esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Acoustic Model
#### 2.1.1 Acoustic Model Output  
The output of the acoustic model is a semitone-scale pitch activity tensor for each voice type and a pitch shifting tensor, given by \(P(p,v,t)=P(t)P(p)P_{t}(p|v)\) and \(P(f,p,v,t)=P(t)P(v)P_{t}(p|v)P_{t}(f|p)\), respectively. By stacking together slices of \(P(f,p,v,t)\) for all values of \(p\), we can create a 20-cent resolution time-pitch representation for each voice type \(v\):  
\[P(f^{\prime},v,t)=P\bigg{(}f^{\prime}\ (\text{mod }5)+1,\bigg{\lfloor}\frac{f^{ \prime}}{5}\bigg{\rfloor}+21,v,t\bigg{)} \tag{8}\]  
where \(f^{\prime}\in\{0,...,439\}\) denotes pitch in 20-cent resolution. The voice-specific 20-cent resolution pitch activation output is given by \(P(f^{\prime},v,t)\), and the overall multi-pitch activations without voice assignment are given by \(P(f^{\prime},t)=\sum_{v}P(f^{\prime},v,t)\). The 20-cent resolution multi-pitch activations \(P(f^{\prime},t)\) are converted into multi-pitch detections, represented by a binary matrix \(B(f^{\prime},t)\), through a binarisationprocess with a fixed threshold \(L_{th}\). Specifically, pitch activations whose values are greater than \(L_{th}\) are set to one in matrix \(\mathbf{B}\), while all others are set to zero.  
This binarised matrix \(\mathbf{B}(f^{\prime},t)\) is then post-processed in order to obtain more accurate pitch activations. In this step, we scan each time frame of the matrix \(\mathbf{B}\), replacing the pitch candidates by the position of spectrogram peaks detected from \(X_{o,t}\) and that are validated by a minimum pitch distance rule:  
\[(\Delta_{peals}(\mathbf{X_{t}},\mathbf{B}(f^{\prime},t))<T_{1})\vee(\Delta_{peals}( \mathbf{X_{t}},\mathbf{B}(f^{\prime},t-1))<T_{2}), \tag{9}\]  
where \(\mathbf{B}(f^{\prime},t)\) represents each binarised pitch activation at time frame \(t\). The function \(\Delta_{peals}\) in (9) indicates the minimum pitch distance between the selected list of peak candidates in \(\mathbf{X_{t}}\) and each pitch candidate \(\mathbf{B}(f^{\prime},t)\) and \(\mathbf{B}(f^{\prime},t-1)\), respectively. In our experiments, we use \(T_{1}=1\) and \(T_{2}=3\), based on density distributions of \(|\Delta_{peals}|\), which were estimated from measurements in our datasets using the pitch ground truth. The use of the previous frame (\(t-1\)) helps to keep the temporal continuity when a pitch candidate is eventually removed by the \(L_{th}\) threshold.
## 2 Literature Review
### Clustering Heads Selection Protocols
The results have been used to optimize the shortest path using a genetic algorithm (GA), which uses an objective function consisting of a network to define the optimal path. In [13], the authors use game theory to choose CH by sending data with game theory incentives and detecting the position of other nodes. Then, using game theory, high-priority data about the proposed method is delivered. In NS3 software, the simulation results show that the suggested technique has satisfactory results in comparison to the artificial bee colony algorithm (ABC), cuckoo search algorithm (CS), firefly algorithm (FA), grey wolf optimization algorithm (GWO), and GA. In [14], the authors provide a novel CH selection method based on (GWO), which considers residual energy and sink distance. The presented approach is validated in several WSN scenarios by varying the number of sensor nodes and CH. The observed findings indicate that the offered method beats existing algorithms to achieve a top network performance. In the paper [15], the author proposes a fuzzy decision-making method for choosing CHs. Decision-making with fuzzy multiple attributes (MADM) is based on three criteria: residual energy, the distance between the nodes to the BS, and the neighbour's number, the method chooses the suitable CHs. In homogenous settings, the simulation results show that this technique outperforms the distributed hierarchical agglomerative clustering (DHAC) protocol in terms of network lifespan extension.
# Aerodynamic Drag Study of the Heat Exchange Equipment with Different Fin Geometries
## 6 Conclusions  
For the patented design of the fins, a computer simulation was carried out in the SolidWorks Flow Simulation environment, with the coefficients of aerodynamic resistance determined for three variants of its location in the flow. The averaged values are given in the work and allow estimation of the influence of the orientation of the fins on the aerodynamic resistance.  
