esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Alternative Training Approach
#### 5.3.2 Combined Subjects Approach  
This approach consists of training only one CNN with examples from all of the subjects for each different trial length and then testing each subject individually on the trained CNN. This approach allows one to decrease the number of CNN to train in order to obtain the average accuracy rate for a single CNN model and therefore the time needed to analyze the results. Using this approach also means that the number of examples for training and testing will increase by the number of subjects. A major drawback of this approach is that subjects who fail to recognize the irregular auditory cues will produce examples that do not contain the P300 even if they are labeled otherwise, negatively affecting the CNN performance. For a single CNN model considering data from all subjects, the average time spent on training was about 32 min for the structures with one convolution layer and approximately 39 for those with two layers.  
The model that exhibited the best performance in Section 4 was chosen to be tested using also the previously explained combined subjects approach to determine if it offered better performance than that of the currently used approach. Table 4 shows the comparison between the results for the single subject and combined subjects approaches for each subject considering the CNN structure with the overall highest accuracy rate (one layer, convolution patch \([4\times 1]\), pool patch \([3\times 2]\)). The difference in the mean accuracy rate is about 6%, in favor of the single-subject approach. If subjects are compared in terms of the two approaches, the combined subjects approach is better only in one out of the nine cases. Subject 9, which produced the lowest accuracy in the single-subject approach, benefited slightly from the combined subjects approach. In the eight cases in which the single-subject approach obtains better results, the accuracy rates between both approaches varies between 0% and 11%, depending on the subject. Figure 6 shows the receiver operating characteristic (ROC) curves for each of the nine subjects for the CNN model with the highest accuracy under the single-subject approach. These curves can serve to better understand the results presented in Table 4 for such an approach.  
Although there is a substantial difference between both approaches of about five times in terms of the amount of time each one required to produce the mean accuracy, the single-subject approach offered better results.  
Figure 6: Receiver operating characteristic (ROC) curves of each subject for the CNN model with one layer, convolution patch \([4\times 1]\) and pool patch \([3\times 2]\) using the single-subject approach.
# Distributed Dynamic Load Identification of Beam Structures Using a Bayesian Method
## 3 Numerical Simulation  
In the previous section, it can be seen that the identification method established in this paper based on the Bayesian method can identify the distributed loads using a recognition system different from the traditional Tikhonov regularization.  
In this section, the numerical examples are presented to verify the proposed method in the frequency domain. The simply supported beam is selected as the simulation model. The simple and complicated distributed loads are all considered in order to verify the feasibility. Simultaneously, the simulation examples with low-level noise and high-level noise are presented, respectively. We employ various error descriptions to evaluate the identification results, e.g., relative error (RE), peak relative error (PRE), and average relative error (ARE), as shown in Equation (37). The identification errors are listed in the tables, including the proposed method, the Tikhonov regularization with L_curve method, and the Tikhonov regularization with generalized cross-validation (GCV) method. This proves the superiority of the method proposed in this paper.  
\[RE=\frac{\left\|f_{idt}-f_{true}\right\|}{\left\|f_{true}\right\|},PRE=\max \left|\frac{f_{idt}-f_{true}}{f_{true}}\right| \tag{37}\] \[ARE=mean\left|\frac{f_{idt}-f_{true}}{f_{true}}\right|,Error= \left|\frac{f_{idt}-f_{true}}{f_{true}}\right|\]  
Figure 2: Schematic diagram of the distributed dynamic load identification.  
The distributed dynamic load \(f(x,t)\) acting on the simply supported beam shown in Figure 1 is given by Equation (38), and the beam parameters are given in Table 1.  
\[f(x,t)=A(x)\sin(\omega_{0}t+\beta(x)) \tag{38}\]  
where \(A(x)\) and \(\beta(x)\) are the amplitude and phase of the distributed load, respectively, while \(\omega_{0}\) is the excitation frequency. We define \(A(x)=\sqrt{\left(F^{r}(x)\right)^{2}+\left(F^{i}(x)\right)^{2}}\), \(\beta(x)=\arctan(\frac{F^{i}(x)}{F^{r}(x)})\). Here, \(F^{r}(x),F^{i}(x)\) are fitted by Legendre orthogonal polynomials.
# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### Utilization of Multilevel Information of Medical Images
#### 4.2.1 3D Convolution  
Medical 3D images are defined on 3D grids, which can have different sizes according to the body parts and resolution of the imaging. The grid size is \(\mathrm{w}\times\mathrm{h}\times\mathrm{d}\), representing the width, height, and depth of the 3D image. Each 3D point on the mesh is called a voxel [81]. Common medical images have 3D information, but when using a single image for medical tasks, it will lose its multidimensional information [82]. As shown in Figure 8, when another convolution kernel adds a dimension (for example, a simple window), the model can also obtain its spatial scene information so that there are scanning results on the x, y, and z axes. [PERSON] et al. [40] used a 3D CNN to directly guide the objective function of training in the lower and upper layers of the network to achieve end-to-end training while fully connecting conditional random fields. Under the training of the 3D depth supervision mechanism, they used the SLiver07 dataset to achieve VOE5.42. [PERSON] et al. [39] proposed H-DenseUNet, which is a collection of 2D DenseNet and 3D DenseNet. Two-dimensional DenseNet is used to effectively extract features within slices to reduce computing costs. Three-dimensional DenseNet context aggregation is used to make full use of spatial information. Under the 3 DIRCADb dataset, the accuracy rate reaches 93.7%. [PERSON] et al. [26] introduced an independent neural network (VINN) by modifying the first layer of the U-Net network structure encoder and the last layer of the decoder. In addition, FastSurf VIN is proposed to solve the problem of resolution independence. It supports 0.7-1.0 mm whole brain  
Figure 7: DenseNet: The arrows in the figure represent the transmission of medical image feature information in DenseNet. The structured information at the lower level of the medical image can be transferred to the higher level through shortcuts.  
segmentation. Under the ABIDE I dataset and compared with the original FastSurf CNN network [20], using FastSurf VIN can achieve an average improvement of 1.46% DSC and 10.06% ASD. [PERSON] et al. [74] proposed a lightweight V-Net (LV Net) for liver segmentation. Its structure is similar to that of 3D U-Net. When tested on the LiTS dataset, the dice value reached 95.43%.
## 3 Design and Implementation of the Algorithm  
### Overall Structure  
The self-fitting algorithm is a process of interaction between subjectivity and objectivity. In addition to ensuring the performance of parameter optimization, reducing user fatigue is also highly important. To improve the efficiency of parameter optimization, two improved strategies are proposed: one strategy builds an expert system to initialize and optimize the parameters of the algorithm and the other improves the genetic algorithm to increase the efficiency of the iterations.  
Figure 1: Different fitting processes for hearing aids. (**a**) Traditional fitting process; (**b**) Self-fitting process.  
The overall self-fitting process for hearing aids is shown in Figure 2. The system includes three components: hearing aid test system, patient and self-fitting system. The basic workflow is as follows. Firstly, the input sound is processed by the algorithm in the hearing aid test system, and the processed sound is presented to the user. Secondly, the user evaluates the sound quality according to subjective criteria. Finally, the evaluation results are fed into the self-fitting system for parameter optimization and adjustment, and the new parameters are returned to the hearing aid test system to generate the new sound. As seen in the figure, the self-fitting system is the key, which is composed of three components: user interface, evolution module and expert system.  
The user interface is the only conduit for human-computer communication between the user and the self-fitting system. Its main functions include hearing threshold measure for patients and sound quality evaluation. The hearing threshold measure is implemented based on simplified pure tone audiometry. The basic test procedure is the same as the traditional tone test [26], and the measured tones are 125, 250, 500, 750, 1000, 1500, 2000, 3000, 4000, 6000 and 8000 Hz. After the tone frequencies are tested, an audiogram of the hearing-impaired person is obtained. The sound quality evaluation is used to assess the generated sound and feed the assessment results back to the evolution module. The assessment results for the sound quality have five levels: bad, poor, medium, good and excellent.  
