esa-sceva/satcom-chunk-collection huggingface dataset:
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 5 Materials and Methods  
### Natural Speech Databases  
In this study, two speech databases containing whisper utterances were employed. The first one is the Whi-Spe database exploited in full capacity [31]. Whisper utterances from this database were utilized for training and augmentation, and neutral utterances are used for inverse filtering (creating pseudo-whisper samples). Recordings from the Whi-Spe dataset consist of fifty isolated words spoken by ten speakers (5 female and 5 male). Each word was repeated 10 times, both in normal and whisperd phonation. As a result, the overall size of the database encompasses 10,000 recordings, which is roughly equivalent to two hours of speech. The recordings were carried out under controlled laboratory conditions. The speech samples were digitized with a sampling rate of 22,050 Hz, 16 bits per sample, using the mono technique and the linear PCM wav format. More details regarding the vocabulary, segmentation procedure, and quality control of this database can be found in [31].  
The second database, known as DBtest, was created in a real-world setting to evaluate the robustness of the ASR systems. Ten volunteer students (5 males and 5 females, as in Whi-Spe) uttered words (two times each) from the Whi-Spe lexicon in a whispered voice. The recordings were made using a laptop and an integrated microphone in various rooms, with ambient noise Sound Pressure Level (SPL) of approximately 35 dBA. The database is publicly available in the repository [32]. To ensure consistency, all recordings from the two databases were resampled to 16 kHz and 16 bits per sample. Furthermore, the audio files in both databases follow the same naming convention.  
Figure 3: Example of inverse filtering on a word in normal speech: (**a**) FFT spectrum of the word, (**b**) LPC spectral envelope, (**c**) FFT spectrum after inverse filtering, and (**d**) frequency response of the inverse filter _IF_ (_z_).
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 2 Model Neurons: Lessons Learnt from Well-Posed Models
### Stochastic Regularization of Convergence to Local Minima Vicinal to the Global Minimum  
Erroneous parameter solutions may be obtained when the parameter search ends at a local minimum of the cost function. This is a rare occurrence which is usually observed when a vicinal local minimum has nearly the same data misfit error as the global minimum, and the difference between the two is smaller than the termination error of the algorithm. In this case, the true solution at the global minimum can still be obtained through the noise regularization method [11].  
Noise has the effect of shifting the positions of local and global minima relative to one another in parameter space, as well as shifting these minima up in energy. An example is stochastic gradient descent [77,78]. The direction and magnitude of the shift depends both on the realization and the standard deviation of noise. By varying these noise parameters in a suitable manner, one can perturb the environment of the minima of the cost function and merge the local minimum, where the parameter search is arrived at, with the global minimum. The judicious management of additive noise until a merger has occurred, and subsequent removal of noise while tracking the return of the global minimum to the zero-noise level, can increase the accuracy on parameters beyond the limitations of gradient descent methods (Figure 3) [11].  
Figure 3: **Regularization by noise.** (**a**) The cost function plotted as a function of one parameter has local minimum \(p_{0}^{l}\) in the vicinity of the global minimum \(p_{0}^{*}\). The parameter estimation algorithm started from an initial guess at the local minimum (pink star). Then, the amplitude of noise was increased. Until a critical value, the parameter solution remains in the local minimum but shifts (blue dot). Above this critical value, at \(\sigma_{3}\), the local minima and the global minimum merge, causing an  
abrupt jump of the solution into the (noise shifted) global minimum. The global solution above \(\sigma_{3}\) then need to be inserted as input to the parameter estimation algorithm, and then, noise is progressively decreased to zero to obtain the global minimum at zero noise. (**b**) Trajectory of the neuron parameter vector projected in two dimensions as noise is varied. A jump is observed from the local to the global solution. (**c**) Schematics of the noise annealing protocol [11].
