esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Numerical Models
Alternatively, other studies present an approach for short-term solar irradiance forecasting based on the Physical Solar Model (PSM) v3 in combination with machine learning models. For example, Ref. [49] suggests a hybrid forecast based on gradient-boosting re gression and bootstrap aggregation machine learning models. Similarly, Ref. [163] proposes a hybrid forecast based on the GB gradient boosting method, but in this case, it utilizes the GFS model. Additionally, Ref. [121] evaluates their PV forecasting method with processed historical data using three different mesoscale, global, and regional numerical models (North American Mesoscale (NAM), GFS, Short Range Ensemble Forecast (SREF)). In contrast, Ref. [164] proposes a corrective algorithm to improve the GHI forecasts given by the ECMWF using ANNs. Ref. [165] presents a hybrid forecast model (Hybrid WT-PSO-SVM) that combines wavelet transform, particle swarm optimization, and the SVM with the data from numerical models such as COSMO, WRF, RAMS, and MM5. Several studies compare numerical models with each other to assess their accuracy and reliability. For example, Ref. [166] analyzes three leading models in Australia, namely GFS, the Australian Community Climate and Earth-System Simulator (ACCESS), and ECMWF Reanalysis v5 (ERA5). Ref. [167] evaluates models such as SolarAnywhere, ECMWF, GFS, high-resolution rapid refresh (HRRR), and the national digital forecast database (NDFD), as well as satellite-based cloud motion.
# Psychaocoustic Models for Perceptual Audio Coding--A Tutorial Review
## 3 Coding of Stereo Signals  
### Binaural Hearing  
Spatial or binaural hearing describes the ability of the human auditory system to analyze the spatial aspects of sound, including localization of sound sources and perception of room (environment) acoustics [41]. Sound waves are shadowed and diffracted by the listener's head and pinnae, depending on frequency and direction of arrival. This corresponds to a direction dependent filtering of the signals that are perceived at the two ears. The human brain interprets the resulting monaural and binaural cues to localize sound. Specifically, binaural cues describe the relationship between the two ear signals. The frequency resolution in which these cues can be distinguished corresponds to the critical bandwidth. The most important binaural cues are:* _Interaural Level Differences_ (ILD), caused by shadowing of sound waves by the head (see Figure 5a);
* _Interaural Time Differences_ (ITD) and _Interaural Phase Differences_ (IPD), caused by the different distances that sound has to travel to both ears (\(d\) in Figure 5a);
* _Interaural Cross-Correlation_ (ICC), influenced by diffuse or reverberant sounds (e.g., different path lengths and head shadowing of direct sound and diffuse sound from wall reflection; see Figure 5b)  
The ILD and ITD cues are commonly associated with the perceived localization and lateralization of sound, whereas the ICC relates to the perceived sound source width and the acoustic properties of the surrounding room.  
Spatial hearing can also improve the perception of single sound sources in the presence of spatially distributed masking sources. This is commonly known as the \"cocktail-party effect\", where binaural hearing provides the ability to focus on the voice of one particular talker in the presence of many concurrent talkers [42]. Consequently, binaural hearing can reduce masking; this effect is described by the _binaural masking level difference_ (BMLD) [41]. The BMLD can lead to a reduction of masking threshold of up to 15 dB, as compared to the classic masker/probe experiments, in which both stimuli are presented monaurally. Those \"unmasking effects\" are most prominent for stereo headphone playback (where each tested signal is identically fed into one ear), but also occur for stereo or multi-channel loudspeakers to a lesser extent (where cross-talk between the signals appears before they arrive at the ears). More details about the properties of spatial hearing can be found in previous studies [24; 41; 43].
