esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Principles and Method  
### Nuclear Magnetic Resonance Technology (NMR) and Small Angle X-Ray Scattering (SAXS)  
NMR has been widely employed to explore the evolution and distribution laws of rock mesopores in recent years [34]. NMR studies the relaxation properties of H atoms in rock pores by means of the magnetic interaction between the external magnetic field and H atom nucleus, and then forms NMR imaging. The recovery time for atomic nuclei from a high-energy state to a low-energy state under the action of radio frequency pulse RF is called relaxation time, which consists of longitudinal relaxation time \(T_{1}\) and transverse relaxation time \(T_{2}\). \(T_{2}\) usually represents the internal pore structure of rocks directly because of its short measurement time. Different relaxation mechanisms of \(T_{2}\) involve volume relaxation, surface relaxation and diffusion relaxation. The explicit expression of transverse relaxation time \(T_{2}\) is as follows [35]:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2 surface}}+\frac{1}{T_{2 volume}}+\frac{1}{T_{2 diffusion}} \tag{1}\]  
The transverse relaxation time is mainly controlled by the surface relaxation, and the last two terms in the above equation can be ignored: \(T_{2}=T_{2 surface}\). As a result, the above equation can be simplified as [36]:  
\[\frac{1}{T_{2}}\approx\frac{1}{T_{2 surface}}=\rho_{2}\bigg{(}\frac{S}{V} \bigg{)}_{pore} \tag{2}\]  
In Equation (2), \(S\) and \(V\) are the pore surface area (cm\({}^{2}\)) and volume (cm\({}^{3}\)), respectively; \(\rho_{2}\) is the rock surface relaxation rate (ms\({}^{-1}\)).  
SAXS is a powerful method to study the overall shape and structural transitions of material, which is different from large angle X-ray diffraction. In SAXS experiments, powdered samples of rocks are usually placed in a plastic disk, and the intensity of the scattered X-rays is recorded by an X-ray detector [37]. The principles of the SAXS test are shown in Figure 1. The resulting scattering mode is relative to the overall shape and size of the particles under exploration [38]. The X-ray scattering occurs when it is projected into the crystal as a kind of electromagnetic wave, and specific diffraction patterns can be obtained by recording the X-ray diffraction line. The different mineral compositions can be distinguished by comparing the position and intensity of the diffraction lines since each mineral has a specific X-ray diffraction pattern.
# Research on the Prediction Model of Blasting Vibration Velocity in the Dahuangshan Mine
## 3 Model  
### EWT-XGBoost Model  
The process of using the EWT-XGBoost model to achieve PPV prediction is shown in Figure 3. The original data of the blasting vibration are denoised by EWT, and the obtained data are divided into the training set and the test set. The cross-validation method can obtain the parameters that optimize the generalization performance of the model. The common hyperparameters of the XGBoost model are the learning rate, the minimum leaf weight, the number and depth of the tree, etc. It divides the dataset into multiple subsets, among which a subset is used as the validation set, and the average error is calculated for multiple trainings to improve the generalization performance of the model. Because the amount of blasting vibration data collected this time is small, the leave-one-out cross-validation method is used to make the number of samples equal to the divided number of training subsets.  
Figure 3: EWT–XGBoost prediction model flowchart.  
When predicting blasting vibration velocity, the amount of explosives (Q), blasting center distance (R), and elevation difference (H) are input as features, and the output is the blasting peak particle velocity (PPV). Both the training data samples and test data samples are derived from the blasting vibration data of the Dahuangshan mine project from July 8 to July 20; the original data samples are shown in Table 1:  
By performing empirical wavelet decomposition on the collected blasting vibration signals, each mra component can be obtained, as shown in Figure 4. The blasting signal can be found by removing the noise, and the maximum value among them can be selected as the new particle peak particle velocity data sample, as input to the EWT-XGBoost blasting vibration velocity prediction model.
