esa-sceva/satcom-chunk-collection huggingface dataset:
# LDPC Coded Massive MIMO Systems
## 3 Joint Detection and Decoding for LDPC Coded Massive MIMO System
### Analysis of Joint Detection and Decoding
Let us define the EXIT function between \(I_{OA}\) and \(I_{OE}\) as  
\[I_{OE}(d_{v})=f_{O}\left(I_{OA}(d_{v}),\frac{E_{b}}{N_{0}}\right), \tag{17}\]  
where \(I_{OE}\) is also a function of \(d_{v}\) due to the dependency of \(I_{OA}\) on \(d_{v}\). Note that \(f_{O}(\cdot)\) is obtained by Monte Carlo simulation [33]. The LLR message \(L_{pc}\) sent from the variable node to check nodes is obtained by summing up \(2n_{R}\) copies of \(L_{o}^{out}\) and \(d_{v}-1\) copies of \(L_{cv}\). Then, the variance of \(L_{vc}\) is obtained by adding the variance of \(L_{o}^{out}\) multiplied by \(2n_{R}\) and the variance of \(L_{cv}\) multiplied by \(d_{v}-1\). It follows that  
\[I_{VE}(d_{v})=J\left(\sqrt{2n_{R}\cdot\left[J^{-1}\left(I_{OE}(d_{v})\right) \right]^{2}+\left(d_{v}-1\right)\cdot\left[J^{-1}\left(I_{VA}\right)\right]^{ 2}}\right). \tag{18}\]  
In the case of irregular distribution of \(d_{v}\), we define averages of \(I_{VE}(d_{v})\) and \(I_{OE}(d_{v})\) over \(d_{v}\) as  
\[I_{VE}=\sum_{d_{v}=2}^{d_{v,\max}}\lambda_{d_{v}}\cdot I_{VE}(d_{v}) \tag{19}\]  
and  
\[I_{OE}=\sum_{d_{v}=2}^{d_{v,\max}}\lambda_{d_{v}}\cdot I_{OE}(d_{v}), \tag{20}\]  
respectively, where \(\lambda_{d_{v}}\) denotes the fraction of edges that are connected to variable nodes of degree \(d_{v}\) and \(d_{v,  
a point with \(\bar{I}_{VE}=1\) at a certain \(E_{b}/N_{0}\), this implies that the JDD converges and the decoding succeeds at this \(E_{b}/N_{0}\). The minimum value of \(E_{b}/N_{0}\) resulting in the JDD trajectory approaching \(I_{VE}=1\) is called the threshold. We can find the threshold value of LDPC coded massive MIMO system by using Algorithm 3 and visualize the JDD behavior by using the 3-D EXIT chart. ```
1 Initialize: \(I_{VA}=0\) and \(I_{OE}(d_{v})=0,\forall d_{v}\)
2 for\(l^{\prime}=1\) to \(N_{S}\)do
3 for\(d_{v}=2\) to \(d_{v,\max}\)do
4 for\(m=1\) to \(N_{det}\)do
5\(I_{OE}(d_{v})\gets I_{OE}\left(I\left(\sqrt{\left(2n_{R}-1\right)\cdot \left[J^{-1}\left(I_{OE}(d_{v})\right)\right]^{2}+d_{v}\cdot\left[J^{-1} \left(\bar{I}_{VA}\right)\right]^{2}}\right),\frac{E_{b}}{N_{0}}\right)\)
6\(\bar{I}_{OE}\leftarrow\sum_{d_{v}}\lambda_{d_{v}}I_{OE}(d_{v})\)
7 for\(l^{\prime\prime}=1\) to \(N_{dec}\)do
8\(\bar{I}_{VE}\leftarrow\sum_{d_{v}=1}^{d_{v,\max}}\lambda_{d_{v}}\cdot I\left( \sqrt{2n_{R}\cdot\left[J^{-1}\left(I_{OE}(d_{v})\right)\right]^{2}+\left(d_{v }-1\right)\cdot\left[J^{-1}\left(I_{VA}\right)\right]^{2}}\right)\)
9\(\bar{I}_{CA}\gets I_{VE}\)
10\(I_{CE}\leftarrow\sum_{d_{v}=1}^{d_{v,\max}}\rho_{d_{v}}\cdot\left(1-J\left( \sqrt{d_{v}-1}\cdot J^{-1}(1-\bar{I}_{CA})\right)\right)\)
11\(I_{VA}\gets I_{CE}\)
