esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They also used this method to determine the absolute values of nonlinear acoustic parameters \(\alpha,\beta,\delta\) by using the temperature change as a driving force of nonlinear response of materials [15]. [PERSON] and [PERSON] [16] derived the theoretical formulas and validated this method on metals. In all the references reviewed above, the CWI method can measure very small wave-velocity changes, which would not be accurately measured using the conventional time of flight method. Many studies used CWI for the measurement of velocity variation in the uniaxial loading test to investigate the acoustoelastic effect in concrete or rock [11; 12; 17; 18; 19; 20]. Because the uniaxial loading test causes inhomogeneous stress field and velocity change in the concrete, CWI analysis results vary with the selected time windows in the signals. Figure 1: Stretching technique on two ultrasonic signals acquired from a stress-free state \(S_{0}\), and a stressed state \(S_{1}\). The red dash line is the stretched and interpolated signal \(S_{1}^{\prime}\) with stretching parameter \(\varepsilon_{\text{max}}\). [PERSON] et al. [17] found the relative wave-velocity changes at different time windows varied between 0.7% and 1.28 % in Berea sandstone due to a uniaxial stress increase of 2 MPa. [PERSON] et al. [12] also reported varying values of \(dV/V\) when determining the third-order elastic constants in concrete using CWI analysis. In both studies, these authors calculated the mean values of velocity variations obtained in different time windows. [PERSON] and [PERSON] [21] applied the stretching technique to the direct wave instead of the coda wave in a concrete beam loading test, where the end of the direct wave was based on the observation that signals did not show significant changes before cracking occurred. [PERSON] et al. [18] directly calculated the velocity changes using full coda waveforms instead of a specific part to remove the time dependence. It can be seen that researchers adopted different time windows in CWI analysis, while some results are significantly affected by the time window selection. With an increasing research interest in coda wave analysis and wide applications of CWI for NDE of concrete, the authors believe there is a need to discuss time window effects on CWI analysis results, and extend CWI to direct wave analysis before diffuse wavefield forms in the medium. Direct wave velocities are often needed in nonlinear ultrasonic analysis and determination of elastic constants. This paper presents three applications of wave velocity monitoring in concrete: (1) early-age hydration, (2) temperature change, and (3) uniaxial loading. In all experiments, the stretching method is applied to the direct waves to extract the relative velocity changes of the direct P wave and S wave in concrete, which are compared with the CWI analysis results. It is found that the CWI cannot be used to determine the third-order elastic constants or acoustoelastic coefficients because coda wave does not maintain clear wave propagation and polarization directions.
## 3 Methodology
### Fusion-Based BIM Structured Query Strings Generation  
The proposed model, termed **BIMCoder**, is a fusion-based architecture designed to translate natural language BIM queries into executable structured commands. It integrates the domain expertise of fine-tuned models with the flexibility of open-domain language models and adds a verification layer to ensure output quality. The architecture consists of three key components: an _expert model_, a _wrapper model_, and a _validator_.  
The **expert model** is fine-tuned using a carefully constructed BIM query dataset to capture domain-specific syntactic and semantic patterns. The **wrapper model**, built upon a general-purpose large language model (LLM), serves as a preprocessing module that standardizes user inputs and enhances compatibility with the expert model. Finally, the **validator** ensures that the output query strings conform to IFC-based JSON syntax and can be successfully executed within BIM platforms.  
The overall workflow is illustrated in Figure 1.  
The detailed process is as follows:  
* **Input:** The system receives user-provided natural language queries targeting BIM-related information.
* **Wrapper Model:** A large language model (LLM) reformulates the input queries into standardized, disambiguated forms suitable for domain-specific parsing.
* **Expert Model:** The expert model, trained on BIM query data, converts the preprocessed input into structured BIM query strings in JSON format.
* **Validator:** The validator checks the syntax and executability of the generated queries to ensure compatibility with downstream BIM platforms.
