esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To generalize the nonradiative recombination, the rate-based calculation of DB considers the nonradiative generation and recombination and its ideal reverse saturation current [1]. Thus, the SJSC can be made to achieve a semiempirical limit by varying the ideal reverse saturation current, which helps explain the nonradiative recombination impacts [1]. Therefore, this method can be applied to the DB of MEGSC. Figure 1: Carrier generation and recombination process of a single-junction solar cell (SJSC) and multiple exciton generation solar cell (MEGSC). (**a**) One electron and hole pair (EHP) generation and recombination of SJSC. (**b**) Multiple EHP generation process by impact ionization and multiple carrier recombination process after Auger recombination (AR) where M is the maximum generated E\({}_{\text{H}}\)s (1, 2, 3,..., M – 1, M). (**c**) Multiple carrier recombination and Auger thermalization via phonon-assisted electron or hole cooling after a carrier excitation. In this paper, we present a study on the DB limit of an MEGSC, which involves a quantitative analysis of the nonradiative recombination impact. For simulation purposes, we altered the DB of MEGSC by varying the ideal reverse saturation current (J\({}_{0}\)) from the nonradiative generation and recombination in order to derive the ideal reverse saturation condition for MEGSC. By varying this parameter, we were able to obtain a semiempirical limit and discuss the impact of the nonradiative recombination of MEGSC.
# Effect of Temperature on the Modal Variability in Short-Span Concrete Bridges
## 2 Materials and Methods  
Two bridges currently in operation were monitored during 7 months through installed accelerometers and thermometers to evaluate the variation pattern of their natural frequencies according to environmental factors such as temperature. The monitored bridges are Seolmun Bridge, a RC (reinforced concrete) slab bridge, and Gajwa Bridge, a RC rahmen bridge. Two different bridge types were chosen to make it possible to assess the extent of the effect of temperature with regard to the bridge type. Figures 1 and 2 show views of Seolmun Bridge and Gajwa Bridge, respectively. Both bridges are 3-span continuous with a length of about 15 m. Gajwa Bridge is straight with a width approximately twice that of Seolmun Bridge, which is skewed.  
Figure 1: Seolmun Bridge (RC slab bridge).  
Figure 3 presents the overall shape and sensor layout of Seolmun Bridge. Each support is equipped by 5 elastomeric bearings. Accelerometers were installed to measure vertical acceleration at the ends and at the center in the width direction at the center of each span. Thermometers were also installed in the center of the first and third spans. Figure 4 shows the overall shape and sensor layout of Gajwa Bridge. The sensor layout is identical to that of Seolmun Bridge.  
Figure 3: Shape and sensor layout of Seolmun Bridge.  
Figure 2: Gajwa Bridge (RC rahmen bridge).  
The accelerometers and thermometers were disposed in protective boxes as shown in Figure 5 to secure safe long-term monitoring. Electric power was supplied to the sensors by means of solar panels and batteries. Measurement started at completion of the installation of the sensors on September 14, 2021 in Seolmun Bridge and two days later, on September 16, 2021 in Gajwa Bridge. The sampling rate was 100 Hz for the accelerometers and 1 Hz for the thermometers.  
Figure 6 plots the acceleration responses measured during a short period in both bridges. The acceleration responses measured in Seolmun Bridge, the RC slab bridge, appear to be larger than those measured in Gajwa Bridge, the RC rahmen bridge. This difference in the amplitude can be attributed to the difference in the bridge types as well as to the fact that Seolmun Bridge has a thinner slab and shorter width than Gajwa Bridge.  
Figure 4: Shape and sensor layout of Gajwa Bridge.  
Figure 5: Accelerometers: (**a**) installation on site; (**b**) shape of sensor.  