```  
**Algorithm 3**Density evolution in terms of EXIT characteristics.
## 3 Results and Discussion
### Turbulence Statistics  
#### 3.2.1 Turbulent Viscosity (\(\mu_{\rm t}\), \(\
u_{\rm t}\))  
As can be seen from Figure 11, the plume turbulent viscosity presents a mushrooming spatial distribution, and the value decays outward from the center of the plume region and the axis of the plume center, and the strongest turbulence mixing exists in the plume region. The maximum turbulent viscosity calculated by rke_case2 and sst_case2 are 100.4 Pa s and 95.4 Pa s. It can be seen from the model itself that the rke model strengthens the ability of standard k-\(\epsilon\) model to predict the flow field of rotating flow, and the sst model strengthens the prediction ability of the standard k-\(\omega\) model in the boundary layer. However, according to the definition, the sst model adopts the algorithm of the standard k-\(\epsilon\) model to calculate the jet region in the range of plume stem region. The hydrothermal plume belongs to local axisymmetric flow [36], and there are vortices, enrolling and rotating flow phenomena. Therefore, the turbulent viscosities calculated by the two turbulence models are not the same.  
The changes in turbulent viscosity of the vent orifice center line in different working conditions along with the height of hydrothermal plume rising are shown in Figure 12a. The trends calculated by the two models are not the same. When the height z is less than 0.8 \(Z_{\rm max}\), the turbulent viscosity calculated by the rke model is larger than that calculated by the sst model. Compared with the sst model, the calculated results of the rke model are closer to those of [PERSON] et al. [14].  
Through dimensional analysis, it is concluded that there is a certain functional relationship between turbulent viscosity, buoyancy frequency and buoyancy flux. Figure 12b shows the linear fitting of the maximum turbulent viscosity of the vent orifice center line with the buoyancy flux and buoyancy frequency. The turbulent viscosities scaling law of the rke model and the sst model are as follows:  
\[\left\{\begin{array}{l}v_{t,\max,r_{\rm k}}=0.016\ B_{ext}^{1/2}\cdot N^{-1 /2}\\ v_{t,\max,sst}=0.014\ B_{exit}^{1/2}\cdot N^{-1/2}\end{array}\right.. \tag{14}\]  
[PERSON] et al. obtained the scaling law of turbulent viscosity through plume experiments, and its scaling coefficient was 0.030, an order of magnitude consistent with that in this paper [12]. We believe that the turbulent viscosity scaling law of the hydrothermal plume is reasonable considering the difference between the experimental environment and the simulated environment. From the numerical value of turbulent viscosity, compared with the sst model, the turbulent viscosity calculated by the rke model is larger. There is intense enrolling and vorticity phenomena in the hydrothermal plume, and there are large flows in and below the neutral buoyant layer, which generates large internal friction force and increases the turbulent viscosity. Therefore, the rke model can predict the turbulent viscosity of hydrothermal plume more reasonably.  
Figure 11: Turbulent viscosity distribution of a quasi-steady hydrothermal plume.
## 5 Methodology, Experiments and Results
### Proposed Methodology
#### 5.1.3 Implementation of Algorithms  
After collecting the data set we prepared Python-based tools for data analysis. These tools were used to draw conclusions about the data. The libraries used to develop the tool are as follows: NumPy [42], Pandas [43], Scikit-learn [44], Matplotlib [45], Seaborn [46] and MLxtend [47].
