esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Blended Biochar Pellet by the Use of Nutrient Releasing Model
###### Abstract  
For the recycling of biomass conversion materials, this experiment was conducted to investigate plant nutrient releasing characteristics, and to determine an optimum blended ratio of biochar for producing a biochar pellet based on a column leaching study. The treatments consisted of only pig manure compost (PMC) as a control, pig manure compost pellets (PMCP), and biochar pellets (BCP) blended with biochar and pig manure compost with the following ratios: 9:1, 8:2, 4:6, and 2:8. Results showed that the accumulated amount of ammonium nitrogen (NH4-N) was in order of PMC > PMCP > BCP (2:8) > BCP (4:6) > BCP (8:2) > BCP (9:1) ratios. The highest accumulated amounts of phosphate phosphorus (PO4-P) and potassium (K) were 1953 and 1917 mg L\({}^{-1}\) in the PMC and PMCP, but the lowest in the BCP (9:1) were 223 and 1078 mg L\({}^{-1}\), respectively. It was shown that the highest accumulated amount of silicon dioxide (SiO2) was 2329 mg L\({}^{-1}\) in the BCP (8:2), but the lowest in the PMC was 985 mg L\({}^{-1}\). The estimations for accumulated NH4-N, PO4-P, K, and SiO2 releasing amounts in all the treatments were significantly fitted with a modified Hyperbola model. The optimum mixing rate was estimated to be BCP (2:8). Therefore, biochar pellets might be useful in obtaining basic information on slow-release fertilizer for sustainable agriculture.  
biochar pellet; modified Hyperbola model; nutrient release; pelletization; pig manure compost Article
## 3 Computational Approach  
The computational approach is structured as a clear progression of steps: initially, the generation of weights, followed by calculating similarities and, finally, recommending resources. At the outset, a user-item preference matrix is created using both tag and time data. This is followed by the derivation of three distinctive user similarities based on the weight type. The culmination of this process is the recommendation of resources, evaluated through specific metrics.  
Digging deeper into the resource recommendation phase, there are two meticulous sub-stages. Initially, neighbors are identified. The CF system recognizes users who exhibit preferences akin to the primary user by employing an array of similarity calculation techniques. The information retrieval domain has yielded various similarity measurement paradigms, with three standing out prominently: Cosine, Jaccard, and Pearson [56; 57; 58]. Based on the chosen similarity measure's outcomes, certain users showcasing paramount similarity are designated as neighbors. It is worth noting that the number of these neighbors profoundly influences the CF's resultant quality [37]. It is imperative for recommendation systems to discern an optimal neighborhood size for effective predictive outcomes. A scanty neighborhood size can jeopardize accuracy. Conversely, an extensive neighborhood, while potentially elevating accuracy, can complicate computations [59]. In the subsequent phase, an array of methodologies is available to amalgamate neighbor ratings to forecast a score for items that the primary user has not rated. Each neighbor's preference rating is typically modulated by the similarity value determined earlier. Essentially, the closer the similarity between a neighbor and the target user, the more profound their impact on the prediction. Post prediction, items that garner the top N predictive scores are put forth as recommendations. The efficacy of the recommendation systems is then gauged by juxtaposing the recommended items against the target users' testing sets.
