esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design of Experiments  
The experimental design applied to this study was a 2\({}^{3}\)-factorial design (three factors and two levels). Three central point experiments were included in this design and used as a source for error estimation. The factorial design was accomplished to study the effect of the factors and their interactions on the HTL process. Analysis of variance (ANOVA) was carried out using Statgraphics Centurion XVIII software (Statpoint Technologies Inc., Warrenton, VA, USA). The statistical significance was considered when \(p\)-value \(<\) 0.05.  
The factors studied were temperature (T), reaction time (t) and microalgal concentration (CS) because of their influence on the HTL process [5,10,23,24]. The levels of the factors are shown in Table 2. The biocrude yields are very low at lower temperatures than the minimum level (200 \({}^{\circ}\)C) chosen [11]. The maximum design temperature was selected based on the limits allowed by the reactor used (320 \({}^{\circ}\)C). The reaction time ranged from 10 to 180 min based on previous experimental studies [4,10,11,25,26]. Finally, microalgal concentration (10-50 wt%) was selected considering the levels reached after harvesting and concentrating the microalgae biomass.  
The responses selected in the design of experiments were the yield to biocrude (Y\({}_{\text{B}}\)) and the contents of nitrogen (C\({}_{\text{N}}\)) and oxygen (C\({}_{\text{O}}\)) in the biocrude. The main objective of this work was to maximise the production of biocrude from HTL and minimise the contents of nitrogen (C\({}_{\text{N}}\)) and oxygen (C\({}_{\text{O}}\)) in the biocrude to improve its properties [13; 27].
## 1 Introduction  
### Background  
Among all of the severe problems confronting mankind in the 21 st century, including the aggravation of human society's dependence on energy, the exhaustion of fossil resources, climate change and environmental problems, the energy crisis, which had arisen in the 1970s, deserves priority concern. Seeking for new energy sources that are sustainable and clean and that can enhance the efficiency of energy utilization is an urgent mission [1,2].  
A micro power grid, directly connecting distributed generation units, electric power network and end users in local area, can effectively optimize and improve the efficiency of energy utilization and provide flexibility, controllability and economic efficiency of power system operation. It is thus in rapid development and has become an effective way of distributed energy integration. There is vast solar and wind resource in the northern region of China, where thermal load demand is also great. The micro grid consisting of combined heat and power (CHP) generation and renewable energy, including wind and photovoltaic (PV) power, has a bright development prospect for its advantages in economic efficiency, applicability and reliability. By 2035, renewable energy will account for nearly half of global generation capacity growth, among which intermittent power supply based more on wind and PV is proportionally 45%. China will be the largest country in increments of the absolute amount of renewable energy generation, exceeding the total amount of the European Union, America and Japan, according to the International Energy Agency (IEA) World Energy Outlook 2013. With the rapid development of energy storage devices and electric vehicles (EVs), which can manage bidirectional energy flow between the power system and the vehicle, new requirements emerge for optimal scheduling of micro grid considering the demand side management (DSM) and EV connection.  
Based on the power and heat supply of the CHP micro grid, the model of mixed energy integrated optimization scheduling considering EVs and DSM mode is constructed in this paper. In the model, the objective function is designed as minimum total operation costs, and the output of each distributed power source is set as an optimization variable. The multi-team particle swarm optimization (MTPSO) algorithm, which can improve the global search capability, is proposed, and a simulation study is conducted in a case system to show the feasibility of the proposed method.
