esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Optimization Hybrid Algorithm
### OPF Solutions IEEE 30-Bus Network [56]
#### 4.1.4 Case 4: Minimization of Real Power Loss and Fuel Cost  
Here, the performance of the TLTFWO algorithm is assessed, where the objective function is formulated such that the quadratic cost function and active power loss are minimized based on Equations (16) and (17). Thirty tests are executed in simulations to solve  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel Cost (USD/h)** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** \\ \hline HFAJAYA & 832.1798 & 0.4378 & 10.6897 & 0.8578 \\
[72] & 832.5596 & 0.4372 & 10.6823 & 0.8539 \\ SP-DE [76] & 832.4813 & 0.43651 & 10.6762 & 0.75042 \\ PSO [77] & 832.6871 & - & - & - \\ TFWO & 832.6598 & 0.4382 & 10.9105 & 0.8410 \\ TLBO & 832.7624 & 0.4380 & 10.9397 & 0.8322 \\ TLTFWO & 832.1584 & 0.4378 & 10.6856 & 0.8618 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal results found by the TLTFWO in Case 3.  
Figure 7: Convergence trends in Case 3.  
the OPF problem repetitively using TLTFWO. Equation (50) gives the objective function of OPF:  
\[\begin{split} J_{4}&=\sum\limits_{i=1}^{NG}\alpha_{i}+b _{i}P_{Gi}+c_{i}P_{Gi}^{2}+\phi_{p}\sum\limits_{i=1}^{NTL}\sum\limits_{ \begin{subarray}{c}j\,\equiv\,1\\ j\,\equiv\,1\end{subarray}}^{NTL}\ G_{ij}\,V_{i}^{2}\,+\ B_{ij}V_{j}^{2}\,-2V_{i} \,V_{j}\cos\delta_{ij}+\,\lambda_{P}(\,P_{G1}-P_{G1}^{\text{lim}}\,)^{2}\\ &\qquad+\lambda_{V}\sum\limits_{i=1}^{NL}\,(\,VL_{i}-VL_{i}^{ \text{lim}})^{2}+\,\lambda_{Q}\sum\limits_{i=1}^{NG}\,(\,Q_{Gi}-Q_{Gi}^{\text{ lim}})^{2}+\,\lambda_{S}\sum\limits_{i=1}^{NTL}\,(\,S_{i}-S_{i}^{\text{lim}})^{2} \end{split} \tag{50}\]  
here \(\phi_{p}=40\) is set, similar to [56].  
Table 3 shows the optimal settings of control variables. Additionally, the convergence behavior of the best result obtained for fuel cost from the implemented algorithm can be provided in Figure 8. Table 7 compares the performance of the proposed TLTFWO algorithm with some other techniques already mentioned throughout the article. The values of fuel cost and active power loss in the case of utilizing the proposed method are 859.0075 (USD/h) and 4.5295 (MW), respectively.  
Figure 8: Convergence trends for Case 4.  
According to Table 5, one can understand that the overall objective function found by the TLTFWO is significantly smaller than those of the previous research reports.
## 2 Methods
### Preprocessing  
Principal component analysis (PCA) is a simple yet powerful non-parametric method for extracting meaningful information from complex datasets. Typically, PCA efficiently defines new features and reduces dimensionality to uncover hidden or simplified structures suitable for classification algorithms during preprocessing [38]. However, given the unique nature of our dataset, which comprised categorical or binary variables instead of continuous ones, PCA was used to convert these variables rather than reduce or adjust dimensions. Raw collected data often contain noise, making them unsuitable for direct input into a deep neural network (DNN) classifier. Therefore, we utilized the PCA maximum-attribute filter to transform binary or categorical variables into continuous variables. This approach effectively minimized data discretization, resulting in a cleaner dataset for model training. Figure 2 illustrates the first and second principal components derived from the data: Figure 2 depicts a non-scaled PCA, while Figure 2 shows a PCA with a quantile transformer scaler. The combination of a DNN and PCA-quantile transformer scaler provided the best performance, demonstrating the effectiveness of this preprocessing strategy in handling our dataset's unique characteristics.
# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 2 Photovoltaic Systems  
PV systems, composed of semiconductor materials, are devices that directly convert sunlight into electrical energy upon exposure to solar radiation. When sunlight strikes the semiconductor material, electron movement occurs between the \(p\)-type and n-type regions, generating an electric current. This is the fundamental operational principle of PV conversion. In grid-connected systems, the generated energy is transferred to the grid after being converted to AC by an inverter, as seen in Figure 1.  
Figure 1: PV system.  
PV systems offer advantages such as long lifespan, environmental friendliness, modularity, and relatively low maintenance and operational costs. However, their efficiency is somewhat limited, and various environmental factors impact their performance. Parameters such as temperature, geographical location, surface cleanliness, and shading are among the key environmental factors affecting PV system performance [12].  
Since the efficiency of PV panels is highly dependent on solar irradiance, different technologies such as bifacial panels, solar tracking systems and maximum power point tracking (MPPT) techniques have been developed to maximize energy yield [1,3].  
In addition to solar radiation, dust accumulation, clouds and shading significantly impact the efficiency of PV systems [1,2,13,14,15]. Dust on the panel surface reduces the effectiveness of solar radiation absorption, while shading and cloud cover limit the available sunlight.  
Humidity is another significant environmental factor, as it not only reduces the amount of solar radiation reaching the panel but also leads to technical issues such as corrosion [16,17].  
Temperature variations also play a crucial role in PV performance, as excessive heat can reduce efficiency, whereas wind may help mitigate temperature effects and naturally remove dust and other particulates [18,19,20,21].  
Due to these environmental dependencies, effective energy management and forecasting are crucial for PV power systems. Proper forecasting enables efficient energy planning, cost reduction, and improved grid stability. While PV systems are a promising renewable energy source, their intermittency remains a challenge, highlighting the necessity of accurate power predictions.  
To address this challenge, numerous studies have been conducted on precise energy forecasting. These studies employ various techniques and approaches to achieve reliable power estimation. Recently, AI techniques, including ANN, fuzzy logic, genetic algorithms, and Extreme Learning Machines (ELM), have been utilized for power forecasting [4, 5, 6, 7, 8, 9, 10, 11]. These advancements contribute to more reliable PV power integration into the energy grid.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.6 Graph Generation  
These methods took advantage of the graph theory applied to input data structures. They modeled the environment into graph structures and were able to predict the room segmentation or classification with graph processing techniques, like node clustering or adjacency statistics. An example is presented in Figure 10. Graph generation was found in 12 articles. [PERSON] and [PERSON] [190] and [PERSON] et al. [191; 194] used them to process the trajectory of a robot in the environment to add semantic meaning to visited places. Similarly, [PERSON] et al. [19] used a motorized tripod for RGBD panoramic picture acquisition. They processed the data to generate a graph of the environment structure for the room segmentation process. [PERSON] et al. [180] equipped their robot with vision sensors prepared for artificial label recognition and used it to create a multilevel graph,  
Figure 9: Visualization of the Floor Plan Generation task. Input data (recorded user trajectory, **left**) is used to estimate a possible floor plan—relying on the paths’ density and their clustering (**right**).  
representing the global topology of the building, and to detect connections between small items in the same local area. [PERSON] et al. in [13; 14] focused on graph generation for topological building representation and its structure decomposition from a pure 3D point cloud. [PERSON] et al. in [80] and later [PERSON] and [PERSON] in [82] took the input structure of the undirected graph representing the indoor environment and discussed how to analyze the graph to segment rooms and predict the global structure of a building. [PERSON] et al. [139] used a Voronoi diagram method for automatic graph creation from a 2D grid map. The proposed \"Area Graph\" was used as a segmentation technique. [PERSON] and [PERSON] [138] presented a similar solution, but added Alpha Shape-based processing to the initial diagram and used it for topology graph improvement. Another complex solution was presented by [PERSON] et al. [67]. They combined a neural network with a distance transform algorithm to reconstruct the topological meaning of indoor space partitions from input RGBD pictures and their colorful point cloud representations.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 3 Results  
### Vegetation Cover Map  
The resulting vegetation cover map (Figure 3) and the derived surface and altitude metrics (Table 1) show specific vegetation patterns for the Valais. Deciduous trees are concentrated in valleys at lower altitudes and correspond to a proportion of 9.3% (i.e., 484 km\({}^{2}\)) of the entire Valais area. Coniferous trees are mainly located at higher altitudes and cover 13.8% of the carton territory, which corresponds to 722 km\({}^{2}\). Grasslands are the most widespread vegetation class with an 1176 km\({}^{2}\) surface area, which is equivalent to 22.5% of Valais. They are mainly located above the tree line, although they are also present in valleys. Their mean altitude is above 2000 m. Shrublands are the vegetation class with the higher altitude mean (i.e., 2103 m). They also have the smallest surface area with a proportion of 2.5% (i.e., 133 km\({}^{2}\)) of the entire Valais. Altogether, these four vegetation classes represent 49.1% of the Canton, the rest being mainly bare rocks, water, and built-up areas.
# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 4 Experiments and Analysis
### Deep Learning-Based Detection Methods and Their Key Hyperparameters  
CNN-SAD [23] and DL-DRA [25] are two representative deep learning-based detection methods. They consist of one layer and two layers of convolutional neural network, respectively. Both have excellent performance when detecting various types of recommendation attack. The reason for choosing these two methods to improve in our experiments are shown as follows. The key hyperparameters are manually and clearly analyzed and determined in both methods. These operations provide a basis for our experiments to select which key hyperparameters to optimize automatically.  
In CNN-SAD algorithm, a rating vector is reshaped into a rectangle as the input of the network. The length of the rectangle denotes the number of items in a recommender system. As a key hyperparameter, the size, i.e., the long side and the short side, of the rectangle is confirmed by manual analysis. Since the long side can be determined by the short side for a rectangle with a fixed perimeter, the short side is selected as one key hyperparameter for the automatic optimization in our experiments.  
In DL-DRA algorithm, the rating vector is resized into a square as the input of the network using the bicubic interpolation technique. The size of the square is a key hyperparameter as shown in the DL-DRA algorithm. In our experiments, we select the size of the square as one key hyperparameter for the automatic optimization.  
In both CNN-SAD and DL-DRA algorithms, the activation function, batch size, and epochs are three key hyperparameters which are manually optimized. The settings and combinations of these hyperparameters can greatly affect the detection performance of the detection algorithms. Therefore, we also set them as key hyperparameters for the automatic optimization in our experiments.  
Based on above discussions, key hyperparameters with their search spaces used in our experiments for automatic optimization are shown in Tables
## 3 Results and Discussions
### Simulation Study
#### 3.1.2 Second-Order Process Model
On the other hand, PI-PD controllers tend to have higher overshoot and longer rise times, but they offer faster settling times. FOPID is known for its precise control among fractional controllers, while FOPI-FOPD is renowned for prioritizing stability with minimal compromise in settling and rise times. In the case of complex sequential controllers, each variant has its own characteristics, making it difficult to choose a controller that caters to specific application requirements. In set-point tracking, the comprehensive evaluation of the figures and table shows that the proposed complex-order controller consistently outperforms its counterpart and performs well. This advantage is not limited to individual cases but extends across target value tracking scenarios. The controller's subtle adjustments and dynamic responsiveness in complex sequences provide the best overall performance and exceed the capabilities of its controller counterpart. This empirical validation highlights the inherent effectiveness of complex sequential controllers in accurately handling various set-point changes, making them ideal for applications where accuracy and adaptability to varying conditions are important performance criteria. Complex-order controllers have proven to be a transformative force in set-point tracking, promising improved control dynamics and greater adaptability in the face of evolving operational needs. Figure 6: Second-order process model’s step response with compared controllers. Figure 7: Second-order process model’s tracking response with compared controllers.
