esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Constructing Ontology Model and Semantic Network  
By analyzing bridge BIM models and relevant knowledge in the field of bridge engineering, the bridge structures are abstracted and classified. Considering the engineering requirements of transportation infrastructure, the bridge structures are categorized into nine major classes: Pre-stressed Component, Foundation Component, Abutment, Pier, Girder Bridge Component, Arch Bridge Component, Cable-Stayed Bridge Component, Suspension Bridge Component, and Bridge Deck and Subsidiary Engineering Component. Based on this classification, a basic structural component ontology model for bridges is established, and further subdivisions are made within each of the nine main classes. With the class hierarchy defined, conceptual relationships are set from top to bottom, and properties and instances are added to the concepts. The relationships between classes in the ontology mainly consist of Part-of and Instance-of relationships. The bridge ontology model constructed in this paper using Protege is shown in Figure 4.  
Figure 4: Bridge ontology model in Protege.  
According to the structure of the bridge ontology model, a semantic network for ontology model mapping is established using Python. Each class in the ontology model corresponds to a node in the semantic network. The nodes in the semantic network need to store information for calculating similarities. The code to set up a node in the semantic network is shown as follows:  
G.add_node ('abutment', \(p\) = '0.27', \(\mathrm{attrs}\) = [('component','string'), ('height', 'double'), ('length', 'double'), ('type', 'abutment')])  
Where G is a directed graph representing the constructed semantic network, and \"\(p\)\" represents the occurrence probability of node concepts, which is based on the frequency statistics of concepts in BIM models. The property groups required for calculating Feature-based similarity are saved in the \"attrs\" string array corresponding to each node.  
In the semantic network, nodes are connected through directed edges. When setting the directed edges, based on the relationships between nodes, the corresponding weights are set. For example, between \"Bridge\" and \"Abutment\" there is a \"Part-of\" relationship. The code for establishing edges in Python is as follows:  
G.add_edge('Bridge', 'Abutment', rel = 'Part-of', weight = 0.5)  
Once the semantic network is established, the calculation of Edge-Counting, Feature-based, and Information-Content similarities can be performed after selecting two concept nodes from the semantic network.
## 2 Fundamentals
### Real-Time Simulation Solution Procedure
It is composed by applying a dedicated matrix stamp for each power system component, which corresponds to its impact on the power system's equations. In the following, as depicted in Figures 1 and 2, we distinguish between two phases in which the substantial computation steps of the MNA based solution procedure and the applied LU decomposition method can be performed. First, the simulator's _initialisation phase_ takes place before the start of the simulation, prepares data structures and performs computations, the outcome of which can subsequently be reused. Second, in the simulator's _simulation phase_, the computations are performed for each simulation step in real time by scheduling them according to wall-clock time. If neither time-varying nor iteratively varying matrix entries are present, the MNA system matrix remains constant over the entire simulation phase. Given this condition, as depicted in Figure 1, the MNA system matrix and its corresponding \(\mathbf{L}\) and \(\mathbf{U}\) decomposition matrices can be computed during the initialisation phase. During the simulation phase, only the RHS vector \(\mathbf{b_{k}}\) is updated according to the components' behaviour at a discrete-time instant \(k\). Based on that, the solution \(\mathbf{x_{k}}\) can be determined by means of forward/backward substitution and reusing the \(\mathbf{L}\) and \(\mathbf{U}\) matrices, which have been precalculated in the initialisation phase. The main advantage is that the computationally demanding preprocessing and factorisation steps of the LU decomposition method can be performed during the initialisation phase, and their outcome can be reused during the simulation phase. As explained above, the incorporation of nonlinear power system components into the system solution may lead to time-varying and/or iteratively varying matrix entries. Nonetheless, as shown in Figure 2, the preprocessing and initial factorisation can be performed during the simulator's initialisation phase. During the simulation phase, the matrix entries are updated according to the corresponding component behaviour that leads to an MNA system matrix \(\mathbf{A_{k}^{(j)}}\), where \(k\) is the discrete-time index, and \(j\) is the iteration index of  
