esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensory Analysis of the Experimental Chips
The left side is a summary of the descriptors, which are sorted by their product discrimination. All attributes were discriminated by the panel; that is, there are differences between the two products for each attribute under consideration. In the same table, the assessors are ranked according to their average rank of the individual effects on all attributes. If the panelist discriminated between the two products (\(p<0.05\)) and agreed with the panel, + is shown; otherwise, \(-\) is shown. Checking panelists' performance showed excellent results for most attributes: all panelists discriminated between the two products and agreed with the rest of the panel (+ cells). For the walnut aroma, four panelists (numbers 1, 4, 5, and 9) were discriminant and disagreed with the rest of the panel (\(-\) cells). Panelist no. 2 was the only one who detected differences between the products by pulse-likeness and disagreed with the panel (\(-\) cell). Also, for the hazelnut aroma, panelists number 1, 5, and 9 were discriminant and disagreed with the panel. The ranking of the panel members based on RankF shows that panel member no. 7 was the most discriminating when all the attributes were considered, while panel member no. 1 was the least discriminating. Our findings highlighted that overall, the experts had a good individual agreement with the whole panel. Here, for the first time, the HSR fortification was analyzed to improve the sensorial analysis of snacks based on lentil flour. Indeed, in previous papers, it was shown that snacks based on lentil flour achieved a substantially low flavor acceptability score, indicating that flavor was the greatest challenge to the development of a successful pulse-containing snack [28; 29]. To the best of our knowledge, in the literature, only one paper applied another strategy to improve the flavor in pulse-based snacks in a way comparable with the present  
Figure 3: PCA loading plot showing the multivariate variation between the two products in terms of sensory attributes and overall judgment. CTRc: control chip recipe; HSc5%: hazelnut skin chip 5% recipe. study. Indeed, [PERSON] et al. fortified \"diamond cuts\" snacks with 9% germinated pulse flour and compared them with 9% non-germinated pulse flour. This strategy improved the overall acceptability score by 1.2 points (from 6.2 to 7.4 in non-germinated vs. germinated pulse flour, respectively); additionally, the maximum percentage of incorporated pulses was low and equal to 9% [30]. Here, similarly, HSR significantly improved the overall judgment score by 1.2 points (from 5.6 to 6.8 in CTRc and HSc5%, respectively), with the pulse-like aroma decreasing from 8.6 to 4.3 but with 65.2% DLF in the recipe.
# Deep Instance Segmentation of Laboratory Animals in Thermal Images
## 1 Introduction
### Two-Stage Methods  
Treating segmentation as an extension of the object detection is adopted by most of two-stage methods [21; 22; 23; 24]. They are designed according to the dominant paradigm for instance segmentation which is: detect-then-segment. First, it detects an object with a box and then segments each object using the box as a guide.  
Mask R-CNN [23] is one of a state of the art two-stage method of instance segmentation. It extends Fast [25] and Faster R-CNN [26] methods by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition [23]. This method detects object bounding boxes, and then crops and segments them to find the objects.  
Many approaches based on Mask R-CNN also have dominated leaderboards of recent segmentation challenges.  
In 2019 Mask Scoring R-CNN algorithm was introduced in [24]. This method focuses on scoring the predicted instance masks. The model learns an Intersection-over-Union (IoU) score for each mask and improves segmentation results by rewarding more accurate predictions during COCO AP evaluation.  
The method named PANet presented in [27] improves Mask R-CNN in three steps by:  
1. creating bottom-up path augmentation,
2. adaptive feature pooling,
3. augmenting mask prediction with fully connected layers.  
The results outperform Mask R-CNN architecture but are re-implemented by the PANet authors.  
Another extension of the Mask R-CNN model, this time with a cascade architecture, is the Hybrid Task Cascade (HTC) [28]. It improves the segmentation accuracy by incorporating cascade and multi-tasking at each stage and explores more contextual information from the spatial context.  