* **Output:** Validated structured query strings, ready to be executed in BIM systems (e.g., IFCQL-compliant JSON), are returned to the user or interfaced applications.  
This fusion-based design improves the robustness, interpretability, and domain adaptability of BIM query generation. By leveraging general-purpose LLMs for preprocessing and domain-specific models for generation, BIMCoder effectively bridges the gap between natural language flexibility and structured query precision.
## 1 Introduction
### Basics of the Smart City
#### 1.1.3 Smart Transportation  
Smart transportation is another key enabler for a sustainable smart city [30]. Smart transportation requires different communication technologies, such as vehicle-to-everything (V2X), vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-cloud (V2C), vehicle-to-device (V2D), etc. Smart transportation avoids collisions, saves energy, and improves road-safety, as well as increases the efficiency of traffic [31]. Thus, smart transportation can play a crucial role in the economic advancement of the smart city.
## 4 Quality-Aware Routing and Channel Assignment in Multi-Channel Multi-Gateway WOBAN
### Complexity of the Proposed Channel Assignment Scheme  
The _average_ case complexity of the proposed channel assignment scheme is discussed as follows: _Conflict graph formation:_ If there are \(m\) edges in the connectivity graph, i.e., \(m\) vertices in the conflict graph, then, for any two of the \(m\) vertices, we need to check whether they are in the interfere each other or not. Thus, the conflict graph formation takes \(\mathcal{O}(m^{2})\) time.  
_Vertex deletion:_ Next, we to calculate the complexity of vertex deletion. First, we need to sort \(m\) vertices based on their degree; this sorting takes \(\mathcal{O}(m\text{log}_{2}m)\) time. After that, for \(\mathcal{O}(m)\) vertices, we need to check whether the deletion of that vertex makes the remaining graph planar or not. Checking of the planarity condition takes \(\mathcal{O}(m)\) time based on a Boyer-Myrvold planarity test [44]. Thus, the total complexity of vertex deletion takes \(\mathcal{O}(m\text{log}_{2}m+m^{2})\) = \(\mathcal{O}(m^{2})\) time.  
_Backtracking:_ The next stage is backtracking; for that, we need to visit \(\sum_{L=0}^{n}2^{-\frac{l}{2}}k^{L}2^{-\frac{l^{2}}{k}}\) nodes on average based on [48] where \(k\) is the number of colors and \(n\) is the number of vertices in the planar subgraph. As in our case \(k=4\), the backtracking takes \(\mathcal{O}\left(\sum_{L=0}^{n}2^{-\frac{l^{2}-20l}{8}}\right)\) times on average.  
_Genetic algorithm:_ Finally, we need to calculate the average complexity of genetic algorithm. First, let us calculate the number of generations the genetic algorithm takes to converge. If the expected number of generations is \(E[i]\), then, from Equation (6), we get \(E[i]=\sum_{t=1}^{\infty}tP(t,l)\). In each generation, it performs crossover and mutation that takes \(\mathcal{O}(U^{2})\) and \(\mathcal{O}(U)\) time, respectively, where \(U\) is the number of vertices in GCL. Thus, the average time complexity of genetic algorithm is given by \(E[i]\mathcal{O}(U^{2})\).  
Figure 7: Genetic operations: illustration of (**a**) two-point crossover and (**b**) mutation.  
Thus, the average complexity of channel assignment scheme is given by \(\mathcal{O}(m^{2})+\mathcal{O}(m^{2})+\mathcal{O}\left(\sum_{L=0}^{n}2^{-\frac{L^ {2}-20L}{8}}\right)+E[i]\mathcal{O}(U^{2})=\mathcal{O}(m^{2})+\mathcal{O}\left( \sum_{L=0}^{n}2^{-\frac{L^{2}-20L}{8}}\right)+E[i]\mathcal{O}(U^{2})\).
