esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 7 Technological Integration and Evaluation
### Evaluation in Real-World Applications  
Real-world applications and evaluations are vital for validating the effectiveness of HRTF technologies. [PERSON] et al. [219] performed a detailed evaluation of individualised HRTFs within a dynamic 3D shooter game environment, emphasising the importance of testing personalised HRTFs in dynamic environments such as gaming. Their findings demonstrate that players equipped with individualised HRTFs experience enhanced spatial awareness, sound localisation, immersion, and improved reaction times compared to those using generic profiles. This research provides insights into how well these personalised profiles translate to real-world applications. It also supports the broader adoption of HRTF technologies in interactive and immersive environments, such as gaming and virtual reality, where precise sound localisation significantly enhances the overall user experience. The study highlights the potential of personalised spatial audio in interactive applications.  
[PERSON] and [PERSON] [181] evaluated various HRTF interpolation methods to determine their computational efficiency in real-time audio rendering systems. Their research highlights the trade-offs between achieving high-quality audio reproduction and maintaining low computational costs. This balance is critical for consumer products, where real-time spatial audio processing must operate with minimal latency while ensuring that sound quality remains high. This study also provides insights for developers of real-time spatial audio systems in terms of optimising performance with limited processing power.  
[PERSON] and [PERSON] [220] evaluate the frequency response of consumer headphones and their role in individualising HRTFs. They introduce a technique using a chain of peak filters, which models the frequency response of headphones, allowing for more personalised HRTF outputs. This approach reduces the need for extensive equipment and offers a practical solution for integrating personalised HRTFs into consumer devices. This work provides a foundation for understanding how spatial audio can be democratised, making high-quality HRTF personalisation more accessible to a wider audience.  
Dolby Atmos and similar spatial audio solutions used in home theatre systems have been evaluated for their performance in various acoustic environments. These evaluations shed light on how HRTF-based technologies perform in everyday contexts, providing avenues for future refinements to improve user experience across different audio applications.
## 4 Results and Discussion
### ECG-ID Dataset Results
#### 1.1.1 Performance at Lower Sampling Rates  
_Convolutional neural networks_: Traditional CNNs mainly extract the morphological features from ECG signals and usually suffer from low sampling rates since they are unable to capture the temporal dependencies. It was proved that although CNNs were effective in spatial feature extraction, missing some important temporal patterns with low sampling rates significantly affected reconstruction quality [16]. The case of the TCN model shows much better consistency in improving Pearson's CC, hence having a better capacity to retain temporal information, which is essential for rebuilding ECG signals.  
The transformer model, on the other hand, uses self-attention mechanisms, which allows it to maintain high fidelity to the original signal even at low sampling rates, even better than TCN, as observed in the results.  
_Recurrent neural networks (RNNs) and LSTMs_: While RNNs are commonly applied for the reconstruction of the ECG signal, methods using them, such as long short-term memory networks, can have the issues of vanishing gradient, mostly at lower sampling rates. This might cause the learning of long-term dependencies to be less effective [30]. On the other hand, the MSE and Pearson's r obtained by the TCN model show a stable improvement with the increment in sampling rates. Therefore, it is more stable and efficient in capturing these long-term dependencies compared to traditional RNNs.  
Figure 14: Scatter plot for transformer model showing actual outcome: (**a**) MSE vs. number of samples; (**b**) RMSE vs. number of samples; (**c**) Pearson’s r vs. number of samples.  
However, the Transformer model outdoes these models by attaining lower MSE and higher Pearson's r, representing a better arrangement with the original signal.
## 2 Poincare Plot and Their Features
### Complex Correlation Measure  
The Complex Correlation Measure (CCM) is a feature that quantifies the temporal variation of the signal based on the Poincare plot. This feature was proposed for the diagnosis of cardiac diseases based on the heart rate interval signals [30]. This feature is calculated from a lagged Poincare plot by using a sliding window of three consecutive samples represented in the Poincare plot. For a 2D Poincare plot, the sliding window includes three consecutive points that forms a triangle, and the area of this triangle is calculated and added by considering all signal samples. This concept is illustrated in Figure 7 for a Poincare plot constructed for a lag \(\tau\). Let \(X=\{x_{1},x_{2},x_{3},...,x_{N}\}\) be the vibration sampled signal, and consider the \(i\) window including the points denoted \(a(x_{i},y_{i})\), \(b(x_{i+1},y_{i+1})\) and \(c(x_{i+2},y_{i+2})\), where \(y_{i}=x_{i+\tau}\). The area of the triangle spanned by points (\(a\),\(b\),\(c\)) is calculated as:  
\[A(i)=\frac{1}{2}\left|\begin{array}{ccc}x_{i}&y_{i}&1\\ x_{i+1}&y_{i+1}&1\\ x_{i+2}&y_{i+2}&1\end{array}\right| \tag{5}\]  
Figure 6: Feature extraction using the symbolic dynamics calculated from the Poincaré plot. (**a**) Poincaré plot from the Lorenz model constructed with the \(x(t)\) component. The twelve angular regions are overlapped, and a single symbol is assigned to each of points included in the region (**b**) Histogram calculated from the Poincare plot for the Lorenz model. The histogram represents the set of features useful for fault classification.  