# Diode Laser Welding/Brazing of Aluminum Alloy to Steel Using a Nickel Coating
## 3 Results
### Fractography  
All of the tensile specimens were fractured at the FZ/Ni coating interface in Al/Ni-coated steel joints. Figure 7 shows the SEM images and XRD pattern of the fracture surfaces for the laser Al/Ni-coated steel joint at different zones. Various fracture surface morphologies were observed at the direct irradiation zone. On the FZ side (Figure 7a), some tearing ridges were clearly observed that had the average chemical composition of 2.9 \(\pm\) 0.4 at.% Al, 74.0 \(\pm\) 2.7 at.% Zn, and 23.1 \(\pm\) 3.0 at.% Ni (Table 6). It was confirmed as Ni\({}_{5}\)Zn\({}_{21}\). Whereas, on the steel side (Figure 7b), fracture morphologies with different characteristics were observed, including a very smooth feature and some scattering rugged features on the top. The average chemical composition of 100 at.% Ni was found at the smooth feature, while the scattering rugged feature contained 4.3 \(\pm\) 0.5 at.% Al, 72.2 \(\pm\) 1.8 at.% Zn, and 21.9 \(\pm\) 1.6 at.% Ni (Table 6). Thus, it suggested that a mixed fracture consisting of shear brittle fracture at the Ni\({}_{5}\)Zn\({}_{21}\)/Ni interface and cleavage brittle fracture in the Ni\({}_{5}\)Zn\({}_{21}\) layer occurred. At the FZ root, the characteristics of intergranular debonding were evident (Figure 7c,d). Based on EDS analysis, the FZ side and steel side had the respective chemical composition of 64.5 \(\pm\) 2.5 at.% Al, 8.5 \(\pm\) 3.3 at.% Zn, 27.0 \(\pm\) 1.3 at.% Ni, and 66.1 \(\pm\) 2.3 at.% Al, 10.0 \(\pm\) 3.1 at.% Zn, and 23.9 \(\pm\) 2.2 at.% Ni (Table 6). Hence, they could be identified as Al\({}_{3}\)Ni. Thus, it inferred that the intergranular debonding occurred in Al\({}_{3}\)Ni layer. Figure 7e shows the XRD patterns of the fracture surface. Diffraction peaks of Ni\({}_{5}\)Zn\({}_{21}\) and Al\({}_{3}\)Ni were detected on the FZ side, which confirmed the SEM and EDS results.  
Figure 7: Fracture surface morphology and X-ray diffraction (XRD) pattern of the Al/Ni-coated steel joint: (**a**) FZ side at the direct irradiation zone; (**b**) steel side at the direct irradiation zone; (**c**) FZ side at the FZ root; (**d**) steel side at the FZ root and (**e**) XRD pattern at the FZ side.  
In terms of the laser Al/bare steel joint, tearing ridges embedded with some light particles were evident in Figure 8a,b. Similar chemical composition was found at both fracture surfaces, which was 53.1 \(\pm\) 2.8 at.% Al, 31.9 \(\pm\) 3.1 at.% Fe, and 15.0 \(\pm\) 1.3 at.% Zn. They were identified as Fe\({}_{2}\)Al\({}_{5}\) and FeZn\({}_{10}\). It suggested that the failure could occur at this reaction layer (Figure 5). Figure 8c shows the XRD patterns of the fracture surface. Diffraction peaks of Fe\({}_{2}\)Al\({}_{5}\) and FeZn\({}_{10}\) were obviously observed, corresponding to the SEM and EDS analysis. Besides, peaks of FeZn\({}_{10}\) were mainly low diffraction peaks, suggesting that these IMCs in the form of particles existed adjacent to the interface.
## Part A On-Line Energy Optimization
### The Protocol  
Let \(d(p_{i},\,p_{j})\) the distance (along the corresponding vertical lines towards \(\mathcal{W}\)) of particles \(p_{i},\,p_{j}\) and \(d(p_{i},\mathcal{W})\) the (vertical) distance of \(p_{i}\) from \(\mathcal{W}\). Let _info_(\(\mathcal{E}\)) the information about the realization of the crucial event \(\mathcal{E}\) to be propagated. Let \(p\) the particle sensing the event and starting the execution of the protocol. In this protocol, each particle \(p^{\prime}\) that has received _info_(\(\mathcal{E}\)), does the following:  
* _Search Phase_: It uses a periodic low energy directional broadcast in order to discover a particle nearer to \(\mathcal{W}\) than itself. (i.e. a particle \(p^{\prime\prime}\) where \(d(p^{\prime\prime},\,\mathcal{W})\,<\,d(p^{\prime},\,\mathcal{W})\)).