Moreover, the faster reduction in the acceleration response in Gajwa Bridge reveals that the RC rahmen bridge has a higher damping ratio.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 1 Introduction
### Aim and Organization of the Paper  
In this paper, we restrict our attention on evaluating the effect of data imbalance on SGMMs classification accuracy and comparing various strategies to address this challenge. Specifically, we assess techniques such as cost-sensitive learning, probability threshold adjustment, threshold selection through cross-validation, and sampling-based approaches, such as downsampling, oversampling, and SMOTE.  
Through a comprehensive set of experiments on diverse datasets, both synthetic and real data examples, we systematically evaluate the performance of GMM classifiers with and without these adjustment strategies. This empirical analysis aims to provide insights into the effectiveness of these methods in handling data imbalance and improving classification outcomes.  
The remainder of this paper is organized as follows. Section 2 is devoted to the presentation of probabilistic models for supervised learning in classification with particular focus on Gaussian mixture models, and the corresponding classification rule that can be derived. Performance measures for imbalanced data are discussed in Section 4, including cost-sensitive learning, different threshold adjustments techniques, and sampling-based approaches. Section 5 presents an extensive investigation through simulation studies on synthetic data and empirical analyses using real-world datasets. Lastly, the final section concludes by highlighting the key contributions of this paper.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Phase and Communication Procedure
#### 3.1.5 \"Termination\" Phase  
The \"Termination\" phase only occurs when the client intends to terminate the communication with the server. This phase starts with the client closing the connections of both the control channel and data links. Then, the server will terminate all processes associated with the client's connections.
## 2 System Structure
### Some Assumptions  
Before receiving processing, some assumptions are given.  
1. Searching in the spreading code domain and Doppler domain, we only consider the correlation between the received signal and the passband reference signal with frequency offsets, omitting the signal length compression/expansion. This is because it has little effect on the correlation results for one single symbol, which is verified through simulations in Section 3. Moreover, although signal sampling conversion can simulate the influence of Doppler frequency offset and data length compression/expansion, the number of calculations is terrific, and it is easy to introduce additional errors.
2. When intercepting the signal for processing, the Doppler factor of the current symbol is not known in advance. It is assumed that the length of the one-symbol-received signal is unchanged. After Doppler estimation, the length of the current symbol is adjusted according to the estimation results. Similarly, the motion speed of AUV is generally below 10 m/s. We believe that the data length error of the order is not enough to affect the despreading performance.
## 3 Experiments and Results
### Study of the Applied Classifiers
#### 3.1.5 \(k\)-NN Classifier
In addition, regularization technique is used for preventing overfitting in a predictive model. Generally, regularization techniques specify constraints on the flexibility of a model and reduce uncertainty in the estimates, which provide better scopes for maintaining bias-variance tradeoff. There are a number of regularization algorithms in machine learning. In this work, ridge regression algorithm is used for regularization. Typically, cross-validation tests are carried out to investigate the bias-variance tradeoff. In the proposed work, cross validation losses are computed and no overfitting is observed. The detailed numeric analyses of the classification performance are reported in the next Subsection.
## 5 Numerical Analysis  
### Numerical Model Establishment  
To further validate the reliability of the segmented calculation model proposed in this paper, the finite element software Abaqus v2023 was employed in Case A to simulate the construction process of box jacking under different lubricant conditions. The model was simplified based on the following assumptions:  
(1) Constitutive model: (i) Assume the soil is a homogeneous, continuous, isotropic elastic-plastic material, and adopt the Drucker-Prager elastic-plastic constitutive model with better convergence [14]. (ii) Neglect the effect of box joint connections. (iii) Surround the box with a 20 mm equivalent layer to simulate the lubricant, employing an elastic constitutive model.  
(2) Construction process: (i) Use the birth and death element method to excavate the soil, simulating the forward jacking of the box via displacement control [28; 29; 30]. (ii) Neglect axial offset and box deflection during jacking, assuming the box jacks forward in a straight line. (iii) Neglect the time effects on soil consolidation settlement.  
