esa-sceva/satcom-chunk-collection huggingface dataset:
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 4 The Utility-Based Ontology Recommendation System
### Ontology Recommendation and Ranking Component  
The ontology recommendation and ranking component receives the scores from the former components (i.e., the ontology utility component, the class utility component, and the feedback management component) and determines the total ontology utility (_TOUI(O,c)_) to calculate the final score for each ontology \(O\) along with its matching concept \(c\). Based on the total ontology utility score, the system ranks the recommendations and provides the user with a list of top-k recommendations that best fit his explicit and implicit preferences. The total ontology utility score for each candidate ontology is calculated using Equation (9):  
\[\textit{TOUI(O,c)}=w_{1}*\textit{OUI(O)}+w_{2}*\textit{CUI(O,c)}+w_{3}*\textit{OA (O)} \tag{9}\]  
where \(w_{1}=w_{2}=w_{3}\) are three equal weights, and their summation equals 1. It is important to note that, as a preliminary design, in assigning weights for those measures we assume that the user is not technically an expert and they are equally important to him. In later versions, we could ask the user to assign them by himself according to his requirements or we could begin with equal weights and learn them during the user's interactions.
## 2 Materials and Methods
### Methods
#### 2.3.2 Theil-Sen Median  
The Theil-Sen median was used to extract the trends of the GPP time series. Theil-Sen median trend analysis is a robust trend statistical method that uses the median slopes between all \(n(n-1)/2\) pair-wise combinations of the time series data, which contribute to reducing the influence of missing or abnormal data and is particularly effective for the estimation of the trend in a time series [37]. The slope of the Theil-Sen median represents an increase or decrease with the GPP series data between 2000 and 2018 on a pixel scale. The calculation of the slope was as follows:  
\[Slope_{GPP}=Median\left(\frac{GPP_{j}-GPP_{i}}{j-i}\right),2000\leq i\leq j \leq 2018 \tag{3}\]  
where \(i\), \(j\) are the number of years in the study period, and \(GPP_{i}\) and \(GPP_{j}\) represent the \(GPP\) values at times \(i\) and \(j\), respectively. An increasing trend is inferred from \(Slope_{GPP}>0\), and a decreasing trend is inferred from \(Slope_{GPP}<0\). The significance of the trend was quantified using the MK test.
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Baseline Models  
The baseline models included traditional machine learning models (Random Forest [49], XGBoost [50]) and deep learning models (DeepFM [51], AFM [52], DCN [53], NFM [54]), along with a street-level prediction model (DSTGCN [12] based on graph convolution).  
To optimize the traditional models, a grid search was used with the following parameter ranges. Random Forest: n_estimators = [100, 200], max_depth = [None, 10, 20], class_weight = [\"balanced\"]; XGBoost n_estimators = [100, 200], learning_rate = [0.01, 0.1], max_depth = [3, 5, 7].  
The hyperparameter settings for the deep learning models were as follows. AFM: attention_factor = 8, l2_reg_linear = 1 \(\times\) 10\({}^{-5}\), l2_reg_embedding = 1 \(\times\) 10\({}^{-5}\); DCN: dnn_hidden_units = (256, 128, 64), dnn_dropout = 0.5, l2_reg_embedding = 1 \(\times\) 10\({}^{-4}\); NFM: dnn_hidden_units = (256, 128, 64), dnn_dropout = 0.5, l2_reg_embedding = 1 \(\times\) 10\({}^{-4}\); DeepFM: dnn_hidden_units = (256, 128, 64), dnn_dropout = 0.5, l2_reg_embedding = 1 \(\times\) 10\({}^{-4}\). The batch size for the aforementioned models was set in the range of {32, 64, 128, 256, 512}, with 64 yielding the best results after experimental comparison. The optimizer used for the models was Adam, and the loss function was binary_crossentropy.  
