esa-sceva/satcom-chunk-collection huggingface dataset:
# Fibers of Failure: Classifying Errors in Predictive Processes
## 2 Materials and Methods
### FiFa: The General Case
#### 2.3.3 Quantitative: Model Correction Layer  
Once failure modes have been identified, one way to use the identification is to add a correction layer to the predictive process. This is done by using a classifier to recognize input data similarly to a known failure mode, and by adjusting the predictive process output according to the behavior of the failure mode in available training data.  
**Train classifiers.** For our illustrative examples, we demonstrate several \"one versus rest\" binary classifier ensembles where each classifier is trained to recognize one of the failure modes (extracted subgroups) from the Mapper graph.  
**Evaluate bias.** A classifier trained on a failure mode may well capture larger parts of test data than expected. As long as the space identified as a failure mode has consistent bias, it remains useful for model correction: by evaluating the bias in data captured by a failure mode classifier we can calibrate the correction layer.  
**Adjust model.** The actual correction on new data is a type of ensemble model, and has flexibility on how to reconcile the bias prediction with the original model prediction--or even how to reconcile several bias predictions with each other. In the example cases used in this paper, we showcase two different methods for adjusting the model: on the one hand by replacing a classifier prediction with the most common class in the observed failure mode, and on the other hand by using the mean error as an offset.  
**Note on Type S and Type M errors.**  
The authors of [64] argue that for model evaluation, the distinction between Type I and Type II errors is less useful than a distinction between Type S (sign) and Type M (magnitude) errors. Drawing on these error types, we will structure our quantitative adjustments in the continuous case with careful attention paid to Type S errors.  
To elaborate, if a failure mode is found to have consistently overly-high predictions, adjusting with the observed bias of the failure mode is likely to produce a better prediction for all points in the failure mode. However, a failure mode that has errors in both directions will exacerbate some errors when adjusting for bias in that failure mode.  
We apply this philosophy primarily in our design of model correction layers: by restricting bias corrections to cases that have large (handling Type M) and consistent (handling Type S) errors, we can rely on a bias correction to improve prediction for all the observations it corrects.
## 2 Experimental Details
### Measurement of Arc Voltage and Angular Distortion  
During and after welding, investigations were carried out to measure the arc voltage and angular distortion in the A-TIG welded joints. The arc voltage is the voltage between cathode and anode during welding, and it can be measured by multimeter set between the material and tungsten electrode. The schematic figure of the arc voltage measuring set up is shown in Figure 3a.  
The schematic for the angular distortion measuring setup is shown in Figure 3b. During measurement, the dial gauge was moved along the \(x\)-axis from the weld center to the edge of the plate through a distance \(\Delta x\). Subsequently, a vertical displacement \(\Delta z\) was obtained in the dial gauge. The angular distortion values were measured before and after welding three times for each sample, and the average value was recorded. The differences in measurement before and after welding revealed the vertical displacement caused by welding; subsequently, the angular distortion value \(\theta\) was calculated using the following equation:  
\[\theta=\tan^{-1}\left(\frac{\Delta z}{\Delta x}\right) \tag{1}\]
###### Abstract  
Aiming at the issue of suboptimal demodulation performance in OFDM signal enhancement processes due to slow response speeds in first-order overdamped stochastic resonance systems, this paper designs an OFDM signal enhancement and demodulation system based on second-order underdamped unsaturated bistable stochastic resonance (SUUBSR). Firstly, a nonsaturated bistable potential function model is constructed by combining a traditional monostable potential function with a Gaussian potential function, and a damping coefficient is introduced simultaneously to build the SUUBSR system. Subsequently, the transient and steady-state output response analytical expressions of the SUUBSR system under OFDM signal excitation are derived, the energy loss of OFDM symbol waveforms caused by the transient response of the system is discussed, and the relationship between the damping coefficient and the steady-state output response of the system is explored. Finally, simulations are conducted to evaluate the enhancement and demodulation process of OFDM signals using the SUUBSR system. The simulation results show that at an input signal-noise ratio of 2 dB, compared to the first-order unsaturated bistable stochastic resonance (FUBRR) system, the proposed system reduces the system response time by 3.956% of a symbol period and decreases the demodulation bit error rate for OFDM signals by approximately 35%.  