State-of-the-art two-stage methods achieve outstanding results. However, due to the cascade strategy, these methods are usually time- and memory-consuming. That is why recently, performing instance detection and segmentation in a single-stage architecture [29; 30; 31; 32] has become more and more popular, even though it does not yet perform as good as the two-stage methods.
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 4 Controller Validation
### Dynamic Simulations
Table 2 shows quantitative data for the comparison of control performance in Figure 5. In regions where the wind speed is lower than the rated wind speed, the performance of the three controllers was similar because they frequently applied the strategy of maximizing power production (i.e., MPPT control). When compared to PI controllers, the mean power differences for the LQR and \(H_{\infty}\) controllers were 0.09% and \(-\)0.01%, respectively, while the fatigue load reductions were \(-\)2.93% and \(-\)3.23%, respectively. In regions with a wind speed higher than the rated wind speed compared to the PI controller, the rotor speed deviations of the LQR and \(H_{\infty}\) controllers were reduced by 45.34% and 62.31%, respectively. Similarly, the power deviations were reduced by \(-\)52.81% and \(-\)60.32%, respectively. Additionally, the fatigue loads were reduced by \(-\)13.22% and \(-\)14.04%.
# SCS-JPGD: Single-Channel-Signal Joint Projected Gradient Descent
## 2 Related Works
### One-Dimensional Convolutional Neural Network  
The 1D Convolutional Neural Network (CNN) has shown outstanding performance in processing time-series and signal data, effectively capturing the temporal features of signals. One-Dimensional CNNs have advantages in computational efficiency and parameter count, making them well suited for vibration signal analysis in industrial fault diagnosis. Numerous studies have demonstrated that 1D CNNs can achieve high accuracy and fast computation in fault classification tasks, making them ideal for real-time fault diagnosis systems.  
Let the input signal be a one-dimensional sequence \(x=[x_{1},x_{2},\ldots,x_{n}]\), where \(n\) is the length of the input signal, and let the convolution kernel (filter) be \(w=[w_{1},w_{2},\ldots,w_{k}]\), where \(k\) is the length of the convolution kernel. The output of the convolution operation \(y=[y_{1},y_{2},\ldots,y_{m}]\) can be computed as shown in Equation (4).  
\[y_{t}=\sum_{i=1}^{k}x_{t+i-1}\cdot w_{i}+b_{t} \tag{4}\]  
In this context, \(y_{t}\) represents the \(t\)-th output value after convolution, \(x_{t+i-1}\) is the \((t+i-1)\)-th element of the input signal, \(w_{i}\) is the \(i\)-th element of the convolution kernel, and \(b_{t}\) is the bias term for the \(t\)-th output value. The application of 1D Convolutional Neural Networks (CNNs) in time-series signal processing demonstrates its unique advantages. Compared to traditional machine learning methods, 1D CNNs can automatically extract deep temporal features from raw signals, avoiding the complexity of manually designed features. This network architecture, with its local receptive fields and sliding mechanism of convolution kernels, effectively captures the temporal variations in the signal and can identify subtle anomalies in complex signals. In vibration signal analysis, 1D CNNs can accurately distinguish between different types of faults, thereby improving both the accuracy and real-time performance of fault diagnosis.
## 1 Introduction
Unlike conventional methods, the proposed method applies to perturb the color information by maintaining the similarity with an original image in the special range we suggest and considering color histogram information of the classified objects. Additionally, the reason why we consider the concept of color jittering is that the noise is related to pixel values. The proposed method automatically determines the optimal perturbation range by calculating the image similarity between the original and noisy images. In addition, we analyzed the color distribution based on the histograms of the training images. The weights were calculated using color perturbation based on the color distribution. We then generated new training images using the color perturbation method based on the image similarity to the calculated weights. Subsequently, the pretrained network was retrained using the images generated by the proposed method. To validate the proposed method, we compared its performance with that of the image classification results of the trained models using the test images. The remainder of this paper is organized as follows: In Section 2, we review related studies on data augmentation, adversarial attacks, and similarity calculations. In Section 3, we describe the proposed data augmentation method that surmounts data deficiencies and guarantees noise robustness. In Section 4, we present the experimental results of the proposed method. Finally, we present our conclusions in Section 5.
