esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 2 Materials and Methods
### Model Description
Note that while here, we refer to the mean duration \(\lambda\), each state has its own estimated duration \(\lambda_{i}\), such that \(\sum_{i=1}^{D}\lambda_{i}/i=\lambda\). The choice of the hyperparameters \(\alpha\) and \(\beta\) is crucial for duration inference. Setting values for \(\alpha\) and \(\beta\) determines the effect that the data will have on the posterior: larger values imply larger confidence. Several values for \(\alpha\) and \(\beta\) were examined to select the most accurate model. The same approach was adopted for the mean state magnitude \(\mu\), and a Gaussian prior with parameters \(\mu_{0}\) and \(\sigma_{0}\) was used. Table 1 shows a summary of the magnitude and duration distributions and their priors with the corresponding parameters. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Duration** & **Observations** \\ \hline
**Distribution** & Poisson & Gaussian \\
**Parameters** & \(\lambda\) & \(\mu\), \(\sigma^{2}\) \\
**Conjugate prior** & Gamma & Gaussian \\
**Hyperparameter** & \(\alpha\), \(\beta\) & \(\mu_{0}\), \(\sigma_{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distributions and parameters for state duration and magnitude and their conjugate priors. Figure 1: The hidden semi-Markov model. Each state \(S_{i}\) contains a \(D_{i}\) number of observations \(y\). The state transitions are modelled by the transition probabilities \(\pi_{i,j}\). T denotes the total observation time. The rest of the notation is explained in the main text. The HSMM model parameters were inferred with the Python library pyhsmm [19].
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods
#### 3.2.5 Kernel Methods  
The last class of regression methods included in this work are kernel methods. Kernel methods are non-linear approaches that implicitly project samples to a high-dimensional space (also called a feature space) where the model is developed. Thus, they are suitable for identifying and approximating non-linear relationships between predictors and response variable(s). In this work, two commonly used kernels were included: the polynomial kernel and the radial basis function (rbf). The former is more suitable for scenarios where the nonlinearity follows a polynomial relationship (quadratic, cubic, etc.), while the latter addresses other more general types of non-linearities. Kernel versions of PCR and PLS were included in the model comparison framework (L&NL-PAC) to enable modelling different types of non-linearities besides those described by tree-based methods (tree ensembles are more suitable when a step-wise relationship exists between the predictors and the response variable).  
Kernel PCR starts by constructing a kernel matrix \(\mathbf{K}\) (with dimensions \(n\times n\)) between all pairs of samples in the training set. The kernel matrix represents the projection of the samples in a non-linear space, where the traditional linear PCA algorithm can be applied. This implies that the relationship modelled in the original data space is non-linear. The application of PCA provides a low-dimensional scores matrix that can be regressed to the response variable. Details for kernel PCR model building and for data scaling are readily available in the literature [36].  
Kernel PLS is a natural extension of PLS that uses the kernel trick to model non-linear relationships. The starting point is the construction of a kernel matrix \(\mathbf{K}\), containing all the similarity/dissimilarity measures between all pairs of samples. The model is then builtby applying the PLS algorithm to the matrix \(\mathbf{K}\) and the response \(\mathbf{y}\). Details for kernel PLS model building and preprocessing the kernel matrices are available in the literature [36].  
Additionally, two alternatives based on PCA and kernel SVR were included in this work. Initially, PCA was applied and the first principal components were extracted. These principal components constitute the predictor set that is used in kernel SVR, with the polynomial or rbf kernels. The motivation for combining PCA and SVR stems from the fact that the original data are high-dimensional, which negatively impacts the performance of SVR. Using a compression stage first (this is often called feature extraction) allows more stable and effective models to be developed.