Figure 7: Poincaré plot showing the triangles for calculating CCM. The Poincaré plot is considering a lag \(\tau\).  
The area is 0 when points \(a\), \(b\) and \(c\) are on a straight line. The area is positive when points have a counter clock-wise orientation. In contrast the area is negative for clock-wise orientation. The CCM considering a Poincare plot with \(M\) points is obtained by adding the area within the sliding window as:  
\[CCM(\tau)=\frac{1}{C_{m}}\sum_{i}^{M-2}A(i), \tag{6}\]  
where \(\tau\) represents the lag used for constructing the Poincare plot, \(C_{m}\) is a normalizing constant set as \(C_{m}=1/(M-2)\). The number of points of the Poincare plot is \(M=N-\tau\) with \(N\) representing the number of samples of the vibration signal.
## 2 Global Appearance Descriptors
### Fourier Signature Descriptor  
The Fourier signature descriptor was firstly used by [PERSON] et al. [19] to create an image-based memory for robot navigation. [PERSON] et al. [21] studied the computational cost and the error in localization by using Fourier Signature (FS) and proposed a Monte Carlo approach to solve the localization problem in indoor environments.  
This description method is based on the use of the Discrete Fourier Transform (DFT). After calculating the FS of a panoramic image, a new complex matrix is obtained \(IM(u,v)\). It can be decomposed into two real matrices, one containing the magnitudes and the other the arguments. The steps to obtain a global appearance descriptor from a panoramic image through the Fourier Signature (FS) are: First, departing from the intensity matrix of the original image, the DFT of each row is calculated. The result is a complex matrix with the same size as the original image (\(IM(u,v)\in\mathbb{C}^{N_{x}\times N_{y}}\)). Second, only the \(k_{1}\) first columns of this matrix are retained since the main information is in the low frequency components. Third, the resultant matrix (\(IM(u,v)\in\mathbb{C}^{N_{x}\times k_{1}}\)) is decomposed into the magnitudes and arguments matrices. The matrix of magnitudes (\(A(u,y)\in\mathbb{R}^{N_{x}\times k_{1}}\)) is invariant against changes of the robot orientation in the movement plane if the image is panoramic. In the last step, the global appearance descriptor is obtained by arranging the \(k_{1}\) columns of the magnitudes matrix in one single column (\(\vec{d}\in\mathbb{R}^{N_{x}\times k_{1}\times 1}\)).
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 6 Simulation Results
### Results of New Tip Loss Correction  
The simplified form (see Section 3.2) of the new correction developed by [PERSON] et al. [33] is used in the present simulations. The results of blade loads for Cases 1, 2 and 5 are displayed in Figure 8a-c, respectively. Case 1 represents a typical condition of the _NREL Phase VI_ wind turbine with the fully attached flow on its blades, while Case 2 represents another condition of flow separation (stall). Case 5 represents the rated operating condition of the _NREL 5 MW_ wind turbine. The results for Cases 3 and 4 are not displayed here for simplicity. (The performance of the correction in Case 3 is similar to that in Case 2, while the performance in Case 4 is similar to that in Case 5.) The Glauert-C correction, which performs best in Glauert-A/B/C, is compared with the new correction. Full CFD results [33] are employed as the reference data for the _NREL 5 MW_ wind turbine. As compensation for the sparse points of the experimental data, full CFD data [8; 33] are also employed for the _NREL Phase VI_ wind turbine.  
Figure 8: \(Cont\).  
It is seen in the result of Case 1, as shown in Figure 7(a), that the full CFD result achieves the best agreement with the experimental data and the new correction is superior to the Glauert-C correction. As compared to the Glauert-C correction, the overestimation of \(F_{n}\) in the tip region is properly corrected towards the reference data by the new correction. A similar tendency is seen from the tangential force distribution. In Case 2, as shown in Figure 7(b), the new correction maintains its accuracy, while the Glauert-C correction produces larger positive error, due to the occurrence of flow separation. The performance of the two corrections is similar to those in BEMT computations [33].  
