esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Extraction with GraphSAGE
### Neighboring Node Feature Aggregation  
The GraphSAGE model has an \(L\)-layer network, and \(L\) usually takes the value of 2. When \(L=2\), the model performs two feature aggregations. During the aggregation process, the target node is able to receive the node information of its first-order neighbors and second-order neighbors. It aggregates the neighbor node information of each layer and updates its own node information through the aggregation function. The average aggregation function is used to obtain the aggregated features of the neighboring nodes in each layer in this paper. First of all, the feature representation of the target node in layer \(k-1\) is concatenated with the aggregated features of the neighbor nodes in layer \(k\). Then the mean value is calculated for each dimension of the vector. Finally, the final node representation is achieved by the nonlinear activation function.  
The initial hidden state of each node consists of the node feature vector \(\{x_{0},x_{1},\cdots x_{N-1}\}\) in the input layer, and the initial hidden state of the target node \(v_{i}\) is \(h_{i}^{0}=x_{i}\). First of all, the process to generate the hidden feature vector \(h_{i}^{1}\) in the first layer of the network is shown in Equation (6):  
\[h_{i}^{1}=\sigma\Big{(}W^{1}\cdot CONCAT\Big{(}h_{i}^{0},AGGR\Big{(}h_{j}^{0}, \forall v_{j}\in N_{i}\Big{)}\Big{)}\Big{)}. \tag{6}\]  
where \(W\) is the parameter matrix and \(\sigma(\cdot)\) is the nonlinear activation function. The neighbor aggregation feature \(h_{N_{i}}^{1}\) of node \(v_{i}\) is obtained by aggregating the initial hidden states \(h_{j}^{0},\forall v_{j}\in N_{i}\) of all sampled neighbor nodes. The aggregation feature \(h_{N_{i}}^{1}\) and the initial hidden state \(h_{i}^{0}\) of \(v_{i}\) are concatenated together, and a nonlinear transformation is performed to generate the hidden feature representation \(h_{i}^{1}\) of the target node \(v_{i}\) in the first network layer. The concatenated operation \(CONCAT(\cdot)\) performs a linear superposition of the feature vectors using residual concatenation. And the aggregation function \(AGGE(\cdot)\) uses mean value aggregation to calculate the mean value of the concatenated vectors. Finally, the final hidden feature vector is obtained using a nonlinear transformation. The mapping process between the middle two hidden layers is shown in Equation (7):  
\[h_{i}^{J}=\sigma\Big{(}W^{J}\cdot CONCAT\Big{(}h_{i}^{J-1},AGGE\Big{(}h_{j}^{J -1},\forall v_{j}\in N_{i}\Big{)}\Big{)}\Big{)} \tag{7}\]  
The hidden feature \(h_{i}^{J-1}\) of layer \(J-1\) is concatenated and aggregated with the neighbor aggregation feature \(h_{N_{i}}^{J}\) to obtain the hidden feature vector \(h_{i}^{J}\) of layer \(J\) for node \(v_{i}\). The output of the last hidden layer is the final hidden feature vector \(h_{i}^{L}\) of node \(v_{i}\). The GraphSAGE model can output the final hidden feature vectors of all nodes in the network. Since the GNN-DRL algorithm proposed in this section is a distributed DRL agent, only the final hidden feature vectors of all nodes are obtained without embedding process.
