esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Adaptation  
The adaptation methods start with a non-individualised HRTF and try to reshape it based on the specific anthropometry of the subject. One of the first works in this domain is that of [PERSON] et al. [153]. The authors propose an approach that utilises specific anthropometric ear parameters (specifically, cavum concha height, cymba concha height, cavum concha width, fossa height, pinna height, pinna width, and intertragal incisure width; see Table 2), combined with a low-frequency head-and-torso model, to match and adapt HRTFs from the CIPIC database [147]. Their results indicate that this method improves both the localisation accuracy and subjective perception of virtual auditory scenes.  
Another approach, proposed by [154], is a hybrid matching-adaptation technique. The authors proposes a method for customising HRTFs using computer vision to address the limitations of traditional direct measurement-based techniques, particularly at low frequencies. Traditional HRTF measurements often struggle with obtaining reliable low-frequency data due to the long signal lengths required, which can be influenced by room reflections and measurement equipment. To mitigate this issue, the paper integrates an analytical head-and-torso model for synthesising low-frequency HRTFs. These synthesised HRTFs are blended with measured HRTFs from the Kemat dataset [41] by performing a matching process based on anthropometric features derived from a computer vision analysis of the user's head, torso, and external ears.  
In 2017, [PERSON] et al. [155] developed an efficient method for HRTF personalisation, utilising a weighted sparse representation of anthropometric features. These features are weighted according to their importance in accurately modelling HRTFs, with greater emphasis placed on those features that have a stronger influence on acoustic characteristics. The authors assume that both HRTFs and anthropometric data could be represented by the same sparse vector, following previous sparse representation techniques. The normalised sparse vector is applied to the log-scale HRTF data from the CIPIC database to synthesize personalised HRTFs.
# Simultaneous SAXS/SANS Method at D22 of ILL: Instrument Upgrade
## 3 Results and Discussion
### Reduction of the Gamma Background by Lead Shielding
The intensity (cts/s) data are shown on a logarithmic scale. The gamma background is originated from the facility environment and nuclear reactions of the neutrons with the sample material. The short collimation length of the SANS instrument (2.8 m) leads to a higher neutron flux compared to longer collimation lengths. The increase of neutron flux and the gamma radiation background level is roughly proportional to the inverse of the squared collimation length. To shield the X-ray detector (at different SDD\({}_{\text{X-ray}}\)) against this high-energy gamma radiation, several preliminary tests were performed to choose the best lead wall dimensions and geometric arrangements for our SAXS system. The detector SF was calculated as the ratio of background intensity for single-energy and double-energy threshold modes. The total suppression factor is theoretically equal to the product of both shielding and detector SFs. An uncertainty of the total SF of \(\leq\) 1% was observed under identical experimental conditions. Finally, large SFs were achieved, especially at short SDD\({}_{\text{X-ray}}\) (cf. Table 1). For SDD\({}_{\text{X-ray}}\) = 0.55 m, the background intensity could be reduced by a factor of 505 and 275 for short (2.8 m) and long (1.7.6 m) neutron collimation lengths, respectively. For both the unshielded and shielded instrument, the background SF for the double-energy threshold detection was almost equal. The gamma background which originates from the neutron interaction with the sample or from the environment and transmitted through the sample towards the detector cannot be shielded. But the double-energy threshold detector is able to reduce this background component. The tremendous reduction in the background signal of the upgraded instrument for the benefit of simultaneous SAXS/SANS measurements is demonstrated in Section 3.3 by SAXS/SANS data from NP samples.
## 2 Theory  
Molecular dynamics calculations describe the position and momentum of each atom, and the equations of the motion of the atoms satisfy the Newtonian assumptions of motion, as in Equation (1). After time discretization, the complex continuous motion curves of individual atoms are transformed into discrete multiple linear motions.  
\[m_{i}\mathbf{a}_{i}=\mathbf{f}_{i}=-\
abla U(\mathbf{r}_{i}) \tag{1}\]  
in which \(i\) denotes the sequence of atoms, and \(m_{i}\), \(\mathbf{a}_{i}\),\(\mathbf{f}_{i}\), and \(\mathbf{r}_{i}\) denote the mass, acceleration, interatomic force, and position of atom \(i\), respectively. In this paper, the alloys are studied and the EAM action potential provides a favorable fit for the alloys. In the embedded atomic method calculation, the interatomic forces can be obtained using the following equation.  