underdamped stochastic resonance; orthogonal frequency division multiplexing; transient response; damping coefficient +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON] Study of the Characteristics of Second-Order Underdamped Unsaturated Stochastic Resonance System Driven by OFDM Signals. _Appl. Sci._ **2024**, 11334. [[https://doi.org/10.3390/appl142311324](https://doi.org/10.3390/appl142311324)]([https://doi.org/10.3390/appl142311324](https://doi.org/10.3390/appl142311324))
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Methods
### Post-Processing  
To determine significantly activated channels, oxy- (\(\Delta\)HbO) and deoxyhemoglobin (\(\Delta\)Hb) concentration changes were evaluated using the general linear model (GLM) provided in the Huppert Toolbox [57], which uses pre-whitening (operation used prior (\"pre\") to GLM to make a time seriesbehave statistically like white noise (whitening)) and robust regression. A constant, \"mean\" term, the regressor itself convolved with the canonical HRF, and its first and second time derivatives (basis = nirs.design.basis.Canonical; basis.incDeriv = true) were used in the GLM analysis. Only the \(t\)-stats for the main term were used. Using this software, adjustment for multiple comparisons is automatically performed and can be found in the output \"SubjStats.q\". For comparison, we also processed the data without the first order derivatives, which does not account for differences in shape and delay of the hemodynamic response, yet is commonly used in studies of activation.  
It is still common practice to consider only oxy- or deoxyhemoglobin changes when determining activation in a channel; however, determining activation only when significant changes in both oxy- and deoxyhemoglobin occur has been shown to be more accurate [48] and may improve specificity. Therefore, for this study, we show determination of a significant channel activation under three different conditions: (1) significant increase in oxyhemoglobin signal, (2) significant decrease in deoxyhemoglobin signal, and (3) significant increase of oxyhemoglobin accompanied by significant decrease in deoxyhemoglobin signals.  
For the evaluation of all steps and methods we used statistical comparisons, conducted with a paired sample \(t\)-test, two-sided and corrected for multiple comparison with the Benjamini-Hochberg method [58]. The \(p\)-values are marked therefore as \(p_{\text{c}}\).
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 7 Conclusions  
In this paper, we analyzed the system performance in terms of OP and EC and optimized the OP of a downlink NOMA-based dual-hop hybrid RF-UOWC system with DF relaying under the practical assumption of imperfect SIC, where the UOWC channels are characterized by EGG distribution. We derived new analytical closed-form expressions for OPs and ECs and asymptotic expressions for the OPs and the \(DO\). To gain more  
Figure 10: ECs over the entire range of power allocation factor at \(\rho=50\) dB.  
Figure 9: The effect of temperature gradient on ECs performance.  
insight, we investigated the influence of system parameters on performance. Consequently, we deduced that the increase in the level of air bubbles and/or temperature gradient leads to a degradation in the OPs and ECs performances, and the outage performance improves when implementing heterodyne detection compared to IM/DD. Moreover, we investigated the feasibility of obtaining an outage-optimal power allocation factor. Finally, we carried out a comparison with a benchmark system, from which we realize that our proposed system is suitable for UIoT applications. As a future work, we may study the a multi-underwater destination system with amplify and forward relay assuming imperfect channel state information.  
