esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Review of Thematic Foci on Extreme Weather Events and Climate Change
#### 3.6.4 Other  
Studies dealing with extreme events that do not yet fit into any of the above categories and that do not investigate trend changes in climatic conditions fall into the \"Other\" category. Only three studies dealing with two other extreme events, namely floods and heavy snowfall, could not be classified. In the study by [PERSON] et al. [114], the authors described how remote sensing data can be used to quantify flood damage in coastal forests. Rising sea levels and more intense storms are flooding the forest and salinizing the soil. This damages the forest and changes the remotely sensed reflection signal from the vegetation. The authors found that the Normalized Difference Wetness Index (NDWI), calculated from the Landsat timeseries, was best suited to capture the effects of storms on coastal forests.  
[PERSON] et al. [115] examined the impact of floods and droughts on the temperate forest in the Hruby Jesenik Mountains (Czech Republic) between 2004 and 2013. The study assessed the influence of various environmental factors, including drought and management, on forest damage. In addition to in situ data, MODIS data were employed for timeseries analysis. The results indicated that strong winters or droughts exert a more pronounced influence on the NDVI than flood events, thereby demonstrating a stronger association with forest decline. The third publication under \"Other\" addressed the issue of forest damage caused by heavy snowfall. [PERSON] et al. [104] described the potential of multispectral UAV imagery to detect damage to individual trees or completely destroyed trees. The use of structure from motion point clouds (photogrammetry) resulted in an overall accuracy of 0.9 for the detection of trees damaged by wet and heavy snow.
## 2 Related Work
Based on location knowledge and contention processes, the proposed cross-layer protocol, CoopGeo [12], aims at providing an efficient, distributed approach to select next hops and optimal relays. Furthermore, it introduces asymmetric encrypting of data packets to protect from malicious node attacks [13]. Energy-efficient beaconless geographic routing (EBGR) [14] was proposed to provide loop-free, fully-stateless, energy-efficient sensor-to-sink routing at a low communication overhead without the help of prior neighborhood knowledge by prohibiting the unsuitable neighbors from participating in the relay contention procedure. Context-aware Adaptive Opportunistic Routing (CAOR) [15] exploits multiple cross-layer context information, such as link quality, geographic progress, energy and mobility for mobile _ad hoc_ network routing. However, these handshake-based routing protocols cannot be directly applied to the error-prone wireless sensor networks because of their strict limitations. They always require three types of successful message exchanges between a sender and neighbors for each data forwarding in a hop: data packet, response packet and acknowledgment packet. Furthermore, they have a significant delay problem in reactively discovering the location information of neighbors. These limitations causes them to not be able to be used in most real-time sensor applications, such as intruder detection and fire alert systems.
## 3 Results and Discussion
### Mass Change  
Figure 3 shows the mass change results of cement mortar specimens with different lithium mica slag contents at specific ages. The results show that the mortar specimens with a 10% addition of lithium mica slag at 14 days have the highest rate of mass change. The rates of mass change of all mortar specimens containing lithium mica slag were almost equal before 28 days. As the age increased, the masses of mortar specimens slightly increased after 150 days. The mortar specimens with 20% and 30% lithium mica slag developed a greater degree of densification in the later stages than the mortar specimens without lithium mica slag, thus increasing the mass in the later stages. The increase in mass, in addition to the changes in the product, may also be related to the degree of water absorption during the preservation process.  
Figure 2: The concentrations of SO4\({}^{2-}\) dissolved in solution at different pH values and immersion times (mol/L).
## 3 Results and Discussion
### Optimization of Experimental Conditions  
#### 3.4.1 The Effect of the Concentration of Aptamer Probe on the Peak Current  
The influence of the concentration of aptamer probe on DPV response was investigated under the reaction with 5.0 \(\times\) 10\({}^{2}\) CFU/mL _E. coli._ As shown in Figure 4, it can be seen that the peak current increases dramatically when the concentration of aptamer probe increases from 0 to 3 \(\upmu\)M. However, the peak current does not change significantly when the concentration of the aptamer probe exceeds 3 \(\upmu\)M. The results can be attributed to the fact that the low amount of the aptamer probes did not completely hybridize with the single-strand capture probes. Therefore, after incubating with _E. coli,_ there were very few aptamer probes left on the gold electrode, leading to a low peak current. With increasing the amount of the aptamer probe, the peak current value gradually increases until reaching a maximum, after which the peak current stops increasing. Consequently, 3 \(\upmu\)M is selected as the optimum concentration of aptamer probe in the following experiments.
