esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Results and Discussion
### The Total Impacts of Crude Oil Price Shocks  
To deepen the understanding of the total impacts of a positive shock to oil price on each price index over time, we calculate the sum of each month's direct and indirect impacts, as shown in Figure 5, which reveals the impact degree and duration. The duration of the total impacts is similar to the direct impacts, and the impacts on EPI last the longest with about 15 months. Furthermore, the impact direction of crude oil price fluctuation on EPI is highly uncertain. Except for EPI, most of the total impacts reach their peak in the second month.  
Based on Tables 1 and 2, we sum the absolute values of the direct and indirect impacts of crude oil price shocks on each price index and calculate the proportion of the indirect impacts for the total impacts, as shown in Table 3. The total impacts on EPI and MPI are the largest, and impacts on CPI are the lowest. Taking a further analysis, the proportions range from 0.03% to 100.00%. Except for 100%, the proportions of the indirect impacts on MPI are larger than other price indices. Furthermore, it could be concluded that the proportions of the indirect impact of Brent oil price shocks are higher than other oil price shocks. Our results prove that the indirect influence cannot be ignored when analyzing the influence of crude oil price fluctuation on the Chinese price level.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Brent** & **WTI** & **Dubai** & **Daqing** \\ \hline MPI & 0.3642 & 0.0633 & 0.3155 & 0.1816 \\ EPI & 0.2297 & 0.0513 & 0.1960 & 0.0942 \\ CGPI & — & — & — & 0.0002 \\ PPI & 0.0536 & — & 0.0446 & 0.0396 \\ PPIM & 0.0579 & — & 0.0481 & 0.0427 \\ RPI & 0.0563 & 0.0001 & 0.0476 & 0.0166 \\ CPI & 0.0316 & 0.0026 & 0.0264 & 0.0065 \\ MAPPI & 0.0534 & — & 0.0444 & 0.0268 \\ \hline \end{tabular} Note: “—” means that the corresponding price index could not be indirectly impacted by crude oil price shocks.  
\end{table}
Table 2: The total indirect impacts on price indices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Brent** & **WTI** & **Dubai** & **Daqing** & **Brent** & **WTI** & **Dubai** & **Daqing** \\ \hline MPI & 1.6492 & 1.3389 & 1.6379 & 1.5140 & 22.08\% & 4.72\% & 19.26\% & 11.99\% \\ EPI & 2.4277 & 0.0513 & 2.3180 & 2.0753 & 9.46\% & 100.00\% & 8.46\% & 4.54\% \\ CGPI & 0.6007 & — & 0.6339 & 0.5209 & — & — & 0.04\% \\ PPI & 0.5530 & — & 0.5213 & 0.0396 & 9.70\% & — & 8.56\% & 100.00\% \\ PPIM & 0.7167 & — & 0.6853 & 0.0427 & 8.07\% & — & 7.03\% & 100.00\% \\ RPI & 0.3503 & 0.2730 & 0.3362 & 0.3098 & 16.07\% & 0.03\% & 14.17\% & 5.37\% \\ CPI & 0.0316 & 0.0026 & 0.0264 & 0.0065 & 100.00\% & 100.00\% & 100.00\% \\ MAPPI & 0.0534 & — & 0.0444 & 0.0268 & 100.00\% & — & 100.00\% & 100.00\% \\ \hline \end{tabular} Note: “—” means that the corresponding price index could not be impacted by crude oil price shocks.  
