esa-sceva/satcom-chunk-collection huggingface dataset:
# Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease
## 5 Conclusions  
In the present paper, a rapid, nondestructive, cost-effective and noninvasive diagnostic method for the detection and discrimination of pediatric onset inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC), is presented. The proposed methodology makes use of Raman spectroscopy in order to probe the changes in secondary structures occurring in proteic extract from fecal samples of pediatric patients that underwent to IBD, i.e., CD or UC, with respect to subjects for which any presence of IBD is excluded. The detected spectral modifications in the protein network were quantified, through a deconvolution procedure into Voigt profiles applied to the broad amide I vibrational band, by the evaluation of the cross-linking ratio \(R\) between nonreducible (trivalent) and reducible (divalent) structures. The aforementioned parameter is found to significantly increase upon inflammation, suggesting a more interconnected proteic network, and in a different extent according to CD or UC disease. Based on its value, then, the distinction between non-IBD and IBD subjects, and among the last ones, between CD and UC, was achieved.  
Accuracy, sensitivity, and specificity of the proposed diagnostic test were determined using the receiver operating characteristic (ROC) curve and Youden index (_J_), which provided excellent results.  
The overall results suggest a possible use of the Raman technique as an early diagnostic tool for such classes of inflammatory bowel diseases of very high incidence, with enormous implications in facilitating, for example, early specific treatments, and for which, at the same time, biopsy and/or any invasive procedure are no longer necessary.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Participants  
A total of 30 badminton players (15 male, 15 female) were recruited from the Chinese Swimming Club, 21 Amber Road, Singapore for this study. Participants were categorized into three skill levels:  
* Novice (n = 10): Less than 2 years of experience (Mean age: 25.0 years, SD: 5.3; Mean experience: 1.5 years, SD: 0.4).
* Intermediate (n = 10): 2-5 years of experience (Mean age: 28.8 years, SD: 3.9; Mean experience: 4.0 years, SD: 0.7).
* Elite (n = 10): More than 5 years of experience and national-level competition participation (Mean age: 26.5 years, SD: 2.9; Mean experience: 8.7 years, SD: 1.9).  
The overall mean age was 27.3 years (SD: 4.4, range: 18-35). The experience range spanned from 1 to 12 years across all skill levels. The elite players included former and current national players. There were university students, accountants, and white-collar workers from IT, healthcare, education, and sports backgrounds. Tables 1 and 2 present the demographics and characteristics of these badminton players.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Gender** & **Age** & **Experience (Years)** & **Skill Level** & **Occupation/Status** \\ \hline N1 & Male & 22 & 1.5 & Novice & University Student \\ N2 & Female & 19 & 1 & Novice & Part-time Tutor \\ N3 & Male & 25 & 2 & Novice & IT Professional \\ N4 & Female & 30 & 1.5 & Novice & Marketing Executive \\ N5 & Male & 18 & 1 & Novice & Junior College Student \\ N6 & Female & 27 & 2 & Novice & Nurse \\ N7 & Male & 35 & 1 & Novice & Business Owner \\ N8 & Female & 21 & 1.5 & Novice & University Student \\ N9 & Male & 29 & 2 & Novice & Graphic Designer \\ N10 & Female & 24 & 1 & Novice & Primary School Teacher \\ I1 & Male & 28 & 4 & Intermediate & Software Engineer \\ I2 & Female & 32 & 3 & Intermediate & Accountant \\ I3 & Male & 23 & 5 & Intermediate & Physical Education Trainee \\ I4 & Female & 26 & 3.5 & Intermediate & Interior designer \\ I5 & Male & 31 & 4 & Intermediate & Sales Manager \\ I6 & Female & 29 & 3 & Intermediate & Journalist \\ I7 & Male & 35 & 5 & Intermediate & Lawyer \\ I8 & Female & 24 & 4 & Intermediate & Banker \\ I9 & Male & 27 & 3.5 & Intermediate & Bank Teller \\ I10 & Female & 33 & 4.5 & Intermediate & Accountant \\ E1 & Male & 26 & 8 & Elite & Primary School Teacher \\ E2 & Female & 24 & 7 & Elite & Sports Club Player \\ E3 & Male & 29 & 10 & Elite & Badminton Coach \\ E4 & Female & 22 & 6 & Elite & Sports Science Student \\ E5 & Male & 31 & 12 & Elite & Former National Player \\ E6 & Female & 27 & 9 & Elite & Semi-Professional Player \\ E7 & Male & 25 & 8 & Elite & Physical Trainer \\ E8 & Female & 30 & 11 & Elite & Bohdminton sports shop \\ E9 & Male & 28 & 9 & Elite & Sports Journalist \\ E10 & Female & 23 & 7 & Elite & National Team Trainee \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key demographic characteristics of participants.  
