esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Stud-Link Chain Controller  
The XRF analysis of the stud-link chain controller showed that it was made of a wrought iron alloy, with a composition of 96.0-97.6 wt% Fe, 1.2-2.3 wt% Si, 0.4-1.5 wt% P, and less than 0.5 wt % of Mn and Ti (ground metal, Table 2). The stud-link chain controller was characterised by LM observation of the conventional metallographic sample, according to ASTM A247-67 [40] as type B grey cast iron with a dendritic pattern (Supplementary Material Figure S24). Black graphite flakes and areas of graphite rosettes surrounded by the ferrite matrix, perlite microstructure, and bright iron phosphide eutectic were observed by LM and SEM (Figure 14 and Supplementary Material Figures S25 and S26). The FMM observation of the chain controller revealed a microstructure similar to that observed by conventional metallography observation, with graphite flakes surrounded by ferrite and perlite (Figure 15).  
Figure 13: SEM-EDS elemental mapping of the deadeye strap (conventional metallographic sample), showing preferentially oriented slag inclusions surrounded by iron ferrite grains and the presence of elements (bright dots) (**a**) iron; (**b**) silicon; (**c**) oxygen; (**d**) phosphorus; (**e**) sulphur; (**f**) manganese.  
Figure 14: Conventional metallographic images of the stud-link chain controller (P-section, after etching): (**a,b**) grey cast iron containing black graphite flakes (LM); (**c**) black graphite flakes surrounded by the ferrite matrix, perlite, and white iron phosphide eutectic (SEM); (**d**) elemental mapping (SEM-EDS).  
Figure 15: FMM LM images of the stud-link chain controller (side wall, P-section, after etching) grey cast iron (L-CS, after etching): (**a,b**) general view; (**c,d**) higher magnifications.  
The SEM-EDS analysis of the metallographic samples of the chain controller ferrite matrix with graphite flakes detected the presence of Fe, Si, O, P, S, Mn, Ti, and V (Supplementary Material Figure S25a). The EDS analysis of the grey cast iron ferrite matrix with phosphide eutectic areas revealed the presence of Fe, Si, O, P, and Mn (Supplementary Material Figures S25b and S25c, respectively). The SEM-EDS elemental mapping of grey cast iron metallographic samples of the chain controller revealed the presence of Fe, C, Si, O, P, S, Mn, Ti, and V (Supplementary Material Figures S26d and S27a-i).  
The SEM-EDS analysis results of the metallographic samples of the stud-link chain controller after omitting carbon peaks have shown that the grey cast iron composition was 95.7-96.9 wt% Fe and 1.5-2.2 wt% Si, as well as less than 1.0 wt% of O, P, S, Mn, Ti, and V (Table 3). The SEM-EDS analysis results of a typical area of Fe3P eutectic morphology (Figure S25c) revealed a composition of 88.9 wt% Fe, 0.5 wt% Si, 10.2 wt% P, and 0.2 wt% of Cr and V (Table 3).
# GazeRayHand: Combining Gaze Ray and Hand Interaction for Distant Object Manipulation
## 5 Results
### Objective Measures
#### 5.1.1 Manipulation Time  
In total manipulation time, the GB condition (\(M=51.63\), \(SD=37.20\)) was significantly slower to complete the manipulations compared to the GRH condition (\(M=30.44\), \(SD=15.38\); GRH-GB: \(Z=-7.667\), \(Z<0.001\)), GRH2 condition (\(M=32.69\), \(SD=17.83\); GRH2-GB: \(Z=-6.931\), \(p<0.001\)), and GP condition (\(M=27.89\), \(SD=20.43\); GP-GB: \(Z=-8.507\), \(p<0.001\)). Additionally, participants completed faster when using the GP condition compared to the GRH2 condition (\(Z=-3.643\), \(p<0.001\)). In the other comparisons, there was no significant effect (GRH-GRH2: \(Z=-1.598\), \(p=0.110\); GRH-GP: \(Z=-2.296\), \(p=0.022\)).  
