esa-sceva/satcom-chunk-collection huggingface dataset:
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 6 Experiments With Simulated Data
### 3-D Space Localization with Mutual Information-Based Feature Selection
#### 6.1.2 Performance Impact of the Feature Vector Length  
From Figures 1 and 2, it becomes apparent that the length of the feature vector \(\bar{\mathbf{v}}\) can directly influence the localization performance. For example, a length smaller than the optimum will result in insufficient spatial information (especially in the case of the median plane), while a greater length could result in increased ambiguity because of the effects of noise. In both cases, the mean angular localization error will be affected; thus, an optimum length for \(\bar{\mathbf{v}}\) that minimizes this error must be computed at the noise power level observed in a particular localization scenario. Hence, the training process described in Algorithm 1 is applied to a range of simulated speech inputs, and the optimum feature vector length and spatial cue combination is obtained dynamically based on their MI content.  
Figure 6 illustrates the relationship between the mean angular errors and the length of the composite feature vector at different noise levels. The results are presented for three different SNRs, where the selected number of spatial cues varies from 10 to 200 in intervals of 10. The result for the 10 dB SNR case clearly illustrates the general behaviour discussed above (similar behaviour is observed at other noise levels as well), indicating an optimum feature vector length of approximately 90 elements. Note that the angular localization error for the 30 dB scenario is larger than that for the 20 dB scenario when the feature vector length is less than 60. This suggests that a short feature vector may lead to unstable localization performance; thus, a minimum length of the feature vector should be guaranteed.  
Figure 6: Localization error with respect to feature vector length.
## 4 Natural Additives in the Context of Active Food Packaging
### Plant Extracts of Various Biomasses  
Quality and quantity of plant extracts strongly depend on their biomass origin and type of extraction processes. The most common extracts have been obtained by conventional solvent extraction methods (infusion, decoction, digestion, maceration, and percolation) using solvents such as water, ethanol, methanol, chloroform, or dimethyl-sulfoxide [289; 290; 291]. Such extracts typically retard bacterial growth and can also introduce antioxidant and photoabsorbing effects. Those properties are reported for various different biomasses including (but not limited to) herbs, flowers, trees, and their fruits [292; 293; 294; 295; 296; 297; 298]. In contrast to essential oils, the effects tend to be less extensive; however, less biomass must be processed to obtain such extracts. Furthermore, as high-concentrated essential oils provide lipophilic surroundings for active substances, the application of polar active substances is only possible by (hydrophilic) extraction, thus introducing a whole new group of active compounds. The specific characteristics observed in plant extracts can be utilized by incorporating them in food packaging materials to positively affect the packed food. Prominent examples for this approach are documented in Table 7. The studies using plant extracts show similar improvements in packaging characteristics to plant essential oils. Decreased water vapor permeability; enhanced moisture and oil resistance; improved mechanical properties; and enhanced antimicrobial, antioxidant, photoabsorbing, and UV-stability are reported. While it is challenging to directly compare the obtained data with each other due to a variety of tests and extraction methods used, most researchers claim a relevant potential exists for plant-based stabilizers in food packaging applications. Again, plant-based active packaging research is typically focused on biomasses that represents foodstuff. ###encapsulated Plant Essential Oils  
Advanced methods use plant essential oils after encapsulation by a variety of different techniques, including formation of nanofibers, nanotubes, and nanoparticles [308]. This way, the essential oils are more resistant against thermal influences [309; 310; 311]. The incorporation of encapsulated essential oil typically also improves the mechanical properties of packaging materials [312; 313]. Encapsulation furthermore facilitates gradual release of active ingredients, leading to a more durable protection of the packed foodstuff. Encapsulated essential oils are also under investigation in other fields and applications including bio-based insecticides and cleaning agents [314; 315]. Recent studies utilizing the encapsulation of essential oils in the context of food packaging are presented in Table 8. The
## 4 Evaluation and Discussion  
In this section, numerical simulations are first conducted for three different algorithms, i.e., our proposed algorithm, the association method based on the maximum capacity, and the association method based on the maximum service time. Then, their performances are evaluated to validate the effectiveness of our proposed algorithm.
