esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Analysis of Fractional Viscoelastic Flow in Circular Pipes: Parametric Study
## 3 Results and Discussion
### Velocity Profiles for Fractional Maxwell Model  
For fractional Maxwell model parameters that can vary are fractional orders \(\alpha\) and \(\beta\). We examine dynamics of normalized velocity along pipe diameter with respect to nondimensional frequency, \(\tilde{\omega}\). Both fractional orders \(\alpha\) and \(\beta\) are varied considering \(\alpha\geq\beta\). Let us first fix \(\beta=0.2\) and vary \(\alpha\): \(\alpha=0.2\),..., \(1\) (See Supplementary Materials animation muz1[[https://youtu.be/wNkKfdEO54Q](https://youtu.be/wNkKfdEO54Q)]([https://youtu.be/wNkKfdEO54Q](https://youtu.be/wNkKfdEO54Q))). Alternatively, \(\alpha=1\) and \(\beta\) is varied as: \(\beta=0.2\),..., \(1\) (animation muz2 [[https://youtu.be/2](https://youtu.be/2) dWZ6 KNwL5J]([https://youtu.be/2](https://youtu.be/2) dWZ6 KNwL5J)). These two velocity profiles have both similarities and distinct differences. Consider similarities first. Oscillatory behavior of the velocity profile is observed with respect to both spatial and frequency coordinates while for the latter these oscillations are also damped. Moreover, up to a certain nondimensional frequency, velocity increases rapidly until forming a nearly parabolic profile. Additionally, with \(\alpha\) and \(\beta\) increasing, resonant peaks of velocity profile shift to higher values of non-dimensional frequency. Upon passing this profile, oscillations emerge along the pipe diameter. The dynamics of initial parabolic profile, however, differ when changing \(\alpha\) and \(\beta\). In particular, when \(\beta\) is fixed and \(\alpha\) increases, peak amplitudes in initial parabolic profile oscillate. In contrast to it, when we fix \(\alpha\) and vary \(\beta\), initial parabolic profile peak amplitude gradually decreases with increasing \(\beta\). What is even more noticeable, for \(\alpha=1\) and  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**ModelParameter** & \(\xi^{2}\) & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) \\ \hline Fractional Maxwell & \(\big{(}\rho\omega^{2}/E\big{)}\Bigg{[}\frac{1}{(\omega\tau)^{\
u}}+\frac{1}{(i \omega\tau)^{\beta}}\Bigg{]}\) & \([0,1]\) & \([0,1]\) & \(-\) & \(-\) \\ \hline Fractional Zener & \(\big{(}\rho\omega^{2}/E\big{)}\Bigg{[}\frac{1}{(E_{0}/E)(i\omega\tau)^{1}+ \frac{(i\omega\tau)^{\beta}+\beta}{i\omega\tau^{\beta}+i\omega\tau^{\beta}}} \Bigg{]}\) & \([0,1]\) & \([0,1]\) & \([0,1]\) & \(-\) \\ \hline Fractional Burgers & \(\big{(}\rho\omega^{2}/E\big{)}\Bigg{[}\frac{1}{(E_{0}/E)\big{(}(i\omega\tau)^{ \gamma}+(i\omega\tau)^{\delta}\big{)}}+\frac{1}{\big{(}(i\omega\tau)^{\delta} \big{)}}\Bigg{]}\) & \([0,1]\) & \([1,2]\) & \([0,1]\) & \([0,1]\) \\ \hline \end{tabular}
\end{table}
Table 1: Fractional models and their parameters.  
\(\beta=0.2\),..., 1, the velocity profile reduces to parabolic for the entire range of nondimensional frequency as \(\beta\) increases. This result is not surprising as for \(\alpha=1\) and \(\beta=1\) we end up with a Newtonian fluid.
## 4 Research Findings
### Agility Constructs
#### 4.1.2 Workforce Agility  
In line with the dynamic capability framework introduced by [PERSON] et al. [16], [PERSON] et al. [60] offered an interpretation of workforce agility as contingent on employees' agile behaviors. They further deconstruct this construct into three distinct subdimensions, namely proactivity, adaptability, and resilience. Proactivity denotes the capacity for individuals to initiate actions in response to a dynamically changing environment. Adaptability encompasses the ability to modify one's behavior to align with evolving circumstances. At the same time, resilience represents the aptitude to cope effectively with stressful events and recover from the most adverse situations [60].  