## 2 Materials and Methods
### Experimental Setup and Cultivation Conditions  
The experiments were conducted using _Haematococcus pluvialis_ cells collected one week after the preparation of the inoculum. Microalgae cells in their logarithmic growth phase were transferred into 250 mL bubble column tube photobioreactors (PBRs), having an outer diameter of 2.9 cm, inner diameter of 2.7 cm, and a height of 56 cm. Each PBR contained 200 mL of fresh medium. A mixture of CO\({}_{2}\) and compressed air (1.5% CO\({}_{2}\) and 98.5% air by volume) was continuously bubbled into the PBRs via a perforated rubber stopper at volumetric flow rate of 30 mL/min, using a cylinder and air pump (Resun, ACO 003, Singapore). The PBRs were kept at room temperature under continuous illumination from fluorescent lamps, providing two different light intensities: 35 umol m\({}^{-2}\) s\({}^{-1}\) and 140 umol m\({}^{-2}\) s\({}^{-1}\). Three sets of experiments were conducted to evaluate growth rate, biomass productivity, and the intracellular concentration of carotenoids and astaxanthin. The details of the experimental setup are provided in Table 2. Each PBR was initially inoculated with a concentration of 0.1 g/L.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BG11** & \multicolumn{3}{c}{**A5 Solution**} \\ \hline NaNO\({}_{3}\) & 1500 mg L\({}^{-1}\) & H\({}_{3}\)BO\({}_{3}\) & 2.86 g L\({}^{-1}\) \\ K\({}_{2}\)HPO\({}_{4}\) & 40 mg L\({}^{-1}\) & MnCl\({}_{2}\)-4H\({}_{2}\)O & 1.81 g L\({}^{-1}\) \\ MgSO\({}_{4}\).7H\({}_{2}\)O & 75 mg L\({}^{-1}\) & CuSO\({}_{4}\).7H\({}_{2}\)O & 0.079 g L\({}^{-1}\) \\ CaCl\({}_{2}\).2H\({}_{2}\)O & 36 mg L\({}^{-1}\) & Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O & 0.39 g L\({}^{-1}\) \\ Na\({}_{2}\)CO\({}_{3}\) & 20 mg L\({}^{-1}\) & ZnSO\({}_{4}\).7H\({}_{2}\)O & 0.222 g L\({}^{-1}\) \\ Citric acid & 6 mg L\({}^{-1}\) & Co(NO\({}_{3}\))\({}_{2}\).6H\({}_{2}\)O & 0.049 g L\({}^{-1}\) \\ Ferric ammonium & 6 mg L\({}^{-1}\) & & \\ EDTA & 1 mg L\({}^{-1}\) & & \\ Trace element A5 solution & 1 mL L\({}^{-1}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of BG11 medium.  
To avoid contamination, all laboratory equipment and glassware were cleaned with water and detergent, thoroughly rinsed with distilled water, sterilized in an autoclave at 121 \({}^{\circ}\)C for 20 min (Hirayama, HVE-50, Ramsey, MN, USA), and then placed in a laminar flow hood (Airtech SW-CJ-2 FD, Jangsu, China) under UV light for 60 min. Additionally, sterile disposable pipettes and loops were used during the process.  
The growth of microalgae was monitored daily over six consecutive days by measuring biomass (g L\({}^{-1}\)) and chlorophyll content. At the end of the cultivation period, final dry weight, carotenoids, and astaxanthin content in the culture were measured.
## 2 Materials and Methods
### Permeability Calculation  
Geometry data were then used to calculate permeability by simulating fluid flow using the Lattice Boltzmann method (LBM). The LBM describes fluid behaviour by simulating fluid molecules on a grid based on the distribution of several particles using the Boltzmann equation. In LBM simulations, fluid behaviour results from the flow of particle collisions on the grid. The simulation was conducted using the Palabos software (version 2.3.0) [32]. The three-dimensional fracture geometry was converted into an input file for the Palabos simulation to establish boundary conditions. Initially, the fluid flow had zero velocity, and the inlet and outlet of the geometry had a constant pressure difference. The simulation used the D\({}_{3}\)Q\({}_{19}\) movement scheme in which D\({}_{3}\), corresponding to the simulation, is three-dimensional and Q\({}_{19}\) indicates the 19 directions of particle movement. The general form of the LBM is shown in Equation (7):  
\[f_{i}(x+c_{i}\Delta t,t+\Delta t)=f_{i}(x,t)+\Omega_{i}(f) \tag{7}\]  
where \(f_{i}(x,t)\) represents the density distribution moving in the \(i\)-th direction with velocity as a discrete velocity vector \(c_{i}\) heading to the nearest point \(x+c_{i}\Delta t\) at time \(t+\Delta t\). Particle movement is also affected by collisions described using the collision operator, \(\Omega_{i}(f)\). The Bhatnagar-Gross-Krook approximation was used to determine the movement and collision of fluid particles and the collision operator is expressed as follows:  
\[\Omega_{i}(f)=-\frac{f_{i}-f_{i}^{eq}}{\tau}\Delta t \tag{8}\]  
where \(f_{i}^{eq}\) is the equilibrium distribution function and \(\tau\) is the relaxation time. The equilibrium distribution function is shown in Equation (9), where \(c_{s}\) is the lattice speed of sound, \(u\) is macroscopic velocity,
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
### Methodology and Literature Selection Criteria  
This review employed a systematic and thematic approach to identify, evaluate, and synthesize relevant studies on the integration of computer vision (CV) and artificial intelligence (AI) in collaborative robotics. The initial search was conducted across major academic databases, including Scopus, Web of Science, IEEE Xplore, and MDPI's Applied Sciences, using keywords such as human-robot interaction, collaborative robots, robotic vision, semantic scene understanding, deep learning in robotics, and sensor fusion for HRC.  