Figure 11 shows the three-dimensional finite element numerical model. The model parameters are shown in Table 10. The dimensions of the box model were 9.1 \(\times\) 5.5 m, with a wall thickness of 0.65 m and a soil cover depth of 9 m. To eliminate boundary effects, the dimensions of the stratum model were 50 m in width, 30 m in height, and 253.6 m in length. The stratum model used a structured mesh, while the box and boring machine used a swept meshing command, and the element type of the box, boring machine, and soil is C3D8R (the solid hexahedron elements). The entire model consisted of 205,584 nodes and 179,492 elements. In the stratum model, the ground surface was unconstrained, and the bottom was constrained in the x, y, and z-direction displacements.  
Figure 10: Comparison of results from different calculation models.
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 4 Results and Discussion
### Examples of Flood Emergency Monitoring  
#### 4.3.1 Flood Emergency Monitoring in Fangshan District  
The optimized DG-LRG method was used to monitor the flood inundation range in the Fangshan area, and the monitoring results are shown in Figure 8. In the figure, the blue area represents the pre-disaster water bodies extracted from optical images, while the red area represents the water body expansion results monitored using GF-3 data on 1 August 2023. Compared with the pre-disaster water bodies, it was found that, by 1 August, having been affected by the rainstorm, the water body area in Fangshan District had expanded by 44.15 km\({}^{2}\) and the flows of Dashi River (Figure 8A), Xiaoqing River (Figure 8B), and Yongding River (Figure 8C) had soared. The villages around Dashi River in Doudian Town, Shilou Town, and Liulihe Town in Fangshan District were seriously affected, with the water body area expansion rate reaching 3.32%. This example demonstrates that the DG-LRG method can quickly extract the range of water bodies while providing more objective and accurate results, which is helpful for the timely and accurate assessment of disaster situations. Thus, it has good applicability in the context of large-scale flood emergency disaster monitoring.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 4 QSA-Based EDGE Deployment Approach  
The Edge Site Deployment Problem (ESDP) is inherently NP-hard, as it involves optimizing the placement of edge servers while minimizing user request latency under complex constraints. NP-hard problems are computationally intractable for large-scale instances, as the solution space grows exponentially with the number of users and potential server locations. Therefore, finding the global optimal solution is impractical for real-world applications, especially in dynamic environments where user requests and network conditions change frequently.  
Given the computational complexity of the ESDP, heuristic algorithms provide a practical and efficient alternative for finding high-quality approximate solutions within a reasonable time frame. While heuristic algorithms do not guarantee global optimality, they are widely used in both academia and industry to solve large-scale optimization problems, particularly in edge computing and network design [22; 23]. The proposed queuing search algorithm (QSA) is designed to balance solution quality and computational efficiency, making it suitable for real-time deployment scenarios.  
The primary goal of the ESDP model is not to find the global optimal solution but to provide a feasible and efficient deployment strategy that significantly reduces user request latency in practical scenarios. By abstracting latency as the number of hops and minimizing the average hop count, the model captures the core challenge of edge server deployment while remaining computationally tractable. This approach aligns with the real-world requirements of edge computing, where near-optimal solutions are often sufficient to achieve significant performance improvements. The QSA algorithm offers several advantages over traditional optimization methods.  
Scalability: QSA can handle large-scale instances of the ESDP, which are common in real-world edge computing environments.  
Adaptability: QSA is designed to dynamically adjust to changes in user requests and network conditions, making it suitable for dynamic environments.  
Efficiency: QSA achieves a balance between solution quality and computational cost, enabling real-time decision-making.
