esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization Considering Porosity Defects in Metal Additive Manufacturing
## 3 Hole Nucleation Method
### Topological Sensitivity  
The topological sensitivity defines the impact on the objective when a small hole is inserted at a certain position in the design domain. The topological sensitivity of the objective \(J(\Omega)\) in a given design domain is defined as:  
\[D_{T}(\mathbf{x})=\underset{r\to 0}{\lim}\frac{J(\Omega_{r})-J(\Omega)}{M(r)} \tag{22}\]  
where \(M(r)\) is the measure of the hole \(B_{r}\). According to reference [47], this paper takes the Lebesgue measure, which can be written as:  
\[\underset{r\to 0}{\lim}M(r)=0 \tag{23}\]  
According to the topological-shape sensitivity method [47], topological sensitivity can be solved by shape sensitivity. The relationship between shape sensitivity and topological sensitivity is expressed as:  
\[D_{T}(\mathbf{x})=D_{T}^{*}(\mathbf{x})=\underset{r\to 0}{\lim}\frac{1}{M^{\prime}(r) \left|V_{N}\right|}\frac{dJ(\Omega)}{d\theta}\bigg{|}_{\theta=0} \tag{24}\]  
This paper aims to study the topology optimization problem based on the compliance, so it is necessary to solve the topological sensitivity of the compliance. According to the research of [PERSON] et al. [48], the topological sensitivity of compliance is:  
\[D_{T}J(\mathbf{x})=\frac{\pi(\lambda+2\mu)}{2\mu(\lambda+\mu)}(4\mu E(\mathbf{u}) \cdot\mathbf{e}(\mathbf{u})+(\lambda-\mu)tr(E\mathbf{e}(\mathbf{u}))tr(\mathbf{e}(\mathbf{u})))(x) \tag{25}\]for \(d=3\)  
\[D_{T}J(\mathbf{x})=\frac{\pi(\lambda+2\mu)}{\mu(9\lambda+14\mu)}(20\mu E\epsilon(u) \cdot\epsilon(u)+(3\lambda-2\mu)tr(E\epsilon(u))tr(\epsilon(u)))(x) \tag{26}\]  
where \(\lambda\) and \(\mu\) are the Lame moduli of the material, which satisfy:  
\[\lambda=\frac{E\
u}{1-\
u^{2}} \tag{27}\]  
\[\mu=\frac{E}{2(1+\
u)} \tag{28}\]
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 5 Summary and Prospect  
With the increase in train speed and the development of modern train structures, the noise problem has become more pressing, and solutions thereto more complicated. The development of high-speed trains also brings new opportunities and challenges for future train noise research. To cope with the train noise problem, it is necessary to follow the development of science and adopt new materials, new research tools, and new theories. In this review, we have identified the following problems and made corresponding suggestions:  
1. The main component of high-speed train noise has gradually changed from mechanical vibration noise to aerodynamic noise, and with the further increase in running speeds in the future, the aerodynamic noise will be further intensified. The structure of the high-speed train pantograph, high-speed train skirt, and high-speed train connection should be further optimized, and the noise problem should be emphasized in the optimization process.
2. The acoustic insulation level of traditional railroad sound barriers is limited and vulnerable to air pulsation stress. The introduction of acoustic metamaterials can largely improve their acoustic insulation performance, but at present, the structure of acoustic metamaterials is complicated, and their processing cost is high, so research on their manufacturing process and batch production is warranted.
3. It is difficult for the current body structure of high-speed trains to cope with low-frequency noise, and the narrow body structure cannot be arranged with thicker materials to realize sound absorption and insulation. Thus, future acoustic metamaterials may be the best means of achieving sound absorption and insulation in the train body.  
Conceptualization, H.Y. and S.X.; methodology, H.Y.; software, H.Y.; formal analysis, H.Y., K.J. and Z.F.; writing--original draft preparation, H.Y., S.X., K.J. and Z.F.; writing--review and editing, H.Y., S.X., K.J. and Z.F.; supervision, S.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51775558). This paper was also supported by the Nature Science Foundation for Excellent Youth Scholars of Hunan Province (Grant No. 2019J[30034]) and the Shenghua Yu-ying Talents Program of the Central South University (Principle Investigator: [PERSON]).  
Not applicable.  
This research was undertaken at the Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, China. The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant No. 51775558). This paper was also supported by the Nature Science Foundation for Excellent Youth Scholars of Hunan Province (Grant No. 2019J[30034]) and the Shenghua Yu-ying Talents Program of the Central South University (Principle Investigator: [PERSON]).  
