise did not differ (RVOT: \(74.86\pm 37.25\) mL vs. 83.11 \(\pm\) 23.11 mL, \(p\) = 0.413; LPA: 43.10 \(\pm\) 27.79 mL vs. 43.15 \(\pm\) 13.29 mL, \(p\) = 0.995; MPA: 80.52 \(\pm\) 40.39 mL vs. 89.27 \(\pm\) 19.41 mL, \(p\) = 0.408). Similarly, net flow was also lower in the RPA (25.84 \(\pm\) 13.35 mL vs. 33.28 \(\pm\) 11.83 mL, \(p\) = 0.049) of PHT patients but otherwise did not differ (RVOT: 72.38 \(\pm\) 36.23 mL vs. 81.06 \(\pm\) 23.71 mL, \(p\) = 0.380; LPA: 43.04 \(\pm\) 27.84 mL vs. 42.58 \(\pm\) 13.52 mL, \(p\) = 0.949; MPA: 79.70 \(\pm\) 40.75 mL vs. 88.25 \(\pm\) 20.00 mL, \(p\) = 0.424). Retrograde flow (0.06 \(\pm\) 0.14 mL vs. 0.57 \(\pm\) 1.15 mL, \(p\) = 0.017) was lower in the LPA of PHT patients but otherwise did not differ (RVOT: 2.48 \(\pm\) 5.89 mL vs. 2.05 \(\pm\) 2.48 mL, \(p\) = 0.715; MPA: 0.81 \(\pm\) 1.01 mL vs. 1.02 \(\pm\) 1.65 mL, \(p\) = 0.637; RPA: 0.24 \(\pm\) 0.65 mL vs. 0.55 \(\pm\) 0.79 mL, \(p\) = 0.162). Furthermore, regurgitation fractions were decreased in the LPA (0.33 \(\pm\) 0.83% vs. 1.57 \(\pm\) 3.18% \(p\) = 0.043) and RPA (0.23 \(\pm\) 0.54% vs. 1.56 \(\pm\) 1.89%, \(p\) = 0.001) but did not differ in the RVOT (3.48 \(\pm\) 5.60% vs. 2.92 \(\pm\) 4.20%, \(p\) = 0.695) or MPA (1.78 \(\pm\) 3.78% vs. 1.32 \(\pm\) 2.21%, \(p\) = 0.588).
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 2 Methodology
### Human State Estimation  
A UWB polar frame \(U\) is attached to the UWB anchor. The robot's polar frame \(R\) is attached to the center of the robot. The UWB tag is fixed to the user's waist. The human position \(H_{U}(d^{*},\alpha^{*})\) in the UWB frame is assumed to coincide with the tag, which can be transformed into the robot frame as \(H_{R}(d,\alpha)\), derived by the following formula:  
\[\left\{\begin{array}{l}d=\sqrt{\left(d^{*}\cdot\sin\alpha^{*}\right)^{2}+ \left(d^{*}\cdot\cos\alpha^{*}-L\right)^{2}}\\ \alpha=\operatorname{atan}2\left(d^{*}\cdot\sin\alpha^{*},d^{*}\cdot\cos \alpha^{*}-L\right)\end{array}\right., \tag{1}\]  
where \(L\) is the length of \(UR\), which is a constant and equal to 0.75 m, \(d^{*}\) denotes the length of \(HU\), \(\alpha^{*}\) denotes the angle from \(UR\) to \(UH\). \(d\) denotes the length of \(HR\), \(\alpha\) denotes the angle from the robot's centerline to \(RH\). Human locomotion is assumed as a holonomic model [38] in this work and its state is denoted as:  
\[X_{H}=\left[\begin{array}{cccc}x_{H}&y_{H}&x_{H}&y_{H}\end{array}\right]^{\top} \tag{2}\]  
where the terms \(x_{H}\) and \(y_{H}\) represent the coordinates of a human in the global frame, while \(x_{H}\) and \(y_{H}\) denote the human's walking speeds in the x and y directions of the global frame, respectively. Within each sampling interval \(\Delta T\), human motion while walking can be regarded as uniform motion. The pose of the robot \(R_{O}(x_{r},y_{r},\theta_{r})\) in the world frame is obtained through visual odometry. Combining \(R_{O}\) and \(H_{R}\), we could obtain the position of the human in the world frame. Then, we fuse it with the human locomotion model to attenuate the noise by utilizing an extended Kalman filter (EKF). The estimated human state is denoted as \(\hat{X_{H}}(x_{H},y_{H},x_{H}^{*},y_{H}^{*})\).
