esa-sceva/satcom-chunk-collection huggingface dataset:
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 8 Results and Discussion
### Resampling before and after Splitting
#### 8.2.1 Random Undersampling before Stratified Splitting
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Oversampling \%** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Macro Precision** \\ \hline
0.1 & Avg & 0.9999 & 0.902 & 0.775 & 0.81 & 0.951 \\  & SD & & 0.125 & 0.236 & 0.156 & 0.063 \\  & Avg & 0.99996 & 0.904 & 0.8 & 0.828 & 0.952 \\
0.2 & SD & & 0.138 & 0.232 & 0.165 & 0.069 \\
0.3 & Avg & 0.99996 & 0.895 & 0.825 & 0.841 & 0.947 \\  & SD & & 0.15 & 0.243 & 0.185 & 0.075 \\
0.4 & Avg & 0.999996 & 0.9 & 0.794 & 0.824 & 0.95 \\  & SD & & 0.147 & 0.249 & 0.185 & 0.074 \\
0.5 & Avg & 0.999996 & 0.908 & 0.815 & 0.839 & 0.954 \\  & SD & & 0.139 & 0.244 & 0.177 & 0.069 \\  & Avg & 0.999996 & 0.907 & 0.846 & 0.857 & 0.954 \\
0.6 & SD & & 0.136 & 0.233 & 0.169 & 0.068 \\  & Avg & 0.999996 & 0.905 & 0.843 & 0.853 & 0.952 \\
0.7 & SD & & 0.139 & 0.24 & 0.175 & 0.07 \\  & Avg & 0.999996 & 0.906 & 0.834 & 0.848 & 0.953 \\
0.8 & SD & & 0.139 & 0.247 & 0.182 & 0.069 \\  & Avg & 0.999996 & 0.899 & 0.844 & 0.851 & 0.955 \\
0.9 & SD & & 0.14 & 0.24 & 0.178 & 0.07 \\
1 & Avg & 0.999996 & 0.899 & 0.848 & 0.852 & 0.949 \\  & SD & & 0.14 & 0.242 & 0.181 & 0.07 \\  & Averages & 0.9999959 & 0.9025 & 0.8224 & 0.8403 & 0.9512 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: UWF-ZeekData22: privilege escalation—classification results for random undersampling before splitting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Welch’s \(t\)-Test** & **Precision \(t\)-Value** & **Recall \(t\)-Value** & **F-Score \(t\)-Value** & **Macro Precision** & **Analysis** \\ \hline
0.1 vs. 0.2 & \(-\)0.042 & \(-\)0.239 & \(-\)0.252 & \(-\)0.042 & 0.1 and 0.2 are statistically equal \\
0.1 vs. 0.3 & 0.108 & \(-\)0.467 & \(-\)0.411 & 0.108 & 0.1 and 0.3 are statistically equal \\
0.1 vs. 0.4 & 0.034 & \(-\  
After analyzing the classification results using [PERSON]'s \(t\)-tests, for Random Undersampling Before Stratified Splitting, it appears that a random undersampling of 0.5 of the majority data before stratified splitting gives the best results when the BSMOTE oversampling is also at 0.5 for two datasets: Worms and credential access. The best results for Shellcode were achieved at a random undersampling at 0.5 and a BSMOTE oversampling of 0.4, and at a BSMOTE oversampling of 0.2 and 0.1 for Backdoors and privilege escalation, respectively.
## 3 Proposed Design Approach
### Design of the Braced Beams  
When the braces are in the elastic range of behaviour, the braced beam is subjected to the axial force (\(N_{\rm Ed,b}\)) produced by the seismic loads and shear forces (\(V_{\rm Ed,G,b}\)) and the bending moments (\(M_{\rm Ed,G,b}\)) produced by the gravity loads in the seismic design situation.  
After buckling of the brace in compression, the braced beam is subjected to an unbalanced vertical force. The proposed procedure admits yielding of the beam under the bending moments produced by this force. Thus, a moderately ductile behaviour of this member is required. For this reason,  
* a cross section classified as Class 1 or Class 2 has to be selected;
* the axial force in beams has to be lower than 0.3 times the plastic axial resistance.  