# Computational Evaluation of Shock Wave Interaction with a Liquid Droplet
## 1 Introduction
The governing equations consist of two continuity equations for the two different phases, the mixture momentum and energy equations, and the volume fraction advection equation [16; 18]. As far as turbulence modeling is concerned, either under-resolved direct numerical simulation (DNS) or large eddy simulation (LES) approaches are usually followed [6; 14]. The main goal of the present work is the computational evaluation of the initial stages in the interaction process between a traveling air shock front and a water droplet following a relatively lighter approach, where the viscous effects are not neglected. Namely, the mean turbulent flow in a virtual shock tube device with circular cross section is simulated by solving the unsteady Reynolds-averaged Navier-Stokes (RANS) equations, supplied with a suitable turbulence closure model. The process is considered in the shear-stripping regime, that is, at relatively high Weber and very low Ohnesorge numbers. In this study, a 3D computational model is developed, which represents the natural extension of our previous two-dimensional work [13]. Numerical calculations are conducted by employing one of the CFD solvers that are commonly and successfully used for building virtual wind tunnels in industrial fluid dynamics research, namely, Ansys Fluent [19]. The compressible flow governing equations are solved by means of a finite volume (FV) numerical technique [12; 20], where the transient tracking of the air-water interface is approximated on the fixed FV mesh by using the volume of fluid (VOF) method [21]. The present CFD modeling procedure is validated against reference data that are provided by both high-fidelity numerical solutions [15; 16] and experiments [22; 23]. The rest of this manuscript is organized as follows. After describing the overall computational model in Section 2, the results of the numerical simulations are presented and discussed in Section 3. Finally, in Section 4, some concluding remarks are drawn.
## 3 Survey Results
### Sensor Note: External Memory  
Used external memory characteristics are described in Table 7. While external memory of several megabytes is available on most sensor motes, it is actually seldom used (only in 25% of deployments). Motes often perform either simple decision tasks or forward all the collected data without caching. However, these 25% of deployments are still too many to be completely discarded.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Nr** & **Codename** & **Available external memory, KB** & **Secure (SD)** & **Digital** & **External memory used** & **File system used** \\ \hline
1 & Habitats & 512 & n & y & n \\
2 & Minefield & 16,000 & n & y & y \\
3 & Battlefield & 512 & n & n & n \\
4 & Line in the sand & 512 & n & n &? \\
5 & Counter-sniper & 512 & n & n & n \\
6 & Electro-shepherd & 512 & n & y & n \\
7 & Virtual fences &? & y & y & y \\
8 & Oil tanker & 0 & n & n & n \\
9 & Enemy vehicles & 512 & n & n & n \\
10 & Trove game & 512 & n & n & n \\
11 & Elder RFID & 512 & n & n & n \\
12 & Murphy potatoes & 512 & n & n & n \\
13 & Firewxnet & 512 & n & n & n \\
14 & AlarmNet & 512 & n & n & n \\
15 & Ecuador Volcano & 1,024 & n & y & n \\
16 & Pet game & 512 & n & n & n \\
17 & Plug & 0 & n & n & n \\
18 & B-Live & 0 & n & n & n \\
19 & Biomotion & 0 & n & n & n \\
20 & AID-N & 1,024 & n & n & n \\
21 & Firefighting & 1,024 & n & n & n \\
22 & Rehabil & 1,024 & n & n & n \\
23 & CargoNet & 1,024 & n & y & n \\
24 & Fence monitor & 0 & n & n & n \\
25 & BikeNet & 1,024 & n & y? & n \\
26 & BriMon & 1,024 & n & y & n \\
27 & IP net & 1,024 & n & n & n \\
28 & Smart home & 512 & n &? & n \\
29 & SVATS & 512 & n & n & n \\ \hline \hline \end{tabular}
\end{table}
Table 7: Deployments: external memory.  
_Design rule 13_: External memory support for user data storage at the OS level is optional; yet, it should be provided.  
Although very popular consumer products, Secure Digital/MultiMediaCard (SD/MMC) cards are even less frequently used (in less than 10% of deployments). The situation is even worse with filesystem use. Despite multiple sensor network filesystems already being proposed previously [70, 71], they are seldom used. Furthermore, probably, there is a connection between the (lack of) external memory and filesystem usage--external memories are rarely used, because there is no simple and efficient filesystem for these devices.  
_Design rule 14_: A convenient filesystem interface should be provided by the operating system, so that sensor network users can use it without extra complexity.