The authors declare no conflict of interest.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 3 Simulation Results and Discussion  
### Rain and Snow Attenuation  
Using Equation (17) and Table 2, the rain effect on the outdoor VLC system can be predicted by calculating the attenuation corresponding to the rainfall precipitation intensity, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modulation Scheme** & \multicolumn{2}{c}{**Minimum SNR (dB)**} \\ \hline OOK & 13.53 & \\ \hline SC-BPSK & 16.54 & \\ \hline L-PPM & 4 PPM & 10.52 \\  & 8 PPM & 8.76 \\  & 16 PPM & 7.5 \\ \hline  & I-4 PPM & 15.29 \\ I-LPPM & I-8 PPM & 17.21 \\  & I-16 PPM & 19.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Necessary signal-to-noise ratio (SNR) corresponding to target bit error rate (BER) = \(10^{-6}\). OOK: ON-OFF Keying, SC-BPSK: Subcarrier Binary Phase-Shift Keying, L-PPM: L-Pulse Position Modulation, I-LPPM: Inverse L-Pulse Position Modulation.  
Figure 7 shows the relationship between the extinction coefficient and rainfall rates using prediction models as provided by the International Telecommunication Union (ITU) [21] and the physical model as provided in [23].  
It is obvious from Figure 7 that the Marshall model underestimates rain attenuation at high rates and low rates. On the other hand, the model proposed in France shows that attenuation increases approximately by 10 dB/km more than the Marshall model in higher intensities. Japan's model predicts the attenuation at higher rates with approximately 5 dB/km more than the French model.  
The explanation for this disparity is that the Marshall model underestimates light rain intensities and (drizzles) on the one hand and high rain intensities on the other hand. The Marshall model is only an average distribution, which made it not accurate and almost gives an attenuation behavior in light rain intensities as very clear weather or less, as shown in Table 5. France models were tested at low rates, while Japan was calculated at higher rates of up to 50 mm/h, as mentioned in [23]. The rate up to 25 mm/h is considered as an acceptable range of precipitation rates in this paper to make a reliable comparison. Rainfall rates are taken from Ref. [25].  
Using Equation (18) and Table 3, the effect of snow attenuation can be predicted. Snowfall rates are taken from Ref. [10]. Figure 8a,b shows the relationship between the extinction coefficient and snowfall rates estimated for two forms of snow. It is clear that the attenuation induced by wet snow is too small compared with the attenuation caused by dry snow at higher rates.  
Figure 8: Extinction coefficient vs. snowfall precipitation rate for \(\lambda=505\) nm (**a**) wet snow (**b**) dry snow.  
Figure 7: Extinction coefficient vs. rainfall precipitation rate for different models.  
The attenuation caused by wet and dry snow at 505 nm is calculated, and the obtained results are listed in Table 5.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 3 Conventional Architectures
### Cloud Computing-Based Architectures  
In a cloud-based architecture, the salient feature is the employment of a cloud computing platform at the application layer. Cloud computing platforms offer immense scalability potential in terms of big data management (data storage, processing, mining, analysis) as well as visualization and web application tools. In the existing literature, popular cloud computing platforms employed are ThingSpeak, Ubidot, and Blynk, which are presented in Table 3. Another advantage is MQTT (Message Queuing Telemetry Transport), which is a lightweight and secure protocol. MQTT, because of its advantages, is supported by most cloud service providers for managing large numbers of connected things.  
In light of the existing literature review, it is evident that some design challenges have gone unaddressed in proposed solutions. By overcoming these challenges, improvement can be embedded with future IoT-based solar monitoring systems. Salient contributions of this work are:  
* Effects of meteorological and electro-physical parameters on solar plant efficiency have been explored.
* Design paradigms for end and edge nodes have been proposed. The underlying design considerations are low cost, scalability, reliability, and big data management.
* An edge computing paradigm has been proposed to overcome associated challenges of centralized and cloud computing-based architecture.
* For big data management, a public cloud platform's architecture has been proposed.
# Towards Adaptive Multipath Managing:
## 5 Discussion  
In this paper, we proposed a lightweight path management mechanism to aid multihomed MPTCP based mobile computing devices towards efficient multipath data transmission, inspired by the TCP congestion avoidance mechanism. The proposed solution can be easily merged with the current MPTCP variants to remove and reconnect the connection in MPTCP adaptively. In this section, we highlight the limitations of our solution and give some interesting directions for future work. We encourage the researchers who are interested in this field to pay attention to and discuss the following interesting challenges.  