Figure 1: MNA based solution procedure for real-time simulation with a constant system matrix. any fixed-point algorithm. Consequently, due to the numerical variation in matrix entries, the LU decomposition matrices \(\mathbf{L}_{\mathbf{k}}^{(\mathbf{j})}\) and \(\mathbf{U}_{\mathbf{k}}^{(\mathbf{j})}\) need to be recalculated; i.e., refactorisation needs to be performed. Based on that, the solution \(\mathbf{x}_{\mathbf{k}}^{(\mathbf{j})}\) for each time \(k\) and iteration \(j\) can be determined by forward/backward substitution. In addition to a full refactorisation of the system matrix, in this work, we consider a partial refactorisation. This is motivated by the fact that, usually, only a few nonzero entries undergo numerical variations. The partial refactorisation approach chosen in this work is explained in more detail in Section 3.1.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 1 Introduction
However, due to the vast improvements in computing resources, physics-based watershed models were designed to cover multiple media and processes, which have become more applicable at various scales since the late 1990s [18]. An increasing number of hydrological models have been developed to represent the exchanges between surface water and groundwater systems coupled in either a fully or a loosely integrated way [19]. The loosely coupled models, such as GSFLOW [20], SWAT-MODFLOW [21; 22; 23], and HSPF-MODFLOW [24] usually combine surface water and groundwater models, linked through a specific parameter. The fully coupled models, such as MODBRANCH [25], MIKE-SHE [26], and WASH123D [18; 27; 28] are physically based models that describe the flow behavior according to partial differential equations. Such models have the potential to help users understand the fundamental factors involved in natural hydrologic regimes, enable mechanistic predictions, and, most importantly, can be coupled and interact with weather/climate models. This study aimed to develop a procedure to assess groundwater level by incorporating seasonal weather forecasts and hydrological models. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with the weather generator [17] to produce seasonal weather forecasts for inputs into the hydrological models. The HEC-HMS developed by the Hydrologic Engineering Center, United States (US) Army Corps of Engineering [29; 30], which has been proven to be reliable and widely used [31], was used to simulate the rainfall runoff in this study. Furthermore, the physically based and fully coupled hydrological model, WASH123D [18; 27; 28], was used to assess the coupled surface and groundwater flow simulations. This model estimates the groundwater via coupled surface and groundwater algorithmic processes, while the other surface and groundwater integrated approximations are mostly done through boundary assignation. Figure 1 shows a flowchart illustrating the integrated seasonal outlook and hydrological modeling of this study.
# A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem
## 2 Problem Description
### Mathematical Formulation  
As mentioned before the OVRP is a variant of the VRP and formulations for the VRP can be adapted to solve the OVRP. Following a formulation for the OVRP is presented. This formulation is useful for solving symmetric and asymmetric OVRP and considers capacity and route length constraints. It is based on a classic formulation of the VRP and the Miller-Tucker-Zemlin subtour elimination constraints for the VRP [41]. Therefore, the OVRP can be formulated using the following variables:  
\[x_{ij}=\left\{\begin{array}{ll}1,&\text{if arc $a=(i,j)$ is part of the solution,} \quad i\in V,j\in V^{+},\\ 0,&\text{otherwise.}\end{array}\right.\]  
\[u_{i}=\text{Load on the vehicle after visiting vertex i}\]  
\[y_{i}=\text{Length travelled after visiting vertex i}\]  
The hierarchical objective function is defined using the variables mentioned above, as follows:  
\[\min\sum\limits_{j\in V^{+};\
eq j}Mx_{0j}+\sum\limits_{i\in V^{+}}\sum\limits _{j\in V^{+};\
eq j}c_{ij}x_{ij} \tag{1}\]  
where \(M\) is a large number. The first term in Equation (1) minimizes the total number of vehicles, while the second term minimizes the total traveling cost.  
