esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Feature Extraction  
In this study, we utilized the TorchAudio library in Python 3.10 to extract features from raw audio waveforms, generating 128-dimensional Mel spectrograms [37; 38] as input to our classification model. The Mel spectrogram is derived by applying a Short-Time Fourier Transform (STFT) to obtain the energy spectrum, followed by mapping the linear frequency scale to the Mel scale using a bank of triangular filters. This non-linear transformation aligns better with the human auditory system, which perceives pitch more accurately at lower frequencies. Compared to raw waveforms or linear spectrograms, the Mel representation preserves structured temporal-frequency features and has been shown to be highly effective in ESC tasks. However, using high-dimensional Mel bands in ESC tasks requires caution. While higher frequency resolution can improve detail retention, it may also introduce redundancy, leading to increased risk of overfitting and greater computational cost. This is especially relevant for compact models like our ResNet-TF, where excessive input dimensionality may hinder convergence. Therefore, we adopt a 128-band setting as a balanced choice, which we further validate in subsequent experiments. To enhance model training stability, the Mel spectrograms are standardized to zero mean and unit variance along each frequency bin. The resulting three-dimensional tensors (temporal \(\times\) frequency \(\times\) channel) are then fed into the deep learning model as the input for urban sound classification.
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Application of the Methodology
#### 2.4.4 Data Imbalance  
After handling missing data, the three articles' research focused on another crucial issue--data imbalance. Prejudices such as the imbalance of classes, where certain classes have fewer samples than others, create havoc on the efficacy of predictive models. This non-uniqueness can result in low-quality models and further rendition of the models to predict specific rare instances. These studies employed appropriate procedures and strategies to deal with this imbalance, enhance the models above, and make the best forecasts.  
The authors integrated two approaches to address the data imbalance issue in the first research [3]. First, they performed upsampling of the minority class by creating more replications of the same class. While this splitting assists in balancing the classes, it may cause over-fitting of the model if used alone. To this end, the authors also employed majority class sample reduction aimed at decreasing the number of samples belonging to the majority class to decrease the latter's impact and obtain a nearly balanced dataset. By using both these techniques, the problem of overrepresentation of the majority class was eliminated, and a better performance was achieved in the case of the minority class.  
Moving to the second research [4], the type of feature missing data was utilized, while for class imbalance, the authors applied SMOTE [4]. This is because SMOTE directly synthesizes new samples from the vicinity of existing minorities, making their number grow immensely. If no SMOTE arguments are provided, the number of observations in minority and majority classes is balanced. These methods enhance the ratio of the classes in the dataset, ensuring higher reliability of the resulting predictive model.  
Finally, like in the second research, the authors of the third research [5] also used SMOTE to address the problem of data imbalance. SMOTE is one of the most used minority class oversampling techniques in the literature [3; 4; 5]. This technique is preferred for generating new synthetic observations, contributing to better balancing classes, and increasing the representativeness of minority classes in datasets.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Free Method Position Sensorless Methods  
To decouple the rotor position from the flux linkage/inductance, some methods without the use of models are proposed for various speed and torque control strategies and without pre-stored flux linkage/inductance.
## 5 Case Study and Results
### Validation Strategy--Design of Experiments
Therefore, from Figure 9, it can be concluded that, in the first scenario, i.e., the \"AS-IS-SITUATION\", the most time spent was on inspection and travelling, as the expert engineer had to travel on site and physically inspect the malfunctioning machine. On the contrary, in the second scenario, i.e., \"PROPOSED METHODOLOGY\", a totally different time distribution has been observed with the most time spent on activities relative to the acquisition of spare parts for the machine repair. In addition to that, it can be concluded that the actual repair time, which is the black portion of the pie graph, in the second scenario is bigger. This observation can be considered normal as, in the second scenario, the maintenance operations have been carried out by a field technician, whereas, in the first scenario, the same operations have been carried out by an expert engineer with solid practical experience on such machines. In Figure 10, a side-by-side comparison of the specific times for the two maintenance scenarios is presented. It is stressed that, with the adoption of the proposed framework, the time for inspection of the machine can be shorter as no travelling time is involved in order to reduce \(MTTR\). However, due to the inexperience of the technician carrying out the maintenance activities, the actual repair operation time is longer, as the expert engineer had to perform additional checks in order to ensure that the repair was successful. Figure 9: Distribution of time AS-IS situation vs. Proposed Methodology.