# Software Integration of Power System Measurement Devices with AI Capabilities
## 3 Proposed Integration Framework
### System Architecture  
Figure 2 shows the sketch of the framework proposed in this work. The system features a modular design, with each tool required to meet the specifications outlined in Section 3.1. Firstly, the electrical monitoring devices are represented. They can be either PMUs using the IEEE C37.118.2 standard [53] or other devices such as PQMs or SMs using generic communications protocols (Modbus, SMTP, MQTT, etc.). An agent is required for each protocol implemented in the system, as well as a PDC to collect the measurements from the PMUs. Multiple PDCs or agents can be added as needed based on the equipment's distribution across the grid.  
Next in the data flow is the database. It was designed to be the core component of the system architecture, differing from systems like the FIWARE platform where a data  
Figure 2: Proposed infrastructure.  
distribution agent is central. This approach is common in tools where data storage is critical [25; 27], helping to avoid unnecessary data streams. However, it requires a database that can integrate with multiple agents and PDCs. With numerous database management systems available, it is crucial to select the one that best meets our needs. Relational databases (SQL) are useful for avoiding data duplication but are slow with large data volumes, making them unsuitable for PMU measurements. In contrast, non-relational databases (NoSQL) are designed to handle large data volumes efficiently and offer faster data handling speeds. These databases are often specialized for specific data types and optimized accordingly. Among NoSQL types, time series database managers (TSDBs) stand out by storing data as value-timestamp pairs, making them more efficient than other database types for time series data [54; 55].  
Finally, back to the framework requirements, this architecture must be user-friendly. In this regard, it was considered essential to allow real-time access to the stored data, both for monitoring and real-time analysis, allowing the incorporation of algorithms that feed back the generated data. Likewise, it will be necessary to be able to access the data offline if a subsequent analysis is required. The software used for this purpose would depend on the client, but the TSDB will need to be widely compatible for better performance.
### Main Work  
1. To address the performance shortcomings of traditional PSI-CA protocols in the face of significant differences in dataset sizes between participants, this paper introduces the first protocol, which is the unbalanced PSI-CA protocol based on Cuckoo filter. This protocol successfully constructs the first unbalanced private intersection cardinality protocol of this article by integrating exchange encryption technologies with Cuckoo filter functionalities for private information retrieval, followed by experimental analysis.
2. To alleviate the computational and storage burden on the small health app developer in the first protocol, the paper further proposes an unbalanced PSI-CA protocol based on single-cloud assistance and conducts experimental analysis. This strategy effectively migrates computational and storage tasks to cloud services, significantly optimizing resource utilization efficiency.
3. To safeguard against data leakage risks inherent in the unbalanced PSI-CA protocol based on single-cloud assistance, which cannot resist collusion attacks, the paper further designs an unbalanced PSI-CA protocol based on dual-cloud assistance. By employing homomorphic encryption and other security technologies, this scheme resolves potential data leakage risks in the single-cloud protocol while effectively preventing potential collusion attacks.  
Based on the unbalanced PSI-CA protocol based on dual-cloud assistance, this paper also designs the PSI-CA network and establishes corresponding data update strategies, significantly enhancing the practicality of the protocol.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 6 Results and Discussion
### Implementation Details
#### 6.1.3 Genetic Algorithm  
The genetic algorithm used to solve the formulated problem and its implementation were performed via the Pymoo Framework [56]. The different settings of the parameters are presented in Table 3.  
The quality of each chromosome was determined by the value of the fitness function. The fitness of each chromosome was defined by the sum of the benefits of the items included in the knapsack while making sure that the capacity of the knapsack was not exceeded. Given the stochastic nature of the GA, we performed 20 independent runs of the algorithm.  
During the inference phase, our tests were carried out on 10,000 test images (patterns); we extracted their features and compared them to all of the outputs provided by the networks. Therefore, the network with the closest pattern iwass selected. This network would return the label when it reached a stable state. Figures 14-16 show the evolution of the fitness over the first 20 generations in the cases of one, two, or three knapsacks. The algorithm found the best solution after 20 generations. Items that were chosen for each knapsack with the max fitness found are reported in Table 4 for the three dataset versions.  