The evolution module is the core of the self-fitting system. Based on the user feedback on the sound quality, the module updates the algorithm parameters based on the proposed evolution algorithm. In this work, the updated parameters are the sub-band gains. Based on these parameters, the evolution module generates the new speech signals and presents them to the user to reassess.  
The expert system analyses the user preferences and calculates the diversity of the population, which forms the corresponding knowledge and feeds it back to the evolution module. Firstly, according to the audiogram and the personal attributes (age, sex and duration of hearing impairment) of the patient, the module initializes or constrains the algorithm parameters to improve algorithm efficiency, and secondly, it interacts with the evolution module to accelerate convergence.
## 3 Results and Discussion
### Possible Practical Recommendations for Engineers  
In practical applications, selecting appropriate materials is key to ensuring the construction quality of cast-in-situ shaft walls. This study recommends using high-strength concrete or substitute mortar with excellent early-stage strength, good flowability, and anti-dispersibility for challenging environments. For example, the A-2 substitute mortar formulation may offer a strong reference for use, and these mix proportions can be adjusted to suit specific conditions like high-viscosity slurry environments.  
Construction techniques would significantly affect the quality of cast-in-situ shaft walls. In slurry environments, advanced vibration methods should be used to reduce air voids, while curing time can be extended to counter slurry infiltration effects, and the design safety margins should consider a strength reduction of about 15-20%, as observed in this study.  
The long-term stress and strain monitoring is crucial for validating concrete or substitute mortar performance in the construction and operation of cast-in-situ shaft walls. Future research should focus on large-scale validation tests of cast-in-situ concrete and advanced monitoring technologies to ensure durability and reliability.
# Reconfigurable Supply Chain Selection: Literature Review, Research Roadmap and New Trends
## 4 New Perspectives for Reconfigurable Supply Chain Selection
### Sustainability  
Environmental, social and economic issues remain a fundamental requirement for supply chain configuration. Although this concept has been widely discussed in the literature for many years, sustainability has acquired a new dimension in recent years due to growing energy problems. Several works emphasize the importance of sustainability in future manufacturing and in the society 5.0. The author of [43] conducted a study on the role of sustainability in Industry 5.0 and its positive impact on supply chains and production systems. To clarify how Industry 5.0 can deliver sustainability values, [PERSON] et al. [44] conducted a content-focused literature synthesis leading to the identification of several functions through which Industry 5.0 can promote sustainability values in terms of resilience, environmental sustainability and human centricity. The author of [45] proposed a framework of Industry 5.0 through the model of the viable supply chain, the reconfigurable supply chain and the human-centered ecosystems, from which sustainable society can be the result of this future industry. In the reconfigurable context, sustainability is becoming important in RMS research [46; 47; 48; 49; 50; 51; 52]. In these works, the authors highlighted the role of RMS in guaranteeing aspects of sustainability. On a larger level, the sustainability of supply chains should be the object of the design parameters of the supply chain configuration, especially in the implementation of reconfigurable supply chains. The challenge is to answer the following questions:  
* How can reconfigurable supply chain contribute to sustainability and improve environmental, social and economic aspects?  
Figure 5: New trends for Supply Chain Configuration in RSC.  
* How to improve the ability of the supply chain to adapt to rapidly changing sustainability requirements?
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Room Acoustic Simulations
#### 3.3.2 Acoustic Properties of Materials in the Church and Calibration of the Model  
The acoustic properties of the various materials in the church needed to be estimated. As an initial step, table values for absorption and scattering from several different sources were used. The full table of material parameters is given in Appendix A.  
