esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2
## 1 Introduction
### Introducing the Graphol Language  
To obtain an idea of Graphol and its relation with UML and ER, in Figure 1 we model a simple situation about students and courses they attend, using a UML class diagram, a Graphol diagram, and a set of DL axioms, all expressible in OWL 2. We assume that the reader is familiar with UML class diagrams [2], DLs [11], and OWL 2 [12]. Furthermore, since we adopt the DL notation throughout the paper to express logical axioms, we use the DL terminology for ontology predicates, e.g., we use \"concept\" to denote a set of objects (i.e., a \"class\" in OWL parlance), \"role\" to denote a binary relationship between concepts (i.e., an \"ObjectProperty\" in OWL parlance), and \"attribute\" to denote a binary relationship between a concept and a domain of values (i.e., a \"DataProperty\" relating objects to datatypes in OWL parlance). In all the three versions of the model, we use the same alphabet for predicates. The model states that a student must attend at least a course, that university students are students, that graduate courses are courses, and that university students can only attend graduate courses. One can see that, in Graphol, concepts are represented through rectangles, analogously to UML, while, as in ER, diamonds are used for roles. Furthermore, solid directed arrows represent inclusions, as in UML and ER (cf. the inclusion between UniversityStudent and Student). However, differently from UML and ER, in Graphol they do not need to involve only named concepts. The use of a diamond to represent a role, i.e., a \"node\" in the diagram, allows us indeed to depict, in a simple graphical way, concept expressions over the domain and the range of a role, i.e., over its first or second component, by connecting the role to possibly labeled blank and solid boxes, respectively. For example, in Figure 1, the blank box labeled with \"forall\" and linked to the role attends denotes the set of individuals that attend only graduate courses. The inclusion drawn between UniversityStudent and this concept expression specifies that a university student can only attend graduate courses, which corresponds to the DL inclusion axiom UniversityStudent\(\sqsubseteq\)Vattends.GraduateCourse. We remark that this property is not directly expressible in a graphical way in UML, where we need to specify it as an external constraint (cf. the note in the diagram), possibly expressed in a logical language such as OCL.  
We notice that the idea of extending or adapting UML or ER to capture OWL is indeed not new. However, Graphol is distinguished from the other proposals with precise semantics, such as [25; 26; 27; 28; 29] by its ability to capture any OWL 2 ontology in a completely graphical way. Indeed, previous UML-inspired approaches typically require to annotate diagrams with formulas corresponding to complex OWL expressions. Clearly, this hinders both the diagrammatic representation of the ontology and its intuitive understanding. A more in depth discussion on related work is given in Section 2.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 3 Establishment of Tower Tilt Calculation Model
### Model Building
#### 3.1.2 Pole-Tower Imaging Model  
The rod-tower model structure is shown in Figure 7c, which includes two groups of similar triangles existing in the imaging model of the tower:  
\[\Delta\text{OAB}\cong\Delta\text{OCD},\ \ \Delta\text{OAS}\cong\Delta\text{OCI}, \tag{9}\]  
Based on the ratio of the three sides of a similar triangle, Formulas (9)-(11) are obtained:  
\[\frac{\text{AB}}{\text{CD}}=\frac{\text{OS}}{\text{OI}}\rightarrow\frac{\text {Py}}{\text{CD}}=\frac{\text{Pz}}{\text{OI}} \tag{10}\]  
Figure 7: Schematic representation of the monocular visual imaging images: (**a**) Schematic of transmission line tower tilt monitoring auxiliary system; (**b**) Schematic diagram of the rod and tower imaging.  
\[\frac{\text{AS}}{\text{CI}}=\frac{\text{OS}}{\text{OI}}\rightarrow\frac{\left|\text{ Py}-\text{Cy}\right|}{\text{CI}}=\frac{\text{PZ}}{\text{OI}} \tag{11}\]  
Among them, Py represents the known vertical height of the tower, Pz represents the known distance from the tower to the camera, Cy represents the known camera installation height, OI represents the image distance, and CD and CI are the pixel height of the tower Py and the height of the projection of the image at the top of the pixel height.
