esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 1 Introduction
### Contribution  
Hence, there is a need for a practical, carrier, and hardware independent deployment solution for DoS attacks in C-IoT that facilitates Green IoT nodes with very low memory in performing sustained real-time collaborative communications among themselves.  
The communication mechanism proposed in this paper is referred to as the Voice Response Internet of Things (VRITH). VRITH proposes a new category of interaction model called dual-channel communication, which multiplexes voice and data channel. It neither polls nor listens continuously, such as request-response or publish-subscribe mechanisms. It is interrupt-driven, provides real-time communication, and incurs low power consumption. Further, qualitative feature analysis of VRITH, along with a few prominent IoT protocols, is done in Table 1. The solution is applicable even to tiny Green IoT devices with ultra-low memory and low power hardware, as shown in Figure 2.  
The main contributions of this paper in the order of priority are as follows:  
* VRITH provides a higher resilience towards DoS attacks during collaborative interactions of nodes. The DoS attack reduces from 82-65% to less than 28% in the nodes.
* The proposed solution operates in an ultra-low memory scenario and still results in improved real-time communications than the existing protocols, such as MQTT, AMQP, XMPP, and CoAP. Moreover, it increases green IoT energy savings by more than 50% in comparison with these protocols.
* Due to coverage issues and poor connectivity in the mountains during the rainy season, the cellular network falls back from 4G or 3G network to 2G only network without any IP-address connection. During this time, our system is able to send selective sensor data through voice channel to the data center.  
Figure 2: Arduino Atto Board with 2 KB RAM, 3.3 V operating voltage.
## 2 Backgrounds and Related Works
### Special Smart Contracts in Dual Sidechains
Let us begin with the scenario where \(sc\) is executed in \(k\) times in the SC segment \(\mathbb{S}_{\sigma}^{[n]}\). When miners in \(\mathbb{S}_{\sigma}\) first encounter the transaction that executes \(sc\) in the SC segment \(\mathbb{S}_{\sigma}^{[n]}\), they update their local database for the state data of CA\({}_{sc}\) by using the data recorded in AC. After executing \(sc\), miners in \(\mathbb{S}_{\sigma}\) update the state data of CA\({}_{sc}\), not in AC, but in their local database. Miners in the dSC network update the state data of contract accounts in AC. Let [PERSON] be the miner in \(\mathbb{S}_{\sigma}\) network who tries to update the state data of CA\({}_{sc}\) in AC based on \(sc\) executions in \(\mathbb{S}_{\sigma}^{[n]}\). She follows each \(sc\) execution to compute correct changes and the final-state data change to be made for CA\({}_{sc}\) during the time when \(\mathbb{S}_{\sigma}^{[n]}\) is being extended. At the moment when the SC segment \(\mathbb{S}_{\sigma}^{[n]}\) ends, the dSC segment \(\mathbb{S}_{\sigma}^{[n+1]}\) starts. During the dSC segment, \(\mathbb{S}_{\sigma}^{[n+1]}\) is extended; she tries to connect the dSC block that contains only one special transaction \(T\) to \(\mathbb{S}_{\sigma}^{[n+1]}\):  
\[T:\left\{\frac{addr_{i}:c_{i},i=1,\cdots,k,}{state_{sc},q_{sc},}\right. \tag{30}\]  
where \(addr_{i},i=1,\cdots,k\) are the addresses of the UCs that are receiving changes \(c_{i}\), \(i=1,\cdots,k\) from \(sc\)-executions, \(\overline{state}_{sc}\) are the state data of CA\({}_{sc}\) at the moment when the SC segment \(\mathbb{S}_{\sigma}^{[n]}\) ends, and \(\bar{q}_{sc}\) is the input value of \(\overline{state}_{sc}\) for \(sc\) (see Table 1). Notice that, unlike other transactions in HMBS, the special transaction \(T\) in (30) does not require UC spending to be recorded in dSC. It is now clear that the objective of dual sidechains is to update AC based on the smart contract execution. Note that if the execution of a smart contract \(sc\) in the SC segment \(\mathbb{S}_{\sigma}^{[n]}\) is not recognized in \(\mathbb{S}_{\sigma}^{[n+1]}\), then the state data change by the execution of \(sc\) will be ignored in the \(\mathbb{S}_{\sigma}^{[n]}\) network. Figure 1 illustrates how the state data of a state-dependent stateful smart contract \(sc\) are updated. In Step 1, miners in SC \(\mathbb{S}_{\sigma}\) and dSC \(\mathbb{S}_{\sigma}\) networks compute state changes by executing \(sc\) and recording them in their local database. In Step 2, state changes formed by executions of \(sc\) in the \(\mathbb{S}_{\sigma}^{[n]}\) segment are collected to form the special transaction (see (30)) in \(\mathbb{S}_{\sigma}^{[n+1]}\). Finally, in Step 3, the final state data change of \(sc\) is updated in AC immediately after the dSC segment \(\mathbb{S}_{\sigma}^{[n+1]}\) to MC.
# Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis
## 2 Materials and Methods
### Numerical Models and Governing Equations
\begin{array}{l}h\frac{\partial u}{\partial t}+hu\frac{\partial u}{\partial x }+hv\frac{\partial u}{\partial y}-\frac{h}{\rho}\left[E_{xx}\frac{\partial^{2 }u}{\partial x^{2}}+E_{xy}\frac{\partial^{2}u}{\partial y^{2}}\right]+\\ gh\left[\frac{\partial u}{\partial x}+\frac{\partial h}{\partial x}\right]+ \frac{\mathrm{g}ur^{2}}{\left(1.486h\right)^{2}}\left(u^{2}+v^{2}\right)^{\frac {1}{2}}-\zeta V_{a}^{2}\mathrm{cos}\psi-2 hv\omega\mathrm{sin}=0\end{array}  \tag{5}\]  
\[ \begin{array}{l}h\frac{\partial v}{\partial t}+hu\frac{\partial v}{\partial x }+hv\frac{\partial v}{\partial y}-\frac{h}{\rho}\left[E_{yx}\frac{\partial^{2 }v}{\partial x^{2}}+E_{yy}\frac{\partial^{2}v}{\partial y^{2}}\right]+\\ gh\left[\frac{\partial u}{\partial y}+\frac{\partial h}{\partial y}\right]+ \frac{\mathrm{g}ur^{2}}{\left(1.486h\right)^{2}}\left(u^{2}+v^{2}\right)^{ \frac{1}{2}}-\zeta V_{a}^{2}\mathrm{cos}\psi-2 hv\omega\mathrm{sin}\Phi=0\end{array}  \tag{6}\]  
\[ \begin{array}{l}\frac{\partial h}{\partial t}+h\left(\frac{\partial u}{ \partial x}+\frac{\partial v}{\partial y}\right)+u\frac{\partial h}{\partial x }+v\frac{\partial h}{\partial y}=0\end{array}  \tag{7}\]  
where  
\(h=\) Water depth;  
\(u,v=\) Velocities in Cartesian directions;  
\(x,y,t=\) Cartesian coordinates and time;  
\(\rho=\) Density of fluid;  
\(E=\) Eddy viscosity coefficient  
for \(xx=\) normal direction on x axis surface,  
for \(yy=\) normal direction on y axis surface,  
for \(xy\) and \(yx=\) shear direction on each surface;  
\(a=\) Elevation of bottom;  
\(r=\) Manning's roughness;  
\(\zeta=\) Empirical wind shear coefficient;  
\(V_{a}=\) Wind speed;  
\(\psi=\) Wind direction;  
\(\omega=\) Rate of Earth's angular rotation;  
\(\Phi=\) Local latitude. Equations (5)-(7) are solved by the finite element method using the [PERSON] method of weighted residuals. The elements may be one-dimensional channel reaches or two-dimensional quadrilaterals or triangles, and may have curved (parabolic) sides. The shape (or basis) functions are quadratic for velocity and linear for depth. Integration in space is performed by Gaussian integration [35]. The SED2D model, which uses the water level and flow velocity values calculated in the RMA2 model, can simulate suspended sand concentrations and riverbed change using the transport and diffusion equation, and the transport and diffusion equation is as follows. \[\frac{\partial C}{\partial t}+u\frac{\partial C}{\partial x}+v\frac{\partial C }{\partial y}=\frac{\partial}{\partial x}\bigg{(}D_{x}\frac{\partial C}{ \partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}D_{y}\frac{\partial C }{\partial y}\bigg{)}+\alpha_{1}C+\alpha_{2} \tag{8}\]  