The analysis of the simulation results made it possible to classify the fins and assign them to bodies with poor flow, which is expressed in high values of the frontal drag coefficient \(C_{X}\). An exception is the situation with orientation in flow from a larger diameter to a smaller one for the turbulent flow regime, in which a drop in the value of the drag co-efficient is observed.  
The lowest value of the drag coefficient is observed when the fins are oriented from a larger diameter to a smaller one. In the laminar regime (Re < 2300), the average value of \(C_{X}\) = 1.04, in the transitional regime (2300 < Re < 10,000), \(C_{X}\) = 0.74, and in the turbulent regime (Re > 10,000), \(C_{X}\) = 0.22.  
The research allows us to conclude that, in practice, during the design of heat exchange elements, it is rational to use fins of the proposed geometry by orienting them in the flow of the coolant exclusively from a larger to a smaller diameter. This orientation of the fins gives the lowest values of the drag coefficient over the entire range of Reynolds numbers.  
The results of this study will be useful for researchers and engineers who design a heat exchanger in which finned tubular elements are use, e.g., radiators of cooling systems for computer chips and processors and devices in which advanced heat and mass exchange surfaces are used as regular nozzles (scrubbers, absorbers, and bioreactors).  
Conceptualization, S.K. and I.K.; Data curation, S.K. and V.S.; Funding acquisition, I.K.; Investigation, I.K., S.K. and V.S.; Methodology, S.K.; Resources, V.M.; Software, S.K.; Validation, I.K.; Writing--original draft, S.K.; Writing--review & editing, I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained with the article.  
The authors
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.4 Effect of Temperature on the Adsorption Process  
To investigate the influence of temperature on the adsorption process, three different temperatures were studied (25 \({}^{\circ}\)C, 45 \({}^{\circ}\)C and 65 \({}^{\circ}\)C), working at a concentration of 50 mg/L and the natural pH of the solution. The obtained results are presented in Figure 11 and Table 7.  
The experimental data presented in Figure 11 show that the increase in temperature determined an increase in adsorption capacity, indicating that the adsorption of anionic dyes on PM4 is an endothermic process. This can be explained vie the reduced aggregation of the dye by the increased temperature, facilitating the diffusion of dye molecules into the pores of the adsorbent [61; 62]. On the other hand, as the temperature increased from 25 to 65 \({}^{\circ}\)C, the time required to reach equilibrium decreased from 160 to 100 min for AY 42, and from 110 to 70 min for AR 213 (Figure 11, Table 7). High adsorption yields were obtained even at room temperature (Table 7): 73.77% for AY 24 and 87.53% for AR 213.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dye Concentration**} & \multicolumn{2}{c}{**AY 42**} & \multicolumn{2}{c}{**AR 213**} \\ \cline{2-5}  & **R** & **\(t_{e}\)** & **\(R\)** & **\(t_{e}\)** \\  & **(\%)** & **(min)** & **(\%)** & **(min)** \\ \hline
10 & 95.01 & 40 & 92.97 & 40 \\
30 & 86.29 & 100 & 90.55 & 60 \\
50 & 73.27 & 160 & 87.53 & 110 \\
100 & 69.76 & 180 & 74.99 & 150 \\
150 & 52.73 & 210 & 55.02 & 170 \\
200 & 38.79 & 250 & 45.53 & 210 \\
250 & 32.46 & 290 & 33.53 & 220 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influence of the initial concentration of dye solutions on the adsorption capacity of PM4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{2}{c}{**AY 42**} & \multicolumn{2}{c}{**AR 213**} \\ \cline{2-5}  & **R** & **\(t_{e}\)** & **\(R\)** & **\(t_{e}\)** \\  & **(\%)** & **(min)** & **(\%)** & **(min)** \\ \hline
25 & 73.77 & 160 & 87.53 & 110 \\
45 & 95.84 & 140 & 94.77 & 90 \\
65 & 98.81 & 100 & 97.86 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Influence of temperature on the adsorption capacity of PM4.
## 5 Conclusions and Future Works  
In this paper, we address the connectivity problem in WSNs. More specifically, we proposed a novel localized adaptive fuzzy-logic topology control algorithm, called FTC, to control the communication range of sensor nodes in WSNs, with the purpose of achieving the desired node degree (namely the  
Figure 8: Average maximum communication range (k = 3).  
Figure 7: Average communication range (k = 3).  
Figure 9: Energy expended ratio (\(EER\)) (k = 3).  
number of one-hop neighbors a node has), therefore in turn improving the network connectivity. Unlike other ways to design fuzzy-logic control system, the fuzzy-logic controller of FTC is constructed from a training data set. One of the great advantages of this approach is that it is easier to design a feasible controller without complicated parameter adjustment for the fuzzy-logic controller, especially when the network is highly dynamic.  