## 3 Results and Discussion
### Strong Seismic Load Waveform Data and Source Parameter Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Event** & \begin{tabular}{c} **Seismic** \\ **Moment/N\_m** \\ \end{tabular} & \begin{tabular}{c} **Source** \\ **Radius/m** \\ \end{tabular} & \begin{tabular}{c} **Radiated** \\ **Energy/J** \\ \end{tabular} & \begin{tabular}{c} **Apparent** \\ **Volume/m3** \\ \end{tabular} & \
\begin{tabular}{c} **Stress** \\ **Drop/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Apparent** \\ **Stress/MPa** \\ \end{tabular} \\ \hline
1 & \(3.08\times 10^{10}\) & 85.91 & \(2.00\times 10^{4}\) & \(7.89\times 10^{5}\) & 21.238 & 0.195 \\
2 & \(4.59\times 10^{10}\) & 71.66 & \(1.00\times 10^{4}\) & \(4.05\times 10^{6}\) & 63.639 & 0.077 \\
3 & \(1.27\times 10^{11}\) & 29.76 & \(1.20\times 10^{4}\) & \(5.63\times 10^{7}\) & 39.378 & 0.153 \\
4 & \(1.03\times 10^{11}\) & 75.34 & \(1.81\times 10^{4}\) & \(1.54\times 10^{7}\) & 10.109 & 0.087 \\
5 & \(5.38\times 10^{9}\) & 9.52 & \(1.49\times 10^{4}\) & \(1.96\times 10^{4}\) & 24.723 & 1.953 \\
6 & \(5.56\times 10^{9}\) & 4.15 & \(1.67\times 10^{4}\) & \(3.09\times 10^{4}\) & 34.045 & 0.901 \\
7 & \(4.25\times 10^{9}\) & 36.65 & \(1.34\times 10^{4}\) & \(2.26\times 10^{4}\) & 41.835 & 0.952 \\
8 & \(2.47\times 10^{9}\) & 56.94 & \(1.37\times 10^{4}\) & \(7.40\times 10^{3}\) & 58.433 & 1.667 \\
9 & \(6.24\times 10^{10}\) & 51.88 & \(1.60\times 10^{4}\) & \(6.01\times 10^{6}\) & 15.831 & 0.674 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical analysis of typical strong dynamic load seismic source parameters. Figure 9: Comparison chart of seismic source parameters for different types of ruptures in mine seismic events.
# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 2 Materials and Methods
### Utterance Selection and HN Score  
As a first step we carried out forty-four single utterance tests (i.e., by using one utterance in each test). Next, we ran diverse tests in which each classifier was trained with multiple utterances (rather than a single utterance). As the number of possible utterance combinations is very high, the following procedure was used to create what might be an _optimal list of utterances_: (1) A (size = 1) list consisting of the utterance providing the highest accuracy in the one-utterance tests (i.e., _base list_) is created (e.g., word _dedo_); (2) a new test is run for each utterance which is not currently included in the base list (i.e., forty-three in the first step); in these tests, training and evaluation use the _base list_ together with one new utterance (e.g., word _dedo_ + syllable sequence _ka_; word _dedo_ + syllable sequence _ki_, etc.); the utterance in the test with the highest accuracy is added to the _base list_; (3) step 2 is repeated while the accuracy increases. This process is repeated separately for SVM, RF and ANN.  
Finally, we explored the feasibility of computing a hypernasality score (HN Score) for each speaker. The HN Score was the result of dividing the number of times that the speaker has been classified as HN by the total number of tests. Thus, the HN Score ranges from 0% (never classified as HN) to 100% (always classified as HN). Note that the HN Scores may vary depending on the actual tests used to compute it, for which it will be necessary to determine which list of tests provides the best results (i.e., to discriminate the patients from the healthy speakers). In order to interpret the results obtained with the HN Score it is important to consider that some participants may score close to 50% (i.e., indicating  
Figure 1: Architecture of the ANN model.  
that the HN Score does not clarify whether or not the speaker is HN). Here, we use these criteria to interpret the results:  
* HN Score < 40%: the speaker is not HN;
* HN Score in the range 40-60%: HN can be neither confirmed nor discarded;
* HN Score > 60%: the speaker is HN.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Evaluation Indicators  
In this paper, mean of average precision (mAP) is used to evaluate the target detection effect; the parameter quantity and FPS are used to evaluate the detection speed; the class deployment platform is extremely important, so the power consumption index is used to  
Figure 9: DIOR dataset image example: (**a**) golffield; (**b**) vehicle; (**c**) transitation; (**d**) chimney; (**e**) groundtrackfield; (**f**) airplane; (**g**) stadium; (**h**) tenniscourt; (**i**) storage tank; (**j**) ship; (**k**) windmill; (**l**) airport.  