## 5 Experiment Simulation
### Performance on the ISP-like Topologies
#### 5.3.2 Comparison in YouTube Traffic Workload  
Figure 12 shows the performance of different caching strategies under YouTube traffic workload. PB-NCC performs well over cache hit ratio and server load and performs the second-best over latency. PB-NCC independently predicts the content popularity in each node, which is closer to the actual situation and can better track the changes of local popularity. In terms of latency, LCE performs well under YouTube traffic workload. One reason may be that both the content set and the cache space are large, and LCE pushes content to the edge more effectively in this condition. PROB performs the worst, meaning that when content space and cache space are large, using the information of cache space to make caching decisions does not work well. In comparison, better performance can be achieved with simple LCE.  
Figure 11: Comparison in ISP-like topologies when the cache size ratio changes. (**a**) performance in EBONE topology under a different cache size ratio; (**b**) performance in TELSTRA topology under a different cache size ratio; (**c**) performance in ABOVENET topology under a different cache size ratio; (**d**) performance in SPRINTLINK topology under a different cache size ratio.
# Special Feature Vibration-Based Structural Health Monitoring
## 2 Vibration Analysis of the Complex Dynamic Systems by Numerical and Experimental Approach  
For the application of vibration analysis to system identification, direct methods analyzing responses from external loads are preferred. Frequency response analysis for vibration prediction of perforated shells was performed for application to statistical modeling techniques and to be used for damage detection [2]. Damage detection using modal rotational mode shapes was proposed [3][PERSON] The vibration mode shapes were measured from the responses detected by a laser Doppler vibrometer. For measurements of ground vibration, the influence of geometric parameters of the measuring base was investigated numerically [4]. The normal modes were calculated to identify vibration interactions. For the vibration analysis of spray-slip oscillation, a minimal nonlinear dynamic model with a variable angle of the inclined spring was proposed [5]. This provided information about vibration characteristics of a frictional sliding system.  
[PERSON] et al. [6] proposed a simple discrete model to understand the random fluctuation arising in human-bicycle motion. Probability density functions were used to evaluate the vibration responses. [PERSON] et al. [7] proposed a experimental method to estimate damping of lightweight timber floors. Damping is an important parameter to be identified accurately. [PERSON] et al. [8] presented dynamic responses of supertall buildings exposed to severe typhoons. The vibration responses were recorded for structural health monitoring. [PERSON] et al. [9] proposed a four-bar mechanism reducing the force transmitted through the base of the operating machine. The reduction of vibration is important to reduce vibration fatigue and increase the accuracy of the vibration-based system identifications. [PERSON] and [PERSON] [10] evaluated the dynamic performance of an in-service glass walkway. The vibration comfort was assessed based on experimental estimates. The pedestrian's perception depended on the ambient vibration responses of the structure.
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
## 3 Electromagnetic Analysis
### Output Power  
For the initial design model of the proposed AFPM generator, the output power under different load conditions can be readily calculated using the power formulation [27]. Equation (6) presents the power formulation, and the voltage equations for each phase can be expressed as Equation (7) [18; 24].  
\[\mathrm{P}=(\mathrm{V_{A}}\mathrm{i_{A}}+\mathrm{V_{B}}\mathrm{i_{B}}+\mathrm{ V_{C}}\mathrm{i_{C}})\mathrm{cos}\,\delta \tag{6}\]  
Figure 8: Equivalent circuit for three-phase of AFPM generator.  