# A Wide-Angle Scanning Leaky-Wave Antenna Based on a Composite Right/Left-Handed Transmission Line
## 3 LWA Design and Experiment Results
### Simulated Results of the LWA with Finite Unit Cells
The reason behind this phenomenon is the same as that given in the gain variation analysis mentioned above. Table 3 shows a comparison among the performances of several previous studies [19; 20; 21] with the proposed LWA. It shows that the proposed LWA achieves the largest beam-scanning range. Figure 8: Scanning angle and half-power beamwidth (HPBW) of the LWA versus \(C_{L,var}\). (**a**) Scanning angle; (**b**) HPBW. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Simulated** & **Measured** & **Overall Length** & **Realized Gain (dBi)** \\  & **Scanning Range** & **Scanning Range** & & \\ \hline
[21] & \(-\)38\({}^{\circ}\)\(+\)30\({}^{\circ}\) & \(-\)17\({}^{\circ}\)\(-\)40\({}^{\circ}\) & 3.25\(\lambda\) & 3.1–6.2 \\
[20] & \(-\)38.6\({}^{\circ}\)\(+\)53.3\({}^{\circ}\) & \(-\)31\({}^{\circ}\)\(-\)4\({}^{\circ}\)\(+\)35\({}^{\circ}\) & 3.25\(\lambda\) & 7.8–11.3 \\
[19] & \(-\)49\({}^{\circ}\)\(+\)50\({}^{\circ}\) & \(-\)35\({}^{\circ}\)\(-\)4\({}^{\circ}\)\(+\)38\({}^{\circ}\) & 5.87\(\lambda\) & \(-\)7.0–18.0 \\ This work & \(-\)66\({}^{\circ}\)\(+\)62\({}^{\circ}\) & \(-\)60\({}^{\circ}\)\(-\)60\({}^{\circ}\) & 4\(\lambda\) & 5–9.9 1  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of electronically controlled composite right/left-handed (CRLH) LWAs. Figure 7: Simulated radiation patterns with capacitance varied from 0.38 pF to 1.9 pF.
## 4 HP-SFC Performance Analysis  
### Implementation  
The performance of SFC protection mechanisms is dependent on the network topology, the number of primary and backup SFs, and the placement of primary and backup SFs. In order to perform a comprehensive evaluation of HP-SFC, two network topologies were used, which are shown in Figure 4. The first topology was a three-tier fat-tree data center network with eight hosts, 20 switches, and 48 links. The second topology presented an enterprise network and was based on the AT&T backbone IP network [24] with 8 hosts, 25 switches, and 52 links. These topologies were selected because most of the SFC use-cases and deployments were in data center and enterprise networks [25; 26], and their implementation for HP-SFC evaluation was carried out using Mininet [27].  
Links in emulated Mininet topologies were configured with a 100 Mbps bandwidth and 1 ms delay, and they were controlled by the RYU SDN controller framework v4.30. The HPM was implemented in the SDN controller for the setup and protection of SFC in the emulated topologies.  
Placement of SFs in the emulated topologies was performed randomly. Four switches in each topology were randomly selected to be the SFF, and four hosts representing the SFs were added and linked with the selected SFF individually. Backup SFs for the four primary SFs were placed in a way that the disjoint path was available to access them, and Figure 4a,b shows the placement of the primary and backup SFs in each topology. Eight distinct SF chains were configured in each topology by using different combinations of primary SFs, and their details are presented in Table 1. Primary and backup paths based on the HP-SFC, local recovery, and global recovery schemes were installed for each SF chain in both topologies at the time of network initialization. Traffic for SFC was generated by the respective source hosts through the IPerf tool, and the controller utilized the ingress switches as classifiers to add label stacks to packets through MPLS headers. The controller and the emulated topologies in Mininet 2.3.0 were deployed in a system consisting of an Intel core i7 CPU @3.40 GHz and 32 GB memory, and the experiment results were logged for performance evaluation.  
