esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
A higher precision algorithm ensures that recurrence predictions are more accurate, reducing the likelihood of false positives. An algorithm that allows for the identification of high-risk patients accurately becomes a powerful tool to help healthcare providers in decision-making. Clinicians could confidently tailor interventions based on the specific risk profile and prioritize follow-up and surveillance for those patients at highest risk of recurrence. For low-risk patients, the application of unnecessary and burdensome treatments or techniques could be avoided. Conversely, patients at higher risk could receive more aggressive interventions, potentially improving survival rates and avoiding potential delays in critical therapeutic measures. Some limitations need to be acknowledged in our study. First, the findings are specific to the prediction of breast cancer recurrence and may not be directly applicable to other predictive modelling tasks. Additionally, the scope of the study is limited only to a cohort of patients from the CHU hospital in Liege, which is not representative of a wider population. Therefore, generalization to other populations, patient demographics, and healthcare settings is uncertain. It would be of great interest to extend this study to a variety of centers to verify if the results obtained are generalizable to other cohorts of patients. Furthermore, only five ML algorithms were analyzed in this study, and the applicability of the results to other algorithms remains unexplored. Moreover, in the field of survival analysis, there are methods such as models based on Cox proportional hazards that offer greater granularity in predictions regarding the time of recurrence. Although binary classifiers such as those used in this study have the potential to achieve greater predictive accuracy and can be useful in situations where a quick and clear decision is critical, they lack the interpretability and flexibility provided by modeling the event probabilities as a function of time [42]. Future research should explore the implementation and comparison of time-sensitive methods for the prediction of breast cancer recurrence. Finally, this study was based on a single hyperparameter selection method, and exploring a broader range of methods could provide additional insights. Future work should compare the search efficiency and overall ML model performance using different hyperparameter selection methods, such as random search, Bayesian optimization, or hyperparameter search using genetic algorithms. Furthermore, it would also be interesting to investigate the interactions among different hyperparameters, and how these affect the selection process and the ability to find optimal values. Understanding how hyperparameters influence each other could provide valuable information to improve the efficiency and efficacy of automatic selection methods.
# Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink
## 6 Finite Element Simulations and Verification
### The Free Vibration, Simulated by ANSYS Software
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Frequency** & **Amplitude** & **Amplitude Error** \\ \hline One-fourth point, neglecting & \(21.43\) Hz & \(/\) \(4.82\times 10^{-3}\) m & \(/\) \(180.23\%\) \\ One-fourth point, considering & \(11.83\) Hz, \(23.67\) Hz & \(1.18\times 10^{-3}\) m, \(1.91\times 10^{-3}\) m & \(-11.32\%,-11.05\%\) \\ One-fourth point (ANSYS) & \(9.29\) Hz, \(25.21\) Hz & \(1.06\times 10^{-3}\) m, \(1.72\times 10^{-3}\) m & \(/\) \\ One-eighth point, neglecting & \(21.43\) Hz & \(/\) \(1.64\times 10^{-3}\) m & \(/\) \(112\%\) \\ One-eighth point, considering & \(11.83\) Hz, \(23.67\) Hz & \(0.35\times 10^{-3}\) m, \(0.65\times 10^{-3}\) m & \(0,-15.58\%\) \\ One-eighth point (ANSYS) & \(9.29\) Hz, \(25.21\) Hz & \(0.35\times 10^{-3}\) m, \(0.77\times 10^{-3}\) m & \(/\) \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of forced vibration with 1 st-order excitation frequency, calculated by ANSYS software (version number:2.2.1) and harmonic balance method, when nonlinear energy sink is at \(1/2\) point of beam. The amplitudes of the one-eighth point of the beam obtained by the ANSYS software (version number:2.2.1) are 0.35 \(\times\) 10\({}^{-3}\) m and 0.65 \(\times\) 10\({}^{-3}\) m. It can be seen that the errors are 0% and \(-\)15.58% when the weight is considered, and the error is 112% when the weight is neglected. Obviously, the results calculated by the harmonic balance method when the weight is considered are closer to the results calculated by the ANSYS software (version number:2.2.1) when the nonlinear energy sink is placed at the half point of the beam.
