esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Acquisition and Processing of Noise Samples  
### Noise Test  
To get close to the real working scene of the Hy-Vo chain system, a noise test is carried out in the indoor reverberation environment. The Hy-Vo chain sample and equipment required for the test are shown in Figure 2. For the chain sample, the pitch is 9.525 mm, the number of links is 84, the chain form is 4 \(\times\) 3, the drive sprocket tooth number is 35, and the driven sprocket tooth number is 37. The measurement microphone (UMIK-1 from miniDSP technology company, Hong Kong, China) is positioned at the same height as the center of the drive sprocket to collect noise. We select two noise acquisition positions: one is at 0.5 m from the center of the drive sprocket and the other one is at 1 m from that. In the test, loads are 500 N and 750 N, respectively, and the speed range is 500-6000 rpm. The noise acquisition process is as follows: we first place the measuring microphone at the position of 0.5 m, start the motor, and adjust the speed to 500 rpm. After the load is set to 500 N, the noise audio is recorded using Adobe Audition software 2022 in a stable running state. The sampling frequency is 48,000 Hz, and the sampling time is greater than 30 s. After the first audio sample is obtained, the load is increased to 750 N and the second audio sample can be taken. The measuring microphone is moved to the position of 1 m, keeping the height and direction of the microphone unchanged, and we collect the third audio sample at the load of 750 N. The load is reset to 500 N and the fourth audio sample is recorded. Each time the speed increases by 500 rpm and the above collection process is repeated, we can finally get 12 \(\times\) 2 \(\times\) 2 = 48 noise samples. For each noise sample, Adobe Audition software 2022 is used to randomly intercept consecutive 5 s clips for subsequent study.  
Figure 3 compares the time-domain waveform between the Hy-Vo chain and the silent chain. Under the same load condition, the noise energy of the Hy-Vo chain system at 1500 rpm is slightly higher than that of the silent chain system, but at 3000 rpm, the noise of the Hy-Vo chain system is significantly stronger than that of the silent chain system. We know that the noise performance of the Hy-Vo chain system is worse at high speed, so it is different from the sound quality of the silent chain system. Therefore, it is necessary and meaningful to study the sound quality of the Hy-Vo chain system.  
Figure 2: Noise test ((**a**) The Hy-Vo chain transmission system, (**b**) audio sampling, (**c**) the measurement microphone).
## 2 Materials and Methods
### Analysis of Influencing Factors  
LAI and meteorological factors (temperature, radiation and precipitation) were selected as the main influencing factors [34; 35], and the relative sensitivity analysis method was used to simulate the impact of different influencing factors on NPP in a certain range, while other input data remained unchanged. Because the meaning of temperature is high and low, the value of temperature cannot express the quantity, so only a simple analysis was made on the temperature. The sensitivity calculation formula of the influencing factor is as follows:  
\[S=\frac{\sum_{i=1}^{n-1}\left|\frac{(NP_{i+1}-NPP_{i})/\overline{NP}}{(Q_{i+1}- Q_{i})/\overline{Q}}\right|}{n-1} \tag{1}\]  
where \(S\) is the sensitivity of a single factor, with a range of 0-1; \(NPP_{i+1}\), \(NPP_{i}\) and \(\overline{NPP}\), respectively, represent the \(i+1\) and \(i\) times of simulation and the average value of both; \(Q_{i+1}\), \(Q_{i}\) and \(\overline{Q}\), respectively, represent the value of the \(i+1\) and \(i\) times input factors and the average value of both, and \(n\) represents the number of changes of factors.  
The study divided the \(S\) value into four levels, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Value of \(S\)** & **Meaning** \\ \hline I & 0 \(\leq S\leq\) 0.1 & Insensitive \\ II & 0.1 \(<S\leq\) 0.25 & Slight Sensitive \\ III & 0.25 \(<S\leq\) 0.5 & Medium Sensitive \\ IV & 0.5 \(<S\leq\) 1 & Highly Sensitive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensitivity level division.  
Figure 3: Topographic factor maps of the study area: (**a**) DEM data; (**b**) the slope map; (**c**) the slope aspect map.