\[U(\mathbf{r}_{i})=\sum_{i}U_{ij}(\mathbf{r}_{i},\mathbf{r}_{j}) \tag{2}\]  
\[\mathbf{f}_{i}=-\
abla U(\mathbf{r}_{i})=0,\ \mathbf{r}_{ij}>\mathbf{r}_{cut} \tag{3}\]where \(i\) and \(j\) denote the sequence of atoms, \(U_{ij}\) is the potential energy between atoms \(i\) and \(j\), \(\mathbf{r}_{ij}\) denotes the distance between atoms \(i\) and \(j\), and \(\mathbf{r}_{cut}\) denotes the truncation radius. The position and velocity calculation of atoms in large atomic systems are determined using the velocity Verlet calculation method [31], and the stress on the atom is calculated using Equation (4) [32].  
\[\sigma_{xy}=\frac{1}{V^{i}}{\sum_{i}}\left[\frac{1}{2}{\sum_{j=1}^{N}}\left(r_{ x}^{j}-r_{x}^{i}\right)f_{y}^{ij}-m^{i}v_{x}^{i}v_{y}^{i}\right] \tag{4}\]  
where the subscripts \(x\) and \(y\) denote Cartesian components, \(V^{i}\) denotes the volume of atom \(i\), and the superscripts \(i\) and \(j\) denote the atomic label. Atomic stress consists of both potential energy and kinetic energy.  
For the purpose of describing the state of high stress around the defect, the stress concentration factor \(K\) is adopted as an indicator to determine the size of the stress concentration. It is defined as the ratio of the maximum stress \(\sigma_{max}\) around the defect to the nominal stress \(\sigma\) at this point, as shown in Equation (5).  
\[K=\frac{\sigma_{max}}{\sigma} \tag{5}\]
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology  
Previous integrated indicator-based risk assessments have largely been conducted at the regional scale, making it difficult to perform detailed building-level evaluations. Therefore, this study proposes a building-level risk assessment method using publicly available data and introduces the Flood Risk Index for Building (FRIB) for this purpose. In this study, 'publicly available data' refers to information accessible to the general public through government agencies, public data portals, or open-source map platforms. This approach aims to overcome the limitations of region-specific datasets and enable effective flood risk management without reliance on specialized datasets. FRIB is evaluated by considering the three attributes that constitute a disaster--vulnerability, hazard, and exposure. Subsequently, a comparative analysis was conducted with a single physical hazard approach (based on flood depth).
## 3 Results and Discussions
### Reactivity Insertion Accident
#### 3.1.1 Unprotected Reactivity Insertion  
The Transient responses of ADS and CNES for unprotected reactivity insertion of 700 pcm are shown in Figure 8. It can be seen from Figure 8a that the ADS power only increases by about 25% after 700 pcm reactivity is introduced into the core, and then the power decreases slightly due to the negative reactivity introduced by the Doppler feedback effect and the coolant temperature feedback effect, and finally reaches a new equilibrium value. Meanwhile, the temperatures of the fuel center, cladding surface, and coolant outlet also ascend with the power increase and then gradually stabilize to a new equilibrium point. This is principally because the ADS reactor is a subcritical core driven by an external neutron source, in which the \(k_{eff}\) is far from the critical point and the neutron multiplication capacity is weak after the core is inserted positive reactivity. Therefore, the power only rises to 1.25 times the steady-state value, and the maximum temperatures of fuel pellet and cladding are far below the safety limit under the unprotected reactivity insertion accident. The results show that the ADS has good inherent safety in the event of reactivity insertion accident, and its margin of criticality safety is large.  
The simulation results also show that the the CNES power rose sharply to nearly 22 times the steady-state value under unprotected reactivity insertion accident. At the same time, the negative feedback effect of reactivity caused by the substantial increase of the fuel and coolant temperatures suppresses the continuous increase of the power, and finally the power drops to about five times the steady-state value. However, the peak temperature of the fuel center reaches 3012 K, which is close to the melting point. The peak temperature of the cladding surface is 1205 K, which means it has exceeded the damage limit. This is mainly because CNES  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **ADS** & **CNES** \\ \hline Proton energy (MeV) & 600 & - \\ Intensity of proton beam (mA) & 2.5 & - \\ Effective multiplication factor \(k_{eff}\) & 0.97 & 1.04 \\ Total thermal power (MW) & & 80 \\ Fuel element height (m) & 0.9 & \\ Fuel pellet outer diameter (m) & \(7.14\times 10^{-3}\) & \\ Cladding inner diameter (m) & \(7.37\times 10^{-3}\) & \\ Cladding outer diameter (m) & \(8.5\times 10^{-3}\) & \\ Coolant flow area (m\({}^{2}\)) & \(9.89\times 10^{-5}\) & \\ Pin pitch (m) & \(1.341\times 10^{-2}\) & \\ Core inlet temperature (K) & & 573 \\ Core outlet temperature (K) & & 673 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main design parameters of the ADS and CNES reactors.  
