esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Training and Evaluating the Performance of the Models
#### 4.3.3 Predicting Qualifications for All Students  
Figure 7 shows the performance metrics for different models predicting qualifications for all students. The model used APS, mathematics and physical sciences as input parameters and qualification as the target. The accuracy of the models ranges from 94.9% to 96.7%, demonstrating their ability to predict the qualifications students enrolled for correctly. Among the models, the decision tree achieved an accuracy of 96.7%, followed closely by the random forest and XGB models, which also achieved an accuracy of 96.6%. In terms of precision and recall, all models consistently performed well, with precision and recall scores ranging from 94.9% to 96.8%.  
The decision tree, KNN, random forest, and XGB models achieved precision and recall scores of 96.8%. The LGBM model also exhibited strong precision and recall at 94.9%. The F1 scores, which balance precision and recall, ranged from 95.8% to 96.7% for the models. The decision tree, random forest, and XGB models achieved F1 scores of 96.7%. The LGBM model attained an F1 score of 95.8%, showcasing its solid performance. The RMSE values ranged from 0.525 to 0.635. The MAE values ranged from 0.082 to 0.122. The decision tree model achieved the lowest RMSE and MAE values, at 0.525 and 0.082, respectively, of the models' overall accuracy.
## 5 Application  
The proposed fault diagnosis system was applied in an actual production environment. The A-class insulation board production line consists of mixing equipment, a caterpillar laminating machine and cutting equipment. It is essential to keep each piece of equipment of an A-class insulation board production line normal and efficient in operation. Therefore, the voltage and current data of key nodes, vessel pressure and vibration signals of key motors of mixing equipment, crawler laminator and cutting equipment were sampled and monitored in real time. The production line of an A-class insulation board production line and its various sensors for key nodes are shown in Figure 10, and the specifications of deployed sensors are listed in Table 5. Data were acquired and sent to a computer by a PLC. All kinds of collected signals were preprocessed to ensure the integrity and credibility for each key node. Detailed procedures of signal preprocessing are described in Section 4. The training and testing datasets consisted of preprocessed multi-sensor data and were fed into the proposed CNN-LSTM network. The proposed CNN-LSTM network and datasets were sent to the server for training to obtain well-trained predictable models. The configuration of the high-performance server used for training is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Device Name** & **Type** \\ \hline
1 & CPU & I9-10900 kf \\
2 & RAM & 64G DDR4 \\
3 & Hard driver & 512G SSD \\
4 & GPU & RTX 2080s \(\times\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specification of the high-performance server.  
Figure 10: The A-class insulation board production line and detailed pictures of various sensors. (**A**) Container pressure sensor. (**B**) Voltage sensor (AC). (**C**) Current sensor (DC). (**D**) Vibration sensor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Input Voltage** & **Output** & **Resolution** & **Range** \\ \hline Voltage & - & RS485 & 600 & 0–450 V \\ Current & - & 450 & 0–300 A \\ Vibration & 12 or 24 V & 4–20 mA & 0.1 mm & 0–100 mm/s \\ Rotation & 24 V & 0–10 V & 0.3\% & 20–10,000 Hz \\ Pressure & 12–36 V & 4–20 mA & 0.06 MPa & 0.1–60 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of different sensors deployed in the production line.  
Data collected by each sensor were processed by a computer and fed into the proposed CNN-LSTM model. The output of the network contained the prediction of the next 10 s states of each piece of equipment, and was classified by the k-NN algorithm to obtain the final diagnosis. Days of industrial testing proved the prediction accuracy and stability of the proposed model. Prediction accuracy over 10 s reached 98.7%, and decreased by 6% when the prediction duration was increased to 15%. In general, a timely response, i.e., load reduction or shutdown, could be made with accurate prediction within 10 s. Hence, the proposed CNN-LSTM model satisfied the requirement of industrial production.
