esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[36] experimentally investigated churning losses of a single spiral level gear and then improved an analytical model of churning losses that introduced the dynamic immersion depth, a similar quasi-analytical model that estimates the churning losses of a spiral level gear considering the influence of windage effect to change the oil-immersed depth ([PERSON] et al. [37]). Aiming at an intermediate gearbox of helicopters under splash lubrication, the CFD method was also employed to predict the energy dissipation and oil liquid flow property [38; 39; 40]. Two other meshless methods, the smoothed particle hydrodynamics and moving particle semi-implicit methods, were also used to address the churning behavior of spiral level gears [41; 42]. In terms of windage loss of spiral level gears, [PERSON] et al. [43; 44] concluded that the shroud geometry causes significant variation in windage-resisting torque levels for an isolated spiral level gear by leveraging the CFD method. [PERSON] et al. [45; 46; 47] numerically and theoretically investigated the windage phenomenon of bevel gears and suggested that the presence of a shroud can  
Figure 1: Rotating parts including disc, spur gear, straight level gear, and orthogonal face gear. significantly reduce losses by approximately 80% compared to an unshrouded gear. Several other researchers conducted related work [48; 49; 50; 51]. Several previous studies concentrated on no-load power losses of orthogonal face gears. [PERSON] et al. [52] conducted CFD simulations on the windage behavior of an isolated shrouded or unshrouded face gear and found that the clearance of the gear with the shroud is crucially important to power losses. [PERSON] and [PERSON] [53] developed an analytical approach for the estimation of churning losses of a dip-lubricated face gear considering the wind effect. Given the above literature, there are still some issues regarding the no-load power losses of various wheels, especially concerning their essential difference and similarities in terms of windage and churning behaviors. Accordingly, we aimed to carry out a series of experiments and analyses of a family of typical wheels. For this purpose, a family of disc, spur gear, straight bevel gear, and orthogonal face gear were lubricated and tested on an ad hoc test bench. For splash-lubrication tests, oil depth and rotational speed were defined as operating condition parameters to measure their influence. Likewise, the related theoretical formulas of the no-load power losses as the sum of the windage and churning losses are also presented. Comparable qualitative and quantitative analyses of windage-churning behavior for various wheels are further provided.
## 4 Experiments and Results
### EEG-TCNet with Different Bandpass Filter Parameters  
In sub-project 2, EEG-TCNet with baseline hyperparameters is trained with different orders of the filter and the minimum attenuations required in the stop band of Chebyshev type 2, described in Table 6. The parameters \(N\) and \(rs\) are defined in cheby2 of SciPy API for Python (3.12.5).  
After training 45 experiments, a fixed result and a variable result are selected. With regard to the fixed result, the best Chebyshev type 2 parameters \(N=7,rs=40\) for all 9 subjects are selected based on the average accuracy. Regarding the variable result, the best Chebyshev type 2 parameters \(N\), \(rs\) for each subject are selected. Table 7 summarises the variable result.  
\begin{table}
\begin{tabular}{c c} \hline \(N\) & 4, 5, 6, 7, 8 \\ \hline \(rs\) & 20, 30, 40, 50, 60. 70, 80, 90, 100 \\ \hline \end{tabular}
\end{table}
Table 6: Experiment EEG-TCNet with different parameters of Chebyshev type 2 filter.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** \\ \hline \(N\) & 5 & 6 & 5 & 7 & 7 & 6 & 6 & 4 & 8 \\ \(rs\) & 80 & 20 & 100 & 40 & 40 & 90 & 20 & 60 & 80 \\ \hline \end{tabular}
\end{table}
Table 7: Variable bandpass filter of EEG-TCNet.  
Figure 9: The column chart shows the accuracy between the EEG-TCNet baseline model, fixed model, and variable model.  
Table 8 compares the accuracy of 9 subjects and the average accuracy between baseline EEG-TCNet, EEG-TCNet with a fixed bandpass filter, and EEG-Net with a variable bandpass filter. The performance of EEG-TCNet decreased after using Chebyshev type 2 in preprocessing in both a fixed bandpass filter and a variable bandpass filter. The detailed accuracy of 9 subjects is in Table 10.  