## 3 Results and Discussions
### Compressive Strength
#### 3.2.1 Influence of Slag Content  
The compressive strengths of group A mixes with varying slag content are shown in Figure 3. It can be seen that the increase of slag content from 10 to 50% resulted in a significant improvement of the compressive strength of GPC mix. The compressive strength of all the mixes in group A increased at a faster rate till the age of 28 days, after which this rate of strength gain slowed down. It can also be observed that the rate of development of strength is significantly dependent on the slag content. The compressive strength of specimen A4-S50 (50% slag content) was more than two times that of A1-S10 (10% slag content) at 28 days. The 28 days compressive strength of A2-S20 (40 MPa), A3-S30 (54 MPa), A4-S50 (64 MPa) was 100%, 170%, and 225% higher respectively than A1-S10 (20 MPa). The 90 days compressive strength of A2-S20 (45 MPa), A3-S30 (62 MPa), and A4-S50 (69 MPa) was 80%, 150%, and 190% higher respectively than A1-S10 (25 MPa).  
The increase in the compressive strength by the addition of slag is due to the development of C-S-H gel in combination with geopolymeric gel, which is produced when slag reacts with the AAS. The C-S-H gel helps GPC matrix to harden at room temperature and reduce the porosity, thus providing more compact and denser microstructure [38; 39]. The C-S-H gel present in the fresh GPC matrix also provides more nucleation sites, consequently activating the geopolymer gel formation at ambient temperature, which leads to the rapid solidification and hardening process. Therefore, an explanation for the increased strength by increasing the slag content is the enhanced production of C-S-H gel during the hardening process. Hence, the more the slag content in the mix, the more amount of C-S-H gel would be available in the matrix, resulting in a higher compressive strength of the specimens.
# A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands +
###### Abstract  
Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure.  
cognitive radio; Internet of Things; non-Markovian stochastic Petri net; GSM/EDGE; QoS
# Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping
## 1 Introduction
The two pointer lines obtained from the left and right images were then rebuild in three dimensions to determine the value of the point instrument [17]. In 2018, real-time measurement of multi-pointer instrument was realized by using ARM (Advanced RISC Machine) Hisilicon mobile processing platform and skeleton extraction technology based on distance transform algorithm [18]. In summary, there are advantages and disadvantages in the measurement methods of the above two models. The first model has the advantage of being easier to measure the reading of the pointer meter, however, the measurement accuracy is lower. If the meter surface is not parallel to the image plane, the changes of the image locations are critical points shown by Figure 1. The measurement accuracy of the second model is greatly improved, but the redundancy parameters also greatly increase the computational complexity and limit its application and promotion. The present method calibrates the extrinsic parameters by the pixel coordinates and world coordinates of the mid-points of the measured meter scales firstly, and then the meter image is made parallel to the surface of the meter by the inverse perspective mapping. The mapping is finished by rotation transformation of the meter image, and the rotation transformation matrix obtained by the Rodrigues formula. The rest of the paper is arranged as follows: the approach of measurement the reading of the pointer meter is presented in Section 2. Section 3 provides experiments and results, and analyzes the effects of certain factors through experiments. Conclusions are given in Section 4. Figure 1: The distance between the each pointer meter and the camera is the same; (**a**) the pointer meter plane is facing the camera at an angle; (**b**) the pointer meter plane is facing the camera.
## 3 Results and Discussion
### Application of Extraction Method Using Portable GC/MS Instrument and Validation  
After the LLME-SPME method has been developed by using the bench-top GC/MS instrument, it was transferred on a portable instrument with no parametric changes to the extraction procedure for the determination of TCE. It is recommended to use an isotopically labeled internal standard (IS) for calibration with SPME [16], especially for field analyses because methods are more susceptible to fluctuations in sensitivity compared to measurements that are obtained in the controlled environment of the laboratory.  