While the existing literature undeniably acknowledges and establishes a robust foundation for workforce agility, it is pertinent to consider two additional constructs within the healthcare context. This integrated construct synthesis amalgamates human resources agility and agile leadership notions with the overarching concept of workforce agility.  
[PERSON] et al. [49] initially examined this construct by referring to it as the organizational agility of human resources but subsequently terming it human resource agility. The authors emphasized the pivotal role of human resource agility in fostering organizational agility, emphasizing the need for internal agility enablers within the organization [43]. While the study does not explicitly define the construct, it identifies five key dimensions of human resource agility: intelligence and awareness, competency, knowledge management, empowerment culture, and information systems.  
More recently, [PERSON] [35] conducted a study exploring the influence of agile leadership on career performance success, focusing on the mediating role of job embeddedness. In this article, agile leadership is characterized as a set of strategies that impact career success in achieving desired outcomes [37]. Agile leadership emerges from adopting employee-empowerment initiatives designed to address unpredictable working scenarios, establishing a clear link to workforce agility. The authors further deconstruct agile leadership into six key dimensions: result-oriented, teamwork-oriented, competency, flexibility, quickness, and change-oriented.  
While there is a notable absence of extensive research, precise definitions, and consensus regarding the construct of workforce agility, all three authors approach agility as the capability of healthcare workers to effectively respond to changes in a timely manner. This perspective aligns with earlier definitions of agility explored within this review and sets the stage for a deeper examination of workforce agility in the healthcare sector. Within this study, workforce agility is defined as the dynamic capability of employees to promptly sense and respond to changes in their medical work environment and the evolving needs of their patients.
###### Abstract  
Urban road travel time prediction is a critical component of intelligent transportation systems. However, accurate prediction remains challenging due to the high volatility and randomness of urban road traffic. This study compares the predictive performance of deep learning methods with traditional shallow learning methods for urban road travel time prediction, and explores the potential for improving prediction effectiveness through multi-run training strategies for hyperparameter optimization and ensemble learning. The study employs urban road travel time data from Shenzhen, China, designing 10 prediction scenarios with different sliding time window lengths and prediction scales. To address the randomness issue in deep learning models, we adopt a strategy of conducting five independent training runs for each hyperparameter configuration and using statistical measures to obtain stable performance evaluations. For model comparison, we implement multiple shallow learning models (linear regression, ridge regression, LASSO regression, random forest) and deep learning models (LSTM-DNN, DNN), and construct ensemble learning models based on linear regression and LSTM-DNN as base learners. The experimental results demonstrate that: (1) deep learning models generally outperform shallow learning models in terms of Mean Absolute Percentage Error (MAPE), particularly the LSTM-DNN model which achieves the best MAPE values across all prediction scenarios with 30 min sliding time windows; (2) in terms of Root Mean Square Error (RMSE), shallow learning models such as random forest perform better in most scenarios; (3) ensemble learning models show certain advantages in some prediction scenarios, but the improvement effects are limited and scenario-dependent. The research results provide empirical reference for method selection in urban road travel time prediction, while also revealing the limitations of single data sources and the differences in model performance under different evaluation metrics, offering directions for future research.  