Conceptualization, A.A.A.E.-B.; Data curation, A.S. and M.E.; Formal analysis, A.S. and M.E.; Funding acquisition, I.S.A. and K.R.; Methodology, A.S. and M.E.; Project administration, B.M.E.; Software, A.S. and M.E.; Supervision, I.S.A., K.R. and B.M.E.; Validation, A.A.A.E.-B.; Writing--original draft, A.S. and M.E.; Writing--review & editing, K.R. and B.M.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### PMC for Geosmin and 2-MIB Removal  
The geosmin and 2-MIB removal efficiencies of the different PMC doses were investigated. In the coagulation process, PMC-based coagulant was injected up to 70 mg/L. The removal efficiency of geosmin and 2-MIB increased with increasing the PMC dose in both processes (Figure 6). The removal efficiency of geosmin and 2-MIB at 20 mg/L of PMC was approximately 46% and 37%, respectively. The removal efficiencies were higher for geosmin than for 2-MIB. This was attributed to the flatter structure and solubility of geosmin than 2-MIB [1].  
Figure 5: Effect of coagulant dose on pH and alkalinity with varying doses of the PMC coagulant (0, 5, 10, 20, 40, and 70 mg/L).  
Figure 6: Trends in geosmin and 2-methylisoborneol (2-MIB) removal by PMC processes at the lab-scale (initial geosmin concentration = 134.58 ng/L; initial 2-MIB concentration = 159.02 ng/L; PMC dose: 0, 5, 10, 20, 40 and 70 mg/L).  
From the results in Figure 6, it was found that PMC coagulation process could remove some geosmin and 2-MIB, and PMC shows higher removal efficiency of geosmin (46%) and 2-MIB (37%) than other chemical coagulant reported in previous studies [32; 33]. After the PMC coagulation, the residual geosmin and 2-MIB was found to be about 73.63 and 99.85 ng/L, respectively. However, the average threshold of geosmin and 2-MIB concentrations is approximately 10 ng/L [34], thus further removal of geosmin and 2-MIB is still needed. Therefore, to enhance the removal efficiency of these compounds a PMC-based coagulation process followed by AOP process was carried out.
## 3 Thermal Error Experiment
### Data Analysis  
In total, 20 batches of experiments were carried out, marked as B1-B20. Each batch of experiments was run at five different spindle velocities and four different ambient temperatures. Furthermore, the measurement of each batch includes temperatures and thermal errors. The 20 batches are displayed in an ascending order of velocity, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Area** \\ \hline T1\(\sim\)T5 & Spindle Area (A) \\ T6\(\sim\)T10 & Spindle Box Area (B) \\ T11\(\sim\)T14 & Machine Box Area (C) \\ T15 & Working Table Area (D) \\ T16 & Surface of Machine Tool (E) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed installment location of transducers.  
Figure 3: Displacement sensors arrangement on the CNC machine tool.  
Figure 4 demonstrates the error curves in X1, X2, Y1, Y2 and Z direction of B5. It is observed that thermal errors of X1, X2, Y1 and Y2 direction rise rapidly to around the 60 th minute. After that, the curves flatten out, while the thermal error of Z direction peaks around 140 min and then declines slowly. Since thermal errors due to axial thermal expansion of the spindle of machine tool account for a majority of the entire thermal errors [51], the thermal error in Z direction is taken as the priority of the research in this paper. In the following section, the methodology is presented to pick up temperature-sensitive variables and establish the model between thermal errors and temperature transducers.
## 2 Materials and Methods
### Application and Testing of Virtual Constellation in Apalachicola Bay
#### 2.4.4 Validation and Performance  
The first validation layer for the virtual constellation compares its prediction accuracy to a simple LR model. The purpose of this comparison is to demonstrate the superiority of the RF and justify the added complexity. In order to quantitatively validate the model, synthetic clouds were generated over areas in an image that have true NDVI values thus providing labeled data for testing. The images chosen for the synthetic cloud validation were purposely excluded from the training data but were within the study area. The metrics used for validation were Root Mean Square Error (RMSE) and Coefficient of Determination (\(R^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**L8 NDVI** & **Month** & **Northing (m)** & **Easting (m)** & **S2A NDVI** & **ASTER NDVI** \\ \hline
0.53 & 12 & 692,842.0 & 3,295,065.0 & 0.49 & 0.37 \\
0.50 & 12 & 692,872.0 & 3,295,065.0 & 0.53 & 0.40 \\
0.41 & 4 & 668,722.0 & 3,298,545.0 & 0.42 & 0.26 \\
0.34 & 4 & 668,752.0 & 3,298,545.0 & 0.69 & 0.21 \\
0.  