## 3 Study Area and Data
### Landslide Predisposing Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predisposing Factor** & **Value** & **FR** & **LAI\({}_{\text{FR}}\)** & **DFR** \\ \hline \multirow{6}{*}{Elevation (m) (\(PF_{1}\))} & 1254–1726 & 0.013 & 1.000 & 0.013 \\  & 1726–2304 & 4.847 & 0.507 & 2.456 \\  & 2304–2904 & 3.283 & 0.477 & 1.567 \\  & 2904–3482 & 2.193 & 0.423 & 0.929 \\  & 3482–3932 & 1.131 & 0.474 & 0.536 \\  & 3932–4381 & 0.132 & 0.500 & 0.066 \\  & 4381–4917 & 0.312 & 0.846 & 0.264 \\  & 4917–6715 & 0.000 & 0.000 & 0.000 \\ \hline \multirow{6}{*}{Aspect (\(\lx@math@degree\))} & \(-1\) & 0.000 & 0.000 & 0.000 \\  & 0–22.5, 337.5–0 & 0.000 & 0.000 & 0.000 \\  & 22.5–67.5 & 0.241 & 0.500 & 0.121 \\  & 67.5–112.5 & 0.492 & 0.731 & 0.360 \\  & 112.5–157.5 & 1.512 & 0.393 & 0.593 \\  & 157.5–202.5 & 1.171 & 0.424 & 0.496 \\  & 202.5–247.5 & 0.876 & 0.618 & 0.541 \\  & 247.5–292.5 & 0.522 & 0.632 & 0.330 \\  & 292.5–337.5 & 0.198 & 1.000 & 0.198 \\ \hline \multirow{6}{*}{Slope (\(\lx@math@degree\))} & 0–7 & 0.046 & 0.400 & 0.018 \\  & 7–12 & 0.563 & 0.500 & 0.281 \\  & 12–16 & 1.069 & 0.400 & 0.428 \\  & 16–21 & 2.773 & 0.361 & 1.001 \\  & 21–26 & 1.028 & 0.732 & 0.752 \\  & 26–30 & 1.700 & 0.443 & 0.752 \\  & 30–35 & 2.842 & 0.525 & 1.491 \\  & \(>\)35 & 0.963 & 0.750 & 0.722 \\ \hline \multirow{6}{*}{Topographic relief (m) (\(PF_{4}\))} & 0–52 & 0.043 & 0.400 & 0.017 \\  & 52–108 & 0.604 & 0.488 & 0.295 \\  & 108–163 & 1.753 & 0.397 & 0.697 \\  & 163–214 & 2.028 & 0.446 & 0.904 \\  & 214–262 & 1.220 & 0.500 & 0.610 \\  & 262–320 & 2.308 & 0.459 & 1.059 \\  & 320–398 & 2.235 & 0.714 & 1.596 \\  & 398–621 & 1.319 & 0.571 & 0.753 \\ \hline \multirow{6}{*}{Rock group (\(PF_{5}\))} & Very hard & 1.587 & 0.526 & 0.835 \\  & Hard & 0.945 & 0.824 & 0.778 \\ \cline{1-1}  & Less hard & 1.705 & 0.440 & 0.750 \\ \cline{1-1}  & Soft & 1.288 & 0.495 & 0.638 \\ \cline{1-1}  & Very soft & 0.529 & 0.356 & 0.188 \\ \cline{1-1}  & Water & 0.738 & 0.500 & 0.369 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: FRs of the landslide predisposing factors.
## 5 Optimization Design for the Framework
### Design Optimization for the Framework  
In the former analysis and determination of the portal structure, a finite element model including the traverse and lifting beam was created to analyze the structural behavior of the composite structure, as shown in Figure 4C. Isotropic aluminum and orthotropic composite materials were used in the analysis of the plates. We used the material specifications for AMPREG21 laminating sandwich panels provided by Gurit (Wattwil, Switzerland). The final design was examined in a detailed finite element analysis, which included all components of the large-scale moving framework. Table 7 lists the analysis results of the maximum displacements of the milling head in 3 coordinate directions after design optimization of the framework.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & & **Before (D0)** & **After (D1)** \\ \hline \multirow{4}{*}{Maximum deformation (mm) (Taking Figure 6 as an example)} & Total & \(\Delta_{\text{total}}=0.166\) & \(\Delta_{\text{total}}=0.080\) \\  & \(x\)-direction & \(\Delta_{x}=0.163\) & \(\Delta_{x}=0.076\) \\  & \(y\)-direction & \(\Delta_{y}=0.015\) & \(\Delta_{y}=0.014\) \\  & z-direction & \(\Delta_{z}=0.108\) & \(\Delta_{z}=0.054\) \\ \hline \multirow{4}{*}{Mass Stiffness material costs} & 100\% & 106\% \\  & \(100\%\) & 189\% \\ \cline{1-1} \cline{2-4}  & \(100\%\) & 109\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of portal’s performance before and after design optimization.  