intelligent transportation system; urban roads; travel time prediction; deep learning; ensemble learning 2020 IEEE/RSJ International Conference on Intelligent Transportation Systems (IEEE), Vol., No., pp. 1-10, 2018-2020,
# The Influence of Ski Waist-Width and Fatigue on Knee-Joint Stability and Skier's Balance
## 2 Materials and Methods
### Measurement Protocol  
The subject was bonded to a robotic ski simulator with his left ski-boot, while the other ski-boot was lifted from the ground throughout the measurement (simulation as if all the weight is on one leg during the turn). The computer-controlled system randomly changed the position of the ground reaction force four times every 10 s, simulating three ski waist-widths: 0 mm (used as a reference value), 60 mm (\"narrow ski\"), and 120 mm (\"wide ski\"). The subject had to maintain 60\({}^{\circ}\) of flexion in the knee joint and 25\({}^{\circ}\) inclination of the plate for 10 s after each ski-width change on the simulator. These predefined values of knee flexion and ski inclination were set to avoid other influences on knee  
Figure 2: A frontal-plane schematic of the apparatus that enabled simulating different ski waist-widths. The elliptic shapes represent the left/outside ski-boot in the simulated right ski-turn. The axis of rotation (pointed by the arrow) represents the inner edge of the left (outside) ski. The simulated width of the ski is equal to the doubled distance between the axis of the rotation (ski edge) and the mid of the boot. The positions “b” and “c” simulated the 60 and 120 mm ski waist-widths, respectively. The position “a” is nonrealistic and was used only to collect reference values. The computer-guided electromotor (not shown on the schema) moved the platform with the ski-boot-binding system between the presented positions.  
kinematics and to focus only on ski width, as well as to enable a skiing-like body position and ground reaction forces [20]. Both knee flexion and ski inclination conditions were monitored in real time using on-screen visual feedback. Sets lasting 40 s were repeated three times with a 2 min resting interval. This was followed by a fatigue protocol, during which the subject performed three series of one-legged squats in a ski-boot to a knee flexion angle of 70\({}^{\circ}\). The knee angle during squats was monitored on the screen in real time by the participant. The participants were loudly encouraged to perform the squats until failure, i.e., until no additional squat could be performed, which enabled us to meet one of the most common definitions of muscle fatigue: \"the exercise-induced decrease in the ability to produce force\" [28]. During each series of squats, the subject had 30 s of rest. The fatigue phase was followed by three additional 40 s random \"waist-width\" load sequences on the simulator: the first immediately after fatigue, the second 2 min after fatigue, and the third 4 min after fatigue.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 2 Related Work
[17] utilized the well-known Faster R-CNN algorithm, noting its inefficiency and low precision for direct application in video surveillance. To overcome these limitations, they proposed an object detection method based on Faster CNN to detect dairy goats in surveillance footage, achieving twice the speed of Faster R-CNN and an average accuracy of 92.49% accuracy, demonstrating how surveillance technologies can be effectively utilized in livestock management. Unmanned Aerial Vehicle (UAV)-based studies have achieved significant successes in areas such as the management of grazing cattle [18] and the processing of dynamic herd images [19]. They implemented a cattle detection and counting system based on convolutional neural networks (CNNs) using aerial images captured by Unmanned Aerial Vehicles (UAVs). Their dataset comprised 656 images of 4000 \(\times\) 3000 pixels captured by UAVs, and a second dataset from a single flight on a clear day contained 14 images. Their study enhanced detection performance through input resolution optimization using YoloV2, achieving precision of 0.957, recall of 0.946, and an F-Score of 0.952. They demonstrated the suitability of deep neural architectures for the automated detection of Holstein Friesian cattle using computer vision. They introduced a video processing pipeline composed of standard components to efficiently process dynamic herd imagery captured by UAVs, illustrating that detection and localization of Friesian cattle could be robustly performed using the R-CNN algorithm with an accuracy of 99.3%. These works highlight the potential of UAV technology for effectively monitoring and managing vast areas. Lastly, the YOLO integration proposed by [PERSON] et al. [20] for preventing venomous animal attacks accelerated animal recognition and enhanced farm animal security. The deployment of YOLOv5 for snake detection and its synergistic integration with ResNet achieved a notable precision of 87% in detecting snake presence. These studies demonstrate how imaging and machine learning technologies can revolutionize agricultural and wildlife monitoring. Our research aimed to explore further how these technologies can enhance livestock security and reduce threats from wildlife.