The second objective function only minimizes the total traveling cost and is defined as follows:  
\[\min\sum_{i\in V}\sum_{j\in V^{+},i\
eq j}c_{ij}x_{ij} \tag{2}\]  
The following constraints are required to complete the model for the OVRP:  
\[\sum_{i\in V,i\
eq j}x_{ij}=1, \forall j\in V^{+} \tag{3}\] \[\sum_{i\in V_{0},i\
eq j}x_{ji}\leq 1, \forall j\in V^{+}\] (4) \[u_{i}-u_{j}+Qx_{ij}\leq Q-d_{j}, \forall i\:inV,j\in V^{+},i\
eq j,d_{i}+d_{j}\leq Q\] (5) \[y_{i}-y_{j}+Qx_{ij}\leq Q-st, \forall i\:inV,j\in V^{+},i\
eq j\] (6) \[d_{i}\leq u_{i}\leq Q, \forall i\in V,j\in V^{+}\] (7) \[st\leq y_{i}\leq L, \forall i\in V,j\in V^{+} \tag{8}\]  
The constraints in Equation (3) guarantee that all clients are visited, and the constraints in Equation (4) guarantee that routes leave the client or finish the route at the client. Route capacity is controlled with the constraints in Equations (5) and (6). The constraints in Equations (7) and (8) control the route length. Note that the constraints in Equation (5) are the Miller-Tucker-Zemlin subtour elimination constraints for the VRP [41], and the constraints of Equation (7) are the same constraints adapted to control route length.
# Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal
## 5 Conclusions  
Limited-angle CT is useful when the range of the collectable data is limited. DBT is the application of limited-angle CT in mammography [25]. DBT can produce quasi-three-dimensional images from the projections, mitigating an inherent limitation of tissue overlap, but TPA degrades the quality of the image reconstructed by the DBT system [26]. TPA arises from the truncation of projection data, and it will obscure tissue at the edges of the image and spread to the middle of the image. In this study, we propose a TPA-eliminating algorithm by completing the projection data through re-projecting the reconstructed im  
Figure 9: Structural similarity comparison between the image reconstructed by our algorithm and by SART.  
ages with TPA to a larger virtual detector plane and then recursively applying the SART reconstruction and the re-projection. The median filter was used to reduce the effects of the reconstructed artifacts on the re-projection data. Our proposed approach achieved more effective visual success in both simulated data and human breast data than diffusion method. By using our reconstruction algorithm, radiologists can discern the details in previously TPA blurred areas in images and make correct diagnoses. This study provides the possibility to improve the image quality of the limited-angle CT and extend its potential application range beyond the detection of early stage breast cancer.  
Methodology, J.Y.; Software, E.S.; Formal analysis, Y.W.; Investigation, P.L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
Important genes in the (p)ppGpp-related stringent response include _hipA_, _hipB_, _relA_, and _rpoS_. HipA and HipB are part of the type II toxin-antitoxin system and can affect glutamate-tRNA ligase [18]. HipA phosphorylates glutamate-tRNA ligase, causing uncharged tRNA (Glu) accumulation, and thereby hindering the translation process [19]. The reduced translation rates increase (p)ppGpp levels, which, catalyzed by RelA/SpoT, activates the stringent response. As a result, RNA polymerase switches from transcribing growth and reproduction-related genes to stress-response-related genes, and (p)ppGpp accelerates amino acid biosynthesis [20]. HipB acts as an antitoxin component and can neutralize the toxicity of HipA [21]. Therefore, knocking out _hipA_ or _hipB_ may affect the stringent response induced by (p)ppGpp. RelA is a GTP pyrophosphokinase, that catalyzes the formation of (p)ppGpp, and a knockout of _relA_ most likely directly affects the synthesis of (p)ppGpp [22]. (p)ppGpp binds to RNA polymerase and responds to stress through the sigma factor \(\sigma^{S}\)[23]. Therefore, directly knocking out the gene _rpoS_, encoding RNA polymerase sigma factor \(\sigma^{S}\), has a potential impact on stress response. In the system dealing with ROS, _sodA_, _sodB_, _soxR_, and _soxS_ play important roles. Superoxide dismutase SodA and SodB can destroy superoxide anion radicals [24]. Therefore, after knocking out these genes, the function of clearing excessively generated ROS in cells will be weakened to a certain extent. The gene _soxR_ encodes a redox-sensitive transcriptional activator responsible for triggering the transcription of the superoxide response regulator SoxS, which plays a role in the removal of superoxide [25]. Consequently, the deletion of _soxR_ or _soxS_ can reduce the clearance of ROS. In this study, we evaluate the correlation between changes in minimum inhibitory concentration (MIC) and growth rate as an indicator for fitness throughout the resistance acquisition process. For this assessment, four single-gene knockout _E. coli_ strains related to the stringent response were used; moreover, four were related to the oxidative stress responses. The fitness costs associated with resistance were investigated for each strain by quantifying the biomass yield on glucose. Evidence for the relationships between mutations and fitness was ultimately discovered through whole-genome sequencing. The overall picture that emerges is that initial mutations causing resistance come at a metabolic cost that can be subsequently compensated using compensatory mutations. The complex cellular response to stress due to exposure to antibiotics, mediated by the stringent response, oxidative stress response, and other cellular processes, allows the cell to overcome non-lethal concentrations of antimicrobials at minimal metabolic costs.