Figure 10 shows a comparison of accuracies between baseline EEG-TCNet, EEG-TCNet with the best bandpass filter parameters (fixed bandpass filter), and EEG-TCNet with the best bandpass filter parameters of each subject from different trainings. This experiment aims to research the impact of different bandpass filter parameters on EEG-TCNet. Figure 10 shows that the bandpass filter does not improve the performance of EEG-TCNet.
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 3 Radio Channel Characteristics and Models
### Multipath Small-Scale Fading  
It is well known that indoor radio channel characteristics are strongly influenced by the specific features of the environment such as building layout, construction materials, building types, furniture arrangements, movements of occupants, etc. [8; 10; 19; 20]. Major propagation mechanisms of radio signals (i.e., electromagnetic waves) include reflection, diffraction, and scattering. In indoor environments, the radio signal generated by a transmitter may result in many plane waves arriving at a receiver with different time delays and signal attenuation because the received plane waves have traveled along different paths and undergone different combinations of reflection, diffraction, and scattering effects [8; 19]. Such a phenomenon is known as multipath propagation and the plane waves arriving at the receiver along different paths are known as multipath signals.  
Multipath signals arriving at a receiver are combined at the receiver antenna, i.e., the signal acquired by the receiver is a vector sum of the multipath signals, each of which has random amplitude and phase as well as randomly shifted frequency if the Doppler effect needs to be considered [8]. Thus, signal strength of the received signal is random in nature, depending on the distribution of the random amplitudes and phases of multipath signals. As a result, when receiver moves over a small distance, the multipath signals arriving at the new location may be dramatically different from that at the original location, making the instantaneous RSS measurements fluctuate significantly over space. In addition,even when the receiver remains stationary, movements of the objects in the surrounding environment can cause random changes in the multipath signals arriving at the receiver and thus can cause random fluctuations in RSS measurements over time.  
The rapid fluctuation of RSS measurements over short travel distance (on the order of signal wavelength) or short time duration (on the order of seconds) is known as multipath small-scale fading [8; 19]. RSS measurements may vary as much as 30 to 40 dB due to the small-scale fading effects when the receiver is moved by only a fraction of a wavelength [8]. Multipath small-scale fading introduces random fluctuations to RSS measurements so that it has detrimental effects on both of the ranging and location fingerprinting approaches for localization applications. Multipath small-scale fading effects are typically removed by averaging multiple measurements collected over a small span in the time and/or space domains [8; 19]. In practice, radio transceiver systems can implement various diversity combining techniques to reduce or remove the multipath small-scale fading effects. In essence, diversity techniques exploit the random nature of radio propagation channels by combining measurements from independent or at least uncorrelated signal paths [8; 20].
## 4 Experimental Validation on Public Datasets  
### Dataset Introduction  
The experimental data utilized in this paper were derived from the FEMTO-ST rolling bearing full-life accelerated degradation dataset [21], which was provided by the IEEE PHM 2012 Data Challenge. This dataset was collected using the bearing accelerated degradation PRONOSTIA experimental platform, as depicted in Figure 7, and encompassed the degradation processes of rolling bearings under three distinct operating conditions. The data acquisition interval was set at 10 s; the data were continuously acquired, with a sampling frequency of 25.6 kHz and a sampling duration of 0.1 s. Detailed dataset information is presented in Table 1. The bearing was considered to be completely degraded and in a failure state when the amplitude of the vibration signal, recorded by the acceleration sensor, exceeded 20 g. To validate the accuracy of the method proposed in this paper, data exhibiting relatively stable signals were selected from the dataset: under operating Condition No. 1, samples 1-1, 1-2, and 1-3 were designated as the training set, while samples 1-4, 1-5, and 1-6 served as the test set. Under operating Condition No. 2, samples 2-3, 2-4, and 2-6 were chosen as the training set, and samples 2-2, 2-5, and 2-7 were utilized as the test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating** & **Rotational Speed** & **Radial** & \\
**Condition Number** & **(r/min)** & **Force/N** & \\ \hline
1 & 1800 & 4000 & bearing 1-1–bearing 1-7 \\
2 & 1650 & 4200 & bearing 2-1–bearing 2-7 \\
3 & 1500 & 5000 & bearing 3-1–bearing 3-7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rolling bearing dataset description.  