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication
### VLC Challenges
Through ceiling lamps, Li-Fi enables high-speed internet connections within a limited range, typically within a few meters between the ceiling and the office floor. Li-Fi technology can provide multiple Gbps connections. The fundamental principle behind Li-Fi is the conversion of internet data into light signals for transmission, which are then received by the recipient device (e.g., mobile terminal) and converted back into electrical signals and digital data. Localization  
VLC-based localization is particularly suitable for indoor environments where GPS systems cannot operate effectively. The presence of LED lighting ubiquitously in indoor spaces, including subways, buildings, hospitals, and airports, presents unique opportunities for VLC-based indoor localization. By utilizing visible light identifiers and extending the system when identifier coordinates are available at the transmitter, VLC-based localization can accurately determine the position of objects or individuals within a room. Different methods can be employed for localization, such as optical tracking and imaging, as well as triangulation/trilateration. A key requirement for all localization applications is the ability of the receiver device to capture signals transmitted from each lighting unit separately [11,58,61]. * Smart Homes  
Smart homes facilitate seamless communication between residents, government entities, infrastructure, the economy, and the environment [58,59]. While most components of a smart city are already in place, achieving integration of all these components necessitates high-speed and reliable wireless communication. VLC technology can leverage existing indoor lighting sources to provide indoor hotspots capable of delivering extremely high data rates. This enables essential services and sustainable networking capabilities within smart homes while preserving the valuable radio frequency spectrum used by smartphones. To ensure smooth integration of VLC technology in smart home applications, a three-layer VLC-based communication structure has been proposed [62]. In this architecture, VLC is used in the first layer to provide users with access and situational awareness. The second layer facilitates communication between different LEDs and sub-gateways. The final layer enables optical communication between the service gateway and multiple sub-gateways. Based on this architecture, various applications have been developed, including event monitoring, intelligent communication, and detection and tracking.
## 3 Results and Discussion
### Analysis of the Orthogonal Experiment Results  
In this section, the orthogonal experiment including three factors (the work parameters) and three levels was designed. Table 2 presents the factors and levels. Moreover, the erosion volume and erosion depth of HBS were taken as evaluation indexes and were analyzed by the range analysis method. The orthogonal experiment results are shown in Table 3.  
Figure 4: Model mesh of the jet source, water domain, and HBS. (**a**) Jet source and water domain; (**b**) the domain of HBS.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 2 Global System Overview
### General Description of the 3D Ultrasonic Receiver  
A 3D ultrasonic receiver assembly has also been designed. It is composed of three receivers, \(R_{A}\), \(R_{B}\), and \(R_{C}\), placed in the three faces of a tetrahedron. This distribution has been adapted to capture ultrasonic transmissions coming from as many directions as possible. All three receivers are wire-synchronized, whereas the general aspect of this 3D  
Figure 1: General view of the 3D scenario considered with three ULPS units installed on three perpendicular planes of the environment and a mobile receiver.  
Figure 2: General view of LOCATE-US ULPS unit.  
ultrasonic receiver is shown in Figure 3a. Three buffers (SD memory) have been added to each receiver to store the acquired signals, where receiver \(R_{A}\) is the master and receivers \(R_{B}\) and \(R_{C}\) are slaves.  
The ultrasonic receiver is a small and portable device, consisting of: an omnidirectional MEMS PU0414 HR5H-SB microphone [28] with suitable response at 41.67 kHz; an STM32F103 module to filter the received signals with a high-pass filter; and an analog-digital converter at a sampling rate of 100 kHz. Figure 3b presents a single ultrasonic receiver [18]. The omnidirectional MEMS microphone at every face of the 3D assembly receives the ultrasonic signals of similar strength from all the beacons, while minimizing the near-far effect (thanks to the beacons' distribution). Moreover, all the ultrasonic links have a similar channel model and, consequently, all inter-symbol (ISI) or multiple-access interferences (MAI) are similar. Each receiver is able to capture a buffer with a length of 0.1 s at 100 kHz, so this acquisition window includes at least a complete transmission from the beacons in the ULPSs [18], where a receiver's position estimate can be obtained in a PC. This feature actually constrains the maximum position update rate of the proposed system to 10 Hz.