###### Abstract
Simulations and experiments verified the registration accuracy and efficiency of the algorithm designed in this paper. The rest of this paper consists of four sections. Section 2 introduces the RANSAC algorithm to achieve initial alignment and proposes a nonlinear convergence factor and adaptive weight coefficient method to improve the whale optimization algorithm to optimize the RANSAC algorithm in order to achieve initial alignment quickly and accurately. Section 3 is the point cloud accurate alignment and proposes a method to improve the ICP algorithm based on the key point normal vector weighted judgment method. Section 4 describes our experiments by comparing the RANSAC algorithm, ICP algorithm, Depth Filtering-ICP, and IWOA-RANSAC-ICP algorithms, and it was concluded that the proposed method in this paper has a small mean square error, low false identification rate, and faster alignment speed. The effectiveness and superiority of the algorithms in this paper are proved. Section 5 summarizes the research in this paper and suggests future research directions.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 3 Proposed Methodology
### MSSOA-Based Routing Selection and Biological Data Aggregation  
The primary goal of this investigation is to determine the best close path between each CH and the appropriate BS. ISSO generates the route between the source CH and the BS using the same fitness function as the selection CH. Each ISSO represents the biological data forwarding path from each cluster to the base station in the route. Each ISSO is the same size as the total number of CHs in the network, adding extra space for the BS. The suggested transmission route between the source node and the BS is updated frequently throughout the routing process. Every particle's size equals the total number of CHs in the applicable transmission channel. The mentioned fitness functions residual energy, distance, and node degree are utilized by ISSO to choose biological data transmission routes. Route request (RREQ) messages are sent from the originating node to neighbouring nodes to adjust the route identification process.  
The message is transmitted back to the originating CH via the reverse path by the next higher fitness level node. After establishing a routing path, the source CH collects messages from neighbouring nodes. Biological data transmission through the network begins once the routing path is generated.  
Based on the functionality of each biosensor, the proposed WBAN cluster routing protocol detects changes in a patient's health status. It converts them into electrical signals for transmission to a medical server. Initially, all biosensors are assumed to have the same energy \(E_{0}\). In our network, there are two gateway human sensors. To collect data through the CH and forward them to the BS, the first is positioned around the waist of the individual. Biological data are immediately collected from biosensors via a second gateway at the patient's neck. The BS is located outside the network. Vital biosensor data are transmitted straight to the relevant gateways. Biosensors transmit biological data to all relevant gateways using clustering algorithms (Algorithm 2). Each iteration's CH is selected dynamically based on the node's proximity to the gateway node and maximum energy. Biological data are collected by the CH from CMs, combined, and delivered through the gateway unit that forwards them to the BS. By accurately capturing the difference between actual and predicted energy consumption, MSE ensures that the algorithm lowers energy use by decreasing decision-making mistakes. To make a stronger case, it is important to note that MSE is designed to assess optimization performance by penalizing larger errors more severely. This makes it a good fit for situations where small changes in energy consumption can greatly impact the overall system performance. In addition, it presumes that the energy usage would be smooth and predictable, with better routing and clustering choices indicated by less MSE.
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
## 4 Channel Capacity and Doppler of V2V MIMO
 \tag{45}\]  
where \(R_{r}\) is the SFC matrix for a two-element MIMO array, the eigenvalues can be solved directly and simply from \(|\lambda E-R_{r}|=0\), which can be derived by  
\[\lambda_{1,2}=1\pm\sqrt{\rho_{k^{\prime}l^{\prime}}(\delta_{T})\times\rho_{k^ {\prime}l^{\prime}}(\delta_{R})}\,. \tag{46}\]  
Then, the CDF \(F(\gamma\leq x)\) of the output \(SNR\)\(\gamma\) can be analyzed and studied when the eigenvalues of the spatial CCF matrix are expressed as  
\[F(\gamma\leq x)=\frac{1}{\lambda_{1}-\lambda_{2}}[\lambda_{1}(1-e^{x/\lambda_ {1}})-\lambda_{2}(1-e^{x/\lambda_{2}})]\,. \tag{47}\]  
It is well known that the Fourier change of the temporal ACF \(\rho_{kl}(\tau)\) can obtain the Doppler Power Spectral Density (DPSD), \(S_{f}(f)=F_{\tau}\{\gamma_{kl}(\tau)\}\). Hence, the DPSD can be expressed as  