\end{table}
Table 3: The proportion of the indirect impact on total impacts.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 4 Attack Methodology
### Step 1--Training Dataset Preparation  
Let N be the number of randomly selected ECC points, in the Jacobian coordinate system, from the elliptic curve E, and M represent the set of ECC points, then each ECC point in M, over curve E, can be represented as follows:  
\[M=\{(X_{i},Y_{i},Z_{i})\text{ where }i=1,2,...N\} \tag{10}\]  
Let K be the least-significant four bits of the 256-bit key. Out of the 4 LSBs, the three bits at locations 2 nd, 3 rd and 4 th, are the target of this analysis. The first bit is not considered, as the double-and-add-always algorithm's implementation starts encryption using a second bit of the key. For each bit location, raw traces of length \(Len_{Trace}\) are collected and then processed to form samples. \(S_{BitLoc}=N*S_{p}t\) samples are collected for N ECC points from the set M, where \(S_{p}t\) represents the number of samples for each ECC point from the pool of N ECC points. As the number of  
Figure 3: Key Bits Under Target.  
\begin{table}
\begin{tabular}{c c} \hline
**Resource** & **Utilization** \\ \hline CORE AREA (LUT) & 26,570 \\ ECPA (LUT) & 14,382 \\ ECPD (LUT) & 11,760 \\ CLK frequency & 100.00 MHz \\ DYNAMIC POWER & 0.20 W \\ TOTAL POWER & 0.313 W \\ \hline \end{tabular}
\end{table}
Table 1: Implementation results on Xilinx FPGA XC7k160 tfbg676-1.  
possible combinations for the last nibble is \(2^{4}\) and there is no point in attacking the first bit, so in total \(S_{N}=S_{BitLoc}*(2^{n}-2)\) samples are collected. For creating a training dataset for machine-learning classification, data samples need to be labeled. After data sample collection, labeling is an important task. To ease the process of attacking and labeling, we have divided the attack into three levels according to the bit location under attack and have categorized the samples into two groups. Each is further explained below.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from HH Polarization  
As shown in Section 3.1, CMOD5.N shows the overall best performance for the GF3-02 SAR VV polarization data. Therefore, for the HH polarization data, the following five models are used: CMOD5.N+PR_Elfouhaily, CMOD5.N+PR_Zhang, CMOD5.N+PR_Ren, CMOD5.N+PR_Wang, and CMODH_HH.  
Figure 5: Comparison of wind speed retrieval results from GF3-02 VV polarization data based on (**a**) CMOD_IFR2, (**b**) CMOD5, (**c**) CMOD5.N, (**d**) CSARMOD2, and (**e**) CMODH_VV and HY-2B SCA wind data.
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 5 Visual Adaptivity by Eye-in-Hand System
### Motion Planning  
Once the image is captured by the eye-in-hand camera of the gripper, the camera coordinates must be converted into global coordinates for path planning to allow the gripper to accurately track the moving object.  
The coordinate frames for a six-degrees-of-freedom robot (Universal Robot 5 in this case) and eye-in-hand robotic gripper system are defined as follows:  
1. Manipulator base coordinate frame \(\left\{\,B\right\}\) ;
2. Tool (end-effector) coordinate frame \(\left\{\,T\right\}\) ;
3. Eye-in-hand camera coordinate frame \(\left\{\,C\right\}\) ;
4. Object coordinate frame \(\left\{\,O\right\}\), which is the surface of the object.  
For simplicity, the eye-in-hand camera was installed in the center of the palm; therefore, the camera frame was the same as the gripper frame, which was not particularly defined in this study. The relationship between the aforementioned frames is illustrated in Figure 23, and the calibration matrix of this system is given by:  
\[M_{B}^{T}(New)M_{T}^{O}\ =\ M_{B}^{T}(Previous)M_{T}^{C}M_{C}^{O} \tag{12}\]  
Here, \(M_{B}^{T}(Previous)\) is the transformation matrix for the previous position and \(M_{B}^{T}(New)\) is the transformation matrix of the position to which the gripper is expected to move. Thus, the gripper can use inverse kinematics to move toward the target posture. In Equation (12), \(M_{T}^{O}\), \(M_{T}^{C}\), and \(M_{C}^{O}\) are transformation matrices, as shown in Figure 24, and are expressed as:  
\[M_{T}^{O}\ =\ \left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&t\\ 0&0&0&1\end{array}\right] \tag{13}\]  
Figure 23: Relationship of coordinate frames of the manipulator, the gripper/camera system, and the object.  
