esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Example Validation
### Calculation and Identification
#### 4.2.2 Results  
Based on the results of the meteorological risk responsiveness of each route carrier calculated by the discriminant layer, the accident-prone sections of the SC2 highway affected by adverse meteorological conditions are obtained.  
In general, special sections such as tunnels are less affected by adverse weather conditions and should be filtered out initially. Section filtering is performed based on the type of tunnel sections provided in the structural data. On this basis, a 40-km stretch from K15 to K55 is selected for computational output, and the processed results are shown in Figure 6.  
In order to further screen the accident-prone road sections under adverse meteorological conditions, and at the same time to provide data and reference for road section analysis and safety and security programs, the meteorological risk responsiveness ratio is calculated based on Equation (8) for the data in Figure 6, and the specific calculation results are shown in Figure 7.  
From the above figure, it can be seen that the rainy weather risk responsiveness ratio of LD_42 and LD_39 road sections is less than 1; the rainy weather risk responsiveness ratio of LD_17 road section is approximated to be 1, which indicates that compared with the general meteorological conditions such as sunny, cloudy, and foggy, the road section is less affected by rainy adverse meteorological conditions, and it does not conform to the definition of accident-prone road sections subject to adverse meteorological conditions, and it should be  
Figure 6: Calculation results of meteorological events.  
sifted out and ultimately, the responsiveness ratio = 1 is used as the discriminative index for screening; the obtained accident-prone roadway table is shown in Table 5.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Adding Workflow Verification to an Execution Entity
#### 3.3.3 Compliance Service Provider (CSP)  
The Compliance Service Provider runs services for signing workflow queues and verifying workflow queue signatures. To ensure the integrity of workflows, the CSP protects the types, order and parameters of workflows. In general, the implementation of the CSP is free to choose the specific cryptographic algorithms--as long as they are sufficiently secure--since the CSP is the only agent performing the cryptographic actions. We will mention the algorithms that we used in our implementation throughout the remaining document. However, depending on the application and implementation constraints, others may be selected. It is set up with the necessary key material for the signing and the verification process as well as a cryptographic hash function for hashing the workflow information. To use the CSP, a suitable representation for a workflow has to be chosen. In our case (see below), we use a JSON formatted representation. For other systems, this may vary.  
The CSP needs to be secured in terms of access by providing authentication and authorization facilities. In the easiest case, also API tokens could be used. A sufficiently sophisticated mechanism can also be used to ensure that only authorized persons trigger the signature of a workflow. Typically, we assume this to be a responsible manager whenever a robot program has gone through verification and validation. By triggering the signing process, this person then takes responsibility for the correctness of the program.
## 5 Discussion  
The direct effect of the aging index (_Aging_) was found to be significant and negative, indicating that population aging has a negative impact on the GTFP of a given province. The more severe aging is, the lower GTFP is in the local province. Population aging profoundly changes the demographic structure. As a result of fewer young people, the ability to innovate declines, thus lowering the local GTFP. Second, population aging increases labor costs and changes the optimal production functions of local enterprises; this too inhibits GTFP growth. Third, population aging suggests that senior labor forces are widely employed, and may lower labor productivity and impair GTFP due to their lower physical strength generally. Fourth, aging causes an increase in local investment in medical expenditures for aging care, which may lead to reduced fiscal spending on other needs such as education, environmental protection, etc., thereby hindering local GTFP growth. On the other hand, the indirect effect of the aging index is significant and positive, indicating that population aging of neighboring provinces can contribute to GTFP growth \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Direct Effects**} & \multicolumn{2}{c}{**Indirect Effects**} & \multicolumn{2}{c}{**Total Effects**} \\ \cline{2-7}  & **Coefficient** & \(p\) **Value** & **Coefficient** & \(p\) **Value** & **Coefficient** & \(p\) **Value** \\ \hline _Aging_ & \(-0.5903\) & \(0.0160\) & \(0.7360\) & \(0.0420\) & \(0.1457\) & \(0.6320\) \\ _FD_ & \(-1.3437\) & \(0.0000\) & \(0.6536\) & \(0.0300\) & \(-0.6901\) & \(0.0090\) \\ _ER_ & \(0.2061\) & \(0.0000\) & \(-0.0347\) & \(0.5750\) & \(0.1713\) & \(0.0010\) \\ _Energy_ & \(-1.7626\) & \(0.0000\) & \(0.5225\) & \(0.0110\) & \(-1.2401\) & \(0.0000\) \\ _Med_ & \(-0.5613\) & \(0.0000\) & \(-0.0442\) & \(0.8450\) & \(-0.6055\) & \(0.0050\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Direct, indirect and total effects of explanatory variables.