## 1 Introduction
The influence of network topology is explicitly considered in order to provide the optimal location of wireless transceivers, in terms of received power levels and interference margins, leading to the final deployment proposed by the network designer and to the radioplanning strategy. One of the main challenges in the analysis of future Internet of Things and Context-Aware Scenarios is to consider the full complexity in terms of overall dimensions and the large amount of elements, in which a potentially vast amount of sensors and transceivers can be located. Different methods can be applied, such as empirical estimations or full wave electromagnetic simulation. The first are strongly site-specific and require intensive measurement campaigns, which usually leads to large average errors in the estimation. The latter are very precise but exhibit very large computational demands, which for large scenarios limit their use in practical terms. Another approach is to use deterministic based algorithms, based on Geometric Optics and Uniform Theory of Diffraction, in which rays launched from the source resemble for a discretized electromagnetic wave-front. The implementation of this method is usually bi-dimensional (in horizontal, vertical or simultaneously both planes), although three dimensional implementations have also been done. In this work, an in house implemented 3D Ray Launching code is employed, which has been specifically adapted to the analysis of scenarios in which wireless sensors and transceivers can be allocated in multiple positions. This approach provides estimations of multiple quality assurance metrics (such as coverage levels, time dependent parameters such as power delay profiles and more elaborate estimators, such as current consumption), in an alternative manner as the common empirical/statistical solutions commonly reported. The rest of the paper is organized as follows: Section 2 is devoted to introduce the indoor scenario as well as the in-house 3D Ray Launching simulation; Section 3 describes the experimental methodology, while Section 4 validates the results obtained by simulation and those obtained by diverse measurement procedures. Finally, conclusions and references end the paper.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses  
Two series of experiments were designed. In the first series of experiment, the best value of \(n\) (number of selected bands) was analyzed. The impact of window size \(m\) and threshold \(\sigma\) on the segmentation performance of the proposed IM-CHD algorithm was investigated. The results can provide references for the parameter selection. In the second series of experiment, our algorithm was compared with the existing superpixel segmentation algorithms of hyperspectral images.  
In the experiment, two datasets with different characteristics, i.e., the Indian Pines dataset (Indian Pines) and the Pavia Center dataset (Pavia), were selected. Indian pines were acquired on 12 June 1992 at the Purdue University Agricultural College in northwestern West Lafayette with an airborne visible/infrared imaging spectrometer (AVIRIS) sensor system with \(145\times 145\) pixels. After removing the noise, the image contains 200 spectral information bands with a resolution of 20 m. There are 16 real objects with labels in the image [30], as shown in Figure 5.  
Figure 4: Flow chart of superpixel segmentation for hyperspectral images based on information measures.  
Figure 5: Ground truth of Indian Pines dataset.  
PaviaC was obtained through the Reflex Optical System Imaging Spectrometer hyperspectral sensor system in the city center near Pavia University in Pavia, northern Italy. After removing the noise, the data contain \(1096\times 175\) pixels. The image has a resolution of 1.3 m and a total of 102 spectral bands. This image contains 9 types of objects [31], as shown in Figure 6.  
In these experiments, under-segmentation error (UE), achievable segmentation accuracy (ASA), and boundary recovery (BR) were used to evaluate the performance of the proposed algorithm [32]. UE measures the fraction of pixel leakage across the truth boundaries of the ground. Smaller value of UE indicates the better performance. In ideal situations, the classification results are all correct, so the calculation result of UE is 0. BR estimates the percentage of true natural boundaries recovered by segmentation of superpixel boundaries. In ideal situations, the superpixel boundaries are the same as real boundaries and the calculation result of BR is 1. A larger value of BR indicates the better segmentation performance. Complete accurate segmentation rate (ASA) measures the upper bound of the performance. ASA represents the highest precision of segmentation in superpixels. A larger ASA indicates the better performance. In the ideal situation, the result of ASA is 1.  