This process retrieved approximately 1800 records published from 2015 through May 2025. Exclusion criteria were applied in several stages: (i) removing non-English papersand non-peer-reviewed sources; (ii) filtering out works unrelated to collaborative robotics (e.g., purely industrial automation or general computer vision without HRC context); and (iii) excluding duplicate entries and low-relevance conference abstracts. This screening narrowed the corpus to around 250 articles for full-text assessment.  
After detailed evaluation of their technical depth, real-world applicability, and relevance to the cobot paradigm, 138 studies were selected for inclusion in this review. These covered diverse topics such as object detection, human pose estimation, scene understanding, visual SLAM, reinforcement learning, and explainable AI as applied to collaborative robotics. A bibliometric analysis revealed notable trends. Publication volume has grown markedly since 2018, reflecting accelerating interest in vision-AI fusion for human-robot collaboration. The included studies span contributions from Europe, North America, and Asia, with strong research clusters in Germany, China, the United States, and Japan. Topical trends indicate increasing emphasis on multimodal sensor fusion, proactive and anticipatory human-robot collaboration, explainable AI, and real-time adaptive planning.  
This multi-stage selection strategy ensures that the review synthesizes a representative, high-quality, and globally relevant body of literature, enabling a comprehensive assessment of current capabilities, challenges, and future directions in vision-AI-enabled collaborative robotics.
## 2 Materials and Methods
### Image Evaluation  
Three observers (two oral and maxillofacial radiologists and one endodontist) with four to eleven years of experience analyzed digital radiographs and CBCT images in a dimly lit room without any time restriction affecting the quality of their judgment. The observers were unaware of the study groups. Images were displayed on a 20\" MSI all-in-one MS-AA8A monitor (MSI, New Taipei, Taiwan) with \(1600\times 900\) pixels resolution. The calibration procedure was performed by demonstration and discussion on five radiographs not related to the current study. Digital radiographs were exported from the Scanora software (Soredex, Tuusula, Finland) and saved in Joint Photographic Experts Group (JPEG) format to be randomly displayed for each observer. CBCT images were analyzed using the dedicated NNT software (NewTom, Verona, Italy). The observers could scroll in axial, sagittal, and coronal planes. In addition, they had the ability to use cross-sectional images in every desired plane. The observers were also allowed to change the magnification, brightness, contrast, and sharpness of all the image sets if needed (Figures 1 and 2) An observer's judgement was recorded by a 5-point scale as follows: (1) definitely present; (2) probably present; (3) uncertain/unable to tell; (4) probably absent; and (5)
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 4 Detection with CESDD
### Mean-Shift-Based NMS  
Directly applying the proposed CESDD-based classifier to all window patches would likely produce highly overlapped detections around target vehicles, as shown in Figure 5(a). To deal with this issue, the mean-shift-based NMS is adapted and applied.  
The detections from the previous classification are assigned bounding box information and scores of confidence. Mean-shift-based NMS initially transforms the detections into points in a \(x\)-\(y\)-scale 3D space, with \(x\) and \(y\) representing coordinates in the image plane and _scale_ representing the scale level in the pyramid. Then, a kernel density estimation map is built by weighted combination of 3D Gaussian distributions centered at these 3D detection points, with the weights as monotonically increasing functions of the detection confidence scores. Then, from the initial positions of the detections in the 3D space, all detection points are moved in the map according to gradients; this moving process will be iterated several times until convergence where all 3D points have reached modes in the map. Finally, the reached modes will be regarded as desired detections. The detailed process could be found in [7]; this process is identical to a mean-shift process.  
Some adaptations are necessary to make the mean-shift-based NMS to fit into the case of vehicle detection in this work. Firstly, the initial detections are not of the same aspect ratio. Thus, for each found mode from the mean-shift-based NMS, the aspect ratios of the group of detection points having reached the same mode are averaged; the average aspect ratio is applied to the output detection bounding box inferred from the mode. Apart from this, modes having attracted very few detection points, like one or two, tend to be false positives and should be dropped, because true vehicle targets always attract more detections around them. This could be observed in Figure 2. Furthermore, a proper threshold should be set to screen out modes with too low kernel density estimation values which correspond to detection confidences.