## 3 Proposed Methodology
### OIChain
#### 3.2.2 Module Design of OIChain  
Users submit OI through the pre-deployed OI management contract. It needs to be verified through a smart contract and submitted to an oracle for further verification. This process is shown in Algorithm 3.  
```
0:\(OIstruct\)
0:\(true\) or \(false\)
1:\(CONTRACT_{OI}:FUN_{submit}\longleftarrow{}O{}Istruct\)
2:if\(identity[Pu_{k},CommunityID]\
eq{}developer\parallel prefix(linkID)\
eq prefix(authenticationLink_{CommunityID})\parallel existLink[linkID]==true\parallel prelinkID\
eq 0\)&&\(CONTRACT_{OI}:FUN_{queryOI}(prelimkID)==null\)then
3:return\(false\);
4:else
5:\(CONTRACT_{Oracle}:FUN_{OI}\longleftarrow{}O{}Istruct\)
6:external: Oracle verifies \(linkID\), Digest & timestamp
7:\(CONTRACT_{auth}:FUN_{OI}\longleftarrow{}verificationResult\)
8:Record O1 struct & various mapping relationships
9:Update \(R_{dev}\)
10:return\(true\);
11:endif
```  
**Algorithm 3** The entire process of OI submission by the user  
After identity authentication, a user has maintained the corresponding relationship between the identity in communities and \(Pu_{k}\) in the OIChain. Therefore, it is simply needed to compare the prefixes of the links submitted twice (generally community domain name + user name) after confirming the corresponding developer identity. The contract also checks whether a \(linkID\) is repeatedly submitted and whether the OI to which the non-zero \(prelimkID\) points is meaningful (the map of some programming languages cannot determine the existence of the value through the existence of the key). With the assistance of the oracle, the last verification is to check the correctness of the \(linkID\), the digital digest and the timestamp submitted to the warehouse. These checks can prevent the same project in a warehouse from being reused or used by others.  
Any user can query the total OI amount and the last submitted \(OIstruct\) of a developer through \(Pu_{k}\). An \(OIstruct\) submitted for the \(num(_{Pu_{k}\to OI})\)th time can be queried through \(<Pu_{k},num(_{Pu_{k}\to OI})>\). After the \(OIstruct\) is queried through a \(linkID\), users can continue to query the previous version through the \(prelimkID\) in this \(OIstruct\). The OSCMS provides one-click version traceability, which is shown in Algorithm 4.  
```
0:\(linkID\)
0:\(\Theta\) //set of \(OIstruct\)
1:\(OSCMS\longleftarrow{}linkID\)
2:\(CONTRACT_{OI}:FUN_{queryOI}(linkID)\)
3:\(\Theta\longleftarrow{}O{}Istruct_{linkID}\)
4:while\(Ostruct.prelimkID\
eq 0\)do
5:\(CONTRACT_{OI}:FUN_{queryOI}(prelimkID)\)
6:\(\Theta\longleftarrow{}O{}Istruct_{prelimkID}\)
7:endwhile
8:return\(\Theta\);
```  
**Algorithm 4** Version traceability process of OI
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## Appendix B Derivation of PLoS for Case 3 and 4: \(p_{3,4}\)  
This Appendix details the derivation of the PLoS expression for cases 3 and 4, denoted by \(p_{3,4}\).  