In this paper, we implemented the generation of false color images on the platform of MatLAB2014a, and implemented the hyperpixel segmentation of hyperspectral images on the platform of VS2008 with the extended Opencv library. The specific experimental design is described as follows.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods
### Modelling Methodology
#### 2.2.1 Traditional ML Algorithms  
The traditional ML algorithms LR, SVM, and RF were used as baseline models [12]. Widely used in statistics, LR estimates the log odds of the output as the linear combination of the input variables. SVM finds a hyperplane that best separates different classes in the feature space, maximising the margin between them. RF is an ensemble learning method that combines multiple decision trees to improve model performance and mitigate overfitting. These algorithms were used to perform the classification task of predicting the risk of decompensation within the 24-h forecast window. However, since they cannot directly model time-series variables, they were trained on extracted hand-crafted statistical features from the input time series, i.e., mean, median, standard deviation, average absolute deviation (AAD), minimum and maximum values, interquartile range, peaks, differences between maximum and minimum values, median absolute deviation, and the count of values above the mean for each feature.
## 5 Results
### Aerodynamics  
Generally, a good accordance between the numerical and experimental results for low AoA values was revealed. In Figure 7, the numerical results of the lift coefficient match the wind tunnel data exactly up to about 7 degrees. Above this value of AoA, a discrepancy with the PANUKL results occurs, which is associated with the presence of vortex lift which is not modeled by the potential flow. Also, a critical angle of attack cannot be determined by potential flow. In the nonlinear part of the lift coefficient, a discrepancy between the MGAERO and wind tunnel data occurs. This is caused by flow viscosity impact on vortex development as well as the surface roughness of the model engaged in wind tunnel tests. In terms of drag coefficient, both MGAERO and PANUKL do not model the viscosity effect, which results in a lack of friction drag. This component of the drag was calculated using a semi-empirical formula based on wetted area and equivalent friction coefficient [42]. However, due to the rocket plane's unconventional configuration, surface roughness, and relatively low Reynolds number, the friction coefficient is underestimated. The minimum drag coefficient for both numerical sets is almost the same because the same empirical formula is used to estimate it. The discrepancy between the PANUKL and MGAERO drag coefficient occurs for the same point where the discrepancy for the lift occurred. This difference in drag is associated with the induced drag, which is greater in the case of MGAERO because the lift is greater due to the presence of vortex lift. Based on the CL(CD) (Figure 8, right), it can be concluded that the impact of the vortex on aerodynamic characteristics is stronger on the lift than the impact on the induced drag. The drag coefficient for 40 degrees is around 1.0, which is typical for designs with LEX, where the lift and drag coefficient for high AoAs are of the same order. In Figure 8, the pitching moment coefficient is shown, and the pitch up effect can be observed for both the wind tunnel data and the MGAERO results. This effect is associated with a crossflow which does not occur for the PANUKL due to the potential flow model. The difference between the wind tunnel data and the MGAERO results caused by the precision of setting the reference point using the Witoszynski's balance.  
Figure 8: Basic aerodynamic characteristics obtained via various methods—pitching moment coefficient vs, angle of attack (**left**); polar drag (**right**).  
Figure 7: Basic aerodynamic characteristics obtained via various methods—lift coefficient vs. angle of attack (**left**); drag coefficient vs. angle of attack (**right**).
# Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars
###### Abstract
This study utilizes GPR data to determine the depth and size of rebar in reinforced concrete structure components such as RC slabs. A novel computer algorithm was devised to accurately extract the hyperbola representing the rebar in the radargram without requiring signal processing steps like background and noise removal. New methods were also employed to determine the time zero and electromagnetic wave velocity, offering significant advantages over existing techniques. Additionally, biquadratic, and theoretical equations for hyperbolas were developed to assess rebar spacing and depth. As a result, matching testing and theoretical curves provided rebar information regarding rebar size.
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 4 Experimental Validations  
### Construction of Monitoring System for Variable Wing Leading-Edge Morphology  
The variable wing leading-edge monitoring experimental system primarily consisted of a dynamic strain acquisition instrument, resistance strain gauges, a fixed platform, laser displacement sensors, the variable wing leading edge, and its actuating mechanism, as depicted in Figure 13a.  