## 4 Analysis of the Experiment Data
### RMS Delay Spread
Figure 13: Three-dimensional plots of CIR at (**a**) 805 m (inset is a partial enlargement of the results of B and SB) and (**b**) 905 m (inset is a partial enlargement of the results of B and SB). At receiving depths within the direct-arrival zone (>3405 m), the energy of the channels was sourced from D, S, B, and SB. The relative delays of these four path clusters were very small and essentially converged. The energy of the channels in this zone was mainly sourced from D, with no interface loss. Therefore, the average RMS delay spread was very low (<0.1 s). The above analysis led to the following conclusions:  
(1) At depths shallower than 500 m in the shadow zone, the average RMS delay spread largely fluctuated under the influence of sea surface fluctuations. (2) When B majorly contributed to the channel in the shadow zone, the depth-dependent fluctuations in the average RMS delay spread were caused by energy changes in this cluster. (3) At depths greater than 2605 m in the shadow zone, the energies of BS and SBS in the channels were small and could not satisfy Equation (2). Therefore, the energy of the channel was sourced mainly from B and SB, with small relative delays, and the RMS delay spread was relatively low (~0.1 s). (4) Within the direct-arrival zone, the path clusters converged and the energy of the channel was mainly sourced from D, with no interface loss. The channel structure was simple and the average RMS delay spread was low (<0.1 s).
###### Abstract  
Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.  
IoT; blockchain; smart campus; smart university; teaching and learning; smart cities; fog computing; edge computing; higher education; context-aware applications +
Footnote †: journal: Applied Sciences
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 4 Methodology
### Abstract Semantic Graph Generation
#### 4.2.2. ASG from Bytecode
The operand normalization mappings used in this paper are provided in Table 2, where we classify the opcodes into six categories according to the types of their operands, i.e., arithmetic, block, logic, memory, store, and bit. The overall workflow of BASG generation is described by Algorithm 2. The bytecode of a smart contract \(B\) is decompiled to opcodes at first time (Line 1), which are grouped in basic blocks according to the opcodes alerting the flow of control (Lines 2-10). Each basic block is regarded as a node of the \(BASG\). Then, control edges consisting of fall edges, conditional edges, and unconditional edges are augmented to the \(BASG\) (Lines 11-13). \begin{table}
\begin{tabular}{c c} \hline \hline
**Opcode** & **Normalized Code** \\ \hline LQG0-LOG4 & LOGX \\ PUSH1-PUSH32 & PUSHX \\ DUP1-DUP16 & - \\ SWAP1-SWAP16 & - \\ POP & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Opcode normalization. \begin{table}
\begin{tabular}{c c} \hline \hline ADD, MUL, SUB, EXP, SIGENEXTEND & ArithData \\ BLOCKHASH, COINBASE, TIMESAMP, & BlockData \\ NUMBER, DIFFICULTY, GASLIMIT & \\ LT, GT, SLT, SGT, EQ, ISZERO & LogicData \\ MLOAD & MemData \\ SLOAD & StorData \\ BYTE, SHL, SHR, SAR & BitData \\ AND, OR, XOR, NOT & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Operand normalization. Finally, opcodes and operands are normalized to their general forms following the rules as aforementioned (Lines 14-17). ```
1:Bytecode of smart contract, \(B\)
2:Bytecode abstract semantic graph, \(BASG\)
3:Decompiling \(B\) to extract \(Opcodes\)
4:\(BB\)\(\leftarrow\)\(\emptyset\)
5:forop in \(Opcodes\)do\(\triangleright\) Obtain basic block
6:ifop is [JUMP JUMPI STOP REVENT RETURN INVALID SELFDESTRUCT] then
7:\(BASG.Node.add(BB)\)\(\triangleright\) Add current basic block
8:\(BB\)\(\leftarrow\)\(\emptyset\)
9:else
10:\(BB.add(op)\)\(\triangleright\) Add current Opcode
11:endif
12:endfor
13:\(BASG.Edge.addFallEdges()\)
14:\(BASG.Edge.addConditionalEdges()\)
15:\(BASG.Edge.addUnconditionalEdges()\)
16:for\(BB\)in\(BASG.Node\)do\(\triangleright\) Normalization
17: NormalizeOpcode(\(BB.Opcode\))
18: NormalizeOperand(\(BB.Operand\))