Further, flexible braced beams sustain large deflection under the unbalanced vertical force transmitted by braces. In turn, this promotes the large inelastic deformation demand of the brace in compression, which is a premise for the premature fracture of the brace. To prevent this unfavourable yielding mechanism, the axial shortening \(\delta_{\rm b}\) of the braces caused by the vertical displacement produced at midspan of the beam by the unbalanced vertical force should be lower than a rate \(k\) of the axial displacement capacity of the brace (\(\delta_{\rm u}\)):  
\[\delta_{\rm b}\leq k\ \delta_{\rm u} \tag{3}\]  
This study investigates different values for the coefficient \(k\), namely, 0.2, 0.6, 1.0, 1.4 and 1.8.  
Under the hypothesis of pinned beam-to-column connections, the axial shortening \(\delta_{\rm b}\) of the braces caused by the vertical displacement at midspan of the beam is calculated as  
\[\delta_{\rm b}=\gamma_{\rm ov}\frac{\left(1.1N_{\rm pl,br}-N_{\rm u,br}\right) \sin^{2}\alpha\cdot L^{3}}{48\ E_{\rm s}I_{\rm b}} \tag{4}\]  
where \(N_{\rm pl,br}\) and \(N_{\rm u,br}\) are the plastic and ultimate resistances of the brace, \(E_{\rm s}\) is the elastic modulus of steel, \(I_{\rm b}\) is the moment of inertia of the beam, \(L\) is the length of the braced bay and \(\alpha\) is the slope of the brace to the horizontal axis. In the equation above, the plastic resistance of the brace in tension is amplified by 1.1 to take into account the effect of hardening.  
Parameter \(\delta_{\rm u}\) is calculated here as the elongation of the brace at yield \(\delta_{\rm y}\) times the ductility capacity of the brace at fracture \(\mu_{\rm f}\), evaluated according to [PERSON] [22] as  
\[\mu_{\rm f}=1+\theta_{\rm f}^{2}\frac{E_{\rm s}}{2f_{\rm y}} \tag{5}\]  
where \(\theta_{\rm f}\) is the rotation of the plastic hinge at mid-length of the brace at fracture. This capacity is calculated by the relation proposed by [PERSON] [8] for HSS cross-sections and depends on the slenderness \(\lambda\) of the brace and on the cross-section shape by the width-to-thickness (\(b_{0}/t\)) and depth-to-thickness (\(d_{0}/t\)) ratios:  
\[\theta_{\rm f}=0.091\left(\frac{b_{0}}{t}\frac{d_{0}}{t}\right)^{-0.1}\lambda ^{0.3} \tag{6}\]
## 2 Materials and Methods
### UPLC-DAD Analysis of F2 and Method Validation
#### 2.2.1 Linearity, LOD and LOQ  
The stock solutions of the standards were serially diluted into five concentrations; astragalain (15.625-250 mg/mL), fustin (62.5-1000 mg/mL), fisetin (15.625-250 mg/mL), sulfuretin (31.25-500 mg/mL), and ellagic acid (31.25-500 mg/mL). The calibration curves were constructed by plotting peak areas (y-axis) versus concentrations (x-axis, mg/mL) for each analyte in a given range. These solutions were analyzed individually in triplicate for the establishment of the calibration curve. Linearity was appraised by the correlation coefficient (R\({}^{2}\)) values [21]. The LOD and LOQ were calculated based on the standard deviation (SD) of y-intercepts of regression lines and slope (S) of the calibration curve.  
\[\text{LOD}=3.3\times\text{SD}_{\text{Y-intercept}}/\text{S}\text{ and LOQ}=10\times\text{SD}_{\text{Y-intercept}}/\text{S}\]
# Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans
## 2 Materials and Methods  
### Dataset and Processing  
We test our method on datasets acquired from different scanners of different medical institutions. The collected dataset composes of 260 CT scans, with a largely varying in slice spacing from 0.45 mm to 5 mm. And 220 CT scans were randomly selected for training, the rest 40 cases for testing. For images pre-processing, the image intensity values were truncated to the range of [\(-\)150, 250] hounsfield unit (HU) to remove the irrelevant details [9].
