esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
### Note 2 Original Samples from Virtual Calibration  
After completing the virtual ground calibration experiment, it is imperative to analyze the response of the strain gauges to ascertain their suitability for integration into the load equation. This analysis primarily involves evaluating the linearity, effectiveness, and sensitivity of the strain gauges. The strain data extracted from the wing finite element model for a load of 5 kN is presented in Table 11. The analysis of the selected strain gauge characteristics is depicted in Figure 11, illustrating that as the load level increases, the strain gauges demonstrate excellent linearity, effectiveness, and sensitivity.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load Level** & **M1 (\(\mu\varepsilon\))** & **M4 (\(\mu\varepsilon\))** & **Q11 (\(\mu\varepsilon\))** & **Q22 (\(\mu\varepsilon\))** & **Q22 (\(\mu\varepsilon\))** & **T11 (\(\mu\varepsilon\))** & **T22 (\(\mu\varepsilon\))** \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0.01 & 7.577 & \(-\)7.577 & 0.399 & \(-\)0.897 & \(-\)0.117 & \(-\)6.854 & 0.616 & 2.005 \\
0.02 & 15.154 & \(-\)15.153 & 0.798 & \(-\)1.795 & \(-\)0.231 & \(-\)13.706 & 1.233 & 4.012 \\
0.035 & 26.52 & \(-\)26.517 & 1.398 & \(-\)3.14 & \(-\)0.399 & \(-\)23.979 & 2.159 & 7.026 \\
0.058 & 43.569 & \(-\)43.563 & 2.299 & \(-\)5.159 & \(-\)0.641 & \(-\)39.376 & 3.548 & 11.557 \\
0.091 & 69.144 & \(-\)69.128 & 3.654 & \(-\)8.188 & \(-\)0.983 & \(-\)62.445 & 5.636 & 18.376 \\
0.142 & 107.509 & \(-\)107.469 & 5.694 & \(-\)12.731 & \(-\)1.449 & \(-\)96.985 & 8.776 & 28.655 \\
0.218 & 165.06 & \(-\)164.967 & 8.771 & \(-\)19.548 & \(-\)2.041 & \(-\)148.646 & 13.501 & 44.193 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Strain data for a load of 5 kn.
# A New SORM Method for Structural Reliability with Hybrid Uncertain Variables
## 2 The Analysis of the Structural Reliability Using the Traditional SORM
### UPSORM: SORM in Polar for Random and Interval Uncertainty  
Assuming that the design point at the failure domain of the structure is \(\mathbf{\omega}^{*}=(u^{*}{}_{1},u^{*}{}_{2},\cdots,u^{*}{}_{n},\delta_{1}{}^{*},\delta_{2}{}^{*},\cdots,\delta_{m}{}^{*})\), the second-order Taylor expansion of \(G(\mathbf{\omega})\) at the design point \(\mathbf{\omega}^{*}\) is described as:  
\[G(\mathbf{\omega})\approx G(\mathbf{\omega}^{*})+\
abla G(\mathbf{\omega}^{*})(\mathbf{\omega }-\mathbf{\omega}^{*})+\frac{1}{2}(\mathbf{\omega}-\mathbf{\omega}^{*})^{T}\mathbf{H}( \mathbf{\omega}-\mathbf{\omega}^{*}) \tag{19}\]  
where \(\
abla G(\mathbf{\omega}^{*})\) is the gradient of \(G(\mathbf{\omega})\) with \(\
abla G(\mathbf{\omega}^{*})=\left.\frac{\partial G}{\partial\mathbf{\omega}}\right|_ {\mathbf{\omega}^{*}}\), \(\mathbf{H}\) is the Hessian matrix, written as \(\mathbf{H}_{ij}=\left.\frac{\partial G}{\partial\omega_{i}\partial\omega_{j}} \right|_{\mathbf{\omega}^{*}}\).  