Figure 6: Results of objective measures: total manipulation time, coarse manipulation time, fine-grained manipulation time, total translation distance, and total rotation degree. (GRH: GazeRayHand; GRH2: GazeRayHand2; GP: Gaze&Pinch; GB: modified Gaze Beam Guided; diamond: mean; \(\alpha\): outliers; blue lines: error bars representing Standard Error of the Mean (SEM); \({}^{*}=p<0.0083\), \({}^{**}=p<0.0016\), and \({}^{***}=p<0.001\) for Wilcoxon signed-rank test—alpha level has been adjusted with Bonferroni correction).  
Figure 7: Results of objective measures: total hand translation, total hand rotation, total head translation, and total head rotation. (GRH: GazeRayHand; GRH2: GazeRayHand2; GP: Gaze&Pinch; GB: modified Gaze Beam Guided; diamond: mean; \(\alpha\): outliers; blue lines: error bars representing SEM; \({}^{*}=p<0.0083\) and \({}^{***}=p<0.001\) for Wilcoxon signed-rank test—alpha level has been adjusted with Bonferroni correction).  
In coarse manipulation time, the GP condition (\(M=24.90\), \(SD=19.32\)) was significantly faster than the GRH condition (\(M=27.87\), \(SD=14.28\); GRH-GP: \(Z=-2.881\), \(p=0.004\)), GRH2 condition (\(M=29.69\), \(SD=17.02\); GRH2-GP: \(Z=-4.163\), \(p<0.001\)), and GB condition (\(M=48.25\), \(SD=36.07\); GP-GB: \(Z=-8.782\), \(p<0.001\)). Furthermore, the GRH and GRH2 conditions were significantly faster than the GB condition (GRH-GB: \(Z=-7.501\), \(p<0.001\); GRH2-GB: \(Z=-7.003\), \(p<0.001\)), but there was no significant effect between them (GRH-GRH2: \(Z=-1.101\), \(p=0.271\)).  
Finally, in the results of fine-grained manipulation time, the GB condition (\(M=3.38\), \(SD=3.41\)) was slower compared to the GRH condition (\(M=2.57\), \(SD=3.16\); GRH-GB: \(Z=-3.558\), \(p<0.001\)) and the GP condition (\(M=2.98\), \(SD=4.25\); GP-GB: \(Z=-3.312\), \(p=0.001\)). We found no significant difference in other pairwise comparisons (GRH2: \(M=3.0\), \(SD=3.52\); GRH-GRH2: \(Z=-1.527\), \(p=0.127\); GRH-GP: \(Z=-0.211\), \(p=0.883\); GRH2-GP: \(Z=-0.623\), \(p=0.553\); GRH2-GB: \(Z=-1.883\), \(p=0.06\)).
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 2 Methodology
### Informer  
#### 2.2.1 ProbSparse Self-Attention  
The ProbSparse self-attention is used in this paper. The previous self-attention is defined as a probabilistic nuclear smoothing method:  
\[A(q_{i},K,V)=\sum\limits_{j}\frac{k(q_{i},k_{j})}{\sum\limits_{l}k(q_{i},k_{l})} p_{j}=E_{p(k_{j}|q_{i})}[p_{j}] \tag{1}\]  
where \(p(k_{j}|q_{i})=\frac{k(q_{i},k_{j})}{\sum\limits_{l}k(q_{i},k_{l})}\) and \(k(q_{i},k_{j})\) select asymmetric exponential kernel \(\exp\left(\frac{q_{i}k_{j}^{T}}{\sqrt{d}}\right)\).  
The attention weight of the \(i\)-th query for all keys can be regarded as a probability distribution \(p(k_{j}|q_{i})\) and some significant attention weights will make \(p(k_{j}|q_{i})\) deviate from the uniform distribution. If \(p(k_{j}|q_{i})\) is close to a uniform distribution \(q(k_{j}|q_{i})=\frac{1}{L_{k}}\), then self-attention is redundant, which indicates that the \(i\)-th query is lazy.  
Figure 1: The structure of the GCN–Informer model.  
Figure 2: Diagram of EncoderStack in our model.  
