esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The streamlined SCN, optimized using singular value decomposition to minimize redundancy, demonstrates superior performance in industrial data analytics, including ammonia-nitrogen prediction in wastewater treatment, as validated across four datasets. In [29], to address the nonlinearity and delay challenges of the main steam temperature (MST) control in power plants, we introduce a Stochastic Configuration Networks (SCN)-based cascade generalized predictive control (GPC) scheme. This approach utilizes a three-layer SCN, developed through initial and real-time phases using stochastic configuration and weighted recursive least squares, respectively, and incorporates Karush-Kuhn-Tucker conditions for constraint optimization in the predictive control model. The principal contributions of this paper are outlined as follows: A mathematical model for a multi-jointed robotic arm with uncertain parameters was developed using the Lagrangian method. Building on this model, an Optimal Sliding Mode Control (OSMC) system was proposed to guarantee zero-error convergence. Additionally, we designed an observer based on a Stochastic Configuration Neural Network (SSCN), which operates in two phases: initial and real-time. In the initial phase, SSCN employs historical data to configure network parameters, effectively shortening the required training duration. Furthermore, the SSCN approach involves a unique node selection strategy for the hidden layer, executed in two stages:'single time', where weights and biases are chosen randomly via a uniform distribution; and 'double time', where these parameters are selected from a normal distribution centered around the previously selected points. This method focuses on enhancing the selection interval around high-quality points to improve both the efficiency and accuracy of the model.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 7 Characteristics of the Developed System
### Short Range Communication (2.4 GHz WiFi Frequency)  
The high power and short range (e.g., WiFi) module is only activated only when data needs to be transmitted. In a similar way, we can find the maximum distance covered by a WiFi signal taking all parameters as considered for \(F_{2}\) frequency, except for a higher masking factor that is 20.  
Receiver sensitivity of a UAV is \(\xi=\ -130\ \mathrm{dBm}\)  
A signal to noise ratio necessary to demodulate is \(SNR=\ 15\ \mathrm{dB}\)  
The maximum path loss constraint is:  
\[M_{\mathrm{PL}}=\mathrm{PW}-\xi-SNR-MF=85\ \mathrm{dB}\]  
Based on Equation (15), we can calculate the maximum distance covered by WiFi communication:  
\[20\ log_{10}(D_{2})=85\ dB-32\ dB-20\ log_{10}(F_{2})=(85-32.45-67.6)dB=-15.05 \ dB\]  
\[\begin{array}{c}Log_{10}(D_{2})=-\frac{15.05}{20}=-0.7525\Rightarrow\\ D_{2}=10^{-0.7525}\cong 0.176\mathrm{Km}\end{array}\]  
The maximum range between the UAV and the cluster head node for data collection is 0.177 Km. We will then consider a maximum distance in the \((x,y)\) plane of the order of \(xy_{UAV}\ =\ 150\ \mathrm{m}\).
## 6 Conclusions  
By prefabricating a single fissure with different angles in quasi-sandstone samples, single freeze-thaw tests and triaxial compression tests after thawing were conducted in a saturated state--unfrozen and with freezing temperatures of \(-10\), \(-20\) and \(-30\)\({}^{\circ}\)C. The effects of the freezing temperature and fissure angle on the crack propagation process caused by frost heave were investigated, as well as the fracture failure characteristics and the damage evolution law of quasi-sandstone samples. The following conclusions were drawn.  
(1) The initiation and propagation of frost-heaving cracks in a fissured rock mass are mainly caused by the frost-heaving force of the prefixes, and the frost-heaving force of the prefixes increases with the decrease in freezing temperature. The greater the inclination angle of the fissure, the greater the absolute value of the freezing temperature necessary to produce a frost heave crack.  
(2) When the fissure angles are 45\({}^{\circ}\) and 75\({}^{\circ}\), the fracture failure of the quasi-sandstone mass occurs along the frost-heaving cracks. The path of the frost-heaving cracks reduces the triaxial compressive strength of the single-fissured quasi-sandstone, and the frost-heaving cracks at a 0\({}^{\circ}\) angle do not necessarily change the fracture failure mode of quasi-sandstone.  