## 3 Results and Discussion
### Brake-Specific Fuel Consumption  
Specific fuel consumption (SFC) is defined as the quotient obtained by dividing the mass flow rate of fuel by the engine capacity. The parameter being referred to is the fuel efficiency of an engine, which quantifies the engine's ability to effectively utilise fuel resources. This metric is determined by evaluating the specific attributes of the fuel in question, with one value being provided as an example [24; 25]. Figure 4 illustrates the performance of the SFC under different engine settings, namely, using diesel fuel (D100), as well as blends, including 5%, 10%, and 15% hydrogen (DP20H5, DP20H10, and DP20H15, respectively). Typically, the SFC exhibits a reduction with an increase in engine load. This observation suggests that the engine exhibits improved performance when subjected to higher levels of workload. The graph illustrates that the SFC of the DP20H5, DP20H10, and DP20H15 engines is consistently lower than that of the D100 engine across all test situations. According to available reports, the lowest SFC for diesel fuel with a hydrogen content of 15% is documented as 142.0 g/kWh. Conversely, the highest BSFC for D100 fuel is recorded as 275.4 g/kWh. When subjected to maximum load, the 10% hydrogen diesel fuel mix exhibited the second lowest SFC efficiency. The SFC exhibits a reduction with the introduction of hydrogen into the diesel fuel, in comparison to the SFC observed for D100 [26; 27].  
Figure 3: BTE (%) with injection timing for hydrogen blends.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Decision Support Systems
#### 4.3.2 Emergency Response Assistance  
In emergency situations, swift and effective response is essential to minimize harm. LLMs can guide crews through emergency procedures by providing step-by-step instructions tailored to the specific circumstances. By understanding the nature of the emergency, the model can prioritize actions and highlight critical steps [130].  
Additionally, LLMs can facilitate communication with rescue teams and authorities by generating concise and accurate reports of the situation [131]. This includes translating distress messages into multiple languages if necessary and ensuring that essential information is conveyed clearly. The model's ability to process and relay information rapidly can improve coordination during emergencies, enhancing overall response effectiveness.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Overflow Buffer and Large Integer Conversion  
* To prevent overflow during the subsequent mixing process, each sample is prefixed with a 1-bit overflow buffer code, calculated as floor(\(log_{2}3\)) = 1 bit. This step is crucial for the big integer conversion.
* The adjusted samples are then concatenated into a single big integer, resulting in 11101100100001100101110.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 3 Simulation and Results  
### Simulation Condition  
To analyze the effectiveness of the proposed FinFET-based TPCA-PUF, the 4-bit cascaded PUF was simulated using a 45 nm bulk FinFET standard process. Table 1 summarizes the device parameters and the simulation conditions.  
For the ability of PUF to uniquely distinguish a chip among the group of other chips, 100 runs of the Monte-Carlo simulation were conducted to emulate the behavior of 100 IC  
\begin{table}
\begin{tabular}{l c} \hline \hline  & - \(\  
PUF chips. This simulation aimed to analyze the effects of the process variation, such as threshold voltage (\(V_{TH}\)) variation, the gate oxide thickness (\(T_{OX}\)) variation, and the ambient temperature variation. The \(\pm\)10% tolerance variation (for \(V_{TH}\) and \(T_{OX}\)) was set for the Monte-Carlo simulation condition to evaluate the uniqueness and the reliability of the proposed adiabatic PUF cell. Moreover, the \(\pm\)20% of supply voltage was applied to further investigate the reliability of the proposed TPCA-PUF circuit. The temperature variation was set at \(-\)40, 0, 27, 50, and 100 \({}^{\circ}\)C.
## 5 Application Results and Analysis
### Existing Wavelet Selection Methods  
To evaluate the denoising performance of the proposed method, it was compared with previous methods. EBWS and SNRBWS, the NewEBWS methods, are selected due to the similar scheme of the scale-dependent mother wavelet with the proposed method.  
