esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Estimation of BC and PM Mass  
As part of the quality control procedure, the measured BC and PM masses were validated by comparing to theory predictions. Combustion-generated soot particles have fractal aggregate characteristics. The open structure of the particles creates extra drag on the particles. This results in decreasing effective density (defined as the ratio of the actual particle mass to the spherical volume of the particle calculated from mobility diameter) with increasing mobility diameter. Studies show that the effective density of combustion soot particles could vary from 2.5 g/cm\({}^{3}\) (for 10 nm particles) to 0.24 g/cm\({}^{3}\) (for 300 nm particles) [50; 51; 52; 53; 54; 55]. Here, the mass of the BC particle, \(M_{p}\) (in \(\upmu\)g), was estimated according to [PERSON] et al. [52]:  
\[M_{p}=6.05\times 10^{-24}.D^{2.34} \tag{1}\]  
where \(D\) is the mobility diameter of the particle in nm. Figure 4a summarizes the variations of the calculated BC mass (based on Equation (1)) against the measured BC mass from the LII. The majority of the data points are in good agreement and fall within the region between the slope of 1 and 0.8. Part of the difference could due to the fact that the particles produced from the current setting in this study are slightly different than those produced from [PERSON] et al. [52], which leads to differences in the mass mobility exponent (or sometimes referred to as fractal dimension), which affects the mass calculations. A study from [PERSON] et al. [42] showed that particles generated from the miniCAST under various conditions contain different amounts of organic materials. Therefore, it is reasonable to expect that some of the organic materials fill up part of the void space in the soot aggregates when the aerosol stream is cooled, which also has an impact on the mass mobility exponent.  
Figure 3: Example of the particle number size distribution measured at the (**a**) pre- and (**b**) post-cabin filter locations during a typical experiment.  
Particle mass was estimated here using the assumption of spherical particle and unity density (1 g/cm\({}^{3}\)), and the results are presented in Figure 4b. Observations in Figure 4b show that the spherical particle assumption overestimates the particle mass by as much as 50% while a majority of the data points were overestimating by 20% due to the aggregate nature of the BC particles.
## 3 Proposed System Design  
The modelling steps are explained appropriately to make a compelling design of the proposed system, capable of operating in certain determined conditions. A BLDC motor with 1 HP power and a solar panel of the maximum power capacity of 800 W under standard test conditions (STC) are deliberated. Figure 2 shows a schematic representation of the solar cell. In Figure 2a,b, the simplified diagram and the equivalent diagram of the solar cell are represented, respectively. The current \(I_{ph}\) represents the current generated due to photons. \(I_{ph}\) mainly depends on the incident radiant energy emitted by the sun and its temperature. One single solar cell is designed by using one photon source current, one diode and one set of series and shunt resistors grouped in a series and parallel fashion, forming a photovoltaic cell.  
Figure 2: Schematic representation of the solar cell. (**a**) Simplified diagram of the solar cell; (**b**) Equivalent diagram of the solar cell.
## 2 HGF Model
### Guided Filtering  
The guided filter not only attenuates the noise but also preserves the edge when the guided image is the original image (Figure 2). Thus, it is an edge-preserving filter. Furthermore, it has applications in image fusion [16; 17], image enhancement [18], target detection [19], defogging, and classification [20]. The principle of the guided filter is  
Figure 2: Schematic of the guided filter.  
For an input image \(p\), the output image \(O\) is obtained after filtering using the guiding image \(I\), which acts as the guided filter. For a pixel point at position \(i\), the filtered output is a weighted average, and the guided filter can be represented using the following equation:  
\[O_{i}=\sum\limits_{j}W_{ij}(I)p_{j} \tag{3}\]  
where \(i\) and \(j\) denote the pixel subscripts, and \(W_{ij}\) is the filter kernel associated only with the guided image \(I\). The filter is linear with respect to \(p\).  
An important assumption of the guided filter is that there is a local linear relationship between the output image \(O\) and the guided image \(I\) over a window \(w_{k}\) centered at pixel \(k\).  
\[O_{i}=a_{k}I_{i}+b_{k}\forall i\in w_{k} \tag{4}\]  
where \(w_{k}\) is a square window of size (\(2r+1\)) \(\times\) (\(2r+1\)), \(i\) denotes the pixel index, and \(a_{k}\) and \(b_{k}\) are coefficients that remain constant in the window \(w_{k}\).
# Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking
## 4 Implementation of SDN Controller in Smart Cities  
SDN Controller is the program which serves as a technical control plane within a software-defined network. Generally, this is the network's \"brains\" [31]. To deploy efficient networks, an controller of SDN establishes flow access to switches/routers (via southbound APIs) and applications and business logic (via northbound APIs). The POX controller is used for the implementation of SDN.  