19:endfor
```  
**Algorithm 2** BASG generation from bytecode.
# Build Orientation Optimization Based on Weighted Analysis of Local Surface Region Curvature
## 3 Methodology
### Weight Distribution
#### 3.2.2 Local Weight  
Although cluster weight represents the variation of local regions, it cannot show the specific changes at the point level. Once we have the curvature map of each cluster, we need to get further information in each cluster from the point level instead of cluster  
Figure 5: K-means cluster, \(k=10\): (**a**) Cluster results. (**b**,\(\epsilon\)) Different results when adopting the same parameter to get key points.  
Figure 6: Curvature shift cluster, bandwidth = 1.2: (**a**) Cluster results. (**b**) Key points when \(\epsilon=0.8\). (**c**) Key points when \(\epsilon=0.6\).  
level. Local weight is proposed to reflect curvature variation of a facet consist of its three vertices. Given a cluster \(C\) with \(m\) points, \(C=\{p_{1},p_{2},\ldots,p_{m}\}\), local weight is obtained as Algorithm 2 shown.  
```
0: the cluster \(C:\{p_{1},p_{2},\ldots,p_{m}\}\)
0: the local weight \(w_{l}\)
1: Calculate \(c_{pj}\) (\(j\in[1,m]\)) in \(C\) by the method in Section 3.1.3.
2:\(c_{av}\leftarrow\frac{\sum_{j=1}^{s}c_{pj}}{m}\), \(P_{temp}\leftarrow\varnothing\)
3:while\(c_{pj}\geq c_{av}\)do
4: insert \(c_{pj}\) into \(P_{temp}\)
5:endwhile
6:\(F\leftarrow\varnothing\), \(t\gets 0\)
7: Assumed \(P_{temp}\) consists of facet set \(F\), \(F\) consists of \(t\) facets
8:for facet \(f\) in \(F\)do
9: get the three vertex belongs to \(f\):\(p_{1},p_{2},p_{3}\)
10:\(c_{f}\gets c_{p1}+c_{p2}+c_{p3}\)
11:\(t++\)
12:endfor
13:return\(w_{li}\leftarrow\frac{c_{fi}}{\sum_{i=1}^{t}c_{fi}}\), \(i\in[1,t]\)
```  
**Algorithm 2** Weight calculation.  
The final weight of the \(i\)-th facet in the model is calculated by the cluster weight and local weight:  
\[w_{fi}=w_{ci}\cdot w_{li} \tag{13}\]  
The weight of the points whose curvatures are 0 indicates the points have no contribution to the optimal orientation. Owing to the weight being based on the facet, the local weight actually means the facet weight based on the facet consisted of the three vertices. In addition, the mean curvature \(c_{m}\) of a cluster also helps to judge the regions with large curvature changes from the center point. If the cluster weight and the local weight have synchronized changes, it indicates the cluster weight has sufficient credibility to reflect the geometry changes in the current region.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods
### Experimental Procedure
#### 2.2.4 Measurement Protocol of the Transfer Function  
The measurement of the transfer function is performed between the transformer substation and the customer's electrical installation. In some cases, these two points of the grid may be composed of several hundreds of independent electricity meters. Thus, it is difficult to use the same vector network analyzer (VNA) both for transmission and reception.  
Equation (1) gives the expression of the transfer function (\(H\)(\(f\))) which corresponds to the ratio between the received signal (\(Y\)(\(f\))) and the injected signal (\(X\)(\(f\))).  
As can be seen in Figure 7, in this study, two VNAs are used. The first one acts as a signal generator (VNA-TX). The second one is used as a receiver (VNA-RX). Two NB-PLC single phase couplers enable to couple the signal with the power line, and to decouple it from the power line on the receiver side. The access impedance of each VNA is equal to 50 \(\Omega\). For all measurements, the number  
Figure 6: Temporal measurement setup in the transformer substation.  
of points was set to 201 in the frequency band ranging from 9 to 500 kHz. The resolution bandwidth (RBW) is equal to 300 Hz, and the sweep time is set to 5 s per measurement.  
\[H(f)=\frac{Y(f)_{VNA-RX}}{X(f)_{VNA-TX}} \tag{1}\]  
\(\bullet\)\(Y(f)_{VNA-RX}\) is the received signal.  