RMSE is defined as:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left({X_{i}-\hat{X}_{i}} \right)^{2}}} \tag{3}\]  
where \(X_{i}\) and \(\hat{X}_{i}\) represent the observed and estimated NDVI values for pixel \(i\), respectively, and \(n\) is the number of pixels in the sample [79].  
Coefficient of Determination (\(R^{2}\)) is a measure of performance when comparing estimated values to observed values, typically in a one-to-one plot. It is defined as:  
\[R^{2}=1-\frac{SSE}{SS_{XX}} \tag{4}\]  
\[SSE=\sum{\left({X_{i}-\hat{X}_{i}}\right)^{2}} \tag{5}\]  
\[SS_{XX}=\sum{\left({X_{i}-\overline{X_{i}}}\right)^{2}} \tag{6}\]  
where \(X_{i}\) is the observed NDVI, \(\overline{X_{i}}\) is the mean of the observed NDVI values, and \(\hat{X}_{i}\) is the estimated NDVI. \(R^{2}\) measures the linear agreement between predictions and observations or the degree to which the predicted values capture the observed values over simply using the mean as the prediction. However, it is sensitive to outliers and should only be used when the data are normally distributed.  
As summarized above, synthetic clouds were created over pixels that had true L8 NDVI values. First, we compared the RF and LR virtual constellation models over the synthetic cloud pixels. Sample data from each season (3 month intervals) were used for performance assessment and validation. We also tested the virtual constellation model for sensitivity to the month and cloud cover percentage of the target image. The purpose of this analysis was to deepen our understanding about the performance of the model with respect to seasonality, check for issues related to [PERSON]'s paradox [54], and investigate the model's sensitivity to the initial percentage of image obscured by clouds. Synthetic clouds were created by obscuring increasing percentages of the images to investigate the influence, if any, of cloud coverage in the target image on the end result and determine the working limitations of the model in this respect.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Performance under Various Noise Levels  
The Urban dataset was used to evaluate the robustness of the proposed algorithm against noisy image observations. To this end, the effects of image noise along with varying percentages of outliers were tested using 81 instances of the Urban dataset. The results are illustrated in Figure 9.  
From the accuracy point of view, the accuracy of estimation (described by \(\mu_{d}\cdot CP\)) did not decrease by increasing the noise level. The different noise synthesized on putative matches varies between zero and four pixels. The robustness of the algorithm to noise means that the sample sets whose points have a lower level of noise can be recognized through the LTS-based objective function. Thus, the estimation of the fundamental matrix would be based on the least-noisy correspondences of the dataset. Regarding the accuracy of inlier detection, the true positive rate was not affected by the noise. However, the true negative rate decreased slightly by increasing the noise level. Since the outliers were synthesized by adding gross errors as low as 10 pixels to correspondences, distinguishing real outliers from noisy inliers at higher noise levels became a more difficult task, and the false positive rate increased. In terms of convergence speed, increasing the magnitude of image noise decreased the number of iterations. The total number of iterations required to find the final solution changed only from 80 to 250 as the ratio of outliers increased to 80% and the level of noise increased to four pixels. This shows the high computational efficiency of the overall algorithm in the presence of high ratios of outliers and noise.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 2 Smart Gateway Antenna  
Figure 2 shows the configuration of the proposed antenna. This antenna is composed of five active elements, four of which (#1-#4) are arranged at equal angular intervals of \(90^{\circ}\) on a circle of radius \(a\), with the final element (#5) located at the center of the circle. The arrangement of the elements is the same as the ESPAR antenna, except that all elements are excited. By controlling the weight \(w\) and phase shift \(\tau\) of the excitation, it is possible to form the directivity of two functions: AOA estimation and beam steering.  
Figure 2: Configuration of the smart gateway antenna.  