\[\begin{bmatrix}\mathrm{V}_{\mathrm{A}}\\ \mathrm{V}_{\mathrm{b}}\\ \mathrm{V}_{\mathrm{c}}\end{bmatrix}=\begin{bmatrix}-\left(\mathrm{R}_{\mathrm{ph} }+\mathrm{R}+\frac{\mathrm{d}}{\mathrm{dt}}\mathrm{L}_{\mathrm{ph}}\right)&0&0\\ 0&-\left(\mathrm{R}_{\mathrm{ph}}+\mathrm{R}+\frac{\mathrm{d}}{\mathrm{dt}} \mathrm{L}_{\mathrm{ph}}\right)&0\\ 0&0&-\left(\mathrm{R}_{\mathrm{ph}}+\mathrm{R}+\frac{\mathrm{d}}{\mathrm{dt}} \mathrm{L}_{\mathrm{ph}}\right)\end{bmatrix}\begin{bmatrix}\mathrm{i}_{ \mathrm{A}}\\ \mathrm{i}_{\mathrm{B}}\\ \mathrm{i}_{\mathrm{C}}\end{bmatrix}+\begin{bmatrix}\mathrm{e}_{\mathrm{A}}\\ \mathrm{e}_{\mathrm{B}}\\ \mathrm{e}_{\mathrm{C}}\end{bmatrix} \tag{7}\]  
In these equations, P is the output power, V\({}_{\mathrm{A}}\), V\({}_{\mathrm{B}}\) and V\({}_{\mathrm{C}}\) are the induced voltages in the phases of A, B and C for the rated-load condition, i\({}_{\mathrm{A}}\), i\({}_{\mathrm{B}}\) and i\({}_{\mathrm{C}}\) are the induced currents in the phases of A, B and C, and \(\delta\) presents the phase angle between induced voltages and induced currents. In addition, R is the load resistance and e\({}_{\mathrm{A}}\), e\({}_{\mathrm{B}}\) and e\({}_{\mathrm{C}}\) are back-electromotive forces for each phase.  
The back-electromotive forces can be obtained using these equations provided:  
\[\mathrm{e}_{\mathrm{A}} =-\omega_{\mathrm{e}}\varphi_{\mathrm{f}}\sin(\theta) \tag{8}\] \[\mathrm{e}_{\mathrm{B}} =-\omega_{\mathrm{e}}\varphi_{\mathrm{f}}\sin(\theta-\frac{2\pi} {3})\] (9) \[\mathrm{e}_{\mathrm{C}} =-\omega_{\mathrm{e}}\varphi_{\mathrm{f}}\sin(\theta+\frac{2\pi} {3}) \tag{10}\]  
where \(\omega_{\mathrm{e}}\) is the electrical angular velocity and \(\varphi_{\mathrm{f}}\) is the maximum flux linkage by the permanent magnet.  
The waveforms of induced currents and voltages under the rated-load condition are shown in Figure 9, in which the output power can reach a maximum value. The active output power of the proposed AFPM generator was obtained as 199 W, which was corrected by considering the power factor of 0.8. However, the requirement for the active output power is more than 250 W. Therefore, it is necessary to increase the output power by improving the shape parameters, as shown in Figure 4. In the present study, an optimal shape design was carried out to meet the target specification, including the active power of 250 W under a constraint of weight minimization.  
Figure 9: Induced currents and voltages for A, B and C phases under rated-load condition by lapse of time.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
###### Abstract  
We consider the problem of the efficient transmit antenna subset (TAS) selection for maximizing the signal-to-interference-plus-noise ratio (SINR) of multiuser space-time line code (MU-STLC) systems. The exhaustive search for optimal TAS selection is impractical since the total number of transmit antennas increases. We propose two efficient TAS selection schemes based on the Woodbury formula. The first is to incrementally select \(N_{S}\) active transmit antennas among the available \(N_{T}\) transmit antennas. To reduce the complexity of the incremental selection scheme, the Woodbury formula is employed in the optimization process. The second is to perform the decremental strategy in which the Woodbury formula is also applied to develop the low-complexity TAS selection procedure for the MU-STLC systems. Simulation results show that the proposed incremental and decremental TAS selection algorithms offer better alternatives than the existing greedy TAS selection algorithm for the MU-STLC systems. Furthermore, in terms of bit error rate, the proposed minimum mean square error decremental TAS selection algorithm turns out to outperform the existing greedy algorithm with significantly lower computational complexity. Finally, we analyze the detection SINR penalty experienced from TAS selection and the analytical quantity is shown to be well matched with simulation results.  