## 3 The ZAPROS III-\(i\) Method
The flowchart with steps to apply the ZAPROS III-_i_ method to rank a set of alternatives was presented in [17] and is shown in Figure 2. In the first stage, problem formulation, the relevant criteria and their values are obtained through the decision-making process. In the second stage, elicitation of preferences, the preference scale is generated based on the decision-maker's preference. As mentioned, this stage occurs in two steps: (i) elicitation of preferences for quality variation of the same criterion, and (ii) elicitation of preferences between pairs of criteria. In the last stage, comparison of alternatives, the alternatives are compared based on the decision-maker's preferences. In the elicitation of preferences stage, decision-maker responses allow ranking of all quality variations (QV) from the scales of two criteria. This ranking is called the joint scale of quality variation  
Figure 2: Procedure to apply ZAPROS III-_i_ Method [17]. (JSQV) for two criteria. All criteria are submitted to the same process. Finally, the scale of preferences for quality variations (JSQV) for all criteria is constructed [17]. As an example of the flow diagram shown in Figure 2, we briefly show that the task of eliciting the preferences of the decision-maker consists of comparing all the quality variations (QV) obtained on a two-criteria scale by means of questions to him. After obtaining all the QV's, a joint result is obtained and the JSQV is obtained, e.g., c1 < a1 < b1 < a2 < b2 < c2 < a3 < b3 < c3. Since each criterion has a set of alternatives (Y1 = {A1, B2, C2} and Y2 = {A2, B1, C2}), we can compare these alternatives with respect to JSQV and obtain a FIQ for each of these alternatives, e.g., Y1 = 3 and Y2 = 9. In this example, Y1 is more preferable than Y2. A more consistent explanation can be found in [17]. As can be seen, VDA has many techniques. ZAPROS III-\(i\) was adopted in this work because it has a method that was developed for VDA and generates a ranking of very good alternatives. To facilitate the decision-making process and to carry it out consistently, a tool called ARAAU was developed [22]. The tool was first developed to support the ZAPROS III method. In this work, we use an updated version of the ZAPROS III-\(i\) method. The ARAAU tool serves as a means of data entry and facilitates the methodology of this work because it has the ZAPROS III-\(i\) method incorporated into it.
## 4 Results
### Dataset X1
#### 4.1.2 Cumulative Explained Variance Ratio for Dataset X1  
Analysis of the explained variance ratio for Dataset X1 yields the following insights:  
* The first principal component accounts for 60% of the dataset's variance.
* The first two principal components collectively explain 83.4% of the variance.
* By the fifth principal component, 99.2% of the variance is explained.
# Impact of Pore Geometry and Water Saturation on Gas Effective Diffusion Coefficient in Soil
## 2 Materials and Methods
### Pore Diameter and Water Distribution  
We quantified the pore size distribution and connectivity of the soil using a morphological model as that in Hu, Huang and Zhang [38] and Hu, Liu and Zhang [39]. In the morphological model, the void space is assumed to be an assembly of overlapped spheres of different diameters and the diameter of each pore is defined as the diameter of the largest sphere that can occupy the pore. For each void voxel shown in Figure 1b, we first calculated its shortest distance to the solid walls and then, we started from the pore voxel that is most distant from the solid walls and sign it as the center of a sphere. The sphere is then expanded until it touches a solid wall; the void voxels within this expanded sphere are together defined as a temporary pore, and its diameter is defined as the temporary diameter of the sphere. We then repeated the above procedures to all the pore voxels and progressively updated their temporary diameters. For overlapped spheres, the pore voxels in the overlapped zone were assigned to the diameter of the largest sphere that covered the voxels. Figure 1c shows the pore-diameter distribution of the soil, and the mean pore diameter was 5.26 voxels.  