evaluate the processing performance of the hardware deployment platform. The relevant formulas are as follows:  
\[FPS=\frac{x}{T} \tag{17}\]  
\[Precision=\frac{TP}{FP+TP} \tag{18}\]  
\[recall=\frac{TP}{FN+TP} \tag{19}\]  
\[AP=\int_{0}^{1}P(x)dx \tag{20}\]  
\[mAP=\sum_{k=1}^{N}\frac{AP_{k}}{N} \tag{21}\]  
In Equation (17), \(x\) represents the number of detected pictures, \(T\) is the time consumed to detect \(x\) pictures, FPS represents the number of pictures detected per unit time: the higher the FPS, the faster the detection speed, the higher the real time. In Equations (18) and (19), \(FP\) is the detected false sample, which is the sample detected in the target detection task where the target class is inconsistent with the real class; \(TP\) is a true sample, indicating that the detected target class is the same as the real class, and \(FN\) is a false negative sample, indicating a sample that actually exists but has not been detected. Equation (21) \(mAP\) refers to the average of the average accuracy (AP), and all classes detected by the model can be drawn from the accuracy recall to form a curve, and the area enclosed by this curve and the coordinate axis is \(AP\), which is represented by Equation (20). N represents the detection categories.  
Xilinx provides the Maxim PowerTool GUI tool and Uart software (AMD Xilinx, San Jose, CA, USA, version 2.32.03) for developers to perform power testing of FPGAs. According to the actual situation, this paper uses the Maxim PowerTool USB cable with chip and the corresponding GUI software to test the real-time power of FPGA. Although the Maxim PowerTool can display real-time current and voltage levels, it cannot save historical data. We programmed a program in Python language to read out memory values in real time and detect average power by reading power values in continuous time. The calculation formula for the power metric is a fundamental equation used to quantify the rate of energy transfer or consumption in a system. It is expressed as Equation (22), where T is the total time of consumption, and \(p(t)\) represents the function of power as a function of time.  
\[\overline{P}=\frac{\int_{0}^{T}p(t)dt}{T} \tag{22}\]
## 3 Experimental Setup
### Instrumentation and Data Acquisition  
The devices utilized for the test included displacement transducers, load cells, and strain gauges, and all data were transferred simultaneously to the data logger. The column  
Figure 2: Details of test setup.  
axial load and the loads applied to the beams' tips were controlled by load cells. For this purpose, in the experimental test setup, three Kyowa load cells (force transducers) with a capacity of 300 kN and 150 kN have been employed to measure the applied force on the column and at the ends of two beams. The positions of these load cells are depicted in Figure 3.  
Additionally, four units of KYOWA UVDT sensors (displacement transducers) capable of measuring deformations up to 100 mm in the test setup have been utilized. Two of the LVDTs were installed at the midpoints of the columns, both at the top and bottom, to measure the deformation of the column under applied axial pressure at the top. The other two LVDTs were placed at the ends of the beams to measure the displacements of the beams when pushing loads were applied to both ends (Figure 3).  
Strains of the reinforcing steel and concrete were gained at different sections by means of electric resistance gauges, bonded to the bars at pre-ground locations for smooth contact.
###### Abstract  
In this paper, we experimentally evaluate and compare the robustness against interference of the OQPSK-DSSS (Offset Quadrature Phase Shift Keying-Direct Sequence Spread Spectrum) and the SUN-OFDM (Smart Utility Network-Orthogonal Frequency Division Multiplexing) physical layers, as defined in the IEEE 802.15.4-2015 standard. The objective of this study is to provide a comprehensive analysis of the impact that different levels of interference produce on these modulations, in terms of the resulting PDR (Packet Delivery Ratio) and depending on the length of the packet being transmitted. The results show that the SUN-OFDM physical layer provides significant benefits compared to the ubiquitous OQPSK-DSSS in terms of interference robustness, regardless of the interference type and the packet length. Overall, this demonstrates the suitability of choosing the SUN-OFDM physical layer when deploying low-power wireless networks in industrial scenarios, especially taking into consideration the possibility of trading-off robustness and spectrum efficiency depending on the application requirements.  
