esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Low-Noise-Supply Modulator Design  
Figure 6 shows a block diagram of the supply modulator. RFT is a noise-blocking technique that blocks noise from the V\({}_{\text{SW}}\) node to the V\({}_{\text{OUT}}\) node. CLs and notch filters (L\({}_{\text{NF}}\) and C\({}_{\text{NF}}\)) are noise-removing techniques used at the V\({}_{\text{OUT}}\) node. In APT operation, the supply noise is easily suppressed by the very low impedance of a large decoupling capacitor. In ET operation, the wideband linear amplifier acts like a large decoupling capacitor. However, the output impedance is increased as frequency increases due to decreased loop gain. In [11], low output impedance is achieved with a linear amplifier at a low frequency and a load capacitor (a few nF) at a high frequency. The voltage-sensing negative feedback loop of the linear amplifier lowers the SM output impedance within the unit loop bandwidth, so the linear amplifier can provide low output impedance as a voltage source. Figure 7 shows the SM output noise open-loop model. In this paper, a load capacitor, resonant-frequency tuning (RFT), and a notch filter are used for noise reduction.  
Figure 6: Block diagram of the hybrid switching supply modulator.  
Figure 7: SM output open-loop noise model and noise contribution depending on frequency offset.  
Figure 8a shows an inductor (LSW) of a switching amplifier with its parasitic capacitor (CPARA) and a variable capacitor (CVAR), which are integrated into the IC. The switching noise at the VSW node is blocked at a certain frequency (fopen) by the high impedance of the parallel LSW and the equivalent capacitance of CPARA+CVAR. Figure 8b shows the equivalent load resistance of the PA, C\({}_{L}\), and notch filter. The PA has a low impedance at a certain frequency (fshort) due to the notch filter and at a high frequency due to C\({}_{L}\). The optimized noise performance for each band is possible by controlling the resonant frequencies of fopen and fshort according to the separation between the uplink and downlink frequencies.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results
### Analysis of Models' Features
#### 5.3.1 Models' Accuracy and Stability by Daily Error Metrics  
Calculation of daily performance measures like nMAE and nRMSE enables meaningful comparison of model predictions. The average error reflects the overall model performance, whereas the median error provides the typical error, indicating model robustness and outlier resistance. A stronger model has lower error variance and a consistent mean and median over multiple days, as it can generalize well across various wind conditions and reduce deviations from factual power generation.  
In Table 8, it is visible that the Hybrid model outperforms all other models in both nMAE and nRMSE metrics with mean and median averaging. The hybrid model has a 15% lower mean nRMSE and 23% lower nMAE than the XGBoost model and a 16% lower mean nRMSE and 19% lower nMAE than the LTC model. The median of errors also decreased. The median nRMSE decreased by 18% in the Hybrid model compared to XGBoost, while the median nMAE decreased by 28%. Compared to LTC, the Hybrid model performed 13% better in median nRMSE metrics and 17% better in median nMAE. This indicates that the Hybrid model demonstrates superior performance and accuracy in both metrics, making it the most accurate model among the evaluated models on a daily level.  
It can also be seen that consistently lower-than-average median errors indicate that there are large outliers that have a significant impact on the mean. However, most forecasts remain more accurate.  
Figure 10 confirms these findings. It shows that all models contain several outliers. And they are visible in the previously mentioned Figure 9, the last 200-time-step interval.  
Table 9 and Figure 11 show the comparison of errors across models and different data subsets. It is visible that XGBoost model validation and test dataset errors increased by ~14% in both nRMSE and nMAE metrics. This indicates that XGBoost does not generalize well to unseen data. On the other hand, the test data subset errors of the LTC model, compared to the training dataset errors, decreased by ~5% in nRMSE and ~8% in nMAE.  
LTC model shows improved generalization, which indicates that the model might be more stable, resistant to overfitting, and effective at capturing general patterns in the dataset. The hybrid model falls between the XGBoost and LTC models. Its errors increase slightly during the test stage (nRMSE increases by 5.5% and nMAE by 4%) but remain lower than the XGBoost model. Even after such an increase, the error rate of the model remains low.