## 2 Materials and Methods
### Model Description and Improvement
#### 2.1.4 Model Improvement  
The default soil profile in Noah-MP has a depth of 2 m, divided into four layers with thicknesses of 0.1, 0.3, 0.6, and 1.0 m from surface to bottom. This configuration restricts root depth and excludes deep soil water uptake. To better represent deep-rooted plantations, we extended the soil depth to approximately 200 m. The number of hydrologically active soil layers (\(N_{bendrock}\)) was restricted to the actual soil thickness (Figure 1).  
_R. pseudoacacia_ develops fine roots beyond 10 m, with some extending deeper than 20 m (Figure 2a) [34; 35; 42; 59]. About 50% of fine roots (\(D_{50}\)) were distributed in the top 3 m, and the soil depth corresponding to 95% of fine roots (\(D_{95}\)) ranged from 9.4 m to 20.4 m across different sites. The mean \(D_{95}\) value of 12.4 m was adopted as the effective rooting depth in the model. In the dynamic root option, vertical carbon allocation is controlled by parameter \(a\) in Equation (10) (Figure 2b), which determines the exponential distribution of fine roots. Smaller values of \(a\) lead to deeper root penetration, while larger values allocate more biomass to shallow layers.  
When bedrock is introduced into the soil structure, the subsurface runoff options in the Noah-MP model may be unsuitable. We incorporated the subsurface runoff scheme and water table scheme from CLM 5.0 into Noah-MP to calculate lateral subsurface runoff and water table depth. When a saturated soil layer exists within the soil column, the model begins to calculate total lateral subsurface runoff:  
\[q_{drain}=\theta_{\text{ice}}K_{baseflow}\tan(\beta)(z_{b bedrock}-z_{\Delta})^{N_{ baseflow}} \tag{17}\]  
where \(K_{baseflow}\) is an adjustable parameter, \(\beta\) is the topographic slope, \(N_{baseflow}\) equals 1, \(\theta_{ice}\) is the ice impedance factor, and \(z_{\Delta}\) is the water table depth, determined by the first soil layer above the bedrock, where the ratio of actual to saturated soil water content is more than 0.90.  
Figure 1: Determination of hydrologically active soil layers in the improved model.  
Figure 2: Vertical distribution of (**a**) fine roots for _Robinia pseudoacacia_ L. in the Loess Plateau and (**b**) carbon allocation in relation to soil depth in the Noah-MP model. [PERSON] et al. (2022), [PERSON] et al. (2023), [PERSON] et al. (2021) and [PERSON] et al. (2021) match references ([PERSON] et al., 2021; [PERSON] et al., 2021; [PERSON] et al., 2021; [PERSON] et al., 2021).
## 4 Experimental Test Results
### Variable Amplitude Fatigue Loading
#### 4.2.2 Behavior of the C-Jointed Precast FDDP under VAF  
The compressive strengths of concrete cylinders for the NSC used to cast the second precast FDDP specimen with C-joint S4 were 67.78, 64.93, 65.63, and 67.81 MPa, with an average value of 66.54 MPa. The compressive strengths of the concrete cylinder for the UHPFRC used to fill the joints were 147.60, 150.69, and 151.45 MPa, with an average value of 149.91 MPa.  
