esa-sceva/satcom-chunk-collection huggingface dataset:
# A Percentile Method to Determine Cold Days and Spells in Bangladesh
## 5 Conclusions  
The temperature thresholds were used with the daily \(T_{min}\) and \(T_{max}\) for the winter seasons from 2000 to 2021 to calculate the cold days, cold spells, and their rate of change. The cold days and spells were further categorized into five classes, namely extreme (\(\leq\)13 \({}^{\circ}\)C), severe (>13 to 14 \({}^{\circ}\)C), very (>14 to 15 \({}^{\circ}\)C), moderate (>15 to 16 \({}^{\circ}\)C), and mild (>16 to 17 \({}^{\circ}\)C). A higher number of cold days occurred in the western and northwestern parts of Bangladesh, with an average of 4-6, 6-8, 1-2, and 12-16 days per year in December, January, February, and the entire winter season, respectively. The period of 11-20 January showed the highest number of cold days (25.54%) in the winter months. In general, higher cold days were observed in the northwest and west, and it declined gradually towards the south, southeast, and east of the country. Likewise, the number of very, severe, and extreme categories of cold days was higher in the western to northwestern regions, reducing linearly towards the east and southeast and found minimum in the coastal areas. On the other hand, the mild and moderate categories of cold days were found to be higher in number in the southern coastal stations and gradually decreased towards the north and northwest. Cold spells followed nearly similar patterns of cold days in time and space, with few exceptions. The significant increasing trends of cold days were found during 11-20 December, 21-31 December, and the whole month of December for a total of 5, 3, and 13 weather stations, respectively. In contrast, significant decreasing trends were noticed during 1-10 January in three stations. Overall, the proposed 10P method portrayed a better scenario of cold days/spells in time and space, compared to the traditional single BMD threshold for the entire country. Based on the above findings, the proposed 10P method could be used in Bangladesh for calculating cold days and spells, with a thorough review before applying it for operational purposes.  
**Author Contributions:** Conceptualization, M.M.A., A.S.M.M., M.R.A. and Q.K.H.; methodology, M.M.A., A.S.M.M., M.R.A. and Q.K.H.; formal analysis, M.M.A., A.S.M.M. and M.R.A.; writing--original draft preparation, M.M.A., A.S.M.M. and M.R.A.; writing--review and editing, Q.K.H.; supervision, Q.K.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Output data are available in the figures and tables of the manuscript.  
**Acknowledgments:** The authors thank Bangladesh Meteorological Department (BMD) for providing air temperature data for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods
### Uncertainty Analysis of Parameters  
Uncertainty theory is the science that examines the unpredictability and fuzziness in systems, processes, or models. The core idea is that many real-world problems lack a clearly defined solution path and are characterized by varying degrees of fuzziness and uncertainty. Entropy, originally introduced as a thermodynamic measure of molecular disorder, was later incorporated into information theory by mathematician [PERSON] in 1948. In this context, entropy quantifies the uncertainty of information and has since been widely applied across various fields. A higher value of information entropy indicates greater uncertainty within the system. [PERSON] established entropy's pivotal role as \"a measure of uncertainty\", thereby creating a universal paradigm for cross-disciplinary uncertainty quantification [22]. Recent years have witnessed a surge in the scholarly adoption of information entropy as a metric for parametric uncertainty quantification [23]. While entropy-based methods provide a robust and computationally efficient framework for uncertainty quantification, it is important to contextualize their use within the broader landscape of uncertainty quantification techniques. Monte Carlo methods, for example, rely on repeated random sampling to estimate outcome distributions, offering high accuracy but often at significant computational cost. Bayesian inference incorporates prior knowledge and updates probabilities with new data, making it suitable for problems with well-defined priors but requiring substantial computational resources and expertise. In contrast, entropy-based methods balance computational efficiency and interpretability, making them ideal for real-time decision-making and large-scale data analysis. By leveraging entropy's inherent properties, our approach offers a practical and scalable solution for quantifying uncertainty in material source parameters, avoiding the need for extensive computational resources or complex probabilistic models.  