Whereas there are many table values for absorption coefficients, scattering coefficients are more difficult to measure and may vary depending on the software used. In ODEON, scattering coefficients for 707 Hz are provided by the user, which are then extrapolated to the full frequency range using a built-in algorithm. For the simulations in this project, table values were primarily used. When these were unavailable, mid-frequency scattering was estimated from the characteristic depth of the structure in question, as discussed in [51].  
To improve the quality of the absorption coefficient estimates, calibration by comparison to modern reverberation time measurements (measured by the integrated impulse response method [52] and a B&K type 2270 analyzer) was performed. As the modern interior of the church differs so significantly from the historical configuration (see, e.g., Figures 1 and 3-5), only a few materials can be calibrated: the vaulted ceilings, the glass windows, and the stone floor. These are assumed to have the same material properties as during the 1470s, whereas the walls which were historically covered by plaster are now bare and can not be calibrated.  
The calibration is performed using ODEON's built-in genetic algorithm optimization tool, in a digital model corresponding to the modern church. This model comprises the exterior shell, modern wooden pews, and the main altar. This corresponds to the conditions under which the modern measurements were performed. The average error of the simulated reverberation time in the calibration model compared to measurements is within \(\pm\)1.4 JND (see in Figure 8 for detailed comparison for each octave band and position). The calibrated material parameters were then used in the historical digital model.  
Measured and simulated \(T_{20}\) in the modern church  
Figure 8: Comparison between simulated and measured \(T_{20}\) values in the modern church. The red triangles show the results of the calibrated simulation, and the black boxes correspond to measurements together with frequency-dependent 1 JND error bars. The results for six different listener–receiver combinations are shown across center octave-band frequencies from 63 to 8000 Hz. For low frequencies, some values could not be extracted from the measurements and only the simulated results are shown. The average error in JND is highest in the 63 Hz and 8000 Hz octave bands with 1.2 and 1.4, respectively; the remaining octave bands have values between 0.2 and 0.7.
## 3 Results and Discussion
### Coagulation/Flocculation and Sedimentation of the C. vulgaris Culture
According to [PERSON] and [PERSON] [10], microalgal flocculation can take place when the pH is higher than 9.0, either through the addition of a strong base or spontaneously (autoflocculation). Moreover, [PERSON] et al. [33] observed that the harvesting efficiency of C. _culgaris_ using a biofocculant (_Moringa_ seed flour) increased with the pH, achieving a maximum of 89% when the pH was increased to 9.2. The higher harvesting efficiencies at higher pH could be associated with the formation of hydroxide-polymer precipitates, which constitutes a sweeping mechanism. According to [PERSON] et al. [35], the precipitate sweeping of microalgae cells leads to cell aggregation, flocculation, and settling. According to [PERSON] et al. [36], green coagulants have a harvesting efficiency of \(>\)70% in acid or basic pH. For them, the greater percentages of biomass recovery depend on the kind of green coagulant and the dose, combined with pH adjustment and longer settling times. Adjustment to either acidic or basic values results in greater efficiency due to the favorable colloidal destabilization  
[PERSON] and [PERSON] [37] reported the ability of _Opuntia focus indica_ to harvest the microalgal biomass of _Chloerella sp_. In their research, they used NaCl (3 N) as an eluent  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Extract** & & & & **Harvesting** & **Harvesting** \\
**Coagulant** & **Eluent** & **(OD\({}_{750\text{ nm}}\))\({}_{1}\)** & **(OD\({}_{750\text{ nm}}\))\({}_{2}\)** & **(OD\({}_{750\text{ nm}}\))\({}_{3}\)** & **Efficiency (\%) after** & **Efficiency (\%) after** \\
**Dosage (g L\({}^{-1}\))** & & & & & **Floculation  
for cactus mucilage and achieved a biomass removal efficiency of approximately 60% of _Chlorella sp_. after 4 h of sedimentation. They also used water as eluent, and the biomass removal efficiency was 40.5 % at the same time as sedimentation. It can be concluded that these eluents were not sufficient for the extraction of the coagulant compounds from the _Opuntia_ species, although [PERSON] et al. [38] obtained better results than these authors when using NaCl as an eluent.