## 3 Results and Discussion  
### Chemical Equilibrium  
The equilibrium parameter values, Ae and Be for the C\({}_{4}\) and C\({}_{5}\) series, as well as for the reaction schemes of the C\({}_{6}\) series presented in Tables 3 and 4, respectively, were determined following the methodology detailed in Section 2.1.  
The validation of this methodology was performed by solving an equilibrium reactor for each hydrocarbon series (C\({}_{4}\) and C\({}_{5}\)) and the reaction schemes of the C6 series. A base feed of 150 kmol/h was used for calculations, and the equilibrium compositions were determined for each temperature within a range from 100 to 200 \({}^{\circ}\)C.  
The model exhibits satisfactory agreement with experimental data for both the C\({}_{4}\) and C\({}_{5}\) series and the studied schemes of the C\({}_{6}\) series. Figure 1 presents the equilibrium reactor modeling results for schemes 1 and 8. The overlap between the calculated curves and the experimental data [20] confirms the validity of the model. Furthermore, in the C\({}_{6}\) series, the individual behavior of each compound can be analyzed while preserving information on the specific distribution of isomers, in contrast to the 2-MP + 3-MP representation [17].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reaction** & \(\mathbf{A}\bigg{(}\frac{\mathbf{mol}}{\mathbf{m^{3}h}}\bigg{)}\) & \(\mathbf{E}\bigg{(}\frac{\mathbf{kJ}}{\mathbf{kmol}}\bigg{)}\) & \(\mathbf{A_{e}}\) & \(\mathbf{B_{e}}\) \\ \hline \(nC_{6}\leftrightarrow 2-MP\) & \(2.0911\times 10^{16}\) & 78,300.00 & \(-2.6386\) & \(1580.30\) \\ \(nC_{6}\leftrightarrow 3-MP\) & \(2.2400\times 10^{17}\) & 86,300.00 & \(-2.9883\) & \(1464.30\) \\ \((2)nC_{6}\leftrightarrow 2-MP+3-MP\) & \(4.8600\times 10^{19}\) & 80,600.00 & \(-5.6357\) & \(3073.50\) \\ \(nC_{6}\leftrightarrow 2,3-DMB\) & \(2.9142\times 10^{13}\) & 74,921.73 & \(-4.0006\) & \(1690.20\) \\ \(nC_{6}\leftrightarrow 2,2-DMB\) & \(3.4772\times 10^{15}\) & 79,357.62 & \(-4.1394\) & \(2285.10\) \\ \(2-MP\leftrightarrow 3-MP\) & \(9.3100\times 10^{15}\) & 86,300.00 & \(-0.3496\) & \(-116.09\) \\ \(2-MP\leftrightarrow 2,3-DMB\) & \(5.2100\times 10^{15}\) & 81,800.00 & \(-1.3620\) & \(109.86\) \\ \(2-MP+3-MP\leftrightarrow(2)2,3-DMB\) & \(6.8600\times 10^{16}\) & 81,600.00 & \(-2.3747\) & \(335.96\) \\ \(2-MP\leftrightarrow 2,2-DMB\) & \(9.1100\times 10^{16}\) & 91,800.00 & \(-1.5014\) & \(705.26\) \\ \(3-MP\leftrightarrow 2,3-DMB\) & \(2.2400\times 10^{17}\) & 86,300.00 & \(-1.0125\) & \(226.02\) \\ \(2,3-DMB\leftrightarrow 2,2-DMB\) & \(5.5137\times 10^{18}\) & 89,900.00 & \(-0.1388\) & \(594.89\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kinetic constants and equilibrium parameters of the reactions in the C6 series schemes.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & \(\mathbf{A}\bigg{(}\frac{\mathbf{mol}}{\mathbf{m^{3}h}}\bigg{)}\) & \(\mathbf{E}\bigg{(}\frac{\mathbf{kJ}}{\mathbf{kmol}}\bigg{)}^{*}\) & \(\mathbf{A_{e}}\) & \(\mathbf{B_{e}}\) \\ \hline \(1\) & \(2.1404\times 10^{15}\) & 79,100.00 & \(-2.1384\)
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 3 THz Channel Modeling
### Deterministic Channel Model for THz Band
They predict the propagation of electromagnetic waves through magnetic and electric fields and, in contrast to the RT technique, [PERSON]'s equations incorporate antennas and other structures. Various authors have explored [PERSON]'s equations to model THz electromagnetic wave propagation. For example, in [97; 98], numerical analysis techniques that approximate the magnetic and electric fields in temporal and spatial domains for THz waves are proposed. Those techniques, denoted FDTD, can resolve small and complex scatterers and rough surfaces in the THz band. However, this approach relies on computers' memory capacity, as they provide numerical solutions to the electric and magnetic field equations used to model the THz electromagnetic wave propagation.