## 4 Numerical Simulation
### Stability Analysis  
#### 4.3.1 Plastic Zone Analysis  
The plastic strain clouds before and after the finite element simulation analysis of microbial reinforcement are shown in Figure 11.  
From the above figure, it can be seen that when t = 0.2, the plastic zone appears above the foot of the slope, and when t = 0.25, the plastic zone above the foot of the slope expands and extends to the inner part of the slope and gradually connects with the plastic zone inside the slope. When t = 0.34, the plastic zone basically penetrates, and the slope is destabilised. Comparing the plastic strain clouds before and after microbial reinforcement, it can be seen that at t = 0.2, the strain in the plastic zone at the foot of the slope after microbial reinforcement is lower and the strain inside the slope body is higher, but the value is smaller than the strain inside the slope body before microbial reinforcement. This may occur because after the slope surface is reinforced by microorganisms, the microorganisms have not completely penetrated into the interior of the soil body and have less influence on the interior of the slope body. The subsequent development of the plastic zone is roughly similar before and after microbial reinforcement. The analysis of the strain cloud diagram at the time of complete penetration shows that the maximum plastic strain before microbial reinforcement appears at the foot of the slope, and the corresponding stress reaches 239.3 kPa, while the maximum plastic strain after microbial reinforcement also appears at the foot of the slope, but its distribution range is greatly reduced, and the maximum stress value is lowered to 174.5 kPa.
# Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
## 3 Evolution Model of a Complex SCN Based on the Deviation of the Power Law Distribution
### Establishment of an Evolution Model of Power-Law Distribution Deviation in SCN
\begin{array}{l}p(k,t)=\dfrac{\partial P(k_{i}(t)<t)}{\partial k}\\ =\dfrac{fm^{\frac{f}{mp_{1}+nqt}}}{mp_{1}+nqt}(\dfrac{cp_{2}t}{f})^{\frac{2f-mp _{1}-nqt}{mp_{1}+nqt}}(k-\dfrac{nq(cp_{2}t)^{2}}{(f-mp_{1}-nqt)(nqt+mp_{1})}) ^{-\gamma_{2}}\\ \end{array}  \tag{18}\]  
Among them, \(\gamma_{2}=3-\dfrac{n(q+qt)}{mp_{1}+nqt}\). In addition, when \(t_{i}\sim t\) is obtained, \(k_{i}(t)=m(\dfrac{t}{t_{i}})^{2}\) can be obtained. Similarly, the expression of the probability density \(p(k)\) of the SCN at this time can be obtained as shown in Equation (19). \[p(k)=\dfrac{\partial P(k_{i}(t)<k)}{\partial k}=\dfrac{1}{2}m^{\frac{1}{2}}k^ {-\gamma_{1}}, \tag{19}\]  
Among them, \(\gamma_{1}=1.5\). Based on the above analysis, it can be found that the SCN generated according to the previously set evolution rule is a scale-free network, and the node degree distribution in the network has power-law characteristics and two different power exponents in the power-law distribution area, which is subject to the two-stage power-law distribution. At the evolution time \(t\), for the nodes with larger degree values generated in the complex SCN, the network node degree distribution will evolve with the power exponent of \(\gamma_{2}\) with time. Different SCNs are in second place due to different parameter values. The node enterprises on the segment power law distribution curve have different evolution speeds. The power exponent \(\gamma_{2}\) in Formula (18) has a relatively dynamic form of change, which is more convenient for characterizing different real-world network evolution dynamics. For the node enterprises generated by the network with a small degree value, the evolution power exponent \(\gamma_{1}=1.5\) has a more stable evolution mechanism.