* In practice, the number of connection interfaces on the end-devices may not be too large, which may constrain the success of our proposal in today's Internet architectures. We argue that end-devices in the future Internet may be equipped with many network interfaces and can see multiple access links. The authors hope to attract more researchers to discuss this topic.  
Figure 11: The comparison of jitter.  
Figure 12: The comparison of throughput.  
* In the Performance Evaluation Section, we discussed the possible reasons why the throughput performance of all the MPTCP variants was far from satisfactory. We noticed that in a sample dual dumbbell simulation topology (see Section 2), the cumulative average throughput of MPTCP was only close to 1.5 Mbps, which corresponded only to 15% of the bottleneck bandwidth of Path A (10 Mbps). For these negative simulation results, we argue that the NS-2 model of MPTCP may not be able to reflect the MPTCP implementation fully. We encourage more researchers to pay attention to and discuss this controversial problem.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Simulation Study
#### 4.2.4 Results on Number of Drone vs. Overlapping Area  
Figure 20 showed the ratio of the overlapping area within the searched area. The overlapping area for the _Centralized_ approach was only about 22% of the total covered area. This meant the _Centralized_ approach achieved high coverage without excessive overlapping when sensing the area. Compared to the _Distributed_ approach, the _Hybrid_ approach had slightly better performance in terms of the overlapping area. However, given the _Hybrid_ approach had a more covered area (Figure 19), the actual size of the overlapping area was less than the _Distributed_ approach. After an increase to 22 drones, the overlapping area for the _Random_ approach became less than the _Distributed_ approach and the _Hybrid_ approach. The reason was that both the _Distributed_ approach and the _Hybrid_ approach select one sub-region at a time. Thus, once the number of drones was closely equal to the number of sub-regions in the area (i.e., 22 drones with 25 sub-regions in simulation), the probability of selecting already sensed sub-regions was higher and the overlapping area was the entire sub-region if the same sub-regions is selected.  
Figure 19: Number of drones vs. coverage area.
# Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs
## 2 Examples of Drugs Metabolized by CYPs
### Nifedipine  
Nifedipine (Figure 13) is a calcium channel antagonist belonging to the 1,4-dihydropyorylines, clinically used for the therapy of blood-pressure-related diseases such as angina pectoris, hypertension and Raynaud syndrome [96]. It is metabolized by CYP3A4 to dehydronorifedipine in humans [97] and studies on nifedipine in _Macaca fascicularis_ demonstrated its high selectivity for CYP2C76 (a CYP identified in the cynomolgus monkey), leading to its methyl hydroxylated metabolite [98]. It was also reported that the CYP2C76 cDNA contains the open reading frame encoding a protein of 489 amino acids that is about 80% identical to any human or monkey P450 cDNAs [99]. It is noteworthy that the nifedipine blood levels can fluctuate due to the interactions with drugs and foods, such as ketoconazole, St. John's wort and graprefruit juice. The long-term treatment with oral ketoconazole extensively inhibits hepatic CYP3A activity and may dramatically increase the bioavailability of nifedipine [100]. St. John's wort, acting as an inducer of CYP3A4, in healthy subjects considerably reduced the area under the concentration-time curve (AUC0-\(\infty\)) for nifedipine, which reflects the body's exposure to the drug after administering a single dose. A simultaneous significant increase in the AUC0-\(\infty\) for dehydronorifedipine was observed. This effect was mediated by the induction of CYP3A4 [101]. Moreover, nifedipine pharmacokinetics have been reported to be impaired by the co-ingestion of graprefruit juice, acting as an inhibitor of CYP3A4, leading to the irreversible inactivation of intestinal CYP3A4, reducing the pre-systemic metabolism and enhancing the oral bioavailability of the drugs undergoing this metabolism pathway [102]. The single exposure to graprefruit juice affects intestinal CYP3A4 activity with a half-time of about 24 h, which is completed within 3 days, whereas large doses of this juice may also inhibit hepatic CYP3A4 [103; 104]. Graprefruit juice markedly reduces nifedipine metabolism by enhancing its AUC and bioavailability [105], even though this effect was observed following the oral administration of nifedipine, and not after intravenous administration, and appears to be mitigated when smaller amounts of graprefruit juice (250 mL vs. 400 mL or 500 mL) were administered [106].  