## 2 Materials  
### Study Area and Field Measurements  
The study area encompassed the Hangjin Rear Banner (40\({}^{\circ}\)26\({}^{\prime}\)N-41\({}^{\circ}\)13\({}^{\prime}\)N, 106\({}^{\circ}\)34\({}^{\prime}\)E-107\({}^{\circ}\)24\({}^{\prime}\)E), Urat Front Banner (40\({}^{\circ}\)28\({}^{\prime}\)N-41\({}^{\circ}\)16\({}^{\prime}\)N, 108\({}^{\circ}\)11\({}^{\prime}\)E-109\({}^{\circ}\)54\({}^{\prime}\)E), Zhenglan Banner (42\({}^{\circ}\)21\({}^{\prime}\)N-42\({}^{\circ}\)56\({}^{\prime}\)N, 115\({}^{\circ}\)30\({}^{\prime}\)E-116\({}^{\circ}\)13\({}^{\prime}\)E), and Horqin Right Front Banner (45\({}^{\circ}\)50\({}^{\prime}\)N-46\({}^{\circ}\)21\({}^{\prime}\)N, 121\({}^{\circ}\)44\({}^{\prime}\)E-122\({}^{\circ}\)25\({}^{\prime}\)E) in the Inner Mongolia Autonomous Region (Figure 1). The predominant crops in the regions are corn, rice, and sunflower. Field measurements were conducted from 17 July to 18 September 2021, encompassing the flowering, grouting, and ripening phases of the crops (Table 1). Parameters including canopy spectrum, LAI, and FVC were systematically collected. Each measurement was executed in a homogenous field with dimensions no smaller than 100 m \(\times\) 100 m. Within the central area of each field, five 10 m \(\times\) 10 m quadrats were established, and the mean of these quadrats was used to represent the field's canopy spectrum, LAI, and FVC measurements. All measurements were performed under clear sky conditions. Crop canopy spectral data were acquired using an ASD FieldSpec4 spectrometer, which boasts a spectral range from 350 to 2500 nm. LAI measurements were obtained using an LAI-2200C plant canopy analyzer. The FVC was assessed through vertical photography. In vivo chlorophyll, flavanol, and anthocyanin content estimations were performed using an MPM-100 Multi-Pigment-Meter (OPTI-SCIENCES, USA) on fully expanded leaves [37]. For these measurements, adaxial leaf surfaces were selected, with care taken to avoid the leaf midribs when positioning the fluorescence detector for readings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sites** & **Crop Type** & **Time** & **Crop Growing** & **Number of** \\  & & & **Season** & **Survey Points** \\ \hline Hangjin Rear Banner & Corn, Sunflower & 17 July 2021–18 July 2021 & Flowering Period & 22 \\ Urat Front Banner & Corn, Sunflower & 19 July 2021–20 July 2021 & Flowering Period & 10 \\  & & 20 August 2021–22 August 2021 & Grouting Period & 15 \\ Zhenglan Banner & Corn, Sunflower & 09 September 2021–11 September 2021 & Grouting Period & 22 \\ Horqin Right Front & & 15 September 2021–18 September 2021 & Ripe Period & 56 \\ Banner & Corn, Rice & 15 September 2021–18 September 2021 & Ripe Period & 56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sites, time, crop type, and growing season information for field survey.
## 3 Background and Related Work
### Triage and Risk Stratification  
Triage is critical for prioritizing patients by urgency, yet traditional nurse-led triage can be inconsistent due to its subjective nature. DL-based systems have been developed to assist triage nurses, standardizing acuity assessments and improving patient flow [31]. Studies report that AI-supported triage improves the identification of high-risk patients, resulting in improved patient outcomes [32]. For example, a DL model trained on triage vitals, symptoms, and notes outperformed conventional methods in predicting critical outcomes, enabling more reliable severity stratification [33].
## 1 Introduction
The CNN learns to map the degraded images to their clean counterparts, effectively removing the blurring effect and restoring the image. Both AGF and Haar transform inherently contribute to edge preservation, considering that the AGF preserves edges while selectively smoothing other regions, and the Haar transform allows for directional analysis, which can further enhance the edge preservation during the restoration process with the DnCNN. Anisotropic Gaussian filters adaptively adjust their parameters based on local image features, allowing for effective noise reduction without sacrificing important image details.
# Measurement of Head-Related Transfer Functions: A Review
## 6 Post-Processing of Measured HRTFs
### Low-Frequency Extension  
The frequency range of measured HRTFs depends not only on the excitation signal, but also on the transfer functions of electro-acoustic systems. Small or mid-size studio monitors, which are commonly used for measuring HRTFs with multi-channel loudspeaker systems, can not reproduce signals at low frequencies with sufficient power (e.g., below, 50 Hz, depending on the size of loudspeakers).  