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
## 4 Results and Discussion  
### Measurement of Terahertz Refractive Index  
To measure the T-ray parameter which indicated the material properties, the THz pulse was obtained from the Shim stock films and GFRP composites in the reflection mode. Figure 5 clearly shows the time difference (\(\Delta t\)) between the surface and the back of the GFRP composites in the reflection mode. GFRP was used for the specimen, and the thickness of specimen was approximately 3.0 mm. The time difference (\(\Delta t\)), which was obtained according to the thickness of the specimen, was 42 ps. Therefore, the optical time difference was calculated using the reflection mode, which is a measurement technique used to obtain the refractive index. The optimal time difference was calculated using Equation (4). In addition, the Shim stock films, GFRP composites, PMMA, and fused quartz specimens were measured under the reflection mode method, as shown in Table 1. When the results were compared with those from the previous references, only a difference within \(\pm\)1% was found [1,6].  
Here, the reflection mode measurement techniques of the terahertz were performed in one direction, and experiments were carried out considering various aspects. In addition, since the measurement methods and the characteristics of the GFRP composites and Shim stock films were different, it was difficult to compare them with the previous data.
# Characterization and Prediction of Air Transport Delays in China
## 5 Effect of Weather on Delay Dynamics
### Statistical Analysis
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Airport** & **Temperature** & **Wind Speed** & **Rain** & **Visibility** \\ \hline ZBAA & \(11.07\to 135.6\) & \(7.88\to 5.72\) & \(15.9\to 35.2\) & \(0.69\to 0.00\) \\ ZSPD & \(11.84\to 120.6\) & \(1.09\to 2.95\) & \(8.58\to 19.8\) & \(3.14\to 2.95\) \\ ZGGG & \(12.16\to 112.5\) & \(0.13\to 0.00\) & \(38.4\to 29.3\) & \(4.56\to 0.17\) \\ ZPPP & \(15.24\to 116.9\) & \(0.00\to 35.0\) & \(4.23\to 52.2\) & \(7.05\to 1.56\) \\ ZGSZ & \(0.229\to 83.72\) & \(14.3\to 1.56\) & \(17.1\to 17.4\) & \(0.07\to 7.31\) \\ ZUUU & \(2.119\to 112.5\) & \(0.88\to 5.72\) & \(0.19\to 13.2\) & \(5.23\to 26.0\) \\ ZLYY & \(5.517\to 94.01\) & \(0.02\to 14.9\) & \(1.41\to 5.02\) & \(10.1\to 10.4\) \\ ZUCK & \(25.18\to 122.8\) & \(2.15\to 0.70\) & \(5.79\to 0.00\) & \(8.57\to 29.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evolution of the weather-delay relationships. Each cell reports the evolution of the \(\chi^{2}\) statistics of a test assessing the presence of a relationship between high delays and a meteorological factor, for a given pair airport-metric, from the first year of the data set (May 2016 to April 2017, left side of the arrow) to the second year (May 2017 to April 2018, right side of the arrow).
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 2 Materials and Methods
### Implementation of Anthropomorphic Phantom Images  
To observe the impact of bilateral filters in clinical scenarios, we obtained anthropomorphic images with scan parameters as shown in Table 2. As before, the bilateral filter was run for 5 iterations on the head anthropomorphic phantom image. The noise was measured using a circular ROI with a radius of 10 mm in the frontal lobe area (Figure 5a). In terms of spatial resolution, we used visual observations on the visible areas of soft tissue, bone, and air (Figure 5b). Comparisons using structural similarity (SSIM) were also carried out to get a more comprehensive description. SSIM was obtained by comparing the filtered image with the original image.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Scanner & Toshiba Alexion \\ Tube current (mAs) & 100 \\ Tube voltage (kVp) & 120 \\ Slice thickness (mm) & 7 \\ Scan option & Helical \\ Pitch & 1.5 \\ Convolution kernel & FC13 \\ Image reconstruction & Filtered-back projection \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scan parameters on anthropomorphic phantoms.  
Figure 4: A homogeneous water phantom image on a tube current of 77 mAs filtered repeatedly. (**a**) Original image, filtered image iteratively in (**b**) 1, (**c**) 2, (**d**) 3, (**e**) 4, and (**f**) 5 times.