Figure 7: PRONOSTIA platform.
## 7 Results and Discussion  
As previously reported in [25], the sun's angular speed as seen from Earth is maximal during the spring and autumn equinoxes (angular speed = 15.00\({}^{\circ}\)/hour) and minimal during the summer and winter solstices (angular speed = 13.76\({}^{\circ}\)/hour). Figure 9 shows the reflection of the north (N) heliostat (see Figure 8) in the target, from 11:30 to 12:30 LCT (with intervals of 6 min), on 22 September (autumn equinox) and 20 June (summer solstice). The heliostat is fixed and aligned with the target center for 12:00, and the image shows  
Figure 9: Radiation collected at the target screen (represented in a blue–red scale accounting for minimum–maximum intensity) after the reflection of the N heliostat (see Figures 7 and 8), calculated with 10\({}^{8}\) ray tracing events. The square (2.0 m \(\times\) 2.0 m) heliostat is fixed and aligned with the target center at 12:00 LCT. The radiation successively received in the target from 11:30 to 12:30 LCT, every 6 min, on 22 September (equinox) and 20 June (solstice) at Protaras is superimposed to illustrate how quickly the collected radiation is drifting away. The results for Julich are similar as the sun’s angular speed is the same.  
that the reflection image quickly drifts away at different times. As the sun travels west, the reflection moves east. The results are almost identical for Protaras and Julich, as the angular speed is the same; only the sun's trajectory is different.  
These results show that the heliostats' orientation must be updated at least once a minute to maintain a reasonably stable thermal distribution over the target. On the other hand, updating the heliostats' orientation too often might result in unnecessary wear on the mechanical rotation components. To simulate realistic conditions, the heliostats' orientation was updated every 30 s (updating the heliostats' orientation every second increases the total rotation for the entire year by less than 0.002%, so the accuracy is unaffected).
## 2 Methods and Data
### Basic Data  
The training data for the ML model are referred to as basic data in this section. Each data point consists of a stack of images constructed by a geometry model developed by [PERSON] et al. [32] and [PERSON] et al. [33]. They developed a fiber model of the micro-structure of Toray 090 GDL. Their studies are based on the stochastic analysis of real fibers. In order to achieve the porosity of the real material, they added binder material, again using a stochastic approach. The binder model of [PERSON] et al. [32] is represented by a width \(b_{r}\) of binder covering parts of the fibers [32]. Figure 2 illustrates this approach in selected images.  
The representation of a GDL section by images of size 130 \(\times\) 512 \(\times\) 512 was shown to be the stochastic equivalent to real data, validated using X-ray tomographs from the BESSY synchrotron in Berlin [32]. The underlying image resolution is 1.5 \(\upmu\)m/px, leading to a GDL section of 768 \(\upmu\)m by 768 \(\upmu\)m with a thickness of 195 \(\upmu\)m. The data in Figure 2A-D were published by [PERSON] et al. [26], both uncompressed and compressed. The total number of available data points is given by  
* the number of the realizations of the fiber geometry: 25;
* the number of parameters used for different binder distributions: 4;
* the number of compression levels, where six steps from 0% (uncompressed) to 50%--in steps of 10%--were used.  
The compression levels were chosen according to the particular way of representing the artificial structures by five identical image layers by [PERSON] et al. [26]. A compression step of 10% was implemented by merging two images of adjacent fiber layers, taking every second fiber layer interface. This procedure was repeated five times. In this way, a total number of \(25\cdot 4\cdot 6=600\) geometries could be available. It was already stated by [PERSON] et al. [26] that not all of the LB simulations did converge. This was especially the case for the higher compressed micro-structures. From this reason, only 541 data points were available for training.  
Based on [PERSON] et al. [32], the underlying fibers were also the subject of a high-accuracy LB simulation by [PERSON] and [PERSON] [34]. They used periodic boundary conditions and a circular cross-section of the fibers, whereas in this work, wall boundary conditions were used, with coarser mesh based on a quadratic cross-section of the fibers. The reason was to obtain a better comparison with real data from X-ray tomography [32; 33].