We used the method as used in [PERSON] et al. [38; 39] to determine the distribution of water in the soil. In this method we assumed that soils are hydrophobic and water distribution in the soil is regulated by capillary pressure which depends on the pore diameter \(\delta\). The associated capillary pressure with the pore was assumed to be \(p=\sigma/\delta\), where \(p\) is capillary pressure, \(\sigma\) is the air-water surface tension, as the air-water interface is part of a sphere with diameter \(\delta\) and all pores smaller than \(\delta\) are filled by water. Water distribution in soil is hysteretic and here we just calculated it under wetting processes as shown in Figure 2.  
Due to the technical limitations and computational reasons, the achievable voxel size by current tomography devices is limited by the sample size, which means that if we needed a good representativeness of the soil sample, we would sacrifice the resolution of 3D soil image [31] and all pores which are smaller than the voxel size cannot be explicitly resolved. To investigate the gas diffusion coefficient in a wild range of scales and avoid taking small sub-samples, we shrunk the voxel size of the soil image from \(10^{-1}\) nm to \(10^{3}\) nm to obtain a series of soil samples with same geometry as shown in Figure 1b. The mean pore diameter of these samples ranged approximately from 0.5 nm to 5 \(\upmu\)m.  
Figure 1: (**a**) 3D image of soil, acquired using X-ray tomography, with the pores in black and solid in grey; (**b**) the cube (sample 1) trimmed from the soil; (**c**) pore-size distribution.
## 4 Numerical Examples and Validation of the Software
### Numerical Examples in 1d
One observes that, for the given examples, the number of iterations of the augmented Lagrangian algorithm do not differ  
Figure 1:2 Concentration profiles (**a**,**d**,**g**), evaluation of the Lagrangian multipliers (**b**,**e**,**h**) and estimators (**c**,**f**,**i**) during the augmented Lagrangian scheme in the 1-st, 44-th and 89-th iteration w.r.t. parameters given in Table 3 and adaptive mesh refinement. between uniform and adaptive mesh refinement, as seen in Figure 1:3 However, one sees, in Table 4, that the usage of the adaptive algorithm is far more efficient than the usage of the uniform refinement. Especially for the nonlinear system of ODEs, as described by the parameter set given in Table 3, a massive reduction of computation time was obtained. All together, one can conclude that the numerical scheme works for the given scheme and examples. Because of the fact that no rigorous proofs are done in this paper, the convergence remains as conjecture, based on the numerical results in this section.
## 3 Results
### Static Identification  
The static torque of the NES was measured to verify the restoring torque predicted in Section 3 with the proposed design. The static identification test was conducted before the NES was installed on the gear pair assembly for dynamic testing, using the custom apparatus shown in the sketch in Figure 8. The NES attachment shaft was rigidly held in a vise. A lever was eccentrically attached on the back side of the NES disk in order to create a mechanical advantage for point B. Incremental weights were hanged from  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Inertia of pinion [kgm\({}^{2}\)] & 0.0004 \\ Inertia of gear [kgm\({}^{2}\)] & 0.005 \\ Radius of pinion [m] & \(30\times 10^{-3}\) \\ Radius of gear [m] & \(60\times 10^{-3}\) \\ Pinion teeth number & 20 \\ Gear teeth number & 40 \\ Gear ratio & 2 \\ Pinion base torque [Nm] & 2 \\ Gear torque [Nm] & 4 \\ Backlash [m] & \(40\times 10^{-6}\) \\ Torque amplitude [Nm] & 0.8 \\ NES inertia [kgm\({}^{2}\)] & 0.0004 \\ NES damping \(c_{N}\) [Nms/rad] & 0.002 \\ Mesh damping ratio [-] & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the gear assembly.  