An anechoic chamber is usually used for HRTF measurements to simulate the free-field environment. In practice, the free-field condition can not be fulfilled at low frequencies (typically below 100-200 Hz), whose cut-off frequency depends on the length of absorption wedges mounted in the chamber. Moreover, the room modes of the anechoic chamber may also influence the measurement results at low frequencies. As a consequence, an appropriate manipulation should be considered for low-frequency HRTFs.  
Besides the use of numerical solutions [185; 186], some studies suggested to model the low-frequency HRTF with a flat magnitude and a linear phase, since the head and pinna barely have influences on magnitude spectra of HRTFs below 400 Hz [78; 126; 187; 188]. [PERSON] [187] corrected the HRTFs at low frequencies by setting the magnitude to a constant value and linearly extrapolating the phase. [PERSON] et al. [78] split HRTFs into two frequency ranges by applying low- and high-pass filters. The original low-frequency component is substituted with a matched low-frequency extension (LFE) generated by a low-pass filtered time-shifted Dirac pulse. The delay and amplitude of the Dirac pulse is based on the group delay and the magnitude around the crossover frequency, respectively. After that, the LFE is filtered through an all-pass filter to match the phase slope of the original low-frequency component around the crossover frequency, and then combined with the original high-frequency component to reconstruct the HRTF over all frequencies. [PERSON] and [PERSON] [188] used a similar approach to [78] to extend the low-frequency components of HRTFs but using different crossover filters.
# Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes
## 3 Methodology
### More In-Depth Simulations and Explanations for the Robustness of ANNs Based on Weight Alterations  
Next, we checked how the alteration of weights impacts the overall prediction process for each of the different implemented ANNs in more detail. In order to achieve this, different sizes of ANN architectures were developed, and random weight alterations were injected into all of the individual developed ANNs. All the individual ANNs for each partition of the different NoC topologies and all the weights were checked. The randomly chosen bit/bits alterations (one and more bit alterations) of the randomly chosen weight/weights were checked in this work. Figure 4 presents the weight alteration process, and explanations are provided below. An exhaustive testing process of all the random possibilities of the weight faults is developed for each individual ANN. Thousands of different simulations are developed based on randomly chosen weights and bits for all ANNs. To generate erroneous weights, we tested all the possible random-weight bit/bits alterations for one or more weight errors. For each of these simulation cases, we used PARSEC workloads. All the results were recorded, and based on the prediction results, we managed to conclude on the most important neurons/connections of the ANNs. For the purposes of our method, a neuron/connection is defined as important if the weight/bit alterations cause the ANN to yield erroneous prediction results. Next, we present some of the different simulation cases (Table 1) with results. Different sizes of the NoCs/ANNs were developed and simulated. We started working with one randomly chosen network-on-chip partition: a dedicated ANN is responsible for each individual partition. All network partitions and different workloads from the PARSEC benchmark suite were verified and presented. Different simulation cases were developed and checked. Table 1 presents some of the developed simulation cases with appropriate descriptions. Figure 5 shows the results concerning the percentage of correct predictions for each of the above-mentioned cases in comparison with the case of no weight faults for different ANN sizes. From the above results, we can conclude that weight changes in the ANNs impact the whole prediction process, and we used thousands of additional simulations to determine the most important ANN neurons for each case (partition and PARSEC workload). In the following section, we present and analyze a method/technique for maintaining the robustness of ANNs in high levels based on replicating the most important neurons/connections. Figure 4: Flowchart presenting the process of weight alterations.
## 4 Simulation and Analysis
### CarSim Model
#### 4.2.2 CCRm  
In the CCRm test scenario, the front vehicle travels at a fixed speed of 20 km/h, while the trailing vehicle runs at 60 km/h, maintaining an initial gap of 60 m. Figure 11a-e illustrate the responses of the inter-vehicle distance, the speed of the trailing vehicle, their relative speeds, the deceleration of the front vehicle, and the time-to-collision value. The results show that the vehicle equipped with a seventh-degree polynomial automatic braking system is also effective in preventing rear-end collisions in this scenario, with a gap of approximately 2 m from the leading vehicle. The trailing vehicle reduces its speed to match that of the front vehicle at around \(t=6.5\) s. The trailing vehicle's deceleration initially increases and then decreases, reaching a peak value at around \(t=5.4\) s. Throughout the braking process, the minimum TTC recorded is 0.75 s, occurring at \(t=5.0\) s.