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 4 Damage Constitutive Model
### Evolution Law of Mesoscopic Damage
#### 4.1.2 Mesoscopic Damage Caused by Loading  
The distribution of the mineral particles' physical and mechanical characteristics in coal is random, so the strength of the micro-element body in coal can be considered to meet a specific statistical distribution type, and hence it is possible to establish the coal's statistical damage constitutive model.  
Scholars have introduced a variety of micro-element strength distribution types in their studies, including dual-parameter Weibull distribution, logarithmic normal distribution, normal distribution, and power function distribution. Studies have shown that the fitting effect of the two-parameter Weibull distribution is better than that of other distribution types [38]. Therefore, this paper assumes that the strength of the micro-element body in coal follows this distribution, and its probability density function is [39]:  
\[P(S)=\frac{m}{n}\bigg{(}\frac{S}{n}\bigg{)}\exp\bigg{[}-\bigg{(}\frac{S}{n} \bigg{)}^{m}\bigg{]} \tag{5}\]  
In Equation (5): \(P(S)\) is the strength distribution function of a coal sample micro-element; \(S\) is the random distribution variable of micro-element strength, it is a combined function of stress, known as stress level; and \(m\) and \(n\) are distribution parameters obtained by analyzing the experimental data.  
When the stress of the coal rises to a particular degree, the number of damaged micro-elements inside the coal is:  
\[N^{\prime}=\int_{0}^{S}NP(x)\mathrm{d}x=N\bigg{\{}1-\exp\bigg{[}-\bigg{(}\frac{ S}{n}\bigg{)}^{m}\bigg{]}\bigg{\}} \tag{6}\]  
In Equation (6): \(N\) is the total number of micro-elements in coal and rock and \(N^{\prime}\) is the number of damaged micro-elements.  
The number of damaged micro-elements divided by the total number of micro-elements is the damage variable \(D_{2}\) of the coal under load:  
\[D_{2}=\frac{N^{\prime}}{N} \tag{7}\]  
The equation for the damage evolution of the coal under loading is obtained by substituting Equation (6) into Equation (7):  
\[D_{2}=1-\exp\bigg{[}-\bigg{(}\frac{S}{n}\bigg{)}^{m}\bigg{]} \tag{8}\]
## 4 Implementation Details
### Interference Detector and SNR\({}_{fe}\) Estimator (IDSE)
#### 4.1.1 Interference Detector
When signal power is more than \(30\) dBm, then even in absence of adjacent interference \(E(\hat{P}_{adj})\) exceeds \(\overline{\hat{P}_{adj}^{thresh}}\). As shown later, when \(P_{sig}\) is high (SNR\({}_{FE}>15\) dB), \(Q_{dig}\) and \(f_{s}\) settings of receiver is a minimum irrespective of outcome of interference detection. Effect of \(P_{sig}\) is less severe on detecting alternate interference as alternate channels are farther in frequency domain. Variance of interference detector reduces with increase in number of pulses utilized for detection. Interference detection is done over four half sine pulses, as the variance does not change much for further increase in duration of detection.
# Fingerprints Classification through Image Analysis and Machine Learning Method
## 3 Proposed Method  
### Fingerprint Image Pre-Processing and Enhancement  
There have been many previous studies on solving the problem of a noisy or distorted image dataset. But, image pre-processing is always a challenging field for researchers to develop image quality enhancement algorithms. We focused on image pre-processing techniques based on computer vision techniques, such as image denoising to consider real-world images from an imperfect image-capturing device.  
Traditionally, image-denoising methods are often used to treat noisy images [30]. Denoising suppresses the small details and perturbations and enhances the edges. This operation can basicallybe represented as a blurring of the image, followed by the enhancement of the edges. Therefore, the resultant image emphasizes edges and suppressed details, thereby suppressing the noise in the image.  
A dataset is directly collected with much noise, especially noise with an extremely high percentage of impulses, which brings a significant challenge for image denoising. So, in the noise filter step we propose a filter method based on the convolutional neural network (CNN) [31]. This process includes two main steps. First, we develop a pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images, to detect the noisy pixels of the image and then smooth them using a Gaussian filter method. In the CNN training step, the pre-processed images are divided into overlapping patches. We use these patches as input for the convolutional neural network. Our network has three layers; in each layer, we define a set of filters and operators to generate mappings. The convolutional result of each patch is corresponding to a n-dimensional feature map. We define a convolutional layer to predict enhancing patches and reconstruct them as a result image in the third layer. In this work, the Gaussian algorithm [32] used to filter noise with mask \(3\times 3\), then a canny algorithm [33] is applied to improve enhance information edge. Therefore, when the morphology operation has processed all image, the result will be better. Image pre-processing steps are as follows (Figure 1).  