(1) Energy based wavelet selection (EBWS) [8]  
According to the energy based wavelet selection method, a wavelet is selected as an optimum mother wavelet if it generates an approximation with the largest energy among all candidate wavelets for selection at each scale. For a one-dimensional wavelet decomposition, \(E_{A_{j}}\) is defined as the energy percentage of approximation coefficients at the scale \(j\) as (22), where \(i\)=1,2,..., \(j\).  
\[E_{A_{j}}=\frac{\underset{k}{\sum}cA(j,k)^{2}}{\underset{k}{\sum}cA(j,k)^{2}+ \underset{i}{\sum}\underset{k}{\sum}cD(i,k)^{2}} \tag{22}\]  
(2) SNR based wavelet selection (SNRBWS) [19]  
The separation of PD pulses and noise will be efficient as the wavelet well represents PD pulses with the least high amplitude coefficients. In this sense, the filters should concentrate the wavelet coefficients energy, rather than spread it. The SNRBWS method is based on the assumption that the highest energy concentration corresponds to the sub-band with the highest absolute coefficient value. At each scale, the optimum wavelet is selected as the one maximizing the difference between the peak amplitudes of the detail coefficients and approximation coefficients.  
(3) New energy based wavelet selection (NewEBWS) [17]  
NewEBWS follows a similar idea with SNRBWS, identifying the sub-band that concentrates most of the PD pulses representation at each decomposition scale. However, the sub-band corresponding to the PD pulses will no longer be identified as the one that has the coefficient with the maximum absolute value, but as the one with the largest coefficient energy.
## 3 Results and Discussion
### Tensile Tests  
Of the five tests performed for each group of 15%, 25%, 50%, 75%, and 100% infill samples, Figure 13 shows the results of the samples whose maximum stress values obtained in the assays were closer to the mean of the corresponding group. Table 2 presents the synthesized results of the mechanical properties obtained through the tensile tests performed.  
Figure 11: Demonstration of the fixation constants of acting forces.  
Figure 12: Allocation of acting forces.  
From the results presented, it is possible to perceive a positive correlation between the internal fill level of the PETG samples and their mechanical properties, increases in relative elongation, yield strength, tensile strength, and stiffness being observed. Importantly, the stiffness values obtained by the slope of the stress-strain curve in the elastic regime, for the present case, are not directly related to the elastic modulus of PETG (estimated at approximately 1 GPa in printed samples) due to the variation in the patterns filler employed when printing each sample group.  
The observed values for yield strength and tensile strength are also a reflection of the internal structure generated in each sample group. Higher fill patterns have a higher effective cross-section and, consequently, higher tensile strength. The higher level of fill gives the samples a greater capacity for absorbing deformation energy, which can be seen by the increased tensile strength and maximum elongation.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 4 Perceptual Evaluation
### Results
#### 4.2.1 Simple Scenes  
The median scores of the simple-scenes test, conducted to determine whether AIO improves the overall perceived similarity between binaural Ambisonic rendering and HRIR convolution, are shown in Figure 13 for each order of Ambisonics across all participants and test sound locations, with non-parametric 95% confidence intervals (CI) [57] (reference and anchor scores are omitted). The different conditions of the test were tested for statistical significance using a Friedman's analysis of variance (ANOVA) test, which showed statistical significance (\(\chi^{2}(5)=203.71,p<0.01\)). From the figure, AIO is shown to produce an increase in overall perceived similarity for all tested orders of Ambisonics. To test whether this improvement is statistically significant, Wilcoxon signed-rank tests were conducted for each Ambisonic order, and Table 2 presents the significance results. For \(M=1\) and \(M=2\), AIO produced a statistically significant improvement in overall perceived similarity between binaural Ambisonic rendering and HRIR convolution. Though an improvement can be observed for \(M=3\), it was not statistically significant at a confidence of 95%.  
To assess whether the perceptual effect of AIO varied with test sound location, a [PERSON]'s ANOVA was conducted, which showed high statistical significance (\(\chi^{2}(7)=39.61,p<0.01\)). Figure 14 illustrates the median scores with non-parametric 95% CI for each individual test sound location \(\psi\) across all participants. Post-hoc Wilcoxon signed-rank tests were conducted to determine which test sound locations produced a significant improvement in overall perceived similarity for AIO, the results of which are shown in Table 3. It is clear that results varied for test sound location differently for each tested Ambisonic order. Additionally, some participants noted minor listening fatigue in the simple scenes due to repeated pink noise bursts, so future tests should look at addressing this.  