## 3 Results and Discussion
### Grey Relational Analysis of Coagulation and Flocculation of OMW
#### 3.2.1 Process of Grey Relational Generation  
Firstly, normalization of data for the responses should be generated considering the lower-the-better or higher-the-better criterion which is called a process of grey relational generation. In our case, the higher-the-better criterion was followed since the targeted pollutants' removal was satisfied and reported in Table 2. Therefore, values of the considered parameters were translated into a comparability sequence as indicated by Equation (1), where \(X_{i}(k)\) refers to the value of each parameter, I is the serial number of the schemes, k is the serial number of the criteria, max \(Y_{i}(k)\) refers to the maximum of \(Y_{i}(k)\), min \(Y_{i}(k)\) refers to the minimum \(Y_{i}(k)\) and \(X_{i}(k)\) refers to the standardized comparability sequence.  
Firstly, the higher-the-better criterion can be expressed as Equation (1).  
\[X_{i}(k)=\frac{Y_{i}(k)-\text{min}Y_{i}(k)}{\text{max}Y_{i}(k)-\text{min}Y_{i}(k)} \tag{1}\]  
Secondly, the lower-the-better criterion, which is preferred, can be expressed as Equation (2).  
\[X_{i}(k)=\frac{\text{max}Y_{i}(k)-Y_{i}(k)}{\text{max}Y_{i}(k)-\text{min}Y_{i}(k)} \tag{2}\]  
Normalized data of responses after step 1 are shown in Table 3.  
All the candidate values of criteria will be scaled into the range (0,1) upon completion of the grey relational generating procedure. It can be observed in Table 3 that experiment number 12 had a lower value of zero for TOC, whereas experiment number 1 gave a lower value for COD, phenols and turbidity since they had minimum values, among others. Maximum values were shown on TOC and COD in experiment 6, whereas maximum values for phenols were shown in experiment 7.  
The normalized data of TOC may be represented with \(k=1\), that of COD with \(k=2\) and Table 3 and that of turbidity with \(k=4\).  
If all its criterion values are closest to or equal to 1, the candidate is optimal. Following this, the deviation sequence is calculated between the reference sequence and comparable sequences to calculate the grey relational coefficient.
# Hierarchical Modelling for CO\({}_{2}\) Variation Prediction for HVAC System Operation
## 3 Data Preliminaries
### Dataset Description  
The dataset used includes IAQ data collected over a period of one year in 2019 in the Nordic climate. As a result, the dataset includes 22.6 million samples collected from 62 nodes mounted in 13 rooms [25]. The IAQ features include temperature (\({}^{\circ}\)C), relative humidity (%), pressure (hecto Pascals), CO\({}_{2}\) concentration (ppm), and activity levels ranging from 0 to 12. Each sample was captured with one-minute granularity. The activity level was calculated using PIR sensors that aggregate movement levels within a five-second interval. The collected data covered 13 rooms, including 11 cubicles that can fit 2-3 people and 2 meeting rooms that can fit 12 people. The code made available by the authors produces continuous chunks of data in separate datasets, which reduced the total number of samples to 6.1 million datapoints distributed over all the rooms [30]. The steps to obtain these datasets are as follows [30]:  
1. Data from different sensors in each room are aggregated;
2. Gaps of less than two minutes are interpolated;
3. Continuous data samples of high variability in CO\({}_{2}\) levels are extracted as testing set to evaluate the developed methodology.
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 3 Supercritical Fluid Extraction
### SFE of Bioactive Compounds from Waste
#### 3.2.2 Carotenoids  
Carotenoids are a group of phytochemicals that are responsible for the yellow, orange, and red colors of foods and vegetables [166]. They can be classified in two broad subcategories: carotenes (such as \(\alpha\)-carotene, \(\beta\)-carotene, and lycopene) and xanthophylls (such as lutein, astaxanthin, and cryptokanthin). From the chemical point of view, xanthophylls are carotenes' oxygenated derivatives. Indeed, they differ for the presence of oxygen atoms in the structure: carotenes are purely hydrocarbons (for example, \(\alpha\)-carotene, \(\beta\)-carotene, and lycopene formula is C\({}_{40}\)H\({}_{56}\)), whereas xanthophylls are formed by carbon, hydrogen, and oxygen (lutein formula is C\({}_{40}\)H\({}_{56}\)O\({}_{2}\), astaxanthin is C\({}_{40}\)H\({}_{52}\)O\({}_{4}\), and cryptokanthin is C\({}_{40}\)H\({}_{56}\)O).  