In this study, information entropy is employed to quantify the uncertainty of material source parameters. The calculation procedure is as follows.  
The value range of the parameter is divided into equal intervals, and the frequency of occurrence for each interval is recorded as \(n\).  
The frequency of each interval is calculated using Formula (1):  
\[P=\frac{n}{101}\times 100\% \tag{1}\]  
where \(P\) is the frequency of the \(i\)-th interval, \(n\) is the occurrence count of the values in the interval, and \(106\) is the total number of samples.  
The entropy value of the parameter is then calculated using Formula (2):  
\[E=-k\sum_{j=1}^{m}P_{j}\ln P_{j} \tag{2}\]  
where \(E\) is the entropy value, \(m\) is the number of intervals, \(k=1/\ln(m)\), and \(P_{j}\) is the frequency of the \(j\)-th interval.  
This process is repeated for all material source parameters. The resulting entropy values are used to assess the degree of uncertainty associated with each parameter. Higher entropy values correspond to higher levels of uncertainty.
## 3 Experimental Results and Discussion
### Stage 2--Production
#### 3.1.2 EDM Fluid--RJS Ageing  
For each material, the specimens immersed in the dielectric fluid underwent two different ageing conditions corresponding to one sharpening cycle and five sharpening cycles. The maximum shear strength values for aluminium and steel specimens are presented in Figure 8a,b, respectively.  
Figure 8: Maximum shear strength of RJS specimens, unaged and aged (in dielectric fluid) for the duration of one and five sharpening cycles. The red dotted line represents the threshold value for the RJS specimen’s minimum allowed joint strength (1.5 kN). **(a)** Aluminium RJS specimens. **(b)** Steel RJS specimens.  
The dielectric fluid had little to no effect on the shear strength of the joints for steel specimens, and a comparatively small decrease in shear load for aluminium specimens. This coincides with the behaviour observed in the gravimetric analysis, meaning that there was nearly no ingress of fluid on the adhesive and that the adhesive itself was not damaged.  
The reduction in strength in the aluminium specimens was attributed to the joint's interfacial properties, which usually are worse for raw aluminium in comparison with steel. Therefore, since the interface also allows for fluid sorption, the aluminium joints were damaged at the interface, resulting in a strength decrease under the aged conditions near the defined threshold developed at the beginning of Section 3.  
The low influence of this hydrocarbon-based fluid on the mechanical properties of the joint is in accordance with that which is reported by the manufacturer of the adhesive, as shown in Table 3.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
###### Abstract  
Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is proposed to classify the common damages in the pipe utilizing the guided wave signals obtained from numerous damage detection tests. The fundamental torsional mode T(0,1) is selected to conduct the guided wave-based damage detection to reduce the complexity of signal processing for its almost non-dispersive property. A total of 520 groups of experimental data under different degrees of damage were obtained to verify the proposed method. Finally, with help of a deep neural network (DNN) algorithm, all response data from the damages in the pipes were all clearly classified with quite high probability.  
pipe; guided wave; torsional mode; damage classification; MLP +
Footnote †: journal: Applied Sciences
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables
#### 4.1.5 Real-Time Data Analytics  
The ability to analyze fermentation data in real-time is a significant advantage of AI systems. Real-time data analytics allow for immediate detection of deviations from desired fermentation conditions, enabling prompt corrective actions. This proactive approach minimizes the risk of fermentation issues and enhances the overall efficiency and reliability of the winemaking process.  
[PERSON] et al. explored the use of real-time data analytics for monitoring and controlling the fermentation process in bioreactors. By employing advanced sensor technology to measure parameters such as dissolved oxygen, carbon dioxide production, and volatile compound concentrations, the researchers developed a robust AI system capable of real-time monitoring and control. The system's ability to process and analyze data instantaneously allowed for immediate adjustments to fermentation parameters, ensuring optimal conditions throughout the process [84].  