The result of image pre-processing of a fingerprint shown in Figure 2. Figure 2a,b show the results of the equalization histogram and noise filter based on CNN, Figure 2c,d show the results of edge detection and morphology.  
Figure 1: Block diagram of fingerprint image pre-processing.  
Figure 2: The result of image pre-processing of a fingerprint. (**a**,**b**) show the results of the equalization histogram and noise filter based on CNN, (**c**,**d**) show the results of edge detection and morphology
## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Glottal Gap Segmentation Techniques  
The literature reports different techniques for the glottis segmentation task depending on the user intervention. They can be grouped in semi-automatic and automatic approaches. The semi-automatic techniques let the user interact as many times as needed in order to solve any inconvenience that might appear during the segmentation process. On the contrary, the automatic techniques process all the data without any previous setting or any user intervention. From a clinical point of view, both methods present advantages and disadvantages but it is worth mentioning that semi-automatic methods are more time consuming for the clinicians, although their accuracy is expected to be better.  
In semi-automatic segmentation, the users select an arbitrary set of images within the video sequence. These frames are representative cases of the glottal cycle, for instance, maximal glottal opening [71] or minimal glottal opening [72]. Later, the user selects one or multiple seed-points belonging to the glottal area [73,74]; the posterior and anterior commissures of vocal folds [75,76,77,71];  
Figure 5: Laryngeal image sequence with its respective notation. (**Left side**) laryngeal image sequence \(I(\mathbf{x},t)\); (**right side**) single image \(I(x,y)\).  
or a region of interest (ROI) that includes the glottal gap area [11; 78]. Lastly, thresholding, region growing or active contours techniques are used to compute the glottis segmentation. A summary of the main semi-automatic algorithms contributions is presented in Table 1.  
With regard to the automatic segmentation, only a few of the existing approaches are designed to be fully automatic [87; 88; 89; 90]. However, in the last few years, the fully automatic glottal segmentation has become an active research field with growing interest [91; 92; 93; 94; 95; 96]. Up to now, there is no standardized procedure to automatically segment the glottal gap from endoscopic high-speed sequences, in spite of the extensive literature devoted to solve such a problem. Roughly speaking the authors divide the problem into three main stages: image enhancement, identification of the ROI, and glottal gap delimitation (see Figure 6). A summary of the main automatic algorithms contributions is presented in Table 2.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
## 3 Results
#### 3.1.3 Ozone--Effects of UV-A Dose  
Compared with a baseline (ambient air) amount of O\({}_{3}\) detected without UV irradiation, the concentration of O\({}_{3}\) was effectively mitigated (up to 100%) by UV-A irradiation (Table 3). This observation was consistent with our earlier UV-A research in lab-scale and pilot-scale (poultry farm) conditions [9; 10]. Therefore, the treatment of the lowest UV dose (1.3 mJ/cm\({}^{2}\)) is the most economical condition if O\({}_{3}\) is the targeted gas. It is also important to mention that the mean O\({}_{3}\) concentration in the UV mobile lab outlet was 4.7 ppb. This concentration is relatively low and likely of low concern for scaling up to farm environments, where abundant VOCs are present to react with O\({}_{3}\) and further reduce the risk of its release to the atmosphere outside the barn.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**UV-A Dose** **(m/cm\({}^{2}\))** & **Light Intensity** **(mW/cm\({}^{2}\))** & **Treatment** **Time (s)** & **Control** **(OU\({}_{\text{E}}\)/m\({}^{3}\))** & **Treatment** **(OU\({}_{\text{E}}\)/m\({}^{3}\))** & **\% Reduction** **(\(p\)-Value)** \\ \hline \multicolumn{6}{c}{UV dose control with light intensity} \\