According to Section 2.3, the \(p_{3,4}\) expression can be written as follows:  
\[p_{3,4} =\mathrm{Pr}\big{\{}\min(h_{T_{x}},h_{R_{x}})\leq h_{V}<h_{R_{x}} <\max(h_{T_{x}},h_{R_{x}})\big{\}}\] \[=\int_{d_{\min}}^{r}f_{a}(a)\int_{h_{\min}}^{h_{\max}}f_{z}(z) \int_{h_{\min}}^{z}f_{y}(y)\int_{y}^{h_{R_{y}}}f_{h_{V}}(x)\;dx\;dy\;dz\;da \tag{17}\]  
where \(y=\min(h_{T_{x}},h_{R_{x}})\), \(z=\max(h_{T_{x}},h_{R_{x}})\), \(f_{a}\) is the PDF of \(a\), and \(f_{z}\) is the PDF of \(z\), where \(f_{z}\) expression is derived as follows:\[f_{z}(z) = \frac{\mathrm{d}}{\mathrm{d}z}\Big{[}\mathrm{Pr}\{\max(h_{\mathrm{r}_ {x}},h_{\mathrm{z}_{x}})\leq z\}\Big{]} \tag{10}\] \[= \frac{\mathrm{d}}{\mathrm{d}z}\Big{[}\mathrm{Pr}\{h_{\mathrm{r}_ {x}}<z\}\ \mathrm{Pr}\{h_{\mathrm{r}_{x}}<z\}\Big{]}.\]  
Now, by using the PDF expression in (11), the expression of \(f_{z}\) is given by  
\[f_{z}(z) = \frac{\mathrm{d}}{\mathrm{d}z}\Bigg{[}\int_{h_{\mathrm{min}}}^{z }f_{h_{V}}(x)\ dx\int_{h_{\mathrm{min}}}^{z}f_{h_{V}}(x)\ dx\Bigg{]} \tag{11}\] \[= \frac{\mathrm{d}}{\mathrm{d}z}\Bigg{[}\frac{z^{2}+h_{\mathrm{min} }^{2}-2h_{\mathrm{min}}y}{\left(h_{\mathrm{max}}-h_{\mathrm{min}}\right)^{2}} \Bigg{]}\] \[= \frac{2(z-h_{\mathrm{min}})}{\left(h_{\mathrm{max}}-h_{\mathrm{ min}}\right)^{2}}.\]  
By using the expressions of \(f_{h_{V}}\), \(f_{y}\), \(f_{z}\), and \(f_{a}\), (10) can be rewritten as follows:  
\[p_{34}=\int_{d_{\mathrm{min}}}^{r}\frac{1}{r-d_{\mathrm{min}}}\int_{h_{\mathrm{ min}}}^{h_{\mathrm{max}}}\frac{2(z-h_{\mathrm{min}})}{\left(h_{\mathrm{max}}-h_{ \mathrm{min}}\right)^{2}}\int_{h_{\mathrm{min}}}^{z}\frac{2(h_{\mathrm{max}}- y)}{\left(h_{\mathrm{min}}-h_{\mathrm{min}}\right)^{2}}\int_{y}^{\frac{z}{h_{ \mathrm{min}}}-y+y}\frac{1}{h_{\mathrm{max}}-h_{\mathrm{min}}}\ dx\ dy\ dz\ da \tag{12}\]  
After evaluating the integrations in (12), with respect to \(x\), \(y\), \(z\), and \(a\), and after some simplifications, the final expression of \(p_{34}\) is given by  
\[p_{34}=\frac{11\ r}{60\ (r-d_{\mathrm{min}})}. \tag{13}\]
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments
### Robustness Analysis  
In order to verify the robustness of the proposed method for the sparse point cloud, 1024, 768, 512, 256 and 128 points were selected as the input of the training model, and the experimental parameters were the same as those in Section 3.3. From Figure 12, it is clearly visible that the classification accuracy of the proposed method is always higher than the other networks with the reduction in sampling points. The classification accuracy of the DGCNN decreases rapidly when the number of points is less than 512, and the classification accuracy of PointNet++, GAPNet and MSHANet decreases rapidly when the number of points is less than 256. This indicates that the proposed method has strong robustness in the field of the classification of sparse points.  