The first 500,000 fatigue load cycles were performed at a frequency of 2 Hz, then by 100,000 cycles at 1 Hz, and finally by 92,866 cycles at 1 Hz, leading to punching shear failure at a total number of cycles of 692,866. Figure 11c depicts the punching shear failure at the wheel footprint on top of the slab, while Figure 11d depicts the crack pattern at the bottom surface of the slab at failure. A fan-shaped crack pattern was observed at the bottom surface similar to those developed for the slab tested to collapse after passing the CAF loading. However, Figure 11d shows greater concrete spalling along the perimeter on the punching shear plane at the bottom of the slab but only from one side of the closure strip. At failure of S4, a major flexural crack appeared at the wheel load location and extended to the free edge of the short FDDP segment, as depicted in Figure 11d. This led to the conclusion that the failure mode is primarily punching shear combined with flexural failure in the adjacent short FDDP. It should be noted that very few flexural cracks appeared at the bottom of UHPFRC in slab S4 tested under VAF loading when compared to intensive cracks through UHPFRC in slab S3 tested under CAF loading, as depicted in Figure 8d. Slab S4 failed at a jacking load of 495.69 kN and a maximum slab deflection of 40.89 mm. It is worth mentioning that such a failure load is about 2.38 times the CHBDC factored design wheelload. Figure 12 depicts the monotonic load-deflection relationship of the slab after each 100,000 fatigue load cycle. It can be observed that the slope of the curve decreased, leading to a reduction in slab flexural stiffness, with an increase in the number of VAF load cycles.  
Figure 11: Views of punching shear failure of slabs S2, S4, and S6 tested under VAF loading to collapse.
## 3 Developed Equipment and Experimental Setup
### Procedure of Feature Extraction  
Feature extraction proceeded from step 1 to step 4 as shown in Figure 7. First, in step 1, the FRFs were tested for all porcelain insulators using the developed equipment of Figure 3, and input data and response data were collected. The input data is a signal collected from the auto impact hammer, and output data is a signal collected by the MEMS microphone. In step 2, the input data and response data were converted into the frequency domain by FFT, and the FRF data of each porcelain insulator were calculated using Equation (1). Then, each set of FRF data was extracted separately according to the five frequency ranges identified in Figure 6. In step 3, a total of four basic data sets were constructed, including the real and imaginary values of the FRF data and the data not exhibiting a linear trend between the two values. The original data of the FRF waveform has real and imaginary values, which means that a total of four basic data sets for one FRF waveform are constructed. Next, 20 characteristics were derived by calculating area, root mean square (RMS), cross-sectional primary moment, center, and standard deviation using the four basic data sets [29]. Removal of trends implies subtracting the mean or the best fit line from the data (meaning the least squares). Removing the trends from the data allows us to focus our analysis on variations in the data for the trends. Linear trends generally indicate a systematic increase or decrease in data. Trends are meaningful, but removing trends provides better insights in some analysis types. Whether it is appropriate to remove trend effects from the data often depends on the analytical goal. Finally, in step 4, the data calculated according to the frequency range is divided into feature sets A to E and then used as basic data for NCA analysis.  
Figure 6: Division of frequency range for feature extraction.
## 2 Forward Model for O\({}_{2}\)_A_-Band Night Glow Limb Radiation in MLT  
Due to the relative positions of the sun, observation points, and instruments, the modes of satellite observation of the atmosphere can be divided into three types: occultation observation, nadir observation, and limb observation. In limb mode, the detector's line of sight points to the atmosphere and can resolve atmospheric radiation from a thinner layer, that is, limb radiation. By using the simultaneous field of view imaging technique of atmospheric vertical profile, a single observation can be used instead of multiple scanning processes required to observe the radiation at all tangent heights. Therefore, spectral information of multiple height layers can be obtained in a single observation to achieve the function of simultaneous collection of spatial information and spectral information, which has significant advantages in the timeliness of detection [6]. The limb observation model combined with the simultaneous separate field of view imaging technique of atmospheric vertical profile is used to detect the fine spectral structure of O\({}_{2}\)_A_-band night glow, and the temperature information is obtained by spectral analysis. The construction of an accurate forward model is the premise and basis for obtaining the global high-resolution atmospheric temperature structure through space-borne instruments.
## 1 Introduction
The proposed protocol improves the threshold of cluster head selection by considering the remaining energy of the node and the distance between the node and the base station. The cluster heads also integrate multi-hop data transmission in order to avoid overhearing and reduce their energy consumption. This improved protocol can use energy efficiently and prolong the networks lifetime.