* _Direct Transmission Phase_: Then, \(p^{\prime}\) sends _info_(\(\mathcal{E}\)) to \(p^{\prime\prime}\).
* _Backtrack Phase_: If consecutive repetitions of the _search phase_ fail to discover a particle nearer to \(\mathcal{W}\), then \(p^{\prime}\) sends _info_(\(\mathcal{E}\)) to the particle that it originally received the information from.  
Note that one can estimate an a-priori upper bound on the number of repetitions of the search phase needed, by calculating the probability of success of each search phase, as a function of various parameters (such as density, search angle, transmission range). This bound can be used to decide when to backtrack.  
For a graphical representation see Figures 2 and 3.  
Figure 3: Example of a Transmission.  
Figure 2: Example of the Search Phase.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 4 Results and Discussion  
### Features Extracted from Cutting Force Signals  
The raw cutting force signal in a single segment is shown in Figure 9. This shows that the magnitude of the force is increased with the cutting depth. The average cutting force and the cutting force coefficients are selected as the input cutting force features of the deep learning model for tool wear monitoring. The average value of the cutting force in each segment are calculated and used as the input features. Cutting force coefficients are calibrated by applying linear regression as per Equations (3) and (4).  
By reconstructing the cutting force signal with the WPD, the cutting force components caused by cutting vibration are obtained. The features related to tool wear are extracted from these two kinds of signals. With the three-layer WPD, the cutting force signals in the 0-1.5 KHZ are decomposed into eight frequency bands. By using 8 db wavelet packet decomposition, the width of each frequency band is 150 Hz. Taking the cutting force signal in three cycles as an example, the reconstructed signals in each band after WPD are shown in Figure 10.  
From Figure 10, the reconstructed signals in different frequency band can be analyzed intuitively. According to the cutting force model Equation (1), the signal in the 0-150 Hz frequency band is related to the static cutting force component; the other signal in higher frequency band can be regarded as the dynamic component caused by cutting vibration and noise. From the results of WPD, the tangential force signal in 150-300 Hz is related to cutting vibration. The reconstructed force is denoted as \(V_{t}\). The radial force signal in 450-600 Hz is related to cutting vibration. The reconstructed force is denoted as \(V_{r}\). These two parameters are selected as the input cutting vibration features of the deep learning model for tool wear monitoring.  
\begin{table}
\begin{tabular}{c c} \hline
**Cutting Parameters** & **Setting Values** \\ \hline Spindle speed & 1200 rev/min \\ Feedrate & 0.05 mm/rev \\ Axial cutting depth & 2 mm \\ Radial cutting depth & 0.5\(\sim\)1.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Cutting parameters in milling experiment.  
Figure 9: Raw signal of cutting force in a single segment.