where  
\(C=\) Concentration;  
\(D_{x}=\) Effective diffusion coefficient in the x-direction;  
\(D_{y}=\) Effective diffusion coefficient in the y-direction;  
\(\alpha_{1}=\) Coefficient for the source term;  
\(\alpha_{2}=\) Equilibrium concentration portion of the source term.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 1 Introduction
### Related Works  
Existing studies of the fully inserted laparoscope utilized in SILS have developed multiple methods of position control, including suturing, piercing, and attracting the laparoscope to the abdominal wall by an external permanent magnet (EPM).  
[PERSON] et al. developed two types of stick-shaped laparoscopic robots, which adopt DC servo motors combined with worm gear mechanisms to enable the pan and tilt motion of the camera [4; 5]. During the surgery, these two laparoscopic robots are fixed by being sutured on the abdominal wall. [PERSON] et al. proposed a wireless miniature anchored laparoscopic robot [6], the pan and tilt motion of its camera is manipulated by two inner motors. By applying a needle to pierce through the patient's abdominal wall, the laparoscopic robot is fixed to the abdominal wall. However, both suturing or piercing methods have two significant shortcomings. One is the extra injures caused by suturing or piercing, the other is the limited reposition ability. Once these laparoscopic robots are fixed, it is difficult to reposition.  
Different from the suturing and piercing methods, some research groups use EPMs to achieve the position control of the laparoscopic robot. [PERSON] et al. proposed a fully-inserted modular wireless surgical robot. By changing the payload in its cylindrical housing, the robot can achieve multiple functions [7]. Through two magnetic caps at both ends of the robot, it can be magnetically attached to the inside of the abdominal wall and achieve pan motion by an EPM handle. [PERSON] et al. designed a wireless laparoscopic camera system which embeds two internal permanent magnets (IPMs) in a capsule-shaped housing [8], two EPMs are adopted to achieve the pan and shift control of the laparoscopic robot, and the rotation of the camera along its axis is achieved by an inner motor. A modified design is proposed in [9]. Compared with these methods of position control, the use of EMPs are more flexible.  
As for orientation control, most of the existing laparoscopic robots use micro motors and gear mechanisms to achieve the tilt motion of the inner camera, and adopt other mechanisms (suturing, piercing, EMP, etc.) to achieve the position control of the robot. However, the inner motor along with a relevant mechanism will complicate the structure, enlarge the dimension, and increase the energy consumption of the laparoscopic robot.  