## 5 Enhancement of Dental Imaging Protocols and Applications
### Imaging-Guided Diagnosis and Treatment Planning
#### 5.2.1 Endodontics  
In endodontics, CBCT's high-resolution capabilities are particularly useful for identifying complex canal morphologies, such as C-shaped or bifurcated roots, which are difficult to detect using 2D modalities. Its role is especially valuable in non-surgical retreatment and apical surgery, where spatial navigation is critical. These modalities are also effective in detecting periapical lesions, internal and external root resorption, and root fractures, all of which can significantly impact prognosis and treatment success [31].  
For surgical endodontics, 3D imaging offers a preoperative insight into the anatomical relationships between roots and adjacent structures such as nerves and sinus cavities, thereby minimizing the risk of complications. Postoperative assessments also benefit from high-resolution imaging, allowing clinicians to monitor healing and confirm treatment efficacy [32].
## 2 Experimental Section
### Sample Characterization  
To confirm successful introduction of B, the chemical bonding of the as-prepared samples was identified by Fourier Transform Infrared Spectroscopy (FTIR, Perkin Elmer, Shanghai, Spectrum Two, USA; wavenumber range: 400-4000 cm\({}^{-1}\), resolution: 0.4 cm\({}^{-1}\), scanning rate: 10 cm\({}^{-1}\)/s, KBr/sample ratio: 100:1). The residual carbon after pyrolysis was attached to the conductive tape and further analyzed by a field-emission scanning electron microscope (SEM, FEI Company, Hillsboro, HELIOS NanoLab 600i, USA) equipped with Energy Dispersive X-ray (EDX) to confirm presence of the B element. The accelerating voltage used in SEM analysis was 20 kV. The B content within B-containing PR was measured as per related literature and National Standards (GB/T 24411--2009). The thermal stability, along with residual carbon content, was evaluated by thermogravimetric analysis (TGA, SDT Q600, TA Instruments, TA Instruments, Wilmington, USA) with a heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\) up to a temperature of 1200 \({}^{\circ}\)C under protection of flow Ar atmosphere (flow rate 40 mL/min). The starting sample weight was 10 mg for TGA. The surface tension of PR and B-containing PR solutions as well as the wettability between B-containing PR solution and carbon fibers were recorded by an optical contact angle meter (SDC-100S, Shengding Precision Instrument Co., Ltd., Dongwan, China). PR or B-containing PR solutions at different concentrations (0.4 and 0.8 g/mL) and temperatures (25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C and 45 \({}^{\circ}\)C) were prepared and placed into a syringe that was part of an optical contact angle meter. The status of PR or B-containing PR solution droplets (squeezed from the syringe) and the wetting state of B-containing PR solution droplet on the surface of carbon fibers were imaged and analyzed by the optical contact angle meter. The system automatically calculates surface tension on assessing the status of solution droplets.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 7 Experimental Procedure
### Machine Learning Algorithms  
While a broad range of algorithms were reviewed in Sections 2-5, only a selected subset of machine learning algorithms was implemented in our experiments:  
* AdaFair: A summary of this algorithm is provided in Section 5.1.
* FairGBM: A summary of this algorithm is provided in Section 5.2.
* Fairlearn-GB: Combines the Exponentiated Gradient technique with a gradient boosting algorithm, implemented using the Fairlearn library.
* Fairlearn-NB: Integrates the Exponentiated Gradient technique with a Naive Bayes classifier via the Fairlearn library.
* Fairlearn-DT: Applies the Exponentiated Gradient technique in conjunction with Decision Trees, utilizing the Fairlearn library.
* Fairlearn-LR: Combines the Exponentiated Gradient technique with Logistic Regression using the Fairlearn library.
* Fair Decision Trees: Summarized in Section 3.8, this algorithm is based on the implementation available in the GitHub repository of ([PERSON] et al., 2018).
* Fair Random Forest: An extension of the Fair Decision Trees algorithm, also described in Section 3.8 and obtained from the GitHub repository of ([PERSON] et al., 2018).
* NNB-Parity: Briefly described in Section 2.3, this algorithm is sourced from the GitHub repository of ([PERSON] et al., 2018).