(3) It is assumed that there are damage elements and nondamage elements in the single-fissured quasi-sandstone after freezing-thawing and triaxial compression, and the damage elements can also bear part of the axial stress. Based on the distribution theory of the micro-element strength and static elastic modulus of quasi-sandstone, a damage constitutive model of rock considering the effects of a single fissure, freeze-thaw temperature, confining pressure, and residual stress was established and verified experimentally.  
(4) An S-shaped damage evolution curve corresponds to each stage of the triaxial compression of quasi-sandstone. With the decrease in freezing temperature, the strength of the quasi-sandstone after thawing decreases, and its brittleness characteristics strengthen.  
Conceptualization, Y.X.; Data curation, T.C.; Formal analysis, Y.X. and T.C.; Funding acquisition, J.R.; Investigation, T.C. and X.C.; Methodology, Y.X.; Project administration, J.R.; Resources, M.Y.; Supervision, J.R.; Validation, Y.X. and T.C.; Visualization, Y.X.; Writing-review and editing, T.C. and M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11872299, Grant No. 12072259) and the Natural Science Foundation of Shaanxi Province (Grant No. 2019 JLP-01).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### UAV Communication Latency
#### 4.1.1 UAV Uplink Communication Latency  
In order to derive the channel characteristics between vehicle k and the UAV, we consider an uplink P-NOMA network where vehicles encode their data packets through superposition coding and transmit them to the UAV over a shared channel. At the UAV, the data packets are relayed using a Decode-and-Forward (DF) protocol. According to the channel characteristics model provided by the UAV-Assisted Vehicular Channel Model and based on the Shannon-Hartley formula, the transmission rate of vehicles in the G2A channel is obtained as:  
\[R_{ku}=W_{up}log_{2}(1+SINR_{ku}), \tag{12}\]  
where \(W_{up}\) is a constant representing the total available bandwidth of the uplink path, and \(SINR_{ku}\) can be obtained from Equation [11] in the previous section. Based on the transmission rate, we can determine the time required to transmit a data packet of size \(\beta\) as:  
\[T_{ku}=\
icefrac{{\beta}}{{R_{ku}}}. \tag{13}\]
## 2 Materials and Methods
### Test Methods
#### 2.2.5 Contact Angle Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Aggages**} & \multicolumn{6}{c}{**Surface Free Energy Parameters (mJ/m\({}^{2}\))**} \\ \cline{2-7}  & \(\gamma\) & \(\gamma\)d & \(\gamma_{\mathrm{p}}\) & \(\gamma^{+}\) & \(\gamma^{-}\) \\ \hline Limestone & 43.20 & 12.55 & 30.65 & 8.42 & 25.32 \\ Steel slag & 46.48 & 15.05 & 31.43 & 5.29 & 27.95 \\ \hline \multicolumn{7}{c}{Note: \(\gamma\) is the total surface free energy of aggregates; \(\gamma_{\mathrm{d}}\) is the dispersion component of aggregates; \(\gamma_{\mathrm{p}}\) is the polarity component of aggregates; \(\gamma^{+}\) is the Lewis acid component of aggregates; \(\gamma^{-}\) is the Lewis base component of aggregates.} \\ \end{tabular}
\end{table} 
Table 7: Surface free energy parameters of aggregates. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Agging Condition**} & \multirow{2}{*}{**Content of Bio-Oil (\%)**} & \multicolumn{6}{c}{**Contact Angle (\({}^{\circ}\))**} & \multicolumn{6}{c}{**Surface Free Energy Parameters (mJ/m\({}^{2}\))**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Distilled Water**} & \multirow{2}{*}{**Formamide**} & \multicolumn{2}{c}{**Ethylene Glycol**} & \(\gamma\) & \(\gamma_{\mathrm{d}}\) & \(\gamma_{\mathrm{p}}\) & \(\gamma^{+}\) & \(\gamma^{-}\) & **Wa** \\ \hline \multirow{4}{*}{Virgin} & 0 & 106.20 & 91.33 & 83.64 & 20.38 & 18.82 & 1.56 & 0.01 & 1.61 & 40.76 \\  & 5 & 104.11 & 88.40 & 85.40 & 23.00 & 21.33 & 1.67 & 0.02 & 1.73 & 46.00 \\  & 10 & 103.35 & 87.52 & 86.07 & 25.50 & 23.46 & 2.04 & 0.09 & 1.86 & 51.00 \\  & 15 & 103.04 & 87.00 & 87.12 & 26.78 & 24.56 & 2.22 & 0.14 & 1.88 & 53.56 \\ \cline{2-10}  & 0 & 101.43 & 85.10 & 78.89 & 21.88 & 19.63 & 2.25 & 0.09 & 1.91 & 43.76 \\  & 5 & 102.50 & 85.56 & 78.98 & 23.57 & 21.61 & 1.96 & 0.005 & 1.64 & 47.14 \\  & 10 & 103.59 & 85.81 & 82.82 & 26.05 & 24.49 & 1.56 & 0.05 & 1.35 & 52.10 \\  & 15 & 104.48 & 86.34 & 78.69 & 27.69 & 26.50 & 1.19 & 0.20 & 1.19 & 55.38 \\ \cline{2-10}  & 0 & 100.90 & 83.79 & 76.13 & 18.19 & 16.23 & 1.96 & 0.68 & 1.68 & 36.38 \\ \cline{2-10}  & 5 & 101.85 & 84.53 & 77.06 & 18.80 & 17.43 & 1.37 & 0.40 & 1.53 & 37.60 \\ \cline{2-10}  & 10 & 102.02 & 84.94 & 78.56 & 19.92 & 18.65 & 1.27 & 0.20 & 1.60 & 39.84 \\ \cline{2-10}  & 15 & 103.00 & 85.20 & 79.23 & 22.83 & 21.83 & 1.00 & 0.10 & 1.36 & 45.66 \\ \hline \hline \end{tabular} Note: Wa is the cohesive work of COBA.  
\end{table} 
Table 8: Surface free energy parameters of COBA.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 5 Open Issues and Future Directions  
Although the EI research field has been gaining traction and becoming a global trend, some issues are still open, without a concrete solution at this stage.  
Security and privacy are very important topics that concern both the industry and academia. How to store data and make it available in a safe and privacy-aware way for training and inference [27]? As presented throughout the text, many techniques, such as model compression, face the burden of managing the tradeoff between low latency and/or light solution and inference accuracy [26].  
The CAP (Consistency, Availability e Partition tolerance) Theorem [162] states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: consistency, availability and partition tolerance. This concept can be translated pretty well to the paradigm of EI, once it involves distributed data storage, transport and processing [71]. Moreover, [PERSON] et al. [51] mention the difficulty of achieving data consistency on edge devices in an efficient and distributed approach.  
Another point that requires the attention of the research community is the adaptability of statically trained models [71]. Since edge devices are known for not having a high power of processing and/or storage, some strategies take the training step to be run in a controlled and powerful environment. Subsequently, the model is offloaded to the nodes. However, this translation has still not been perfected.  
New researches increasingly refer to the growing demand for research related to the heterogeneous environment present at the edge, with the need for ubiquitous computing, communication, and caching resources. According to [PERSON] et al. [163] and also [PERSON] et al. [43], the strength of 5G technology is likely to be an essential enabler for the IoT intelligence environment. This technology, by offering intelligent cloud services and close to the production environment with low latency and lower cost, should allow the evolution of many other lines of research to support edge intelligence, as well as for the improvement in terms of network security and preventing vulnerable transactions from malicious nodes.
## 3 CB and Power Control
### Power Control  
With max-min power control, cell-free massive MIMO provides the same QoS to the terminals in the service area. We propose that instantaneous CSI participate in power control in a slowly varying channel environment to avoid performance loss due to insufficient channel hardening.  