## 1 Introduction
Since prevention is crucial in managing AD, the development of new trial designs, diagnostic approaches, and technological solutions using data mining techniques are anticipated to enhance its effectiveness and have a greater impact in the field of prevention. To date, there has been no minimally invasive, straightforward, and cost-effective procedure available for the early detection or prevention of AD. Apart from the cerebrospinal fluid (CSF) approach, which is unpleasant and unsuitable for routine use, a range of candidate biomarkers from blood samples have been proposed. Although these biomarkers have not yet been validated, they represent a potentially more cost-effective and relatively less invasive approach that could offer valuable information for the early diagnosis or prognosis of the disease or serve as a screening tool [5; 6; 23; 24; 25; 26]. In recent years, buccal mucosa cells and saliva have emerged as promising tool for the potential diagnosis, prognosis, or screening of AD. This interest stems from their shared embryological origin with the central nervous system (CNS) from differentiated ectodermal tissue. These materials are expected to provide insights into changes related to brain neurodegeneration and the AD pathology [11; 27; 28; 29]. Many studies have shown significant structural and buccal epithelium differences during normal aging or in AD and MCI patients compared with healthy individuals [7; 8; 9; 10]. Buccal cells, associated with the immunocytochemical expression of peripheral pro-inflammatory cytokines, including TNF-\(\alpha\), IL-1\(\beta\), and IL-6, have demonstrated the association between peripheral inflammation and CNS degeneration in individuals with MCI and AD or the elderly. This includes the activation and release of various peripheral pro-inflammatory cytokines and antigens, such as TNF-\(\alpha\), IL-1\(\beta\), and IL-6, that can penetrate the BBB and provoke specific responses within the brain. Additionally, factors such as insulin levels, insulin receptor resistance, and adipose tissue are implicated in increasing the risk of AD [30; 31; 32; 33; 34; 35]. This study aims to bridge the gap between clinical and computational methodologies to enhance the understanding and detection of Alzheimer's disease. By integrating detailed cellular observations with advanced data processing and machine learning techniques, this research seeks to identify significant biomarkers and patterns associated with AD. The goal is to develop minimally invasive, cost-effective, and reliable methods for the early diagnosis and prevention of AD, thereby contributing to better management and potentially mitigating the impact of this neurodegenerative disorder on the aging population.
## 3 Results and Discussion  
### Quantification of the Active Pharmaceutical Ingredient (API)  
The calibration curve was prepared using known contents of acetaminophen as the active ingredient, and from this curve, the content in the biopolymeric film was determined according to the Beer-Lambert law. The analysis was performed by ultraviolet-visible spectrophotometry at 243 nm, preparing five calibration standards (20, 200, 400, 600, and 800 g:L\({}^{-1}\)) from a 1% aqueous solution prepared with reference standard acetaminophen. Figure 1 shows the results of the absorbances at each of the known contents. The experimental value of the proportion in the film was determined from the equation of the straight line obtained by linear regression. The calibration curve obtained for quantification presented good linearity and a high correlation coefficient value (R\({}^{2}\) = 0.9983), ensuring the accuracy of the calculated values.  
The absorbance measurement of the film was 2.815, corresponding to a calculated experimental proportion of 0.47% (\(w/w\)), as shown in Figure S1. The formulated concentration was then multiplied by the dilution factor, which in this case was 25, resulting in a final proportion of 11.75% (\(w/w\)) API in the film. To determine the theoretical proportion of the ODF with API, the calculation was based on the initial proportion of the polymer mixture before oven drying, which was theoretically 0.5%. Given that 23 mL of the film-forming solution was used, the total amount of API was 0.11965 g. This proportion gave a recovery percentage of 98% when compared to the experimental result.  
API (g) = (0.5 g API/100 g) \(\times\) 23.93 g = 0.11965 g  
Figure 1: Calibration curve for determination of acetaminophen content.  