Figure 14: Median simple-scene scores with non-parametric 95% CI across all participants for each test sound location (\(\psi\)), reference and anchor results omitted. Score indicates overall perceived similarity between binaural Ambisonic rendering and HRIR convolution.  
Figure 13: Median simple-scene scores with non-parametric 95% CI across all participants and test sound locations (\(\psi\)), reference and anchor results omitted. Score indicates overall perceived similarity between binaural Ambisonic rendering and HRIR convolution.
# Shear Wave Velocity Estimation Using the Real-Time Curve Tracing Method in Ultrasound Elastography
## 2 Method and Principle
Finally, the connected domain of the directional filter in image \(p_{i}(x,y)\) is regarded as the shear wave front, and the center points in each transverse direction in the connected domain of the shear wave front are connected into a smooth curve to complete the real-time curve tracing of the shear wave front. From the perspective of image processing, the real-time curve tracing method utilizes a curve to fit the discontinuous shear wave with a certain width, which can realize the real-time visualization of shear wave propagation. Based on the above principle, we reconstruct the shear wave velocity image by using the real-time tracing curve of shear wave propagation. For the selected region of interest (ROI), the shear wave velocity \(V(x,y)\) of the pixel point \((x,y)\) is given by Equation (7). \[V(x,y)\ =\ \frac{\Delta x}{\Delta t} \tag{7}\]  
where \(\Delta x\) is the displacement of the shear wave tracing curve relative to the previous frame, and \(\Delta t\) is the interval time between each frame of the shear wave. Equation (7) can be used to calculate the propagation velocity of the shear wave in the region of interest at each pixel, so as to reconstruct the shear wave velocity image. Certainly, interpolation and filtering algorithms can make the speed reconstruction image have better resolution and contrast. Then, the reconstructed shear wave velocity image is transformed into a two-dimensional ultrasonic elastic image by Equation (8). \[E\ =\ 3\rho c^{2} \tag{8}\]  
where \(E\) is [PERSON]'s modulus, \(\rho\) is the density of the tissue and \(c\) is the shear wave propagation velocity.
## 5 Conclusions  
Satellite data offer tremendous advantages for irrigated area mapping problems at various temporal and spatial scales. However, for more effective use of remote sensing, the analyst should be aware of the limitations and advantages of satellite data and should choose from the available irrigation mapping options accordingly. For example, methods that work in local areas may not lend themselves easily to regional and global applications. Although satellite remote sensing cannot provide the detailed information available from aerial photography or field studies, it can provide complimentary information to these conventional mapping techniques. It can also identify areas where changes are occurring and where more detailed information must be gathered.  
Techniques for improving the identification of irrigated areas using remote sensing data include the use of multi-temporal imagery and ancillary data and these methods hold true across all spatial scales considered here. In general, it was found that multi-temporal imagery and ancillary information such as climate, soils, or slope improved irrigation classification. Multi-temporal imagery provides the greatest accuracy for delineating irrigation from other land cover types. While the ideal dates will differ depending on the type and location of irrigation being studied, it is possible to make use of high frequency observations at coarse spatial resolutions even in local area investigations. In general, ancillary data, when available, improves irrigation classification. Machine learning and rule-based classification methods generally provide better results than conventional statistical classification approaches. If detailed information is required at a finer spatial resolution than the satellite sensor can provide, then a subpixel classification scheme should be used. Several methods have been developed for this purpose. In conclusion, mapping of irrigated areas with satellite imagery is difficult but possible. As with many remote sensing problems, satisfactory results may require techniques specific to the location on a case by case basis.  
Finally, multisensor data fusion provides an effective paradigm for remote sensing applications by synthesizing data from multiple sensors or sources. For example, high-resolution Landsat style observations can be integrated with low-resolution high temporal frequency observations to complement each other for the improved information extraction in irrigated settings. High spatial resolution images can also be processed by the state-of-the-art image segmentation algorithms to generate individual objects that often correspond to physically meaningful entities, e.g., a cropland unit growing wheat.