The hyperparameter settings for DSTGCN and our model DSTGAT were as follows: optimizer = Adam, loss function = binary_crossentropy, learning rate = 0.001, weight decay = 0.001, hop = {1, 2, 3, 4} with 2 yielding the best result, batch size = {64, 128, 256, 512} with 512 yielding the best result.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Multiple-Mode Shunt-Damping Circuit  
In this section, the \"current-flowing\" shunt circuit proposed by [PERSON] et al. [13; 14; 15] is used to model the electromechanical system equations for simultaneously damping the multiple vibration modes of the piezoelectric composite system.  
The \"current-flowing\" shunt circuit is shown in Figure 2. Each circuit branch \(C_{i}-\hat{L}_{i}-\widetilde{L}_{i}-R_{i}\) of the \"current-flowing\" shunt corresponds to the structural mode chosen to be damped and consists of two sub-branches: a current-flowing branch \(C_{i}-\hat{L}_{i}\) and a series single-mode shunt-damping branch \(\widetilde{L}_{i}-R_{i}\). The current-flowing sub-branch \(C_{i}-\hat{L}_{i}\) is tuned at the target resonance frequency \(\omega_{i}\) of the structural system and is an approximately open circuit at all the other frequencies. This is achieved by selecting \(C_{i}\) and \(\widetilde{L}_{i}\) so that:  
\[\widetilde{L}_{i}=\frac{1}{\omega_{i}^{2}C_{i}} \tag{36}\]  
When the current flows at frequency \(\omega_{i}\), the shunt-damping sub-branch \(\widetilde{L}_{i}-R_{i}\) is connected in series to the capacitor \(C_{p}\) of the piezoelectric patch and can be independently tuned to that target frequency \(\omega_{i}\). by imposing:  
\[\widetilde{L}_{i}=\frac{1}{\omega_{i}^{2}C_{p}} \tag{37}\]  
It is obvious that the shunt circuit shown in Figure 2 can be replaced by an equivalent or simplified shunt circuit with total inductance \(L_{i}\) of each branch given by:  
\[L_{i}=\widetilde{L}_{i}+\widetilde{L}_{i}=\frac{C_{p}+C_{i}}{\omega_{i}^{2}C_ {i}C_{p}} \tag{38}\]
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Segmentation in Skeletal Muscle  
Skeletal muscle ultrasound imaging is the largest area of deep learning research regarding muscle ultrasound images [20]. There were seven studies on the use of deep learning with two purposes, with four studies on segmentation and three classification studies. In the segmentation of skeletal muscle ultrasound images, the output was helping clinics with landmark identification. The problems in skeletal muscle tissue, in general,  
Figure 4: Various network architectures used in deep learning studies on muscle ultrasound images  
Figure 3: The development of deep learning studies for muscle ultrasound images from 2018 to 2020.  
are noise on the image and are related to movement [30]. Because of these problems, for segmentation in skeletal muscle images, it is necessary to modify the network architecture to achieve the maximum performance results. The Dice similarity coefficient is common to be used in segmentation task [19]. The Dice similarity coefficient is also considered superior because it does not only measure the proper marking of the number of pixels but also the precision of boundary segmentation [43].
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 9 Ensuring Integrity  
As the goal exchange process involves two agents that are physically separated machines and they communicate through a wireless link the goal exchange process is at the risk of failure. Using a reliable communication protocol like Transmission Control Protocol (TCP) and dividing the process into a sequence of stages acknowledged by the remote host the fault-tolerance of the system can be increased. Assuming that the transmitted packets are encrypted (which is standard nowadays) and the implementation is relatively simple (the author believes that software bugs can be corrected) byzantine fault tolerance (BFT) [27] is not considered here.  
The first stage of the goal exchange process is establishing the connection between agents. It is proposed to use the TCP connection because this protocol uses sequence numbers, acknowledgements, checksums and it is the most reliable, widely used communication protocol. Agents know network addresses of the other agents. They can be given in advance, provided by the higher-level system or obtained using a dedicated network broadcasting service. The attempt to connect to the agent that is already involved in the goal exchange process should be rejected. This can be easily and effectively implemented using TCP.  