# Galerkin's Spectral Method in the Analysis of Antenna Wall Operation
## 3 Radiation Field Analysis of Unrestricted Antenna Array
### Spectral Representation of the Field--The Case of Plane Wave Excitation  
For the case of uniform phase modulation of the antenna excitation, i.e., excitation by an incident plane wave, we obtain the following expressions for the field distribution in the antenna plane:  
- The incident field:  
\[\begin{array}{l}\underline{E}_{t}^{s}(x,y,0)=\underline{u}_{t}^{i}exp \left(i\underline{k}_{x}^{i}x+i\underline{k}_{y}^{i}y\right)\sum_{m,n} \underline{E}_{tmn}^{i}(x,y,0)=\\ \sum_{m,n}\underline{E}_{t00}^{i}(x-x_{m},y-y_{n},0)exp\left(im\underline{ \phi}_{x}^{i}+in_{y}^{i}\right)\end{array} \tag{68}\]where  
\[\underline{E}_{tmn}^{i}(x,y,0)=h_{mn}(x,y)\underline{n}_{t}^{i}exp[i\Big{(}k_{x }^{i}x+k_{y}^{i}y\Big{)}] \tag{69}\]  
The stimulating field (\(U_{mn}=-\big{[}1+\Gamma^{i}\big{]}\equiv U=const.\)):  
\[\begin{array}{l}\underline{E}_{t}^{e}(x,y,0)=-U\underline{E}_{t}^{i}(x,y,0)= -U\underline{n}_{t}^{i}exp[i\Big{(}k_{x}^{i}x+k_{y}^{i}y\Big{)}]=\sum_{m,n} \underline{E}_{tmn}^{e}(x,y,0)=\\ -U\underline{\sum}_{m,n}\underline{E}_{t00}^{i}(x-x_{m},y-y_{n},0)exp\Big{(} im\varnothing_{x}^{i}+in_{y}^{i}\Big{)}\end{array} \tag{70}\]  
where  
\[\underline{E}_{tmn}^{e}(x,y,0)=-U\underline{n}_{mn}(x,y)\underline{n}^{i}exp [i\Big{(}k_{x}^{i}x+k_{y}^{i}y\Big{)}] \tag{71}\]  
The scattered field:  
\[\underline{E}_{t}^{s}(x,y,0)=\sum_{m,n}\underline{E}_{tmn}^{s}(x,y,0) \tag{72}\]  
where  
\[\underline{E}_{tmn}^{s}(x,y,0)=\underline{E}_{t}^{s}(x,y,0)h_{mn}(x,y) \tag{73}\]  
The total area:  
\[\underline{E}_{t}(x,y,0)=\sum_{m,n}\underline{E}_{tmn}(x,y,0)=\sum_{m,n} \underline{E}_{tmn}^{e}(x,y,0)+\underline{E}_{tmn}^{s}(x,y,0) \tag{74}\]  
where  
\[\underline{E}_{tmn}(x,y,0)=\underline{E}_{t}(x,y,0)h_{mn}(x,y). \tag{75}\]  
In addition, the following condition is met on the surface of the metal planar elements of the antenna:  
\[\begin{array}{l}A(x,y)\underline{E}_{t}^{s}(x,y,0)=UA(x,y)\underline{E}_{t}^ {i}(x,y,0)=U\underline{\sum}_{m,n}a_{m,n}(x,y)\underline{n}_{t}^{i}exp\Big{(} ik_{x}^{i}x+\mathrm{i}k_{y}^{i}y\Big{)}=\\ \sum_{m,n}a_{m,n}(x,y)\underline{E}_{tmn}^{s}exp(x,y,0)=U\underline{\sum}_{m, n}\underline{E}_{t00}^{i}a_{oo}(x-x_{m},y-y_{n},0)exp\Big{(}im\varnothing_{x}^{i} +in_{y}^{i}\Big{)}\end{array} \tag{76}\]  
where  
\[a_{mn}(x,y)\underline{E}_{tmn}^{s}(x,y,0)=-Ua_{mn}(x,y)\underline{n}_{t}^{i} exp\big{[}i\Big{(}k_{x}^{i}x+k_{y}^{i}y\Big{)}\big{]} \tag{77}\]  
\[a_{mn}(x,y)\underline{E}_{tmn}(x,y,0)=0 \tag{78}\]
## 4 Discussion
### Use and Application  
Due to the poor fit of XRF in situ to AAS results, using XRF as a quantitative tool for predicting in situ concentrations is limited. However, XRF can be used as a qualitative screening tool for established risk criteria, and then the conversion factor of 1.7 can be applied to samples analyzed ex situ to predict AAS results. This enables estimates of mercury concentrations in sieved soil, which is more appropriate when estimating risk related to human exposures. Any soils confirmed to exceed risk criteria can then be considered for intervention and/or remediation. By using XRF to pre-screen, sampling efforts can be focused on areas with elevated levels of mercury that are of high-risk to the population. Focusing sampling efforts in this manner saves time and laboratory resources.  