IEEE 802.15.4; smart utility networks; OQPSK-DSSS; SUN-OFDM; interference +
Footnote †: journal: Accepted: 13 September 2019; Published: 18 September 2019
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 4 Analysis of ACF and PSD
### Modulation Parameter Analysis
#### 4.1.2 Effect of Subcarrier Interval on Navigation Characteristics  
The experimental conditions are as follows: the number of subcarriers \(K\) is 128, useful time of one symbol of the OFDM is \(T_{u}=1/\Delta f\), time domain sampling interval is \(\Delta t=1/N\Delta f\), cyclic prefix length is \(T_{g}=16\Delta t\), and subcarrier interval \(\Delta f\) is 0.05, 0.1, 0.2, and 0.3 MHz, respectively. Figures 5 and 6 shows the change law of the ACF and PSD of the four sets of OFDM navigation signals with the value of \(\Delta f\). When the other conditions remain unchanged, as the subcarrier interval increases, the main peak of the ACF gradually narrows, and the code ring tracking accuracy becomes larger. The sub-peak of the ACF has basically no impact, but it will affect the position of the secondary peak; when the secondary peak is closer to the main peak, it is easy to lock secondary peak and this lock is false lock. By analyzing several sets of OFDM-modulated PSD, it can be found that when the subcarrier interval increases, the main lobe of the PSD density increases, the amplitude of the main lobe gradually decreases, and the width of the depression at the center of the main lobe becomes larger.  
Figure 4: The PSD of OFDM navigation signals with a different number of subcarriers.  
Figure 5: The ACF with different subcarrier intervals.
## 2 Materials and Methods  
All of the experiments were performed in a combined MBE-STM-ARPES ultra-high vacuum (UHV) system with a base pressure of \(\sim\)2 \(\times\) 10\({}^{-10}\) mbar. First, to obtain a clean Si(111)-(7 \(\times\) 7) reconstructed surface, a Si(111) substrate (n-doped by P) was initially degassed at 650 \({}^{\circ}\)C, followed by flash-annealing to \(\sim\)1250 \({}^{\circ}\)C for 20 cycles. To passivate the dangling bonds of the Si(111)-(7 \(\times\) 7) surface and enhance the surface diffusion for the growth of SnSe\({}_{2}\), we evaporated 1/3 ML of Sn on the Si(111)-(7 \(\times\) 7) surface at 550 \({}^{\circ}\)C, leading to a well-ordered \(\sqrt{3}\times\sqrt{3}\)\(R\)30\({}^{\circ}\)-Sn reconstruction surface on the Si(111) (\(\sqrt{3}\)-Sn/Si(111)) surface. Next, the SnSe\({}_{2}\) films were grown by co-depositing high-purity Sn (99.99%) and Se (99.99%) from standard Knudsen cells separately onto the \(\sqrt{3}\)-Sn/Si(111) substrate at 200 \({}^{\circ}\)C. The temperatures of the Sn and Se sources were maintained at 920 \({}^{\circ}\)C and 130 \({}^{\circ}\)C, respectively, keeping the flux ratio at Sn:Se \(\sim\)1:20, and leading the growth rate of SnSe\({}_{2}\) films as about \(\sim\)15 min/layer. During the film growth, an in situ RHEED was used for real time monitoring and surface structural characterization. The electron beam energy of RHEED was kept at 18 keV. After growth, the surface morphology was further characterized by an in situ room temperature STM (RT-STM), whose set point was kept at \(V_{b}\) = 1.0 V and \(I_{l}\) = 100 pA. Subsequently, to investigate the electronic structures, the in situ low temperature (\(\sim\)10 K) XPS and ARPES were performed by a shared DA30 analyzer (Scienta Omicron AB, Uppsala, Sweden). The monochromatic X-ray of XPS and the ultraviolet light source of ARPES were generated from an Al electrode excitation source (Al K\(\alpha\), 1486.7 eV) and a Helium lamp with a monochromator (He I, 21.218 eV), respectively.