The skin of the variable wing leading edge in the monitoring system is fabricated using an epoxy resin matrix reinforced with SW100A/6511 woven glass-fiber prepreg. The specific material parameters are detailed in Table 1.  
The root of the wing's leading edge was mounted on a fixed support platform, and when the rocker's arm rotated around loading point P, four connecting rods drove the main spar to achieve precise control of the leading-edge deformation.  
Experimental validation of the variable wing leading-edge morphological reconstruction method was conducted for four leading-edge deformation states with deflection angles  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**E\({}_{1}\) (GPa)** & **E\({}_{1}\) (GPa)** & **Nu\({}_{12}\)** & **G\({}_{12}\) (GPa)** & \(\epsilon_{\text{t}}\) (\(\mu\))** & \(\epsilon_{\text{c}}\) (\(\mu\)) \\ \hline
47.7 & 13.3 & 0.12 & 47.5 & 33,166 & 13,538 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the glass-fiber prepreg.  
Figure 13: (**a**) Experimental system of wing leading-edge deformation reconstruction; (**b**) integration between strain sensor and leading edge.  
of 3\({}^{\circ}\), 6\({}^{\circ}\), 9\({}^{\circ}\), and 12\({}^{\circ}\), respectively. The strain responses measured by strain gauges attached to the leading-edge skin surface were utilized as inputs for the morphological reconstruction method, as shown in Figure 13b.  
To enhance the density of strain response information collection caused by the deformation of the wing leading edge, three complementary and staggered layouts of sensing points were designed along the chordwise direction of the wing. Combining the optimization results of the sensing point quantity in Section 3.2.2, three strain-sensing paths, namely, Path A, Path B, and Path C, were established on the surface of the wing leading-edge skin. Each path was equipped with 17 strain-sensing points arranged at equal intervals, with a spacing of 57.5 mm between adjacent points.  
Among these 51 sensing points, 26 points (highlighted in yellow) were selected to gather data as inputs for the morphological reconstruction method proposed in this study, while the remaining 25 points (highlighted in blue) were used for evaluating the curvature/strain reconstruction effectiveness, as illustrated in Figure 14.
# A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion
## 2 Literature Review
### The Implication of Road Traffic Congestions for Road Traffic Collisions  
This section of the study explores the implications of road traffic congestion for road traffic collisions. Road traffic congestion refers to the high number of vehicles trying to use a certain road that cannot handle the number of vehicles [28]. Road traffic congestion mainly happens during peak hours of the day. A road traffic collision happens when a vehicle on a public road collides with another vehicle or involves hitting a stationary object,a pedestrian, or an animal. Road traffic collisions, which are commonly referred to as \"road traffic accidents\", can often result in injuries, or even a fatality [29]. The effects of road traffic congestion globally resulting in collisions imposes a burden on the economy and communities. It is well-established in the literature that road traffic congestion and collisions negatively impact road traffic safety [11; 30; 31]. An increased travel time due to road traffic congestion imposes a high cost to commuters in fuel expedition, replacement of vehicle parts, and much more.  
Reducing road traffic congestion and collisions is beneficial to commuters and transport planners, economists, and policy-makers. Researchers [30; 31] have studied the relationship between road traffic congestion and collisions; however, there is no consensus regarding the relationship between road traffic congestion and collisions. On the one hand, studies suggest that road traffic congestion is likely to result in light injuries due to the slow vehicular traffic flow. On the other hand, road traffic congestion can lead to high-impact collisions, resulting in serious injuries or fatalities and overall road traffic risks. In summary, it implies that high road traffic congestion is directly propositional to high road traffic collisions resulting in casualties. This can result from various factors, including environmental conditions, the state of the road infrastructure, weather conditions, increased car ownership, stopped vehicles, and also on-road users' behaviour which is out of the scope of the study.