The polarization characteristics of the radio wave radiated from the sensor, which is attached to the human body, will change significantly depending on the mounting orientation of the sensor and human motion, regardless of the radiation characteristics of the sensor antenna. We had previously reported a weighted-polarization wearable MIMO antenna, which can have controlled polarization characteristics by considering the variation in the cross-polarization power ratio of the incoming wave and the antenna inclination angle, which depends on the human motion [26]. However, the development of this sensor is not considered in this study. Although it is desirable for the gateway antenna to receive both polarizations, this study deals only with the vertical polarization. Therefore, all the antenna elements in Figure 2 are for a dipole antenna perpendicular to the ground. \(l\) is the length of an element. The radius \(a\) and the length \(l\) of the proposed smart gateway antenna were redesigned to increase the radiation gain and improve the estimation accuracy of the incoming wave based on the results of a previous antenna that is composed of nine elements [24; 25]. The operating frequency is 2 GHz.
## 1 Introduction  
Chitosan, a natural polysaccharide-based biopolymer, has attracted considerable attention in various scientific and engineering applications [1]. For instances, chitosan was extensively investigated in metal extraction [2; 3; 4], chemical synthesis reaction [5; 6] and electrolyte-based fuel cells studies [7; 8]. Chitosan was explored to be a mild and direct solid catalyst in organic synthesis without any post-modification [6; 9; 10]. However, most of the research efforts have been reported to prepare functional derivatives of chitosan by chemical modifications to serve the desired useful purpose [11]. The benefits of using chitosan in different applications include the following: (1) it is low-cost and eco-friendly; (2) it has the desirable property of hydrophilicity; (3) it allows chemical or physical modifications to customize its property; (4) it is chemically stable (e.g., high thermal stability up to 280 \({}^{\circ}\)C), nontoxic and biodegradable; (5) it contains metal anchoring functional groups [1, 7, 12, 13, 14, 15, 16].  
As recent developments for greener, cleaner and more sustainable chemistry are being driven by a shift from petrochemical-based feedstocks to biological materials [17], chitosan is of particular interest for application in catalysis. Chitosan exhibits great potential in chemical applications and is commonly used as a catalyst supporters to promote the efficiency of chemical reactions [5, 18]. The presence of readily functionalizable hydroxyl and amino groups on the chitosan and its insolubility in organic solvents make it very attractive in the application [19, 20]. Besides, chitosan can be easily modified chemically or physically to become a versatile supporting material [21].  
Among all types of catalysis systems, recent advances in catalysts for chemical synthesis have switched from homogeneous systems to heterogeneous ones. Heterogeneous catalysis systems provide the advantages of easy catalyst removal, recovery and recycling [13, 22]. Moreover, the use of chitosan as novel supporting materials for heterogeneous catalysis is on the rise, mainly due to its high affinity to metal ions [13, 14, 23]. The unique properties of chitosan also enhanced its applicability for further functionalization and immobilization of metal complexes through covalent attachment [20].  
Recent efforts in chitosan researc have been made to explore new applications in the area of catalysis. Chitosan-metal catalysts have been investigated in the chemical reactions of hydrogenation [5, 24, 25] and adsorption [26]. In addition, chitosan was employed to immobilize photocatalysts for easy recovery as well as enhancement of photodegradation [27, 28]. This review intends to provide an overview of current applications of chitosan as heterogeneous catalysts supports. A variety of appropriately functionalized catalysts using chitosan as supports are discussed. Table 1 summarizes various chitosan-metal catalysis systems that are currently reviewed in this study.
## 2 Related Work
[17] designed an ECA-based real-time database system for smart building control, while [PERSON] et al. [18] discussed cloud-based room automation via rule sets based on occupancy. Such architectures highlight distributed control models for smart buildings. All these efforts point toward intelligent, networked, and secure HVAC systems. Whether in terms of AI-based temperature control systems, Reinforcement Learning, or safe Modbus TCP-based systems, integration of sensing, prediction, and real-time control is defining next-generation energy-efficient, occupant-centric building spaces.