esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical (CFD) Model of the Vertilated Solar Wall  
Computational fluid dynamics, which is based on the solution of a standard set of conservation equations, can accurately describe the flow regime, velocity, and temperature of the airflow in the cavity, as well as determine the heat transfer coefficients in the given system. The main advantage of this method is its ability to collect very accurate information on thermofluidic behaviors in walls and cavities; however, this leads to a significantly long time being required to complete the calculation. This implies that such a method is only appropriate for studying transient state phenomena under steady-state conditions or when they are of relatively short duration, not for studying transient states [30].  
With CFD modeling, the geometric domain of the VSW is divided into many cells. The three conservation equations of mass, momentum, and energy are solved for each cell to predict the field variables (temperature and velocity). Ansys Fluent code was used for this CFD study to investigate the fluid flow and heat transfer characteristics in the ventilated cavities of the solar wall described in the previous section. The partial differential equations were then numerically solved using the finite volume method.  
A two-dimensional steady-state laminar (Re \(<\) 2300) flow was assumed. A pressure-based double-precision solver was selected to solve the set of equations used. The discrete-ordinates (DO) radiation model was employed to solve the radiative transfer equation (RTE). Solar radiation was modeled as internal heat gains added to both glass panes, and for the massive wall it was modeled as surface heat generation at its external surface using the solar energy absorption factors determined previously (Section 2). Simulations considering and not considering the gravity force were performed. At first, simulations not considering the gravity force were performed, as this reduces the simulation time. However, after we noticed that gravity affected the velocity and temperature profiles and subsequently the convective heat transfer coefficients, we finally took it into account. Second-order upwind discretization schemes were imposed for pressure and momentum and first-order upwind for energy and DO. Convergence was reached when the scaled residuals reached values of \(10^{-6}\) for the continuity, \(10^{-8}\) for the velocity and DO intensity, and \(10^{-7}\) for the energy. A uniform mass flow rate was imposed at the inlet of the geometry. The mass flow range was chosen in such a way as to achieve laminar flow.  
Air entered within the VSW at an external (cold) temperature, \(T_{ext}\), equal to \(0\ ^{\circ}\)C. On the internal (warm) side of the VSW, the temperature of the air, \(T_{int}\), was fixed at \(20\ ^{\circ}\)C. The indoor and outdoor radiative temperatures had the same values as the indoor and outdoor air temperatures.
## 2 Methods Used for Determining the Compressive Strength of Concrete
### Non-Destructive Testing of Concrete
#### 2.2.1 Elastic Rebound Method  
The elastic rebound method is fast, cheap, convenient and is one of the most widely used diagnostic non-destructive methods for determining the surface hardness of concrete. It uses a correlation dependence and the probabilistic compressive strength \(\mathrm{f_{c,Schmidt}}\) in new and existing reinforced concrete structures.  
It is based on the relationship between the compressive strength and the magnitude of the rebound R (the rebound number) from the concrete surface by a hammer that activates a movable mass. After the impact, energy is induced, which causes the rebound [4; 7; 21; 22; 23; 24]. The rebound number, which is a measure of surface hardness, is measured on a graduated scale. The most widely used device in this method is the Schmidt hammer. The magnitude of the rebound is affected by the characteristics of the concrete components (the size and type of coarse aggregate, the class and quantity of cement, the water-cement ratio); the age, moisture and carbonation degree of the concrete cover; the size and shape of the specimen; the temperature, type and inclination of the hammer during testing and other factors [1; 4; 11]. A disadvantage of the method is that if the influence of these parameters is not taken into account, the probabilistic compressive strength can differ by up to 70% from the actual one. To reduce these influences, the test is repeated at least 10 times within a test region, and the result is given by the median of these measurements. If more than 20% of all results differ from the median value by more than 30% all results are dismissed, according to EN 12504-2 [25]. Measurements must be carried out on well-cleaned surfaces, without defects.  
With this method, the compressive strength is determined at a depth of 2-3 cm from the surface, and for this reason it should be combined with other methods that determine the strength inside the elements [26; 27].  
More than 80 empirical models have been established, giving a relationship between the compressive strength of concrete and the rebound number [1; 4; 6; 26].