Figure 6: Comparison of the portal’s structure and FEA deformation cloud map (taking load 1 as an example) before and after design optimization: (**A**) structure of D0; (**B**) total deformation of D0; (**C**) \(x\)-direction deformation of D0; (**D**) \(y\)-direction deformation of D0; (**E**) \(z\)-direction deformation of D0; (**F**) structure of D1; (**G**) total deformation of D1; (**H**) \(x\)-direction deformation of D1; (**I**) \(y\)-direction deformation of D1; (**J**) \(z\)-direction deformation of D1.  
From Table 7, it is observed that the displacements of the milling head were less than the design requirement of 0.25 mm in all load cases. Meanwhile, the stress inside the laminate was much lower than the allowable material limits. This indicated that the 3-step design optimization approach proposed in this study for the optimal design of a large-scale moving framework structure was feasible and successful.
## 3 Proposed Methodology
### IoT Architecture  
The IoT has revolutionized future networks in recent years with the potential to improve the overall quality of life efficiently. It contains a vast network of interconnected internet-enabled devices called IoT devices, equipped with a wide range of sensors, storage, computational, and communication capabilities. It generates a massive amount of critical data shared over the internet and is needed to be secured.  
A typical three-layer IoT architecture [67] is depicted in Figure 2. It consists of a perception layer, network layer, and application layer. The perception layer is the lowest, also called the physical layer. It involves the different devices, sensors, actuators, etc., that constantly gather information and then exchange using different communication standards and protocols like Bluetooth, Wi-Fi, ZigBee, and 6 LowPAN, etc. The network layer, also called the transport layer, is responsible for the smooth transmission of the packets using the different communication standards as 4G, 5G, Wi-Fi, ZigBee, IPv6, etc. The final layer is the application layer, which processes the data for visualization for end users' applications, e.g., smart health monitoring. Some of the protocols used in this layer are the Constrained Application Protocol (CoAP) and Data Distribution Service (DDS) etc. [68].  
The most suitable position to deploy the NIDS for the three-layered IoT architecture is the entry points of the network--e.g., an edge router as shown in Figure 2--to provide the needed protection to the network against anomalies. In this study, the considered NIDS is a two-stage solution, comprised of the data capturing and preparation stage (Stage-1 in Figure 2), followed by the DL-based anomaly detection stage (Stage-2 in Figure 2). In stage-1, the data will be collected and intercepted either within the network such as IoT devices or from outside of the IoT network through the internet. The useful feature extraction task will be performed followed by the data preparation for the DL stage. In  
Figure 2: A three-layered IoT architecture.  
stage-2, the prepared data will be processed by the DL-based anomaly detection model to detect the anomaly traffic to protect the IoT network.
## 5 Minimization of Errors Due to Laser Intensity Fluctuations  
Laser intensity fluctuations can be due to laser power supply fluctuations, drift or aging [2; 19]. In the dual path correlation spectroscopy arrangement both detector outputs will change in the same proportion as the laser intensity varies. Therefore, by calculating the depth of modulation, these fluctuations will be canceled. In order to probe this point, in our experiment we intentionally varied the laser line intensity while the gas concentration was left fixed. As an example, the gas cell was filled with a mixture of 9% C\({}_{2}\)H\({}_{2}\) and 91% N\({}_{2}\) and after a certain time we reduced the laser intensity five times. This consequently affected the signal levels of both channels (Figure 7a). By using these signals the depth of modulation was calculated and the result is presented in Figure 7b, where it can be appreciated that the depth of modulation is practically unaffected by laser intensity fluctuations. As a further example, we performed an additional measurement, which started with a 0% C\({}_{2}\)H\({}_{2}\) concentration, after we filled the cell with the mixture of 40% C\({}_{2}\)H\({}_{2}\) balanced with N\({}_{2}\). At this point the measurement channel signal started to drop (Figure 7c) while the \(D_{M}\) started to increase until it reached its constant state (Figure 7d). Afterwards, we reduced the light intensity in two different times, affecting both channel signals (Figure 7c) while the \(D_{M}\) remained constant (Figure 7d). Finally, the light intensity was increased to its initial value and both channels signals recovered their levels (Figure 7c), while the \(D_{M}\) continued quite stable over the time (Figure 7d). These measurement results are very important since they clearly demonstrate that the dual path correlation spectroscopy is able to minimize large laser intensity fluctuations (Figure 7a,c) by calculating the depth of modulation (Figure 7b,d).