\[M_{T}^{C}\ =\ \left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&d\\ 0&0&0&1\end{array}\right]\text{, and } \tag{14}\]  
\[M_{C}^{O}\ =\ \left[\begin{array}{cccc}\cos(R_{Z})&-\sin(R_{Z})&0&\frac{ \Delta u\times z}{\Delta r_{Z}}\\ \sin(R_{Z})&\cos(R_{Z})&0&\frac{\Delta u\times z}{\Delta r_{Z}}\\ 0&0&1&z^{\prime}\\ 0&0&0&1\end{array}\right]\text{,} \tag{15}\]  
respectively. The \(M_{T}^{O}\) is the transformation matrix from object coordinates to tool coordinates, \(t\) is the distance between the tool frame and the object frame, whereas the \(M_{T}^{C}\) is the transformation matrix from the camera coordinates to the tool coordinates, and \(d\) is the distance between the tool frame and the camera frame. Last but not least, the \(M_{C}^{O}\) is the transformation matrix from object coordinates to camera coordinates, in which expression \(R_{Z}\) is the orientation of the target object, \(z\) is the distance between the camera frame and the object frame, \(z^{\prime}\) is the distance between the expected grabbing position and the object frame, \(\Delta u\) and \(\Delta v\) are the pixels between the center of the target object point and the center of the image, and \(\alpha\) and \(\beta\) are the calibrate parameters in the intrinsic matrices of the camera.
## 3 Metrics for Jamming Attack Detection  
[PERSON] _et al._[1] define a jammer _'to be an entity who is purposefully trying to interfere with the physical transmission and reception of wireless communications'_. This can be achieved by the jammer by attacking at the physical layer or at the data-link layer. At the physical layer, the jammer can only jam the receiver by transmitting at high power at the network frequency and lowering the signal-to-noise ratio below the receiver's threshold; however, it cannot prevent the transmitter from transmitting, and hence it cannot jam the transmitter. At the data link layer, it can jam the receiver by corrupting legitimate packets through protocol violations, and can also jam the transmitter by preventing it to transmit by capturing the carrier through continuous transmission (another form of protocol violation). With this _modus operandi_ of the jammer at the background, we examine the suitability of various metrics, as suggested by different scholars, for detecting jamming attack on a WSN.
# Learning Translation-Based Knowledge Graph Embeddings by \(N\)-Pair Translation Loss
## 6 Conclusions  
This paper has proposed simple and effective learning for translation-based knowledge graph embeddings by introducing a new loss function. The proposed loss function receives multiple negative triples per one positive triple and allows the positive triple to be compared against the multiple negative triples at a single parameter update. Therefore, learning vector representations with the loss can utilize the information obtained by interacting with multiple negative triples. The experimental results have shown that the proposed loss function does not achieve only fast convergence, but also produces better vector representations. (Our code is available at [[https://github.com/songhyunje/kge](https://github.com/songhyunje/kge)]([https://github.com/songhyunje/kge](https://github.com/songhyunje/kge))).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Embeddings**} & **Dataset** & \multicolumn{2}{c}{**WN18 RR**} & \multicolumn{2}{c}{**FB15K-237**} \\ \cline{2-6}  & **Metric** & **MRR** & **Hits10** & **MRR** & **Hits10** \\ \hline \multirow{4}{*}{TransE} & Margin & 14.5 & 37.6 & 27.0 & 43.6 \\  & \(N\)-pair & **23.7** & **53.0** & **32.6** & **50.5** \\  & KBGAN [15] & 21.0 & 47.9 & 27.8 & 45.3 \\  & NSCaching [17] & 20.5 & 47.4 & 30.0 & 47.4 \\ \hline \multirow{4}{*}{TransD} & Margin & 15.0 & 38.7 & 25.4 & 43.3 \\  & \(N\)-pair & 22.6 & **49.4** & **31.8** & **50.3** \\ \cline{1-1}  & KBGAN [15] & **27.7** & 45.8 & 21.5 & 46.9 \\ \cline{1-1}  & NSCaching [17] & 20.1 & 48.4 & 28.8 & 48.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on link prediction.  