# Practical Implementation of a Blockchain-Enabled SDN for Large-Scale Infrastructure Networks
## 5 Experiments and Results
Figure 2 provides informative insights regarding how many packets were captured from different size intervals, how much they represent from the total number of packets and which is the mean value for the size of a packet from the mentioned categories. As can be seen in Figure 8, the packet sizes are within the middle sections of the distribution curve, indicating that the network is efficiently handling traffic. In the experimental part, a total of 1173 packets were exchanged, with multiple TCP three-way handshakes indicating various conversations. The TCP three-way handshake duration ranged from 4.19 to 16.48 milliseconds, with 18 to 21 TCP segments transmitted between nodes until connection closure. UDP packets were also captured, usually exchanged after TCP connections were established or closed, with varying lengths and timing. Additional connections and interactions with other blockchain nodes were observed, but they did not significantly impact specified node communication. Background interactions between nodes remained consistent in timing, with regular TCP connections and intermittent UDP datagram exchanges. A communication delay was noted, attributed to route differences causing varying durations in the three-way handshake process between hosts. From the conducted experimental work, it can be noticed that both TCP and UDP communication between the Geth-enabled nodes is encrypted (Figure 9). Figure 8: Packet sizes during communication between two SDN blockchain nodes. Figure 7: The graphic of the exchanged packets over a period of time. To conclude the information provided regarding the experimental work in this section, it can be noted that the communication among the nodes appears to be robust, without deviations or problems that would disrupt the information exchange, with default encryption handled by the Geth protocol which manages the underlying communication endpoints. Also, the packet sizes usually fall in the average section, which means that the data exchange is expected to be balanced. Taking into consideration the points that were just mentioned, the integration of the blockchain node inside the SDN controller is anticipated to impose a manageable computational overhead on the hosting SDN controller.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 3 CW Phosphate Fiber Lasers and Amplifiers
### Yb-Doped Phosphate Fiber Lasers and Amplifiers  
The first CW cladding-pumped Yb-doped single-mode phosphate fiber laser emitting in the 1 \(\upmu\)m region was demonstrated in 2006 by [PERSON] et al. [8] using a Large Mode Area (LMA) fiber with diameters of 10/240/355 \(\upmu\)m for the core, inner cladding and outer cladding, respectively. In view of breaking the symmetry of the fiber design and thus increasing the pump absorption, the core of the fiber was offset from the center and an air hole parallel to the core was added to the inner cladding. With a length  
Figure 4: Schematic of cladding-pumped double-cladding fiber laser.  
of 84.6 cm of this fiber, doped with 12 wt % of Yb\({}_{2}\)O\({}_{3}\) and pumped with 80 W at 940 nm, an output power of nearly 20 W at 1.07 \(\upmu\)m, with a slope efficiency of 25.8% against launched pump power, was demonstrated. With this very same fiber configuration, few years later, power scaling to 57 W was demonstrated with a slope efficiency of 50.6% by varying the pump wavelength to 977 nm, where Yb\({}^{3+}\) absorption is much higher and quantum defect is lower [43]. In both reported Yb-doped fiber lasers, the slope efficiency was reduced by the quite high loss of the fiber, around 3 dB/m, which was measured to be mainly due to impurity absorption (77%) and scattering.  
Recently, an Ytterbium-doped phosphate glass single-mode fiber laser with noticeably high slope efficiency of 66.6% in relation to the launched pump power was reported. The fiber consisted in a double-cladding fiber with a 19 \(\upmu\)m diameter step-index core and a pump waveguide with a high numerical aperture due to the external air-cladding. An output power of 11.6 W was obtained by using 4 cm-long fiber sample pumped with a multi-mode laser diode. This is the best result, in terms of slope efficiency, ever reported for a phosphate single-mode fiber laser pumped with a multi-mode laser diode [44].  
Concerning Yb\({}^{3+}\)-doped phosphate fiber amplifiers, the first watt level Yb\({}^{3+}\)-doped phosphate fiber MOPA was reported in 2009 [43] with an output power of 16.3 W corresponding to a gain of 27 dB with respect to the launched signal power (\(\sim\)0.36 dB/cm). The gain medium was a 74.5 cm-long single-mode double-cladding phosphate fiber doped with 12 wt % of Yb\({}_{2}\)O\({}_{3}\).
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.4 Demand-Response Charging  
Demand-response charging capabilities may be included in advanced DC fast charging infrastructure. With this mode of control, the charging station can dynamically modify the charging power depending on the capacity of the grid and the electricity demand [129]. Reducing the charging power during heightened demand or when the electrical grid is strained allows for grid resources to be utilized efficiently.