Although PLE has been used to extract carotenoids, the non-polar nature of some of these components makes SFE using scCO\({}_{2}\) the most suitable extraction technique for this category of compounds [17]. Supercritical carbon dioxide has been used alone or with low quantities of ethanol as cosolvent to extract those substances from different sources, such as tomato, vegetables, or seafood waste. Table 2 provides an overview of studies focused on the supercritical carbon dioxide extraction of carotenoids from food waste. The source from which the active principle has been extracted, the eventual amount of cosolvent added to scCO\({}_{2}\), the operating conditions in terms of pressure, temperature and extraction time, and the recovery of the carotenoid are reported. The recovery values reported in the table are the ratios between the dry weight of the carotenoid in the extracts and the dry weight of the carotenoid contained in the original sample.  
It can be noted that for the extraction of this class of compounds, in some cases CO2 alone is used; in most cases, the addition of a cosolvent is required, which in all cases is ethanol. The amount of ethanol (when present) varies from 5 to 15.5%. Carotenes are extracted in general at 300-350 bar (only in one case has a pressure of 450 bar been adopted to extract lycopene [170]), whereas in the case of xanthophylls, a wider range of pressures can be observed (astaxanthyn is extracted from crustacean waste at 200 bar [50] and focoxanthin is extracted from brown seaweed waste at 400 bar) [172]. From the perspective of operating temperatures, carotenes are extracted at higher temperatures (from 55 to 80 \({}^{\circ}\)C) than are xanthophylls (where the range of 40-59 \({}^{\circ}\)C is sufficient). The recovery values are generally high, confirming that sc-CO2 extraction is the most appropriate technique for carotenoids.
## 2 System Description
### Two-Stage System
The target is a straight line along the horizontal axis (\(\mathbf{X}\)-axis), as shown in Figure 2, and the system output is the position of the abrasive nozzle. Our variation refers to the deviation in the abrasive nozzle position on the \(\mathbf{Y}\)-axis. According to this model, an input of the system--the current position of the master controller, \(\mathbf{y}_{0}^{t}\)--is processed through stage 1, the human operator stage, and is moved to the next desired position by the human operator, \(A_{k-1}^{t}\). The output from stage 1, \(\mathbf{y}_{1}^{t}\), includes the bias, \(B_{1}^{t}\mathbf{u}_{1}^{t}\), and precision, \(\mathbf{w}_{1}^{t}\), of the operator and a measurement error, \(\mathbf{m}_{1}^{t}\), which result in the master controller's position deviating from the desired position. The total variation in the stage 1 output is represented by \(Cov[\mathbf{z}_{1}^{t}]\), as expressed in Equation (5). In stage 2, the machine receives a command to move to a human-controlled position. During this process, a variation occurs due to a machine calibration error, \(\mathbf{B}_{1}^{t}\mathbf{u}_{1}^{t}\), a machine precision error, \(\mathbf{w}_{1}^{t}\), and a measurement error, \(\mathbf{m}_{1}^{t}\), which, in combination with the input,results in the abrasive nozzle position (output of the system, \(\mathbf{z}_{2}^{t}\)) deviating from the position received from stage 1. It is obvious that the variation propagates through the system. The output in the stage in which the variation occurs is affected. Likewise, the following stage and the system output are also affected. Therefore, a decrease in variation in the initial stage would result in a decrease in the total variation. Although variation caused by human operators has been referenced in the literature [20], variation arising from this cause has not been studied in this context. Additionally, as in stage 1, minimizing the variation caused by human arm movement error will reduce the covariance such that the variation in stage 2 is minimized. Furthermore, according to the previous experiment, the human operator produces a relatively large amount of the variation. Therefore, we focused on minimizing the variation caused by human movement error in stage 1. The variation in position caused by human arm movement error can be considered to be related to accuracy (bias) and precision. A variation in the blasting line created by the human operator could relate to either or both of these. Furthermore, it should be noted that any given operator's behavior is inconsistent. By using a conventional approach, it is, therefore, difficult to define a model to estimate the variation for a specific person. However, the approach we propose herein is designed to cope with exactly this problem.