Figure 4: Emulated network topologies in Mininet for the performance evaluation of SFC protection mechanisms. (**a**) Emulated three-layer fat-tree data center topology. (**b**) Emulated enterprise network topology based on the AT&T IP backbone network [24].
## 3 Results and Discussion
### Method Simplicity  
The simplest of the methods used is by far direct hydrosol analysis. There was no sample pre-preparation, and no organic solvents were used. Sample preparation using extraction is longer, since it takes time to prepare the mixture of sample and solvent, and the mixture has to be shaken for some time. We used overnight shaking to assure an equilibrium. However, in additional experiments, we concluded that the shaking time can be shorter, approximately 3 h (results not shown). Additionally, very little sample was used by direct hydrosol analysis; the volume of injected hydrosol was 1 \(\upmu\)L. In hydrosol extraction, more sample is used, since extraction is difficult to carry out with very small volumes. About 1 mL of hydrosol is used for the 1:1 extraction (a larger volume can be used to make the solvent separation easier), and 10 times more sample is used for the 10:1 extraction. In the published literature, adding sodium chloride is often reported [18; 19; 20]. In our experiment, sodium chloride did not prove to be of much benefit, as it did not affect the area % significantly, while it decreased the number of detected compounds in the 1:1 extraction. We also compared extraction ratios of 1:1 and 10:1 (vol:vol, hydrosol:solvent). By using a 1:1 ratio, less sample was used. In conclusion, a direct hydrosol analysis is simplest; however, if extraction is used, the most convenient method is using a volume ratio of hydrosol to solvent of 1:1 with no sodium chloride, it uses less sample and gives good results.
## 2 Wireless Multimedia Sensor Networks
### Real-Time Video Data
#### 2.2.3 Prioritization and Differentiated Service  
WMSNs encompasses both camera sensors and a variety of scalar sensors. As a result, heterogenous types of traffic including video, audio, and scalar data often coexist at the same time in WMSNs. This no doubt adds to the complexity of QoS provision because each type of traffic possesses distinct attributes and has different QoS requirements. Moreover, since WMSNs are no longer employed for a singular application, every application will also have its very own QoS demands. Even for the same application, however, different events may as well require different QoS guarantees. Not to mention there are extreme cases where different packets can have different QoS requirements. For example, packets containing I-frames in a video stream have higher QoS demands than packets with B-frames and P-frames. To tackle these issues, communication protocols needs to consider the usage of prioritization schemes which assign separate priority levels to different traffic types (or different applications, events, and packets) for providing differentiated services [4]. Commonly, there will be a classifier to assign proper priority levels to the packets and a scheduler to determine which packets should be first served according to the assigned priority levels [54]. With the priority levels set for each packet, routing protocols and MAC protocols can adjust their behavior in ways  
Figure 1: Reliability Provision Methods in wireless multimedia sensor networks (WMSNs).  
such that packets with higher priority will be sent on a shorter path, or be given earlier access to the medium.
## 5 Unsupervised Learning for Power Allocation
### Training Algorithm for ULNN  
To reduce the system load, the power allocation uses the same experience pool shared by the channel allocation. In the training process of the proposed attention-based ULNN, a batch of the channel gain information \(H^{t}\) and the JA \(A_{\text{best}}^{t}\) sampled randomly from the experience pool are first fed into the proposed attention-based ULNN to obtain the power allocation scheme \(P\). Next, the energy efficiency of the system is calculated based on the power allocation scheme \(P\), the channel gain information \(H^{t}\), and the channel allocation scheme \(G_{\text{best}}^{t}\). Finally, the negative expectation of the system energy efficiency is calculated as the loss function. To prevent overfitting during the training process, we additionally incorporate the \(L2\) regularization into the loss function, it can be expressed as  
\[Loss=\text{E}_{G_{\text{best}}^{t},P}\Big{[}-E+\lambda\|\mathbf{w}\|_{2}^{2} \Big{]} \tag{30}\]  
where \(\lambda\) is the regularization parameter used to control the strength of regularization and \(\|\mathbf{w}\|_{2}^{2}\) is the sum of the squares of parameters in the weight vector of the proposed attention-based ULNN. The gradient of the loss function \(\
abla_{\theta}Loss\) is calculated, and the parameters of the proposed attention-based ULNN are updated using the Adam optimizer.  