Figure 11: Semantic segmentation results of the proposed method on the S3 DIS dataset, and different colors represent different objects.
## 8 Conclusion  
This paper discusses the issue of reliability in WSNs. We deliberate that the gateway nodes in WSNs can play an important role to improve the reliability of source to sink communications. We proposed a hotline-based topology to enhance the reliability of inter-gateway paths. The proposed topology improved reliability of end-to-end WSN communications by exploiting a concept similar to small-world graphs. We showed through mathematical analysis that hotline assisted routing gives noticeably better reliability in comparison to traditional all-wireless ad hoc routing. Our proposed topology provides reliability which is independent of the number of nodes in a given cluster or area. This property makes it a good choice for high density WSN deployments. We have also observed the impact of additional cost with respect to the achieved reliability. Our SBR scheme provides lesser delays from source to the sink communication. SBR vests intelligence to the gateways and help them make smart routing decisions. This property makes our topology ideal for all the data critical  
Figure 24: Average End-to-End Delay comparison of SBR with maodv-sim [14]  
applications. Finally we simulated the topology and showed that our proposed topology achieves a marked improvement in the reliability of WSN in comparison to all wireless routing. Our simulation results show at least 50% improvement in the reliability as compared to the all wireless routing.
# Three-Phase PV CHB Inverter for a Distributed Power Generation System
## 3 Numerical Analysis
In addition to the above analysis, circuit-level simulations were performed in the PSpice environment in order to compare the proposed modulation strategy in terms of power efficiency with respect to a standard Phase-Shifted PWM (PS-PWM). A seven-level single-phase CHB inverter was constructed; each cell, composed of four PowerMOS (IRFP4568 PBF, \(V_{ds,max}=150\) V, \(I_{d,max}=170\) A @ 25 \({}^{\circ}\)C, typical \(R_{ds,ON}=4.8\) m\(\Omega\)), powered an individual PV panel. The behavior of the PV panel was described by a circuital model with the same parameters reported in Table 1 and an irradiance of 800 W/m\({}^{2}\). As in the Matlab/Simulink simulations, the CHB inverter was equipped with a 5 mH line inductor. Moreover, the PSpice simulation was conducted at a higher switching frequency (i.e., 10 kHz instead of 5 kHz) in order to deeply stress the switching behavior. Numerical results showed that the proposed modulation strategy allows a decrease of about 50% of the overall power dissipation, thus making the efficiency pass from 98.8% in the PS-PWM case to 99.4%. Figure 10: Partial shading: comparison among (**a**) phase currents \(i_{grid,p}\); and (**b**) phase modulated voltages \(v_{imv,p}\). Figure 11: Dynamic partial shading: the operating point position corresponding to three panels in the \(p\)-th phase leg (namely, panel 1, \(p\) (pink squares), panel 2, \(p\) (orange circles), and panel 3, \(p\) (green triangles)) is highlighted with respect to the individual panel P–V curves. The solid line P–V curves correspond to the panels 1, \(p\), 2, \(p\), and 3, \(p\) under sunny condition, while the dashed line describes panel 2, \(p\) under partial shading condition. (**a**) Phase leg 1; (**b**) phase leg 2; (**c**) phase leg 3. Figure 12: Dynamic partial shading: time behavior of the output currents compared to grid voltages related to (**a**) phase 1; (**b**) phase 2; and (**c**) phase 3.
## 8 Open Challenges and Research Directions
### Using Artificial Intelligence  
Nowadays, ML algorithms and tools are commonly used in several fields, and VNs are no exception to this practice. In recent years, many works proposed to use ML, DL, and AI algorithms for task offloading and resource management. However, there are still many opportunities to use AI for predicting future network situations and performing and migrating of tasks. Furthermore, the challenge of offloading AI algorithms themselves within VNs presents an intriguing and complex problem. Ensuring that the right AI models are deployed on the right vehicles, as well as considering their computational capabilities and communication constraints, is a non-trivial task. Developing efficient mechanisms for AI model selection, deployment, and updates is crucial for the seamless integration of AI into VNs.