antenna selection; multiple input multiple output (MIMO); precoding; zero forcing (ZF); minimum mean square error (MMSE); multiuser; space-time line code +
Footnote †: journal: Sensors
## 5 Discussion
### Limitations  
Despite its strengths, this study has limitations. The dataset used, while real-world and e-commerce-specific, represents only a single domain. Performance dynamics may differ in domains with different itemset distributions, such as healthcare or cybersecurity. Additionally, the FP-Growth implementation is dependent on memory for tree construction, which may affect the results in ultra-large or memory-constrained environments.  
Furthermore, the study does not explore parallel or distributed implementations (e.g., Apache Spark), which are increasingly relevant in big data scenarios. These aspects offer valuable avenues for future research.  
Figure 5: Memory usage comparison: Apriori vs. FP-Growth.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### Convolutional Neural Network (CNN)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Model** & **Purpose** & **Category** & **Feature Set** & **Experimentation** \\ \hline \multirow{4}{*}{[PERSON] et al. (2021)} & Convolutional & \multirow{4}{*}{\begin{tabular}{c} To capture the \\ sentence-level and \\ word-level \\ similarity \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Short answer \\ questions (Chinese) \\ Language) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} SticinethBank, \\ Chinese Wikipedia \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Python 3.7, \\ Intel(R)i7-7700, \\ 3.60 GHz processor \\ 16 GB memory \\ \end{tabular} } \\ \cline{1-1}  & & Convolutional & & & & Ubuntu 18, Python \\ \cline{1-1}  & neural network & & & & 3.6, PyTorch 10.0, \\ \cline{1-1}  & (CNN) and & & & & \\ Word2 Vec & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al. (2020) \\ [17] \\ \end{tabular} } & Dual embeddings & To understand the & \multirow{4}{*}{\begin{tabular}{c} Short text \\ information from \\ short text \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Knowledge base \\ Chinese, Movie \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Python 3.6, \\ PyTorch 10.0 \\ corpus of news \\ \end{tabular} } \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of convolutional neural network (CNN)-based studies in STSS.
###### Abstract  
Artificial intelligence (AI) is rapidly shaping the global financial market and its services due to the great competence that it has shown for analysis and modeling in many disciplines. What is especially remarkable is the potential that these techniques could offer to the challenging reality of credit fraud detection (CFD); but it is not easy, even for financial institutions, to keep in strict compliance with non-discriminatory and data protection regulations while extracting all the potential that these powerful new tools can provide to them. This reality effectively restricts nearly all possible AI applications to simple and easy to trace neural networks, preventing more advanced and modern techniques from being applied. The aim of this work was to create a reliable, unbiased, and interpretable methodology to automatically evaluate CFD risk. Therefore, we propose a novel methodology to address the mentioned complexity when applying machine learning (ML) to the CFD problem that uses state-of-the-art algorithms capable of quantifying the information of the variables and their relationships. This approach offers a new form of interpretability to cope with this multifaceted situation. Applied first is a recent published feature selection technique, the informative variable identifier (IVI), which is capable of distinguishing among informative, redundant, and noisy variables. Second, a set of innovative recurrent filters defined in this work are applied, which aim to minimize the training-data bias, namely, the recurrent feature filter (RFF) and the maximally-informative feature filter (MIFF). Finally, the output is classified by using compelling ML techniques, such as gradient boosting, support vector machine, linear discriminant analysis, and linear regression. These defined models were applied both to a synthetic database, for better descriptive modeling and fine tuning, and then to a real database. Our results confirm that our proposal yields valuable interpretability by identifying the informative features' weights that link original variables with final objectives. Informative features were living beyond one's means, lack or absence of a transaction trail, and unexpected overdrafts, which are consistent with other published works. Furthermore, we obtained 76% accuracy in CFD, which represents an improvement of more than 4% in the real databases compared to other published works. We conclude that with the use of the presented methodology, we do not only reduce dimensionality, but also improve the accuracy, and trace relationships among input and output features, bringing transparency to the ML reasoning process. The results obtained here were used as a starting point for the companion paper which reports on our extending the interpretability to nonlinear ML architectures.  