Figure 12: Two-dimensional principal component analysis projection of AWGN noise.  
Figure 13: Two-dimensional principal component analysis projection of AWGN+Contrast noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Contrast**} & \multicolumn{2}{c}{**AWGN**} & \multicolumn{2}{c}{**MOTION**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Max.**} & \multicolumn{1}{c}{**Items Chosen**} & \multicolumn{1}{c}{**Max.**} & \multicolumn{1}{c}{**Items Chosen**} & \multicolumn{1}{c}{**Fit.**} & \multicolumn{1}{c}{**Items Chosen**} \\  & \multicolumn{1}{c}{**Found**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**Found**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**Found**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**Found**} & \multicolumn{1}{c}{**Items Chosen**} \\ \hline One knapsack & 5 & 1,3,5,6,8 & 4 & 1,2,4,10 & 3.588 & 1,4,6,8 \\ \hline Two knapsacks & 7.267 & 1,5,9,10 & 5,942 & 1,2,3,4 & 6.680 & 5,6,7,8,10 \\ Three knapsacks & 7.669 & 6,7,8,9 & 6.691 & 5,6,7,8 & 7.06 & 5,6,7,8 \\  & & 10 & 9,10 & & 9,10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The chosen items for different numbers of knapsacks with the max fitness found.  
Figure 14: GA fitness vs. the number of generations for AWGN+Contrast noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Dimension of the problem (N) & K \\ Number of runs & 20 \\ Number of generations & 20 \\ Population size (POP) & 100 \\ Max number of generations (ITER) & 100 \\ Mutation rate (MUT) & 0.1 \\ Crossover rate (CR) & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the genetic algorithm.
## 1 Introduction
### Previous Related Works  
Since XPD is a spatio-temporal parameter, several studies have been conducted in many regions to report the statistical estimation of XPD. The impact of rain height variability on XPD in the 10-35 GHz frequency range in India was reported by [PERSON] and [PERSON] [25]. Five years of measured rain height data obtained from the India Meteorological Department were used to compute CPAs and XPDs. The research revealed that CPA varies significantly with rain height, as XPD depends on CPA, frequency, and elevation angle. [PERSON] et al. [13] utilized kernel density estimation to analyze the spatio-temporal distribution of rain heights in South Africa, revealing seasonal dependencies in rain-induced attenuation. The outcome of the research recommends the use of seasonal rain attenuation values instead of annual mean values in link budget design. Similarly, [PERSON] [20] highlighted the variation in XPD in Malaysia, showing that rainstorms in tropical regions exhibit more severe depolarization effects than those in subtropical regions. [PERSON] [26] studied the relationship between XPD and its dependent parameters such as CPA, frequency, and elevation angle using ITU-R P618-7 [27] in three stations in Nigeria. The research employed fixed annual mean rain heights for the computation of rain-induced attenuation and CPA. It was observed that dual-polarized orthogonal channels operating at a 23\({}^{\circ}\) elevation angle are prone to interference due to cross-polarization. [PERSON] and [PERSON] [28] estimated cross-polarization  
Figure 1: Schematic presentation of an Earth–space path showing rain height and parameters required to compute rain-induced attenuation [23].  
discrimination due to rain in some stations in South Africa. The research utilized an annual mean rain height to compute rain-induced attenuations for all study locations. Recent research observed significant variation in rain-induced attenuation due to seasonal rain height variability [13]. None of the previous work considered the impacts of seasonal rain height variability on rain-induced attenuation and XPD in Pretoria, which is the major focus of this study. Pretoria, like other areas in South Africa, experiences four distinct weather seasons annually. It has a subtropical climate characterized by warm, wet summers and mild, dry winters. The summer (December-January) is the hottest wet season when most of the annual rainfall occurs. The autumn (March-May) is a transition period with gradually cooling temperatures, while the winter (June-August) is the coldest season characterized by cool, dry weather. The spring (September-November) is a transition season when a gradual rise in temperature signifies the onset of the next summer season. Further details about the climatology and weather of Pretoria can be found in [PERSON] et al. [13] and [PERSON] and Nxumalo [29].