API (%, \(w/w\)) = (0.11965 g de API/0.9994 g) \(\times\) 100 = 11.97% (\(w/w\))
## 3 Satellite Characteristic Fundamentals
### Satellite Link Multiple-Access Techniques  
In order to exploit adequately the precious satellite resource, an opportune multiple-access technique has to be used in communications. The used technique allows users to share the satellite channel, avoiding collisions when accessing the bandwidth resource. Generally, in satellite communications, four types of multiple-access techniques are used, namely:  
* Frequency-division multiple access (FDMA);
* Time-division multiple access (TDMA);
* Code-division multiple access (CDMA);
* Multi-frequency-TDMA (MF-TDMA), a hybrid solution that exploits the FDMA and TDMA characteristics.  
Moreover, LMS satellites are able to provide their services in a widespread range of spectrum bands [16], such as: L-band 1-2 GHz, S-band 2-4 GHz, C-band 3.4-6.725 GHz, Kuband 10.7-14.8 GHz, Ka-band 17.3-21.2 GHz, 27.0-31.0 GHz, and Q/V-bands 37.5-43.5 GHz, 47.2-50.2 GHz and 50.4-51.4 GHz, and other bands as well; see Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Altitude** & **Orbit** \\ \hline Low Earth orbit (LEO) & 500–1500 km & circular \\ Medium Earth orbit (MEO) & 7000–25,000 km & circular \\ Geostationary Earth orbit (LEO) & 35,786 km & fixed \\ Highly elliptical orbit (HEO) & 400–50,000 km & elliptical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite Orbit Classification.
###### Abstract  
The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence thatLESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy ofLESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of \(10\times 10\)-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (\(r=0.63\)) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS \(\geq 5\) errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.  
**Keywords:** anterior cruciate ligament; automation; drop jump; injury risk; deep learning; machine learning; movement screen; OpenPose +
Footnote †: journal: Applied
## 3 Research Methodology
### Modeling (Phase B)  
The next phase was the modeling phase. In this phase, simulations of existing methods such as the Eagle Strategy (ES), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) were carried out by using the Netlogo simulation tool with the same number of datasets and environments. Netlogo is a multi-agent programmable modeling environment. The ES method gave the better performance compared to the other methods, and researcher [15] mentioned the same, in which ES outperformed others compared to the standard algorithms such as Genetic Algorithm (GA), WOA, Discrete Guided Artificial Bee Colony (DGABC), Hybrid Genetic Algorithm (HGA), and Greedy Randomized Adaptive Search Procedure (GRASP).  
Based on Figures 2 and 3, we employed 500 food items to replicate WOA and PSO using a simulation. Figure 4 depicts the results of the same simulation conducted for ES. The results suggest that ES outperformed WOA and PSO in terms of performance (time). As a result, we selected ES and proceeded with the simulation using a reduced space, as illustrated in Figure 5.  
Similar good performance can be seen from our simulation in Figures 3-5. In Figures 3 and 4, the ES method was seen to work better in terms of performance on selecting the services when the search space was small. The advantage of ES was utilized fully in this research, where ES was able to select the services in less response time (indicating better performance) on a small-scaled repository. Additionally, existing ES approaches only focused on non-functional attributes of services, which is the QoS. Our proposed method focused on both functional and non-functional attributes of services as well as focusing on the response time on a large-scale repository. Thus, we named our proposed method the Improved Eagle Strategy (IES).  
Figure 1: Flow of the activity.  
Figure 3: Simulation of particle swarm optimization from beginning at time = 0 with the number of food = 500, until the end, the number of food = 1.  
Figure 2: Simulation of the whale optimization algorithm from beginning at time = 0 with the number of food = 500, until the end, the number of food = 1.  
Figure 5 shows the graph of the time taken to complete the iteration by standard ES for 1000 services. The time taken to complete the iteration was less than when the search space was small. ESNormal indicated the standard search space by Netlogo simulation; meanwhile, ESReduced indicated the search space was reduced at 50% from the standard search space to evaluate the efficiency of the ES algorithm on selecting the services, especially in a large-scale repository.  
It can be seen in Figure 4 that the time taken for ESNormal had been increasing somewhat linearly with the increase in the number of services compared to ESReduced, which was somewhat constant; therefore, our proposed method had selected services from the large-scale repository where the services would be clustered into small groups based on the functional attributes of the services. These attributes were based on the method names that were retrieved from the Web Service Description Language (WSDL).