1.3 & 0.14 & 9.5 & 378 \(\pm\) 13 & 229 \(\pm\) 75 & 39.4 (0.12) \\
2.5 & 0.26 & 9.5 & 352 \(\pm\) 8.0 & 239 \(\pm\) 24 & **32.2 (0.04)** \\
3.9 & 0.41 & 9.5 & 653 \(\pm\) 32 & 277 \(\pm\) 22 & **57.5 (0.01)** \\ \hline \multicolumn{6}{c}{UV dose control with treatment time} \\
1.3 & 0.41 & 3.2 & 198 \(\pm\) 59 & 234 \(\pm\) 60 & \(-\)18.6 (0.61) \\
2.0 & 0.41 & 4.8 & 212 \(\pm\) 31 & 206 \(\pm\) 75 & 2.9 (0.93) \\ \hline \multicolumn{6}{c}{UV dose control with light intensity and treatment time} \\
5.8 * & 0.41 and 0.04 & 9.5 and 47.6 & 653 \(\pm\) 9.2 & 243 \(\pm\) 64 & **62.7 (<0.01)** \\ \hline \end{tabular} Note. * Irradiation with 5 UV-A lamps per each chamber (#1-#12, a total of 60 lamps turned on) with additional 100 portable UV-A lamps turned on in chambers #2 and #3, 160 lamps turned on total. Inlet and outlet air temperature = 19 \(\pm\) 2 \({}^{\circ}\)C and 22 \(\pm\) 5 \({}^{\circ}\)C.  
\end{table}
Table 1: Mitigation of odor with the different UV-A doses (1.3, 2.0, 2.5, 3.9, and 5.8 mJ/cm\({}^{2}\)) irradiating gaseous emissions from swine manure. Bold signifies statistical significance.
## 3 Controller Design
### Pressure Tracking Strategy  
The nonlinear controller includes a disturbance observer-based pressure tracking controller to design the virtual control flow to the system.  
The dynamics of the tracking error \(\widetilde{x}_{c}\) is given by:  
\[\dot{\widetilde{x}}_{c}=h\big{(}x_{p}\big{)}\big{[}-A_{1}\dot{x}_{p}-d+Q_{in} (x_{1},x_{p},\alpha)\big{]}-\dot{x}_{d} \tag{15}\]  
A virtual controller \(Q_{in,d}\) for \(\widetilde{x}_{c}\) is designed as:  
\[Q_{in,d}=A_{1}\dot{x}_{p}-\hat{d}-\frac{c_{1}}{a}\widetilde{x}_{c}+\frac{1}{a }\dot{x}_{d} \tag{16}\]  
where \(c_{1}\) is the control gain.  
As mentioned earlier, the proportional valve responds more quickly than the pump, but its stroke is limited. This paper introduces a mid-range controller for allocating the flow command between the valve and pump, utilizing the valve's wider bandwidth while maintaining the spool position close to the middle of its functional range. Based on the respective frequency characteristics of the valve and pump, two filters, a high-pass filter \(H_{\text{p}}\) for the valve and a low-pass filter \(H_{\text{p}}\) for the pump, are introduced:  
\[\begin{split} H_{p}(s)&=\frac{Q_{p,d}}{Q_{in,d}}= \frac{2\zeta_{in}T_{d}s+1}{T_{d}^{2}s^{2}+2\zeta_{in}T_{d}s+1}\\ H_{\text{p}}(s)&=\frac{Q_{p,d}}{Q_{in,d}}=\frac{T_{d}^{ 2}s^{2}}{T_{d}^{2}s^{2}+2\zeta_{in}T_{d}s+1}\end{split} \tag{17}\]  
Therefore, we can obtain:  
\[\alpha(s)=H_where \(\alpha(s)\), \(\alpha_{p}(s)\) and \(\alpha_{v}(s)\) are the transfer functions between the actual supply flow of the cylinder chamber and the virtual command flow, the real pump flow and the desired pump flow, and the actual valve flow and the ideal valve flow, respectively.  
When in the low-frequency range, the pump and valve supply flow can easily track the desired flow, i.e., \(\alpha_{p}(s)\approx 1\), \(\alpha_{v}(s)\approx 1\), and we can obtain  
\[\alpha(s)\approx H_{p}(s)\cdot 1+H_{v}(s)\cdot 1\approx H_{p}+H_{v}=1 \tag{19}\]  
Similarly, when in the high-frequency range, we can obtain  
\[\alpha(s)\approx H_{p}(s)\cdot 0+H_{v}(s)\cdot 1\approx H_{v}(s)\approx 1 \tag{20}\]  
Therefore, \(\alpha(s)\) is approximately constant.  