## 4 Case Study: Application of APIRM Model at Istanbul Airport  
Istanbul Airport, selected for the implementation of the APIRM model, is located 40 km northeast of Istanbul city center. The airport's first flight took place on 29 October 2018, and it features a terminal with an annual passenger capacity of 90 million. As depicted in Figure 6, the airport has five runways, two of which are auxiliary and run from north to south. All of the runways are asphalt paved (flexible pavements), except for the first 375 m of runways 18-36, which are concrete-paved. Some taxiway pavements are asphalt, while others are concrete. The aircraft parking areas and passenger boarding bridge surroundings are also concrete paved [44]. According to data from 2023, the airport averaged 1386 aircraft movements and 208,251 passengers per day. In the first eight months of 2024, these figures increased to 1420 aircraft movements and 218,894 passengers per day [45]  
A survey was conducted with the experts responsible for inspecting the maintenance and repair of pavements in the operation, construction, and safety management system (SMS) departments of Istanbul Airport. The experts provided data on the following:  
- Physical information on runway aprons and taxiways  
- Pavement types  
- Distresses (hazards) identified in the pavements  
- Methods used for pavement condition assessments at Istanbul Airport  
- Definitions of hazards according to APMS criteria  
- Likelihood, frequency, and consequence values used to calculate hazard risk scores via the Fine-Kinney method  
The identified hazards in the airport's runway, apron, and taxiway pavements were defined according to the APMS framework. Risk scores for these hazards were calculated using the Fine-Kinney method. The likelihood, frequency, and consequence values were determined through the expert survey, and the calculated risk scores were subsequently evaluated.  
Figure 6: Istanbul Airport layout plan [46].
## 4 Case Study
### Deployment of the CVROSS
#### 4.2.2 Vehicle Recognition  
To determine the dimensions of specific vehicles, the technique of geometric matching was used to recognize and match two trucks with different appearances. It was found that the CVROSS was able to detect, recognize and match vehicles from all traffic lanes on the road and those in an individual traffic lane. General traffic conditions could be interpreted using the information from the matching results for all traffic lanes on the road, for example, for the issue of vehicles remaining double-parked. On the other hand, information from an individual traffic lane was capable of illustrating situations in a particular traffic lane, to determine the level of traffic congestion and identify loading and unloading activities. Figure 13 illustrates the use of geometric matching in vehicle recognition. The coordinates of vertices are located for measuring the corresponding length and width of the vehicles via a geometric technique.
## 3 Contributions  
Comprehensive research on D2D communications has been administered using FFR scheme for sharing resources between the cellular users and D2D users. Motivated by the literature works, we propose a resource allocation and power control technique for D2D communications with FRF of 2, where the locations of the D2D users are assume to be static. Hence, the contributions of this paper are summarized as below.  
* We introduce a novel resource allocation algorithm, which supports frequency reuse factor of 2.
* We analyze the outage probability of D2D pairs and overall system with respect to distance between D2D-Rx and eNB, SINR threshold, and number of available D2D pairs. The proposed scheme provides lowest outage probability as compared with other existing schemes.
* We improve the D2D throughput and overall system throughput with less computational complexity.
* We minimize the average energy consumption of the system with varying number of available D2D pairs.
* We discuss the fairness distribution of resources by considering [PERSON]'s fairness index.  
To the best of our knowledge, this is the first work that proposes resource allocation and power control scheme based on the fairness index by cell sectorization mechanism for FRF of 2. The formulation in this paper yields tractable results that can be applicable in future cellular networks.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)  
### Algorithm Overview  
The _XSQ-Learning_ algorithm extends traditional Q-learning by incorporating similarity-based value propagation. The core intuition is that state-action pairs with similar expected returns should receive related updates during the learning process. Formally, given a similarity function \(f\) that quantifies the relationship between state-action pairs, the algorithm propagates temporal-difference updates proportionally to their pairwise similarity.  
This method reflects analogical reasoning principles by transferring learned behaviors across structurally similar experiences. Unlike standard Q-learning which treats each state-action pair in isolation, XSQ-Learning creates a network of related experiences that inform each other's updates.