esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 General Discussion
### Limitations and Future Directions  
In the current research, only four reader profiles were used for generating LLM-adapted texts. While this number provided sufficient proof-of-concept for exploring personalized adaptations and demonstrating the capabilities of NLP analyses in evaluating personalized content, the limited number of profiles potentially constrains the generalizability of our findings. Specifically, the reader profiles might not capture the full complexity and range of individual differences in a diverse student population. Additional characteristics such as interests, motivation, cultural backgrounds, learning disabilities beyond dyslexia, or cognitive characteristics were not sufficiently represented in these profiles [75, 76, 77]. To accurately personalize and evaluate educational materials, it is essential to capture these broader individual differences beyond reading skills and prior knowledge [78]. Future research should explore more nuanced reader characteristics and incorporate a more diverse set of reader profiles to improve external validity. In addition, it would be beneficial to test on a larger and more diverse text domain.  
Another limitation of these two experiments is that each text was prompted to be modified only once for each reader profile. LLMs are inherently non-deterministic, and each iteration can produce varying outputs even when using the same prompts [79]. The variability in responses across identical prompts even within the same model is particularly problematic, especially when they are used for educational tasks that require consistency [80]. As such, conducting single-run trials limits the ability to capture and quantify this internal variability. Future research can systematically analyze repeated text generations to examine reliability and consistency across LLM outputs.  
The current research demonstrated how LLMs can be used to generate personalized educational content, as well as how NLP can be leveraged to assess and validate content quality. Notably, however, a persistent challenge is the risk of hallucinations. LLMs inherently generate information that is potentially inaccurate or irrelevant [81, 82, 83]. While semantic similarity between the original and revised texts was automatically calculated to detect inconsistencies, we still had to have a human in the loop to manually review whether the generated content preserved key concepts and remained relevant to the original text (see Appendix E for the rubric guiding evaluation process). While automating this process would require multi-agent programming in which a separate LLM checked accuracy and relevance, NLP offers an efficient means of supporting quality assessment and iterative refinement of content. This approach is valuable because personalized content can be dynamically and continuously adapted to students' evolving knowledge, interests, and skills.
## 2 Materials and Methods
### Statistical Analysis and Mathematical Background of the HRA Parameters  
The i/e-ratio-related changes of PAT and HRA parameters (GI, PI) were compared using the Friedman test as a primary assessment in which \(p<0.5\) was considered statistically significant. The three possible post hoc Wilcoxon paired-sample tests were performed by each parameter with the following i/e ratio pairs: (1) single-paced to 1:2, (2) single-paced to 1:1, and (3) 1:2 to 1:1 breathing ratios. According to Holm-Bonferroni correction, the post hoc significance level was corrected to \(p<0.01667\) (0.5/3 = 0.01667) [37]. The Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) with StatistiXL package (v. 2.0, 2008, Broadway-Nedlands, Australia) was used for statistical analysis.  
The PI and GI are expressed in the form of percentages. They are calculated using the following Equations (1)-(3) [16; 17]:  
\[PI=\frac{N(\Delta RRi-)}{N(\Delta RRi\
eq 0)}\times 100 \tag{1}\]  
in which the \(N(\Delta RRi-)\) represented the number of the points below the identity line (y = x), and \(N(\Delta RRi\
eq 0)\) is the number of the points that are not on the identity line on the Poincare plot [17].  
\[D_{i}=\frac{|RR_{i}-RR_{i+1}|}{\sqrt{2}} \tag{2}\]  
\[GI=\frac{\sum_{i=1}^{N^{+}}(D_{i}^{+})^{2}}{\sum_{i=1}^{N-1}(D_{i})^{2}}\times \tag{3}\]  
Figure 3: **(A)** ECG signal. The starting point for PAT calculation is at the 1/2 amplitude height of the R-wave. **(B)** PPG signal and the reference points: the peak (T\({}_{3/3}\)), 2/3 height (T\({}_{2/3}\)), 1/2 height (T\({}_{1/2}\)), 1/3 height (T\({}_{1/3}\)) of the ascending slope, the local minimum (T\({}_{\rm LM}\)), the extrapolated point (T\({}_{\rm EP}\)), the time interval between T\({}_{1/3}\) and T\({}_{2/3}\) (T\({}_{1/3}\)T\({}_{2/3}\)).  
in which the \(D_{i}\) value is the distance of the \(i\)th point from the identity line, \({D_{i}}^{+}\) is the distance of the \(i\)th point above the y = x line, and \(N+\) represents the number of points above the identity line [16].
# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 3 Methodology
### Deep Learning-Based Named Entity Recognition
#### 3.2.2 Bidirectional Long Short-Term Memory Network  
After the original text is processed by the pre-training layer, it is transformed into a vector with semantic representations, which is then passed into the Bidirectional Long Short-Term Memory (BiLSTM) network. The BiLSTM consists of a forward LSTM unit and a backward LSTM unit. Each LSTM unit is composed of an input gate, output gate, forget gate, and memory cell. The hidden state from the previous time step and the input vector at the current time step are first input into the forget gate to determine the information to be forgotten, as shown in the following formula:  
\[F_{t}=\sigma(W_{F}[H_{t-1},X_{t}]+b_{F}) \tag{1}\]  
In the above formula, the dimensions of \(X_{t}\) are \([n,d]\), the dimensions of \(H_{t-1}\) are \([n,h]\), \(W_{F}\) represents the weight parameter with dimensions \([d,h]\), and \(b_{F}\) is the bias parameter with dimensions \([1,h]\). \(\sigma\) represents the sigmoid activation function, whose formula is as follows:  
\[S(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
Figure 4: Example of RoBERTa-zh processing.  
Next, the memory gate is calculated to select the information to be remembered. The formula is as follows:  
\[I_{t}=\sigma(W_{I}\cdot[H_{t-1},X_{t}]+b_{I}) \tag{3}\]  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[H_{t-1},X_{t}]+b_{C}) \tag{4}\]  
In the formula, \(\widetilde{C}_{t}\) represents the candidate memory cell, which is used to store information and overcome the vanishing or exploding gradient problems faced by traditional RNNs when processing long-term sequence data. The activation function \(\tanh t\) used here is  
\[tanhx=\frac{sinhx}{coshx}=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{5}\]  
After the memory gate, the current cell state needs to be calculated. The formula is as follows:  
\[C_{t}=F_{t}\times C_{t-1}+I_{t}\times\widetilde{C}_{t} \tag{6}\]  
In the formula, \(C_{t}\) represents the cell state at the current time step. Then, the output gate and the update function for the hidden state at the current time step are computed, ultimately resulting in a hidden state sequence with the same length as the sentence.  
\[O_{t}=\sigma(W_{O}\cdot[H_{t-1},X_{t}]+b_{O}) \tag{7}\]  
\[H_{t}=O_{t}\times tanh(C_{t}) \tag{8}\]  
The BiLSTM combines gated mechanisms with memory cells to facilitate the transfer of information, enabling it to effectively capture bidirectional contextual information. This results in a richer context representation, while also addressing the gradient vanishing or explosion problems encountered in traditional RNN models with long sequences. Additionally, the BiLSTM generates a hidden state representation for each input position that integrates comprehensive contextual information.
## 6 Discussion  
This paper presents a low-complexity digital beamforming architecture designed to generate 32 simultaneous RF beams using a 32-point DFT approximation, effectively eliminating multiplication operations. We validated the proposed method on a fully operational 32-element digital 1-D receiver array operating at 5.7 GHz. The 1-D array employs 32 parallel ADCs for sampling the antenna outputs and uses an ADFT implemented on a Xilinx FPGA to compute 32 RF beams in real time. Additionally, this paper discusses the enhancement of our existing 32-channel 5.7 GHz spectrum sensing system through the implementation of 32 parallel 1024-bin temporal FFTs on a ROACH-2 hardware platform. This experimental verification with real-time measurements across a band of frequencies and angles of incidence demonstrates the capability of the architecture to sense information from multiple directions with a frequency resolution of 100 kHz and directional resolution of about 6\({}^{\circ}\).  
The authors plan on expanding the ADFT to larger numbers of elements and create multi-dimensional ADFT beamformers, for beam-steering in both elevation and azimuthal planes. For example, using a 32 \(\times\) 8 array, we could expand the set of beams to a rectangular grid of size 32 \(\times\) 8 where both 32-point and 8-point FFTs are replaced by the corresponding ADFT algorithms based on sparse matrix factorization [103; 104; 105; 106].  
Conceptualization and system design by [PERSON] Theory and design of ADFT and fast algorithm by [PERSON]. Multi-dimensional signal processing review by C.U.S.E. Review of radio astronomy interference issues by [PERSON] Digital implementation of ADFT core by [PERSON] Experimental setup, digital design of CASPER tools, calibration, and real-time experimentation by S.S. and B.U.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by NSF (under the grants FuSE 2329012, SWIFT 2229471, ICORPS 2243346, SpectrumX), NTIA, CNPq (Brazil) under the grant 315047/23-2 and Senate Research Committee, University of Mortuwa under the grant SRC/LT/2020/08.  