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 3 Design of Bottom Guidance Law Based on Sliding Mode Control Theory
### Design of the Impact Time Control Guidance Law  
#### 3.2.1 Design of the Guidance Law for Stationary Target  
For stationary targets, considering the impact time control, the sliding mode surface is designed as below:  
\[s=t_{f}-t_{f}^{d}=t+t_{go}-t_{f}^{d}=t_{go}-t_{go}^{d} \tag{19}\]  
where, \(t_{f}^{d}\) and \(t_{go}^{d}\) are the desired impact time and the desired time-to-go respectively. \(t_{go}\) is the time-to-go under proportional navigation law and the expression is shown in Equation (18).  
The time derivative of Equation (19) is expressed as follows:  
\[\begin{array}{ll}\dot{s}&=\dot{i}_{go}-\dot{t}_{go}^{d}\\ &=(1+K_{1})+(K_{2}+K_{3})\dot{\sigma}_{M}\\ &=(1+K_{1})+(K_{2}+K_{3})\Big{(}\frac{\sigma_{M}}{V_{M}}+\frac{V_{M}\sin\sigma _{M}}{r}\Big{)}\\ &=(1+K_{1})+(K_{2}+K_{3})\frac{\dot{V}_{M}\sin\sigma_{M}}{r}+(K_{2}+K_{3}) \frac{\sigma_{M}}{V_{M}}\end{array} \tag{20}\]  
where, \(K_{1},K_{2}\) and \(K_{3}\) are the corresponding coefficients, and the specific expressions can be expressed as:  
\[K_{1}=-\cos\sigma_{M}\Big{(}1+N\sigma_{M}^{2}\Big{)}\bigg{(}\frac{\sigma_{M}} {\sin\sigma_{M}}\bigg{)}^{\frac{1}{N-1}} \tag{21}\]  
\[K_{2}=\frac{r}{V_{M}}\frac{1}{N-1}\bigg{(}\frac{\sigma_{M}}{\sin\sigma_{M}} \bigg{)}^{\frac{1}{N-1}-1}\frac{\sin\sigma_{M}-\sigma_{M}\cos\sigma_{M}}{\sin ^{2}\sigma_{M}}\Big{(}1+N\sigma_{M}^{2}\Big{)} \tag{22}\]  
\[K_{3}=\frac{r}{V_{M}}(2N\sigma_{M})\bigg{(}\frac{\sigma_{M}}{\sin\sigma_{M}} \bigg{)}^{\frac{1}{N-1}} \tag{23}\]  
According to the sliding surface designed by Equation (19), the impact time control guidance law based on [PERSON] non-linear control theory is designed as follows:  
\[a_{M}=a_{M}^{eq}+a_{M}^{sw} \tag{24}\]  
\[a_{M}^{eq}=-\frac{V_{M}}{K_{2}+K_{3}}\bigg{(}(1+K_{1})+(K_{2}+K_{3})\frac{V_{ M}\sin\sigma_{M}}{r}\bigg{)} \tag{25}\]  
\[a_{M}^{sw}=-ks\sin\sigma_{M}-M(p\text{sign}(K_{2}+K_{3})+1)\text{sign}(s) \tag{26}\]  
where, \(a_{M}^{eq}\) and \(a_{M}^{sw}\) are the equivalent part and switching part of the guidance law, respectively, and the parameters \(k>0,M>0,p>1\). The equivalent control item is used to control the line-of-sight angular velocity to ensure that the UAV can impact the target, and to maintain the sliding mode surface reaching law \(\dot{s}=0\). The switching control term is to satisfy the impact time constraint, while ensuring that the designed sliding mode guidance law Equation (24) satisfies the Lyapunov stability condition as well as being non-singular, that is, not containing singular points.
