esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### The AHP-TOPSIS-2N Method
Obtaining the standard decision matrix: The four standardization procedures most used in the literature are described by [11] as explained below: \(\bullet\) Standardization procedure N\({}_{1}\): by using the maximum value of the scores (1). \[p_{ij}=\ \frac{x_{ij}}{\max x_{ij}}\ \text{where}\ i=1,\ \ldots,m;\ \text{and}\,\ j=1,\ldots,\ n.\] (1)
* Standardization procedure N\({}_{2}\): by using the ratio between the difference of the scores and the minimum value of the scores, and the difference between the maximum value and the minimum value of the scores (2). \[p_{ij}=\frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}\,\ \text{where}\ i=1, \ldots,\ m;\ \text{and}\,\ j=1,\ldots,\ n.\] (2)
* Standardization procedure N\({}_{3}\): by using the sum of the scores (3). \[p_{ij}=\frac{x_{ij}}{\sum_{i=0}^{m}\ x_{ij}}\ \,\ \text{where}\ i=1, \ldots,\ m;\text{and}\,\ j=1,\ldots,\ n.\] (3)
* Normalization procedure N\({}_{4}\): by using the square root of the sum of the squares of the scores (4). \[p_{ij}=\frac{x_{ij}}{\sqrt{\sum_{i=0}^{m}\ x_{ij}^{2}}}\ \,\ \text{where}\ i=1, \ldots,\ m;\text{and}\,\ j=1,\ldots,\ n.\] (4)  
We emphasize that, in this study, all four normalization procedures mentioned were tested, but only two of them had consistent results in terms of the order of alternatives. As such, the AHP-TOPSIS-2N method considers the normalization procedures N\({}_{2}\) (Equation (2)) and N\({}_{4}\) (Equation (4)). Normalizations N\({}_{1}\) and N\({}_{3}\) gave many discrepant results, corroborating with the results obtained by [11], who identified the most appropriate normalizations to the hybrid methodology. 5. Construction of the Weighted Standard Decision Matrix: weighted matrices are obtained by multiplying the weights calculated in step III by the normalized matrices: 6. Obtaining the PIS (A+) and NIS (A\(-\)) (5): \[A^{+}=\left\{p_{1}^{+},p_{2}^{+},\ldots,p_{m}^{+}\right\};\ \ \ \ A^{-}=\left\{p_{1}^{-},p_{2}^{-},\ldots,p_{m}^{-}\right\}\] (5)  
It is important to note that, for criteria of the cost type, the PIS will be the smallest module value, and the NIS will be the value of the highest absolute value (6). \[A^{+}=\left\{p_{1}^{-},p_{2}^{-},\ldots,p_{m}^{-}\right\};\ \ \ \ A^{-}=\left\{p_{1}^{+},p_{2}^{+},\ldots,p_{m}^{+}\right\} \tag{6}\]  
7. Calculation of the Euclidean distances of each of the alternatives to PIS (D+) and NIS (D\(-\)) (7) and (8): \[D^{+}=\sqrt{\sum_{j=1}^{n}w_{j}\Big{(}d_{ij}^{+}\Big{)}^{2}}\] (7)\[D^{-}=\sqrt{\sum_{j=1}^{n}w_{j}\left(d_{ij}^{-}\right)^{2}}\] (8) where \(d_{ij}^{+}=P_{j}^{+}-P_{ij}\), with \(i=1,\dots,\ m.\ e\ d_{ij}^{-}=P_{j}^{-}P_{ij}\), with \(i=1,\dots,\ m\). 8. Calculation of proximity to the ideal alternative (8): \[C=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}}\] (9)
9. Ordering preferences.
## 3 Results
### Stokes Regime  
The first regime is that of very low Reynolds numbers characterized by dominating viscous effects that determine the flow. In this context, the governing dynamics is a balance between pressure and viscous forces that is expressed by the Stokes equation \(\
abla p=\mu\
abla^{2}\mathbf{u}\); this does not have inertial terms, and therefore cannot involve the fluid density \(\rho\). On the other hand, the drag force is commonly written as \(F_{D}=\rho U^{2}SC_{D}/2\), with \(S\) a reference surface that--for this problem--is the blockage area \(S=4\pi Ra\). The only way for \(F_{D}\) to get rid of \(\rho\) is that \(C_{D}\sim 1/Re=\mu/(\rho UR)\), and this is a common feature of all very low \(Re\) flows. Figure 5 shows that indeed also for the torus the drag coefficient follows this power law; here, in the interest of brevity, we show only the results for \(\Gamma=0.1\) and \(\Gamma=1\) even if the scaling \(C_{D}\sim 1/Re\) is observed for all values of \(\Gamma\).  