enters a prompt supercritical state after 700 pcm (about 2 s) reactivity is instantly introduced into the critical core, and the power grows exponentially. The comparison results show the power and the temperatures of fuel pellet, cladding, and coolant of the CNES core are much higher than those of the ADS core during the reactivity insertion accident, which means ADS has a better safety advantage than CNES.
## 2 Materials and Methods
### Meteorological Data  
Daily meteorological data was obtained from Met Eireann, the Irish national meteorological service, for Ireland and from the UK Met Office Integrated Data Archive System (MIDAS) for Aldergrove, located in Northern Ireland. Daily maximum and minimum temperature, global radiation and precipitation were obtained for the years 2011-2013 for JC, 2013-2016 OP and 2013-2015 for AG, RP (Table 1). Reference evapotranspiration was derived based on the FAO-Penman Monteith equation.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 5 Implementation  
Based on the proposed approaches, we develop a prototype, dubbed DGF-CFGConstruct. The implementation details of each component are as follows.  
**Static CFG Construction**: We build up the CG and CFG based on LLVM's IR. In the CG, functions are taken as the nodes of the graph and identified by functions' signatures. We discover the CG's edges through analyzing all call instructions. Specifically, if the jump address of a call instruction can be determined by the LLVM's API \(CallInst::getCalledFunction\), the jump relation should be added to the CG as an edge. Otherwise, the call instruction is considered as an indirect jump location. In addition, we can obtain the CFG of each function by another LLVM's API \(llvm::WriteGraph\). Then, the static iCFG is constructed by combining the CG and CFGs according to the method in Section 4.1.  
**DGF-based Test Case Generation**: The DGF-based test case generation is established on AFLGo. The indirect jump locations are considered as the target sites. We calculate the basic block-level distance calculation in a Python script and instrument the seed distance calculation in a LLVM's _pass_. In order to balance the exploration and exploitation, we retain the annealing-based power schedules in AFLGo.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Basic Block** & **Recalculate or Not** & **Distance on Old iCFG** & **Distance on New iCFG** \\ \hline a & Yes & \(+\infty\) & \(2\cdot\alpha+1\) \\ b & Yes & \(+\infty\) & \(2\cdot\alpha\) \\ c & No & \(\alpha+1\) & \(\alpha+1\) \\ d & No & \(\alpha\) & \(\alpha\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of updating distance.  
**Indirect Jump Instrumentation and Monitoring**: The DGF-Constructor records the executed indirect jump relations by instrumenting at the indirect jump locations and the entry of each function. The instrumentation is implemented in another LLVM's _pass_. When a indirect jump is triggered, the relevant jump relation is recorded to a file. After all test cases are executed, we can obtain all indirect jump relations discovered at runtime in the result file.  
It should be noted that although the prototype is implemented to analyze the source code at present, in principle, our techniques are also suitable for binary code. We will extend it to binary analysis by replacing the corresponding tools in the further work.