A study by [PERSON] et al. demonstrated the integration of virtual data augmentation and deep neural networks (DNNs) to predict volatile fatty acid (VFA) production in anaerobic fermentation processes. By utilizing real-time data from multiple sensors, the AI system could accurately predict VFA levels, allowing for dynamic adjustments to the fermentation environment. This approach highlighted the potential of real-time data analytics in enhancing the predictive accuracy and control capabilities of AI systems in fermentation processes [85].  
These examples illustrate how the integration of advanced sensors and real-time data analytics can significantly improve the management and optimization of the fermentation process, leading to higher quality and more consistent wine production.
# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 5 Current Research Challenges  
Through this study, we gained insight into the current trends of online MOT methods that can be utilized in robotics applications and the challenges faced. The first challenge would be the online feature. The MOT algorithm should be able to operate in real-time for most robotics applications in order to be able to react to environmental change. The second challenge would be the accurate track trajectory across multiple frames. The lack of this problem can cause multiple identity switches and difficulty keeping a concise description of the surroundings. In addition, the issue is segmenting the detections at pixel level and tracking them. The bounding box provides wrong information about the object in shape and size, along with noises from the background. Finally, the motion feature has proven its value in tracking, but it is not simple to track random moving objects such as people, animals, cyclists, etc.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
###### Abstract
Finally, our analysis across different configurations of PE and LN further supports the LTE model's suitability for orbital datasets, demonstrating its predictive accuracy and efficiency. The structure of this paper is as follows: Section 2 introduces the dataset, preprocessing, model architecture, experimental setup, and baseline models. Section 3 presents an analysis of the prediction performance of the LTE model, along with its efficiency in terms of execution time and parameters, as well as the impact of layer removal on the LTE model. Section 4 discusses the limitations and future work. Finally, Section 5 provides a summary of the study and presents our key findings.
## 3 Benefits of an Optimized Enterprise Network System
### Enhanced Security Provisioning  
In order to detect security risks and cyber-attacks early, enterprise network optimization is crucial. With a well-developed network, a security vulnerability can be found quickly enough to stop extensive data compromise. Furthermore, in the event of hardware loss or breach of security, network optimization eliminates data manipulation, which promotes improved disaster management and speedy recovery [52, 53].  
Big businesses are striving for real-time detection of security threats for better monitoring and protection from attacks. An enterprise network serves as a main threat detector by implementing real-time security mechanisms to complement the roles of firewalls, secure internet gateways, and security applications [54]. Some of these functions are accomplished through profiling, access control, network monitoring, verification, and device management and identification.
## 3 Results and Discussion  
### Characterization of Dendrimer Modified CdTe/CdS QDs  
Figure 1 shows the TEM and HR-TEM images of the CdTe core (Figure 1a,b) and dendrimer modified CdTe/CdS core/shell (Figure 1d,e) QDs, which both the CdTe and dendrimer modified CdTe/CdS QDs displayed good dispersibility and water-solubility. As shown in Figure 1c,f, the average size of the CdTe and dendrimer modified CdTe/CdS QDs is about \(3.25\pm 0.2\) nm and \(4.05\pm 0.3\) nm, respectively. Figure 1g shows the powder XRD pattern of the CdTe core and dendrimer modified CdTe/CdS core/shell QDs, and the XRD of CdTe/CdS QDs is matched previous reported results. Moreover, these maindiffraction peaks of (111), (220) and (311) crystal plane also were indicated in SAED patterns (shown in Figure 1h). The hydro diameter and zeta potential of our products are shown in Figure 1i,j, which is about 20.3 nm and +9.73 eV, respectively.  
Figure 1: _Cont._  
Figure 2: **(a)** UV-vis and Photoluminescence spectra of CdTe (dashed line) and CdTe/CdS QDs modified with different generations of dendrimer (solid lines); **(b)** Fluorescence decay curves of a CdTe core (dashed line) and CdTe/CdS QDs modified with different generations of dendrimer (solid lines).  