## 3 Risk Factors for Developing Secondary Osteoporosis  
### Immobilization and Secondary Osteoporosis  
Immobilization is normal in children who are wheelchair-dependent. Cerebral palsy and neuromuscular disorders, such as Duchenne muscular dystrophy, are examples of diseases with immobilization. However, also severe neurodevelopmental disorders and spina bifida are frequent causes of immobilization.  
The degree of immobilization in children with cerebral palsy is classified with the Gross Motor Functional Classification Scale (GMFCS) in five levels [24]. GMFCS level 4 means that a child can walk indoors for short distances with assistance but relies on a wheelchair outdoors. Children with GMFCS level 5 are wheelchair dependent. Immobilization in children with cerebral palsy reduces biomechanical bone loading, leading to thinner long bones and less trabecular bone formation [25]. In children aged 2-19 years with moderate to severe cerebral palsy, classified as GMFCS level 3-5, low BMD was found in 97% of children unable to stand and older than 9 years. This leads to reduced periosteal apposition in lower extremity bones, reducing cortical thickness. Consequently, fractures occur most commonly in the distal femur and tibia. Fractures occurred in 26% of children who were older than 10 years [26]. Other factors that contributed to low BMD (z score \(\leq-2.0\)) were feeding difficulty and the use of anticonvulsants. Other studies showed an incidence of fractures of 4% per year [27].  
The major role that immobilization plays in secondary osteoporosis and low BMD is also illustrated by boys with Duchenne muscular dystrophy. In the era preceding treatment with glucocorticoids, [PERSON] and [PERSON] found that BMD was only slightly decreased (z-score lumbar spine \(-0.8\)) when the boys were ambulatory but decreased significantly after the loss of ambulation (z-score lumbar spine \(-1.6\)) [28]. This was also shown by [PERSON] et al. in boys with Duchenne muscular dystrophy treated with glucocorticoids who became non-ambulant [29]. They showed that 44% of the boys sustained a fracture. Two-thirds of these fractures involved the lower extremities, and there were no vertebral fractures. Moreover, 44% of the boys who walked with support at the time of fracture never resumed walking after the fracture. In addition, [PERSON] et al. showed an absence of clinical vertebral fractures in glucocorticoid naive boys [30]. In the authors' experience, vertebral fractures can occur in DMD among steroid-naive patients if routine screening is part of the bone health evaluation; however, this occurrence is rarely related to boys with DMD who are receiving glucocorticoid therapy.  
Percinical studies are useful for a more in-depth understanding of the relationship between immobilization and secondary osteoporosis. Animal models may be used to study immobilization and the cellular mechanisms of secondary osteoporosis. A recent systematic review gives an overview of known animal models [31].
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 4 Simulations Results and Analysis
### Simulation Research under Different Road Conditions  
Different road conditions are used in simulations including dry asphalt road, wet asphalt road, and icy road, as shown in Figures 4-6, and changing road surface conditions are shown in Figures 7-9.  
From the simulation results, the vehicle can track the vehicle speed well and the slip ratio is always maintained at the desired value through the adjustment of the adaptive controller regardless of the road surface condition. The braking torque is within the limit value and decreases as the road adhesion coefficient decrease. Thus, the proposed method presents strong robustness for the road surface conditions. Although the stopping time and braking distance are increased along with the  
Figure 8: Vehicle and wheel speed under varying road conditions.  
Figure 6: Braking torque under different road conditions.  
Figure 7: Road adhesion coefficient observer output.  
Figure 9: Slip ratio under varying road conditions.  
decrease of the road adhesion coefficient, the controller exhibits good brake performance. Even on the road with a low adhesion coefficient, the control system is very stable and the wheel cannot be locked.  
In order to verify the effectiveness, an extreme situation is considered in Figures 7-9. In this case, the road condition is constantly changing from 0.8 to 0.3. With the help of the real-time road condition observer and the controller the adaptive feedback regulation to the road disturbance, the brake performance is not affected. Furthermore, better road adaptability of the adaptive controller has also been verified.