**Author Contributions**: Conceptualization, [PERSON]-[PERSON] and A.-Y.K.; methodology, [PERSON]-[PERSON] and A.-Y.K.; visualization, A.-Y.K.; validation, H.-J.S., A.-Y.K. and [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, H.-[PERSON]; writing--review and editing, [PERSON]-[PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research received no external funding.  
**Acknowledgments**: This paper was supported by research funds for newly appointed professors of Jeonbuk National University in 2019 and Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2013-0-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Problem Statement  
We consider a sensor network that operates in a data-centric way in which the queries do not need to identify an individual destination node. As shown in Figure 1, in order to collect sensing data, thesink node disseminates a query that describes the data of interest, e.g., temperature or humidity, to the network. Upon receiving a query message that matches their data, the corresponding sensors reply to this query. The sink node is not concerned with knowing which node should reply or whether sensor nodes have received the query. This will cause an essential transport problem that the sink node will not detect sensing source loss in the network. For example, in Figure 1 the nodes \(A\), \(B\), \(C\), \(D\), and \(E\) should reply to a query of brightness data, but nodes \(B\) and \(C\) do not receive the query due to loss or node failure. In this case, the sink node will not know that sensor nodes \(B\) and \(C\) are also data sources for this query. In the case that \(B\) and \(C\) have critical and unique data that are essential to this query, this situation will lead to an incorrect judgment and decision at the sink node.  
Due to the large number of queries from various users and the large size of sensor networks, it is difficult to adopt conventional reliable broadcast schemes with high complexity. Furthermore, packet losses caused by temporary failures of sensor nodes cannot be solved by any reliable broadcast method.  
Detection and recovery of packet losses in the query dissemination at the object nodes are essential to enable the success of data collection. In case the sink node has all information of which nodes should reply to each query, a general approach can be that the sink node recovers the loss of query by retrieving the data again from those nodes. Since unicast can be more reliable and easily controlled, the recovery is generally reliable with regard to the success of packet delivery. This approach is straightforward and can be widely applied assuming that each sink node knows every sensor node that is corresponding to each query in the entire network.  
However, this sink based recovery approach suffers from the problem of scalability with increasing sensor network size. The route length of recovery refers to the length of the route between the sink node and an object sensor node. Such route length increases with the network size. In case there are several query losses that need to be recovered, the sink node will have a large cost for recovering over a large  
Figure 1: (**a**) Data query and (**b**) data collection in WSN. Loss of data query messages leads to loss of sensing data.  
distance. The large length of recovery increases the network traffic and recovery delay and consumes much energy at sensor nodes. Therefore, a scalable detection and recovery mechanism of query losses is essential for sensor networks, especially for those with large network size.
# Offloading of Atomic Tasks in Satellite Networks:
## 5 Numerical Experiments
### Parameter Setting  
As shown in Table 2, where the parameter '_gamma_' denotes the kernel coefficient and its values are selected in the range of \(0.01,0.1,1,10\), the parameter '_n_clusters_' denotes the dimension of the projection subspace and its values are selected in the range of \(2,3,4,5,6\), the parameter '_n_neighbours_' denotes the number of neighbors to use when constructing the affinity matrix using the nearest neighbors method and its value is 10, the parameter '_degree_' denotes the degree of the polynomial kernel and its value is 3. We select the optimal aggregation result from the above set by training, and the set of three categories after the clustering is completed selected by default. The default minimum start time for all tasks should be greater than 0.  
As shown in Table 3, where the parameter \(lb\) denotes the minimum value of each independent variable, \(ub\) denotes the maximum value of each independent variable, and the maximum value of each variable comes online as the number of resources currently owned by the satellite. _prob_mut_ denotes the variance probability, \(w\) denotes the inertia weight of algorithm \(PSO\), \(c1\) denotes the individual memory of algorithm \(PSO\), \(c2\) denotes the collective memory of algorithm \(PSO\), \(F\) denotes the variance coefficient of algorithm \(DE\), \(size\_pop\) denotes the population size.