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 2 Materials and Methods
### The Cabin Model
#### 2.2.2 Ventilation and Infiltration  
In order to enable proper operation of the HVAC system, windows are considered closed and the air conditioning system works in the air recirculation mode. As the vehicle body is not perfectly sealed, infiltration of external air occurs inside the vehicle cabin as a function of the pressure difference between the internal and external environments. The amount of air entering from the outside was experimentally studied by [PERSON] and [PERSON] (1994) [28] in the case of a stationary vehicle and in the case of a vehicle in movement. The infiltration flow rate can be calculated as follows [28]:  
\[m_{a,ext}=\rho_{air}\cdot\dot{V}_{a,ext}=\rho_{air}\cdot 0.1C\Big{(}\frac{ \rho_{air}}{2}\Big{)}^{n}v^{2n}\,, \tag{38}\]  
where \(C\) and \(n\) are experimental coefficients and depend both on vehicle typology and operative conditions [28], while \(v\) is the speed of the car. Therefore, the thermal load due to infiltration can be expressed as  
\[\dot{Q}_{in}=m_{a,ext}\cdot(h_{a,out}-h_{a,in})\,. \tag{39}\]  
The specific air enthalpy \(h_{a}\) can be calculated as follows:  
\[h_{a}=T_{a}c_{p,a}+\big{(}T_{a}c_{p,w}+r\big{)}x_{a}\,, \tag{40}\]where \(T_{a}\) is the temperature of the air, \(c_{p,a}\) is the specific heat of the air, \(c_{p,w}\) is the specific heat of water vapor, \(r\) is the latent heat of the vaporization of the water, \(x_{a}\) is the humidity ratio and \(\varphi\) is the relative humidity. The humidity ratio is a function of saturation pressure \(P_{sat}\) as follows:  
\[x_{a}=0.622\frac{\varphi P_{sat}}{101325-\varphi P_{sat}}\,, \tag{41}\]  
where  
\[P_{sat}=e^{\left(65.81-\frac{70.627}{T_{a}+273.15}-5.976 ln(T_{a}+273.15)\right) }\,. \tag{42}\]  
When the vehicle is not in motion, a volumetric air flow rate equal to \(10^{-3}\) m\({}^{3}\)/h is assumed.
## 2 Materials and Methods
### Laboratory Test on Mechanical Parameters of Filling Weak Surfaces
#### 2.1.2 Direct Shear Test of Layered Paste Filling  
The direct shear test was conducted with RMT-150C rock mechanics experimental system, as shown in Figure 1, and the inner wall size of the direct shear test box was 150 mm \(\times\) 150 mm \(\times\) 150 mm. The direct shear specimen was placed in the shear box, and a height of 10 mm was reserved between the upper and lower shear boxes for shearing, as shown in Figure 2. The direct shear test adopts the force (large) displacement control method of the test system. According to the standard for test methods of engineering rock mass, the axial force is controlled by force stroke, and the loading rate is 500 N/s; shear force is controlled by displacement; and the loading rate is 5 \(\times\) 10\({}^{-5}\) m/s. A total of four groups of direct shear tests of filling weak surfaces under normal stress (0.5 MPa, 1.0 MPa, 1.5 MPa, 2.0 MPa) were conducted. The test loading process was vertical preload \(\rightarrow\) horizontal preload \(\rightarrow\) horizontal load \(\rightarrow\) specimen failure.  
In the direct shear test, the position of the shear failure surface and the filling weak surface basically coincide, and the shear failure surface was neat and regular, and the shear failure surface was smooth and flat. It can be concluded that when the layered paste filler was subjected to large shear load as a whole, shear slip is most likely to occur at the weak  
Figure 1: Rock mechanics experimental system.  
Figure 2: Shear failure diagram.  
filling surface. Layered paste filler had fewer other failure types near the weak surface during shear-slip process, which fully indicates that the mechanical strength of the weak surface is much lower than that of surrounding filler.  
The shear stress-strain curve divides the shear failure process of filling weak surfaces into two stages, strain-softening stage and strain-hardening stage, as shown in Figure 3a. The peak shear stress of filling weak surface increases with the increase in normal load and the strain reaching the peak shear stress migrates backward. Based on Moore-Coulomb strength criterion, as shown in Figure 3b, the parameters of shear strength of filling weak surfaces are obtained by linear fitting with least squares method, with cohesion of 0.105 MPa and internal friction angle of 20.49\({}^{\circ}\).
# A Study of Precision Current Efficiency Curve Measurement with a Casing-Type Anode
## 6 Conclusions  
This study proposed a highly accurate current efficiency curve measurement technique with a casing-type anode. In this method, the specimen being measured was designed into two parts: the mandril and the casing. With this design, the edge stray corrosion becomes concentrated on the casing, and only the current distribution on the mandril can thus be taken into consideration for the calculations of the value of current efficiency. The working principle of the proposed method was demonstrated by carrying out the electric field simulations and the current efficiency measurements of 304 SS. Compared to the conventional method, the side and edge effects were significantly reduced, and the current distribution on the end face of the mandril was considered uniform. Thus, by using the proposed measurement method, the precision of current efficiency measurements can be highly improved, especially in the low current density region.  