Figure 3: Drag coefficient as function of the Reynolds number for the flow around a sphere (\(\Gamma\to\infty\)): \(\circ\) present results, filled \(\vartriangle\) data from [13].  
For small aspect ratios \(\Gamma\), the flow tends to that around a two-dimensional cylinder (\(R/a\to\infty\)) and, accordingly, already for \(\Gamma=0.1\) the \(C_{D}\) is almost indistinguishable from that for a circular cylinder. It is worth noting that the Reynolds number for the cylinder \(Re_{d}\) is defined with the cylinder diameter, while here we use the toroidal radius \(R\); this implies that the two Reynolds numbers are related through \(Re_{d}=Re\Gamma\), which should be kept in mind when comparing the two flows.  
The behaviour is qualitatively similar for the aspect ratio \(\Gamma=1\), although the fits have different coefficients than for the two-dimensional cylinder: here we have found in the Stokes regime \(C_{D}\simeq 10/Re\). We report for comparison the data for the same flow computed in [9], which show a perfect agreement with the present ones.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 2 Related Works  
### Projection Models for the Catadioptric Imaging Systems  
The catadioptric imaging systems can be categorized based on the shape of the mirror and the positional relationship between the camera and the mirror. There are two main types of mirror shapes: plane [10; 11; 12] and curved [5; 6; 7; 8]. Using plane mirrors in imaging systems allows for acquiring 3D information by capturing viewpoints from multiple angles. This approach has a simple projection model but is limited to observing a single object or a relatively narrow scene. A plane mirror of a corresponding size is required to observe wider areas, such as open spaces, resulting in larger imaging equipment. On the other hand, curved mirrors reflect light from various directions, enabling the observation of a wide field of view. Existing methods for acquiring 3D information using catadioptric imaging systems with curved mirrors involve using multiple curved mirrors, employing multiple systems, or moving the systems. However, these methods do not provide a wide range of 3D estimation from monocular images using compact devices.  
In this research, we propose a method to obtain 3D information from a single image captured by a catadioptric imaging system comprising a curved mirror and an omnidirectional camera with a fisheye lens. To simplify the analysis of the catadioptric imaging system, we use a spherical mirror with a shape of either a complete sphere or a spherical cap cut in a plane. Analyzing such a system requires information about the incident direction of light rays from an object onto the mirror surface and the position of their reflection on the mirror surface. The projection model varies depending on the positional relationship between the camera and the mirror. When the relationship is fixed [4; 5; 6], the projection modeling is relatively straightforward. However, this approach presents challenges, such as the equipment size and accommodating changes in the object's size. On the other hand, when the relationship is variable [8], the projection modeling becomes more complex. However, it allows for observation of the object from any camera position. [PERSON] et al. [8] proposed a method for 3D estimation using multiple curved mirrors, which has shown successful results. However, the estimation process requires bundle adjustment with more than three curved mirrors. Therefore, in this research, we adopt a variable method using curved mirrors to enable observation with a high degree of freedom without limiting the observation range in open spaces.  
Figure 1: Flow of 3D Estimation Using an Omnidirectional Camera and a Spherical Mirror: The capture of two images from different viewpoints using an omnidirectional camera and a spherical mirror, and the estimation of the 3D information of the scene using stereo vision.