The second stage is transmission of the robot location coordinates and the goal from agent one to another. The receiver computes \(\sigma\) (Equation (32)) and sends back the obtained value. If \(\sigma=0\) agent closes the connection, otherwise they go to the third step.  
The third step is a goal exchange that is the most critical part of the process. It must be guaranteed that no goal stays unassigned and no goal can be assigned to more than one robot in the case of agent/communication failure. To fulfil this condition the goal exchange must have all properties of database transaction: it must be atomic, consistent, isolated and durable. In practice this idealistic solution can be approximated by applying one of the widely used algorithms: two-phase commit protocol [28], three-phase commit protocol [29], or Paxos [30]. All of them introduce a coordinator block that is the central point of the algorithm. It can be run (for example as a separate process) on the one of the machines involved in the goal exchange procedure. Notice that in the case of failure (communication error, agent failure, etc.) the operation of goal exchange is aborted. This leads to slower convergence of robots to their desired values but is not critical for the task execution.  
Figure 11: Two robot-goal assignment—case 5.
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 3 Novel Rate Adaptation Method  
Considering the limitations of the methods that can be used in heterogeneous WBANs with the TDMA, there is a need to develop a novel method for the allocation of data streams with simultaneously operating RIs.
## 3 Results and Discussion
### Effect of NP Number Density on the Oil-Water IFT
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **7 nm CSNP** & **12 nm CSNP** & **22 nm CSNP** & **12 nm FSNP** \\ \hline Total mass of bare NPs used for & & & 0.0199 & \\ emulsion formation (M tot. NP), g & & (0.5 wt\% in the aqueous phase) & & \\ \hline Density of bare NPs (\(\rho_{\text{ NP}}\)), g/nm\({}^{3}\) & \(2.50\times 10^{-21}\) & \(2.36\times 10^{-21}\) & \(2.42\times 10^{-21}\) & Approximately \\ \hline Total volume of NPs in emulsion & & & & \\ (V tot. NP = M tot. NP/\(\rho_{\text{ NP}}\)), nm\({}^{3}\) & \(7.93\times 10^{18}\) & \(8.41\times 10^{18}\) & \(8.19\times 10^{18}\) & \(3.98\times 10^{19}\) \\ \hline Mean diameter of bare NPs (D NP), nm & 7 & 12 & 22 & 12 \\ \hline Volume of bare NPs & & & & \\ (VNP = 1/6 \(\pi\times\) D NP\({}^{3}\)), nm\({}^{3}\) & 179.59 & 904.77 & 5575.27 & 904.77 \\ \hline Total number of NPs in emulsion & & & & \\ (N tot. NP = V tot. NP/VNP) & 4.42 \(\times\) 10\({}^{16}\) & \(9.30\times 10^{15}\) & \(1.46\times 10^{15}\) & \(4.39\times 10^{16}\) \\ \hline Number of NPs per unit & & & & \\ surface area & 3002 & 569 & 101 & 3225 \\ (N surf. NP = N tot. NP/S tot. drop.), 1/um\({}^{2}\) & & & & \\ \hline Interfacial area covered per NP & & & & \\ (A inter. NP = Stot. drop./ N tot. NP), nm\({}^{2}\) & 333.10 & 1754.51 & 9836.82 & 309.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interfacial areas covered by single NPs. Figure 6: (**a**) IFT reduction as function of the interfacial area covered per NP and (**b**) the desorption energy required to remove one NP.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 2 Full-Scale Test of Corrugated Steel
### Test Results and Analysis  
#### 2.2.1 Test Results  
During the test, vertical loading was applied in incremental steps. Following each step of vertical loading, the servo jack system and vertical displacement meter were employed to measure the respective vertical forces exerted by the two jacks, as well as the midspan deflection of CSP. Given the minimal discrepancy between the two vertical loading forces, their average was calculated as the effective vertical loading force, F. By subtracting the initial value from each vertical displacement meter reading, the deflection at each point was determined, establishing the relationship between vertical loading force and deflection in the full-scale corrugated steel test, as depicted in Figure 3.  