Conceptualization, H.W.; investigation, H.W.; Writing--Original draft preparation, H.W.; resources, D.Z. and J.L.; supervision, J.L.; Writing--Review and editing, J.L. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Joint Funds of Natural Science Foundation of China and Guangdong Province (Grant U1601201), the National Natural Science Foundation of China for Creative Research Groups (Grant 51921003), National Science and Technology Major Project (Grant 2017-VII-0004-0097).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
### Techno-Economic Analysis Research on e-Fuel Production
With an electricity price of 0.03 USD/kWh for water electrolysis and a 45 V tax credit, the cost of SNG reaches paritywith fossil natural gas (NG). The 45 V tax credit enhances the economic feasibility of SNG, making it a competitive alternative to fossil NG [74]. Quantitative economic analysis. Studies have evaluated the techno-economic feasibility of e-fuel production across various countries, focusing on metrics such as minimum fuel selling price (MFSP), levelised cost of methanol (LCoM), and levelised cost of electricity (LCoE). For instance, the MFSP for e-gasoline and e-diesel in KSA is USD 3.24 and USD 2.89 per litre, respectively. Economic analysis in Italy indicates that the LCoM is 960 EUR/t, and another study in Germany projects the production costs of e-fuels to drop to 1.8-3.1 EUR/kg by 2050. Technical parameters. Key technical parameters considered in these studies include efficiency, effectiveness, and specific energy consumption. For example, a study in the UK reports a power-to-liquids efficiency of 25.6% and a carbon conversion efficiency of 88%. In the USA, another study details a 70% process energy efficiency for FT synthesis processes.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements  
After the implementation of Release 13 features, studies erupted along with field trials that revealed the need for further enhancements to improve the quality of service as well as user experience. In this regard, 3 GPP introduced further enhancement features to NB-IoT.  
The enhancements features in Release 14 include positioning update, multicast services, and a new UE output power class in which the NB-IoT system throughput, mobility, service continuity and non-anchor carrier operation are improved [29; 30].
# Impact of Mega Constellations on Geospace Safety
## 4 Impact of Starlink on Control Avoidance Strategies for low-orbiting Satellites in Geospace
### Impact on ACPL Values for LEO Space Target Satellites
YAOGAN-35C's ACPL value of \(10^{-4}\) increases the frequency of avoidance control by about 20.76 times, and the collision risk is 2.63 \(\times\)\(10^{-6}\) and 1.65 \(\times\)\(10^{-4}\), respectively, becoming 62.73-times higher before Starlink is considered. In Figure 8, the relationship between the frequency of control avoidance and its corresponding risk coefficient and risk, the following conclusion can be drawn: after considering Starlink, the growth of the operation frequency does not guarantee that the risk coefficient decreases or even returns to the original tier, and the growth of the operation frequency is even accompanied by the growth of the risk coefficient in the case that the growth of the operation frequency is not of a large order of magnitude.
## 3 Results and Discussion
### Atomic Force Microscope Indentation  
Table 3 and Figure 11a show the stiffness values and the force-displacement curves of the AFM indentation on the free-stand two-layer graphene and Gr/AgNW/Gr composites. Figure 11b,c shows the SEM images of the two-layer graphene and the Gr/AgNW/Gr nanocomposite after the AFM indentation. The cut-wire phenomenon observed on the nanocomposite indicates the strengthening effect of the AgNW on increasing the mechanical property of the nanocomposite. Compared with two-layer graphene, the stiffness of the composite increases with higher AgNW concentrations, while it becomes stable after 0.6 mg/mL with the value of around 2150 N/m, which indicates the maximum stiffness enhancement by the AgNW network reaches 0.6 mg/mL. This is owing to the limited interfacial bonding strength between the graphene layer and the AgNW network. When the AgNW concentration is higher than 0.6 mg/mL, the interfacial bonding between the Gr and AgNW dominates the stiffness of the nanocomposite. Therefore, the continuously increased AgNW concentration after 0.6 mg/mL cannot enhance the mechanical strength of the nanocomposite.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 5 Simulation Results and Analysis
### Average Number of Hops and Average Energy Consumption  
(1) Average number of hops  
To some extent, the average number of hops (ANH) can qualitatively reflect the data transmission delay in the network, that is, the smaller the average hops is, the faster the data can be transmitted to the sink node. Figure 6 is a comparison of the average hops of the three algorithms. It can be seen that, with the increase of the network size, the ANH curve has an upward trend, but DS-EERA remains the lowest, this is because the \"energy transmission ratio\" of the algorithm in this paper considers the shortest path as much as possible, so as to reduce the number of hops. At the same time, the \"idleness degree\" index considers the time of packet queuing and processing, and the combination of the two can effectively reduce the delay.  
(2) Average energy consumption  
The average energy consumption (AEC) represents the average energy consumption of all nodes in each data transmission round, which is used to measure the energy consumption rate of the network. Figure 7 is a comparison of average energy consumption using different algorithms, as can be seen from that, the DS-EERA in this paper has the lowest average energy consumption and can effectively balance the energy utilization, so its network lifetime is also the highest (Figure 4). The AEC and EV of MCRP and FLEOR algorithms are large, so the energy utilization is insufficient, resulting in lower network lifetime.  
Figure 6: The average number of hops (ANH) comparisons with varying network size.