_POX Controller_  
SDN-based IoT network application that sits on top of the SDN controller provides the solution's primary logic. Using the SDN infrastructure, it rapidly generates and controls end-to-end channels of communication from IoT devices to the cloud using TCP/UDP, the program orchestrates cloud resources and controls IoT traffic between different clusters and cloud services. OpenFlow SDN Controllers are included in this Python-based open source framework SDN control program. It enables rapid prototyping and growth [27]. Features that are offered in POX are: Discovery of topology, follows similar visualization tools as NOX, it can run anywhere and is a pythonic open flow interface with support from platform like Windows, MAC OS, Linux.  
Main Functions and Classes of POX Controller  
* **dl_type:** It is used to specify whether the packet is arp (0x806) or IP(0x800) type.
- **dl_src_dl_dst:** It is for specifying layer 2 source and destination MAC address.
- **in_port:** The port through which packet came
- **tp_dst:** This is to specify TCP/UDP destination port
* **hard_timeout:** After how many seconds the flow entry will be removed. The default is no timeout.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variables:** & \(cluster_{i}\), \(req_{i}\), \(TH_{i}\) \\
**Procedure input:** PacketIn Packets accepted from the controller \\
**Begin:** & \(Cluster_{i}\) \\
1 & If \(TH_{i}\) = \(current_{req_{i}}\) \\
2 & send request to \(cluster_{i+1}\) \\
3 & else \(cluster_{i}\) processes the request \\
4 & request received by \(node_{j}\) \\
5 & If \(current_{req}\)\(node_{j}<Max_{req_{node_{j}}}\) \\
6 & \(node_{j}\) process the request \\
7 & else \\
8 & send request to \(node_{j+1}\) \\
9 & **endif** \\
10 & **endif** \\
**End** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm for Cluster IoT nodes mechanism.  
**idle_timeout**: After how many seconds the idle flow entries will be removed. The default is no timeout.
**- priority:** relative importance of flow entries.
**- buffer_id:** The buffer id of the packet.
**- off_packet_out**: It is an OpenFlow message sent by the controller to the switch to send the packet out. The packet sent out could be the one that was received by the switch and delivered to the controller after buffering or the packet created by the controller itself. The main fields are:  
**- data:** raw data that you want to send. No need if sending buffered data.
**- buffer_id:** The buffer id of the packet
**- action**: list of actions in_port: the port on which the packet arrived. Specify OFPP_NONE for the packet created at the controller.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 3 Wireless Power Transfer of Power Industry Optical Wireless Communications
### Other Technologies of Wireless Power Transfer
#### 3.3.2 Laser Emission Wireless Power Transfer  
Laser WPT technology research is moving toward the high transmission power, high transmission efficiency, and long transmission distance development [113]. The transmitting part of laser transmitting WPT system mainly includes laser driver, laser and optical transmitting antenna. The laser is a key part of laser emission. Lasers with a wavelength of around 800 nm are known to be better suited for wireless energy transmission. Currently available high-power lasers with a wavelength of approximately 800 nm include solid-state lasers and semiconductor lasers. As for conversion efficiency, solid-state lasers only reach 40%, while semiconductor lasers can reach 55-60%. Semiconductor lasers have the advantages of high electro-optical conversion efficiency, small size, light weight, and low cost. In recent years, the electro-optical conversion efficiency of AlGaAs semiconductor lasers has been improved steadily. Experiments show that the efficiency can reach 70% and will be higher in the future [114]. The transmitting antenna of laser-transmitting WPT can be designed to approach the diffraction limit and obtain the minimum receiving point as far as possible. In order to reduce the influence of receiving aperture and increase the distance, it is necessary to reduce the emission angle of laser beam.  
Thus, in the transmitting system, it is usually necessary to converge the launching lasers and achieve the requirements of the collimation system through beam forming launching technology. [PERSON] et al. studied the wireless optical energy transmission based on optical beam forming, and the beam forming technology realized by the spatial light modulator realized wireless energy transmission [98]. In order to support the power and distance requirements of the Internet of Things and mobile devices, literature [115] proposed a distributed laser charging (DLC) system. The self-aligning feature of DLC provides a more convenient way to charge the Internet of Things and mobile devices without specific positioning or tracking, as long as the transmitter and receiver are within LOS of each other. In 2017, Japan in [116] put forward a scheme in which a photovoltaic cell is fixed on the top of the car and a solar-pumped laser is used on both sides of the road to receive the solar lamp and directly convert it into a laser, and then the laser is irradiated to the photovoltaic cell to provide energy for the car, as shown in Figure 28 [113].  
Figure 27: Power generation ratio obtained: (**a**) irradiated directly by light source; (**b**) irradiated by mirror [106].