Then, (19) is simplified as:  
\[\begin{array}{rl}G(\mathbf{\omega})&\approx b-y(\mathbf{\omega}^{*})-\
abla y(\mathbf{ \omega}^{*})(\mathbf{\omega}-\mathbf{\omega}^{*})-\lambda(\mathbf{\omega}-\mathbf{\omega}^{*})^ {T}(\mathbf{\omega}-\mathbf{\omega}^{*})\\ &=b-y(\mathbf{\omega}^{*})+\sum_{k=1}^{n+m}\left(\left.\frac{\partial y}{\partial \omega_{k}}\right|_{\mathbf{\omega}^{*}}\omega_{k}{}^{*}\right)-\sum_{k=1}^{n+m} \left(\left.\frac{\partial y}{\partial\omega_{k}}\right|_{\mathbf{\omega}^{*}} \omega_{k}\right)\\ &+2\lambda\sum_{k=1}^{n+m}\omega_{k}\omega_{k}{}^{*}-\lambda\sum_{k=1}^{n+m}( \omega_{k}{}^{*})^{2}-\lambda\sum_{k=1}^{n+m}\omega_{k}{}^{2}\end{array} \tag{20}\]  
In polar space, the new reliability domain is \(d+\bigg{(}\dfrac{2\lambda}{v_{1}^{*}}-D\bigg{)}v_{1}v_{2}-\lambda{v_{1}}^{2}>0\), and the failure domain is \(d+\bigg{(}\dfrac{2\lambda}{v_{1}^{*}}-D\bigg{)}v_{1}v_{2}-\lambda{v_{1}}^{2}\leq 0\). The limit stat surface is:  
\[d+\bigg{(}\dfrac{2\lambda}{v_{1}^{*}}-D\bigg{)}v_{1}v_{2}-\lambda{v_{1}}^{2}=0 \tag{28}\]  
By functions (18) and (19), it is obtained that:  
\[P_{R}^{U}=1-\int_{\sqrt{m}}^{+\infty}\int_{-1}^{1}f_{v_{1}}(\xi_{1})f_{v_{2}}( \xi_{2})d\xi_{1}d\xi_{2} \tag{29}\]  
\[\text{where}\,f_{v_{1}}(x)=\dfrac{2^{1-n/2}(x^{2}-m)^{(n-1)/2}}{\Gamma(n/2)} \exp(-\dfrac{x^{2}-m}{2})\text{, and}\]  
\[f_{v_{2}}(x)=\dfrac{1}{\sqrt{1-x^{2}}\int_{0}^{\pi}\sin^{n+m-2}\alpha d\alpha }(\sin^{n+m-2}(\arccos x)+\sin^{n+m-2}(\pi-\arccos x)),\]  
\[P_{R}^{L}=1-\int_{0}^{+\infty}\int_{-1}^{1}f_{v_{1}}(\xi_{1})f_{v_{2}}(\xi_{1 })d\xi_{1}d\xi_{2} \tag{30}\]  
\[\text{where}\,f_{v_{1}}(x)=\dfrac{2^{1-n/2}x^{(n-1)}}{\Gamma(n/2)}\exp(- \dfrac{x^{2}}{2})\text{, and}\]  
\[f_{v_{2}}(x)=\dfrac{1}{\sqrt{1-x^{2}}\!\!\int_{0}^{\pi}\sin^{n+m-2}\alpha d \alpha}(\sin^{n+m-2}(\arccos x)+\sin^{n+m-2}(\pi-\arccos x)).\]
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 4 Results
### Comparison of Experimental Results  
Table 3 shows the encoding results of the proposed algorithm and the HM-16.20 benchmark algorithm, including the video sequence name, target bit rate, actual bitrate, and YUV-PSNR. Table 4 shows the average BD-Rate and BD-PSNR. As a result, Figure 5 shows the corresponding rate-distortion curve.  
Figure 4: Frames of four test sequences. (**a**) AerialCity; (**b**) DrivingInCity; (**c**) DrivingInCountry; (**d**) PoleVault.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Sequence** & **BD-Rate (\%)** & **BD-PSNR (dB)** \\ \hline AerialCity & \(0.11\) & \(-0.01\) \\ DrivingInCity & \(-3.33\) & \(0.17\) \\ DrivingInCountry & \(-1.14\) & \(0.03\) \\ PoleVault\_le & \(-3.07\) & \(0.09\) \\
**Average** & \(-1.86\) & \(0.07\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: BD-Rate and BD-PSNR results.  
Figure 5: R-D curves of different test sequences. (**a**) AerialCity; (**b**) DrivingInCity; (**c**) DrivingInCountry; (**d**) PoleVault.  
During video transmission, if the actual bitrate does not match the network bandwidth, the mismatch will increase greatly. If the actual bitrate exceeds the network bandwidth, packet loss or stutter may occur during transmission. When the actual bitrate is lower than the network bandwidth, the video quality will be degraded, and the phenomenon of blur and distortion will appear. Therefore, it is very important to design a reasonable algorithm so that the actual bitrate is close to the target bitrate. As can be seen from Figure 6, the proposed algorithm can provide a lower mismatch than the HM-16.20 benchmark algorithm, which can effectively improve the coding performance. By observing the rate-distortion curves of the four video sequences in Figure 5 under the two algorithms, it can be found that the rate-distortion curve of the proposed algorithm is slightly higher than the rate-distortion curve corresponding to the HM-16.20 benchmark algorithm, which shows that the algorithm proposed by this paper can obtain excellent rate-distortion performance.