The proposed attention-based ULNN for the power allocation is described in detail as Algorithm 2.  
```
0: State space 5; The experience pool; The initialized ULNN Output: The well-trained ULNN; power allocation scheme
1:for\(t\)do
2:if the number of the sample data in the experience pool reaches a certain level then
3:\(s^{t},A_{\text{best}}^{t},G_{\text{best}}^{t},\ r^{t}\leftarrow\) the sample data of the experience pool
4:\(P\leftarrow\) output of the ULNN \(\gets s^{t}\), \(A_{\text{best}}^{t}\)
5:\(E_{\text{num}}=\sum_{i}^{M}\sum_{1}^{N}\sum_{1}^{K}\frac{E_{\text{num}}\log \left(1+SINE_{\text{num}}^{n}\right)}{P_{\text{num}}+P_{\text{num}}}\)
6:\(Loss=\text{E}_{G_{\text{best}}^{t},P}[-E_{\text{num}}+\lambda\|\mathbf{w}\|_{2}^{2}]\)
7:\(\theta\gets Adam(\theta,\
abla_{\theta}Loss(\theta))\)
8:\(\text{Test}\leftarrow\) ULNN
9:endif
10:endfor
```  
**Algorithm 2** ULNN for power allocation
## 3 System Model
### Antenna Model  
The antenna model is critical for assessing the time-dependent UE performance. Thus, in this paper, we assume there are planar symmetric antenna arrays at both BS and UE.  
Specifically, the antenna radiation patterns are first generated by following 3 GPP TR 37.977 [46]. Accordingly, the antenna pattern is represented as a superposition of element patterns leading to the array factor in the following form  
\[\tilde{\mathbf{W}}=\mathbf{V}\mathbf{W}, \tag{7}\]  
where  
\[\mathbf{V}(\phi,\theta)=[v_{1,1},v_{1,2},\dots,v_{N_{H},N_{V}}]^{T} \tag{8}\]  
is the phase shift caused by the array placement, and  
\[\mathbf{W}=[w_{1,1},w_{1,2},\dots,w_{N_{H},N_{V}}]^{T} \tag{9}\]  
is the weighting factor, which determines the level of side lobes and the value of electrical down-tilt; \(N_{H},N_{V}\) are the total number of antenna elements in the horizontal and vertical planes.  
A single element \(w_{m,n}\) of the weighting factor \(\mathbf{W}\) can be computed as  
\[w_{m,n}=\frac{1}{\sqrt{N_{H}N_{V}}}\mathrm{exp}\Big{[}2i\pi\Big{(}(n-1)\frac{d \
u}{\lambda}\mathrm{cos}(\theta)+(m-1)\frac{d_{H}}{\lambda}\mathrm{sin}( \theta)\mathrm{sin}(\phi)\Big{)}\Big{]}, \tag{10}\]  
where \(m=1,2,\dots,N_{H}\), \(n=1,2,\dots,N_{V}\); \(\phi\) and \(\theta\) are the horizontal and vertical angular shifts; \(d_{H}\) and \(d_{V}\) are the distances between two antenna elements in the horizontal and vertical planes.  