For the _NREL 5 MW_ rotor, as shown in Figure 7(c), the new correction again makes a better correction to the blade loads in the tip region. The corrected curve of \(F_{n}\) for the new correction is closer to the reference data compared to that for the Glauert-C correction. The difference between the two corrections in tangential force (\(F_{t}\)) is small, and both the corrections can be considered to be doing well.
## 3 Results and Discussion
### Principal Component Analysis and Outlier Detection  
Principal component analysis (PCA) was conducted on the red, yellow, and orange bell pepper samples. As depicted in Figure 2A-C, the first two principal components collectively accounted for 94% and 3%, 94% and 4%, and 74% and 18% of the total variance in the red, yellow, and orange varieties, respectively. Consequently, the first two components  
Figure 1: Absorption spectrum of red (**A**), yellow (**B**) and orange (**C**) bell pepper varieties.  
explained 94%, 94%, and 74% of the data variability for the corresponding pepper types. Given the potential influence of technical issues, data collection errors, or inappropriate sampling on the dataset [55; 56], outlier detection was performed using [PERSON]'s T\({}^{2}\) distribution and residual F. The residual F quantifies the distance between a sample and the model, while [PERSON]'s T\({}^{2}\) indicates how well the model describes the sample [56]. Unscrambler X software facilitated the visualization of outliers as ellipses in the score plot [57]. Outliers can significantly impact analysis results [58]. As shown in Figure 2D, one red and two orange bell pepper samples were identified as outliers and subsequently excluded from further analyses. Principal component analysis (PCA) was used to categorize the antioxidant activity of red pepper powder based on near-infrared spectral data [30]. To validate their findings, the researchers opted for full cross-validation, a method known to enhance correlation and statistical power. The results demonstrated the efficacy of this approach in classifying the antioxidant activity of red pepper powder. Variance exceeding 70% for PC1 and PC2 indicates a robust two-dimensional visualization on the score plot [30].  
Figure 2: Results of the principal component analysis (PCA) (**A–C**) and [PERSON]’s T\({}^{2}\) test (**D–F**) for red, yellow, and orange varieties, respectively.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 2 Materials and Methods
### Registration of DICOM-Based and RGBD Pointclouds
#### 2.3.3 Evaluation of Registration Error  
The registration error was determined by evaluating the average error between randomly chosen points on the pointclouds. Specifically, a set of \(N\) random points on the RGB pointcloud was chosen, and for each of them, the closest point in terms of euclidean distance on the DICOM-based pointcloud was identified. Finally, the average error \(E_{N}=(E_{N_{x}},E_{N_{y}},E_{N_{z}})\) was evaluated. Specifically: \(E_{N_{x}}\) is the average error on the \(x\) axes, \(E_{N_{y}}\) is the average error on the \(y\) axes and \(E_{N_{z}}\) is the average error on the \(z\) axes. This procedure was repeated for increasing values of \(N\), defining: \(E_{100}\), \(E_{1000}\), \(E_{10,000}\), \(E_{20,000}\) and \(E_{30,000}\). Additionally, to investigate how the registration error was spatially distributed, a heatmap representing the registration error between the RGBD and the DICOM-based pointcloud was performed.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
Among them, neural collaborative filtering (NCF) stands out as a notable attempt [30]; this method enhances the expressiveness of the developed model by expanding the inner product within a multilayer  
Figure 1: The development and classification of recommender system methods [28; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. perceptron (MLP). The success of this approach has further motivated researchers to explore the integration of deep learning techniques using traditional recommendation algorithms. This trend is effectively illustrated in the deep factorization machine (DeepFM) [31], which cleverly integrates a shallow factorization machine (FM) [50] with an MLP. This approach effectively combines the FM and deep components to achieve a unified representation of both low-order and high-order feature interactions, significantly enhancing the model's expressive power. This approach is similar to traditional machine learning, but its training is divided into two phases. Therefore, it can converge better and faster. [PERSON], [PERSON] et al. proposed FedDeepFM [32], a model grounded in federated learning, which offers a mechanism that delivers high-quality recommendations while enhancing user privacy. This approach is particularly suitable for scenarios that prioritize privacy preservation or data transmission reduction. It generates training data using real data combined with interaction-based synthetic data. Built upon DeepFM, FedDeepFM offers a mechanism for delivering high-quality recommendations while enhancing user privacy. The \"multi-layer neural network + output layer\" allows for a more extensive interaction of user/item vectors, introducing additional non-linear features and enhancing the learning capability for sparse features. By incorporating attention mechanisms and introducing attention layers between the embedding layer and the multi-layer perceptron, it has evolved into deep interest networks (DIN) [51]. Furthermore, there are variations such as deep interest evolution networks (DIEN) [52], which incorporate sequence models to simulate changes in user preferences, and multi-interest networks with dynamic routing (MIND) [53], which utilize capsule networks to extract diverse user interests and introduce tag-based attention mechanisms for dynamic path selection. As one of the most widely used recommendation algorithms at present, collaborative filtering exhibits strong generality and significant effectiveness. However, collaborative filtering always faces serious issues of data sparsity and cold start. The integration of deep learning with collaborative filtering recommender systems not only simplifies engineering implementation but also alleviates these problems. Content-based recommendation, which coexisted with collaborative filtering, has also evolved in a similar manner.