## 2 Related Works/Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author and Reference** & **Model** & **Merits** & **Demerits** \\ \hline \multirow{4}{*}{[PERSON] et al. [16]} & \multirow{4}{*}{LSTM and AE} & \(\bullet\) & It can handle longer sequences. & \(\bullet\) & Previously reliable autoencoders frequently learn completely connected networks, which are prone to overfitting. \\  & & \(\bullet\) & It is able to train the data. & \(\bullet\) & Typical DNN and CNN models lead to restricted results. \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & Two different ML models combined with extra tree classifier. & \(\bullet\) & It has high stability and accuracy when handling difficult issues. & \(\bullet\) & It leads to poor generalization of data. \\  & & & & \(\bullet\) & Network intrusion has been generalized. \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{Decision tree twin SVM and hierarchical clustering} & \(\bullet\) & The model has higher speed. & \(\bullet\) & High complexity. \\  & & \(\bullet\) & It is very easy to understand and use. & \(\bullet\) & Consumes more time. \\ \hline \multirow{4}{*}{[PERSON] et al. [19]} & Hybrid attention mechanism combined with enhanced algorithm & \(\bullet\) & The model has faster convergence. & \(\bullet\) & It has high false alarm rate. \\  & & & & \(\bullet\) & Only three datasets lead to generalizing the results and precision accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Different models with their merits and demerits. Many researchers have chosen to develop an automatic IDS method for cyber-attacks. An expert has developed a process by combining LSTM and AE. However, this model had overfitting issues. To detect cyber-attacks accurately, experts introduced two different ML models combined with an extra tree classifier. However, the model needed better data generalization. Likewise, another model named decision tree twin SVM and hierarchical clustering was utilized to identify cyber-attacks. However, this model consumed more time. Another enhanced model, the hybrid attention mechanism, combined with an enhanced algorithm, was employed to detect cyber-attacks. However, the model had a high false alarm rate and it did not perform well. Therefore, to overcome the above-mentioned problems, this research focuses on developing an advanced model for cyber-attack detection.
## 1 Introduction
These challenges include (1) The small size of _Polylepis_ stands limits their detection with freely available satellite data. (2) Accessibility to these forests is complicated, making it challenging to obtain field validation data. (3) The spectral signature of _Polylepis_ is similar to those of other shrub vegetation in the study zone. Given this background, the objective of this study is to (a) Compare classification scenarios at spatial resolutions of 5 and 10 m and evaluate if there is any difference in the efficiency of _Polylepis_ detection based on the spatial resolution of the input data; (b) evaluate the combination of optical, radar, and topographic data for _Polylepis_ stands identification; and (c) identify the minimum variables needed to classify _Polylepis_ stands using a pixel-based approach.
## 2 Related Work
Experimental results in a testbed and real-world deployment show that the network provides reliable and energy-efficient improvement. The autonomous approach is currently a significant focus of researchers, in particular because of its advantages over the other two aforementioned approaches. Accordingly, it is the approach studied in this work.
## 4 Results and Discussion
### Experimental Validation
#### 4.2.2 Sinusoid Track  
The input was then changed to the sinusoidal signal with an amplitude of 20 N and frequency of 0.2 Hz, and the response curve is shown in Figure 17. The data collection rate was 1 sampling/10 ms, and the total number of samples was 900 in 9 s. The error data between the set value and the actual value collected in Figure 17a was calculated and statistically analyzed, and the probability distribution of the 900 error data within \(\pm\)3 N is shown in Figure 17b.  
The sinusoid curve tracking in the experiment was not as smooth as that in the simulation (Figure 13), and the response curve fluctuated up and down around the target curve. Among the three controllers, the BPNN-SP-PID gave the smoothest response and the smallest fluctuation. The maximum error was 1.88 N, and the average error was only 0.68 N. Meanwhile, the maximum errors of the BPNN-PID and PID controllers were both above 2 N, and the average errors reached 0.74 N and 0.98 N, respectively. The force control accuracy was expected to be \(\pm\)1 N in some robot contact operations, such as robot grinding, and BPNN-SP-PID could meet the requirements well.  
According to the histogram of the tracking errors, it can be seen that the error distribution of BPNN-SP-PID was concentrated around zero, showing better precision than the other two controllers. The addition of the neural network and Smith predictor made the control system more robust and quicker, resulting in a reduction of the tracking error.  