# Post-Occupancy Evaluation in High-Rise Apartent Buildings in Vietnam
## 2 Materials and Methods
### Method for Evaluating Long-Term Thermal Comfort
Therefore, this paper applies the adaptive comfort model given in the draft Vietnamese Standard TCVN 14214:2024 [36] (see Figure 2) to evaluate the indoor thermal environment of the investigated apartments when AC systems are not in use. The comfort temperature equation used in this model was developed by [PERSON] et al. [37]. According to this approach, an indoor environment is classified as overheating or overcooling if the indoor operative temperature of an occupied space exceeds or falls below the upper or lower 80% acceptability limit. This method is validated through point-in-time thermal comfort surveys, in which only 8% of the 87 respondents reported dissatisfaction when the indoor operative temperature remained within the 80% acceptable range. In principle, the PMV model can be used to evaluate the thermal comfort in occupied spaces during AC operation. However, its application for long-term evaluation presents challenges in practice. A key limitation is that some required input parameters, such as metabolic rate, clothing level, and air velocity, are difficult to monitor over an extended period continuously. Using standardized values--for instance, 0.5 clo of clothing level and 0.2 m/s of air velocity under summer conditions--may lead to the overestimation of PMV values, as adaptive behaviors such as clothing adjustments and the increased air velocity from electric fans are not accounted for. Among the 24 surveys on thermal comfort during AC operation, only 8% of respondents reported discomfort. This issue was primarily observed during the transition period, when the air conditioner was first turned on and the room had not yet reached the desired temperature. Once stabilized, most occupants found the indoor environment satisfactory. Given this, this study assumes that the thermal conditions during AC operation are generally comfortable, allowing for a simplified evaluation while avoiding PMV overestimation due to missing data on occupant-specific adaptations. Figure 2: Acceptable operative temperature ranges for naturally conditioned spaces, modified from TCVN 14214:2024 [36]. Since occupied hours vary across rooms, comparing overcooling and overheating hours is inappropriate. Instead, this study introduces overcooling and overheating probabilities as a comparative metric for thermal comfort in different rooms. These probabilities are calculated using Equation (1) and Equation (2), respectively:  
\[EP_{hot}=EH_{hot}/OH \tag{1}\]  
\[EP_{cold}=EH_{cold}/OH \tag{2}\]  
\(EP_{hot}\) and \(EP_{cold}\) indicate the probability of overheating and the probability of overcooling, respectively. They are defined as the ratio of overheating hours (\(EH_{hot}\)) and overcooling hours (\(EH_{cold}\)) for a given period to the occupied hours (\(OH\)).
# Multi-Probe Measurement Method for Error Motion of Precision Rotary Stage Based on Reference Plate
## 4 Experimental Results and Discussion
Table 2 systematically compares several measurement methods across critical dimensions such as setup complexity and measurement accuracy, demonstrating that the proposed method surpasses alternatives in terms of setup simplicity and cost effectiveness. While the multi-probe method provides a reliable metrological performance, its implementation requires strict axial alignment of the fourth sensor with the rotary stage to ensure that both the axial error motion measurement and the subsequent tilt error motion calculation are accurate. Furthermore, the methodology's inability to account for radial error motions limits its applicability in scenarios requiring comprehensive error analysis.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 4 Results and Discussion
### MARS Modeling
#### 4.2.2 Piecewise-Nonlinear Method  
In the second method, instead of using linear nodes, nonlinear conditions are considered. As in the first method, different states of influence of the input parameters on the output are obtained, and finally, using them, the MARS model is created. Figure 9 illustrates the results of nonlinear states. The accuracy of this new model is given in Table 5. As can be seen from this table, the results of the first method were better in predicting the slope deformation of earth dams. The results of the piecewise-cubic method are presented in Table 6 below. These functions eventually provide a relationship to predict the slope deformation of earth dams in Equation (10).  
\[\begin{split}\text{y}=4.8-22\times\text{BF1}+11\times\text{BF2}+8.3\times 10^{2}\times\text{BF3}+38\times\text{BF4}-5.5\times\text{BF5}+1.3\times \text{BF6}+5.1\times\\ \text{BF7}+1.4\times\text{BF8}-2.3\times\text{BF9}+0.26\times\text{ BF10}+4.9\times\text{BF11}-4.4\times\text{BF12}-14\times\text{BF13}+1.6\times\\ \text{BF14}-2.5\times\text{BF15}-50\times\text{BF16}-8.9\times\text{ BF17}+9\times\text{BF18}-8.5\times\text{BF19}-1.2\times\text{BF20}-2.2\times\\ \text{BF21}+1.2\times\text{BF22}\end{split} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training** & \multicolumn{2}{c}{**Testing**} \\ \hline MAE & 0.3057 & MAE & 0.2767 \\ MSE & 0.2571 & MSE & 0.2165 \\ RMSE & 0.5070 & RMSE & 0.4653 \\ RRMSE & 0.3456 & RRMSE & 0.3072 \\ R\({}^{2}\) & 0.8905 & R\({}^{2}\) & 0.8956 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of slope deformation of earth dam predicted by the MARS model based on the piecewise-linear method.  
Figure 8: The various effects of variables on MARS models based on the piecewise-linear method.  
Figure 9: Cont.  