\[S_{f}(f)=F_{\tau}\{\gamma_{kl}(\tau)\}=\int_{-\infty}^{+\infty}\gamma_{kl}( \tau)e^{-j2\pi f\tau}d\tau\,. \tag{48}\]  
From Equation (48), two important metrics to measure DPSD \(S_{f}(f)\), i.e., the average Doppler shift \(f_{shift}\) and the Doppler spread \(f_{spread}\) are defined as the first moment of \(S_{f}(f)\) and the square root of the second central moment of \(S_{f}(f)\), respectively. \(f_{shift}\) and \(f_{spread}\) can be computed alternately by using Fourier transform techniques. Therefore, we have  
\[f_{shift}=\frac{1}{2\pi j\cdot\rho_{kl}(\tau)}\cdot\frac{\partial\rho_{kl}( \tau)}{\partial\tau}|_{\tau=0} \tag{49}\]  
\[f_{spread}=\frac{1}{2\pi}\sqrt{\frac{1}{\rho_{kl}^{2}(\tau)}\cdot(\frac{ \partial\rho_{kl}(\tau)}{\partial\tau})^{2}-\frac{1}{\rho_{kl}(\tau)}\cdot \frac{\partial^{2}\rho_{kl}(\tau)}{\partial^{2}\tau}|_{\tau=0}}\,. \tag{50}\]
# Transient Analysis of a Selective Partial-Update LMS Algorithm
## 2 Proposed SPU-LMS-M-Min for a Single Block
### SPU-LMS-M-Min for Multiple Blocks  
The ensuing derivations facilitates the understanding of the generalized formulation of the algorithm, in which the designer intends to update \(B\) blocks in each iteration. Consider these indexes are denoted by \(\mathcal{I}_{B}=\{i_{0},i_{1},\ldots,i_{B-1}\}\). Equation (8) can be extended by the following _generalized_ optimization problem:  
\[\begin{split}\min_{\mathbf{w}_{\mathcal{I}_{B}}(k+1)}\frac{1}{2}\| \mathbf{w}_{\mathcal{I}_{B}}(k+1)-\mathbf{w}_{\mathcal{I}_{B}}(k)\|^{2}\\ \text{s.t. }e_{p}(k)=\bigg{(}1-\beta B\frac{N}{M}\sigma_{x}^{2} \bigg{)}e(k),\end{split} \tag{13}\]  
where \(\mathbf{w}_{\mathcal{I}_{B}}(k+1)\triangleq\Big{[}\mathbf{w}_{i_{0}}^{T}(k)\quad\mathbf{w} _{i_{1}}^{T}(k)\quad\ldots\quad\mathbf{w}_{i_{B-1}}^{T}(k)\Big{]}^{T}\).  
Using similar steps to those in (9), one may describe the SPU-LMS-M-min algorithm that updates \(B\) blocks by  
\[\mathbf{w}_{\mathcal{I}_{B}}(k+1)=\mathbf{w}_{\mathcal{I}_{B}}(k)+\beta\mathbf{x}_{ \mathcal{I}_{B}}(k)e(k), \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Multiplication** & **Addition** & **Comparison** & **Division** \\
**LMS-Based** & & & & \\ \hline Standard & \(2N+1\) & \(2N\) & \(-\) & \(-\) \\ \hline M-min & \(N+BL+1\) & \(N+BL\) & \(2[log_{2}N]+2\) & \(-\) \\ \hline Periodic & \(N+(N+1)/S\) & \(N+N/S\) & \(-\) & \(-\) \\ \hline Sequential & \(N+BL+1\) & \(N+BL\) & \(-\) & \(-\) \\ \hline Stochastic & \(N+BL+3\) & \(N+BL+2\) & \(-\) & \(-\) \\ \hline M-max & \(N+BL+1\) & \(N+BL\) & \(2[log_{2}N]+2\) & \(-\) \\ \hline Norm. Selective & \(N+BL+2\) & \(N+BL+2\) & \(2[log_{2}N]+2\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 1: Number of multiplications, additions, comparisons (i.e., if one value is greater than another, required to find the maximum or minimum value of a vector), and divisions at each iteration of the algorithms and their respective selective partial update variants [10; 19]. \(S\) denotes the period of coefficient updates (i.e., it is assumed that updates occur only every \(S\) consecutive input vectors).  
where \(\mathcal{I}_{B}\) can be computed by the following rule:  
\[\mathcal{I}_{B} = \arg\min_{J_{B}\in S}\|\mathbf{w}_{J_{B}}(k+1)-\mathbf{w}_{J_{B}}(k)\|^{2} \tag{15}\] \[= \arg\min_{J_{B}\in S}\|\beta\mathbf{x}_{J_{B}}(k)e(k)\|^{2}=\arg\min_{J _{B}\in S}\|\mathbf{x}_{J_{B}}(k)\|^{2}\] \[= \arg\min_{J_{B}\in S}\sum_{j\in J_{B}}\|\mathbf{x}_{j}(k)\|^{2}.\]
## 1 Introduction
Calculation of the electrical output of a boiler in the operating system requires the knowledge of 232 operating parameters. To reduce the dimensionality of the input dataset, principal component analysis is performed. Finally, an extreme learning machine is established to predict the boiler output power, whose inputs are the 16 principal components. To validate the effectiveness of the combined approach of PCA-ELM, it is compared with ELM in testing accuracy and simulation time. Further, the ELM model is designed with various activation functions, and the number of hidden neurons and the results for simulation time and testing accuracy are compared. From earlier statements, the following hypotheses can be levied:  
1. to investigate the prediction of boiler output power in a thermal power plant using an extreme learning machine
2. to reduce the dimensionality of the input dataset by performing principal component analysis
3. to validate the effectiveness of the combined approach of PCA-ELM over ELM and
4. to determine the simulation time and testing accuracy of PCA-ELM designed for various ELM network architectures.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Environmental Monitoring
#### 3.2.2 Adaptability to Dynamic Environments  
Environmental conditions can change rapidly, requiring the monitoring system to adapt [31];
* BA principles can be used where robots adjust their paths using simulated echolocation. This helps them navigate dynamic and challenging environments like forests, wetlands, and underwater ecosystems.