The difference between the distribution \(p(k_{j}|q_{i})\) of a query and the uniformly distributed \(p(k_{j}|q_{i})\) can be measured by KL divergence:  
\[KL(q||p)=\ln\sum_{l=1}^{L_{K}}\exp(\frac{q_{i}k_{l}^{T}}{\sqrt{d}})-\frac{1}{L_{ K}}\sum_{j=1}^{L_{K}}\frac{q_{i}k_{l}^{T}}{\sqrt{d}}-\ln L_{K} \tag{2}\]  
By removing the constant, the sparsity measure of the \(i\)-th query can be defined as:  
\[M(q_{i},K)=\ln\sum_{j=1}^{L_{K}}\exp(\frac{q_{i}k_{l}^{T}}{\sqrt{d}})-\frac{1}{ L_{K}}\sum_{j=1}^{L_{K}}\frac{q_{i}k_{l}^{T}}{\sqrt{d}}-\ln L_{K} \tag{3}\]  
The process of ProbSparse self-attention is expressed:  
\[A(Q,K,V)=soft\max(\frac{\hat{Q}K^{T}}{\sqrt{d_{k}}})V \tag{4}\]  
This improved self-attention filters out the most important query, reduces the computational complexity, and solves the computational complexity of the square level of self-attention.
## 3 Results
### Vorticity Distribution and Its Influence on Recirculating Flow
The variation of \(E_{z}\) vs. recirculation zone length displays three distinct subzones' response to the position of Groin B relative to Groin A, i.e., increase in the upstream far-field, decrease in the near field, followed by increase in the downstream far-field. The relationship between vertical vorticity flux and recirculation zone length in each subzone is shown in Figure 16. When Groin B is located upstream far-field of Groin A, the recirculation zone length behind Groin A increases at the fastest rate with increasing vertical vorticity flux. When Groin B is in the downstream far-field of Groin A, the recirculation zone length behind Groin A increases at the slowest rate with increasing vertical vorticity flux. The change rate of recirculation zone length is between the two above if Groin B is placed in the up- or down-stream near-field of Groin A. In summary, the accumulation effect of the vertical vorticity flux near Groin A is at its maximum when Groin B is located in the upstream far-field from Groin A.  
Figure 13: Vertical vorticity in the horizontal plane for (**a**) single groin, (**b**) two symmetrical groins. Figure 14: Vertical vorticity in the horizontal planes for alternating groin configuration with Groin B at 5 to 55 cm upstream (left column) and downstream (right column) from Groin A. As Groin B approaches Groin A on the upstream side, the advection and diffusion of vorticity induced by Groin A in the horizontal plane are enhanced and the extent of high vertical vorticity flux area increases, resulting in a larger recirculation zone. In case 7, i.e., symmetrical groins, a critical threshold appears in the extension of vertical vorticity flux produced by Groin A both in transverse and longitudinal directions, which demonstrates that the transverse extension of vertical vorticity is strengthened whereas the longitudinal propagation of vertical vorticity is weakened significantly. It follows that the high-vorticity zone shrank, leading to a shorter recirculation zone behind Groin A. When Groin B is 0.15 m downstream of Groin A, the streamwise extension of the vertical vorticity induced by Groin A is inhibited the most by Groin B so that the length of the recirculation zone behind Groin A drops to the minimum value of 0.16 m (see Table 1). When the groins are  
Figure 16: The relationship between vertical vorticity flux and recirculation zone length of Groin A when Groin B is upstream far-field (black *), downstream far-field (orange triangle) and near field (red circle) from Groin A.  
Figure 15: Variation of recirculation zone length behind Groin A with the vertical vorticity flux induced by Groin A for different spacing between groins. further apart, downstream Groin B promotes the increase in vertical vorticity near Groin A, therefore promoting the development of the recirculation zone behind groin A. Although the vertical vorticity induced by Groin A is a nonlinear function of the Groin B distance from Groin A, the variation rate of vertical vorticity flux and the recirculation zone length induced by Groin A remains almost constant.