Figure 9: The various effects of variables on MARS models based on the piecewise-cubic method.  
This relationship can be a good solution for issues that need to be more realistic. This formula is one of the advantages of the MARS method that can be used in various fields of geotechnical and civil engineering.  
Finally, the best model obtained for the slope deformation of earth dams distance prediction is presented in Table 6. As can be seen, its performance is better than the GMDH and second method of MARS. As a result, the first method of the MARS model (piecewise-linear) can be successfully applied to making such predictions in a reasonably accurate way.
## 2 Background on ML-Based Optimisation of Wireless Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Operational Timescales** & **Network Functions** & **Examples of Key Research Studies** \\ \hline \multirow{8}{*}{Near-real-time RIC} & & DL for joint subcarrier allocation and power control in the downlink of multi-cell networks [47]. \\  & & Joint downlink power control and bandwidth allocation in multi-cell networks using NNs and Q-learning [48]. \\  & & DNNs for interference-limited power control that maximises the sum-rate under a power budget constraint at the base station [49]. \\  & Resource allocation & Predictive models on spectrum availability for distributed proactive dynamic channel allocation and carrier aggregation for maximising the throughput of LTE small cells operating in unlicensed spectrum bands [50]. \\  & & A DRL agent learns to select the best scheduling policy, including water-filling, round-robin, proportional-fair, or max-min, for each base station and RAN slice [51]. \\ \cline{2-3}  & Interference management & Centralised joint beam management and inter-cell interference coordination in dense mm-wave networks using DNNs [52]. \\ \hline \multirow{8}{*}{Non-real-time RIC} & & Predicting the capacity of RAN slices and the congestion of network slices using NNs, and optimally combining them to sustain the quality-of-experience (QoE) [53]. \\  & E2E network slicing & An AI/ML module predicts the states of network resources in runtime and autonomously stitches together RAN and core slices to satisfy all the user intents [54]. \\ \hline \multirow{4}{*}{Network dimensioning and planning} & & SINR-based coverage evaluation using stochastic geometry & Deep transfer learning for selecting the downlink transmit power level that optimises the energy efficiency in cellular networks given the density of base stations [55]. \\  & & NNN-based prediction of the coverage probability given the base station density, the propagation pathloss and the shadowing correlation model [56]. \\ \cline{1-1} \cline{2-3}  & Network maintenance & Fault detection and compensation & Supervised learning method to detect performance degradation and the corresponding root cause of the fault [57]. \\ \hline \end{tabular}
\end{table} 
Table 2: Example key studies utilising model-based ML techniques for improving the performance of wireless networks in near real time (10 ms to 1000 ms), and non real time (longer than 1000 ms).
###### Abstract
According to documented reports on vehicular navigation, the total traffic flow between Johannesburg and Pretoria during on-peak days on the N1 freeway is over 160,000 vehicles per day [11]. This research study focuses on the South African Road transportation system, especially the busiest N1 Allandale interchange road, which has the highest traffic volume in Southern Africa. The novelty of this study is in the application of a hybrid artificial neural network trained by a particle swarm optimization model for modelling traffic flow at signalized road intersections. Also, transportation researchers to date have focused majorly on road intersections rather than on signalized road intersections. This study aimed to address the following research questions:  
* Can a hybrid artificial intelligence algorithm such as an artificial neural network trained by particle swarm optimization (ANN-PSO) be used as a predictive approach in modeling traffic flow at a signalized road intersection? * Can traffic flow parameters such as speed of vehicles, traffic density, time, number of different classes of vehicles on the road, and traffic volume be used to model vehicular traffic flow at a road intersection? This paper is divided into five parts. The first section introduces the research study and outlines the primary aim and significance of this research study. The second section discusses prior research related to the engineering application of Artificial neural network-particle swarm optimization (ANN-PSO) and the theoretical framework of the model. The third section concerns the methodology used for this research study, including location, collection of the traffic datasets, and how the model was developed. The fourth section presents the findings of this research study. The final section comprises the conclusion and implications of this study on the road transportation system.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 2 Previous Studies
### Issues with Previous Studies
#### 2.2.2 (B) Issues with Voyage Speed Calculations  
As discussed in Section 2.1, [PERSON] et al. used data on the departure and destination ports from operation data on the same vessel to calculate the average voyage speed. However, future weather conditions were not considered despite their potential effect on the voyage speed. The voyage speed also varies with the transit area, such as harbor, offshore, and strait, examples of which are shown in Figure 2. This figure is a graph created using Marine Traffic operation data on the voyage speed distribution of vessels around the Kanmon Straits, Kyushu, Taiwan, and Hong Kong ports in November 2019. The vertical axis depicts the percentage of vessels that made the voyage at the speed depicted on the horizontal axis. Figure 2 shows vessels travel at a high speed offshore but at a low average speed of 10 knots or less near straits and harbors. [PERSON] et al. did not consider these regional aspects of the voyage speed.  