Figure 16: Comparison of the step responses of three controllers in the experiment.
## 5 Experimental Setup
### Evaluation Stage and Metrics Applied  
In computer vision, precision and recall serve as prevalent evaluation metrics for object detection [61]. Precision signifies the accuracy of predictions (as per Formula (4)), while recall characterizes the percentage of objects detected by the model (as per Formula (5)).  
During evaluation, accuracy values are computed the using IoU [62] threshold, indicating the extent of overlap between the predicted and ground-truth bounding boxes. Using this value, the algorithm assesses the confidence value of the detected object (as per Formula (6)).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\;, \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\;, \tag{5}\]  
* TP: true positive: correct predictions;
* FP: false positive: incorrect predictions;
* FN: false negative: number of unidentified objects. \[\text{Confidence}=\text{Pr}(\text{Object})*\text{IoU}(\text{truth},\text{ pred}),\] (6)
* Pr (Object): binary value which is equal to 1 if the object is present in the specified cell, and 0 otherwise;  
After the training phase, we conducted a validation process by evaluating every tenth checkpoint file to determine the models' average precision values. The models with the highest precision values were selected. Among the various learning rate techniques tested, the cosine annealing method yielded the most favorable outcomes. The accuracy values reached 92.25% at epoch 89 (day) and 95.15% at epoch 112 (night). We exported the resulting checkpoint files to ONNX (Open Neural Network Exchange) format for deployment with detectNet. Figures 6 and 7 illustrate the relationship between the loss function and accuracy values across epochs, along with the associated learning rate.  
Figure 6: Comparing validation loss and accuracy variation for the daytime model.  
Since the \"jetson-inference\" library calculates only the average precision of the model on each checkpoint file, the recall metric cannot be determined. Thus, after exporting the networks, we evaluated the checkpoint files on the test dataset images to compute the average recall value with Excel and Formula (5). (Predictions with a confidence value of at least 50% were considered true): The average recall values were 50% (day) and 48.87% (night).  
Throughout the validation process, it became evident that employing low-resolution input images (300 \(\times\) 300) led to a substantial information loss, particularly affecting the detection of smaller objects like road signs from a distance (Figure 8). To mitigate this challenge, we opted for a neural network capable of handling larger input images. As the \"jetson-inference\" library lacks support for models designed for larger input images, an alternative approach was pursued to address this limitation.
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 5 Evaluation
### Utility for Audio Applications
#### 5.2.2 Results and Discussion  
Figure 5e,f show the voice signal separated using Audlet_hann and Audlet_gfb, respectively, for \(R_{\text{t}}=4\) and \(\beta=1/6\). The objective quality measures are listed in Table 3. Audio files are available on the companion webpage: [[http://www.kfs.oeaw.ac.at/audletFB](http://www.kfs.oeaw.ac.at/audletFB)]([http://www.kfs.oeaw.ac.at/audletFB](http://www.kfs.oeaw.ac.at/audletFB)). The following observations can be made.  
First, system Audlet_gfb outperformed trev_gfb in most conditions. This demonstrates the role of perfect reconstruction in the quality of sub-channel processing. In other words, using the Audlet framework can improve the reconstruction quality. Note that for \(\beta=1/6\), the performance of trev_gfb improved with increasing \(R_{\text{t}}\) and tended towards the performance of Audlet_gfb. This is due to the decrease in the amount of aliasing with increasing \(R_{\text{t}}\). For trev_gfb and \(R_{\text{t}}=4\), very little aliasing was present and a good performance was achieved despite the approximate reconstruction of trev_gfb.  
Second, the performance of Audlet_hann was comparable to that of Audlet_gfb in almost every measure. Although the filter shape did not play a major role in this particular example, it may have a larger impact in other applications.  
Third, for all configurations, reducing \(\beta\) from 1 to 1/6 generally improved all quality measures. This suggests that, depending on the application, a departure from the human auditory perception may improve signal processing performance. In the present application, for instance, finely tuned filters are required to resolve all harmonics and therefore properly separate the signals.  
Finally, while STFT_hann performed comparably to Audlet_hann at the highest \(R\), the performance of STFT_hann dropped at mid and low redundancies. This suggests that using an auditory frequency scale may improve signal processing performance at low redundancies.