To address the above problems, this paper proposed an innovative configuration of laparoscopic robot, which adopts external magnetic field to achieve position and orientation control of the laparoscopic robot. Therefore, the inner motor-based motion mechanism can be removed, and the laparoscopic robot will have simpler structure, less energy consumption, and more room for other sensors.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 4 Hybrid Beamforming Design for FD AF Relay Systems
### System Model
In this correspondence, the exponential model is adopted to design the covariance matrixes of CSI error [14; 15; 32; 33], i.e., \(\mathbf{\Phi}(i,j)=\sigma_{\mathbf{e}}^{2}\mathbf{\Theta}^{|i-j|}\), \(\mathbf{\Theta}(i,j)=\alpha^{|i-j|}\), with \(\alpha,\beta\in\mathbb{R}\) being correlation coefficients and \(\sigma_{\mathbf{e}}^{2}\in\mathbb{R}\) being estimation error variance. \((i,j)\) represents the element at the intersection of row \(i\) and column \(j\). _(3) SI Channel Model:_ In mmWave FD relay communication systems, SI consists of the LOS part and the NLOS part, where the former is much bigger than the latter on account of the path loss of mmWave signals. On one hand, NLOS SI is caused by the reflection of transmit signals on adjacent obstacles. Typically, the reflection channel \(\mathbf{H}_{\text{NLOS}}\in\mathbb{C}^{N_{\text{R}}\times N_{\text{R}}}\) can be modelled as (2), i.e., the mmWave channel [15]. On the other hand, the direct path SI is caused by transmit signals of the FD relay node through the LOS channel \(\mathbf{H}_{\text{LOS}}\in\mathbb{C}^{N_{\text{R}}\times N_{\text{R}}}\) between the transmitter-receiver link. According to [34], it can be formulated as the near-field model, whose \((m,n)_{th}\) element can be expressed as  
\[\mathbf{H}_{\text{LOS}}^{(m,n)}=\mu_{mn}e^{-j\frac{2\pi}{\lambda}d_{mn}}, \tag{29}\]  
where \(\mu_{mn}\) is the normalization factor, \(d_{mn}\) denotes the distance between the \(m_{th}\) receive antenna and the \(n_{th}\) transmit antenna. Consequently, the SI channel can be modelled as  
\[\mathbf{H}_{\text{SI}}=\sqrt{\frac{\kappa}{1+\kappa}}\mathbf{H}_{\text{LOS}} +\sqrt{\frac{1}{1+\kappa}}\mathbf{H}_{\text{NLOS}}, \tag{30}\]  
with the scalar \(\kappa\) being the Rician factor. In practice, the transmit and receive arrays of the FD AF relay node are close and fixed. In this paper, it is assumed that there is an accurate estimation for \(\mathbf{H}_{\text{LOS}}\) while uncertainty for \(\mathbf{H}_{\text{NLOS}}\)[35]. As a result, the estimated SI channel is given by [30]  
\[\overline{\mathbf{H}}_{\text{SI}}=\sqrt{\frac{\kappa}{1+\kappa}}\mathbf{H}_{ \text{LOS}}+\sqrt{\frac{1}{1+\kappa}}\overline{\mathbf{H}}_{\text{NLOS}}, \tag{31}\]  
where \(\overline{\mathbf{H}}_{\text{NLOS}}\) denotes the estimated reflection channel. Due to the channel estimation errors, the SI constraint should be transformed into \(\mathbf{G}_{1}^{H}\overline{\mathbf{H}}_{\text{SI}}\mathbf{G}_{2}=\mathbf{0}\), which means the LOS part can be completely eliminated, while the NOLS part cannot. Considering the NLOS SI is much weaker than the LOS SI, we ignore the influence of residual SI in the subsequent problem formulation and algorithm design [30; 35], but its impact on spectral efficiency will be shown in the simulation results.
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 4 Simulation Experiments and Comparison of the Fusion Algorithm
### Experimental Results and Analysis  
Figure 8 displays the 3D trajectory curves of optimal paths generated after running the different algorithms 50 times on a 3D map. Figure 9 shows the objective function iteration curves of the algorithms, while Figure 10 compares the planning times over 50 runs. From Figure 8, it is clear that all four algorithms effectively avoid dynamic obstacles and eventually reach their target points. The PSO-DWA algorithm, however, tends to get trapped in local optima, resulting in the longest path, whereas the paths generated by the GA-DWA, SSA-DWA, and NRBO-DWA algorithms are of similar length. A further review of Figure 8 indicates that the GA-DWA algorithm stabilizes after 40 iterations, the PSO-DWA after 38 iterations, the SSA-DWA after 23 iterations, and the NRBO-DWA achieves stability in just 13 iterations, demonstrating better overall performance and a lower objective function.  