* NNB-DF: Also detailed in Section 2.3, this algorithm is likewise obtained from the GitHub repository of ([PERSON] et al., 2018).
* FairBoost: An overview of this algorithm is presented in Section 5.6, based on the work of ([PERSON] et al., 2018).
* Prejudice Remover Regularizer: Utilized via its implementation in the AI Fairness 360 (AIF360) library.  
This selection was guided by practical considerations such as algorithmic diversity, code/library availability and relevance to the characteristics of the datasets described earlier.
## 2 Related Work
### Methods Leveraging Multi-View Information  
Apart from URL-based methods, researchers have also explored alternative approaches that incorporate additional information, such as website content, visual appearance, and behavioral patterns, for phishing detection. [PERSON]. [31] used a hybrid CNN-LSTM model to extract URL features and combined it with page content features to train XGBoost for phishing detection. Ref. [32] proposed a hybrid identity-based phishing detection technique that leverages the webpage's visual and textual identity, incorporating novel image features and brand-specific keywords derived from the webpage content using textual analysis methods. An empirical evaluation on multiple benchmark datasets showed that this joint visual-textual identity detection approach significantly improves the phishing detection performance, with an overall accuracy of 98.6%, while reducing the false-positive rate by up to 3.4%. Ref. [3] used website images and URLs, along with CNNs, to extract features and classify them into benign and phishing pages. HTMLish [33] is a language-independent and client-side strategy for webpage phishing detection that utilizes deep-learning-based approaches, specifically CNNs, to learn semantic dependencies in the textual content of HTML webpages and achieved an accuracy and true-positive rate of over 93% on a dataset of more than 50,000 HTML documents. Phish-Sight [34] is a machine-learning-based framework that uses dominant color features and popular brand names embedded in URLs' webpages to detect phishing websites through a visual inspection strategy.  
There is also some work using a mixture of features [35; 36; 37; 38]. These approaches may require more computational resources and be more complex to implement compared to URL-based methods. Additionally, they may be susceptible to adversarial manipulation of website content or visual appearance. We propose a robust multi-view learning approach that leverages various readily available website information to achieve accurate and resilient phishing detection.
## 4 Simulations
Figure 5: Comparison of ADR performance between the proposed 2D-DFC scheme with iterative processing and the conventional 2D-DFC scheme that employs a reference image. where \(\hat{\mathbf{D}}_{t}[i](=\mathbf{F}_{P}^{H}\hat{\mathbf{D}}_{F}[i]\mathbf{F}_{Q}^{H})\) is the reconstructed image in the spatial domain and \(\mathbf{D}_{t}[i]\) is the transmitted spatial domain image given by (6). We can then compute the PSNR between the transmitted and reconstructed images as:  
\[\text{PSNR}=10\log_{10}\left(\frac{R^{2}}{\text{MSE}}\right), \tag{25}\]  
where \(R\) is the maximum pixel value. As shown in Figure 6, the proposed iterative 2D-DFC scheme ensures the perceptual unobtrusiveness of the data embedding, as the reconstructed image quality improves with an increasing number of iterations. The visual features of the image were primarily located at low frequencies, whereas details and noise were present at higher frequencies. Because data embedding occurs in the high-frequency subband, visual artifacts are hardly noticeable. Although the proposed iterative method results in a slightly reduced PSNR performance compared to the conventional method, the data rate is nearly doubled. Therefore, the proposed method is beneficial for applications requiring high-speed data transmission through D2C links.
## 2 Error Sources Analysis of Antenna Holographic Measurement
### Error Sources  
Figure 2 shows a 26 m radio telescope, of which the antenna type is the Cassegrain dual-reflector antenna and the reflector of which deforms under the gravitational load. The purpose of microwave holography measurement is to analyze the deviation between the actual shape and the ideal shape of the antenna surface according to the difference in the received signal, so that the surface deviation can be corrected and the received radio signal can be improved.  