# Effect of Subglottic Stenosis on Expiratory Sound Using Direct Noise Calculation
###### Abstract  
Subglottic stenosis (SGS) is a rare yet potentially life-threatening condition that requires prompt identification and treatment. One of the primary symptoms of SGS is a respiratory sound that is tonal. To better understand the effect of SGS on expiratory sound, we used direct noise calculation to simulate sound production in a simplified axisymmetric configuration that included the trachea, the vocal folds, the supraglottal tract, and an open environmental space. This study focused on flow-sustained tones and explored the impact of various parameters, such as the SGS severity, the SGS distance, the flowrate, and the glottal opening size. It was found that the sound pressure level (SPL) of the expiratory sound increased with flowrate. SGS had little effect on the sound until its severity approached 75% and SPL increased rapidly as the severity approached 100%. The results also revealed that the tonal components of the sound predominantly came from hole tones and tract harmonics and their coupling. The spectra of the sound were greatly influenced by constricting the glottis, which suggests that respiratory tasks that involve maneuvers to change the glottal opening size could be useful in gathering more information on respiratory sound to aid in the diagnosis of subglottic stenosis.  
flow-sustained tones; hole tone; whistle; respiratory sound; subglottic stenosis Article
## 3 Results
### Test 2: On-Water Slalom  
The participant performed n = 25 on-water slalom runs on the same course with a mean \(\pm\) SD (range) duration of 53.35 \(\pm\) 3.31 s (46.35-64.00) with a total of 1329 data samples. Data were calculated using 26 \(\pm\) 2 (24-31) satellites within the constellation, where athlete trajectory was 96.25 \(\pm\) 1.52 m (94.14-102.1) with an average speed of 1.81 \(\pm\) 0.09 m\(\cdot\)s\({}^{-1}\) (1.60-2.05). Upon a visual inspection of geographical trajectory, no errors were identified in the dataset for the on-water test. The mean \(\pm\) SD (95% CI) accuracy at the start of the test was determined to be 0.315 \(\pm\) 0.109 (0.265-0.364) m. The overall Hrms was 0.35 \(\pm\) 0.05 (0.349-0.354) m, with a corresponding HDOP 0.56 \(\pm\) 0.03 (0.555-0.558) and PDOP 1.13 \(\pm\) 0.03 (1.123-1.236). The frequency distribution of Hrms and dilution of precision data is shown in Figure 5 where the classification [23] of dilution of precision was ideal for HDOP, while it was excellent for PDOP.  
Comparison between dry-land simulation and on-water tests using a Kruskal-Wallis one-way ANOVA presented significant differences for the number of satellites used within the constellation (H\({}_{(3,75)}\) = 22.15, \(p\)! 0.0001, Figure 6A), but no significant differences for Hrms (H\({}_{(3,75)}\) = 4.036, \(p\) = 0.133, Figure 6B), while significant differences were found for HDOP (H\({}_{(3,75)}\) = 24.71, \(p\)! 0.0001, Figure 6C) and PDOP (H\({}_{(3,75)}\) = 39.49, \(p\)! 0.0001, Figure 6D), even though all data were classified as ideal for HDOP and excellent for PDOP. Post hoc analysis showed no differences between the dry-land simulation courses (\(p\)! 0.05) for any of the dependent variables, while there were significant differences between both the dry land course simulations and the on-water test for satellites used, HDOP and PDOP data (Figure 6).  
Figure 5: Frequency distribution for (**A**) number of satellites used by device, (**B**) the Hrms (m), (**C**) the horizontal dilution of precision (HDOP), and (**D**) the 3D position dilution of precision (PDOP), for every data point for the on-water slalom simulation.
# Address Space Layout Randomization Next Generation
## 7 ASLR-NG: Address Space Layout Randomization Next Generation
### Algorithm  
When a process is created, the area reserved to avoid fragmentation is defined by setting the variables min_ASLR and max_ASLR. This is the range that will be used to allocate objects (allocation area).  
The direction of a specific-zone is a random bit with a probability of pointing towards the middle of the allocation range inversely proportional to the distance of the base address to the middle--the expression is in Listing 1. In other words, if the base address is close to the border of the allocating range, then the direction is more likely to point toward the other side of the range. This way, objects will not accumulate at the borders of the allocation area.  
```
new_zone(low,high,zone){ zone.base=randomize_range(low,high); zone.direction=randomize_range(low,high)<zone.base?TOPDOWN:DOWNTOP; } do_exec(){... reserved=VM_SIZE*percentage_reserved/100; min_ASLR=reserved*(rand()%2); max_ASLR=min_ASLR+VM_SIZE-reserved; new_zone(min_ASLR,max_ASLR,mmap_base); new_zone(min_ASLR,max_ASLR,huge_pages); new_zone(min_ASLR,max_ASLR,thread_stacks);... }
```  
Listing 1: ASLR-NG initialization pseudo-code.  