The cost of the Niton(tm) XL3t hand-held XRF GOLDD+ analyzer, which can analyze nearly an unlimited number of soil samples, is roughly equivalent to an average laboratoryanalytical cost of 220 soil samples. By using XRF and the conversion factor, SHL and MOH will have the ability to save both time and money, allowing more resources for screening programs and human health intervention to address the elevated NCD rates in the Batken region [21].  
In addition, to providing SHL with an XRF mercury conversion factor, the presented methodology can be used to determine mercury site-specific conversion factors at other sites. Since conversion factors are site-specific, the methodology must be repeated for each location. Soil sampling protocols may deviate from the presented methodology to incorporate local sampling procedures.
## 8 Discussion
### Recommendations
#### 8.2.2 Manufacturing with High Precision  
One of the most important goals of Industry 4.0 is to eliminate the need for human intervention in industrial processes through the use of automatic control systems and communications technology. For high-precision manufacturing, this results in extremely high dependability (on the order of 1-109) and extremely low latency (0.1-1 ms round-trip time) [396]. Aside from that, industrial control networks demand real-time data transfer and excellent determinism, which translates into extremely low delay jitter, on the order of 1 ns, which is translated into extremely low delay jitter.
## 2 Background and Motivation  
It appears that HAP or near-space vehicle can provide an optimal platform for passive BiSAR due to the following reasons [9, 10, 21]:HAP includes free-floater and maneuvering vehicle, but this paper considers only the free-floater. Free-floater has extremely small radar cross section (RCS), making it relatively invulnerable to most traditional track and location methods. At this altitude the acquisition and tracking will be a difficulty since few weapons are designed to engage a target with a very low RCS. Even if the acquisition and location problems are resolved, HAP-borne sensors are still difficult to be destroyed. Free-floater can be manufactured in two basic types: super-pressure and zero-pressure. Super-pressure free-floater is inflated and sealed like a child's helium balloon. Zero-pressure free-floater has a venting system that ensures the pressure inside the balloon is same as the surrounding atmosphere and thus it is less vulnerable to puncture.  
HAP operates in the altitude above the troposphere and atmosphere region where most weather occurs. There are no clouds, thunderstorms and precipitation in this altitude. Free-floater can stay at a specific near-space site almost indefinitely to provide a stationary observation. Maneuvering vehicle can station-keep over a specific position and fly at a high speed. Moreover, HAP can use some propulsion scheme to overcome unusual winds.  
HAP is much closer to the desired targets than its orbital cousins. Distance is critical to receiving low power signals. Consider a point at nadir, HAPs are 10-20 times closer to the targets than a typical 400 km LEO satellite. This distance differential implies that HAP radar could detect much weaker signals (10 dB to 13 dB weaker). Moreover, the footprint covered by a HAP is also satisfactory for regional remote sensing.  
The last advantage is low cost. The inherent simplicity, recoverability, relative lack of requirement for complex infrastructures, and lack of space-hardening requirements all contribute this strong advantage.
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
###### Abstract  
This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria--namely, being recent, high-quality XAI application articles published in English--and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI's expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems.  
explainable artificial intelligence; applications; interpretable machine learning; convolutional neural network; deep learning; post-hoc explanations; model-agnostic explanations +
Footnote †: journal: Medical Imaging  
1 Faculty of Information Technology, University of Jyvaskyla, F.O. Box 35, FI-40014 Jyvaskyla, Finland 1  
2 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; vili.podgorelec@um.si