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 7 Results
### Road Traffic Conditions Classification Results  
Table 5 shows the accuracy, the precision, the recall and the F-score of road traffic conditions classification, while Table 6 shows the confusion matrix of the predictions.  
Table 7 shows the feature importance for road traffic classification using the permutation feature importance. SVM relied on vehicle instant speed for classification, while decision trees and random forests relied more on the average speed. Fuel consumption ranked as the third important feature for decision trees and random forests, while it was not in the top seven important features for SVM. Manifold absolute pressure was more important to SVM than the other two algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Decision Trees**} & \multicolumn{2}{c}{**Random Forests**} & \multicolumn{2}{c}{**SVM**} \\  & \multicolumn{2}{c}{**Trees**} & & \multicolumn{2}{c}{**Forests**} & \multicolumn{2}{c}{**SVM**} \\ \hline True/Predicted & High & Low & Normal & High & Low & Normal & High & Low \\  & traffic & traffic & traffic & traffic & traffic & traffic & traffic \\ High traffic & 599 & 2 & 1 & 599 & 6 & 0 & 504 & 84 & 11 \\ Low traffic & 80 & 3694 & 2 & 11 & 3744 & 2 & 47 & 3693 & 35 \\ Normal traffic & 39 & 43 & 549 & 25 & 35 & 549 & 13 & 119 & 486 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for road traffic conditions classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Feature Importance Rank**} & \multicolumn{2}{c}{**Decision Trees**} & \multicolumn{2}{c}{**Random Forests**} & \multicolumn{2}{c}{**SVM**} \\ \hline
1 & Engine Coolant Temperature & Engine Coolant Temperature & Engine Coolant Temperature & Engine Coolant Temperature \\
2 & Fuel Consumption Average & Intake Air Temperature & Intake Air Temperature \\
3 & Vehicle Speed Average & Fuel Consumption Average & Vehicle Speed Average \\
4 & Intake Air Temperature & Vehicle Speed Average & Vehicle Speed instantaneous \\
5 & Engine RPM & Engine RPM & Engine RPM \\
6 & Vehicle Speed Variance & Manifold Absolute Pressure & Longitudinal Acceleration \\
7 & Manifold Absolute Pressure & Vehicle Speed Variance & Manifold Absolute Pressure \\ \hline \hline \end{tabular}
\end{table}
Table 4: Road surface conditions feature importance using the permutation feature importance approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Decision Trees**} & \multicolumn{2}{c}{**Random Forests**} & \multicolumn{2}{c}{**SVM**} \\ \hline Accuracy & 0.951 & 0.979 & 0.938 \\ Precision & 0.973 & 0.989 & 0.938 \\ Recall & 0.973 & 0.981 & 0.938 \\ F-score & 0.973 & 0.985 & 0.938 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Road traffic conditions classification results.
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
## 2 Equations of Motion  
Practical blade-disc-shaft assemblies tend to possess complex geometries, which are hardly modeled by lumped parameter models without several simplifications. Three-dimensional finite element models could accurately describe complex structures without simplifications. As bladed discs tend to be axially symmetric, their governing equations contain periodically time-variant terms if analyzed in inertial frames of reference. Therefore, the rotating frames are employed in this paper. For a 3D finite element model of a mistuned blade-disc-shaft assembly, its governing equation of motion at a constant rotating angular velocity \(\Omega\) has the following general form:  
\[\mathbf{M}\ddot{\mathbf{u}}+[\mathbf{C}_{d}+\mathbf{C}_{c}(\Omega)]\dot{\mathbf{ u}}+[\mathbf{K}_{e}+\mathbf{K}_{c}(\Omega)-\mathbf{K}_{s}(\Omega)+\mathbf{K}_{ \delta}]\mathbf{u}=\mathbf{f}, \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{C}_{d}\), and \(\mathbf{K}_{e}\) are the mass, damping, and elastic stiffness matrices of the assembly, \(\mathbf{C}_{c}(\Omega)\), \(\mathbf{K}_{c}(\Omega)\), and \(\mathbf{K}_{s}(\Omega)\) represent the speed-dependent Coriolis, centrifugal stiffening, and spin softening matrices, respectively, \(\mathbf{K}_{\delta}\) denotes the additional stiffness matrix due to mistuning, \(\mathbf{u}\) is the vector of response, and \(\mathbf{f}\) represents the vector of external forces. As the 3D finite element models are employed, the above matrices are obtained via linear assumption. In this paper, the external forces acting on the blades are assumed to be traveling wave excitations, where the forces on the \(i\)th blade can be expressed as  
\[\mathbf{f}_{i}(t)=\mathbf{B}_{0}\cos(\omega t-\varphi_{i}),\;\;i=1,2,\cdots,N, \tag{2}\]  
where \(\mathbf{B}_{0}\) denotes the forcing amplitude vector, \(\omega\) represents the angular frequency of excitation, and \(\omega=C\Omega\), where \(C\) is the engine order (EO) of excitation, and \(\varphi_{i}\) is the interblade phase angle defined as \(\varphi_{i}=(i-1)C\theta_{0}\), where \(\theta_{0}=2\pi/N\), and \(N\) is the number of blades.