Figure 7: (**a**) Experimental setup; (**b**) NES disk.  
point B, calculating the angular deflection of the NES disk at each increment based on measurements of h (Figure 8) and known trigonometric relations.  
The static identification tests were repeated three times in each direction, clockwise and anti-clockwise, leading to the static restoring torque shown in Figure 9. The experimentally measured static torque of the NES was found to be in very good agreement with the torque profile predicted in Section 3. Note that Figure 9 compares the experimentally recorded static torque with the Taylor expansion in Equation (6). However, as was shown in Figure 6, Taylor expansions up to the fifth order both yield reasonably accurate expressions. This reaffirms confidence in the presented numerical approach in Section 2.
## 1 Introduction
Key technical requirements for effective composting include the regulation of critical parameters, such as the temperature, moisture content, aeration, carbon-to-nitrogen (C/N) ratio, and pH. Optimal moisture levels, typically between 50% and 60%, are essential to facilitate microbial activity while preventing anaerobic conditions or desiccation of the composting material. Adequate aeration is crucial to supply oxygen for aerobic respiration and to dissipate excess heat and moisture; this can be achieved through passive air flow, mechanical turning, or forced aeration systems. The C/N ratio is a vital factor influencing microbial metabolism and should generally be maintained between 25:1 and 30:1 to optimize decomposition rates. pH levels are also important, with neutral to slightly alkaline conditions (pH 6.5-8.0) being favorable for most microbial activity [33]. The technical implementation of composting requires the careful consideration of system design and operational strategies. The selection of the composting method--such as window, aerated static pile, or in-vessel systems--depends on factors like the available space, desired processing time, and environmental controls. Monitoring equipment for temperature, oxygen levels, and moisture content is necessary to ensure that the conditions remain within optimal ranges throughout the process. Additionally, the use of bulking agents or amendments, such as wood chips or straw, can enhance the physical structure of the sludge, improving the porosity and facilitating air flow. This study evaluated the characteristics of the product obtained through improved composting (AC). This method was previously proposed by [PERSON] [33]; however, in this research, it was modernized and based on dynamic composting with temperature regulation and enhanced raw material for product production. Furthermore, the scientific novelty of this study lay in examining the characteristics of fertilizers derived from low-organic-content wastewater sludge typical of small treatment facilities in the Arctic regions. These areas require accessible fertilizers to enhance the soil fertility, making this research particularly relevant for these regions. The composted sludge was compared with sludge subjected to accelerated laboratory treatment (freeze-thaw-drying cycles in a climatic chamber) and with sludge collected from a disposal site under real conditions after five years of storage. In agriculture, the effectiveness of fertilizers is largely determined by their nitrogen and phosphorus contents. For substantial agronomic benefits, fertilizers should contain between 5% and 46% nitrogen and between 5% and 52% phosphorus (expressed as P\({}_{2}\)O\({}_{5}\)) by weight. In wastewater sludge after drying, the nitrogen content typically ranges around 2-5%, and phosphorus (as P\({}_{2}\)O\({}_{5}\)) is about 1-4% [18]. These values may slightly increase after composting due to the retention of nutrients.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Optoelectronic Oscillator
#### 3.3.3 Optoelectronic Devices and Modulation Formats' Applications in the 5G Network  
To improve the network performance, innovative functional splits and related standards have been introduced in 5G. In this context, new quality-of-service requirements are imposed on optical transport. Based on the system requirements, several advanced transceiver configurations and modulation formats can be employed for different 5G use cases. Furthermore, various transceiver components can be selected based on the network demand. Table 3 summarizes some of the presented modulation formats and advanced optoelectronic devices for 5G applications considering optical transceivers discussed in Section 2.3.2. In this regard, we focus on different transceiver configurations for 5G X-haul (FH, MH, and BH). The related wavelength and the achievable transmission distance are considered.