All participants provided informed consent, and the study was approved by the institutional ethics committee.
## 2 Preliminaries
### Fuzzy Number  
Fuzzy numbers are a type of mathematical concept used to represent uncertainty and imprecision in data. [PERSON] et al. [22] suggest that unlike traditional numbers, fuzzy numbers are not precise values but rather a range of possible values. They are represented by a membership function that assigns a degree of membership to each element of the universe of discourse. The degree of membership ranges from 0 to 1, with 0 representing no membership and 1 representing full membership. According to [PERSON] [23], fuzzy numbers have found application in numerous domains, including engineering, economics, and decision making. They serve as a valuable tool for modeling uncertainty, pattern recognition, weather control systems, computer vision, and addressing vagueness in real-world scenarios. In the field of image processing, fuzzy numbers have been utilized for encryption, segmentation, clustering, and image classification. They offer a means to express the level of confidence in a statement or describe the quality of a product or service.  
In triangular fuzzy membership function, which is depicted in Equation (5), there are three parameters that used to control inputs: \(a\) as minimum value; \(c\) as maximum value; and \(b\) as a middle value which is located the peak of triangle, while \(a\) and \(c\) are located at the bottom side of a triangle, thus \(a\leq b\leq c\).  
\[f(x,a,b,c)=\max\left(\min\left(\frac{x-a}{b-a},\ \frac{c-x}{c-b}\right),0\right) \tag{5}\]  
In this proposed technique, we have utilized the triangular membership function to create a hybrid of the Henon map and triangular membership values called fuzzy Henon map. This results in a complex and random sequence number which is utilized as a secret key during the encryption process. Furthermore, we have designed a hybrid of the sine map and logistic map (the logistic sine map) to generate additional random sequence numbers that are also used as secret keys. In both hybrid maps, the triangular membership function has been employed to generate parameters that are used to modify the initial keys of the map equations. This approach enables us to generate keys that are highly suitable for image encryption.
# Wind Dispersal of Natural and Biomimetic Maple Samaras
## 3 Experimental Methods
### Windage  
Windage was estimated from the field experiments. We use the widely used and simplistic (kinematic) empirical model of windage [25; 31; 32], which holds that the average  
Figure 4: (**a**) Landing positions of each samara for each session of field experiments, aligned with the mean wind direction. As in Figure 3, natural Norway maple seeds are shown with orange circles, artificial Norway maple seeds are shown with green triangles, and artificial silver maple seeds are shown with yellow inverted triangles. (**b**) Wind roses for each experimental session. The color of each segment represents the wind speed and the size of each segment indicates the number of occurrences. Black arrows show the mean wind direction for the session. (**c**) Aerial view of Virginia Tech field site. The release point is marked with a star and a 25 m scale bar is shown. Samaras were dropped from the release point at a height of 3.05 m above the runway. Satellite imagery: Google, Commonwealth of Virginia, Maxar Technologies. (**d**) Histograms of samara dispersal distances, for each of the wind conditions, combining the two low wind (\(\approx\)1 m/s) sessions and the two high wind (3–4 m/s) sessions.  