Figure 3 reveals several key observations: (1) The maximum deflection does not exceed 3 mm, and the deflection of the corrugated steel plate increases linearly with the vertical loading force, indicating elastic behavior throughout the loading process; (2) the vertical loading force-deflection curves for measuring points D2, D3, and D4 exhibit a consistent two-stage linear relationship. Ordinary least squares fitting of the two-stage data identifies stiffness values of 14.12 kN/mm and 24.94 kN/mm at a critical point of 10 kN vertical load; (3) the curves for measuring points D1 and D5 exhibit a linear relationship up to 10 kN vertical loading, followed by significant fluctuations; (4) compared to D2, D3, and D4, the D1 curve is notably larger, whereas the D5 curve is smaller. This discrepancy arises from the inclination of the CSP toward D5 during loading, causing significant deflection  
Figure 3: Vertical load–deflection curve of corrugated steel in full-scale test.  
deviation at the edge from the expected range. Consequently, to accurately represent the mechanical response of the CSP, data from vertical displacement meters D1 and D5 will be excluded from subsequent analyses.
## 5 Discussion  
The numerical simulations of quasi-static and dynamic tests were also performed using Johnson-Cook damage model with the constants obtained by [PERSON] and [PERSON] (2010). The results showed similar specimen profile after fracture for quasi-static test simulation, whereas in the high strain rate test J-C model predicted the specimen profile after fracture slightly more accurate than that predicted for GTN model. The reason was that both J-C and GTN models are coupled with material models which are normally constructed on the basis of experimental stress-strain curve. More clearly, material models are required for implementing the damage model. Johnson-Cook damage model makes use of Johnson-Cook material model that involves five constants and considers the effects of strain rate and temperature ([PERSON] and [PERSON], 2010). This is while GTN damage model employs the material model defined by Equation (4) which involves only three constants and is simpler than the J-C material model and does not take any account of the effects of strain rate and temperature. Therefore, although GTN model is an analytical approach and involves more constants in damage analysis compared to Johnson-Cook damage model which is purely an empirical relation, it may be less accurate than Johnson-Cook damage model as the latter takes account of the effect of strain rate and temperature indirectly through the material model.  
Although, Kriging method is mathematically more complicated and more expensive than polynomial regression method, it may be more accurate as it requires a smaller number of initial samples. As a matter of fact, Kriging method is advantageous over the polynomial regression method, especially in cases where generation of samples is costly. In addition, the Kriging method is quite flexible in the required number of initial samples which makes it superior the polynomial regression method for which there is a requirement of having a specific number of samples.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 3 Digital Twinning throughout the Life Cycle of Physical Assets
### Uses and Life Cycle Phases of Physical Assets
#### 3.1.1 Planning and Design  
In the planning and design phase of physical assets, the asset begins as an idea, which subsequently evolves into design and detailed blueprints, all without physical existence. Prior to effective construction, the asset may be replicated in the form of a physical prototype. This prototype can undergo testing, generating crucial data to validate design assumptions or make design changes before construction. Although planning and design represents a relatively small portion of the expected lifespan (especially in the context of infrastructure assets), it has a significant impact on the asset's life cycle (Figure 3).  
During planning and design, there is a significantly greater ease of making changes and influencing the life cycle costs when compared to subsequent phases, where this flexibility diminishes sharply as the design is executed and the asset materialises [51]. In this phase, digital simulation and optimisation models prove valuable in reducing potential errors in the following phases, thus mitigating risks related to quality, safety, delays, and financial performance, among others. Although the design may need to be modified due to unforeseen circumstances during the construction phase, there is a range of opportunities for reducing the quantity and the impact of \"avoidable\" errors. These opportunities include joining and updating design versions, clash detection, and anticipating changes, assisted, for example, by interactive and immersive design visualisation.  