The max-min power allocation problem for downlink transmission is as follows:  
\[\begin{split}\max_{\mathbf{\eta}}&\min_{k}~{}~{}SE_{k}\\ \text{s.t.}&\sum_{i=1}^{K}\eta_{mi}\hat{g}_{mi}\hat{g} _{mi}^{*}\leq 1,m=1,\dots,M\\ &\eta_{mi}\geq 0,m=1,\dots,M,i=1,\dots,K.\end{split} \tag{21}\]  
\(SE_{k}\) is replaced with (19) and (20). This problem is solved via the bisection method [25]. Set \(t\) as the target available rate for each terminal. Repeatedly use the bisection method to solve the following feasibility problem that obtains the power control coefficients.  
\[\begin{split}&\text{find }\zeta\\ &\text{s.t.}~{}\sqrt{\rho_{ap}}\Bigg{(}\sum_{m=1}^{M}\zeta_{mk} \hat{g}_{mk}\hat{g}_{mk}^{*}\Bigg{)}\geq\sqrt{t}\Big{\|}[\mathbf{\alpha}_{k}^{(1)},\mathbf{\alpha}_{k}^{(2)},\mathbf{\alpha}_{k}^{(3)},\sigma_{w}]\Big{\|},k=1,\dots,K \\ &\sum_{i=1}^{K}\zeta_{mi}^{2}\hat{g}_{mi}^{*}\hat{g}_{mi}^{*}\leq 1,m=1,\dots,M\\ &\zeta_{mi}\geq 0,m=1,\dots,M,i=1,\dots,K,\end{split} \tag{22}\]  
where \(\zeta_{mk}=\sqrt{\eta_{mk}}\), \(\mathbf{\zeta}=[[\sqrt{\eta_{1,1}},\dots,\sqrt{\eta_{M,1}}]^{T},\dots,[\sqrt{\eta_{ 1,K}},\dots,\sqrt{\eta_{M,K}}]^{T}]\), and \(\mathbf{\alpha}_{k}^{(1)}\), \(\mathbf{\alpha}_{k}^{(2)}\), and \(\mathbf{\alpha}_{k}^{(3)}\) are  
\[\mathbf{\alpha}_{k}^{(1)}=\Re\Big{(}\mathbf{g}_{k}^{T}(\xi_{-k}\circ\hat{\mathbf{G}}_{-k}) \Big{)}, \tag{23}\]  
\[\mathbf{\alpha}_{k}^{(2)}=\Im\Big{(}\mathbf{g}_{k}^{T}(\xi_{-k}\circ\hat{\mathbf{G}}_{-k} )\Big{)}, \tag{24}\]  
\[\mathbf{\alpha}_{k}^{(3)}=vec(\zeta\circ abs(\hat{\mathbf{G}}))\sqrt{diag(\mathbf{\beta}_ {k}-\tau_{k})}. \tag{25}\]
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 4 Robustness Measure for JSP with Preventive Maintenance
### Monte Carlo Simulation  
To address stochastic methods regarding RMBs, a set of scenarios randomly generated according to the probability distribution of machine failure are used to simulate RMBs. The scheduling quality robustness can be approximately given by the average of the makespan delay in all scenarios, which can be described as follows:  
\[QR_{MC}\left(\delta_{p}\right)=\frac{1}{N}\sum_{k=1}^{N}\left(C_{max}(\delta_{ k})-C_{max}\left(\delta_{p}\right)\right) \tag{5}\]  
where \(QR_{MC}\left(\delta_{p}\right)\) denotes the Monte Carlo approximation of the schedule \(\delta_{p}\) quality robustness, \(N\) is the number of scenarios, and \(C_{max}(\delta_{k})\) is the actual makespan of the schedule \(\delta_{k}\) with breakdowns in the kth simulated scenarios.  
The scheduling stability robustness can be approximately given by the average of the total delay time for all operations in all simulated scenarios, which can be described as follows:  
\[SR_{MC}\left(\delta_{p}\right)=\frac{1}{N}\sum_{k=1}^{n}\left(\sum_{i=1}^{n} \sum_{j=1}^{m}\left(C_{i,j}(\delta_{k})-C_{i,j}(\delta_{p})\right)\right) \tag{6}\]  
where \(SR_{MC}\left(\delta_{p}\right)\) denotes the Monte Carlo approximation of the schedule \(\delta_{p}\) solution robustness and \(C_{i,j}(\delta_{k})\) is the completion time of operation \(O_{i,j}\) in the schedule \(\delta_{k}\).  