horizontal velocity of the object, \(u\), is linearly related to the average horizontal wind velocity \(u_{w}\),  
\[u=C_{w}u_{w}, \tag{1}\]  
by a windage coefficient \(C_{w}\), where \(u_{w}\) is the horizontal wind velocity averaged between the release height, \(h\), and the ground. To estimate \(C_{w}\), we determine the downwind distance traveled by the dropped samara, \(d\), as compared with the distance, \(d_{w}\), traveled by a theoretical tracer following the horizontal wind for the same drop time. Using each day's average wind speed, \(u_{w}\) and the drop time of each samara, \(t\), the effective distance traveled by a passive tracer in the wind may be calculated as,  
\[d_{w}=\bar{u}_{w}t. \tag{2}\]  
The windage coefficient, \(C_{w}\), therefore, is the ratio of the actual downwind distance traveled to the effective distance of a passive tracer,  
\[C_{w}=\frac{d}{d_{w}}. \tag{3}\]  
Ignoring turbulence [33; 34], the drop time is approximately related to the release height and terminal velocity of the seed, that is, the seed descent rate in still air (\(v_{d}\)),  
\[t=\frac{h}{v_{d}}, \tag{4}\]  
thus, the distance traveled is estimated as,  
\[d=C_{w}h\frac{u_{w}}{v_{d}}. \tag{5}\]  
We note that this approach differs from a simple ballistic model used in some previous seed dispersal studies [34; 35; 36], which effectively assumed a windage coefficient of 1.
###### Abstract  
The aim of this study was to explore the possibility of assessing the health status of a cow's udder using infrared thermography. We studied the effect of mastitistis on cow milk yield, the effect of mastitistis on udder surface skin temperature, and the dependence between severity of mastitis and udder temperature. We determined the presence of a significant relationship between the udder surface skin temperature and the milk yield of mastitistis cows (Coefficient of determination = 0.886, linear Pearson correlation coefficient = \(-\)0.96), as well as the absence of a significant relationship between the udder surface skin temperature and the milk yield of healthy cows (Coefficient of determination = 0.029, linear Pearson correlation coefficient = 0.16). We substantiated the temperature ranges of the udder surface of healthy cows [32-35.9 \({}^{\circ}\)C] and mastitis [36.1-39 \({}^{\circ}\)C]. The obtained data made it possible to form an algorithm that allows a quick assessment of the herd for the presence of udder disease, using infrared images of the udder surface skin temperature.  
infrared thermography; dairy herd; milk yields; mastitis; udder surface skin temperature 2022
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 2 Basic Concept of Logic Selectivity
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acronym** & **Name** & **Function** \\ \hline CB & Circuit breaker & Device able to trip the circuit during a fault; its operations are controlled externally by the PR. \\ \hline PR & Protection relay & Device able to: (i) detect faults; (ii) control the respective CB; (iii) exchange messages with the other PRs. \\ \hline SB & Substation breaker & Breaker positioned in the primary substation. \\ \hline SR & Substation relay & Relay controlling the SB \\ \hline IB & Interconnection breaker & Breaker normally open that, if closed, connects the line to other distribution networks. \\ \hline TR & Tie recloser & Relay controlling the IB. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Devices involved in logic selectivity scenario. Figure 1: Example of fault management in a medium-voltage (MV) network according to the logic selectivity paradigm. (**a**) Healthy conditions. (**b**) Fault between protection relay 2 (PR2) and PR3; transmission of blind signals. (**c**) Circuit breaker 2 (CB2) tripping, and transmission of the Trip signal to PR3. (**d**) CB3 tripping and transmission of the Close signal to Tie recloser (TR). The time it takes to clear the fault depends only on the communication between two successive PRs, so it will always be the same, regardless of the section of line in which the fault occurs.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 5 Sex Ratio Impact on Ecosystem Stability Model (SRIES Model)
### Application and Analysis of Extended Models  
We conducted experimental simulations of symbiotic and parasitic relationships associated with lampreys, respectively, and the results obtained are shown below.  
As shown in Figure 12, we also explored salmonids that have a symbiotic relationship with lampreys to plot the number of interactions between them over time. It can be found that their population number changes are basically synchronized, which is consistent with the basic model of a mutually beneficial symbiotic relationship. However, a noticeable delay persists, given that their populations are simultaneously constrained by environmental capabilities.  
As shown in Figure 13, our model further explores four parasites that have parasitic relationships with lampreys, plotting their number over time at 20-year intervals. It can be seen that a gradual increase in the sex ratio of lamprey populations is accompanied by an increase in the number of Nematodes and Trematodes, in contrast to the changes in Arthropods and Protozoans. Thus, the proportion of males in lampreys is positively correlated with Nematodes and Trematodes.  