## 2 Materials and Methods
### Biomechanical Property Studies
#### 2.2.1 Wss  
This study investigated the effect of different pore sizes, cell sizes, and cell shapes of the unit cell structure as well as different flow velocities and temperatures on the WSS within the scaffold. WSS is an important factor to consider when designing bone tissue engineering scaffolds due to the fluid flow inside the scaffold as it affects the cellular processes involved in new tissue formation [32]. It has been shown that different WSS leads to different biosignals affecting mesenchymal stromal cell differentiation and ECM mineralization, leading to differences in tissue repair outcomes. WSS also affects the self-assembly and directional alignment of collagen fibers, which is critical for the mechanical properties of bone tissue.  
The formula for studying WSS is Equation (2).  
\[\text{WSS}=\eta\left(\frac{\partial v_{i}}{\partial x_{j}}+\frac{\partial v_ {j}}{\partial x_{i}}=\right)\Bigg{|}_{x_{j}\in\Gamma_{s}}, \tag{2}\]where \(v_{i}\) and \(v_{j}\) are velocity vectors in different directions, while \(x_{i}\) and \(x_{j}\) are the i-th (or j-th) space coordinates.  
The expression of WSS shows that the fluid velocity and viscosity are the main causes of WSS. Numerous studies have documented the influence of WSS on the differentiation of bone mesenchymal stem cells (MSCs). Current recommendations suggest applying mechanical stimulation with WSS levels in the range of 0.1 to 10 mPa to promote the osteogenic differentiation of MSCs. Conversely, WSS in the range of 10 to 30 mPa has been shown to enhance chondrogenesis [25]. Furthermore, in vitro studies have demonstrated that WSS ranging from 0.05 to 25 mPa can effectively facilitate tissue growth [33]. [PERSON] et al. investigated the impact of different flow velocities on mineralized tissue growth generated by human mesenchymal stromal cells (hMSCs) cultured on scaffolds. By integrating WSS distributions obtained from CFD simulations with three-dimensional micro-computed tomography data, they demonstrated that shear stress below 0.39 mPa correlates with increased DNA content, while shear stress between 0.55 mPa and 24 mPa promotes osteogenic differentiation [34]. [PERSON] et al. reported that an average surface shear stress of \(5\times 10^{-5}\) Pa (0.05 mPa) is associated with high cell viability and osteoblast proliferation, whereas a peak shear stress of 5.7 \(\times\) 10\({}^{-2}\) Pa (57 mPa) leads to cell death [6]. The observed discrepancies among these studies likely stem from variations in experimental parameters, including scaffold structure, dimensions, material types, and specific conditions (such as flow velocity and frequency within bioreactors). Additionally, local variations in surface chemistry and morphology can significantly impact cell-biomaterial interactions, thereby influencing cellular responses to mechanical stimuli. Consequently, accurately predicting fluid shear stress and flow paths via CFD simulations is critical for optimizing the design parameters of scaffolds and bioreactor systems.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 4 Preliminaries
### Integration Between RPL and IEEE 802.15.4  
Our proposal attempts the coupling of the RPL routing protocol and the beacon-enabled IEEE 802.15.4 to support different QoS requirements of the emergency monitoring IIoT application. This integration enables efficient data delivery while reducing the energy consumption. These standards operate in two different layers therefore the network structure maintained by RPL is different with topology required in IEEE 802.15.4. In RPL structure, one node may associate with multiple parent nodes, while in IEEE 802.15.4 multi-hop structure, a node can associate with only one parent node. As a result, this structure may suffer from the beacons collisions while networking synchronization. Therefore, the superframe needs to schedule in a way that collision-free slots can be assigned and handling the overlapping superframes. Therefore, in our proposition, we implement the simple distributed beacon scheduling algorithm presented in [47]. Basically, the algorithm assigns the superframe slots randomly as well as on a greedy basis.