## 3 Proposed Procedure
### Algorithms and Optimization Procedures for Efficient Processing of Vector Data
#### 3.4.1 Data Simplification  
To effectively utilize vector data in 3D models, a technique that reduces computational complexity while maintaining the original data's integrity is essential. This is achieved by implementing Level of Detail (LOD) techniques to adjust data granularity and improve rendering performance. The Douglas-Peucker algorithm is employed during the simplification process to optimize linear vector data. The simplification process is executed by calculating the perpendicular distance between a curve point and the reference line using the following mathematical approach:  
\[d(P_{i},\ P_{0}P_{n})=\frac{\big{|}\left(x_{LOD},n-x_{LOD},0\right)*\left(y_{ LOD},0-y_{Orig},t\right)-\left(x_{LOD},0-x_{Orig},t\right)*\left(y_{LOD},n-y_{ LOD},0\right)\big{|}}{\sqrt{\left(x_{LOD},n-x_{LOD},0\right)^{2}+\left(y_{ LOD},n-y_{LOD},0\right)^{2}}}\]
# The Spotting Distribution of Wildfires
## 2 A Transport Model for Firebrand Transport and Combustion
### Solution of the Transport Model  
The above transport model can be explicitly solved using the method of characteristics. We reformulate the above model by using the product rule for the partial derivatives:  
\[\frac{\partial p(t,x,z,m)}{\partial t}+w(z)\frac{\partial p(t,x,z,m)}{\partial x}+v(t,m)\frac{\partial p(t,x,z,m)}{\partial z}+f(t,z,m)\frac{ \partial p(t,x,z,m)}{\partial m} \tag{9}\] \[= -\frac{\partial f(t,z,m)}{\partial m}p(t,x,z,m). \tag{10}\]  
The characteristic equations are  
\[\frac{dx}{dt} = w(z), \tag{11}\] \[\frac{dz}{dt} = v(t,m),\] (12) \[\frac{dm}{dt} = f(t,z,m), \tag{13}\]  
where Equation (11) describes horizontal transport due to wind, Equation (12) describes vertical movement due to gravity, buoyancy _etc._, and Equation (13) describes the combustion process of a burning ember. Detailed physical models are given in the Appendix. Each of the above equations needs to be equipped with an initial condition  
\[x(0)=x_{0},\quad z(0)=z_{0},\quad m(0)=m_{0}, \tag{14}\]  
which describes the initial location \(x_{0}\), the initial height \(z_{0}\) and the initial mass \(m_{0}\) of a fire brand. We introduce \(Y:=(x,z,m)\) and we assume that the functions on the right hand side are globally Lipschitz continuous such that the IVP for the System (11)-(13), together with the initial conditions in Equation (14) has a unique solution  
\[Y(t,Y_{0})=(x(t;x_{0}),v(t;v_{0}),m(t;m_{0})),\quad\mbox{with}\quad Y_{0}=(x_ {0},v_{0},m_{0}), \tag{15}\]  
such that \(x(0;x_{0})=x_{0}\) etc., which we call the characteristics of the system. Along the characteristics, \(p\) satisfies an ordinary differential equation  
\[\frac{d}{dt}p(t,Y(t,Y_{0})) = \frac{\partial p(t,Y(t,Y_{0}))}{\partial t}+\
abla_{Y}p(t,Y(t,Y_ {0}))\frac{\partial Y(t,Y_{0})}{\partial t} \tag{16}\] \[= -\frac{\partial f(t,Y(t,Y_{0}))}{\partial m}p(t,Y(t,Y_{0})), \tag{17}\]where we used Equation (10) in the last step. Equation (17) is solved by the exponential  
\[p(t,Y(t,Y_{0}))=p(0,Y_{0})\exp\left(-\int_{0}^{t}\frac{\partial f(s,Y(s,Y_{0}))}{ \partial m}ds\right). \tag{18}\]  
This solution \"lives\" on the characteristics \(Y(t)\). To get a full solution to our problem, we consider a given point \(Y(x(t;x_{0}),z(t;z_{0}),m(t;m_{0}))\) and we follow the characteristics backwards to its origin. We denote the backwards solution of the characteristics equation as \(Y^{-1}(t,Y)\), such that \(Y^{-1}(t,Y(t,Y_{0}))=Y_{0}\). Using this we can write the solution as  
\[p(t,Y)=p\Big{(}0,Y^{-1}(t,Y)\Big{)}\exp\left(\int_{0}^{t}\frac{\partial f( \sigma,Y^{-1}(\sigma,Y))}{\partial m}d\sigma\right). \tag{19}\]  
A schematic for the use of the method of characteristics, in case of constant wind and constant terminal falling velocity, is shown in Figure 2a.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 4 Implementation of the Indoor Channel
### Indoor Environment  
The indoor environment block represents the indoor space of the VLC system. This block is defined by the user from a set of input parameters: the size \(D_{E}\), number of reflections \(k\), reflectance of each wall \(R_{W}\), and resolution \(\Delta D\). The resolution establishes the size of the smaller areas used to compute the NLoS propagation. An indoor environment-type object method was developed to create a grid of points on each wall. Each point is a 3D Cartesian coordinate representing the center of a smaller area. This block computes the pairwise parameters of the grid of points, including the Euclidean distance and the cosine of the two angles formed between two points. Figure 3 shows that the angles \(\phi_{0}\) and \(\theta_{0}\) are formed with respect to the normal vector at each point and the vector between two points. The indoor environment block computes the cosine of these angles. The encapsulation of the indoor environment's parameters is represented by \(\mathbf{IE}_{e}\) in Figure 4.