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 3 Results
### Gene Coexpression Network Analysis  
The gene coexpression profiles of pepper in response to cold stress and rewarming conditions were analyzed using WGCNA. Seventeen coexpression modules and correlation coefficients were identified (Figure 8). The resistance of cold was positively correlated with the MEturquoise, and the correlation coefficient was 0.85. The MEyellow (0.76), MEmidnightblue (0.97), and MEblue (0.91) modules had higher correlation coefficients at T1, T3, and T4, respectively. Further, it was found that CT-T1 was more positively correlated with the MEtan (0.97).  
The genes related to cold stress and recovery of pepper were mainly concentrated in the MEturquoise, MEmidnightblue, MEblue, and MEtan modules. Therefore, the hub genes in each module were selected for further analysis and drawing the network (Figure 9). The genes, including _Capana08g000846, Capana10g001816, Capana10g000688, Capana07g001882, Capana03g003720, Capana04g00027, Capana04g002349_, and _Capana11g000347_, which encoded translation initiation factor 3 subunit F (EIF3F), zinc finger protein, two-component response regulator-like APRR9, cytochrome P450 family 4 subfamily X (CYP), CCR4-associated factor 1 (CAF1), calmodulin-lysine n-methyltransferasease (CAMKMT), bzip transcription factor, and heat shock 70 kda protein 8 (HSP70), respectively, had higher indegrees in four modules (Figure 9 and Supplementary Table S6).  
Figure 8: Module–trait relationship with samples. The number represents the correlation coefficient about modules with samples. The number in the bracket means \(p\)-value. Red color represents high correlation coefficients; green color represents low correlation coefficients.
## 6 Discussion
### Mobility on Demand  
In this paper, we focus on the registration and resolution of the micro-mobility of entities in common scenarios, such as walking speed and vehicle speed, which are less than 500 km/h. We refer to the macro-mobility as UE moving from one top-level HEA to another peer HEA, and refer to micro-mobility as UE moving in the same HEA or from one non-top-level HEA to another. We suppose that if there is a car driving on the road whose moving speed is 120 km/h, a passenger sitting in it is watching a live 4k or 8k video, since the longest session interrupt time in the 5G control-plane allowed for enhanced Mobile Broadband (eMBB) is 150 ms [54]. Over the duration of session interruption, the car can move forward no more than 5 m; even for a high-speed train whose moving speed is predicted to be 500 km/h, the moving distance is less than 21 m, thus the moving distances are still in the geo-coverage of a bottom-level HEA. Therefore, we use the HM of bottom-level HEAs, which is considered to be fine-grained enough as a transit node to handover the name registration and resolution requests to guarantee the continuity of service.  
In order to ensuring the continuity of services at different moving speeds and guarantee the QoS of latency-sensitive services or applications, avoiding frequent registering or updating of name binding to reduce the signalling overhead, we developed DNHT-based mobile name registration and resolution mechanisms in ENRS to support fast and seamless mobility name resolution, which is a trace-based solution. In the trace-based mobile name resolution approach, when a mobile UE sends the name registration and resolution requests to the ENRS, the requests will be forwarded and propagated within the HMs in the DNHT based on Mobility Parameter (MP), and the ENRS will track the moving path of UE, i.e., the previous PoA and its HEA, and the forwarding path of requests. Then, the response messages will be propagated along the reverse direction the message passed by. The MP will decide the direction of message exchange in DNHT. The historical statistics of the UE's mobility can be used to predict the UE's moving trajectory. Thus, the name registration and resolution requests can be forwarded to the neighbouring HEAs on the UE's predicted moving path. ENRS will maintain some mobility parameters or state parameters when tracking the mobile UE, which may improve the maintenance cost of name registration and name lookup.