# Influence of Open Differential Design on the Mass Reduction Function
## 4 Numerical Analysis of Differential Transmission
### Submodeling of the Pinion and Bevel Gear  
The submodel includes the geometry of the entire pinion (excluding the rest of the drive shaft) and the 1/6 of the ring gear (Figure 9). The influence of stresses and displacements on the 1/6 pinion due to the degree of overlap of the gearing in the mesh between the driving and driven gear was observed and analyzed. By submodeling the pinion and bevel gear, the finite element mesh was further refined at the points of contact and small radii at the tooth root of both gears. In this way, the stress values converge and the numerical calculation results obtained are more accurate. The submodel represents a separate analysis in relation to the analysis of the global model. The connection between the submodel and the model is reflected in the transfer of time-dependent values stored in the analysis of the global model for relevant nodes or essential boundary conditions of the submodel. The data transfer was achieved by connecting the results of the analysis of the global model with the settings of the new analysis. Since it is a separate analysis, the submodel can serve as the global model of the next submodel.  
Figure 8: Equivalent stress fields according to [PERSON] in MPa, pinion and bevel gear.  
Figure 9: Submodel of the pinion and ring gear: (**a**) finite element mesh; (**b**) distribution of total displacement in mm.  
In the creation of the submodel, care was taken to apply [PERSON]'s principle [43], which states that there is no influence on the mechanical behavior of the structure if one is far enough from the locations where the boundary conditions and loads are given. With this method, the global model can be evaluated with a coarser mesh, resulting in a faster solution, and then the areas of the considered domain can be analyzed with a finer finite element mesh. Thus, the analysis in the global model can omit certain details, such as welds, radii and holes, which can be considered later in the submodel. Displacement fields are applied to the submodel, as shown in Figure 9b. In addition to the displacement field, contacts and boundary conditions are also defined. The submodel is meshed with the same finite elements as the global model. The finite element mesh of the submodel consists of 590,776 finite elements and 829,486 nodes (Figure 9a).  
The obtained value of the maximum von Mises equivalent stresses in the submodel is closer to the values of the analytical calculation (Table 5), indicating that the submodeling procedure gives good results (Figure 10).
###### Abstract  
With increasingly prevalent wireless sensors and devices, low power and lossy networks (LLNs) play an essential role in the realization of ubiquitous computing and communication infrastructure, which, in turn, leads to enhanced data accessibility and availability. A multicast protocol for LLNs (MPL), has been standardized to provide both efficient and reliable multicast communication. Due to the shared wireless medium, lack of tamper resistance, and inherent resource constraints, MPL-based LLNs are undoubtedly vulnerable to various Denial-of-Service (DoS) attacks. In this paper, we propose a heuristic-based detection scheme, called HED, against the suppression attack in MPL-based LLNs, where a malicious node multicasts a series of spoof data messages with continuous sequence numbers to prevent normal nodes from accepting valid data messages and cause them to delete cached data messages. In the HED, each node maintains an increment rate of the minimum sequence number in the Seed Set to detect the potential malicious node by comparing the recent increment of sequence numbers with the heuristically calculated increment threshold of sequence numbers. We evaluate the proposed scheme through extensive simulation experiments using OMNeT++ and compare its performance with original MPL with and without adversary, respectively. The simulation results show high detection rate and packet reception rate but low false detection rate, and indicate that the proposed scheme is a potentially viable approach against the suppression attack in MPL-based LLNs.  
Keywords:Denial-of-Service attack; low power and lossy networks; multicast protocol; suppression attack +
Footnote †: journal: Sensors
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results
### App to Display Patient Information  
During the hospital test, a registered physician was responsible for constantly checking the patient's temperature, blood oxygen, heart rate, respiration rate, galvanic resistance and location, using the PlaIMoS App. Furthermore, electrocardiogram information was automatically updated every second, where a still shot was produced and magnified for more detailed study (Figure 21). During the hospital test, the PlaIMoS App correctly presented all required information without crashing because the application was developed natively for each operating system [31].  
Figure 21: Electrocardiogram View.
# Improved Durability of Wood Strand-Based Panels Using Guayule
## 3 Results and Discussion  
### Physical and Mechanical Properties  
To understand the effect of guayule on the physical and mechanical bonding performance of wood strands, in-situ specimens with 5.00% guayule were submitted to IB and tensile tests. In Figure 3, the results of these tests are compared to that of control specimens that were submitted to the same test in our previous research [19]. The IB and tensile strengths of in-situ specimens were 55.00% and 26.00% lower than those of control specimens, respectively. As mentioned previously, sprayed and brushed specimens were not subjected to IB and tensile testing as they have the same mechanical properties as WSB controls, since a thin coat of guayule does not affect these properties.  