# Ab Initio Study of Martensitic Transformation in NiTiPt High Temperature Shape Memory Alloys
## 1 Introduction  
NiTi shape memory alloys (SMAs) exhibit the shape memory effect and superelasticity which are attributed to reversible thermoelastic martensitic transformations (MTs). The NiTi alloys have found diverse commercial use in biomedicine and engineering applications since 1963 [1, 2]. The structure, mechanics, and martensitic phase transformation in binary NiTi shape memory alloy were widely studied [3, 4, 5, 6, 7]. As is known, the dependence of the phase transformation temperature on alloy composition is of utmost importance with regard to the application of NiTi SMAs [2, 8]. Some alloying additions, for example, Fe, Co, Al, Mn, V, or Cr will decrease the martensite start temperatures (\(M_{\mathrm{s}}\)) of NiTi-based alloys [8, 9]. Unfortunately, some special applications such as aerospace, automotive and power generation, and chemical processing industries demand a high transition temperature, which are greatly limited due to the low \(M_{\mathrm{s}}\).  
Over the past decades, considerable efforts have been made in developing high-temperature shape memory alloys (HTSMAs) with significantly elevated transformation temperatures based on ternary NiTi\(X\) (\(X\) = Pd, Pt, Au, Hf, and Zr) compositions [10]. Experimental studies indicate additions of hafnium above 3 at.% increase the transformation temperatures of Ti-Ni-Hf alloys [11]. For NiTi alloys, replacing titanium or nickel with Zr or Pd above 10 at.% can increase the transformation temperatures [12, 13]. There are many reports on the crystal structures [11, 14, 15, 16, 17], phase transformations [18, 19, 20, 21, 22, 23, 24], shape-memory behaviors [25, 26, 27, 28, 29, 12, 25, 27, 29], and precipitate phases [30, 31, 32, 33, 34] in NiTiX (\(X\) = Pd, Hf, and Zr) alloys.  
Of these HTSMAs, the (Ni,Pt)Ti system, owing to the highest potential use temperature, in spite of the high costs, has been studied as the promising SMAs for demanding applications, such as in the aerospace, automotive, power generation, and chemical processing industries. Yet, compared to the NiTiX (\(X\) = Pd, Hf and Zr) alloys, Ti-Ni-Pt alloys have not received a similar level of attention. Recently, [PERSON] et al. [35] reported that themartensitic transformation temperature of the TiNiPt system increases sharply when Pt content exceeds 10 at.%. The \(M_{\text{s}}\) can be increased to near 1000 \({}^{\circ}\)C in TiPt alloy. Moreover, NiTiPt alloys show the low thermal hysteresis [36; 37], good work output [38], and excellent oxidation resistance [39].  
The elastic property of the B2 phase in NiTiPt has been investigated by density functional theory (DFT) [40; 41]. To the best of our knowledge, few studies discuss the mechanism of phase transition. No theoretical study on the martensitic transformations of TiNiPt has been published. DFT has been used successfully to examine the influence of the Hf, Pd, Cu, or Zr on NiTi properties [42; 43; 44; 45; 46; 47]. Consequently, the MTs of ternary TiNiPt HTSMAs as a function of the Pt content is investigated in the present research by first principles.
## 4 Experiments and Result Analysis
### Comparison of Actual Detection Effect  
To more intuitively compare the performance differences between the original YOLOv5 model and its improved version in the actual defect detection task, both models were used to detect five types of weld seam defect images from the test set. The results are shown in Figure 10. In the practical detection process, the optimized model outperforms the original model in overall detection performance. Notably, the detection performance for defects categorized as linear, non-fusion, and crack has significantly improved. The recognition confidence for linear defects and non-fusion increased by 17% and 11%, respectively. The original model misclassified crack defect as non-fusion defect, while the optimized model correctly identified and classified the crack defect. These results demonstrate that the proposed YOLOv5 optimization model meets the requirements for industrial pipeline X-ray weld seam defect detection, offering higher detection accuracy and robustness.  
Figure 10: Effect comparison of weld defects detected by YOLOv5 model and its improved version.