## 3 Datasets and Methods
### Datasets  
The proposed methodology is applied on the ASL alphabet dataset with 80% training data and 20% testing data. In addition, the methodology is also demonstrated on datasets associated with state-of-the-art techniques to draw a comparative analysis on the results obtained and any significant improvement observed. These datasets include the Cambridge hand gesture dataset, the ASL finger-spelling dataset, the NUS dataset I and II and ISL digits. A glimpse of these datasets is shown in Figure 1 whereas Table 2 outlines the details of the datasets used in this methodology.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 3 Results
### Aeration-Related Characteristics of Aeration Devices
#### 3.2.1 Air Duct Air Velocity and Air Demand  
Four aerators were arranged in the spillway tunnel. No. 1, No. 2 and No. 3 are two-dimensional aerators, all located at the oblique-line section. The No. 4 aerator is a three-dimensional aerator located at the end of the ogee segment section. Two typical aerators, No. 2, and No. 4, were chosen to measure air velocity in their air ducts, and results at five different flow rates are shown in Figure 8a.  
For the No. 2 aerator, air velocity is from 29.4 to 89.3 m/s for the left air duct, and from 28.5 to 80.0 m/s for the right. For the No. 4 aerator, this value ranges from 25.3 to 79.7 m/s for the left air duct, and 23.9 to 76.4 m/s for the right. Air velocities in left and right air duct are not exactly the same, but the difference is very small, indicating that the airflow evenly flows into the cavity through the left and right air ducts of an aerator. Air velocity in air duct increases with the increase of flow rate, but the growth rate is gradually decreased. When the flow rate is \(>\)1462 m\({}^{3}\)/s, air velocities in all air ducts exceed 60 m/s, and the maximum value is close to 90 m/s. The risk of surface damage of air ducts will probably prevail owing to high-velocity airflow [31].  
Air demand, which is the air quantity supplied by air ducts of aerator, is an important design parameter in terms of the aerator efficiency. It is equal to the average air velocity multiplied by the area of air ducts. Results of air demand are shown in Figure 8b. The changing trend of air demand with flow rate is similar to that of air duct air velocity with flow rate. It increases with increasing flow rate, but the growth trend gradually slows down. With increase of the flow rate, the water depth increases simultaneously, and the turbulence intensity of water flow close to jet lower surface also increases.  
Figure 8: Air velocity (\(V_{\text{a}}\)) in air ducts (**a**) and air demand (\(Q_{\text{a}}\) ) of aerators (**b**) at different flow rates.  
Figure 7: Relationship between the noise generated by air vibration and the flow rate in the spillway tunnel.  
As inhibited by water depth, increasing rate of turbulence intensity is not as fast as that of flow rate. Air demand, apart from being influenced by turbulence intensity, is also restricted by the size of air duct, so the increasing rate of air demand is not always a constant value. Varying trends of air velocity in the air duct with flow rate can be explained similarly.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 3 Simulation Results
### Transmission Performance
Note that a better performance when not using the equalizer as compared to using a linear and/or a nonlinear equalizer in the 10-15 km range is obtained because the transmitter bandwidth was chosen, subject to an optimum dispersion tolerance and cost trade-off, rather than only an optimum power sensitivity criterion [18]. The use of MLSE brings about an enormous enhancement in optical power sensitivity, compared to cases without MLSE at different fiber lengths. This verifies the strong capability of MLSE in alleviating both the linear and nonlinear effects of a channel with memory. It is noticeable that the increase of the number of MLSE states from two to four enables only a slightly improvement in power sensitivity until the fiber lengths exceeds 40 km. The performance for the PAM-4 system is shown in Figure 4c. Compared to optical/electrical Duobinary, PAM-4 has the best benchmark dispersion tolerance: it is the only scheme that can support 20 km of SMF transmission without using equalizations. For SMF lengths below 20 km, the use of linear or nonlinear equalization does not bring about a significant improvement in optical power sensitivity. This is because of the relatively small fiber dispersion that PAM-4 experiences, since it has half the symbol rate compared to other two schemes. Considerable optical power sensitivity improvement occurs only beyond 20 km SMF lengths where fiber dispersion becomes more and more strong with increasing fiber length. Similar to other systems, nonlinear equalization outperforms linear equalization regarding fiber dispersion tolerance, although the improvement enabled by linear or nonlinear equalization is far less compared to that of the electrical Duobinary case. The reason might be because PAM-4 is more vulnerable to a strong eye skew at a long fiber distance [11] because PAM-4 has more amplitude levels compared to the other two systems. By introducing a 4-state MLSE, PAM-4 shows moderate enhancement in power sensitivity for fiber lengths beyond 20 km.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 3 Proposed Method
### LSTM for Short-Term and Majority Voting for Long-Term Fire Decision  
We aggregated the changes in the extracted spatial features using two-stage LSTM network in a short period, and determined whether it was a fire or a non-fire object at every \(T_{LSTM}\)[3]. Figure 6 shows the part of LSTM network used to accumulate and decide a fire in a short-term period.  