Figure 3: Comparison between (**a**) internal bond (IB) and (**b**) tensile strengths of wood strand-based (WSB) control and WSB in-situ specimens. COV = coefficient of variation, which is equal to the ratio between the standard deviation and the mean.  
Water absorption and thickness swelling test results are given in Figure 4. In the first two hours, both water absorption and thickness swelling results of the in-situ specimens were lower than those of control specimens. However, after 24 h and 72 h immersion in the water, the in-situ specimens showed higher water absorption and thickness swelling compared to the control specimens.  
The results of IB strength, tensile strength, water absorption, and thickness swelling tests of in-situ specimens revealed that adding guayule during the manufacturing process along with PF resin has a negative effect on the bonding performance of wood strands, and in turn reduces the IB and tensile strength and increase the WA and TS of the panel. Considering such negative effects on the structural performance of WSB panels, adding guayule as a treatment during the manufacturing process is not recommended.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 3 Load-Transfer Model Considering Excavation Effects  
### Load Transfer Relationship of a Single Pile  
Ignoring the weight of the pile and considering the vertical equilibrium condition of the pile element at depth \(z\), the relationship between pile axial force \(P(z)\) and shaft resistance \(\tau_{sp}(z)\) can be expressed as:  
\[\tau_{sp}(z)=-\frac{1}{\pi d}\frac{dP(z)}{dz} \tag{3}\]  
where \(\tau_{sp}\) is the shaft resistance on the elementary length \(dz\); \(z\) is the depth; \(d\) is the pile diameter; \(P\) is the pile axial force. The pile displacement at depth \(z\) can be determined as:  
\[\omega_{p}(z)=\omega_{p}(0)-\int_{0}^{z}\frac{P(z)}{E_{p}A_{p}}dz \tag{4}\]  
Figure 1: Pile behavior during excavation unloading and pile loading.  
where \(\omega_{p}(0)\) is the pile head displacement; \(E_{p}\) is the elastic modulus of pile; \(A_{p}\) is the cross-section area of pile. Differentiating Equation (4), the relationship between pile displacement \(\omega_{p}(z)\) and pile axial force \(P(z)\) can be expressed as:  
\[d\omega_{p}(z)=-\frac{P(z)}{E_{p}A_{p}}dz \tag{5}\]  
Substituting Equation (5) into Equation (3), the load transfer differential equation of a single pile can be obtained as:  
\[\frac{d^{2}\omega_{p}(z)}{dz^{2}}-\frac{\pi d\tau_{sp}(z)}{E_{p}A_{p}}=0 \tag{6}\]  
The load-transfer method evaluated the pile response by solving the load transfer differential equation of the single pile, i.e., Equation (6). Hence, the shaft resistance \(\tau_{sp}(z)\) should be determined to solve the load transfer differential equation. The shaft resistance \(\tau_{sp}(z)\) is related to relative displacement at pile-soil interface \(\omega_{sp}\). According to Equations (1) and (2), the shear-induced soil displacement \(\omega_{sr}\), soil greenfield heave displacement \(\omega_{s0}\), and residual pile-soil relative displacement \(S_{p}\) should be determined to calculate the shaft resistance \(\tau_{sp}(z)\).
## 5 Conclusions  
This paper innovatively evaluated the safety of human head exposure to different frequencies by simulations and used the UQ method to analyze the electromagnetic safety issue regarding the human head exposure to the mobile phones. Firstly, the safety of the electromagnetic radiation was evaluated by obtaining quantitative analysis results from an established electromagnetic simulation model of mobile phone and human head. Then, considering the uncertainty of the electromagnetic parameters of each component of a cell phone, the SAR uncertainty problem of the human head exposed to a mobile phone electromagnetic radiation was quantified by using the gPC method. Comparing the SAR calculation results of the gPC method with those of the MC method at different frequencies, it was proved that the SAR calculation based on the gPC method is a solution that can efficiently solve the uncertainty problem of electromagnetic radiation from mobile phones to human head. Finally, the sensitivity analysis method combining gPC and Sobol was used to calculate the sensitivity indices of different input variables, which intuitively showed the degree of influence of mobile phone electromagnetic materials on human head electromagnetic safety. In summary, this study puts forward scientific guidance for EMC design and the optimization of cell phones with respect to issues related to the exposure of the human head to electromagnetic radiation from mobile phones.  
