esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.3 Weibull Distribution (W3)  
For estimation with MOM, parameter \(\alpha\) can be approximated, depending on the skewness, using the \"approximation 1\" of the rational function type presented by [PERSON], valid for a maximum skewness of 2, or using the new approximation called here \"a better approximation\" valid for a skewness in the range of 0-8:  
Approximation 1, for \(\text{C}_{8}\leq 2\), [11]:  
\[\alpha=\frac{1}{\begin{array}{l}0.2777561+0.3219\cdot\text{C}_{8}+0.061566 \cdot\text{C}_{8}^{2}-0.017376\cdot\text{C}_{8}^{3}-\\ 0.00771\cdot\text{C}_{8}^{4}+0.00398\cdot\text{C}_{8}^{5}-0.00051\cdot\text{C} _{8}^{6}\end{array}} \tag{64}\]  
a better approximation, called here approximation 2, valid for \(0<\text{C}_{8}\leq 8\):  
\[\alpha=\frac{\begin{array}{l}3.5947875-3.7374529\cdot\text{C}_{8}+2.7023151 \cdot\text{C}_{8}^{2}-1.1835102\cdot\text{C}_{8}^{3}+\\ 0.4318107\cdot\text{C}_{8}^{4}-0.0042306\cdot\text{C}_{8}^{5}+0.0045545\cdot \text{C}_{8}^{6}-0.0046984\cdot\text{C}_{8}^{7}\\ 1+0.0931316\cdot\text{C}_{8}-0.12783\cdot\text{C}_{8}^{2}+0.0671082\cdot\text{ C}_{8}^{3}+0.1113667\cdot\text{C}_{8}^{4}-\\ 0.0425475\cdot\text{C}_{8}^{5}+0.0673629\cdot\text{C}_{8}^{6}-0.0157628\cdot \text{C}_{8}^{7}\end{array}} \tag{65}\]  
\[\beta=\frac{\sigma}{\sqrt{\Gamma\left(1+\frac{2}{\alpha}\right)-\Gamma\left(1+ \frac{1}{\alpha}\right)^{2}}} \tag{66}\]  
\[\gamma=\mu-\beta\cdot\Gamma\left(1+\frac{1}{\alpha}\right) \tag{67}\]  
For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on \(\tau_{3}\):Approximation 1, a rational function presented by [PERSON] et al. [11]:  
\[\alpha=\frac{1}{7.85890\cdot\left(\frac{2}{3-\tau_{3}}-\frac{\log(2)}{\log(3)} \right)+2.9554\cdot\left(\frac{2}{3-\tau_{3}}-\frac{\log(2)}{\log(3)}\right)^{2}} \tag{68}\]  
Approximation 2, polynomial form, for positive \(\tau_{3}\), presented by [PERSON] [15]:  
\[\alpha=285.3\cdot\tau_{3}^{6}-658.6\cdot\tau_{3}^{5}+622.8\cdot\tau_{3}^{4}-31 7.2\cdot\tau_{3}^{3}+98.52\cdot\tau_{3}^{2}-21.256\cdot\tau_{3}+3.516 \tag{69}\]  
a better approximation, named here approximation 3, which is a rational function, valid for negative and positive values of L-skewness:  
\[\alpha=\frac{3.528107902-6.294082546\cdot\left|\tau_{3}\right|+2.767652838 \cdot\tau_{3}^{2}}{1+4.599024923\cdot\left|\tau_{3}\right|-7.993601572\cdot\tau _{3}^{2}+2.423742593\cdot\left|\tau_{3}\right|^{3}} \tag{70}\]  
\[\beta=\frac{\mathrm{L}_{2}}{\Gamma\left(1+\frac{1}{\alpha}\right)\cdot\left(1 -2^{-\frac{1}{\alpha}}\right)} \tag{71}\]  
\[\gamma=\mathrm{L}_{1}-\frac{\beta}{\alpha}\cdot\Gamma\left(\frac{1}{\alpha}\right) \tag{72}\]
## 3 The Proposed Method
### The Continuous Wavelet Transform (CWT)  
When the continuous wavelet transform is used to analyze signals whose frequency changes over time, a time-frequency diagram is produced. The technique used to convert to the time-frequency domain is crucial in pattern-recognition techniques. For this transformation, the wavelet transform is well suited because nonstationary signals, such as EEG, ECG, and EMG, may be effectively transformed using this technique [38; 39]. The signal is changed using wavelet functions, such as Daubechies, Morlet, Symlet, and Gaussian in a wavelet transform.  