## 2 Materials and Methods
### Data Source and Processing
#### 2.2.3 Other Data  
Digital elevation model (DEM) data: The data on elevation (Figure 2) came from the radar topography mapping the Shuttle Radar Topography Mission (SRTM) data of the US Space Shuttle Endeavor. The dataset was generated by resampling based on the latest SRTM V4.1 data, including one map data of the whole country with three precisions of 1 km, 500 m, and 250 m. The data were projected using a WGS84 ellipsoid. This study selected the 1 km-precision data for unified resolution.  
Available water capacity (AWC): The data came from the Harmonized World Soil Database (HWSD) v1.2. AWC data (Table 2) were extracted from the HWSD soil database using ArcGIS 10.2.  
Asian irrigated area map (2000-2010): This product updates previous estimates of irrigated areas using MODIS satellite data and uses relatively higher resolution imagery and classification based on the vegetation seasonal profile method, significantly improving the results. In this study, the Asian irrigation area data for 2010 were clipped using ArcGIS 10.2, and the nearest neighbor interpolation method was resampled to 1 km for data processing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Products** & **Date** &
\begin{tabular}{c} **Time** \\ **Resolution** \\ \end{tabular} & **Amount of Data** \\ \hline MOD11A2 & 1 January 2001–31 December 2020 & 8 d & 918 \\ MOD13A3 & 1 January 2001–31 December 2020 & Month & 240 \\ MCD12Q1 & 1 January 2001–31 December 2019 & Year & 19 \\ MOD17A2 HGF & 1 January 2001–31 December 2020 & 8 d & 920 \\ MOD16A2 GF & 1 January 2001–31 December 2020 & 8 d & 920 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MODIS products used in the study.  
Figure 2: Elevation in the HHHP.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Static Traffic Patterns  
This section evaluates the performance of TAROA for different traffic loads and numbers of stations in a static network, which means that stations stay active from the beginning to the end of the simulation and do not change their transmission interval. Three different total traffic loads are simulated for each scenario, i.e., \(T\) = {0.75, 0.85, 1.2} Mbps for the HT scenario, and \(T\) = {0.095, 0.11, 0.15} Mbps for the LT scenario. Each station \(s\) randomly and uniformly chooses an integer value \(v^{s}\) in the interval \([1,20]\); therefore, the total value for station set \(S\) is  
\[V^{S}=\sum_{s\in\mathcal{S}}v^{s}.\]  
For each station \(s\) its traffic load is calculated as:  
\[\hat{T}^{s}=\frac{T\times v^{s}}{V^{S}}.\]  
Its actual packet transmission interval is subsequently calculated as:  
\[\hat{t}^{s}_{int}=\frac{PayloadSize\times 8}{\hat{T}^{s}\times\hat{t}^{b}},\]  
where \(\hat{t}^{b}\) represents the beacon interval time. Given the packet payload size and data rate, the maximum throughput that can be achieved is about 1.049 and 0.124 Mbps for HT and LT, respectively. As such, \(T\) = {1.2, 0.15} Mbps represents a saturated state (\(\eta\) = 114%, 120%), \(T\) = {0.85, 0.11} Mbps represents a medium traffic load (\(\eta\) = 81%, 88%), and \(T\) = {0.75, 0.095} Mbps results in low traffic load (\(\eta\) = 71%, 76%). Here, \(\eta\) denotes the ratio between traffic load and maximum practical throughput that can be achieved. Together with the number of stations, \(\eta\) is used to describe the density of the network. A network without and with hidden nodes is both simulated. As a benchmark, we compare to the traditional 802.11 channel access method based on EDCA/DCF and fixed RAW groups.