FTC has been compared with four representative localized algorithms: NONE, FLSS, k-neighbors and LTRT. The simulation results show that FTC is able to trace the desired node degree as k-Neighbor algorithm which is based on the locations of neighbors, but FTC does not depend on location information. On the contrary, NONE, FLSS, and LTRT are unable to achieve the desired node degree. The average communicating range of FTC is very close to FLSS and k-Neighbor, but lower than NONE and LTRT. It implies that the energy consumption of FTC is lower than that of NONE and LTRT. In addition, the average maximum communication of FTC is the lowest, which means that the highest energy-consuming node in the network running FTC protocol consumes the lowest power than running other algorithms. The Energy Expended Ratio (EER) of FTC is very close to LTRT, but higher than FLSS and k-Neighbor. In summary, without knowing the neighbor location information, our localized FTC shows the capability of achieving desired node degree, maintaining network connectivity, and it is energy-efficient.  
In this paper, all nodes are stationary after they are deployed. In our future works, we will take into account the mobility of sensor nodes. Furthermore, our validations are based on computer simulations. In order to validate FTC protocol, real experiments will be carried out on our Sun SPOT sensor platform in the near future. The node deployment is very unlikely to be an ideal random and uniform distribution, so we will calculate all sensor nodes' locations through computer simulation prior to deploying them in the field.
## 2 Methodology  
### Model  
The model of the soil-structure interaction system (SSI) consists of a superstructure, a three-dimensional rectangular foundation, and a horizontally multi-layered half-space, as shown in Figure 1. The superstructure is simplified to a single-degree-of-freedom (SDOF) oscillator, and its dynamic properties are characterized by mass \(M_{\rm b}\), spring coefficient \(k_{\rm b}\), damping coefficient \(c_{\rm b}\) and height \(H\), so the fixed-base frequency is \(\omega_{\rm b}=\sqrt{k_{\rm b}/M_{\rm b}}\) and the damping ratio is \(\xi_{\rm b}=c_{\rm b}/2M_{\rm b}\omega_{\rm b}\). The foundation is represented by a rigid rectangular foundation of mass \(M_{0}\), width \(a\), length \(b\) and embedment \(c\). The half-space consists of a soil layer of thickness \(D\) on a bedrock, and each sub-layer, as well as the bedrock, is assumed to be elastic and isotropic. The material properties of the \(j\)th sub-layer are characterized by shear-wave velocity \(\beta_{j}\), Poisson's ratio \(\
u_{j}\), mass density \(\rho_{j}\) and damping ratio \(\xi_{j}\) (\(j=1\), \(2\), \(3\), \(\ldots\)), and those of the bedrock are characterized by shear-wave velocity \(\beta_{\rm R}\), Poisson's ratio \(\
u_{\rm R}\), mass density \(\rho_{\rm R}\) and damping ratio \(\xi_{\rm R}\). The SSI system is excited by a train of harmonic SV waves coming from the bedrock, with motion in the XOZ-plane. The circular frequency of the excitation is \(\omega\), and the incident angle is \(\theta\), measured horizontally.  
Figure 1: The SSI model consisting of a superstructure, a three-dimensional rectangular foundation, and a horizontally multi-layered half-space.  