## 1 Introduction and Literature Review
There is a large and growing body of work in using UAVs in disaster response applications, in particular with assisting SAR teams in identifying where people are in need of aid or transporting medical and first aid supplies to people in need of aid. The advent of wireless sniffer technologies allow for SAR UAVs to detect the presence of wireless devices (i.e., cell phones). With the adoption of cell phones in much of the world, the combination of these two technologies allows for the use of UAVs in responding to emergencies where people in need of aid may not have line-of-sight to the sky. When responding to emergencies and disasters, while there are some over-arching principals when developing SAR procedures, domain-specific data and knowledge can go a long way in creating searches that can be conducted faster (e.g., understanding ocean currents to identify where overboard and stranded sailors may be). In the context of searching after a tornado strikes, we can use broad understandings of tornadoes to better capture and identify the extent of the damage. A UAV equipped with wireless sniffers can also estimate the number of people that may be in need of aid at any given area, even if they are buried. To the best of our knowledge, there is very little research in utilizing meteorological and tornado data to help rescuers identify where victims may need aid [11; 12]. In addition, these past works do not attempt to identify the extent of the damage as quickly as possible; they simply assume the entire area must be assessed. This paper presents a method of attempting to identify the damaged area of a tornado quicker than trying to search the entire area. It also attempts to demonstrate that intelligently incorporating historical data into the decision-making process can assist the tools to make better decisions. The remainder of the paper is broken down into five sections. Section 2 contains the problem context and other rules and assumptions that are used in this paper. Section 3 discusses how the dynamic program will work. Section 4 details the experimentation, including a method for generating \"artificial tornado instances\" to provide additional cases for the paper. We test the method developed on historical cases in Section 5. We then conclude this paper in Section 6 by outlining our contribution and discussing research opportunities. Appendix A has additional results for Section 5 and Appendix B outlines the location of the publicly available data this paper is building on.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 5 Conclusions  
In this paper, a series of work on the deformation characteristics of bored pile is carried out by using BOTDR distributed optical fiber sensing technology. The following conclusions are obtained:  
1. The field loading test is carried out. The BOTDR distributed optical fiber sensing technology is used to monitor the deformation of pile, and the strain of the pile is obtained to provide a basis of data for studying the deformation characteristics of bored pile. It shows the advantages of distributed optical fiber sensing technology in the deformation of pile monitoring.
2. The wavelet function is chosen to decompose the strain monitoring data of the pile. The Stein based unbiased risk threshold rigrsure processing method is used to process the signal and obtain the denoised signal. The ideal result is obtained.
3. Using the translation mean method, the Savitzky-Goiay method, percentile-filter method and [PERSON]'s unbiased risk threshold Rigrsure processing method, the optical fiber data is processed. By comparing the smoothing results, the percentile-filter method cannot well reflect the change trend of the deformation of bored pile in large fluctuations, the overall smoothing effect of the Savitzky-Goiay method is worse than that of the translation average method, and the smoothing degree of the curve after smoothing by the translation average method is higher, which can effectively reflect the deformation change trend of the bored pile.  
[PERSON] designed the project. [PERSON] and [PERSON] performed the experiment. [PERSON] and [PERSON] analyzed the data and prepared the manuscript.  
Financial support comes from the National Natural Science Foundation of China (Grant No. 51508159), the Fundamental Research Funds for the Central Universities of Hohai University (No. 2019B12914, No. 2019B44114), the Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University (No. GHXN201904) are gratefully appreciated.  
The authors declare no conflict of interest.
# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 5 Related Work: AIS and IoT
### Network Layer  
So far, most of the AIS- based studies have been carried out in the network layer to handle the IoT security. A signature-based IDS proposed by [PERSON] et al. (2016). This IDS contains memory detectors that simulate the antigens in the human body and classify datagrams as normal and malicious. In spite of that theory mathematically analyzed and detected a various number of intrusions, it has a high computational running and the researchers did not specify how to implement it in limited resources devices. Additionally, a dynamic approach called Artificial Immune System Response Model (AISRM) was produced by [PERSON] et al. (2016). The proposed model captures the IoT data packets and transforms them into immune antigens then detects and responds to attacks. Although this is an adaptable model proven through a simulation experiment, the central server scalability is a significant problem where all communication passes through.