In summary, sea lamprey populations provide an advantage to other species in the ecosystem, while there are only general symbiotic interspecific interactions with species in symbiotic relationships. Changes in sex ratios may affect the host selection and transmission strategies of parasites, as lampreys of different sexes may be different physiologically and behaviorally. This may affect parasite life cycles and spread rates, alter the dynamics of host-parasite relationships, and affect species diversity and stability in ecosystems. Models can be used to predict and develop strategies to control the spread of parasitized populations, especially when considering how to reduce parasitism pressure by managing host population sex ratios.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 4 Data Categorization  
Data collected within V2I communications can be divided into several categories in several ways according to (Figure 6):  
the source;  
the method of collection;  
character [13].
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 1 Introduction
Here, we make use of PSO algorithm to obtain the gains of the IDA-PBC controller which provides an effective way to select/tune these gains thus improving the control performance of DP system of the ship. Moreover, we provide full proofs for stability and matching between the open-loop and closed-loop models necessary to prove that the Hamiltonian structure and passivity properties are preserved despite the deployment of state-transformation method in the course of controller design procedures. Finally, we show that the proposed method guarantees robustness of DP system with respect to environmental disturbances induced by wind, waves and currents in seas and oceans. The rest of the paper is organized as follows: Section 2 describes the dynamical model of the ship based on the PH formalism. In Section 3, the dynamic controller based on IDA-PBC method is proposed. Section 4 introduces the PSO algorithm and the suggested cost function used to tune the gains of the IDA-PBC controller. The proposed control scheme and tuning algorithm are simulated and compared in Section 5 to verify the performance of the newly PSO-tuned IDA-PBC controller. Finally, concluding remarks and future work are given in Section 6.
# Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events
## 2 Weibull Distribution  
The Weibull distribution is also referred to as the type III extreme value distribution [6]. This distribution is characterized by three parameters: the location parameter \(\mu\), the scale parameter \(\rho\), and the shape parameter \(\gamma\). The location parameter \(\mu\) is typically set as the minimum value in the distribution. In the context of survival or failure analysis, it is common to select \(\mu\) as 0, which results in a two-parameter distribution.  
The cumulative distribution function (CDF) for a two-parameter Weibull distributed random variable is denoted as:  
\[F_{T}(t;\rho,\gamma)=1-\exp\biggl{[}-(\frac{t}{\rho})^{\gamma}\biggr{]} \tag{1}\]  
where \(t\geq 0,\rho>0\), and \(\gamma>0\).  
The probability density function (PDF) of the Weibull distribution is given as:  
\[f_{T}(t;\rho,\gamma)=F^{{}^{\prime}}(t;\rho,\gamma)=\frac{\gamma}{\rho}(\frac {t}{\rho})^{\gamma-1}\exp\biggl{[}-(\frac{t}{\rho})^{\gamma}\biggr{]} \tag{2}\]  
The survival function of the Weibull distribution is given as:  
\[S_{T}(t)=1-F_{T}(t)=\exp\biggl{[}-(\frac{t}{\rho})^{\gamma}\biggr{]} \tag{3}\]  
The mean survival time or mean time to failure (MTTF) is given as:  
\[E(T) =\int_{0}^{\infty}S(t)dt\] \[=\int_{0}^{\infty}\exp\biggl{[}-(\frac{t}{\rho})^{\gamma}\biggr{]} dt\qquad\quad\text{let}\ (\frac{t}{\rho})^{\gamma}=u\Rightarrow t=\rho u^{\frac{1}{ \gamma}}\] \[=\int_{0}^{\infty}e^{-u}\frac{1}{\gamma}u^{\frac{1}{\gamma}-1}du\] \[=\rho\frac{1}{\gamma}\Gamma(\frac{1}{\gamma})\qquad\qquad\quad \text{note}\ \frac{1}{\gamma}\Gamma(\frac{1}{\gamma})=\Gamma(\frac{1}{\gamma}+1)\] \[=\rho\Gamma(\frac{1}{\gamma}+1) \tag{4}\]
## 4 Results and Discussion
### Build-Up and Characterization of a Solid Cuboid  
To further evaluate the effectiveness of the shielding gas concept, a solid cuboid was built up applying a shielding gas flow rate of 40 l/min, as shown in Figure 11a. The outer surface exhibited a bright metallic finish, with only marginal discolorations visible on the side walls. Figure 11b shows a cross-section of the deposited cuboid. No oxide inclusions could be observed in the cross-section, as they would typically occur with an insufficient shielding gas coverage using a conventional lateral nozzle [27]. This demonstrates that no unacceptable oxide layers were formed even under the high thermal load during the build-up of a solid component.  