A detailed analysis of the distribution of the objects at the borders of the allocation area is beyond the remit of this paper, but for now we can say that the presented algorithm to determine the direction gives a fair distribution along the whole range, with no accumulation areas (addresses with higher probability), regardless of the number of objects in the zone and the workload mix.  
The algorithm employed to allocate an object works by first selecting a value as a hint address, in order to place the object, and then to look for a free gap in which to actually place the object. The algorithm is as follows:  
1. Obtain the hint address and the direction: * if it is to a specific-zone, then the hint address and the direction are the ones from the specific-zone. * if it is an isolated object, then the hint address is a random value from the allocation range [min_ASLR, max_ASLR] and the direction is top-down.
2. Look for a gap large enough to hold the request from the hint address to the limit of the allocation area determined by the direction. If found, then succeed.  
Look for a gap large enough to hold the request from the hint address to the limit of the allocation area determined by the reverse direction. If found, then succeed.
4. Look for a gap large enough to hold the request from the full VM space, starting from the allocation area and working towards the reserved area. If found, then succeed.
5. Out of memory error.  
Even if there is no reserved area, step 4 is necessary to guarantee that the whole virtual memory is covered properly. For example, as illustrated in the Figure 16d, the gaps [Id.so \(\leftrightarrow\) mmap_base] and [mmap_base \(\leftrightarrow\) vDSO] are not suitable for a large request, but the gap [Id.so \(\leftrightarrow\) vDSO] can be used if a global search is done.
# A Creep Model of Asphal Mixture Based on Variable Order Fractional Derivative
## 1 Introduction
[12] analyzed the creep and creep recovery test data and adopted the fractional Burgers model to characterize the viscoelastic behaviors of asphalt mixture. [PERSON] et al. [13] proposed a fractional rheological model consisting of a fractional Maxwell model and a modified fractional Kelvin model to describe the static viscoelasticity of asphalt mixture. [PERSON] et al. [14] proposed a modified fractional Zener model to quantitatively describe the symmetric dynamic viscoelastic properties of asphalt mixture. Nevertheless, the mentioned fractional differential constitutive models have their limitations, such as no change in the fractional differential order and without considering the rheological property change in creep process. Variable order fractional calculus developed from the basis of classic fractional calculus theory [15; 16; 17], has attracted much attention in recent years. [PERSON] et al. [18] adopted the variable-order fractional Maxwell model to analyze the creep behavior of salt rock at low stress level. [PERSON] et al. [19] further proposed a creep model derived from variable-order fractional calculus in order to describe the nonlinear creep deformation of rock. Although some researchers have proposed viscoelastic and viscoplastic constitutive models for asphalt mixture on the basis of constant-order fractional derivative, there is no report about the analysis of the mechanical behavior of asphalt mixture based on variable-order fractional differential theory. In this paper, a new viscoelasticity creep damage model based on a variable-order fractional differential will be constructed to study the creep behavior of asphalt mixture at various stress levels. The model can describe the whole creep process including primary, steady and tertiary stages, it uses variable-order fractional elements to model the primary and steady creep, and uses time-varying viscoplastic element to consider the damage induced tertiary creep stage. The paper is organized as follows: the basic constitutive models of the constant order fractional viscoelastic element and the variable-order fractional viscoelastic element are briefly derived in Section 2. In order to model the damage induced tertiary creep, the dashpot element with variable viscosity is introduced in Section 3. The new viscoelastic-crep damage model based on variable-order fractional differential is then constructed in Section 4 and verified with experiments in Section 5. Finally, some conclusions are drawn in Section 6.