In this process, there are some factors that will lead to a difference between the received signal and the actual situation, thus affecting the accuracy of the antenna surface error reconstruction, among which the feed offset error and the pointing error are the two main interfering factors, as shown in Figure 3. Any small shift in the feed position may cause the phase center to deviate from the focus, making the received signal different from the actual situation, thus reducing the accuracy of the surface shape error reconstruction. The actual existence of surface shape errors on the antenna surface can also lead to misalignment of the focus with the phase center of the feed source. The purpose of antenna holography measurements is to measure such surface shape errors that actually exist on the antenna surface. Therefore, in this paper, we will only discuss the effect of feed source offset.  
Figure 1: The schematic diagram of the geometric relationship of the reflector antenna.  
Similarly, if the pointing of the antenna is not precise enough, i.e., there is a pointing error, the antenna will also deviate when pointing to the actual radio source position, which also reduces the accuracy of the reconstructed antenna surface error. The systematic error is a constant pointing error that results in a linear distribution of the aperture phase, which is subtracted out by a two-dimensional fit to the aperture field phase. The scanning error is mainly manifested in the fact that the antenna pointing deviates from the predetermined scanning trajectory point during scanning, and the phase error of the aperture field caused by it is not regular and difficult to deal with. In the face of the increasing demand for the accuracy of large-aperture reflector antennas, even small scanning errors cannot be ignored. This paper focuses on the scanning error in the pointing error.
## 4 Discussion
When we tracked the stable, normal artificial signal of the pulse oximeter stimulator, the ratio of those cycles in which we could detect c and d points was 100% and the variability of the aging index and d/a was minimal (CVs below 1%) (Table 1; Normal condition). This shows that our engine reliably analyzes the second derivative curve. However, when we analyzed the abnormal signals offered by the pulse oximeter simulator, the success of c and d point detection became less reliable. We tested two different abnormal signal settings: Abnormal 1 setting generates a pulse signal of high heart rate and almost totally absent second derivative c-d points, whereas Abnormal 2 is a signal that simulates a weak pulse wave (e.g., similar to that observed in case of vasoconstriction due to cold). Second derivative c-d points are absent in this setting as well. With these settings, the calculation of other pulse contour parameters was still highly repeatable (CVs below 2%). However, c-d point detection ratio, the parameter which expresses the percent of those pulse cycles in which c and d points are recognized by the algorithm, fell below 5% for each setting (Table 1; Abnormal 1 and Abnormal 2 conditions). This increased the variability of all the parameters that are derived from c and d values, namely aging index and d/a. When performing repeated human measurements, we also observed diminished reliability of c and d point analysis. Parameters derived from c and d points of the second derivative of the DVP became more variable (aging index, d/a) (Table 2). This concurs with the relatively high variations in c-d point detection ratio of consecutive measurements. The optimum solution for this problem is to improve the automated algorithm in order to make the identification of second derivative c and d points more reliable. However, literature data suggest that this may have limitations (reviewed by [PERSON] [22]). Although we recognize that attempts to make c-d point detection more precise are inevitable, we also propose the use of the c-d point detection ratio as a tool that aids clinical assessment of parameters derived from the second derivative. If the c-d point detection ratio reaches a certain value, we can reliably use parameters derived from the second derivative to support patient evaluation; however, when it is low, these parameters should be neglected. Determination of the minimum c-d point detection ratio that allows valid second derivative parameter interpretation requires further studies; however, based on our preliminary observations, it is around 30% (data not shown). Moreover, in the follow-up of a patient, a sudden or progressive change in c-d point detection may be evaluated as a warning for pulse wave abnormalities.
# Competitive Detection of Volatile Compounds from Food Degradation by a Zinc Oxide Sensor
## 4 Conclusions  
In the work presented, a resistive sensor based on ZnO oxide was prepared and tested for possible use in the monitoring of food degradation phenomena. The ZnO sensor showed a good sensitivity to low concentrations of hexanal, the target compound used in this work as an indicator of meat degradation. In a realistic scenario, the sensor must be able to discriminate among other organic volatiles released at the same time, so tests of the sensitivity were carried out by exposing the sensor to different mixture of vapors of the main competing organic compounds, 1-pentanol and 1-octen-3-ol. In this condition, the sensitivity to hexanal was reduced in the range of low concentrations (5-10 ppm) while at 50 ppm, the sensor's response was comparable with the hexanal quantity. The experiments with binary and ternary mixtures showed, however, that the transient response of the ZnO sensor was significantly influenced by the presence of hexanal in the vapor mixture at all the concentrations tested. Although not so evident in terms of the intensity of the signal R\({}_{0}\)/R, the detection of hexanal in the vapor mixtures was characterized by a steep decrease with time of the sensor's resistance even at low concentrations, which was not shown by the other organic components.  