# Repurposing Historic Drugs for Neutrophil-Mediated Inflammation in Skin Disorders
### Autoimmune Bullous Disorders  
Bullous diseases feature blisters and bullae on the skin and mucous membranes, driven by autoantibodies [1]. Common types include pemphigus (vulgaris and foliaceous) and pemphigoid (bullous and mucous membrane), with less common forms including epidemolysis bullosa acquisita, dermatitis herepetiformis, and linear IgA bullous dermatosis.  
The management of these diseases often involves immunosuppressive therapies, often carrying significant side effects. Bullous pemphigoid (BP), the most common autoimmune blistering skin disease, predominantly affects the elderly and is characterized by intense itching, erythema, and tense blisters caused by autoantibodies attacking the dermal-epidermal junction. Oral prednisolone still represents the standard treatment, though its use in elderly patients can result in significant adverse effects. Super-potent topical corticosteroids provide a safer alternative but may not be practical for all patients.  
Oral corticosteroids are also the primary treatment for pemphigus vulgaris, while the use of traditional corticosteroid-sparing agents remains controversial [2]. Currently, rituximab is established as a highly effective first-line adjunctive therapy in pemphigus vulgaris [3].
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Video Source Encoding  
Input videos are encoded using the VVC standard via the VVenC encoder. The encoder compresses raw YUV420-format videos to reduce bandwidth without significant visual degradation. Experiments are conducted using the following parameters:  
* Resolutions: \(320\times 180\), \(1280\times 720\), \(1920\times 1080\)
* Frame rates: 20, 30, and 50 fps
* GOP sizes: 8, 16, and 32  
The compressed output is a binary bitstream, which can be represented as Equation (1).  
\[\mathbf{b}=\{b_{1},b_{2},...,b_{N}\},\quad b_{i}\in\{0,1\} \tag{1}\]
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Signature-Based Intrusion Detection Systems (SIDS)  
According to the academic study in [30], signature intrusion detection systems (SIDS) work by giving high true-positive rates for label patterns. The detected patterns are also known as the signatures--they are stored in the database and compare current user activity with a stored pattern to identify authorized or unauthorized users. This system is lightweight and easy to implement [12].  
Figure 1 represents the model of the SIDS approach. The primary concept is to aggregate intrusion signatures' records, compare the current actions with existing signatures, and generate an alarm if they are matching. In the work in [7], we can see that SIDS habitually achieves excellent performance in precision rate for predicting known intrusions. The SIDS approach is employed in many common tools to categorize attacks involving a considerable number of network packets. The IDS is required to analyze the content of previous packets; thus, it is essential to extract the information of signature over various packets as the current malware is extra challenging to detect. Nevertheless, SIDS is less effective in detecting zero-day attacks because no matching labeled signature can be found in the database to predict the new signature that will be generated and stored in the system. The growing sophistication of zero-day attacks has caused the SIDS process to become less successful as there are no previous signature records for such attacks. Moreover, polymorphic variations of malware can degrade the detection rate to a level which can be unsatisfactory. Therefore, alternatives must be considered, such as the anomaly-based IDS presented in the next session.
## 2 Related Research
### Method Using Statistical Models
#### 2.2.2 Bayesian Filter  
Unlike fingerprinting methods using CNN, k-NN, and SVM, Bayesian filters [27] view the state estimation problem in the state-space model as probabilistic inference and transform the location estimation of a geodetic point into a calculation of the posterior probability density. Position estimation is performed by calculating the probability of positioning points and reference fingerprint points.  
It is difficult to apply the Bayesian filter directly. For different cases, the Bayesian filter is divided into Kalman filter [28] (KF) and particle filter [29] (PF). The Kalman filter is applied to linear and Gaussian state spaces. However, for indoor positioning, particle filters are often used for nonlinear and non-Gaussian state spaces, where it is difficult to satisfy linear and Gaussian [30].