To use deuterated TCE (TCE-_d_) as an IS could cause 'cross contribution' problem in the mass spectra which is the contribution of intensities of TCE and TCE-_d_[21]. However, the application of classical least-squares (CLS) can effectively model the overlapping peaks between an analyte and its corresponding isotopic IS. The details of using TCE-_d_ as an IS for the quantitation of TCE were reported earlier [22]. The calibration was constructed using samples prepared with the same extraction method discussed above with a linear dynamic range of 10-1000 ng mL\({}^{-1}\) of TCE in water. The validation results are listed in Table 3. The limit of quantitation (LOQ) of this method is 10 ng mL\({}^{-1}\). The accuracy as relative error (RE) was in a range of \(-12\%\)-10% and precision as relative standard deviation (RSD) ranged from 4.4%-11.9%. The method was also applied to the spiked river samples at different concentrations, and the prediction results are all in the acceptable range with the relative errors between \(-1\%\)-10%. A representative GC chromatogram and mass spectrum for the spiked river sample containing TCE at 10 ng mL\({}^{-1}\) is given in Figure 6. Note that the chromatographic peaks at 1.04 min and 1.17 min are the PDMS peaks from septum in crimp seal of glass vial which do not affect the analysis of TCE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**TCE** & \multirow{2}{*}{**TCE in the vial (ng mL\({}^{-1}\))**} & \multicolumn{2}{c}{**SPME**} & \multicolumn{2}{c}{**LLME-SPME**} & \multirow{2}{*}{**Enrichment factor**} \\
**concentration** & & & **TCE on column** & & & \\
**(ng mL\({}^{-1}\))** & & **(ng)** & **(\%)** & & & \\ \hline
10 & 100 & \(1.1\pm 0.1\) & 11 & \(2.9\pm 0.4\) & 29 & \(2.6\pm 0.2\) \\
100 & 1000 & \(17\pm 1\) & 17 & \(41\pm 2\) & 41 & \(2.4\pm 0.4\) \\
300 & 3000 & \(48\pm 3\) & 16 & \(107\pm 14\) & 36 & \(2.2\pm 0.3\) \\ \hline \end{tabular}
\end{table}
Table 2: Absolute recoveries and enrichment factor of TCE by LLME–SPME (_n_ of 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Added concentration** & **Measured concentration** & & \\
**(ng mL\({}^{-1}\))** & **(ng mL\({}^{-1}\))** & & **RSD (\%)** & **RE (\%)** \\ \hline
10 (LLOQ) & \(10\pm 1\) & 10.8 & 2 \\
30 & \(28\pm 3\) & 11.9 & \(-7\) \\
100 & \(88\pm 5\) & 5.9 & \(-12\) \\
300 & \(330\pm 15\) & 4.4 & 10 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy and precision for the determination of TCE (_n_ of 3).
## 1 Introduction  
In the 21 st century, the exploitation of aquifers has become one of the main objectives of the agricultural sector, both to expand and improve existing farms. This growing trend has sparked a keen interest among researchers, who are focused on finding different methods to address this issue in different geological contexts [1; 2; 3; 4; 5; 6].  
Currently, the techniques used, such as geophysics, require considerable physical effort and face difficulties of access due to topography or vegetation. Therefore, one of the main concerns among geophysicists is the need to adopt methods that are more convenient, faster and able to cover larger areas.  
In this paper, we propose an advanced methodology for the effective characterisation of geological strata. We focus in particular on the application of electromagnetic soundings in order to deepen the understanding of the Tertiary strata located in the study region. These soundings are presented as a valuable complement to magnetometry techniques. Wealso integrate the use of unmanned aerial vehicles (drones) as an essential component in the generation of detailed mapping of the metamorphic materials found in this research area.  
In general, metamorphic materials tend to be aquifers with low levels of permeability, making them difficult to exploit. In many cases, they are hardly viable for use. To obtain more interesting flow rates, it is necessary to locate fracture zones or contacts between different materials [7; 8; 9]. In this sense, the mapping of the area by means of magnetic prospecting is of great help in the strategic location of these boreholes.  
Indeed, magnetic surveying has the ability to differentiate between different types of metamorphic rocks, a feature that can be difficult to achieve using other geophysical variables. This distinguishing ability makes magnetic surveying a valuable tool for the characterisation and detailed study of metamorphic formations, which in turn helps to improve accuracy in the identification of potential aquifers and areas of interest for exploitation [10].