## 3 Literature Review
### Cognitive Aspects  
In this category, cognitive aspects such as cognitive load, cognitive abilities, cognitive processes, and aspects regarding learners' attention are combined. Out of the analyzed publications, 39 deal with these aspects.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Category** & **Year of publication** & **2009** & **2010** & **2011** & **2012** & **2013** & **2014** & **Count** \\ \hline
**Acceptance aspects** & 10 & 4 & 7 & 9 & 6 & - & **36** \\
**Business aspects** & 7 & 10 & 6 & 11 & 4 & - & **38** \\
**Cognitive aspects** & 8 & 10 & 6 & 8 & 7 & - & **39** \\
**Course-related aspects** & 4 & 3 & 3 & 2 & 1 & - & **13** \\
**Demographic differences** & 6 & 6 & 5 & 3 & 7 & - & **27** \\
**Influences from prior knowledge and experience** & 7 & 5 & 12 & 9 & 6 & - & **39** \\
**Instruction aspects** & 3 & 8 & 2 & 3 & 4 & - & **20** \\
**Learners’ learning aspects** & 20 & 25 & 15 & 26 & 15 & 1 & **102** \\
**Learners’ requirements** & 3 & 6 & 6 & 5 & 4 & - & **24** \\
**Learning success** & 44 & 40 & 28 & 37 & 37 & - & **186** \\
**Mind-set \& feelings before TEL** & 6 & 4 & 2 & 3 & 8 & - & **23** \\
**Mind-set \& feelings during TEL** & 14 & 21 & 19 & 23 & 25 & - & **102** \\
**Motivational aspects** & 17 & 17 & 12 & 12 & 16 & - & **74** \\
**Requirements on teachers** & - & 2 & - & - & 1 & - & **3** \\
**Self-regulation aspects** & 8 & 9 & 7 & 3 & 9 & - & **36** \\
**Social aspects** & 2 & 1 & - & 1 & - & - & **4** \\
**Support processes** & 3 & 6 & 6 & 6 & 6 & - & **27** \\
**Teachers’ teaching aspects** & 2 & 3 & 3 & 3 & 1 & - & **12** \\
**Technical infrastructure aspects** & 7 & 3 & 5 & 3 & - & - & **18** \\
**Technology-related aspects** & 5 & 3 & 6 & 6 & 10 & - & **30** \\ \hline
**Count** & **176** & **186** & **150** & **173** & **167** & **1** & **853** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identified categories of aspects per year of publication.  
For example, [PERSON] [26] studies the effect of individual cognitive differences on performance. In detail, her study deals with the impact of multimedia combinations, and the results show that display design influences the attention and cognitive load.  
[PERSON], [PERSON], [PERSON], and [PERSON] [27] evaluated in their study the cognitive impact on students while playing a teacher-created video game. Their study shows that although no effect on learning outcomes could be measured, the influence on the participants' level of engagement was significant.  
A way to track and explore learners' attention is shown by [PERSON] and [PERSON] [28]. They used eye tracking to investigate learners' attention in learning scenarios with complex animations within two experiments.
###### Abstract  
Physical rehabilitation of stroke patients is based on the daily execution of exercises with face-to-face supervision by therapists. This model cannot be sustained in the long term, due to the involved economic costs, the growing number of patients, and the aging population. Remote rehabilitation tools have emerged to address this unmet clinical need, but they face the double challenge of motivating patients and ensuring an effective remote rehabilitation. In this context, exergames allow patients to play while performing repetitive therapeutic tasks in a safe and ecological environment. This work proposes the design of Personalized Exergames Language (PEL), a language whose sentences can be processed via software in order to automatically generate exergames. The definition of exergames through PEL, guided by an effective methodology of the design and generation of personalized exergames, will include both game mechanics and the necessary metrics to monitor, guide, and adapt the rehabilitation of each patient. The integration of authoring tools are considered to visually guide the therapist when designing exergames. A study has been carried out with stroke patients and therapists from a hospital and two community centers, in order to evaluate several exergames, automatically generated using PEL, in terms of usability, understanding, and suitability.  
exergames; rehabilitation; tertiary digital prevention; stroke +
Footnote †: journal: Applied Sciences