A signal's CWT may be expressed as:  
\[Z(a.b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}s(t)\psi^{*}\left(\frac{t-b}{a} \right)dt \tag{4}\]  
where (\(t\)) is a signal with finite energy, \(\psi^{*}\) is the mother wavelet's complex conjugate, and \(a\) and \(b\) are parameters that affect the wavelet's scaling and translation, respectively. Smaller scale values cause the wavelet to contract and disclose high frequencies in the signal, whereas larger (scale) values cause the wavelet to expand in time and show low-frequency  
Figure 5: Diagram of the proposed method.  
information in the signal [40]. By constantly changing the a and b variables along the range of scales and lengths of the signal, respectively, the continuous wavelet transform is computed.  
Similar to a spectrogram made with a quick Fourier transform, a scalogram is a visual depiction of a signal's CWT (STFT). By permitting variable-size analysis windows at different frequencies, the CWT effectively outperforms the STFT in terms of temporal and frequency resolution. The frequencies that are present at various points in the signals' scalograms are evident.
# Large Language Model-Powered Automated Assessment: A Systematic Review
## 3 Results
### Details of LLMPAA Research
#### 3.2.3 LLM's and Evaluation Metrics
For example, study [17], which investigated the reliability of ChatGPT as an AES tool by comparing its performance with that of an experienced human evaluator, reported that GPT-4 exhibited weak correlation and low consistency with human ratings (r = 0.359; ICC = 0.447). In contrast, study [40], which compared the reliability and consistency of four different LLMs in assessing the writing of English language learners, found that GPT-4 demonstrated the highest consistency among the models examined (ICC = 0.927; r = 0.731). When examining the LLMs used in the studies, BERT and its variants (RoBERTa, XLNet, etc.) have consistently shown high performance across various tasks. Especially when fine-tuned, BERT has been observed to give the best results in various text evaluation metrics (F1, QWK, r, etc.). It has been determined that GPT-4 and fine-tuned BERT models stand out as LLMs achieving the best results in LLMPAA, demonstrating high performance and positioning themselves among the most powerful and effective automated assessment tools. As a result,  
* While LLMPAA have frequently demonstrated good or excellent agreement with human raters, some studies have reported moderate or low alignment, suggesting that their effectiveness may vary depending on the context and application. * BERT and its variants have generally provided a strong foundation for assessment tasks, although performance can differ based on task type and fine-tuning strategies. * GPT-4 has shown promising results for human-like assessment in many studies, but findings also highlight variability in accuracy and consistency across different scenarios. * The effectiveness of each model depends on the specific requirements of the task, dataset characteristics, and implementation context. * Fine-tuning has been shown to significantly improve model performance in several cases, though its impact may depend on the quality and diversity of training data.
## 3 Research Method
### Configuration of the Testing Environment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Prometheus \& Grafana** & **Sysdig \& Sysdig Dashboard** \\ \hline \multirow{2}{*}{Ease of installation} & Difficult initial configuration & \multicolumn{2}{c}{Very easy configuration.} \\  & Can be automated with Helm Charts & The service is enabled in the IBM Cloud console. \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Multi-cloud \\ implementation \\ \end{tabular} } & Can be deployed on every Kubernetes cluster; it & \multirow{3}{*}{In this form, labeled as the IBM Cloud monitor, Sysdig can only be deployed in IBM Cloud.} \\  & may also be used with other cloud providers, not just with IBM. & \\ \hline \multirow{3}{*}{Ease of use} & Due to the presence of PromQL, the query of & Can be integrated with Prometheus and PromQL. \\  & Prometheus data may be difficult at first and & Can be used as a query language for \\  & not straightforward. & Prometheus DB. \\  & The dashboards in Grafana are easy to use. & Easy to use, with having a very \\  & & straightforward interface. \\ \hline \multirow{3}{*}{Resource consumption} & Prometheus and Grafana are known for & In comparison with Prometheus and Grafana \\  & resource consumption. & solution, it consumes less memory than \\  & & Prometheus \& Grafana \\ \hline \multirow{2}{*}{Pricing} & \multirow{2}{*}{Open Source} & From 35 \$/month with the graduated tier plan \\  & & Free to use in the first month with the free tier. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prometheus vs. Sysdig. The final computed metrics are for the cluster (Table 3). This is a very important metric to follow in the case of an application that tends to consume lots of resources. It is usually used to determine the need for resource scaling (CPU, memory scaling for the worker nodes).