In the cross-section of the built-up cuboid, a semi-quantitative chemical analysis of the oxygen content was performed using EDS. For this purpose, a line scan was performed over the entire height, as illustrated in Figure 12. The detected oxygen content varied only slightly over the entire length of the scan. In particular, there was no significant difference in the oxygen content between the substrate material and the deposited material. Furthermore, no pronounced peaks could be identified, which would indicate oxide inclusions in the material. It is worth mentioning that the measurement procedure is not necessarily suitable for an absolute quantification. Still, the measurement indicates that possible process-related changes in the oxygen content were negligible.  
The results of the Vickers microhardness measurements are shown in Figure 13. The hardness values measured varied in the range between 184 HV and 238 HV. For all three  
Figure 11: (**a**) Multi-track multi-layer cuboid deposited using the optimized shielding gas nozzle; (**b**) one half of a cross-section of the deposited cuboid.  
Figure 12: EDS line scan of the oxygen distribution in the built-up cuboid along the z-direction.  
measurement locations, a significant trend of decreasing hardness values with an increasing build height could be observed. This decrease in hardness is characteristic for DED processes and can be explained by the lower cooling rates in higher layers and the corresponding slower velocities of solidification [66]. Between the three measurement locations, no significant differences in the microhardness distribution were apparent, indicating the deposition of homogeneous layers. In summary, it can be concluded that a high-quality solid cuboid without defects or unacceptable oxidation was successfully built up.
# InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage
## 2 InstaDam: Design and Features
### User Interface Design
#### 2.3.2 Image Processing Techniques Implemented  
Several different image processing techniques (IPTs) were implemented in InstaDam for mask generation. The IPTs were selected from the literature based on their relevance for automated detection of damage and the processing time. Two simple operations of direct thresholding and thresholding following a Gaussian blur [32] were implemented for cases where the contrast between the damage pattern and the background is high. Otsu thresholding [33], which is also commonly used in various image processing applications, and morphology operations [34] that allow the users to dilate and erode selected regions, were incorporated as well. The canny edge detection method [32], proposed for use in damage identification by [PERSON] et al. [35], was implemented for fine edges. Distance filtering in the hue-saturation-intensity color space was implemented for corrosion detection based on the method proposed by [PERSON] et al. [36]. The ridge filter is often used in the medical field for segmentation of veins and blood vessels [37], and is incorporated for detecting fine lines such as cracks. Additionally, the guided filter [38] and local adaptive thresholding [39] available in opencv were incorporated. The time taken by each of the implementations to process one image of size 1 MP on a Windows PC with an i7 processor is provided in Table 1. From the standpoint of usability, the processing time is very important, as delays in generation of the mask will likely result in the user avoiding use of the particular IPT. Table 1 also provides details about the tunable parameters for each of the IPTs that have been implemented in InstaDam. In addition to the IPTs mentioned, another option available is to create a mask out of existing annotations. This mask option is called the label mask and is useful when annotations are to be made by excluding other annotations.
## 4 Results  
In this section, we present a comprehensive evaluation of the results obtained. First, we assess the accuracy of the WL\({}_{a}\) time series derived using the proposed chain for FFSAR- and UFSAR-processed data. Second, we examine the advantages introduced by the water mask extension in improving the analysis. Third, we investigate the noise characteristics observed in the satellite passes prior to water level aggregation. Fourth, we focus on the Itoiz reservoir, which features a transect below 300 m. Fifth, we analyze the correlation between measurements obtained at two nearby locations along the Mequinensa Reservoir. Finally, we provide a case study of a narrow river characterized by a transect width of approximately 50 m.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 2 Hierarchical Visual Place Recognition Based on Semantic-Aggregation  
Research [19] shows that the features extracted from the middle layer of CNN exhibit strong robustness against the severe image appearance changes caused by illumination, season, or weather conditions. On the contrary, high-level features are more semantically meaningful and more robust with respect to viewpoint variations.  
We propose a novel coarse-to-fine hierarchical method based on semantic aggregation, making use of the mid-level convolutional features and semantic features to realize place recognition. Figure 1 shows the whole process. Our approach is a coarse-to-fine visual place recognition pipeline, and contains two parts: coarse matching and fine matching.