The deformation of the structure is represented by the relative horizontal translation \(u_{\text{b}}\) with respect to the \(z^{\prime}\)-axis, which is fixed on the foundation, and the foundation motion is represented by the generalized vector \(\mathbf{U}=\left\{u,\varphi\right\}^{\text{T}}\text{e}^{i\omega t}\), with \(u\) of the horizontal translation and \(\varphi\) of the clockwise rotation about the reference point (0, 0, \(c\)), and \(\text{e}^{i\omega t}\) is the harmonic time factor, omitted hereafter. The vertical displacement, as well as the torsional motion of the SSI system, are not considered in our problem.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 3 Results
### ACL Tears Diagnostic Accuracy of Physical Examination and MRI
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Area Under the Curve**} \\ \hline  & \multicolumn{6}{c}{**Asymptotic \%5\% Confidence Interval**} \\ \hline
**Tested Variables** & **Area Under the Curve** & **SD** & **Asymptotic Significance** & **Inferior Boundary Value** & **Superior Boundary Value** & **Sensitivity** & **1-Specificity** \\ \hline Thessally in extension MM & 0.604 & 0.058 & 0.079 & 0.491 & 0.718 & 0.604 & 0.395 \\ Thessally in flexion for MM & 0.698 & 0.054 & 0.001 & 0.591 & 0.805 & 0.854 & 0.458 \\ McMurray for MM & 0.698 & 0.054 & 0.001 & 0.591 & 0.805 & 0.875 & 0.479 \\ Apley compression for MM & 0.635 & 0.057 & 0.022 & 0.524 & 0.747 & 0.668 & 0.417 \\ Apley direction for MM & 0.883 & 0.058 & 0.159 & 0.469 & 0.668 & 0.417 & 0.250 \\ Medical joint line palpation & 0.594 & 0.058 & 0.113 & 0.480 & 0.708 & 0.583 & 0.396 \\ \hline Thessally in extension LM & 0.746 & 0.068 & 0.001 & 0.613 & 0.879 & 0.650 & 0.158 \\ Thessally in flexion for LM & 0.771 & 0.065 & 0.000 & 0.644 & 0.898 & 0.700 & 0.158 \\ McMurray for LM & 0.739 & 0.068 & 0.001 & 0.606 & 0.873 & 0.650 & 0.171 \\ Apley compression for LM & 0.767 & 0.070 & 0.000 & 0.630 & 0.904 & 0.600 & 0.066 \\ Apley direction for LM & 0.705 & 0.076 & 0.005 & 0.557 & 0.853 & 0.450 & 0.009 \\ Lateral joint line palpation & 0.568 & 0.07 & 0.348 & 0.423 & 0.714 & 0.400 & 0.263 \\ \hline Lachman & 0.883 & 0.042 & 0.000 & 0.800 & 0.966 & 0.844 & 0.078 \\ Pivot shift & 0.711 & 0.062 & 0.001 & 0.589 & 0.832 & 0.438 & 0.016 \\ Anterior drawer & 0.813 & 0.053 & 0.000 & 0.709 & 0.916 & 0.688 & 0.063 \\ Lelli & 0.805 & 0.055 & 0.000 & 0.696 & 0.913 & 0.625 & 0.016 \\ \hline MRI for MM & 0.740 & 0.052 & 0.000 & 0.638 & 0.841 & 0.958 & 0.479 \\ Logistic model for MM & 0.726 & 0.053 & 0.000 & 0.623 & 0.830 & 0.917 & 0.542 \\ \hline MRI for LM & 0.778 & 0.065 & 0.000 & 0.650 & 0.905 & 0.7 & 0.145 \\ Logistic model for LM & 0.767 & 0.070 & 0.000 & 0.630 & 0.904 & 0.6 & 0.066 \\ \hline MRI for ACL & 0.836 & 0.050 & 0.000 & 0.739 & 0.933 & 0.75 & 0.078 \\ Logistic model for ACL & 0.914 & 0.050 & 0.000 & 0.739 & 0.933 & 0.875 & 0.078 \\ \hline \end{tabular}
\end{table} 
Table 2: Relation of specificity to sensitivity for physical examinations and MRI for MM, LM and ACL.
## 2 Literature Review: Offshore Maintenance and Asset Management
### Decision Support Systems for Offshore Maintenance and Asset Management  
ML techniques are increasingly applied to asset management and predictive maintenance in various industries, including power distribution networks [43] and railways [44]. These techniques improve reliability, availability, maintainability, and safety [45]. ML models can enhance decision-making processes, predict the remaining useful life, and detect faults in real time [46]. However, challenges remain in data management, the models' interpretability, and performance evaluation [44]. Despite these challenges, machine learning-based predictive maintenance offers substantial potential to boost efficiency, minimise downtime, and promote sustainability in various industries [47].  
Recent research on offshore O&M optimisation has focused on various approaches to address challenges in the maritime and renewable energy sectors. Digital twin technology has been investigated as a means to enhance O&M efficiency and reduce costs in offshorewind farms [48]. Multi-criteria decision-making (MCDM) methods, such as spherical fuzzy AHP and WASPAS, have been applied to optimise the selection of offshore wind power station sites [49]. Researchers have also investigated condition monitoring, fault diagnosis, and prognosis techniques to enhance maintenance strategies [50]. The integration of MCDM methodologies with quality function deployment has been proposed to improve decision-making processes in offshore renewable energies [51]. These advancements aim to minimise maintenance costs, increase reliability [52], and ensure the sustainable development of offshore wind power systems [53].  