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 3 Proposed Scheme
### Numerical Examples  
#### 3.4.1 Generating Two Cover Images  
Suppose that a pixel in the original image is provided by  
\[x_{j}=[1\;0\;0\;1\;1\;1\;0]^{T}.\]  
Following the generation procedure described in Section 3.1, two cover pixels are derived as follows:  
(1) Initialization: Copy the original pixel to create two initial cover pixels  
\[x_{j}^{1} =[1\;0\;0\;1\;1\;1\;0],\] \[x_{j}^{2} =[1\;0\;0\;1\;1\;1\;0].\]2. Virtual Pixel Construction: A virtual pixel \(y\) is constructed by selecting four LSB bits from \(x_{j}^{1}\) and three LSB bits from \(x_{j}^{2}\), as defined in Equation (4): \[y=[1\;1\;1\;0\;1\;1\;1].\] This process compresses the essential information of two pixels into a compact 7-bit structure, facilitating efficient error management.
3. Decimal Conversion: The selected bits are separated into two groups and converted into decimal: \[\begin{split}\tilde{\alpha}&=b2d([1\;1\;1\;0])=14,\\ \tilde{\beta}&=b2d([1\;1\;1])=7.\end{split}\]
4. Codeword Selection: To ensure that the virtual pixel belongs to a valid codeword set, the closest matching codeword is identified by minimizing the error defined in Equation (5): \[\begin{split}\varepsilon&=\min\Bigl{(}(\alpha_{i}- \tilde{\alpha})^{2}+(\beta_{i}-\tilde{\beta})^{2}\Bigr{)}\\ &=\min\Bigl{(}(15-14)^{2}+(7-7)^{2}\Bigr{)}=1.\end{split}\] Thus, the optimal codeword \(h\) is determined as: \[h=[1\;1\;1\;1\;1\;1\;1].\]
5. Cover Pixel Reconstruction: Using the selected codeword \(h\), the two cover pixels are reconstructed according to Equation (6): \[\begin{split} x_{j}^{1}&=[1\;0\;0\;1\;1\;1\;1],\\ x_{j}^{2}&=[1\;0\;0\;1\;1\;1\;1].\end{split}\]
6. Assignment: The updated pixels \(x_{j}^{1}\) and \(x_{j}^{2}\) are assigned to their respective positions within the cover images \(\mathbf{I}_{1}\) and \(\mathbf{I}_{2}\).
7. Iteration: The above process is repeated for each pixel of the original image to complete the construction of the two cover images.
## 4 Results and Discussion
### Association Rules among Multiple Elements
#### 4.2.2 Comparison with Bivariate Spatial Cross Correlation
The mining of association rules shows that there are positive correlations among all sulfophile elements with HH clustering, that is, [As (HH)] \(\Rightarrow\) [Sb (HH)], [Cd (HH)] \(\Rightarrow\) [Zn(HH)], [Cu (HH)] \(\Rightarrow\) [Co (HH)], [Bi (HH)] \(\Rightarrow\) [Cu (HH)], [Zn (HH), Ag (HH)] \(\Rightarrow\) [Cd(HH)], [Cd (HH), Ag (HH)] \(\Rightarrow\) [Zn (HH)], [Cd (HH), As (HH)] \(\Rightarrow\) [Zn (HH)], [As (HH), Zn (HH)] \(\Rightarrow\) [Cd (HH)], [Zn (HH), Sb (HH)] \(\Rightarrow\) [Cd (HH)], [Cd (HH), Sb (HH)] \(\Rightarrow\) [Zn (HH)], and [Zn (HH), As (HH)] \(\Rightarrow\) [Sb (HH)]. In rules [Mo (HH)] \(\Rightarrow\) [Sb (LL)], [Cu (HH), La (LL)] \(\Rightarrow\) [Co (HH)], and [Co (HH), Sb (LL)] \(\Rightarrow\) [La (LL)], there are positive correlations between sulfophile elements with HH clustering and oxyphile elements with LL clustering. We next compared the bivariate spatial cross correlations and association rules for Cu and Co (Figure 12), as well as As and Sb (Figure 13). The distributions of \(I_{\text{CuCo}}\) HH clustering and \(I_{\text{AsSb}}\) HH clustering are spatially similar to the association rules [Cu (HH)] \(\Rightarrow\) [Co (HH)] and [As (HH)] \(\Rightarrow\) [Sb (HH)], respectively; however, \(I_{\text{CuCo}}\) HH clustering and \(I_{\text{AsSb}}\) HH clustering cover wider areas. In addition, \(I_{\text{CuCo}}\) and \(I_{\text{AsSb}}\) reveal not only high HH clustering but also LL, LH, and HL clustering, which shows the simultaneous relationship between two elements but does not scale efficiently to massive data sets. In contrast, association rule mining is suitable for revealing the association among items in a large geochemical dataset. Figure 12: (**a**) Association rule mining result, [Cu (HH)] \(\Rightarrow\) [Co (HH)], and (**b**) bivariate spatial cross-correlation indicator, \(I_{\text{CuCo}}\), of Cu and Co.