## 4 Numerical Calculation
### Influence of Pile-Soil Contact Surface Detachment  
When the slenderness ratio of the pile is 20, Figure 6 illustrates the deformation and internal force of the laterally loaded long pile in the layered foundation under different pile-soil contact surface properties. To analyze the effect of pile-soil contact surface separation on the mechanical properties of the pile, three conditions were considered: (1) no contact surface separation: the pile maintains full interaction with the surrounding soil throughout its length; (2) complete separation of the pile-upper soil contact surface: in this case, the contact surface between the pile and the upper soil layer is fully separated, simulating a detachment at the pile top; (3) complete separation of the pile-middle soil contact surface: for this condition, only the second soil layer (midsection of the pile) experiences complete separation, simulating detachment at the middle of the pile, no contact surface separation, complete separation of the pile-upper soil contact surface, and complete separation of the pile-middle soil contact surface. The results show that the separation of the pile-upper soil contact surface has the most significant impact on the mechanical properties of the pile. The pile head displacement increases from 2.5 mm to 5.0 mm, an increment of approximately 100%; the pile head rotation increases by 2 times; the maximum bending moment increases  
Figure 5: Effect of parameter \(\lambda\) on the mechanical characteristics of a laterally loaded long pile. (**a**) The displacement. (**b**) The rotating angle. (**c**) The bending moment. (**d**) The shearing force.  
from 1577.6 kN\(\cdot\)m to 3155.2 kN\(\cdot\)m; and the negative shear force increases by 3 times. In comparison, the separation of the pile-middle soil contact surface has a smaller effect, with pile head displacement increasing to 3.1 mm, an increment of approximately 24%; the maximum bending moment increases by only 13%; and the negative shear force rapidly decreases to near zero. These results indicate that the separation of the pile-upper soil contact surface causes the pile to lose critical support, leading to a significant increase in pile head deformation and a rapid increase in internal forces. On the other hand, the separation of the middle soil contact surface mainly affects the distribution of internal forces in the middle region of the pile but has a smaller effect on pile head deformation. Therefore, in practical engineering, particular attention should be given to monitoring and controlling the contact state between the pile and the upper soil layer to prevent contact surface separation from causing a decline in pile performance.
###### Abstract  
The diagnosis of epilepsy primarily relies on the visual and subjective assessment of the patient's electroencephalographic (EEG) or intracranial electroencephalographic (iEEG) signals. Neurophysologists, based on their experience, look for characteristic discharges such as spikes and multi-spikes. One of the main challenges in epilepsy research is developing an automated system capable of detecting epileptic seizures with high sensitivity and precision. Moreover, there is an ongoing search for universal features in iEEG signals that can be easily interpreted by neurophysologists. This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. The study presents standard approaches for analyzing iEEG signals, including chaos theory, energy in different frequency bands (alpha, beta, gamma, theta, and delta), wavelet transform, empirical mode decomposition, and machine learning techniques such as support vector machines. It also discusses modern deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are most commonly used in the field of seizure detection. Detection results were tested on a separate dataset, demonstrating classification accuracy, sensitivity, precision, and specificity of seizure detection. The best results for seizure detection were obtained with features related to iEEG signal energy (accuracy of 0.97, precision of 0.96, sensitivity of 0.99, and specificity of 0.96), as well as features related to chaos, Lyapunov exponents, and fractal dimension (accuracy, precision, sensitivity, and specificity all equal to 0.95). The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). Additionally, the use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection.  
EEG; iEEG; epilepsy; seizure; deep learning; seizure detection Article
## 2 Materials and Methods
### Image Quality Calculation  
For a more reasonable comparison of the image quality using different scanning protocols, the fluence of each protocol was adjusted so that the image noise of different protocols was normalized to the same as the scanning protocol without any attenuation or current modulation. Then, the noise power spectrum (NPS) or NPS isotropy, which can demonstrate the noise magnitude, texture or spatial resolution directionality, was used as an index to compare the image quality [37]. Through Fourier transformation, the 2D NPS was first calculated for the largest central square region of the same imaging task using different scanning protocols. Then, the radial profile of the NPS was sampled from the 2D NPS at an interval of 1\({}^{\circ}\). The area under the 1D NPS profile at each angle can be calculated as:  
\[A(\varphi)=\int_{0}^{\infty}NPS_{\text{1D}}(\varphi)kdk \tag{3}\]  
where \(\varphi\) is the polar angle, \(k\) is the spatial frequency and the NPS is calculated as follows:  
\[NPS=\frac{b_{x}b_{y}}{L_{x}L_{y}}|DFT[g(x,y)-g_{\text{bkg}}(x,y)]|^{2} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scanning Protocol** & **Attenuator** & **TCM** \\ \hline NoAtt\_tcm0 & No & Off \\ SB\_tcm0 & Static bowtie filter & Off \\ FDB\_tcm0 & Fluid dynamic bowtie filter & Off \\ NoAtt\_tcm1 & No & On \\ SB\_tcm1 & Static bowtie filter & On \\ FDB\_tcm1 & Fluid dynamic bowtie filter & On \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scanning protocols are simulated in this study.  