## 5 Discussion  
By now, SPP is still the most popular mode for custom-grade positioning and navigation applications, in which the Saastamoienen model with standard meteorological parameter, the UNB-series models and the EGNOS model are prevailingly applied, other than those newer grid-wise models, e.g., the GPT and VMF series, since frequent data communication and higher operational burden are required by the latter. In contrast, the latter generally have higher accuracy than the former. Therefore, it is worth to anticipate how much the uncorrected residual ZTD would sway the SPP solution in different locations around the world, so that SPP users can optimally determine which ZTD model is used to seek the highest computation efficiency under required positioning precision. For reference, the following recommendations are provided:  
(1) If users just request meter-level horizontal positioning accuracy and are not concerned with vertical positioning accuracy, the simplest Saastamoienen model is preferred.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Altitude** & **\textless{}100 m** & **100–300 m** & **300–500 m** & **500–1000** & **1000–2000** & **\textgreater{}2000 m** \\  & & & & **m** & **m** \\ \hline Num & 185 & 80 & 27 & 58 & 43 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of ICS stations (Num) at different altitudes.  
Figure 11: RMSs of the vertical SPP biases by using the nine models at different heights (unit: cm).  
(2) For users located at 30\({}^{\circ}\)S-90\({}^{\circ}\)S, the Saastamoienen model is a better choice compared with UNB3/UNB3m/EGNOS models.  
(3) For users located near the equator areas, the Saastamoienen model is not an optimal choice, since the SPP biases caused by uncorrected residual ZTD would be at least three times as large as those caused by using other models.  
(4) For users at Northern Hemisphere, UNB3m model would be optimal. Since it can achieve similar accuracy to the saas+GPT series models and the computation efficiency is significantly higher.  
(5) If the vertical SPP biases produced by residual ZTD are required to be lower than 10 cm, only the VMF3 model can be used.  
(6) It is anticipated that the SPP solution on vertical direction would be higher than the real value when using Saas+ models and would be lower than the real value when using UNB3/UNB3m/EGNOS models.
# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Tests on the Swedish Leaf Dataset
#### 3.2.3 Performance Test of the Fusion Method on Swedish Leaf Dataset  
The classification performance of the proposed combination method is evaluated and compared with alternative methods in this experiment. Table 4 shows the classification results of these methods on the Swedish leaf dataset. It can be seen that the classification accuracy obtained by the MEQ + EC-ResNet50 method reaches 100%, which is higher than that of all other compared methods. More concretely, the accuracy of the fusion method is 1.52% higher than that of the shape-and-texture-based MTD + LBP-HF method. Moreover, the classification accuracy of the MEQ + EC-ResNet50 method is 3.17% and 2.46% higher than that of the HGO-CNN and Deep-Plant methods, respectively. Among other leaf-recognition methods, the accuracy of the IMTD + relu5_2 method on the Swedish leaf dataset reaches 99.47%, but it is still about 0.5% lower than that of the MEQ + EC-ResNet50 method. The abovementioned comparison results are sufficient to prove that the MEQ + EC-ResNet50 method has a better classification performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy \%** \\ \hline Dual-path CNN [29] & 96.28\% \\ HGO-CNN [30] & 96.83\% \\ Deep-Plant [31] & 97.54\% \\ MFD [32] & 97.60\% \\ MaskCOV [33] & 98.27\% \\ MTD + LBP-HF [2] & 98.48\% \\ VGG16 + relu5_2 [34] & 98.67\% \\ IMTD + relu5_2 [11] & 99.47\% \\ MEQ + EC-ResNet50 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of classification results of different methods on the Swedish leaf dataset.  
Figure 10: Validation accuracy and validation loss of CNN models on the original and standardized Swedish leaf datasets. (**a–f**) enumerate the validation result curves of AlexNet, GoogLeNet, VGG16, ResNet18, ResNet50, and ResNet101 in turn.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 2 Literature Review  
### EEG-Based Emotion Recognition  
Electroencephalography (EEG) has become a key tool in affective computing, enabling the measurement of neural activity associated with human emotions [4]. Due to its non-invasive nature and high temporal resolution, EEG is widely used for real-time emotion recognition, supporting applications in human-computer interaction (HCI) [2], medical diagnostics [3], and brain-computer interfaces (BCIs) [5]. Research has demonstrated that specific EEG frequency bands, including alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and gamma (30-100 Hz) waves, correlate with different affective states [6].  
Several benchmark datasets facilitate EEG-based emotion recognition research. The DEAP dataset, widely used for emotion classification, provides EEG and physiological signals recorded while participants watched emotionally evocative videos [17]. The SEED dataset, developed by the BCMI laboratory at Shanghai Jiao Tong University, offers EEG recordings from participants viewing emotion-inducing film clips categorised into positive, neutral, and negative states [20].  