## 3 Colpitts Chaos Oscillator
### Study on the Colpitts Chaos Oscillator in a Simulation Environment and Prototype
#### 3.3.3 Simulation and Experimental Study Comparison  
The comparison between simulation and prototype results enlightens critical aspects of the Colpitts chaos oscillator models. The attractor projections obtained from the simulation (Figure 4) and prototype measurements (Figure 7) align well, proving the agreement between the LTspice model and the real-world prototype. Differences between these projections are attributed to inherent imperfections within the electronic circuit components, contributing to minor deviations.  
Examining the signal spectra in Figure 5 (simulation) and Figure 8 (prototype) reveals notable similarities, indicating a convergence in their fundamental frequency. However, a marginal disparity in noise levels is evident, with the prototype exhibiting slightly higher noise in its spectrum compared to the simulation. This discrepancy can be attributed to real-world conditions affecting the prototype's signal quality, a factor often absent or minimized in the controlled environment of simulations.  
The cross-correlation functions depicted in Figure 6 (simulation) and Figure 9 (prototype) present a narrow central section with a rapid amplitude decline, characteristic of chaotic dynamics. However, discernible differences arise in the prototype results, where increased noise levels and higher oscillation amplitudes are observed. This variation leads to a more significant minimum bit length required for effective data transfer in the prototype compared to the simulation.  
In summary, while the simulation and prototype align in fundamental behavior, slight discrepancies in noise levels, oscillation amplitudes, and the necessary bit length for data transmission highlight the influence of real-world conditions on the prototype's performance. These findings underscore the importance of considering practical imperfections and real-world influences when translating simulated models into physical prototypes.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
###### Abstract  
The aim of this paper was to develop a master-slave model with fluid-thermo-structure (FTS) interaction for the thermal fatigue life prediction of a thermal barrier coat (TBC) in a nozzle guide vane (NGV). The master-slave model integrates the phenomenological life model, multilinear kinematic hardening model, fully coupling thermal-elastic element model, and volume element intersection mapping algorithm to improve the prediction precision and efficiency of thermal fatigue life. The simulation results based on the developed model were validated by temperature-sensitive paint (TSP) technology. It was demonstrated that the predicted temperature well catered for the TSP tests with a maximum error of less than 6%, and the maximum thermal life of TBC was 1558 cycles around the trailing edge, which is consistent with the spallation life cycle of the ceramic top coat at 1323 K. With the increase of pre-oxidation time, the life of TBC declined from 1892 cycles to 895 cycles for the leading edge, and 1558 cycles to 536 cycles for the trailing edge. The predicted life of the key points at the leading edge was longer by 17.7-40.1% than the trailing edge. The developed master-slave model was validated to be feasible and accurate in the thermal fatigue life prediction of TBC on NGV. The efforts of this study provide a framework for the thermal fatigue life prediction of NGV with TBC.  
thermal fatigue; thermal barrier coat; master-slave model; life prediction; nozzle guide vane +
Footnote †: journal: Applied Sciences  
Article
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 6 Discussion
### Machine Learning in Gait, Balance, and RoM  
Machine learning has been playing an important role in gait, balance, and RoM analysis in recent years. Machine learning techniques can be used to quantify gait, balance, and ROM parameters [35; 36; 62; 66; 70], distinguish between populations and conditions [45; 46; 51; 61], and estimate assessments scores [74; 75]. The techniques used in the literature showed the efficiency of machine learning to reduce and create gait, balance, and RoM parameters. Machine learning has the capability to converge to global optimum, even in non-linear datasets. Additionally, studies in this review that used machine learning techniques showed the highest accuracy, 88-99%, for both parameter quantification and population/condition classification. Moreover, recent studies showed that gait and balance assessments scores, such as Berg Balance Score and Tinetti Test Scores, can be estimated by using features extracted from wearable devices and machine learning models [74; 75].  
However, the amount of data from different subjects and the time needed to train the algorithms in order to have a reliable and accurate model is immense. Additionally, most of the research builds classification models focused on a binary classification, healthy versus non-healthy, limiting the adaptability and reliability for different targeted population. Also, the selection of features might be constrained by the number of subjects that participate in the study [74]. At last, when using models based on activities from gold standard assessments, the person needs to perform all the activities inwhich the model was constructed. Therefore, the model may not be feasible in a free living environment due to that some activities may be difficult to perform in such environment [75].