All original contributions from this study are documented within the article. For further details, please contact the corresponding authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ADC Analog-to-digital converter; ADFT Approximate discrete Fourier transform; AI Artificial intelligence; ALMA Atacama large millimeter array; BRAM Block random access memory; CASPER Collaboration for astronomy signal processing and electronics research; CSI Channel state information; CW Continuous wave; DFT Discrete Fourier transform; DL Deep learning; DSA Dynamic spectrum access; DSP Digital signal processing; FCC Federal communication commission; FFT Fast Fourier transform; FPGA Field-programmable gate array; GNSS Global navigation satellite system; IF Intermediate frequency;
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 4 Experiments
### Observation and Analysis
(2) Among the six methods compared, the MT method stood out as the domain-adaptive algorithm with the most effective detection in both experiments when using TGRS-HRRSD-Dataset and LEVIR as target domain datasets. Although the results of the experiments with airbus-ship-detection as the target domain dataset do not show a clear pattern, our framework for domain-adaptive object detection algorithms still improves the detection results of training with synthetic data. This text demonstrates how synthetic data can overcome the challenge of limited real data and highlights the effectiveness of the algorithmic framework. (3) In our generated datasets, SASI-UNITY outperformed SASI-ZERO and SASI-RANDOM in detecting small objects, but it was weaker in detecting large objects. This is because the ship models in SASI-UNITY are derived from real ships, allowing for more control over the size proportions of ships in the images, thus resulting in more small objects. On the other hand, SASI-ZERO and SASI-RANDOM were generated using Stable Diffusion-based prompts, and since ships are the central content of the images, they tend to be larger in size. Generating small objects in a target-oriented manner is generally more challenging than controlling the generation of large objects with the Stable Diffusion model. (4) In the detection experiments conducted on the airbus-ship-detection dataset as the target domain, the domain-adaptation algorithm exhibited comparatively less enhancement in the detection results compared to the experiments conducted on the other two target domain datasets. This discrepancy can be attributed to the fact that synthetic image datasets were employed to address the issue of insufficient data, whereas the airbus-ship-detection dataset encompasses a significantly larger volume of data than both the TGRS-HRRSD-Dataset and LEVIR datasets, as well as the synthetic image datasets. Consequently, the improvements achieved by the domain-adaptation algorithm in terms of the detection results remained relatively minor. In future research, it would be advisable to broaden the scope of the target domain datasets and augment the number of synthetic images to further corroborate this perspective.
# Removal of Organic UV Filters Using Enzymes in Spent Mushroom Composts from Fungicultures
###### Abstract  
Organic UV filters in sunscreen products are released to aquatic ecosystems through human recreational activities and urban wastewater treatment plant effluents. The biodegradation of three organic UV filters, 2-ethylhexyl salicate (EHS), homosalate (HMS) and ethylhexyl methoxycinamate (EHMC), which cannot be effectively removed by conventional wastewater treatment plants, was investigated in this study. Spent mushroom compost (SMC), a waste product of the mushroom industry, which contains white-rot fungus extracellular enzymes, was tested for its ability to remove the three organic UV filters. The results of batch experiments revealed that the SMC enzyme extract of _Pleurotus dignor_ exhibited the highest ability for EHS and HMS removal. The results of bioreactor experiments indicated that direct application of SMCs may be a feasible solution to remove EHS and HMS from urban wastewater. The application of SMCs for the removal of organic UV filters can be developed into a green and sustainable technology.  
sunscreen; lacases; wastewater; mycoremediation +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Water Retention Best Fit Curve
#### 3.2.2 Best Fit Curve of the Water Retention Curves for CL and CPCL  
The parameters and crucial points in the best fit curve of the CPCL and CL WRCs are tabulated in Table 8. Using the tabulated fitting parameters, the best fit curve was plotted as shown in Figure 22.  