* The flexibility of SI allows robots to respond in real time to changes in the environment, such as shifting weather conditions, water currents, or the presence of obstacles.
## 3 Circuit Design
### Analog Front-End
#### 3.1.1 Noise Analysis and First Stage Sizing
Considering the general expression of the transconductance given by Equation (4) and the total input-referred thermal noise given by the first two terms of Equation (2), the theoretical \(NEF\) for this preamplifier becomes:  
\[NEF_{th}=\frac{\sqrt{2}}{\kappa}\cdot\sqrt{1+\frac{2\gamma\kappa}{\sqrt{IC_{n }}}}\cong 2.15 \tag{8}\]  
where \(\gamma\approx 2/3\), \(\kappa\approx 0.7\) and \(IC_{n}\) is the inversion coefficient of current mirror transistors. In reality, the \(NEF\) of the proposed amplifier is slightly larger compared to this theoretical limit for three main reasons:  
* The transconductance of the input transistors is slightly lower than \(\kappa I_{D}/U_{T}\) since their inversion coefficient is larger than zero. For the present design, the input transistor \(IC\) is \(0.075\) (see Table 1), and consequently the rms input noise and the \(NEF\) increase by a factor of \(1.034\). * The input-referred noise of the overall amplifier is larger by a factor \((1+1/G_{1}+C_{p}/C_{1})^{2}\) than the one of the first operational amplifier, as stated by Equation (1). Taking into account that the parasitic input capacitance is approximately \(0.5\) pF, mainly due to the OTA input transistors, this factor is equal to \(1.065\). A further contribution derives from the strays associated with the input capacitor plates that was drastically reduced by connecting the capacitor bottom plate (which has the largest parasitism) to the amplifier input and by connecting the top plate to the OTA terminal. In this way, a parasitic capacitance larger than \(1.5\) pF was avoided. * The current drawn by the second amplifying stage contributes to the total current in Equation (7) but does not lower the input-referred thermal noise. Therefore, the \(NEF\) increases by a factor of \(\sqrt{(1+I_{2}/I_{1})}\cong 1.037\), where \(I_{1}\) and \(I_{2}\) are the currents drawn by the first and the second operational amplifier respectively. These three factors increase the estimated \(NEF\) of the overall amplifier to about \(2.45\). Finally, note that the adoption of a telescopic cascode amplifier, known to have a small output dynamic, is not a limiting factor for the input signal amplitude. In fact, the positive voltage swing is \(\approx 450\) mV \((|V_{T,p}|+V_{cas})\) while the negative swing is about \(1\) V \((1.5\) V \(-V_{ov,n})\), thus assuring a maximum amplitude for the input signal of about \(7\) mV.
# Review of Adaptive Shock Control Systems
## 2 Literature Review of Adaptive Concepts for Shock Control  
A typical wing of conventional transport aircraft consists of movable parts for flight control such as flaps, slats, ailerons and spoilers. While the slats are located on the leading edge (LE), the flaps (inboard) and ailerons (outboard) form the trailing edge (TE) of the wing. The spoilers are mounted upstream of the flaps and responsible, e.g., for the airbrake, lift dumping and enabling coordinated turns. Hence, they must bear comparable high loads during the landing phase.  
In this section, the results of a literature study are summarized. There are several ways to approach the challenge of designing adaptive SCBs or similar systems for shock control. Since conventional transport aircraft generally use spoilers and flaps for flight control, and since the transonic shock is often located on the spoiler, several concepts focus upon this wing region. Advantages of using a morphing spoiler to form the SCB is that there is already an actuation system present and that there is a good chance of having enough installation space for additional components--in contrary, e.g., to the wing box where an implementation of additional parts would decrease the tank volume or the aileron region where the wing is rather thin.  