## 4 Conclusions  
The edible coating incorporated with MAP (gas composition of 70% CO\({}_{2}\)/30% N\({}_{2}\)) has positive effects to maintain the excellent qualities/characteristics and extend shelf life for 8-12 days of HTS fillets during refrigerated storage. The water dynamic changes were evaluated by LF-NMR and MRI and three types of water were distinguished: bound water (T\({}_{21}\)), immobile water (T\({}_{22}\)), and free water (T\({}_{23}\)). The pT\({}_{22}\) diminished progressively while pT\({}_{23}\) increased during storage, and pT\({}_{22}\) accounted for the largest proportion of three types of water, followed by pT\({}_{23}\). The weighted MRI provided the internal structure information associated with different treated samples during storage. We also demonstrated the excellent correlation of LF-NMR measurements and quality parameters based on the linear regression analysis and PCA. Above all, the combination of LF-NMR and MRI was able to give more information about water distribution and migration, demonstrating the feasibility of LF-NMR pulse MRI to monitor quality deterioration and predict shelf life of HTS fillets during refrigerated storage.  
Conceived and designed the experiments: [PERSON], [PERSON], and [PERSON] Performed the experiments: [PERSON], [PERSON], and [PERSON] Analyzed the data: [PERSON], [PERSON], and [PERSON] wrote the paper: [PERSON] and [PERSON].  
This research was funded by the National Natural Science Foundation of China (31601414), National Key Research and Development Program (2016 YFD0400106), and Construction Project of Public Service Platform for Shanghai Municipal Science and Technology Commission (17 DZ2293400).  
The authors would like to express their profound gratitude to [PERSON] from the Instrumental Analysis Center of Shanghai Ocean University for his technical assistance.  
The authors declare no conflicts of interest.  
Figure 4: Principal component PC1 and PC2 score plot from the principal component analysis (PCA) of HTS fillets during refrigerated storage (CK1: distilled water + air packing; CK2: edible coating + air packing; C1: edible coating + MAP (60% CO\({}_{2}\)/40% N\({}_{2}\)); C2: edible coating + MAP (65% CO\({}_{2}\)/35% N\({}_{2}\)); C3: edible coating + MAP (70% CO\({}_{2}\)/30% N\({}_{2}\))).
# Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities
###### Abstract
Previous research showed the routing stage of the resources to be interconnected with the central office of a public or private company can incorporate a heterogeneous wireless sensor network that accommodates multi-hops [11,12]. The proposed scenario considers a series of sensors that can be deployed in an urban area and used in various innovative city applications, such as in fixed rubbish collectors, electric energy meters, drinking water meters, charging centers for electric vehicles, electric energy micro-grids, CO\({}_{2}\) emission sensors, and vibration sensors in footbridges, among others. Therefore, the principal idea will be to transmit the information from these sensors to the central offices of public or private companies [13,14]. The main contribution of this paper is a novel idea that enables the routing of several IoT sensors in an authentic and geo-referenced scenario in which a heterogeneous wireless sensor network can be deployed for multiple smart city services. This paper describes the scenario and the problem with a mathematical approach. It illustrates simulation results to demonstrate the evaluation of the system with respect to variations in input variables, such as the numbers of DAPs and sensors, DAP capacity, DAP coverage, and capacity of the links in comparison with related work [15]. This article is organized as follows. Section 2 describes the related work. Section 3 describes the problem's formulation. In Section 4, an analysis of the simulation results is carried out. Finally, we conclude this paper in Section 5.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method
### Feature Construction
#### 2.1.2 Derivative-Based Features (U5-U8)  
These feature vectors are designed to capture dynamic changes in voltage and current signals, which are especially useful in detecting transient disturbances caused by faults.  