The precision of measure mainly depends on the number of repetitions of the simulation. However, the computation becomes intensive when \(N\) is large [3].
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
## 3 Energy Efficiency Optimization of Secure Multicast Transmission
### Discussion  
In the case of small numbers of antennas, the deterministic equivalent expression \(\overline{R}_{k}\left(\mathbf{\Gamma}\right)\) and \(R_{k}\left(\mathbf{\Gamma}\right)\) are approximately equal [45; 46]. Besides, with respect to \(\mathbf{\Gamma}\), \(\overline{R}_{k}\left(\mathbf{\Gamma}\right)\) is still observed to be concave from (33). Thus, each subproblem in (32) does not change the convexity and still guarantees the convergence. Finally, we obtain our algorithm by two-level iteration. The optimal power allocation is obtained by the inner iteration and assigned to the outer iteration. Then, the initial power allocation of the inner layer is updated after the outer iteration. The proposed energy efficiency optimization algorithm of massive MIMO secure multicast transmission is described in detail in Algorithm 1.  
```
1:The beam domain channel transmit statistics CSI \(\mathbf{\Omega}_{k}\) and \(\mathbf{\Omega}_{\text{ev}}\), initial power allocation \(\mathbf{\Lambda}^{(0)}\), the threshold \(\epsilon\)
2:Optimal power allocation matrix \(\mathbf{\Lambda}\)
3:Initialization: \(\ell=0\)
4:Calculate \(\mathbf{\Lambda}^{(0)}\) using (26), (27), and (33)
5:repeat
6: Update \(\ell\leftarrow\ell+1\)
7: Initialization: \(t=0\), \(\mathbf{\Gamma}^{(0)}=\mathbf{\Lambda}^{(\ell-1)}\), \(\eta^{(0)}\) using (31)
8:repeat
9: Update \(t\gets t+1\)
10: Calculate \(\mathbf{\Gamma}^{(t)}\) by using (30) with \(\eta^{(t-1)}\)
11: Update \(\eta^{(t)}\) using (31)
12:until\(\|\mathbf{\Gamma}^{(t)}-\mathbf{\Gamma}^{(t-1)}\|\leq\epsilon\)
13: Return \(\mathbf{\Lambda}^{(\ell)}=\mathbf{\Gamma}^{(t)}\)
14:until\(\|\mathbf{\Lambda}^{(\ell)}-\mathbf{\Lambda}^{(\ell-1)}\|\leq\epsilon\)
15:Return \(\mathbf{\Lambda}=\mathbf{\Lambda}^{(\ell)}\)
```  
**Algorithm 1** Energy Efficiency Optimization Algorithm for Secure Multicast Transmission
## Materials and Methods
### Pyrolysis Compound Specific Isotopic Analysis (Py-GC-/IRMS)  
The carbon isotope composition of individual compounds was determined by direct pyrolysis compound specific isotopic analysis (8\({}^{13}\)C Py-CSIA). This was performed using a double-shot pyrolyzer (model 3030D; Frontier Laboratories Ltd., Fukushima, Japan) attached to a GC IsoLink(tm) HRMS System (Thermo Fisher Scientific, Bremen, Germany). The system consists of a trace GC Ultra system, fitted with the same chromatography column type, and configured with the same chromatographic conditions as previously described, connected to a GC-Isolink System equipped with a micro-reactor for combustion (C) set at 1020 \({}^{\circ}\)C and coupled with a Delta V Advantage isotope ratio mass spectrometer via a ConFlo IV universal interface.  
The samples were weighted (1.5 mg) and pyrolyzed under the same conditions as previously described and the evolved gases directly injected into the GC-C/IRMS system for analysis. All samples were analyzed in triplicate (\(n=3\)). Each chromatographic compound was gasified in the Isolink System, and pure CO\({}_{2}\) gas was mixed into the He carrier flow as pulses of reference gas.  