Figure 1: **(a)** Transmission electron microscopy (TEM) and **(b)** high-resolution (HR)-TEM images of CdTe QDs; **(c)** size distribution analysis of CdTe quantum dots (QDs); **(d)** TEM and **(e)** HR-TEM of dendrimer modified CdTe/CdS QDs; **(f)** size distribution analysis of dendrimer modified CdTe/CdS QDs; **(g)** X-ray powder diffraction (XRD) diffraction patterns of CdTe QDs and dendrimer modified CdTe/CdS QDs; **(h)** Selected area electron diffraction (SAED) pattern of dendrimer modified CdTe/CdS QDs; **(i)** zeta potential and **(j)** hydro diameter distribution of freshly prepared QDs.  
As shown in Figure 2a, in the UV-vis absorption spectrum, there is a blue shift of the absorption band as the generation of the dendrimer is increased. The absorption shift that is observed for the different generations of dendrimers confirms that the amine groups are participating with dendrimers in the capping of the QDs. In the PL spectrum, the CdTe QDs exhibited emission at 562 nm with the excitation wavelength set of 365 nm. Compared to CdTe QDs, the emission wavelength of dendrimer modified core/shell CdTe/CdS QDs shifted to 650 nm which has been taken as a feature of QDs with spatial separated charge carriers [12]. Furthermore, the excitonic peak of the core/shell CdTe/CdS QDs shifted to shorter wavelength companying with increasing the generations of dendrimers. This phenomenon of blue shift in emission peak is consistent with the known theory of quantum size effects.
# Nucleotides as Bacterial Second Messengers
## 2 The c-di-GMP Nucleotide
### Pili as c-di-GMP Targets  
Pili or fimbiae are formed by proteins that assemble to build non-flagellar appendages on the external surface of bacteria. Fimbirae have characteristics of adhesion to abiotic and biotic surfaces, which are associated with biofilm formation, and c-di-GMP is involved in the adhesion of biofilm, suggesting a possible regulation of fimbiae by this c-di-nucleotide. The type 3 fimbiae of _Klebsiella pneumoniae_ helps biofilm formation on abiotic surfaces or human extracellular matrix-coated surfaces. DGC YfiN activity increases the c-di-GMP levels, which then induces transcription of the type 3 fimbiae mRNA. MrkJ PDE activity reduces the c-di-GMP levels, which down-regulates type 3 fimbiae expression. The mechanism of c-di-GMP regulation of the expression of type 3 fimbiae is by linkage of c-di-GMP to the transcriptional factor MrkH, since MrkH has a PiliZ domain, making c-di-GMP-MrKH able to interact with the promoter of type 3 fimbiae and induce its expression [28].  
Another type of fimbiae is the Cup, which is also a regulated by c-di-GMP. _P. aeruginosa_ has five Cup types (A to E); Cups participate in biofilm formation because they modify cell adhesive properties. C-di-GMP can regulate the transcription of all Cup fimbiae except CupE. Some _P. aeruginosa_ strains from cystic fibrosis patients with a variant small colony phenotype show increased YfiN and MorA DGCs activity to produce high c-di-GMP levels and expression of CupA fimbiae [29].  
On the other hand, type IV pili is very diverse and ubiquitous. Type IV pili is associated with twitching mobility because these pili can polymerize and retract. Type IV pili and twitching mobility are essential for the maturation of biofilm. In _P. aeruginosa_, c-di-GMP signaling regulates type IV pili biogenesis and contractile motility [30].