The variable displacement angle command is given as follows:  
\[\alpha_{d}=\frac{Q_{p,d}}{k_{p}} \tag{21}\]  
From (10), the valve control voltage is given by  
\[u_{v}=\begin{cases}\frac{Q_{v,d}}{k_{q}k_{x}\sqrt{F_{s}-x}},&\text{ if }Q_{v,d}\geq 0\\ \frac{Q_{v,d}}{k_{q}k_{x}\sqrt{x-F_{t}}},&\text{ if }Q_{v,d}<0\end{cases} \tag{22}\]
###### Abstract
Their mechanical parameters, such as their modulus and loss factor, vary with temperature and loading frequency. Therefore, the parameters are considered constant, which affects the accuracy of the dynamic performance analysis of the track structure. The elastic modulus of composite sleepers is much smaller than that of concrete sleepers, meaning the application of composite sleepers increases the elasticity of the track structure and can reduce the vibration of the substructure. The vibration of the track structure and the underlying foundation caused by the wheel-rail interaction is actually a process of energy transfer. The use of composite sleepers changes the energy transfer characteristics. At present, there has been almost no analysis of the vibration characteristics of composite sleepers from an energy perspective, and with economic development in China, a large amount of industrial waste is produced. Therefore, it is necessary to systematically study the vibration characteristics of composite sleeper-ballasted tracks from the perspective of vibration energy to provide a theoretical basis for their promotion and application in China. As a means of vibration energy analysis, the power flow method has been proposed since the 1980s [19; 20] and has been used in aerospace, aviation, shipbuilding, and other fields [21; 22; 23]. In recent years, it has also been applied to track structures [24; 25; 26]. In this paper, a vehicle-track dynamic coupling model is established, and the viscoelastic properties of the composite sleeper are considered. The power flow method is adopted to compare these sleepers with concrete sleepers in order to explain the distribution and transfer characteristics of composite sleepers from the perspective of vibration energy and reveal the vibration characteristics of composite sleeper-ballasted tracks.
# Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
## 3 Results
### Transformer  
This section summarizes original studies with augmentation, classification, detection, generation and segmentation based on the transformer. As stated above, different transformers would be optimal for different tasks for the early diagnosis of GID, i.e., vision transformer (accuracy 96.9%) in the case of classification [26], multi-modal transformer (IOU 79.5%) in the case of detection [29] and multi-modal transformer (Dice 89.5%) in the case of segmentation [35]. Here, the original transformer, which consists of the encoder and the decoder, generates or classifies text data by using a combination of positional information (positional vectors), context information (embedding vectors) and attention mechanisms [17]. The vision transformer generates or classifies image data by considering image patches as if the original transformer considers text tokens [20]. The multi-modal transformer accepts both text and image data [29;35].  
A recent study with the vision transformer (i.e., TransMT-Net) aimed to develop and validate deep learning classification models for GID [26]. A major criterion for the testof the trained models was accuracy. The vision transformer and Alex (baseline) were trained, tested and compared. Based on the results of this study, the accuracy of the vision transformer was higher than that of Alex: 96.9% vs. 95.7%. Another recent study used 1886 images and the multi-modal transformer (EGCCap) for the detection of gastric cancer [29]. The multi-modal transformer and the endoscopist (baseline) were trained, validated, tested and compared. According to the findings of this study, the IOU of the multi-modal transformer was higher than that of the endoscopist: 79.5% vs. 74.9%. Another recent study with the multi-modal transformer (BiFTransNet) strived to develop and validate deep-learning segmentation models for gastrointestinal cancer [35]. The source of data was 41,987 images from public sources (UW-Madison Gastrointestinal Segmentation, Synapse Mult-organ Segmentation). A major criterion for the test of the trained and validated models was Dice. The multi-modal transformer and Unet (baseline) were trained, validated, tested and compared. The DICE of the multi-modal transformer was found to be higher than that of Unet: 89.5% vs. 88.3%.