## 5 Experimental Evaluation  
### Experimental Setup  
In this section, the system performance of the FTN signal detection scheme will be evaluated in different channel simulation scenarios. Under the 64-bit Windows 10 operating system, the simulation was performed on the Intel (R) Core (TM) i7-6700K @ 4.00 GHz CPU, Nvidia GeForce GTX 970 (4 GB / Nvidia) on Matlab2018B and PyCharm platforms. Among them, the training and test data sets are randomly generated on the Matlab simulation platform through different channels. The proposed neural network is implemented by Keras, written in Python, and uses TensorFlow as the backend to perform tensor operations. The main parameters of the FTN signal are shown in Table 1, the main simulation parameters of deepearning are shown in Table 2, and the main parameters of the system in multipath channel analysis are shown in Table 3[30].  
Unless otherwise specified, the mobile rate of the simulated multipath channel is the result of aower mobile rate by default, that is, TU-6 is 30 km/h, PB is 3 km/h, and VA is 60 km/h. The hyperparameters Dropout rate and Num unit are updated based on the validation set (the same way as the training set test set is generated). Specifically, in the training phase of the model, after training for a certain epoch (verification too frequently will affect the training speed), a validation set will be run to analyze whether theoss function and evaluation indicators are in the expected target (gradual convergence) to update the hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Time squeezing factor \(\tau\) & 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 \\ Up-sampling rate & 10 \\ Type of pulse-shaping filter & root-raised cosine (RRC) \\ Roll-off factor of filter & 0.3 \\ Signalength \(N\) & 32 \\ SNR range \(E_{k}/N_{0}\) & 0 dB to 15 dB \\ Sampling frequency (MHz) & 15.36 \\ Modulation scheme & BPSK,QPSK \\ Channel model & AWGN, TU6, PB, VA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental settings for FTN signaling generation.  
In addition, the FTN signal generation process and the adopted deepearning model are built on the basis of traditional models, which can be easily transplanted to dedicated computers (such as FPGA).
# Quality Assessment of Components of Wheat Seed Using Different Classifications Models
## 3 Results

Table 1: Ranked features by the Relieff algorithm, with only the first 40 features presented (Morphological (M), Color (C), and Texture (T)). To predict seven-grain classes based on independent data, only the models created with 35 top features were employed. The results of the five selected models after the application of independent data are shown in Table 2. The least classification accuracy for seven-grain classes was 95%, observed in the linear discriminate analysis. The highest accuracy--97.6%--was observed in quantized support vector machine. The results of classification with independent data were not significantly different from those with training data, proving the interoperability of the models in predicting the class of new data. The classification accuracy of shrunken grains was the lowest in all models, and it was the factor for diminishing the accuracy in all models. Barley grains were classified with the highest accuracy in all models. In four of the models, they were classified with 100% accuracy (Table 2). This is in line with the studies reporting the high distinguishability of barley grains from wheat [11]. The time required for training support vector machine models is much more than that for statistical classification models. However, there was no significant difference in the classifying speed of the created model in encountering independent data. Thus, by relying on the importance of training time in relation to classification accuracy, one can choose the suitable model. Here, the quantized support vector machine was selected as the best model because of its highest accuracy. The interference matrix of this model in identifying new grains is presented in Table 3. The grains of sound white wheat, sound white wheat, broken white wheat, red wheat, barley, and rye had acceptable classification accuracies. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification Models**} & \multicolumn{6}{c}{**Kernel Classes**} & \multicolumn{3}{c}{**Mean of**} \\  & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **Classifyication Accuracy** \\ \hline LDA & 99.7 & 93.7 & 99.7 & 95.7 & 98.7 & 94.7 & 83 & 95 \\ QDA & 98 & 98.3 & 100 & 98 & 98.3 & 97.7 & 86.3 & 96.7 \\ LSVM & 99 & 98 & 100 & 98 & 98.7 & 98.3 & 90 & 97.4 \\ QSVM & 98.7 & 98 & 100 & 97.3 & 99 & 99.3 & 90.7 & 97.6 \\ CSVM & 99 & 97.7 & 100 & 98 & 99 & 99.3 & 87.7 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of classification produced by five models on test data using the first 35 features. The kernel classes are: (a) sound white wheat, (b) small white wheat, (c) barley, (d) rye, (e) red wheat, (f) broken white wheat, and (g) shrunken white wheat. Figure 3: Classification accuracy results obtained with respect to the number of ranked features employed using five classifiers (LDA, QDA, LSVM, QSVM, and CSVM).
# Total Internal Reflection Lens for Optical Wireless Receivers
## 2 Concept, Design, and Assembly
### Fabrication and Assembly  
Figure 3 shows a lens prototype. It is fabricated with an injection molding process. The tool was formed by an ultra-precision diamond-turning/-milling process. The milling tip features a radius of 100 \(\upmu\)m. The surface roughness is classified as SPI A-2, i.e., the optical interfaces have an average surface roughness \(R_{\text{a}}\) of 25 nm to 50 nm. The lens material is black polycarbonate with a refractive index of \(n=1.57\). The material absorbs ambient light for \(\lambda<\)700 nm and provides a high transmittance of \(T>0.9\) for \(\lambda>800\) nm. This makes the material an ideal candidate for near-infrared (NIR) signals. The lens has a height of 6.9 mm and an optical input aperture of \(\mathcal{O}\)8.8 mm. The planarity of the top surface can be seen from the reflection. The diameter of the lens cavity is 3.1 mm. The inner sides of the cavity are inclined by \(2^{\circ}\), which is required for the diamond-turning.  
The lens is fixed to the PD by using a low-outgassing adhesive, which is transparent at the wavelength of communication. Because the glue is not applied to the top of the PD, the glue does not necessarily have to be transparent. However, it must be low-outgassing to avoid an undesired covering of the lenses' center cavity. When the lens is placed on the PD, it wiggles slightly, indicating that the mounting structures do not fit exactly. This issue can be eliminated by a selective revision of the tool.  
Figure 3: TIR lens prototype. (**a**) Perspective top view. (**b**) Perspective bottom view.
## 4 Results and Discussion
### Maintenance Phase
#### 4.2.4 Visual Maintenance  
This project applied BIM and MR technology to field maintenance. The maintenance methods of different plants in different growth stages in the landscape can be encapsulated in text or animation into the corresponding BIM model or MR device in advance. Field landscape staff could use the Hololens2 device to compare the real plants with the virtual models, as shown in Figure 13. Moreover, field landscape staff could also extract specific treatment measures for specific plants to realize required visual maintenance procedures for achieving meticulous and scientific maintenance of plants.  
Figure 12: On-site model information display.
## 5 ML Setup and Results Analysis
### LAGD Architecture  
From the results discussed along with the practical testing, the idea of the Layered-Architecture Geofence Division (LAGD) shown in Figure 26 can be introduced. The problem of using the Wi-Fi metrics only for the GDS generation is their absence in the outdoor environment. However, they provide good indication features in indoor environments. The opposite happens with the magnetic data, as they are significantly good for classifying the outdoor open area environments of the buildings from their insides. However, in indoor environments, the general readings of magnetic data are similar. The proposed solution was using the idea of active geofencing (GPS-based service) as a first layer of detecting the user's current workplace or organization. Then, the magnetic data are used as classification features for the detection of buildings as the second layer of detection of the user by using MF-ML models. Then, after the detection of the user's building location, the indoor rooms WF-ML models of this building are invoked for applying the third layer of detection.  