An illustration of the pattern for a \(15\times 4\) antenna array is shown in Figure 2. Then, the main antenna lobe is assumed to be symmetric with respect to the antenna boresight axis. The antenna gain \(G(\beta_{H},\beta_{V})\) model is then provided by  
\[G(\beta_{H},\beta_{V})=\epsilon\rho_{H}(\beta_{H})\rho_{V}(\beta_{V}), \tag{11}\]  
where \(\epsilon\) represents the maximum directivity along the boresight, and \(\rho_{H}(\beta_{H})\) and \(\rho_{V}(\beta_{V})\) are the directivity functions of the angular deviation from the boresight in horizontal \(\beta_{H}\) and vertical \(\beta_{V}\) directions, whereas \(\beta_{H},\beta_{V}\in[0,\pi]\).
## 3 Results and Discussion
### Total C Export by Harvest--Quantities and Spatial Variability  
To convert fresh phytomass into a map of total C export, the linear relationship between fresh and dry phytomass depicted in Figure 14 was used. The result was then multiplied by the averaged total carbon content (44%) to quantify the C export. Due to the linear transformation, the spatial patterns across and within the individual plots remain the same (Figure 18). The majority of values (99%) range between 75 g\(\cdot\)m\({}^{-2}\) and 225 g\(\cdot\)m\({}^{-2}\). In order to evaluate effects of terrain and prevalent soil type on the total C export, the median (M) for each individual plot was calculated (observations were normally distributed). The lowest C export is at plot 7 (M = 124 g\(\cdot\)m\({}^{-2}\)), which represents an extremely eroded soil at steep slope. The C export from colluvial soils in the hollow (plot 9, M = 164 g\(\cdot\)m\({}^{-2}\); plot 10, M = 163 g\(\cdot\)m\({}^{-2}\)) is only slightly higher than from non-eroded soils at the flat hilltop (plot 1, M = 162 g\(\cdot\)m\({}^{-2}\); plot 5, M = 154 g\(\cdot\)m\({}^{-2}\) and plot 4, M = 151 g\(\cdot\)m\({}^{-2}\)). The internal spatial heterogeneity is higher at flat hilltop positions (plots 1, 4 and 5) and at steep slopes (plot 7). The plots in other terrain positions (plots 9, 10 11 and 12) appear more homogenous with coincidently higher phytomasses.  
Although plots 12 and 11 are located in a similar flat slope position, the difference between the respective exports (M = 157 g\(\cdot\)m\({}^{-2}\) and 146 g\(\cdot\)m\({}^{-2}\) respectively) is higher than from other adjacent plots (9, 10 and 4, 5). This effect may be the result of a manipulation experiment that was conducted in 2010 to simulate landscape-scale erosion processes [25]. However, the interpretation of this effect is beyond the scope of this paper.  
Weather conditions in 2014 were ideal for growing since plant growth was not limited by rainfall input. Therefore, differences across the plots are not very distinct. Generally, the observations match the spatial arrangement of soil types in the respective terrain positions, which is characteristic for this hummock soil landscape. The Calcaric Regosol (plot 7) represents widespread extremely eroded soils with very dense parent material (glacial till) at 30 cm depth. Therefore, the rooting space is rather limited. The Endogleyic Colluvic Regosol (plots 9 and 10) in the hollow has the highest organic matter and nutrient stocks compared to other soils and shows local groundwater level is approximately 80 cm, hence additional water supply for an enhanced plant growth by capillary rise. The strongly eroded  
Figure 17: Spatial distribution of fresh phytomass of lucerne within the eight plots of the CarboZALF experimental area. Outclipped areas are disturbed areas due to experimental devices (autochambers, pathways, _etc._).  
Calcic Cutanic Luvisol (plots 11 and 12) at midslope and the non-eroded Albic Cutanic Luvisols at the flat hilltop are generally fertile and characterized by high available water capacities and good root penetration.