Conceptualization, M.Y. and B.W.; methodology, M.Y. and Y.Z.; software, M.Y.; validation, B.W., Y.Z. and T.S.; formal analysis, M.Y.; investigation, B.W.; resources, Y.C.; data curation, Y.Z.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y. and Y.C.; visualization, T.S.; supervision, Y.Z.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Laboratory for Comprehensive Energy Saving of Cold Regions Architecture of the Ministry of Education, Jilin Jianzhu University, under grant number JJLZHDKF202203, and the Jilin Scientific and Technological Development Program, under grant number 20210509048 RQ.  
Not applicable.  
The authors would like to thank the technical staff of the Key Laboratory for Comprehensive Energy Saving of Cold Regions Architecture of the Ministry of Education, Jilin Jianzhu University and the College of Instrument Science and Electrical Engineering, Jilin University for their technical support.  
The authors declare no conflict of interest.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Definition of the Risk Factor for Discharge of Overhead Conductors Under Ice-Covered Conditions
For a 30 mm ice cover, the vertical sag of the conductor's lowest point is 16.646 m, while that of the ground line is 14.888 m. The maximum conductor bounce height is 18.815 m. The minimum conductor-ground line distance of 2 m or less defines the risk area, which spans 102-298 m and constitutes 49% of the total line length.
## 4 Risk Analysis for Current Protocols
### NMEA 2000  
NMEA 2000 is the marine industry open networking standard on all types of vessels and it is used extensively by manufacturers. This standard is based on the Controller Area Network (CAN) Bus protocol.  
CAN Bus protocol is an industrial and automotive networking protocol widely adopted in vehicle electronics systems [28]. The protocol utilizes a message-based architecture and is capable of operating at either 512 Kbps (high-speed variant) or 128 Kbps (fault-tolerant variant) [14, 29]. Each device that transmits CAN Bus signals is considered a node. The lower a node's numeric identifier (ID), the higher the priority for its messages. During transmission, a node continuously checks the signal on the bus and compares it with the signal it is transmitting. If a mismatch occurs, an error is raised (except for during arbitration in which case the node will just stop transmitting). Nodes maintain internal error counters to prevent having broken nodes continuously flood the network with invalidated messages. The right of a node to raise errors can be disabled depending on the value of these counters. This guarantees that communication over CAN Bus is reliable, since, even with broken nodes that raise errors, messages will eventually be successfully transmitted.  
By default, NMEA 2000 signaling rate is 250 Kbps at the range of 250 m (0.15 miles). Data messages are transmitted as a series of data frames that incorporate robust error checking, confirmed frame delivery and guaranteed latency times [5]. This standard is primarily intended to support relatively brief data messages.  
NMEA 2000 does not incorporate any native security features pertaining to confidentiality, authentication, authorization, or non-repudiation. Controls for these attack vectors are left to be implemented by nodes in their own application-specific protocols operating on top of the marine network protocol. We further elaborate more on the potential vulnerabilities of this protocol based on the representative attacks that we have selected.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 6 Conclusions  
In this paper, the DCGAN and an improved SSD algorithm were combined to establish a kilometer post detection model. Firstly, the effective data set of kilometer posts was established by introducing the DCGAN algorithm. This provided a prerequisite for the construction of the detection model. Secondly, by inheriting the existing optimization methods in reference and further simplifying the design of the SSD network according to the specific application scenario in this paper, the kilometer post detection model based on an improved SSD algorithm was established. From the analysis of the experimental results, we can see that our model has a higher detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. When IoU was 0.7, the AP of our model was 26.09% and 16.6% higher than that of Faster R-CNN and YOLOv3, respectively. When IoU was 0.5, compared with Faster R-CNN and ZRNet models, the detection time of our model was 169.2 ms and 13.57 ms less, respectively. A series of comparative data show that the detection model established in this paper is better than other commonly used deep learning detection models. Moreover, under the influence of different weather conditions and trackside interferences, the model can also achieve accurate detection of kilometer posts.  
To sum up, this paper presents an optimized kilometer post detection method. However, the optimization of the kilometer post recognition method is not considered in this paper. In future work, we will propose a kilometer post recognition algorithm, so as to form a complete kilometer post detection and recognition system, and then carry out a feasibility experiment on a high-speed train.  
Figure 9: Detection and location results of kilometer post (kp).  
**Author Contributions:** Conceptualization, Y.W.; methodology, Y.W. and Y.S.; validation, Y.W. and Y.S.; investigation, Y.W.; writing--original draft preparation, Y.S.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No.U1734203) and Beijing Municipal Science & Technology Commission (No.Z181100001018032)  
**Acknowledgments:** The authors wish to thank the reviewers for their valuable comments and suggestions concerning this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.