# Frame-Based Phone Classification Using EMG Signals +
## 2 Materials and Methods
### Acquisition Setup
#### 2.1.2 Electrode Setup  
Previous studies have employed various approaches in determining the optimal electrode setup, such as targeting muscles specifically [31; 35; 36; 37; 38], analyzing anatomical regions [20], and looking for patterns in a high-density electrode setup [39]. Knowing that an activation potential travels along the muscle as a wave, the most appropriate way to use bipolar acquisition is to place the two electrodes longitudinally over the muscle. We decided to target muscles individually and performed a pilot study that consisted of targeting all superficial muscles in the face and neck to find the muscles that were most useful for the task. The final setup (see Figure 3) slightly differs from those used in the previously mentioned studies. These are the targeted muscles (using one channel each):  
1. Levator labii superiors (channel 1)
2. Masseter (channel 2)
3. Risorius (channel 3)
4. Depressor labii inferioris (channel 4)
5. Zygomaticus major (channel 5)
6. Depressor anguli oris (channel 6)
7. Anterior belly of the digastric (channel 7)
8. Stylohyoid (channel 8)  
Figure 2: A personalized 3D mask. The holes are used as reference points to find the positions of the electrodes on the subject’s face.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
###### Abstract  
The services provided to users in the environment associated with the Internet of Things (hereinafter referred to as IoT) begin with the information collected from sensors. It is imperative to transmit high-quality sensor data for providing better services. It is also required to collect data only from those authenticated sensors. Moreover, it is imperative to collect high-quality data on a sustainable and continuous basis in order to provide services anytime and anywhere in the IoT environment. Therefore, high-quality, authenticated sensor networks should be constructed. The most prominent routing protocol to enhance the energy consumption efficiency for the sustainable data collection in a sensor network is the LEACH routing protocol. The LEACH routing protocol transmits sensor data by measuring the energy of sensors and allocating sensor groups dynamically. However, these sensor networks have vulnerabilities such as key leakage, eavesdropping, replay attack and relay attack, given the nature of wireless network communication. A large number of security techniques have been studied in order to solve these vulnerabilities. Nonetheless, these studies still cannot support the dynamic sensor group allocation of the LEACH routing protocol. Furthermore, they are not suitable for the sensor nodes whose hardware computing ability and energy resources are limited. Therefore, this paper proposed a group sensor communication protocol that utilizes only the four fundamental arithmetic operations and logical operation for the sensor node authentication and secure data transmission. Through the security analysis, this paper verified that the proposed scheme was secure to the vulnerabilities resulting from the nature of wireless network communication. Moreover, this paper verified through the performance analysis that the proposed scheme could be utilized efficiently.  
dynamic group management; distance-bounding; group authentication; IoT; sustainable sensing; LEACH routing protocol; sensor network +
Footnote †: journal: _Sustainability_
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 3 Proposed Approach  
In this section, we introduce our proposed SVM algorithm as well as the new traffic flow guidance algorithm, the dynamic weighted vehicle density feedback strategy (DWVDFS). First, we present the assumptions related to the classification setup, the input feature vector, and the input datasets. Then, we present the SVM kernel selection and the flow guidance feedback strategy.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol
### Waiting Times
#### 4.3.1 Waiting Times at Source  
The source manages two kinds of waiting time and one deferment as shown in Figure 6. In the _channel reservation_ phase, after transmitting RTS, the source waits for CTS from the destination during the period of:  
\[d^{\text{S}}_{\text{w\_CTS}}=2\times\tau_{\text{S,D}}+T_{\text{CTS}} \tag{2}\]  
which is easily obtained from Figure 6a. After receiving CTS, the source intentionally delays DATA transmission to avoid a collision with RTS sent by a hidden node (H) who starts a new communication with the destination as shown in Figure 6b. The amount of delay required is denoted by \(d^{\text{S}}_{\text{delay}}\). To avoid the RTS-DATA collision, the DATA should be scheduled to arrive after the possible RTS arrival (Figure 6b). That is:  
\[2\times\tau_{\text{S,D}}+d^{\text{S}}_{\text{delay}}>2\times\tau_{\text{D,H}} +T_{\text{RTS}} \tag{3}\]  
where the \(\tau_{\text{D,H}}\) specified in CTS is a propagation delay between the destination and its farthest neighbor who may be hidden from the source. By rearranging Equation (3), \(d^{\text{S}}_{\text{delay}}\) is obtained by:  
\[d^{\text{S}}_{\text{delay}}=\min(2\times(\tau_{\text{D,H}}-\tau_{\text{S,D}}) +T_{\text{RTS}},\ 0). \tag{4}\]  
After DATA transmission, the source waits for ACK from the destination. The waiting time for ACK might be augmented according to the number of cooperators participating in the retransmission. The time segment augmented by cooperators \(j\) is given by (Figure 6c):  
\[\Delta_{\text{C}_{j}}=2\times\tau_{\text{C}_{j,D}}+T_{\text{NACK}}+T_{\text{ DATA}}. \tag{5}\]  
Thus, the maximum time spent by the source until receiving ACK is:  
\[d^{\text{S}}_{\text{w\_ACK}}=(2\times\tau_{\text{S,D}}+T_{\text{ACK}})+\sum_{ \text{C}_{j}\in\mathbb{N}_{\text{loop}}}\Delta_{\text{C}_{j}}. \tag{6}\]  
If CTS and ACK are not received within the aforementioned time periods \(d^{\text{S}}_{\text{w\_CTS}}\) and \(d^{\text{S}}_{\text{w\_ACK}}\), respectively, the source restarts the whole procedure beginning with sending a new RTS after a random backoff time that follows the binary exponential backoff (BEB) algorithm.  