where \(b_{x}\), \(b_{y}\) are the pixel dimensions, \(L_{x},L_{y}\) are the number of ROI voxels in each dimension, \(DFT\) refers to the discrete Fourier transform and \(g\) is the single image acquisition. Then, the NPS score isotropy is estimated as:  
\[\epsilon_{\text{NPS}}=\langle\frac{A(\varphi)}{A_{\text{max}}}\rangle \tag{5}\]  
where \(A_{\text{max}}\) is the maximum \(A(\varphi)\).
## 2 Materials and Methods  
The initial SAW experiment was conducted in the absence of metal powder additions to establish a base case (BC) as reference. Then, for the same welding conditions of welding heat input and using the same weld wire, metal powders (Al and Ni) were added to investigate the chemical interaction of nickel with aluminium. The latter is added to control the partial oxygen pressure at the weld pool-molten slag interface to control the weld pool oxygen content.  
The welded plate was sectioned at the middle position of the weld run to remove cross section samples. A band saw was used to make sample cuts. Individual volume samples were cut of the whole weld bead from each cross section sample by using a manual saw, following surface preparation and 2% Nital solution etching to reveal the weld metal bead boundary in the base plate. One volume sample was analysed for major element analyses by inductively coupled plasma optical emission spectrocopy (ICP-OES). The second volume sample was used for total oxygen and carbon analysis by the combustion method. A third weld metal cross section sample was analysed by SEM (scanning electron microscope) following polishing to a mirror finish and etching with 2% Nital solution to reveal the weld metal boundary in the base plate. The SEM equipment consisted of a Zeiss (Oberkochen, Germany) crossbeam 540 FEG (Field emission gun) SEM with EDS (energy dispersive spectrometer) operated at 20 kV.  
The flux selected for the welding tests is a commercial agglomerated flux of composition in Table 2. The main consideration in the selection of this flux, following initial testing of different fluxes, is its thermodynamic stability in the presence of aluminium metal [23]. This flux is classified as an aluminate basic flux (Basicity Index (BI) = 1.4) and its chemistry and mineralogy were extensively investigated previously [23]. This flux is agglomerated and has a 0.2-1.6 mm agglomerate particle size. Metal powders of pure Al (99.7% Al and 1 mm) supplied by Sigma-Aldrich, and pure Ni (99.5% Ni and 0.25 mm) supplied by GoodFellow were used.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
###### Abstract  
In the past years, many techniques have been researched and developed to detect and identify the interference sources of Global Navigation Satellite System (GNSS) signals. In this paper, we utilize a simple and portable application to map interference sources in real-time. The results are promising and show the potential of the crowdsourcing for monitoring and mapping GNSS interference distribution.  
interference detection; Android; GNSS software receivers +
Footnote †: journal: Sensors
# High Brightness Diode Lasers Based on Beam Splitting and Polarization Combining
## 4 Discussion  
In summary, we demonstrated a new approach to enhance the brightness of a diode laser by beam-waist splitting and combining. It shows the noticeable advantages over the method based on reducing the divergence angle in the lateral direction [24; 25; 26], including the low power loss, significant brightness improvement, and simple device structure. The applicability of this approach was demonstrated in the diode laser of single emitter and laser array after combining. In total, 46% and 48% improvement of beam quality in slow axis were realized in these two types of laser sources with 9.5% and 7% power penalty. The brightness 42.4 MW cm\({}^{-2}\) sr\({}^{-1}\) and 195.8 MW cm\({}^{-2}\) sr\({}^{-1}\) were demonstrated in a single emitter and a commercial BA diode laser array combined by SBC. The brightness of these two types of laser source were enhanced 84.21% and 80.6%, respectively. We believe these results will contribute to the development of high brightness diode laser.