Figure 12: The structure of tamoxifen and its metabolites.
## 3 Results and Discussion
### Results of Biocontrol with Trichoderma spp. in the Field
Several control strategies have been tried, such as planting cocoa varieties with improved genotypes, chemical treatments and the use of BCA. Breeding programs and chemical treatments have led to the development of varieties with improved genetic resistance to diseases and tolerance to adverse environmental conditions, but they are not effective against _Moniliophora roreri_ because the pathogen evolves in response to genetic improvements. However, the use of _Trichoderma_ spp. as a BCA exhibits characteristics that make it a promising tool in the control of this pest since this fungal genus is also constantly evolving, activating defense genes and producing antimicrobial metabolites, and is compatible with other management strategies.
## 4 Calibration and Characterization
### Calibration Procedure
#### 4.2.1 Hot-Cold Calibration  
The hot-cold calibration is the simplest method used in calibration of microwave radiometers. It is based in a two point linear approximation of the measured power as a function of the antenna temperature. One point corresponds to the power \(P_{hot}\) and antenna temperature \(T_{abs}\) measured at the hot calibration point (looking to the microwave absorber), and the other corresponds to the power \(P_{cold}\) and antenna temperature \(T_{sky}\) measured at the cold calibration point (looking to the sky at zenith). The hot-cold calibration can be modeled as the linear system of equations  
\[P_{hot}=a\,T_{abs}+b \tag{13}\] \[P_{cold}=a\,T_{sky}+b\]  
where  
\[a=\,G_{d}\,G_{r}\,k_{B}\,\eta_{A}\,B_{r}, \tag{14}\]  
and  
\[b=\frac{a}{\eta_{A}}\left((1-\eta_{A})\,T_{pA}+T_{r}\right)+P_{d}. \tag{15}\]  
The physical temperature of the microwave absorber can be obtained using either an external sensor, or MERITXELL's TIR camera. Besides, the zenith sky temperature can be approximated by the temperature of the cosmic background plus the downwelling atmospheric temperature (\(T_{sky}\sim 6\) K), which are nearly independent of the atmospheric conditions between 1 and 10 GHz ([1], p. 287).  
Therefore, measuring the values of \(P_{hot}\) and \(P_{cold}\), \(a\) and \(b\) can be obtained from (13) as  
\[a=\frac{P_{hot}-P_{cold}}{T_{abs}-T_{sky}}, \tag{16}\]  
and\[b=\frac{P_{cold}\,T_{abs}-P_{hot}\,T_{sky}}{T_{abs}-T_{sky}}. \tag{17}\]  
Once the \(a\) and \(b\) parameters are obtained for each band, the relationship between the output and the input of the radiometer is fully characterized. In addition, the power level \(P_{load}\) measured when the switch is commuted to the matched load can be expressed using (14) and (15) as  
\[P_{load}=\frac{a}{\eta_{A}}\left(T_{load}-\left(1-\eta_{A}\right)T_{pA} \right)+b. \tag{18}\]  
Equation (18) can be also used for the hot-cold calibration, only if \(T_{load}=T_{pA}\). In MERITXELL, the identity \(T_{load}=T_{pA}\) is fulfilled to within \(\pm 0.5^{\circ}\)C thanks to the thermal insulation and stabilization sub-systems.