## Results and Discussion
### Biological Evaluation  
#### 3.2.1 Antimicrobial Evaluation of 6a-j  
Synthesized \(N\)-benzylisatin-aryl hydrazones (6a-j) were evaluated for their antibacterial activity against two Gram-positive Staphylococcus aureus ATCC 29,213 and _Bacillus subtilis_ ATCC 3366, four Gram-negative, _Escherichia coli_ ATCC 25922, _Klebsiella pneumoniae_ ATCC 13883, _Salmonella typhi_ ATCC 25,566 and _Pseudomonas aeruginosa_ ATCC 27,853 bacterial strains. As expected, the result of the antibacterial evaluation revealed no effect of these compounds (6a-j) on selected strains of bacteria in concentration up to 100 ug/disc (where no significant inhibition zones were detected). In case of antifungal activity, no inhibition zone was detected against _Candida albicans_ NRRL Y-477. Excitingly, our hypothesis matched with the antimicrobial data of the evaluated compounds 6a-j, which are all having low molecular weight (353-418 Da), conforming to [PERSON] and [PERSON]'s findings. In addition, the antimicrobial data of 6a-j also meet the rule of five (RO5) with logP value of 9.36-11.8 (please see Section 3.4), which is far more than to have anti-microbial activity. In case of antifungal activity, there were no inhibition zones detected against _Candida albicans_ NRRL Y-477 either.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 5 Conclusions and Future Studies  
The incorporation of plant-based by-products into pasta production offers significant nutritional advantages, particularly by increasing fiber content and bioactive compounds such as polyphenols and carotenoids. However, this inclusion alters the culinary quality of the pasta, generally reducing optimal cooking times, increasing cooking losses, modifying water absorption during cooking, and darkening the color of the pasta, thereby changing its hue. The main drawback of these incorporations is a decrease in the organoleptic quality of the pasta, particularly leading to lower consumer acceptability. Obviously, these effects, both negative and positive, are less pronounced when the amount of by-product incorporated is reduced.  
Most studies conducted to date have focused on analyzing the impact of by-product incorporation on pasta quality. However, no studies have examined various factors of interest related to these effects. It would be useful to investigate how pre-processing treatments of the by-products, such as drying and especially milling (and thus particle size), influence the quality and acceptability of the resulting pasta. Likewise, it would be beneficial to explore how pasta quality can be improved through adjustments in formulation and processing when by-products are included. Although some studies have addressed this topic through methods such as enzymatic reinforcement of the gluten network [70] or the incorporation of gums [93], these are scarce, and further research is needed. Additionally, studies aimed at improving the acceptability of the resulting pasta through its final cooking methods, including the use of sauces or other seasonings, should not be overlooked.  
Conceptualization, M.G.; writing--original draft, M.G., M.B., R.F., and F.P.; writing--review and editing, M.G. and F.P. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the FEDER Cooperacion Interreg VI A Espana--Portugal (POCTEP) 2021-2027 through the project TRANSCoLAB PLUS 0112_TRANSCOLAB_PLUS_2_P.  
Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors would like to acknowledge the financial support from the University of Vallalodid and Santander Bank for the predoctoral fellowship of F.P.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Compressive Strength
#### 3.1.1 Influence of the Amount of Portland Cement Replacement by GGBS  
As shown in Figure 10, the compressive strength at 7 days of age is higher for Portland cement-only concrete, and decreases with an increase in the addition of GGBS. The rate of hydration of GGBS is usually lower than that of Portland cement, which results in a lower rate of strength gain for concrete with additions of slag. Consequently, GGBS concrete will present a lower compressive strength at early ages. Furthermore, given that the reactivity of the slag is lower than that of the Portland cement concrete, the compressive strength at early ages will be lower for concretes in which the contribution of slag for the compressive strength is higher, i.e., concrete mixes where slag replaces a higher amount of cement [2].  
Even though the rate of strength development is different for all the mixes, the average compressive strength obtained for all concrete qualities at 28 days is very similar. In the present investigation, the k-factor concept was used. The efficiency factor is used in order to obtain similar compressive strength at 28 days for concrete with additions as the comparable Portland-cement concrete. The results obtained show the adequacy of using the efficiency factor in the production of concrete with addition to obtain similar compressive strength at 28 days of age. The efficiency factor is, therefore, used to offset the slower hydration of the supplementary cementitious materials, which results in a higher compressive strength than it would be obtained if slag would replace Portland cement on a one-to-one basis.  
Figure 9: Compressive strength test set-up.  
The results also show that, for 56 days of age, the compressive strength increases with an increase in slag content, at least for concrete with percentages of replacement of 100% by mass of GGBS of the Portland cement content (50% of the total binder content), with the reference Portland cement concrete presenting the lowest compressive strength. These results are also in agreement with the literature. The hydration of GGBS continues for longer periods of time, when compared to the hydration of Portland cement, resulting in a higher compressive strength of the GGBS concrete at later ages. On the other hand, the denser microstructure of GGBS concrete also contributes to a higher strength, as long as sufficient hydration has occurred [4].