* **U5--**Measures the rate of change in the current, highlighting rapid variations indicative of fault inception.
* **U6--**Captures sudden voltage drops or fluctuations caused by fault conditions.
* **U7--**Provides insights into frequency variations and phase shifts due to faults.
* **U8--**Tracks dynamic phase shifts in voltage waveforms, particularly useful in detecting unbalanced faults.  
These features improve the fault classification accuracy, as traditional magnitude-based methods often fail to distinguish between different types of faults, particularly HIFs, which cause minimal changes in magnitude but affect the waveform dynamics.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 4 Results and Discussion
### Non Uniform Irradiance Condition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & PSC Case 1 & PSC Case 2 & PSC Case 3 \\ \hline \multirow{4}{*}{\(\text{PSPC}\)} & \(\text{P}_{\text{mpp}}\) (KW) & 31.35 KW & 64.15 & 48.03 \\ \cline{2-5}  & \(\text{T}_{\text{s}}\) (s) & 0.41 & 0.416 & 0.425 \\ \cline{2-5}  & \(\text{n}\) (\%) & 99.94\% & 99.94\% & 99.85\% \\ \hline \multirow{4}{*}{\(\text{PSPC}\)} & \(\text{P}_{\text{mpp}}\) (KW) & 30.57 & 63.30 & 46.38 \\ \cline{2-5}  & \(\text{T}_{\text{s}}\) (s) & 0.43 & 0.435 & 0.43 \\ \cline{2-5}  & \(\text{n}\) (\%) & 97.45\% & 98.61\% & 96.43\% \\ \hline \multirow{4}{*}{\(\text{PSPC}\)} & \(\text{P}_{\text{mpp}}\) (KW) & 31.28 & 63.59 & 47.92 \\ \cline{2-5}  & \(\text{T}_{\text{s}}\) (s) & 0.435 & 0.42 & 0.44 \\ \cline{2-5}  & \(\text{n}\) (\%) & 99.71\% & 99.07\% & 99.63\% \\ \hline \multirow{4}{*}{\(\text{PSPC}\)} & \(\text{P}_{\text{mpp}}\) (KW) & 30.79 & 63.75 & 44.91 \\ \cline{2-5}  & \(\text{T}_{\text{s}}\) (s) & 0.42 & 0.425 & 0.432 \\ \cline{2-5}  & \(\text{n}\) (\%) & 98.15\% & 99.32\% & 93.37\% \\ \hline \multirow{4}{*}{\(\text{PSPC}\)} & \(\text{P}_{\text{mpp}}\) (KW) & 29.56 & 59.92 & 44.55 \\ \cline{2-5}  & \(\text{T}_{\text{s}}\) (s) & 0.426 & 0.42 & 0.438 \\ \cline{1-1} \cline{2-5}  & \(\text{n}\) (\%) & 94.23\% & 93.35\% & 92.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of PSC simulation results. upheld an efficacy of about 93.38% in 32 ms. The lowest efficiency of approximately 92.62% was obtained by the PSO algorithm in 38 ms. The RSA-based MPPT algorithm demonstrates promising computational complexity properties, rendering it a reliable and efficient solution for solar power harvesting under various conditions. Therefore, there are many applications where the proposed RSA algorithm can be used, especially, for low voltage and power harvesting applications. One example of a similar application is the optimization of wind turbine performance. Since the proposed algorithm shows a good performance in tracking the global maximum power point of PV systems, the algorithm can be easily used to optimize the wind turbine performance by constantly adjusting the pitch and yaw of the turbine blades based on the measurement of wind's speed and direction. This algorithm can be also used in a solar tracking system to adjust the orientation of PV panels based on the sun's position. By training the algorithm on a dataset of panels' orientations and corresponding power outputs, it can quickly predict the optimal orientation of a PV panel for a given time and location.