\(\bullet\)\(X(f)_{VNA-TX}\) is the injected signal.
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
###### Abstract  
The traditional hyperbolic Radon transform suffers from the major problem of how to both obtain a high resolution and preserve the amplitude variation with offset (AVO). In the Radon domain, high resolution (sparseness) is a valid criterion. However, if a sparse model is obtained in the Radon domain due to averaging along the offset direction, then it is not possible to preserve the AVO in the inversion data. In addition, hyperbolic Radon transform has a time-variant kernel based on a traditional iterative algorithm, the conjugate gradient (CG), which requires significant computation time. To solve these problems, we propose a Radon transform based on waveform that contains both cycle and amplitude characteristics of seismic waves. The new transform entails creating an upper envelope for the seismic data and computing a preliminary forward Radon transform in the time domain. The forward Radon transform incorporates a priori information by measuring the energy of each slowness (p) trace to obtain the high-resolution result of the Radon domain. For AVO preserving, the proposed method uses polynomials to describe the AVO characteristics in the inverse Radon transform based on the least-squares inversion. Besides amplitude preserving and high resolution, the proposed method avoids using CG and greatly reduces the cost of computing hyperbolic Radon transform in the time domain. In applications to both synthetic and field data, waveform Radon transform (WRT) has a better performance than the conjugate gradient Radon transform (CGRT).  
radon transform; AVO; data construction; waveform Article
## 4 Discussion  
### Burned Area Distribution and Causes of Forest Fire  
The burned area distribution is supported by previous studies. For example, [PERSON] et al., (2017) have demonstrated that the burned area is mainly distributed in eastern China, especially in the forests of Heilongjiang Province in Northeast China [36]. [PERSON] et al., denoted that the probability of forest fires in eastern China is higher than that in the western regions, and the probability of forest fires in the north and south is higher than that in Central China [37]. The results of these studies are consistent with our study. Meanwhile, forest fire is driven by various environmental factors [36]. Climatic change and anthropogenic factors are likely altering the fire distribution in most regions of China. The increasing forest fire in northeastern China may have resulted from an increased temperature and decreased precipitation and humidity [38]. In addition, longitude and latitude had the greatest influence on the occurrence of forest fires. This result is due to the uneven distribution of forest resources and regional differences in forest resources in China. The higher the vegetation cover the more likely they are to cause problems related to forest fires [37]. The Intergovernmental Panel on Climate Change states that \"climate variability is often the dominant factor affecting large wildfires\" [39]. [PERSON] et al., (2010) denoted that climate is a dominant control on fire activity, regulating vegetation productivity and fuel  
Figure 6: The slope map of burned area (**left**) and GPP (**right**) change from 2009 to 2018.  
Figure 7: Forest fire causes the GPP loss area.  