Because the data for training the LSTM network should be videos clips differently from those for Faster R-CNN. We collected video clips of fire and non-fire, and constructed a dataset, from which many frames were extracted and added for training Faster R-CNN. The consecutive \(d\)-dimensional spatial features calculated from the trained Faster R-CNN for a video clips were prepared as input streams for the LSTM training.  
As the same way as in DTA [3], the LSTM network reflects a temporal behavior in a short-term period, such as a person's quick glance, and its decisions are integrated by taking majority voting to make a reliable fire decision in a long-term period \(T_{vot}\). The fire decision in this stage was made by the ratio of fire to non-fire decisions during the time window \(T_{vot}\), and fed into Bayesian net as a prior probability of fire \(\text{P}(F)\).  
Figure 5: The spatiotemporal feature extraction from Faster R-CNN [26].
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 2 Materials and Methods
### DTH Drilling Setup  
The experimental setup used in this research is composed of a pneumatic drill rig driven by a 4 kW, 380 V, 1440 rpm, 50 Hz induction motor as a primary rotary actuator that provides the required torque to the rotary bit through a gearbox. The pressured air from a 33 kW rotary screw compressor is distributed in the pneumatic control console to feed a DTH hammer for percussive action and detritus cleaning. The rated speed of the drilling tool bit is 90 rpm. For detailed technical specifications about these components, see [16]. Figure 2 shows the main components of the experimental setup and their interconnections, where the yellow arrows indicate the air pressure lines, the green arrows indicate the location of a pressure sensor for direct measurement of operating pressure, and the light blue arrows indicate the location of the power line outputs of the VFD and power lines on the drill motor connection box. Table 1 summarizes the relevant technical specifications of the current and pressure sensors used in this study to implement different techniques for estimating the impact frequency. It should be noted that three-phase motor currents signals are measured at the output of the VFD (not shown). All readings from the sensorsare collected and processed in a dedicated data acquisition (DAQ) system (MicroLabBox processor [36]) with advanced processing capabilities that enable the implementation of the different MCSA algorithms proposed. The pressure and current sensor data analyzed in this paper are captured at a fixed sample frequency of 50 kHz, while the drilling machine rotates at rated speed.  
Additionally, a Sony HDR-XR500V video camera recorder (not shown in the figures) is used to capture the environmental sound signal during the operation of the drilling rig with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **Range** & **Output** & **Bandwidth** \\ \hline Current clamp & DC + AC peak: 30 [A] & \(\pm\)100 [mV/A] & 20 [kHz] \\ Hammer pressure & 0–10 [bar] & 0–10 [V] & 1 [kHz] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drilling setup sensor specifications.  
Figure 1: Schematics of percussion DTH drilling operating principle.  
Figure 2: Descriptions of the DTH drilling setup, including the main components and their interconnections. The setup extends the one described in [16] with additional sensors.  
a sample rate of 48 kHz. The sound signal is analyzed independently of the other sensors and is used only as a confirmation method to validate the identified impact frequency using the hammer pressure signal.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
###### Abstract  
With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%.  
Keywords:Internet of Things; network security; network device identification; machine learning; deep learning +
Footnote †: journal: _Sci._