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 2 Related Work  
Many researchers have done an extensive study on the coexistence of WiFi and other networks that operate concurrently in unlicensed spectrum bands. In the next sections, we will discuss existing coexistence schemes, particularly on the solutions proposed for the fair coexistence of coordinated and uncoordinated LTE and WiFi networks.
## 2 Numerical Setup
### Mesh Independence Study and Validation  
In this study, the multi-block structured mesh is refined on all solid walls and boundaries to provide better resolution on local flow with large velocity gradient, as shown in Figure 3. The mesh refinement guarantees that the value of \(y^{+}\) on the surfaces of blade, hub, and shroud is normally \(y^{+}\) \(<\) 10 in most regions, while near the outlet of the impeller, the value could increase to about 15. To assess the influence of the mesh number on the accuracy of the results, four different meshes are tested and the pumping head of the impeller is given in Table 2. It can be seen from Table 2 that the difference of the pumping head between Mesh 2 and Mesh 1 is 0.09 m and that the difference between the Mesh 4 and Mesh 3 is 0.07 m. Considering the balance of numerical accuracy and computational cost, we believe the configuration of 7.6 million meshes is sufficient for the present simulation, especially for the near-wall flow.
###### Abstract  
Urban air pollution has received increasing attention in recent years. To investigate the interaction between several heavy metal elements and the degree of atmospheric pollution, the leaves of three evergreen tree species--Chinese juniper (_Juniperus chinensis_ L.), dragon juniper (_Juniperus chinensis_ L.), dragon juniper (_Juniperus chinensis_ L. 'Kaizuca'), and cedar (_Cedrus deodara_ (Roxb. ex D.Don) G.Don)--were collected from main road intersections in the urban area of Tianjin, China. Two different treatments--water washing (WW) and ethanol washing (EW)--were used, and the contents of Cu, Mn, Cd, and Zn were measured in both washed and unwashed (UW) leaves. It was found that the heavy metal contents within Chinese juniper and dragon juniper were ranked as Mn > Zn > Cu > Cd, and the metal accumulation index (_MAI_) value was higher for dragon juniper. For the three plants, water washed off 5.36% to 58.58% of the total heavy metals in the needles, while ethanol washed off 16.08% to 71.60% of the total. Both washes were more effective for Cu, Zn, and Cd, and especially for the element Cd. Ethanol could clean off 38.64% to 71.60% of the total Cd from the leaves. Ethanol had a better elution effect compared to water, and the trend of the Cd content in the leaves of the three plants showed a change after the use of different washing methods, which suggests that the water washing may have masked the real difference.  
everygreen tree species; needle leaves; heavy metals; urban environment; ethanol +
Footnote †: journal: Applied Sciences