## 4 Multihoming and Interworking  
In Section 3, we have introduced the fundamental building blocks of the GLI-Split architecture considering only single-homed GLI-domains. In the multihomed case, some additional mechanisms are required, especially for supporting communications with classic IPv6 nodes either in classic IPv6 domains or in GLI-domains. In the following section, we first briefly explain the issues in the multihomed case and then introduce mechanisms to solve these problems. After that, we introduce advanced mechanisms which facilitate several paths like multipath transfer and traffic engineering.
## 1 Introduction  
Conventional heat-transfer fluids, such as water, oil, and Ethylene Glycol, are widely used in numerous industries, including the fields of transportation, medicine, supply, heating, ventilation, and air conditioning (HVAC), power generation, chemical production and air-conditioning [1; 2; 3; 4]. However, the low thermal conductivity of working fluids is a major limiting factor in design, limiting the compactness, efficiency, and capability of systems. High heat flux devices, including X-rays, high powered lasers, computers, and microelectronics are examples of technologies which are limited by the heat-transfer capabilities of currently available cooling fluids [2; 5; 6].  
Miniaturization, power enhancement, and a possibility of increasing the efficiency of devices, motivated scientists to develop a new generation of working fluids, called suspension fluids. Initially, suspension fluids were fabricated by dispersing micro-scale particles within a base liquid. However, it has been found that suspending micro-scale sized solid particles in heat transfer fluids leads to problems such as sedimentation and erosion. By instead suspending nano-sized particles in heat transfer fluids, there is potential to avoid these problems while significantly enhancing the thermal conductivity [4]. Base fluids containing stably suspended nanoparticles, ranging from 1-100 nm in size, are called nanofluids [3; 7]. Measurements of the thermal conductivities of micro-particle suspensions were first performed in the middle of the last century [8]. Generally, the nanofluid experiments need to be conducted with great care in a fume hood. It is necessary to be aware of particular dangers for humans and the environment before conducting any experiments using nanoparticles. Before conducting an experiment, the hazards identification, first aid measures, firefighting measures,accidental release measures, handling and storage, exposure controls and personal protection, physical and chemical properties, stability and reactivity, toxicological information, ecological information, disposal considerations, transport information and regulations need to be studied in detail, which nowadays can be found in Material Safety Data Sheets (MSDS).  
Experimental data has shown that nanofluid thermal conductivity is a function of multiple parameters. This paper reviews and discusses the effects of different factors which influence the thermal conductivity of nanofluids, including the effects of characteristics of nanoparticles (size, shape, material), nanoparticle concentration, temperature, surfactant, and base liquid. The effects of the addition of carbon nanotubes on the thermal conductivity of a base liquid, along with the effects of adding multiple types of nanoparticles to a single base liquid to produce a hybrid nanofluid are also studied. Finally, the mechanisms which lead to increased nanofluid thermal conductivity are briefly discussed, along with the numerical models which describe these phenomena.