## 3 Key Technologies Achieved by the System
### Duplicated Data Filtering and Missing Data On-line Filling
#### 3.3.3 Algorithm Fusion and Implementation  
According to the self-decision and on-line filling method, if there are other nodes that have strong spatial correlation with a parameter of the missing record, the estimated value (\(\hat{y}i\)) includes the regression estimated value \(\hat{y}i_{\text{A}}\) and the interpolation-fitting estimated value \(\hat{y}i_{\text{B}}\) in the relation of \(\hat{y}i=\alpha_{\text{A}}\hat{y}i_{\text{A}}+\alpha_{\text{B}}\hat{y}i_{ \text{B}}\), where \(\alpha\)A and \(\alpha\)B are weights of corresponding algorithms and \(\alpha_{\text{A}}+\alpha_{\text{B}}=1\). SSE of estimation is [16]:  
\[SSE=\sum_{i=1}^{N}{(y_{i}-\hat{y_{i}})}^{2}=\sum_{i=1}^{N}{[\alpha_{\text{A}} \ \alpha_{\text{B}}]}{\left(\begin{array}{cc}{e_{\text{A}}}^{2}&{e_{\text{A}}} {e_{\text{B}}}_{i}\\ {e_{\text{A}}}{e_{\text{B}}}_{i}&{e_{\text{B}}}_{i}^{2}\end{array}\right)}{ \left[\alpha_{\text{A}}\ \alpha_{\text{B}}\right]}^{T} \tag{1}\]  
where \({e_{\text{A}}}_{i}\) and \({e_{\text{B}}}_{i}\) are errors of regression estimation algorithm and interpolation-fitting estimation algorithm at time \(i\), respectively, and \(N\) is the number of missing records. When ESS is minimum, \(\alpha_{\text{A}}\) and \(\alpha\)B are the optimal weight values.  
\[\text{Let}\ E=\left(\begin{array}{c}\sum_{i=1}^{N}{e_{\text{A}}}^{2}&\sum_{ i=1}^{N}{e_{\text{A}}}{e_{\text{B}}}_{i}\\ \sum_{i=1}^{N}{e_{\text{A}}}{e_{\text{B}}}_{i}&\sum_{i=1}^{N}{e_{\text{B}}}^{2 }\end{array}\right),\ \alpha=[{\alpha_{\text{A}}\ \alpha_{\text{B}}}],\]  
\[\begin{cases}\min(ESS)=\alpha E\alpha^{T}\\ \text{s.t.}\ \alpha_{\text{A}}+\alpha_{\text{B}}=1\,\text{and}\ \alpha_{\text{A}}>0\ \ \alpha_{\text{B}}>0\end{cases} \tag{2}\]  
then the optimal solution is:  
\[\left[\alpha_{\text{A}}\ \alpha_{\text{B}}\right]=\frac{E^{-1}X^{T}}{XE^{-1}X^{T}}\,\text{ where }X=[11].\]  
Root mean square error (RMSE) between estimated value and true value is used as the basis for judging the effectiveness of the system estimation algorithms. The smaller the RMSE, the more accurate the estimation method.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{100}{\left(\hat{y}_{i}-y_{i}\right)}^{2}}{10\,0}} \tag{3}\]  
where \(\hat{y}i\) is system estimation value and \(yi\) is system acquisition value.  
Separate sub threads are created for the on-line filling of missing data so that the performance of other tasks will not be affected. Time field difference between the record acquired in the current cycle and the latest record of the corresponding node in database is used to judge whether there are data missing. The API function provided by MySQL is used to connect databases. The new time value obtained by subtracting system sampling cycle from system current time is used as the time field of estimation data. After successfully stored, estimation data are saved again in current acquisition data to make sure that the data storage order in database is consistent with the actual acquisition order.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 2 Materials and Methods  
As shown in Figure 1, the high-speed motor rotation system is mainly composed of the stage impellers, the spindle, the fixed nuts, and so on. Thereinto, the spindle consists of the sheath, the end covers, and the magnet, which constitutes the interference fit structure.  