Figure 6: \(Cont.\)
# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
## 4 Results and Discussion
### Object Detection Results  
Faster R-CNN achieved impressive accuracy in object labeling and detection tasks [31]. For this purpose, three feature extraction models, namely, the _Inception-Resnet-v2 Atrous_, _NAS_, and _Resnet101_ models, which were all pre-trained from this detector, were evaluated.  
These models were trained with our data composed of 20,000 images captured from the drone and through online collection. The object class in this study was trees. All the images were annotated, and 10% of the dataset was randomly selected for testing. Based on the mean average precision (mAP) for each model in our dataset, the Faster-RCNN Inception-Resnet-v2 Atrous model achieved a mAP value of 92.0%, Faster-RCNN NAS achieved a mAP value of 85.0%, and _Faster-RCNN Resnet101_ achieved a mAP value of 95.0% (the highest value). Hence, _Faster-RCNN Resnet101_ was selected as the ideal model to be implemented in our proposed framework.
## 2 Coal Sample Preparation and Experiments
### Determination of Initial Damage to Coal Samples  
The RSM-SY5 sonic velocimeter was used to test the propagation velocity of sound waves in the coal sample pieces at the same acoustic emission frequency to determine the initial damage to the coal samples, from which 48 specimens were selected as experimental samples to avoid large dispersion of data due to different initial damage. The wave velocity values of the 48 selected coal samples are shown in Table 1. The RSM-SY5 sonic velocimeter equipment is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Wave Speed**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Wave Speed**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Wave Speed**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Wave Speed**} \\  & & & & & & & & **m/s** \\ \hline
1 & 1593 & 11 & 1648 & 21 & 1566 & 31 & 1577 & 41 & 1561 \\
2 & 1509 & 12 & 1553 & 22 & 1543 & 32 & 1629 & 42 & 1652 \\
3 & 1625 & 13 & 1512 & 23 & 1579 & 33 & 1572 & 43 & 1662 \\
4 & 1576 & 14 & 1652 & 24 & 1679 & 34 & 1651 & 44 & 1599 \\
5 & 1576 & 15 & 1513 & 25 & 1501 & 35 & 1601 & 45 & 1647 \\
6 & 1700 & 16 & 1699 & 26 & 1667 & 36 & 1735 & 46 & 1677 \\
7 & 1539 & 17 & 1527 & 27 & 1579 & 37 & 1542 & 47 & 1537 \\
8 & 1639 & 18 & 1639 & 28 & 1529 & 38 & 1542 & 48 & 1560 \\
9 & 1618 & 19 & 1680 & 29 & 1581 & 39 & 1679 & & \\
10 & 1730 & 20 & 1585 & 30 & 1684 & 40 & 1550 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wave velocity of specimen.  
Figure 1: Coal samples.  
Figure 2: RSM-SY5 acoustic velocimeter.  
As can be seen from Table 1, the wave velocities of the 48 coal samples were all between 1500 and 1700 m/s, with a small difference in wave velocity, and the initial damage level of the 48 specimens can be regarded as consistent. In this paper, the initial damage is regarded as a no-damage state.