Research on offshore maintenance and asset management highlights the complexity of planning and optimising maintenance strategies for offshore assets, particularly wind farms. Decision support systems incorporating machine learning, multi-criteria decision-making, and linear programming are being developed to address the challenges in resource allocation, cost minimisation, and condition monitoring [54]. These systems aim to improve fault diagnosis, prognosis, and maintenance efficiency [55]. The literature reveals a clear trend toward more data-driven, intelligent, and holistic approaches to maintenance planning and optimisation [56]. This aligns closely with the objectives of the proposed prototype, which aims to integrate multiple data sources and advanced analytical techniques to support more effective decision-making in offshore painting planning. The proposed study, with its comprehensive approach integrating 3D modelling, machine learning, and multi-criteria optimisation, has the potential to address some of these challenges and contribute significantly to the field.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 5 Future Directions
### Approximation Factors
It is an immediate but still interesting direction to prove tight parameterized approximation ratios for these (and more) problems. Digressing, we remark that this survey does not include FPT-approximation of counting problems, such as approximately counting number of \(k\)-paths in a graph. The best \((1+\varepsilon)\)-multiplicative factor algorithm known [288; 289] for counting number of \(k\)-paths runs in time \(4^{k}f(\varepsilon)poly(n)\) for some subexponential function \(f\) (cf. [290]). So a natural question is: can we count \(k\)-paths approximately in time \(c^{k}\), where \(c\) is as close to the base of running time of the algorithm of deciding existence of \(k\)-Path in a graph (the best currently known \(c\) is roughly 1.657 [291; 292])?
## 1 Introduction
### Motivation and Context  
This study is driven by the need to address key challenges in PV power forecasting, including weather dependency, short-term and spatial variability, data limitations, and scaling difficulties. Tackling these issues requires advanced modeling techniques, high-quality data, and robust computational approaches. Achieving high-accuracy PV power forecasts is essential for grid stability, effective energy management, and seamless integration of RE into the power grid. Moreover, accurate forecasting enables a balanced electricity supply and demand, minimizes grid disruptions caused by fluctuations in solar energy, and enhances the efficiency of energy storage systems. It also optimizes the planning of various power sources, improves market operations by providing reliable data for energy trading, lowers operational costs, ensures regulatory compliance, and maximizes the utilization of RE resources. Besides the accurate prediction power of the trained models, the generalization ability is crucial. Using more general features, such as areal meteorological parameters, may increase training loss. However, it can assist researchers in conducting a robust and generalizable evaluation of repeating the experiment in unseen locations, which can lead to reducing bias. Therefore, we preferred to utilize local weather parameters instead of information from module-embedded sensors.
## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.1 Tree Segmentation and Parameters Extraction
[40] proposed a method for analyzing the vegetative state of almond crops based on multitemporal data acquired using a MSP sensor, extracting individual tree parameters, and calculating NDVI for orchard monitoring. Their results revealed significant temporal variation in the vegetative state of almond trees. [PERSON] et al. [41] used LiDAR data and UAV images to gather structural and geometric parameters, as well as VIs, establishing management zones (MZs) in hedgerow almond orchards. The study showcased the potential of LiDAR and UAV data in defining MZs for precision agriculture. [PERSON] et al. [42] correlated ground-based canopy light absorption data with UAV-captured RGB images to predict crop production variability. [PERSON] et al. [43] employed UAV-acquired data and object-oriented classification for high-resolution forest mapping in an Iranian shrub forest, outperforming pixel-based classification in overall accuracy (OA). [PERSON] et al. [44] conducted work involving several MSP VIs and extracted geometric and structural parameters using 3D LiDAR point clouds. Correlations revealed robust relationships between NDVI and both maximum width and cross-sectional area. Lastly, [PERSON] et al. [45] used RS techniques to study the impact of weed management on almond tree growth, employing UAVs with MSP cameras for data collection. Their research evaluated practices such as ground covers, mulches, and herbicides, identifying the most effective approach as combining pre-emergent herbicides and ground cover, resulting in higher almond yields and improved quality parameters. As presented in Figure 9a, the UAV was the most adopted platform in 77% of the studies, followed by satellite in 15%, while MAV accounted for only 8%. Figure 9: Remote sensing platforms implemented in tree segmentation and parameters extraction studies (**a**) and the extracted parameters (**b**). TH: tree height; NT: number of trees; TCA: tree crown area; CV: crown volume; MW: max width; CS: canopy size; CW: crown width. Several structural parameters were extracted from the studies mentioned above. As can be seen in Figure 9b, the most frequently collected parameter was tree height (TH). A total of 29% of the studies extracted this type of information. This was followed by the number of trees (NT) and tree crown area (TCA), both of which were collected in 18% of the cases. Among the parameters that were extracted less frequently, canopy size (CS) was included in only 6% of the studies and crown width (CW) in 5% of the studies.