Digital models, when used in a systematic and coordinated way, can also expedite permit emission processes by extracting crucial information required for accurate, transparent, and efficient licensing, such as minimum dimensions. However, it is important to note the existence of certain challenges, including the current lack of municipal capacity to handle this innovative methodology and the risk of data tampering, which should be mitigated through research and development. In this regard, ongoing projects like the European Network for Digital Building Permits at the European level and the Portuguese LiMA Platform are already addressing these issues.  
Furthermore, there is potential to leverage more advanced analysis tools, such as machine learning techniques, to optimise and expedite the design phase. For example, machine learning methodologies can be applied to modular construction projects. They can help to identify the most suitable base module based on space characteristics and design requirements, reducing the process duration while ensuring a high-quality solution [52].
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 3 Experiment and Its Results  
### Study Area  
This research was performed in a 2.6-ha meadow on a rural landscape in the central region of South Korea located at 36\({}^{\circ}\)56\({}^{\prime}\)56.8\({}^{\prime\prime}\) N 127\({}^{\circ}\)54\({}^{\prime}\)40.8\({}^{\prime\prime}\) E. The experiment ground was sufficiently large and positioned away from urban synonyms, as shown in Figure 4a. This position provided the best chance to observe significant experimental results. The nearest potentially interfering buildings were 620 m distance. The research was conducted in sunny weather with a mean annual temperature of 14 \({}^{\circ}\)C, and recorded extremes of 9 \({}^{\circ}\)C and 24 \({}^{\circ}\)C. We chose a playground as an experiment zone, as the area's visible borders helped define the actual path, as shown in Figure 4b. The total length of the line is 345 m, the width is 68 m, and the height is 104 m. In all field experiments, we visually observed individual goat behavior using the smart collar, (as illustrated in Figure 1a), and occasionally, series of experiments were provided on any given day. In the current research, we provided experiments in seven days.
# Durability of Fabric-Reinforced Cementitious Matrix (FRCM) Composites: A Review
## 3 Conditioning Environments
### Freeze-Thaw Cycles  
Four methods were employed in the literature to evaluate the durability of FRCM composites subjected to freeze-thaw cycles. All the methods recommended performing thawing in water or in a humid environment to increase the degree of saturation of the materials, which can play a fundamental  
Figure 1: Sketch of (**a**) clevis-grip and (**b**) clamping-grip uniaxial tensile test set-ups.  
role in the durability of the material. In general, if the material saturation is lower than a certain threshold, the material is considered resistant to frost, i.e., dry materials should not be affected by frost [22].  
ASTM C666 [39] recommended a low temperature range of (4 to \(-\)18 \({}^{\circ}\)C) with a short cycle length of 2-5 h. EN 12467 [38] adopted freezing at \(-\)20 \({}^{\circ}\)C for 1-2 h and thawing at 20 \({}^{\circ}\)C for 1-2 h. [PERSON] et al. [14] performed 6 h of freezing at \(-\)25 \({}^{\circ}\)C followed by 2 h of thawing in water at 20 \({}^{\circ}\)C. Finally, AC 434 (2018) [23; 28] and CSLLPP (2019) [24] utilized a wide temperature range and a long cycle period (4 h at \(-\)18 \({}^{\circ}\)C and 12 h at approximately 38 \({}^{\circ}\)C).  
The combination of cycle duration and number of cycles affects the material response. Available studies reported that high number of cycles (more than 100 cycles) is required to develop small deteriorations in TRC composites [22; 26]. Accordingly, the methods proposed by ASTM C666 [39] and EN 12467 [38], which adopt short cycle lengths allowing for performing a high number of cycles within a short time frame, can be suitable candidates to evaluate the long-term behavior of FRCM. However, it is important to mention that the main purpose of these methods is to determine the resistance of the material to the repeated cycles of freezing and thawing. They can be used to compare between different FRCM composites, but they are not intended to provide a quantitative measure of the material service life [22; 39].