## 3 Results
### Effects of HRT on CW Effluent Quality  
In runs 16-19 from 26 November through 25 December 2022, wastewater containing 50 mg/L RY86 was treated in the CWs under HRTs of 3, 1, 10, and 15 days. The air temperature, evapotranspiration ratio, and the effluent RY86 concentrations for these runs are also presented in Figure 2. The above-ground parts of the plants withered in winter, but the roots remained. Root elongation of cattail and papyrus after run 19 in the CWs was also depicted in Figure 1. The roots and stem of both cattail and papyrus were colored yellow, indicating that RY86 was attached on and uptaken by the plants.  
For run 16 at 13 \({}^{\circ}\)C, the evaporation/evapotranspiration ratio was only 2% in the unplanted CW and 6-9% in the planted CWs with a 3 day HRT. The RY86 concentration was 47.8 mg/L in the unplanted CW, 35.5 mg/L in the Cattail-CW, and 26.7 mg/L in the Papyrus-CW. For run 17 at 16 \({}^{\circ}\)C, the evaporation was only 4% in the unplanted CW; evapotranspiration in the planted CWs was 1-4% with HRT of 1 day. The RY86 concentration was 48.2 mg/L in the unplanted CW, 40.5 mg/L in the Cattail-CW, and 33.2 mg/L in the Papyrus-CW. For run 18 at 9 \({}^{\circ}\)C, the evaporation ratio was 10% in the unplanted CW; evapotranspiration in the planted CWs was 25-30% with a 10 day HRT. The RY86 concentration was 49.2 mg/L in the unplanted CW, 33.3 mg/L in the Cattail-CW, and 22.0 mg/L in the Papyrus-CW. Finally, for run 19 at 6 \({}^{\circ}\)C, the evaporation/evapotranspiration was 13% in the unplanted CW and 52-55% in the planted CWs with a 15 day HRT. The RY86 concentration was 50.0 mg/L in the unplanted CW, 30.8 mg/L in the Cattail-CW, and 21.7 mg/L in the Papyrus-CW.  
Effects of HRT on RY86 removal in the CWs for 50 mg/L RY86 are summarized in Figure 5. The RY86 removal was less than 12% in the unplanted CW, even with a 15 day HRT. With increased HRT from 1 day to 15 days, the RY86 removal increased from 22% to 71% in the Cattail-CW and 34% to 81% in the Papyrus-CW.  
Figure 4: Effects of the R86 concentration in wastewater on the RY86 mass removal in CWs under HRT of 5 days.
## 4 Results and Experiments
### Dataset Description and Preprocessing Overview
#### 4.1.1 NF-UNSW-NB15-v2 Dataset  
The NF-UNSW-NB15-v2 dataset, as described in Section 3.1, captures diverse network behaviors, including normal and malicious traffic across various attack types, providing valuable features for IDS development. However, it faces challenges such as missing values, duplicates, and class imbalance, which are addressed through preprocessing outlined in Section 3.2. This included handling missing values, eliminating duplicates, applying outlier detection techniques like z-score and LOF, performing feature selection to reduce dimensionality, and normalizing numerical features using MinMaxScaler. Advanced resampling methods, such as ADASYN for oversampling and ENN for undersampling, were applied, along with dynamic class weights during training to improve class representation. This comprehensive preprocessing optimized the dataset for both binary and multi-class classification tasks.