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Software
In the future, we will also include support for the \"Ultrasound File Format\" proposed by ultrasound beamforming researchers in 2018 [21]. The visualization of the measured data is shown in real time on the user interface. Plots are showing the A-Scans (received amplitude over time) of individual transducer element receive signals or a single reconstructed beam after beamforming. The unreconstructed single element channel data can be viewed as a wide B-Scan showing the brightness of the received echoes as a 2D image. After spatial reconstruction with the selected beamforming, the resulting volume data are visualized using a custom orthogonal slice display and direct volume rendering techniques. The orthogonal slice display shows three images that correspond to single slices of the volume in the XZ, YZ and XY orientation. These views are linked and can be navigated by the user to browse through the insights of the data set. Furthermore, a volumetric rendering of the whole data set is computed on the GPU using direct volume rendering techniques programmed in OpenCL. Using ultrasound imaging-specific visualization parameters, such as echogenicity-to-density mapping of the voxel data and multiple channels for (flow- or stiffness) overlay data on top of the B-Mode grey levels, this rendering technique is still able to compute in real time at more than 60 fps. A cine loop buffer continuously stores incoming and processed ultrasound data during normal operation in the PC system RAM. As soon as the user pauses the acquisition process, the data from the cine loop can be played back and reprocessed by the processing pipeline. The ultrasound device operating software includes these tools for live operation, including volume reconstruction and interactive imaging for predefined beamforming techniques, such as plane wave imaging and diverging waves with virtual point sources, and additional software tools for individual beamforming measurements using custom precomputed transmit delay sets and storage of the received single-element channel data. On top of these standard components, the unit can be programmed using multiple software development kits for the programming languages C#, C++, MATLAB and Python. As our main ultrasound system software core was developed in Microsoft.NET, the C# SDK uses these components directly. In the same way, the integration in MATLAB and Python (IronPython) uses our libraries using their own integrated common language interface (CLI) implementation directly. The often-used C++ SDK was implemented using a C++/CLI library that wraps the managed object of.NET to the native environment of C++. Both the MATLAB and C++ interfaces encapsulate the integration and usage of the.NET components without exposing these language-specific elements to the user.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 3 Experiments and Results
### Satellite Routing Planning  
The premise of inter-satellite routing planning is based on spatial grid modeling. The simulation time is divided into several equal time periods, and each time period is considered static. According to the visibility between the cells themselves (look-up table) to determine the visibility between the satellites falling in different cells, a satellite routing association matrix is generated. The ground point is set to a certain point (taking Shenzhen (114\({}^{\circ}\) E, 22\({}^{\circ}\) N, 50 m) as an example), and a connection graph is established between the satellites according to the visible topological relationship (where 1 is visible; \(-\)1 is invisible; 0 indicates that the calculation object is the satellite node itself and no further calculation is required). Taking 10 satellites as an example (a total of 11 network nodes including ground points), the visible connection graph is shown as Equation (5):  
\[S=\left[\begin{array}{cccccccccccc}0&-1&-1&-1&-1&1&-1&-1&-1&-1&-1\\ -1&0&-1&1&1&-1&-1&1&-1&1&1\\ -1&-1&0&-1&-1&-1&1&-1&-1&-1&-1\\ -1&1&-1&0&1&-1&-1&1&1&1\\ -1&1&-1&1&0&-1&-1&1&-1&1&1\\ 1&-1&-1&-1&-1&0&-1&-1&-1&-1\\ -1&1&-1&1&1&-1&-1&0&-1&1&1\\ -1&-1&-1&1&-1&-1&1&-1&0&-1&1\\ -1&1&-1&1&1&-1&-1&1&-1&0&-1\\ -1&1&-1&1&1&-1&-1&1&-1&0&1\\ -1&1&-1&1&1&-1&1&1&1&1&0\end{array}\right] \tag{5}\]  
Combining the selection strategies of the shortest path and the smallest number of steps, the shortest path is calculated according to the routing path algorithm proposed in Figure 8a. The path to the ground point for both strategies is the same for the matrix above, owing to the small size of the selected constellation. When the constellation size increases, such as selecting 100 satellites, there may be different alternate paths using the two different strategies; for example, from 100 to 43, by the shortest distance is (100, 12, 24, 43) and the minimum number of steps is (100, 20, 43), using the Dijkstra algorithm [25].  
The experimental results of the two strategies are shown in Table 5 and a comparison is shown in Figure 11. Overall, the efficiency of the grid method is significantly better than that of traditional algorithms. In path planning based on the shortest distance strategy, the efficiency is improved by an  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Number of Satellites** & **500** & **1000** & **2000** & **4000** & **6000** & **8000** & **10,000** & **12,000** \\ \hline Grid lookup table & 0.00001 & 0.00007 & 0.00024 & 0.00081 & 0.00171 & 0.00559 & 0.0062 & 0.01148 \\ Grid visibility & 0.00010 & 0.00072 & 0.00370 & 0.01757 & 0.02984 & 0.07247 & 0.07921 & 0.13709 \\ Traditional Visibility & 0.00014 & 0.00145 & 0.00612 & 0.02559 & 0.06208 & 0.11249 & 0.17162 & 0.23701 \\ \hline \end{tabular}
\end{table}
Table 4: Visibility calculation results (ms).  
average of almost 29.74 times; and for the minimum number of steps, the improvement is approximately 23.31 times (mainly related to the visibility between satellites, so the efficiency improvement is close).