The benefit of applying this technique is reducing the inference and processing time as we would invoke the geofences models only after entering the workplace. So, after the user is identified in a geofence or a hall by the WF-ML model, the LAGD architecture would only invoke the WF-ML models in the user's detected workplace and building. Then, it would only return to the previous layers if they were not in any of the geofences of the formally detected building to recheck if the user was still in the building and the workplace. Thus, the GPS would not be used continuously but invoked only when needed to reduce power consumption and save battery life. Furthermore, taking two types of features together would require more processing power of the user's smartphone along with increasing the power consumption. In addition, it would simplify the GDS datasets collection and the ML models creation for each type of geofence.
# Exploring the Oncogenic Potential of Bisphenol F in Ovarian Cancer Development
## 2 Materials and Methods  
### Cell Culture and BPF Treatment  
OC cell line, SKOV3, was cultured in Roswell Park Memorial Institute (RPMI) 1640 growth media (Gibco, Grand Island, NY, USA) in the presence of 10% FBS (Gibco, Grand Island, NY, USA), and 1% penicillin/streptomycin antibiotic in an incubator maintained at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). BPF was purchased from Sigma Aldrich (Catalog number: 51453-100 mg, Saint Louis, MO, USA), and dissolved in 1 mL dimethyl sulfoxide (DMSO) (Sigma, Saint Louis, MO, USA) to prepare a stock solution of 499.5 mM. A range of BPF dilutions (0.01-250 uM) were prepared in DMSO. Moreover, 100 uM of working solution of BPF was prepared in DMSO, and 0.1, and 1 uL of this solution was added to a total of 100 uL plain media to achieve 0.1 and 1 uM final BPF concentrations, respectively. To obtain a final concentration of 10-100 uM BPF in a total of 100 uL plain media, 10 mM working solution of BPF was prepared and 0.1, 0.2, 0.3-1.0 uL of the working solution was added. Meanwhile, 50 mM working solution was prepared to reach 150 and 250 uM BPF concentrations by diluting 0.3 and 0.5 uL, respectively, in a total of 100 uL plain media. For negative control (NC), the cells were treated with the same volume of DMSO only.
## 3 Results
### Studies Implementing Vision Systems (VSS)
#### 3.2.3 Experimental and Real-World Scenarios  
Implementing vision systems, especially in the case of SHM, implies addressing typical problems, including environmental conditions (for example, lighting and weather events), long-distance measurements, calibration procedures, and stability issues. The in-depth analysis revealed that many studies implemented experimental tests to verify the effectiveness of the proposed solutions only in controlled experimental scenarios.  
In many cases, the authors chose experimental indoor scenarios because these allowed the effects of environmental conditions to be limited and solutions to be tested only on scaled models. This choice applies to the following studies: [40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]. In a few cases, the experimental scenarios were outdoors where, on the other hand, it was necessary to consider, at least in part, the environmental conditions and the problems previously indicated, testing the solution on scale models in most cases as well. The following studies chose this option: [32, 36, 65].  
In addition, some of the studies that implemented experimental tests in indoor or outdoor scenarios also ventured out into the field, verifying the behavior of the proposed solutions in real-world environments and on full-scale targets, that is, under more challenging conditions. This is the case for [32, 34, 38, 40, 41, 45, 55, 60, 66, 69, 71, 75, 81, 82].  
In contrast, the authors of [39, 46, 47, 52, 56, 68, 79, 83, 84, 85] tested the proposed solution only directly on real scenarios. The authors of [48] also mentioned an in-field experiment (i.e., estimation of building vibration using a video recorded from the roof of a building). However, they did not present any results regarding this experiment.  
Finally, a few studies only verified the proposed solutions through numerical simulations [33, 37, 57]. In [69, 71], numerical simulations were used to validate part of the proposed framework before experimental and in-field tests.