Table 4 presents the average experimental results from 50 runs of the different algorithms on the 3D map, including path length, planning time, and objective function values. Analyzing the data in Table 4 reveals that the average path planning cost of the NRBO-DWA algorithm is reduced by 15.40%, 30.13%, and 14.99% compared to the GA-DWA, PSO-DWA, and SSA-DWA algorithms, respectively. Additionally, the average planning time of the NRBO-DWA algorithm is decreased by 23.48%, 26.54%, and 14.69% compared to the GA-DWA, PSO-DWA, and SSA-DWA algorithms, respectively.  
In conclusion, the NRBO-DWA fusion algorithm demonstrates superior adaptability compared to the other algorithms within the given environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Starting Point & (1000,1600,500) \\ Ending Point & (5000,7600,1000) \\ Population Size & 100 \\ Maximum Iterations & 100 \\ \(a_{1}\) & 0.5 \\ \(a_{2}\) & 0.3 \\ \(a_{3}\) & 0.2 \\ Linear Velocity (m/s) & 1.5 \\ Linear Acceleration (m/s\({}^{2}\)) & 0 \\ Safety Distance (mm) & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental environment parameter settings.  
Figure 8: Comparison of path planning curves of different algorithms in a dynamic environment, (a) GA-DWA; (b) PSO-DWA; (c) SSA-DWA; (d) NRBO-DWA.  
Figure 9: Iteration curves of the objective function for different algorithms.
## 5 IETF ROLL Working Group
### Leveraging Upon CoAP to Realize the IoT
#### 6.3.2 Congestion Control  
The base CoAP draft only defines a very basic congestion control when using reliable message transmissions and does not provide any congestion control when using the non-reliable transmissions mode, that is likely to carry the majority of traffic. To overcome this shortcoming a few proposals try to provide more advanced congestion control schemes. These proposals can generally provide more optimized performance in exchange for more implementation complexity and/or a narrower field of application. For example, draft-bormann-core-cocoa [114] defines some more advanced CoRE congestion control mechanisms. The main idea here is to provide a way to better estimate the RTT than that implied by the default initial timeout of 2 to 3 seconds. Further suggestions for the enhancements to this estimation of the RTT are presented in [115].  
Another mechanism for congestion control is proposed in [116] by adding an option that allows a server to indicate its desire for some pacing of the requests sent to it by one client; enabling a form of server load control.
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Reference** & **Year** & **Algorithms** & **Datasets** & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{3-13}  & & & & **A** & **R** & **P** & **L** & **F** & **G** & **Others** \\ \hline
[1] & 2016 & CVM-NB, SVM, NB & Datumall & & X & X & & & & & & \\ \hline
[10] & 2021 & BERT, Bidirectional LSTM, KNN, NB & Spumble-Span filter from Kaggle & X & & & & & & & \\ \hline
[11] & 2021 & Regression, Zgboost, SVM, RF, WE, LSTM & The Top Span Dataset-300, Emcon & X & X & X & X & & & \\  & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline
[12] & 2021 & NN, CNN, SVM, LSTM & Udumaldataset & X & X & X & & & & & \\ \hline
[13] & 2021 & CNN, LSTM, BS, SVM, NB, DT & Emcon & X & X & & & & & \\ \hline
[14] & 2020 & NN, DT (48), RNN & SpumAssain & X & X & X & & & & \\ \hline
[15] & 2021 & LR, CNN, KD, IT, Adaboost, & Lingpuran, PU, SpumAssain, Emcon & X & X & X & & & X & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline
[16] & 2020 & SVC, KNN, DN, DT, Edaboost, & Bagging & Spumary from Kaggle & X & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline
[17] & 2021 & SVM, CNN-FG, Rotation Forest, & Spum, ham, dataset from Kaggle & X & X & X & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline
[18] & 2021 & NN, LV, SVM, DL, LSTM & Self-collected & X & & & & & & \\ \hline
[19] & 2021 & SN, DT (48) & Dataset from Kaggle & X & X & X & & & & \\ \hline
[21] & 2021 & SVM, DT, LR, MNB & Dataset from Kaggle & X & X & X & & & & \\ \hline
[22] & 2021 & NB, DT (48), LTC & Self-collected & X & & & & & \\ \hline
[23] & 2021 & NB & Spumbase, Spumdata & X & X & X & & & \\ \hline
[24] & 2020 & NB, SVM & Dataset from github & X & X & X & & & \\ \hline
[25] & 2022 & BERT & Emcon & X & & & & & & \\ \hline
[27] & 2020 & BERT & Emcon & & & & & & & \\ \hline
[28] & 2020 & BERT & Emcon & & & & & & & \\ \hline
[29] & 2020 & Addaboost, RF, DT (48), SVM, Bagging, DL & CSIMC2010, SPAM & X & X & X & & & \\ \hline
[40] & 2022 & Adaboost, Freble Bayes, NN, RF, SVM & SpumAssain & X & X & X & & & \\ \hline
[41] & 2022 & KNN, MPNN, LR, RF, XGBout, MNB, CIRB, & Emcon & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & \\ \hline
[42] & 2021 & LDA-k-norms, LSTM-le, SVM, MLP, CNN & Twitter, StanAvibo & X & X & X & & & \\ \hline
[43] & 2021 & GAN, NB, LSTM _SVM_ & Twitter Spum & & & X & X & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies on email spam detection. A—accuracy; R—recall author; P—precision; L—loss, F—\(\delta\)score; F1 score; G—G-mean.
# An Initial Load-Based Green Software Defined Network +
## 1 Introduction
At present, several works have focused on the energy waste problem in SDN based on coordinated sleeping and considered the problem from the different aspects [13, 14, 15, 16, 17, 18]. The closest work to this paper is [13], where a typical architecture called ElasticTree is proposed for improving energy efficiency in SDN. The traditional energy saving strategies also can be deployed in ElasticTree to determine which devices can be shut down. While ElasticTree treats all the traffic demand as new traffic demand (as described in our previous work [14]), for energy saving, the paths of the uncompleted traffic demands generated before current optimization will be changed, which may result in route oscillation, and causes additional energy consumption for state transition. In [14], to avoid these problems, we designed a new green scheme named GreSDN for energy saving, and presented two routing algorithms that can be used in GreSDN, the constant weight greedy algorithm (CWGA) and the dynamic weight greedy algorithm (DWGA). In this extended version, with regard to the previous work [14], the main contributions of this paper are summarized as follows:  
* The process of GreSDN has been improved to make GreSDN more rigorous. * To find the optimal solution of energy saving in GreSDN, the problem is formulated as a mixed integer linear programming (MILP) problem, and the problem is found to be a NP-hard problem. * As proved in our previous work, for the two green algorithms, CWGA performs better than DWGA in energy saving, but the computation complexity of CWGA is higher than that of DWGA. In this extended version, we further work on decreasing the computation complexity of CWGA, and the improved algorithm is named as improved constant weight greedy algorithm (ICWGA). * In addition to studying the performance profile of ICWGA, we further examine the effect of some other factors on the performance of ICWGA, such as link density and the processing sequence of the traffic demands. The remainder of this paper is organized as follows. In Section 2, previous energy-saving research for SDN networks is reviewed. In Section 3, we describe the green scheme and then formulate the problem in Section 4. In Section 5, the improved heuristic green routing algorithm is presented in detail. Then the performance of the green routing algorithm is evaluated through simulation in Section 6. Finally, in Section 7, we conclude the paper.