The interference fit is adopted between the sheath and the rest of the spindle, in which the contact position between the sheath and the end cover is welded, as shown in Figure 2.  
The diameter of the magnet \(d_{m}\) is 28.5 mm and the shaft length \(l_{m}\) is 70 mm. The outer diameter of the sheath \(d_{s}\) is 36 mm and the shaft length of the sheath \(l\) is 90 mm. The length of the end cover and the part of the interference fit of the sheath \(l_{e}\) is 10 mm.  
The materials and physical properties of the magnets, sheath, and end covers are shown in Table 1 below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parts** & **Magnet** & **Sheath** & **End Covers** \\ \hline Materials & Sm2 Co17 magnet & Stainless steel 3 Cr13 & Stainless steel 3 Cr13 \\ \hline Density(kg/m\({}^{3}\)) & 8400 & 7750 & 7750 \\ \hline Poisson’s ratio & 0.24 & 0.28 & 0.28 \\ \hline Modulus of elasticity (GPa) & 117 & 206 & 206 \\ \hline Compressive yield strength (GPa) & 509 & 540 & 540 \\ \hline Tensile yield strength (MPa) & 35 & 540 & 540 \\ \hline \end{tabular}
\end{table}
Table 1: Material performance parameters of interference fit components.  
Figure 1: The rotor system structure of the centrifugal compressor.  
Figure 2: Structural diagram of the rotor system.
## 3 Research Approach
Evaluation was done by combining the analysis of historical data and data gathered during the product development projects carried out in context of the collaborative project \"OptiAMix\" (www.optiamix.de), funded by the German Federal Ministry of Education and Research (BMBF). Additionally, end-user workshops on correctness and completeness of functionality as well as unexploited leverages were conducted. For application and success evaluation, expert questionnaires about the status quo, the method and software prototype as well as future research potentials were used and elaborated by findings from semi-structured interviews (60-90 min) with developers and project leaders. Results of DS-II are presented in Section 5.
###### Abstract  
Immobilized TiO\({}_{2}\) was prepared by adding a small composition of polyethylene glycol (PEG) as a binder, and this paper reported for the very first time the formation of C=O from oxidized PEG, which acted as an electron injector in enhancing photoactivity. Water-based TiO\({}_{2}\) with PEG formulation was deposited by using a brush technique onto double-sided adhesive tape (DSAT) as a support binder to increase the adhesiveness of immobilized TiO\({}_{2}\). The photocatalytic activity of immobilized TiO\({}_{2}\)-PEG was measured by photodegradation of 12 mg\(\cdot\)L\({}^{-1}\) methylene blue (MB) dye. The optimum condition of immobilized TiO\({}_{2}\)-PEG was observed at TiO\({}_{2}\)/PEG-2 (TP2) with 10:0.1 for the TiO\({}_{2}\)/PEG ratio, which resulted in a 1.8-times higher photodegradation rate as compared to the suspension mode of pristine TiO\({}_{2}\). The high photodegradation rate was due to the formation of the active C=O bond from oxidized PEG binder in immobilized TiO\({}_{2}\)-PEG as observed by Fourier transform infrared and X-ray photoelectron spectroscopy analyses. The presence of C=O has escalated the photoactivity by forming an electron injector to a conduction band of TiO\({}_{2}\) as proven by higher photoluminescence intensity obtained for TP2 as compared to pristine TiO\({}_{2}\). The TP2 sample has also increased its adhesiveness when DSAT is applied as a support binder where it can be recycled up to eight times and comparable to recent photocatalysis cycle developments.  
immobilization; titanium dioxide; oxidized PEG; support binder; methylene blue +
Footnote †: journal: Applied Physics Letters B