The ZnO sensor belongs to the family of conductometric sensors and for this reason, it is affected by some common limitations, such as reduced selectivity, electrical drift, and response reproducibility. The results obtained in this study indicate the possible use of ZnO for the monitoring of food degradation; however, further characterization of the material behavior is needed, and durability and repeatability studies, to confirm the observed potential regarding its use as a sensor.  
Conceptualization, L.B., A.D. and P.F.; methodology, A.F., A.G. and L.B.; formal analysis, L.B. and A.F.; investigation, A.F. and A.G.; data curation, A.F. and A.G.; writing--original draft preparation, A.F. and A.G.; writing--review and editing, L.B. and P.F.; supervision L.B., A.D. and P.F. All authors have read and agreed to the published version of the manuscript. This research was funded by Regione Calabria, POR Calabria FESR/FSE 2014-2020: Asse 12 Azione 10.5.6 \"Mobilita internazionale di dottorandi\".  
The authors declare no conflict of interest.
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 3 Problem Definition and Features Designation
### Features for Bitcoin Price Fluctuation Prediction
#### 3.2.2 Technical Trading Indicators  
Technical trading indicators such as SMA, BB, and MACD [32] used in Bitcoin price fluctuation prediction are based on their widespread usage among individual investors. We make a brief introduction as follows.  
**SMA**, \(f_{2-1}\) averages the last \(N\) intervals (e.g., seconds, minutes, or days) of Bitcoin price. Formally, it is computed as:  
\[\text{SMA}=\frac{\sum_{i=1}^{N}P_{i}}{N}, \tag{3}\]  
where \(P_{i}\) represents the price on \(i\), \(\text{SMA}/P_{N}-1\) is regarded as our feature. Meanwhile, we use different intervals \(N\) to calculate various features.  
**BB**, \(f_{2-2}\) is a technical indicator that creates two bands around a moving average. These bands are computed by the standard deviation of Bitcoin price. The lower and upper BBs can be calculated as:  
\[L=M-2\times\sigma_{m} \tag{4}\] \[U=M+2\times\sigma_{m}, \tag{5}\]  
where \(\sigma_{m}\) stands for the volatility of the moving average \(M\), \(U/L-1\) is regarded as the feature. Moreover, we use different values of \(m\) to construct various features.  
**MACD**, \(f_{2-3}\) is a technical indicator that subtracts two exponential averages from each other, namely the short and the long interval exponential average. The mathematical formula for the MACD is:  
\[D_{i}=E[\text{short}]_{i}-E[\text{long}]_{i}, \tag{6}\]  
where \(E\) is the exponential moving average, computed as \(E_{i}=\frac{2}{N+1}*(P_{i}-E_{i-1})+E_{i-1}\). The value \(D_{i}\) is as the feature. Meanwhile, we use different short and long periods to form various features.
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 3 Problem Statement and Mathematical Formulation  
### Assumptions  
In this study, we consider a prefabricated construction project with \(n\) activities, whose activities are constrained by renewable resources and supply of prefabricated components to minimize the project duration. The following hypotheses are introduced to facilitate the research:  
1. Assume that the first activity \(1\) at the beginning of the project and the last activity \(n\) at the end of the project are dummy work, which does not consume any time and resources.
2. The process activity adopts the end-to-start (F-S type) logic relationship to express the sequence relationship between the activities. Assume that the time lag between the processes is zero.
3. Assume that each activity is executed in a non-preemptive manner, once each process activity starts, it will be executed uninterrupted to the end.
4. Assume that each activity has only one execution mode, and the time consumption and resource requirements of each activity during the execution process remain unchanged.
5. In order to save inventory costs and ensure the smooth progress of construction, it is assumed that the maximum stacking volume of prefabricated components on site each time shall not exceed the on-site inventory space, and the stock volume is sufficient for the assembly construction of the next process.