Despite advancements, EEG-based emotion recognition remains largely confined to controlled laboratory conditions, limiting its adaptability in real-world contexts [21]. Traditional machine learning models, including support vector machines (SVMs) and random forest classifiers, require extensive feature engineering and preprocessing [22]. Deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated superior classification performance [23], yet their implementation in real-time interactive environments like architectural spaces remains largely unexplored [24].
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Effectiveness and Robustness of the AEs in Novelty Detection
#### 5.1.1 Evaluating the Novelty Detection Task in Normal Operating Conditions
The normalized error is used as the anomaly score. The higher the error, the more anomalous the instance is [25]. The One-Class Support Vector Machine is another unsupervised learning algorithm that is trained only on the \"normal\" data, in our case the negative examples. It learns the boundaries of these points and is, therefore, able to classify any points that lie outside the boundary as outliers [26]. Online clustering can be considered a distance-based novelty detection approach, in which the \"normal\" class is characterized by a small number of prototype points in the data space [9]. During the prediction step, the distance between the \"normal\" points and new points is computed. A threshold is fixed to determine whether the current pattern belongs to the same cluster as the normal one, or creates a new cluster. Discussion. The average results reported in Table 4 show that our approach largely outperforms the state-of-the-art approaches in all scenarios. High F1 score values (100% in most of the cases) are measured with all deep learning implementations. The evaluation in Table 3 shows that, conversely from the competing approaches, the AEs perform well in both tasks of recognizing a novel or a known operating condition. We observe that, to be fair, we applied the same hyper-parameters to all AE approaches in all scenarios (i.e., the ones selected via a grid search analysis to maximize the overall accuracy on all scenarios). The performance of the AE approaches can be significantly improved by applying hyper-parameter fine-tuning to the specific datasets.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rule and Mathematical Analysis of Convergence
#### 3.4.3 Convergence Analysis of the Improved Algorithm  
The improved pheromone update formula introduces an additional term, \(\Delta\omega_{ij}^{*}(t)\), which strengthens the correlation between the pheromone concentration on a given path and its performance in the previous generation. For the shortest path, a larger amount of pheromone is deposited, thereby increasing the likelihood of that path being selected in subsequent iterations. Conversely, for the worst-performing path, the pheromone concentration is decreased, reducing its probability of being chosen. This adjustment aids in preventing the algorithm from becoming trapped in local optima, thereby enhancing overall search efficiency.  
Mathematical comparison of convergence speed; in the classic ant colony optimization (ACO) algorithm, the transition probability for path selection is determined by the following formula:  
\[P_{ij}(t)=\frac{\left(\omega_{ij}(t)\right)^{\alpha}\left(\eta_{ij}\right)^{ \beta}}{\sum_{k\in allowed}\left(\omega_{ik}(t)\right)^{\alpha}\left(\eta_{ ik}\right)^{\beta}} \tag{19}\]  
\(\alpha\): controls the importance of pheromone concentration in path selection. \(\beta\): controls the importance of heuristic information in path selection.  
In the improved formula, the incorporation of the difference between the best and worst paths through \(\Delta\omega_{ij}^{*}(t)\) leads to a more rapid rate of change in pheromone concentration, particularly for the optimal path. As a result, for the optimal path \((i,j)\), the pheromone concentration increases after each iteration according to the following expression:  
\[\omega_{ij}(t+1)^{*}>(1-\sigma)\cdot\omega_{ij}(t)+\sum_{k=1}^{m}\frac{Q}{L_ {min}}-\
u\cdot\frac{Q}{L_{max}} \tag{20}\]  
Compared to the original formula, the increase of \(\omega_{ij}(t+1)^{*}\) is more significant, which makes the path selection probability \(P_{ij}(t)\) more inclined to choose the better paths, thereby accelerating the convergence speed of the algorithm.
## 2 Methodology
### A Concrete Temperature Prediction Model Based on SSA-ISVR
#### 2.2.3 SSA Optimization Algorithm Theory  
Additionally, the SVR model's performance largely depends on the training samples and model parameters. To achieve more accurate results, the SSA optimization algorithm was utilized for the selection of SVR parameters.  
The SSA [33] is an emerging swarm intelligence optimization algorithm proposed by [PERSON] et al. in 2020. This algorithm simulates sparrows' foraging and anti-predation behaviors to update and optimize their positions. Compared to other optimization algorithms, SSA features a simple structure, fast convergence speed, and outstanding performance in escaping local optima [34]. It only requires the configuration of basic parameters such as population size and maximum iterations, thereby reducing the workload of parameter tuning and enhancing the algorithm's applicability and stability.