It can be observed in Figure 22 that there are differences in the matric suction of CL and CPCL at the lower suction range and at the higher volumetric water content. However, at some point, the matric suction of the two specimen types for a given cycle is equal, which can be located at the intersection of the curves with said conditions. Moreover, there are less noticeable differences between the matric suction of CL and CPCL at the higher suction range. The behavior of the plotted actual WRC and best-fit curve of the WRC agrees with the expected behavior from previous studies that the soil structure will not significantly affect the water-holding capacity at the higher suction range. However, factors  
Figure 22: Best fit curves of the WRCs for CL and CPCL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Specimen Type and Number of Cycles** & **CL-1** & **CL-2** & **CL-3** & **CPCL-1** & **CPCL-2** & **CPCL-3** \\ \hline Slope at the transition zone, \(S_{1}\) & 0.228 & 0.219 & 0.190 & 0.206 & 0.159 & 0.193 \\ Slope at residual zone, \(S_{2}\) & 0.070 & 0.067 & 0.061 & 0.071 & 0.081 & 0.073 \\ Fitting parameter, \(m\) & 6.066 & 5.198 & 6.499 & 5.230 & 4.997 & 5.845 \\ Fitting parameter, \(n\) & 0.436 & 0.387 & 0.381 & 0.385 & 0.292 & 0.381 \\ Fitting parameter, \(a\) (kPa) & 30,000 & 30,000 & 39,000 & 25,500 & 26,000 & 26,500 \\ Volumetric water content at inflection point, \(\theta_{i}\) & 0.180 & 0.228 & 0.160 & 0.214 & 0.228 & 0.181 \\ Saturated volumetric water content, \(\theta_{i}\) & 0.939 & 0.939 & 0.939 & 0.889 & 0.889 & 0.889 \\ Air entry value, \(\psi_{b}\) (kPa) & 14.07 & 17.00 & 3.10 & 13.49 & 1.81 & 5.69 \\ Residual volumetric water content, \(\theta_{r}\) & 0.076 & 0.094 & 0.071 & 0.096 & 0.134 & 0.088 \\ Residual matric suction, \(\psi_{r}\) (kPa) & 85,388 & 123,299 & 11,4608 & 95716 & 10,1849 & 79,979 \\ \hline \end{tabular}
\end{table}
Table 8: Fitting parameters of the best fit curve of the WRCs for CL and CPCL.  
that affect the soil structure, such as polyurethane content and the number of wet-dry cycles, significantly affect the matric suction at the lower suction range.
## 4 Experiment  
### Evaluation Metrics  
In order to evaluate the performance of a model in a binary classification prediction task, four metrics are employed: Precision, Recall, F1, and G-mean. These metrics reveal the model's predictive performance for different sample types, offering evaluations from a range of perspectives, as given in Equations (8)-(11). In addition, AUC, which stands for Area Under the Receiver Operating Characteristic Curve (ROC), is a performance measurement for classification problems at various threshold settings, as given in Equation (12). Each of these metrics is defined on a scale from 0 to 1, with higher values indicating a more effective model performance.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{10}\]  
\[G-mean=\sqrt{\frac{TP}{TP+FN}\times\frac{TN}{TN+FP}} \tag{11}\]  
\[AUC=\frac{1}{2}\bigg{(}1+\frac{TP}{TP+FN}-\frac{FP}{FP+TN}\bigg{)} \tag{12}\]  
where \(TP\), \(FP\), and \(FN\) represent the number of true positives, false positives, and false negatives, respectively.  
The prediction of defects in strip steel is a multi-classification task. If the binary classification evaluation index is directly applied, the model performance evaluation is susceptible to bias due to the number of dominant category samples. To avoid this bias, a macro-average strategy is employed when appraising the performance of the multi-class  
Figure 10: Model framework.  
model. This strategy involves the computation of evaluation metrics for each category independently, followed by the calculation of the arithmetic mean of these metrics. This approach provides a comprehensive measurement of the overall model performance, as given in Equations (13)-(16).  
\[\mathit{Macro\_Precision}=\frac{1}{N}\sum_{i=1}^{N}\mathit{Precision}_{i} \tag{13}\]  
\[\mathit{Macro\_Recall}=\frac{1}{N}\sum_{i=1}^{N}\mathit{Recall}_{i} \tag{14}\]  
\[\mathit{Marco\_F1}=\frac{2\times\mathit{Macro\_Precision}\times\mathit{ Macro\_Recall}}{\mathit{Macro\_Precision}+\mathit{Macro\_Recall}} \tag{15}\]  
\[\mathit{Macro\_AUC}=\frac{1}{N}\sum_{i=1}^{N}\mathit{AUC}_{i} \tag{16}\]
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Respiration Rate Extraction
#### 2.5.1 Chest ROI Detection  
The chest ROI that was used to determine breathing motion is derived from the location and size of the face ROI. It is defined as a region directly below the face ROI. Its height is equal to the height of the face ROI, and its width is three times that of the face ROI.
# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 4 Case Study
### Field Data Analysis Based on GPA Model  
Firstly, the physical-model GPA method, described in Section 3.1, is applied to extract the fault features from the field data. As an example, four parameters (\(T_{1}\), \(p_{1}\), \(n_{1}\), and \(n_{2}\)) of the field data \(\mathbf{x}\) and \(\mathbf{y}\) collected from the GE LM2500 PLUS gas turbine are displayed in Figure 7. The gas turbine experienced a sudden shutdown at the moment of 128 s. Before the shutdown, the range of the four parameters was \(T_{1}\in[26.2~{}^{\circ}\)C, \(26.8~{}^{\circ}\)C], \(p_{1}\in[0.911\) bar, \(0.927\) bar], \(n_{1}\in[9545\) rpm, \(9714\) rpm], and \(n_{2}\in[5589\) rpm, \(5930\) rpm].  
Secondly, the collected field data are inputted into the GPA model to obtain the six fault features of the compressor (\(DGC\), \(DEC\)), high pressure turbine (\(DGT\), \(DET\)), and power turbine (\(DGP\), \(DEP\)). In this study, the gas path fault occurs in the compressor. Correspondingly, the extracted compressor fault features of \(DGC\) and \(DEC\) are selected to analyze the compressor fault mode. The variations of \(DGC\) and \(DEC\) are shown in Figure 8. The \(DGC\) varies within a range of 1% before the sudden shutdown. However, the \(DEC\) shows an exception (reaching about 5%) at the moment of about 110 s. This \(DEC\) exception indicates that the gas path fault would already occur at the moment of about 110 s. The results show that the GPA method can detect the gas path fault 18 s in advance, compared with the vibration monitoring system.  
Figure 6: One of the compressor blades damaged by the foreign objects.  
Figure 7: Field data of the LM2500 PLUS gas turbine; (**a**) inlet temperature and pressure; (**b**) speed.
# A Review and Future Directions on the Deterioration Evaluation of Concrete Utility Poles
## 4 The Current Studies of Wood Utility Pole
### Deterioration Factors and Dynamic Performance  
The deterioration factors of wooden utility poles have been identified as decay and damage caused by fungi and termites and damage inflicted by woodpeckers [37; 38]. The decay of wooden utility poles generally starts at the point of contact with the ground and spreads throughout the pole shown in Figure 3[39]. Currently, studies are being conducted on proposing probabilistic deterioration prediction models and applying non-destructive testing (NDT) technologies.  
Additionally, numerous cases of collapse due to hurricanes have been reported for wooden utility poles [40; 41; 42; 43; 44]. In the United States, it is estimated that power outages caused by hurricanes result in approximately $270 million in annual repair costs [41]. Therefore,  
Figure 3: Deterioration of a wooden utility pole: (**a**) external damage as a reduction in cross-section; (**b**) internal damage due to fungal attack; (**c**) internal damage as a reduction in cross-section due to termites [39].  
efficient and cost-effective replacement plans are crucial for utility pole maintenance. Moreover, it is necessary to account for the increase in damage associated with the growing intensity of hurricanes due to recent climate change [45].  
Numerous studies on probabilistic deterioration prediction models are currently being undertaken in response to such deterioration phenomena in wooden utility poles. Further details on previous studies will be discussed later. While the deterioration factors for wooden utility poles have been organized, unlike concrete utility poles, limited research focuses on the mechanisms of deterioration factors. Most existing studies use non-destructive testing methods to predict and detect damage caused by deterioration.  
Regarding mechanical properties, [PERSON] et al. [46] focused on reinforcing corrooded wooden utility poles using FRP (Fiber-Reinforced Polymer). They demonstrated that FRP reinforcement effectively extends the service life of wooden utility poles.  
There are fewer studies on mechanical properties for wooden utility poles than for concrete utility poles. The studies identified primarily aim to verify the effectiveness of reinforcement methods. However, sufficient investigations into the ultimate state and other mechanical properties have not been conducted.  
Although deterioration evaluation methods using probabilistic simulations, as will be discussed later, have been extensively studied as an approach for efficient replacement evaluation, the mechanical performance of actual utility poles has not been thoroughly examined. Previous studies have also highlighted this as a future challenge for properly evaluating deterioration and damage in wooden utility poles, similar to concrete utility poles [47].