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Long Short-Term Memory Networks to Spectrum Sensing
#### 3.2.2 LSTM-Based Spectrum-Sensing Methods
Based on the experimental results, we can observe that the LSTM network exhibits significant advantages for the time-series problem and outperforms the traditional MLP network in terms of prediction performance. While the previously reviewed articles considered spectrum sensing in static environments, in [83], [PERSON] et al. considered how to improve the performance of LSTM-based spectrum-sensing methods under a dynamic signal-to-noise ratio. The authors' idea is to improve the LSTM network to adapt to the dynamic environment by first extracting the signal spectral features as LSTM inputs through CNNs and then introducing the Cuttle Fish algorithm [84] to optimize the hyperparameters in the LSTM to improve the performance. They conducted experiments to compare the detection probability of the proposed model in the paper with CNN-LSTM, LSTM, energy detection, and cyclostationary feature detection methods, all with the same false alarm probability. The results show that the proposed method performs slightly better than CNN-LSTM, and the proposed method performs even better for spectrum sensing at several different frequencies, which indicates that the proposed method has a better advantage for dynamic spectrum sensing. In the future, the authors will consider adding reinforcement learning to further improve the performance. As can be seen from Table 6, scholars have mainly learned autocorrelation information among signals, correlation information between signals, or other time-series features through LSTM and achieved favorable results. On the other hand, research has shown that LSTM-based spectrum-sensing methods have some problems. For example, these methods need a lot of time and data to be trained to work well. Also, the performance of LSTMcan change depending on which hyperparameters are chosen, which could mean that a lot of experiments are needed to find the best configuration. These issues need to be looked into further.
## 3 Approximation by Removing the Number of Paths with Smaller Coefficients
### Approximation of Gram Matrix  
Gram matrix [25] is frequently used in machine learning and other areas [33; 34; 35]. For a given data matrix \(X\) (the column vectors represent the data), it is defined by the following product:  
\[G=X^{T}X. \tag{12}\]  
Matrix \(G\) is positive semi-definite, and its rank depends on the number of independent data vectors in \(X\). In the simulations, we have used the normalized \(vec(G)\) produced from the default random number generator used in the Python-numpy package. The following Python code is used to generate data with different distributions. (The simulation code can be found on [[https://github.com/adaskin/app_with_schmidt.git](https://github.com/adaskin/app_with_schmidt.git)]([https://github.com/adaskin/app_with_schmidt.git](https://github.com/adaskin/app_with_schmidt.git)), (accessed on 27 February 2023)):  
import numpy as np n = 8 N = 2**n  dist = \"normal\"  rng = np.random.default_rng()  if dist == \"normal\":  X = rng.normal(size = (N,N))  elif dist == \"uniform\":  X = rng.uniform(size = (N,N))  elif dist == \"exponential\":  X = rng.exponential(size = (N,N))  elif dist == \"poisson\":  X = rng.poisson(size = (N,N))  
Using different distributions with default parameters, an example of the output, which shows the histogram of the probabilities of the paths, is presented in Figure 2 (Most output generates a similar result. Therefore, we only show one instance for each to explain the behavior in each distribution). As seen in the figure, in the cases of the exponential, Poisson, and uniform distributions, matrix \(G\) can be approximated with high accuracy by using only one term. This is because the Schmidt decomposition is related to the eigenvalues of the data matrix, and the variance of the eigenvalues in these cases is large: i.e., there is one dominant eigenvalue. When the distribution is normal, although there is a clear cut-off point, the approximation accuracy is less than the others.  
Here note that one can also use the same technique to directly approximate \(X\). Figure 3 shows the direct approximations for instances of \(\left|\psi\right\rangle=vec(X)\) generated randomly from different distributions. As can be seen from the figure, although a cut-off point still exists in all except the normal distribution, the accuracy is much lower. We also note that in the normal distribution, since the data depends on most of the paths, the probabilities are much lower. As a final note, one can also use data that are not powers of two, which may require filling \(vec(X)\) with zeros up to the power of two.  
Figure 2: Histogram of the coefficients for \(G\) produced by randomly generated \(X\) with different distributions. \(n\) represents the number of qubits required for \(vec(G)\): i.e., the dimension of \(vec(G)\) is \(2^{n}\).