As described above, SCBs are the most promising approach for wave drag reduction by weakening the transonic shock which is why most of the following concepts include the formation of a bump on the wing suction side. To get an understanding of this kind of shock control, the aerodynamic phenomenon is depicted in Figure 1.  
Figure 1: Aerodynamic principle to mitigate the transonic shock (**a**) by using a shock control bump (SCB) with smooth transition (**b**) or wedged bump with a kink to form the \(\lambda\)-shock structure (**c**) based on [15; 25] (Ma: Mach number).  
Depending on the design and the shape of the SCB, the main shock is bifurcated by the bump into several isentropic compression waves for a smooth transition from airfoil to bump (Figure 1b) or into the so called lambda shock structure (\(\lambda\)-shock) with a front and a rear leg if the SCB is more wedge-shaped and has a kink on the transition from airfoil to bump (Figure 1c) which is highly beneficial in terms of wave drag reduction [14; 15; 22; 23; 59; 60].  
As [PERSON] et al. [15] investigated, the bump shape plays only a minor role in terms of drag reduction and the SCB location and height are the two most important parameters. However, as also shown in Figure 1, the type of shock interference depends on the shape of the bump. According to [PERSON] [6], the smooth transition leads to isentropic compressions waves and might perform better whilst the kink anchor with a wedge-shaped bump is more robust towards flow condition changes [61]. However, the SCB height, position as well as the location of the crest are very important for an effective and robust bump design [27; 55; 59; 60]. This is why an accurate control of the SCB should be enabled by an adaptive system. The following section reviews several SCB concepts with regard to such functionalities.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 3 Materials and Methods
### Feature Selection  
As discussed earlier, the CES-CICIDS2018 dataset [29] has over 80 features. The large number of input features significantly influences the performance of the deep learning model. Feature selection plays a crucial part in a deep learning model's success. Feature selection is also a pivotal element in the effectiveness of any intrusion detection system (IDS) approach. Its impact is manifold, including reducing model complexity, which minimizes computational costs. Additionally, it simplifies model debugging, thus enhancing the model's interpretation of the learning results. Further, it massively reduces the model training time and improves the training accuracy [32]. We applied Pearson correlation matrix analysis to the CES-CICIDS2018 dataset to calculate the correlation matrix heatmap. This helps identify features that are highly correlated with each other, allowing one to make informed decisions about which features to potentially drop from the data.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
###### Abstract  
Microorganisms capable of accumulating lipids in high percentages, known as oleaginous microorganisms, have been widely studied as an alternative for producing oleochemicals and biofuels. Microbial lipid, so-called Single Cell Oil (SCO), production depends on several growth parameters, including the nature of the carbon substrate, which must be efficiently taken up and converted into storage lipid. On the other hand, substrates considered for large scale applications must be abundant and of low acquisition cost. Among others, lignocellulosic biomass is a promising renewable substrate containing high percentages of assimilable sugars (hexoses and pentoses). However, it is also highly recalcitrant, and therefore it requires specific pretreatments in order to release its assimilable components. The main drawback of lignocellulosic pretreatment is the generation of several by-products that can inhibit the microbial metabolism. In this review, we discuss the main aspects related to the cultivation of oleaginous microorganisms using lignocellulosic biomass as substrate, hoping to contribute to the development of a sustainable process for SCO production in the near future.  
oeaginous microorganisms; microbial lipids; lignocellulosic pretreatment; enzymatic hydrolysis; inhibitors +
Footnote †: journal: Applied Physics Letters  
18 September 202  
that may inhibit microbial growth and/or lipogenesis are released [15]. Several approaches to overcome the inhibitory effects of the by-products of lignocellulosic pretreatment and increase lipid productivities have been proposed. Among them, pretreatment techniques that generate less inhibitory compounds or a detoxification system able to reduce their concentration have been considered [16]. Additionally, genetic and metabolic engineering techniques [17; 18], evolutionary adaptation [19; 20] and the use of specialized microbial consortia [21; 22; 23; 24] offer new approaches to lignocellulosic biotechnological valorization. However, the technological level achieved so far is still insufficient to support cost-effective SCO production.  
The aim of this review is to discuss recent research dealing with lignocellulosic biomass pretreatment and provide a comprehensive view concerning the growth of oleaginous microorganisms and lipid production on substrates of lignocellulosic origin.