moisture [40]. Precipitation suppresses the forest fire activity and promotes flammable material accumulation. Additionally, the temperature provides burning conditions for the flammable materials. Human activity is now the primary source of ignition in tropical forests, savannas, and agricultural regions [41; 42]. In our study, the forest fire primarily located in Northeast China is mainly caused by natural factors. This result is consistent with some previous studies. However, we only discuss the temperature and precipitation as the natural factor and the nighttime-light as the human action. More factors and quantitative analysis methods should be discussed. Meanwhile, different data sources will bring a lot of uncertainty to the results. Therefore, multi-source data on fire range identification and GPP impact factor identification process will be considered in future work.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### Fuzzy Controller Drive  
The response curves pertaining to the five-phase PMBLDC drive with a fuzzy controller is depicted in Figure 7 and is summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Load** & **Characteristics** & **Time Interval (s)** & **Overshoot \%** & **Peak Time (s)** & **Rise Time (s)** & **Settling Time (s)** & **Steady State Error** \\ \hline \multirow{4}{*}{Step load variation} & Starting & 0–1 & 22.50 & 0.3214 & 0.1069 & 0.4350 & \(-\)2 rpm \\  & Loading & 1–1.2 & \(-\)3.429 & 1.0056 & 0.1948 & 1.1996 & 42.2 rpm \\  & Unloading & 3.3–3.5 & 0 & 3.5 & 0.00059 & 3.4989 & \(-\)163 rpm \\  & Load removal & 3.5–5 & 22.27 & 3.6020 & 0.0288 & 4.2401 & \(-\)1.5 rpm \\  & Starting & 0–1 & 22.50 & 0.3214 & 0.1069 & 0.4350 & \(-\)2 rpm \\ Linear & Gradual increases of load & 1–2.5 & 11.11 & 1.0850 & 0.3325 & 2.4838 & 135 rpm \\  & Constant Max Load & 2.5–3.5 & 0.4088 & 3.1671 & 0.0365 & 3.4975 & 2.3 rpm \\  & Gradual decrease in load & 3.5–4.5 & 14.28 & 4.2666 & 0.0216 & 4.4988 & 2.8 rpm \\  & No Load & 4.5–5 & 14.28 & 4.2666 & 0.0216 & 4.4988 & 2.8 rpm \\ \hline \hline \end{tabular}
\end{table}
Table 2: PI Controller Characteristics.  
Figure 6: Transient performance of the drive with a PI controller.
## 4 Data Acquisition and Parallel Design and Implementation of MIR Algorithm  
For this work, the input data is obtained by using an array of six antennas in a hat-like structure around the head. The signals are obtained using the mono-static radar mode operation; where the same antenna is used to transmit and receive signals. The antennas were arranged around the inner side of the absorber with an equal distance of 9 mm. The signals are generated by the Vector Network Analyzer (VNA) having a frequency range of 1 to 3 GHz. This operation is performed using HP 8753 VNA, with a dynamic range of up to 100 dB. Cylindrical scanning is performed to get the back-scattering signal s11, using six antennas on different locations covering the whole head. The data is stored in text format, showing S11 parameters along with a frequency range of 1-3 GHz. Six sets of data, one for each antenna, making 1206 points in total are then used as input to the algorithm for image reconstruction. Detailed information on the experimental setup, VNA used, and wearable device can be found in our previous work [52].
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 5 Experiments
### Experimental Results
#### 5.3.3 The Contribution of Model Components  
As demonstrated in Figure 3(a),b, JARUA constantly outperforms all four variants across all training proportions. Here, we take a 30% training proportion as an example to discuss the contributions of JARUA components. Figure 5 shows the performances of the variants of JARUA. Clearly, JARUA performs better than JARUA-s by 25.1% on WD and 28.2% on DBLP, which can be attributed to incorporating attributes information. JARUA beats JARUA-a by 4.7% on WD and 12.2% on DBLP, suggesting that utilizing network structural information benefits the UA tasks. However, this effect is not so strong as attribute information in our data sets. Thus, JARUA is quite promising when attribute information is sufficient. It is found that JARUA achieves Hit-Precision values 3.0% higher than JARUA-v on WD and 7.8% higher on DBLP. This suggests that the iterative training algorithm significantly improves the model performances. Moreover, JARUA beats JARUA-t by 25.6% on WD and 3.3% on DBLP. This demonstrates that the proposed multi-granularity attribute embedding performs well in modeling multi-type text attributes.
## 3 Methodology  
The routing algorithm proposed in this paper is based on link state routing protocols [10, 11, 24, 28, 44]. Considering the number of hops, bandwidth, load and delay we have created a new metric. This metric uses artificial neural network logic in order to optimize route selection, optimize network resources and relax link occupancy. In this way, the probability of network blocking should be minimized. The proposed routing algorithm is based on two different Hopfield neural networks. The first one is dedicated to mobile agent routing logic, in order to ensure that every change of network topology or network parameters is distributed to the network as fast as possible [72, 73, 74]. The other one is dedicated to the routing protocol and the route selection problem based on previously collected information. All these artificial logics are implemented in our algorithm bearing in mind that WMN's attributes (dynamic topologies changes, route stability, and relation between bandwidth and load) need very specific logic and good potential redundancies.