Moreover, as mentioned above, the method of [PERSON] et al. [19] uses equations of motion considering weather conditions and wave/wind parameters to calculate the voyage speed. Parameters such as the number of engine and propeller rotations are also considered. The effect of these parameters is small near ports and large offshore, and therefore they reflect the regionality of the voyage speed. However, just like the study by [PERSON] et al. [20], the equations of motion also involve other parameters such as the viscous resistance that are difficult to determine as suitable for actual marine environments. In addition, operation data provided by an AIS do not include dynamic vessel information, such as the number of engine and propeller rotations, and some ships may not provide such log data, making the acquisition of these data difficult.  
In summary, the previous methods may not predict the voyage speed, and in turn the arrival time, so accurately for voyages with constantly changing weather conditions.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 3 Results and Discussion
### Endurance Test of PIT  
The endurance time of the PIT was evaluated by setting the communication cycle to 1 s, 10 s, and 30 s, respectively, and recording the duration of PIT power supply voltage consumption from full voltage to below 3.4 V. The experimental results are shown in Figure 13. In the case of a communication cycle of 30 s, the running time was about 25 h, which meets the design requirements of the signaling system.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 3 Details of Damst
### Damst Point List and Packet Format  
#### 3.2.1 Damst Point List and DAMST Angle List  
As shown in Figure 1, if a mobile sink is interested in the status of a region, it appoints a node in the region as a RA while it passes by the RA. The RA is responsible for collecting data in the region of interest. After leaving the region, the mobile sink periodically chooses nodes on or near its trajectory as footprints, obtains their location information by its own GPS and records their ID and location information. If a time interval to designate two successive footprints is set as \(t\), a communication radius of each node is set as \(r\), and a speed of the mobile sink is set as \(v\), these parameters are required to satisfy the following inequality:  
\[t\times v\,<\,r \tag{1}\]  
The mobile sink's movement angles are called as DAMST angles and defined in Section 3.3.1. While the mobile sink moves, its DAMST angles are recorded in a DAMST angle list and its footprints are put into a DAMST point list as DAMST points. After each footprint appointment, the mobile sink puts the new footprint into the DAMST point list, and calculates a new DAMST angle then puts it into the DAMST angle list. Meanwhile, for obtaining an energy efficient path to the RA, the mobile sink cancels DAMST points on detours via analysis of the DAMST angles in the DAMST angle list. After that, both the DAMST point list and the DAMST angle list need to be updated.
# Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems
## 3 A Review of Approaches to Irregular Object Packings
### Algorithmic and Heuristic Approaches
Additional considerations, such as load balancing, give rise to even more challenging non-standard packing problems. The article first considers the orthogonal packing of \"Tetris-like\" items within a convex domain, and then the packing of polygons with continuous rotations in a convex domain. The proposed solution approaches are based on mixed-integer linear or nonlinear programming (MILP, MINLP). [PERSON] [13] summarizes the results of his research aimed at tackling non-standard packing issues arising in space engineering and logistics. In these applications, the necessity of exploiting the spacecraft load capacity, as much as possible, represents a paramount challenge. He proposes a global optimization framework based on MILP, MINLP, and heuristic strategies. His study offers insights related to possible applications across several engineering and industrial sectors, from transportation to manufacturing. [PERSON] [27] describes a general algorithm for nesting irregular shapes. The key idea is to inscribe a selection of circles in each irregular shape and then relax the non-overlap constraints for the shapes by replacing them with non-overlap constraints for the inscribed circles. A specialized branch-and-bound algorithm with added heuristics is introduced to find the initial inscribed circles that approximate the shapes. This work shows an interesting connection between general circle packings (with possible overlaps among the circles) and general irregular packings. [PERSON] et al. [17] review mathematical models for handling irregular packing problems. These hard nesting problems had been addressed earlier, both in the scientific literature and in real-world applications, applying heuristic and meta-heuristic techniques. More recently, a variety of mathematical models have been proposed for nesting problems. These models can be used either to prove optimal solutions for certain types of nesting problems or can frequently serve as the basis of problem type-specific heuristic approaches to improve the approximate solution found by optimization. [PERSON] et al. [28] consider packing irregular solid objects in a cuboid of minimum volume. Each considered object type is composed of a number of convex shapes, such as oblique and right circular cylinders, cones, and truncated cones. New analytical tools are introduced to state placement constraints for oblique shapes. Using the \(\Phi\)-function technique originally proposed by [PERSON] and subsequently utilized in studies with his colleagues, the packing problem is formulated as a nonlinear programming problem. The solution approach is illustrated by numerical examples.