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.6 Hardware Complexity  
IBSN sensor nodes have much less resources than conventional sensor networks due to their strict size restrictions and bio-compatibility. In such cases, introducing additional hardware only to increase the performance of wireless communication is not an option. However, with the advent of nano-technology and system-on-chip design, the addition of minimal hardware components, such as a wake-radio has been made possible without increasing the total surface area of the chip itself [14]. Although the addition of minimal hardware is a viable option, it will have consequences in terms of energy consumption and may even affect performance of the wireless communication.
# Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis
## 3 Hierarchical Time-Dependent FMEA Model
### Separation of a Loss Function  
The loss can be considered as the point whether the FC is detected before the SF or not; (i) detected before an SF and (ii) detected after an SF. In general, if an FC is found and repaired before an SF occurs, there will be no additional loss due to the elapsed time from the time of the occurrence of the cause to the time of the detection of the cause, other than the cost of repairing the cause. Therefore, in case (i), the cost of repairing the SSF can be set to a constant value \(\alpha\). On the other hand, if the FC is searched after the SF, as in case (ii), the opportunity cost will be incurred due to the production interruption, due to the system inoperability. Since this will be proportional to the system downtime, \(s-t_{0}\) (\(s\) is the detection time and \(t_{0}\) is the SF time), we set the variable cost to \(\theta(s-t_{0})\) according to the system downtime. In addition, the fixed cost will be added to repair the SSF and SF. If we set the repair cost of SF be \(\beta\), the repair cost of SSF and SF would be \(\alpha+\beta\).  
Now, we separate the loss function in two functions such that \(L_{1}(s,\ t_{0})\) and \(L_{2}(s,\ t_{0})\). First, we set \(L_{1}(s,\ t_{0})\) be the repair cost of the SSF, which is trivially a constant \(\alpha\) regardless of the detection time. Second, we set \(L_{2}(s,\ t_{0})\) to be the repair cost of the SF and the opportunity cost of the system downtime. If the detection time, \(s\), is before the SF time, \(t_{0}\), \(L_{2}(s,\ t_{0})\) is 0. Otherwise, \(L_{2}(s,\ t_{0})\) is the sum of the repair cost of SF, \(\beta\), and the opportunity cost, \(\theta(s-t_{0})\), of system downtime. Thus, the loss function, \(L(s,\ t_{0})\), is expressed as follows:  
\[L(s,\ t_{0})=\ L_{1}(s,\ t_{0})+L_{2}(s,\ t_{0}), \tag{1}\]  
where  
\(s\) is the detection time of the FC (\(n+1^{\text{th}}\) level); and  
\(t_{0}\) is the failure time of the system (\(0^{\text{th}}\) level),  
with  
\[L_{1}(s,\ t_{0})=\ \alpha, \tag{2}\]  
\[L_{2}(s,\ t_{0})=\ \left\{\begin{array}{ll}0,&s<t_{0}\\ \beta+\theta(s,\ t_{0}),&s\geq t_{0}\end{array}\right.. \tag{3}\]
## 4 Validation and Results
### Silvi Marina Test Case
Finally, among the runs that assimilate only one variable, WIND and ZTD_GNSS3h_1 ist have the highest number of best scores at low threshold for both IFS and GFS while, as already argued, ZTD_GNSS3h_1 ist guarantees the best performances for higher thresholds. Also the ZTD_INSAR experiment improves the forecast, as indicated by the high number of improved indices with respect to the OL (Tables A7-A9), in particular concerning the rainfall pattern location. This confirms the importance of water vapour data for rainfall events corresponding to convective equilibrium conditions (type I). The substantial difference of the InSAR and GNSS derived ZTD observations is in the spatio-temporal resolution, namely the InSAR ZTD is characterized by high spatial and low temporal resolution, while the opposite holds for the GNSS ZTD signals. Despite the assimilation of both InSAR and GNSS derived ZTD has an added value, the fact that this event is long-lived can be the reason why a constant update of the ZTD every 3 h from GNSS during the entire event allowed to obtain the best result with respect to a single update performed with the Sentinel observations. Furthermore, in this test case both the OL simulations have already a very good performance, thus it is more difficult to obtain a significant improvement from data assimilation. Figure 13: GFS-driven cases. QPF values for the 12 h time period from 18 UTC on 9 September 2017 to 06 UTC on 10 September 2017, where a = SST, b = SM, c = WIND, d = ZTD_INSAR, e = ZTD_GNSS3h_1 ist, f = WIND+SM+ZTD_INSAR_GNSS3h_1 ist, g = OBS, h = OL. For details about the 3 DVAR approaches the reader is referred to the main text.