credit fraud detection; explainable machine learning; interpretability; feature selection
# Study on Mechanical Properties and Application of a New Flexible Bolt
## 6 Engineering Application
### Geological Background  
Flexible bolts were applied in Yaoqiao Coal Mine, Xuzhou city, Jiangsu province, China. As a relatively large coal mine in the east of China, the mine has an annual raw coal output of 4.45 million tons. The mining area extends 13.70 km from east to west, and 4.65 km from south to north, covering an area of approximately 63.8 km\({}^{2}\). The mining depth stretches from \(-\)135 to \(-\)1300 m. The complex geological environment of this coal mine makes roadway deformation a prominent problem in the actual mining process. Therefore, it is highly appropriate to apply flexible bolts in this coal mine. This test lays a solid foundation for the future application and promotion of flexible bolts.  
The material roadway of the 7704 working face is selected as the testing zone. The coal seam where this roadway is located is the 7# coal seam whose burial depth is 170-1335 m and whose thickness is 1.38-9.86 m. The average thickness of coal seam is 4.8 m and the average dip angle of the coal seam is 13\({}^{\circ}\).
## 2 Literature Review
### Model Updating Theories  
Recent advancements in inverse analysis for bridge structures, particularly in the context of model updating and damage assessment, have been significantly influenced by the works of [PERSON] and [PERSON], as well as [PERSON] [33; 34]. [PERSON]'s contributions, for instance, focus on the identification of elastic-plastic material parameters through indentation tests and finite element simulation. His innovative approach includes measuring the imprint geometry alongside indentation curves utilizing a deterministic approach for inverse analysis with conventional algorithms. Additionally, [PERSON]'s research extends to the area of fracture mechanics, particularly in the calibration of cohesive fracture models for quasi-brittle materials, emphasizing the combination of experimentation, experiment simulation, and the minimization of discrepancies between measured and computed quantities.  
Building upon this foundation, model updating plays a crucial role in enhancing the accuracy of finite element (FE) models, which are vital for predicting the behavior of bridges under various loads and conditions. Traditional FE models often rely on idealized assumptions that may not accurately reflect the actual state of a bridge. Updating these models with real-world data, such as those obtained from structural health monitoring (SHM) systems or 3D laser scans, allows engineers to refine the model's accuracy and ensure it mirrors the current condition of the structure.  
The process of model updating can be approached through various techniques, including deterministic and probabilistic methods. Deterministic model updating involves adjusting parameters to minimize the discrepancy between measured and predicted responses. On the other hand, probabilistic approaches account for uncertainties in the data, providing a spectrum of possible outcomes ([PERSON], & [PERSON] (2015) [35]). Bayesian inference has gained popularity in model updating due to its ability to integrate prior knowledge and address uncertainties in parameter estimation ([PERSON], 1998 [36]). Moreover, recent developments in model updating have incorporated machine learning techniques, which can autonomously adjust model parameters based on sensor data, thereby enhancing the efficiency of the updating process ([PERSON], [PERSON], Wang, [PERSON], & [PERSON] (2022) [37]).  
In addition, recent advancements have shown the effectiveness of combining wavelet transform with artificial neural networks (ANNs) for structural damage detection. This adaptive method preprocesses data using wavelet transform to extract relevant features while minimizing noise interference, thus enhancing the accuracy of damage identification. The ANNs then classify the structural state based on these features, demonstrating a high level of precision in detecting damage even under challenging conditions. This approach is particularly beneficial in addressing the complexities associated with real-world data, making it a valuable contribution to the field of structural health monitoring and model updating [38].