# Strategies and Methods for the Fault-Tolerant Function Development of Multi-Domain Systems +
## 6 Product Development Examples
### Accommodation of Fault \"Inclination Angle Disturbance\" in a Balanced Scooter  
Another example in multi-domain system development concerns a balanced two-wheel scooter, which is one of the use cases of a research project investigating engineering frameworks based on graph-based design languages GBDLs [52]. One main challenge in the operation of this special kind of vehicle is to maintain a desired inclination angle in all driving conditions. A frequently occurring fault is an inclination angle disturbance. In theory, several sensors, which are currently available on the market, can accurately measure this inclination angle. It is also, in general, possible to choose drive motors which possess the dynamic performance to act as actuators and realise the intended inclination angles. Consequently, it should be possible to synthesise an effective control algorithm; commercial systems with well-functioning control algorithms have been available on the market for several years. However, in the development project, it was a continuous challenge to control and maintain the inclination angle of the balanced scooter. Domain-specific countermeasure possibilities could include better sensors and better actuators. However, such singular attempts were not successful. A detailed analysis led to the insight that the problem was partly caused by irradiations and that a shielding of the electronics and accelerometers was an effective countermeasure. In this case, it was realised by applying a 3D-printed housing for these components and a shield by means of an aluminium adhesive tape [72], as shown in Figure 11.  
The functional accommodation and the implementation are summarised in Table 6.  
The problem was also partly caused by fluctuations in the sensor output; here, the application of appropriate filters considerably improved the system behaviour. Again, a combination of functionally diverse accommodation possibilities, including measures which isolate certain system elements, were helpful countermeasures. This is also true for the final example of the development of a multi-domain technical system--the development of a gear system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technical System** & **Fault** & **Health Risk** & \begin{tabular}{c} **Functional** \\ **Accommodation** \\ \end{tabular} & \begin{tabular}{c} **Implementation Success** \\ \end{tabular} \\ \hline Balanced scooter & \begin{tabular}{c} Inclination angle \\ disturbance \\ \end{tabular} & \begin{tabular}{c} Distortion of the \\ driving position/injury \\ of the driver \\ \end{tabular} & \
\begin{tabular}{c} Solutions which \\ isolate components \\ \end{tabular} &
\begin{tabular}{c} Improved drive ability of \\ the balanced scooter \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accommodation of the fault “inclination angle disturbance”—summary.  
Figure 11: Accommodation of the fault “inclination angle disturbance” in an balanced scooter.
# Dynamic Gesture Recognition Based on MEMP Network
## 5 Conclusions  
Gesture recognition plays an important role both in daily life and in the direction of computer vision. The current method based on deep learning is the main research aspect of gesture recognition. In this paper, we proposed a MEMP network for gesture recognition research. The advantage of MEMP network is that 3D CNN and convLSTM are mixed several times to extract and predict the video gesture information multiple times, so as to get higher accuracy. The MEMP Neural Networks has achieved high accuracy in LSA, IsoGD and SKIG data sets, and it also indicates that this network is applicable in many medium and large video data sets. ResNet can easily realize good accuracy of image classification and location tasks. In future studies, we will use the residual network to classify gesture recognition.  
**Author Contributions:** Methodology, Review, Writing and Editing, X.Z.; Validation, X.L.  
**Funding:** This work is partially supported by Shanghai International Cooperation Fund Project (No. 12510708400) and Shanghai Innovation Action Plan Project (No. 16511101200) of Science and Technology Committee of Shanghai Municipality.  
**Conflicts of Interest:** We have no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Dataset** & \multicolumn{2}{c}{**SKIG**} \\ \cline{2-3}
**Records** & **Method** & **Accuracy** \\ \hline
1 & LPSNet (RGB) [28] & 96.7\% \\
2 & LPSNet (RGB-Depth) [28] & 98.7\% \\
3 & 3D CNN and ConvLSTM (RGB) [3] & 95.93\% \\
4 & 3D CNN and ConvLSTM (RGB-Dep) [3] & 98.70\% \\
5 & MEMP network (RGB) & 97.01\% \\
6 & MEMP network (RGB-Dep) & 99.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: SKIG experimental results.  
Figure 6: Accuracy on SKIG dataset using MEMP network.