Structural features of specific chromatographic compounds (peaks) were inferred by comparing and matching the mass spectra obtained with the Py-GC/MS analysis with the Py-CSIA chromatograms obtained using the same pyrolysis and chromatographic conditions. Background subtractions and isotope abundances were calculated using the ISODAT 3.0 software (Thermo Scientific, Bremen, Germany).  
The precision of 8\({}^{13}\)C Py-CSIA and accurate of the measurements was evaluated and corrected against the reference material \(n\)-alkane mixture C4 (5 dissolved \(n\)-alkanes C-17 to C-25 in equal concentrations: Indiana University, Bloomington, IN, USA). The linear correlation between standard and measured (IRMS) 8\({}^{13}\)C values from the C4 mixture was used to derive sample 8\({}^{13}\)C values relative to the VPDB scale [43]. The reference \(n\)-alkane mixture was analyzed in a daily routine, before and after each analytical run. Standard and measured isotopic values fitted well along a straight line, with a linear regression R\({}^{2}\) no lower than 0.99 in all cases.
###### Abstract  
This paper presents a fault-tolerant control scheme for the sensor fault in the acceleration process of the variable cycle engine. Firstly, an adaptive equilibrium manifold model with multiple inputs and multiple outputs is established. Combined with the Kalman filter bank, sensor fault diagnosis is carried out to realize the diagnosis and signal reconstruction of the engine in the case of a single sensor and double sensor faults. On this basis, isolation and group isolation are used to diagnose sensor faults and reconstruct signal in speed closed-loop control. Then, the control plan of the acceleration process is optimized based on the target shooting method, aiming to simulate the variation of various variables in the engine acceleration process more accurately, so as to verify the feasibility of the sensor fault-tolerant control scheme. Finally, a hardware-in-loop simulation platform is built based on the idea of distributed control, and the fault-tolerant control scheme of the sensor proposed previously is verified based on this platform. The results show that the proposed scheme can accurately diagnose the sensor faults and reconstruct the signal within 0.2 s, and the actual speed can rise from 67.87% to 99.9% in 4 s, ensuring the safe and rapid completion of the acceleration process.  
fault-tolerant control; sensor fault; acceleration process; variable cycle engine; adaptive equilibrium manifold model; target shooting method +
Footnote †: journal: Applied sciences
## 4 Reduced Element  
### Sturcture of the Reduced Element  
Let \(u^{h}\) denote the FE solution of the conventional element of degree \(m+1\), which hereinafter will be called the 'full order solution' (or simply 'full solution'). Also let \(u_{\text{R}}^{h}\) denote the associated solution of the reduced element of degree \(m\), which will be called the'reduced order solution' (or simply'reduced solution'). As mentioned above, the extraction of the reduced solution from the full solution is as follows: after the full order solution \(u^{h}\) has been obtained, it is reduced to the solution \(u_{\text{R}}^{h}\) of one order lower by ignoring the \((m+1)\)-degreed term. With the hierarchical shape functions employed, it is extremely simple to extract the reduced solution simply by ignoring the term \(\hat{N}_{m+1}(t)u_{m+2}^{h}\) in Equation (5) as follows:  
\[u_{\text{R}}^{h}=\hat{N}_{1}(t)u_{1}^{h}+\sum_{k=2}^{m}\hat{N}_{k}(t)u_{k+1}^ {h}+\hat{N}_{2}(t)u_{2}^{h}. \tag{13}\]  
The term \(\hat{N}_{m+1}(t)u_{m+2}^{h}\) is not thrown away and will serve as an error term \(\varepsilon_{\text{R}}^{h}=u^{h}-u_{\text{R}}^{h}\), which will be further explained later. Also note that \(u_{\text{R}}^{h}\) is not a genuine FE solution since it is not directly solved for by an FE procedure and it is simply a certain kind of \(m\)-degreed polynomial interpolation to the full order solution \(u^{h}\). Therefore, the structure of the full solution is viewed to be composed of two parts: the reduced solution \(u_{\text{R}}^{h}\) and an error estimator \(\varepsilon_{\text{R}}^{h}\). Figure 1 schematically shows the structure of the reduced linear element.