## 3 Results
### Historical Background of Monitoring and Control Systems' Development in Aviation
These systems require sophisticated sensors, actuators, and control algorithms to operate safely without direct human intervention, pushing the boundaries of what is possible in aviation technology. 8. Health monitoring systems and predictive maintenance: As aircraft systems became increasingly complex, the need for sophisticated health monitoring systems became apparent. These systems are designed to continuously assess the condition of an aircraft's critical components, from engines to structural elements, using advanced sensors and data analysis techniques. By monitoring vibration, temperature, and other key indicators, HM systems can identify signs of wear or impending failure in components, allowing for preemptive maintenance actions. 9. Integration of HM systems with health management systems: Health management systems take the data provided by health monitoring systems and integrate them with maintenance databases, operational schedules, and logistic support systems. This integration allows for a holistic approach to aircraft maintenance and operations, optimizing the performance and availability of the aircraft while minimizing downtime and maintenance costs. For example, if a sensor detects an abnormality in an engine's operation, the HMS can automatically schedule a maintenance check, ensuring that the issue is addressed before it leads to a failure. 10. Advanced diagnostics and prognostics: Within the realm of health monitoring and management, advanced diagnostic and prognostic capabilities play a vital role. Diagnostics are concerned with identifying the nature and cause of a problem, while prognostics focus on predicting the time until a system or component will no longer perform its intended function. Together, these capabilities enable operators to not just react to problems but to anticipate them, planning maintenance and replacements in a way that maximally preserves the aircraft's operational availability and safety. 11. AI and machine learning: Looking forward, the integration of AI and machine learning into health monitoring and management promises to further revolutionize these systems. AI and ML can analyze vast amounts of data from various sensors more efficiently than traditional methods, identifying patterns and predicting failures with greater accuracy. This capability will enhance the predictive maintenance strategies, further improving the reliability and safety of aviation operations.
## 5 Conclusions  
This paper proposes two new ensemble methods for supervised classification, CTS and CTS-SR, which are based on confidence and incorporate the \"surprisingly popular\" concept. By using confidence levels instead of direct classification results, we generalize HMTS to a more general case. CTS obtains the prior and posterior probabilities of an event by statistically analyzing the prediction consistency and confidence levels of learners. This allows us to determine whether to recover the correct answers held by a minority group, rather than simply applying majority voting. CTS-SR uses confidence to represent the real-world scenario where the correct answer is known, thereby achieving adherence to the correct decisions of a few experts with lower computational overhead. Experiments demonstrate the applicability of two proposed ensemble methods in various situations, and we explain the reasons behind the results. A key aspect of the CTS algorithm in this  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **\# of Recovery/\# of Test** & **Proportion** \\ \hline Australian & 3/345 & 0.9\% \\ German & 12/500 & 2.4\% \\ spam & 36/2301 & 1.6\% \\ biodeg & 10/528 & 1.9\% \\ magazine & 33/1187 & 2.8\% \\ all-beauty & 5/254 & 2.0\% \\ musk & 19/334 & 5.7\% \\ Hilly & 84/606 & 13.9\% \\ \hline \hline \end{tabular} \# indicates that the value represents a quantity.  
\end{table}
Table 8: Number and proportion of recoveries on each dataset.  
paper is treating the classification confidence of base learners--an inherent attribute--as an additional one-dimensional feature of the samples. This enables the regression model to utilize sample information more comprehensively, leading to significant improvements in the following aspects:  
1. Enhancing feature representation capability: It can capture the uncertainty information of sample classifications under different classifiers, enriching the representation of sample features.
2. Improving the model's robustness: Introducing classification confidence increases model diversity, reduces the risk of overfitting, and enhances robustness.
3. Boosting prediction performance: Classification confidence reflects the consistency of samples under different classifiers. By integrating this information, samples can be predicted more accurately.