## 1 Introduction
In these types of medical applications, where the results obtained will significantly affect the patient's life, explainability and interpretability will increase the confidence in the correctness of the decision. ML and DL models are supported by explainable artificial intelligence (XAI), which reveals which features or which regions of the image are effective in the decision made regarding an image, and, thus, transparency and understandability of the decisions are ensured [13; 14]. In this way, medical experts understand the reason for the decisions made. This enables the model to be used as an aid in making medical diagnoses with increased confidence in its results. For classifying brain tumors, a novel hybrid model is proposed in this paper. The main contributions of this study are as follows:  
1. By fusing the strengths of deep learning models for feature extraction from images and machine learning for classification, a CNN + XGBoost hybrid model has been developed for multi-class brain tumor classification. 2. The study incorporates XAI techniques to improve the interpretability of the results. This makes it possible to comprehend the decision-making process of the model in greater detail and offers insightful information about the variables affecting the classification results. 3. A reliable and accurate model that can be used by medical professionals is provided, both by performing high-accuracy multi-classification and by providing visual reasons for the model outputs. 4. A comprehensive evaluation showing the ability of the proposed model to effectively classify brain tumors and the interpretability of results through XAI is provided. Thiscontributes to the growing medical image classification field by offering a robust and explainable model. The paper is structured as follows. For classifying brain tumors, related work in the literature is presented in Section 2. Our proposed methodology, including the details of the CNN architecture, XGBoost algorithm, and Grad-CAM method is described in Section 3. Section 4 offers the results and its performance comparison with other models. Section 5 discusses the general conclusions of the paper.
## 3 Proposed Method
### Convolutional Neural Network (CNN)  
A convolutional neural network (CNN) was used as the backbone model in this study. The advantages of CNN include feature extraction ability, spatial hierarchy, and scale invariance. The convolutional layer extracts facial features automatically and trains the model based on facial features. The facial feature is a multiple spatial hierarchy including local features (e.g., ear, eyes) and global features. A CNN model can extract a spatial hierarchy by multiple convolution layers and pooling layers and allow the model to understand images better. Due to the different sizes in input images, our system must  
Figure 8: AdaBoost diagram for Haar-like feature selection.  
possess the attribute of scale invariance. Multi-layers of convolutional kernels in the CNN with varying field sizes enable the processing of features at different scales, thereby achieving scale invariance. Because of the three attributes of a CNN, we ultimately selected the CNN model as the primary network for achieving facial expression recognition.  
Our CNN model was composed of four convolution layers, four pooling layers, a flattened layer, and two fully connected layers. The overall architecture is shown in Figure 9. The filters slide the input image and emphasize object edges to realize the extraction effect of facial features in the convolution layer. The output example of the convolution layer is illustrated in Figure 10. We employed max pooling as the model's pooling method because it can preserve high-frequency features more effectively than average pooling can. Additionally, it reduces the sensitivity of the convolutional layers to edges, thus achieving the goals of feature preservation, dimension reduction, and a decrease in the number of learnable parameters. In the flattened layer, facial features are mapped into a one-dimensional vector and fed into the fully connected layer as input. The flattened layer diagram is illustrated in Figure 11. In the fully connected layer, features are integrated, and calculated probabilities correspond to seven classes of emotions. Lastly, expression recognition was conducted through _softmax_. Cross-entropy loss was computed to facilitate subsequent model training and evaluation. The _softmax_ and cross-entropy functions were defined by Equations (4) and (5), respectively.  
\[f(s)_{i}=\frac{e^{s_{i}}}{\sum\limits_{j}^{C}e^{s_{j}}} \tag{4}\]  
\[L=-\sum\limits_{i}^{C}t_{i}log(f(s)_{i}) \tag{5}\]  
Here, \(C\) represents the number of neurons in the fully connected layer, \(s_{j}\) represents the values of each neuron, and \(s_{i}\) denotes the value obtained for the \(i\)-th class.  
Figure 10: Convolution layer output.  
Figure 9: Architecture of the proposed CNN.