## 3 Experimental Results and Discussion  
### Failure Mode  
The overall performance of the specimens was satisfactory. Indeed, the reinforcement of the blocks simulating the old and new concrete was sufficient to prevent cracks not related to the interface behavior. As intended, the first recorded crack, in all specimens, formed along the interface. It is important to note that the opening of that crack was measurable and visible for small values of the imposed shear slip, in some cases as small as 0.10 mm (Figure 4a). However, as shown in detail in the following Sections, the width of the crack along the interface is affected by the parameters investigated within the experimental program.  
In several specimens (Table 2), an inclined crack or multiple inclined cracks occurred in block \"B\" (Figure 4b,c) following cycles at increased slip values. This crack, visible for an imposed shear slip approximately equal to 0.50 mm, was initiated at the interface (at the location of one of the interface bars) and propagated within block \"B\" with an angle of approximately 45\({}^{\circ}\). In some cases, especially where the embedment length of the bars was small, the occurrence of this crack did not allow the test to continue. The formation of such oblique cracks was observed by other researchers too ([PERSON] et al. [4], [PERSON] and [PERSON] [14], [PERSON] et al. [29]) and their occurrence was attributed to the effect of high normal stresses on the interface, either subjected to an external compressive stress [14] or provided with a high percentage of reinforcement [4; 29].  
In one case (Specimen Re-26/B/6/0.1), provided with post-installed bars anchored at 6d in the old concrete, a crack parallel to the interface formed in block \"A\", simulating the existing concrete. This crack occurred at a distance almost equal to the anchorage length of the post-installed bars (Figure 4c). Similar cracks parallel and eccentric to the interface were also observed in other tests, in specimens which had been reinforced with post-installed bars of limited embedment length ([PERSON] et al. [4], [PERSON] et al. [30], [PERSON] [31]).  
Figure 3: Position of LVDTs to measure shear slip and crack opening.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 5 Factors Affecting Acoustic Methods in Pipeline Localization
### Background Noise  
There is no doubt that detection results of acoustic methods can be susceptible to interference from noise, such as that produced by aircraft, automobiles, trains, and electrical transformers. For most locators in commercial use, filters are often built in receivers to intercept external noises. In addition, the employment of phase information rather than sound intensity provides an option to obtain rather robust results against the interference of background noise.
## 3 Results and Discussion
### Development of Prediction Models  
Based on the findings in Section 3.2, the soil properties, including S, P, CaO, Mn, TFe\({}_{2}\)O\({}_{3}\), TOC, and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\), were selected as the input variables and the BAF\({}_{\rm Zn}\) as the output variable for the model development. The dataset of 371 rice grain and root soil samples from the study area was divided into 80% for the training set and 20% for the validation set to build two models: the artificial neural network (ANN) and random forest (RF).  
The ANN model structure is illustrated in Figure 5, with two hidden layers containing six and four neurons, respectively. The larger first layer and smaller second layer allow for the extraction of low-order features in the first layer, which are then used to extract higher order features in the second layer, potentially improving the ANN performance. The RF model had 800 trees (ntree = 800) and three features considered at each split (mtry = 3). The feature importance for the RF model is shown in Figure 6, where mtry plays a more significant role in optimizing the model compared to ntree.  
Figure 5: ANN model.  
The models' accuracies were assessed using R\({}^{2}\), the NME, the MRE, and the RMSE, and the results are summarized in Table 3. Both the ANN and RF models had an NME of 0.01, indicating that both models slightly overestimated the BAF\({}_{\rm Zn}\) values. The ANN model had MRE and RMSE values closer to zero than the RF model, although the difference was small. The predicted BAF\({}_{\rm Zn}\) values from both models were highly correlated with the measured values, and the predicted-to-observed ratio was nearly 1:1 (Figure 7). The R\({}^{2}\) value for the ANN model was slightly higher than for the RF model, indicating that both models are robust and provide reliable predictions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Validation Sample Size** & **R\({}^{2}\)** & **NME** & **MRE (\%)** & **RMSE** \\ \hline Artificial Neural & 75 & 0.88 & 0.01 & 11.02 & 0.06 \\ Network & 77 & 0.85 & 0.01 & 15.09 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of various models.  
Figure 6: Importance score in random forest model.