esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Performance Baseline and Metric Audit
#### 6.12.2 Datasets  
The datasets in Table 14 reveal a concerning pattern in this research group. While these papers make valuable contributions by connecting performance drift to understandable explanations, their use of public datasets is inconsistent. Only two of the four studies clearly reference accessible datasets, while the other two do not provide clear data sources. This lack of transparency creates several problems. For example, [PERSON] et al. [57] analyze changes in clinical data related to a new antibiotic flag, but without access to their dataset, other researchers cannot verify whether their selected features truly explain the observed drift. Similarly, [PERSON] et al. [29] show how data leakage affects accuracy by comparing different train/test splits, but without shared data, it is hard to know whether their solutions work beyond their specific case. These gaps risk reducing potentially useful research to theoretical ideas rather than practical tools. The publicly available datasets that are used, like BPIC process logs or MIMIC-II clinical data, focus heavily on healthcare and business operations. While these areas are important due to their strict performance requirements, this narrow focus raises questions about whether these methods would work equally well in other fields, like finance or social media, where data behaves differently. The limited variety of datasets may hide potential weaknesses in the methods.
# Advanced Metal Matrix Nanocomposites
## 5 Strengthening Mechanisms
### Orowan  
Orowan strengthening is based on hard particles that are distributed as homogeneously as possible in the matrix. These particles hinder the smooth movement of dislocations through the matrix on its slip planes. Typically, this effect can be neglected for particles with a diameter >1 \(\mu\)m [145]. When the dislocations interact with a particle, they first bend around the particle. Furthermore, the dislocations on the other side reconnect with themselves and form a dislocation loop around the particle which lead to an increase in strength. The dislocation itself can then continue to propagate through the matrix. The increase in strength is inversely proportional to the interparticle distance; therefore, the smaller the particles are, the higher the strength increase that can be achieved with the same proportion by volume or weight of particles. Equation (8) describes the contribution to the strength increase by Orowan strengthening [146] and Equation (9) describes the geometric description of the interparticle distance \(\lambda\)[147]. It is b the Burgers vector, \(G_{\rm m}\) the shear modulus, \(d_{\rm p}\) the average diameter of the nanoparticles, \(V_{\rm p}\) the Volume fraction of nanoparticles.  
\[\Delta\sigma_{\rm OR}=\frac{0.13\rm{b}G_{\rm m}}{\lambda}\ln\frac{d_{\rm p}}{2 \rm{b}} \tag{8}\]  
where  
\[\lambda=d_{\rm p}\left[\left(\frac{1}{2V_{\rm p}}\right)^{\frac{1}{3}}-1\right] \tag{9}\]  
AZ91D reinforced with 1 wt% AlN nanoparticles has been produced using an ultrasound-assisted casting process wherein the yield strength of the two materials with and without AlN nanoparticles is 144 and 100 MPa, respectively; [PERSON] et al. [26] attributing this 44% increase to Orowan strengthening and calculated its theoretical contribution to be 35.8 MPa, whereas the missing 8.2 MPa is explained by a small grain refining effect of the nanoparticles.  
A composite material based on an Mg-2 Zn alloy with an SiC nanoparticle content of 14 vol% [137] was obtained via ultrasound-assisted casting of an Mg-6 Zn alloy with a SiC particle content of 1 vol% SiC followed by evaporation of Mg and Zn. Orowan strengthening as a component of the increase in yield strength is calculated to be 113 MPa. The high value is a result of the small interparticle distance of such a high volume fraction of SiC nanoparticles.
## 4 Methodology
### Video Processing  
The features of the visual data were gathered from multiple different classical and machine learning (ML)-based methods for real-time classification, enabling the capture of various activities, identification of regions of particular interest, and the extraction of additional information, notably for motion activity monitoring and different nursing activities. Our semantic segmentation methodology relies on the fact that distinct motion patterns and areas gain prominence in different behavioral states of the neonate. For instance, key regions include the infant's abdominal region, where respiratory activity is observable, and the infant's face, where open eyes are visible during periods of wakefulness. In this study, four such ROIs were defined and segmented, as visualized in Figure 2.  
To elucidate our rationale for selecting and extracting features from these aforementioned ROIs, first we elaborate on our criteria for ROI selection. Our motivation relies on the observation that abdominal movements often correlate with respiration, a vital physiological signal in discriminating sleep-wakefulness stages. Accordingly, we have employed a UNET architecture for the segmentation of the infant's abdominal region in images. It is worth noting that this method may encounter limitations when dealing with draped infants. However, this challenge is mitigated by our supplementary motion-based approach for identifying respiratory movement locations. Once the abdominal region is segmented, we can easily identify the surrounding area within a specified distance from the abdomen, a region of particular importance, as it may encompass the infant's limbs, depending on the ring's parameter settings. Consequently, we can obtain insights into limb movements independently of respiration. Finally, the outermost area, farther from the neonate's body, serves to suppress external events.  
Figure 1: An overview of the proposed behavioral state classification algorithm. (**a**) ROIs are detected on each frame, coming from the video feed, and \(1\)-dimensional motion-related features are extracted from the detected areas. (**b**) PRV statistics are generated and combined. (**c**) Then, principal component analysis is performed on the extracted feature set. (**d**) The classification is done hierarchically, where the first classifier checks whether the input feature set belongs to the deep sleep class (Block 1). If not, then the 2 nd, 3 rd, 4 th binary classifiers check whether it belongs to the light sleep (Block 2), drowsy (Block 3), or awake (Block 4) classes, respectively, step-by-step.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 7 Conclusions  
This paper proposed a deep learning method for estimating the Wiener filter for speech enhancement. Following the classical speech enhancement processing framework, this paper evaluated two spectral domain speech estimator algorithms: LSA and OMLSA. The obtained results indicated that despite LSA introducing very low distortion, the best trade-off among the speech enhancement, the signal distortion, and the objective quality metrics (PESQ, STOI, CBAK, COVID) was for the system based on OMLSA with \(Gmin=0.0562\). Further studies on the combination of the DL-based Wiener filter estimation and other speech estimator algorithms would be analyzed in the next steps.  
The method was evaluated for speech enhancement on a simulated noisy speech database and compared with state-of-the-art methods. Results showed that the proposal improves the statistical-based OMLSA, providing it with a robust version that accurately performs in simulated and real speech data. Regarding the state of the art, the proposal achieved better or similar performance. However, it proposes a better-suited formulation for a deep learning solution. This consists of directly targeting the estimation of the Wiener filter without the use of compensations for avoiding practical issues with the training convergence or the accuracy of the estimation. On the other side, the comparison with the state of the art shows that more sophisticated deep learning networks could produce more accurate estimation results, such as multi-head attention mechanisms and transformers. Audio examples of the enhancement performed in simulated and real noisy speech are available ([[http://dayanaribas.vivolab.es/DEMOenhancement/index.html](http://dayanaribas.vivolab.es/DEMOenhancement/index.html)]([http://dayanaribas.vivolab.es/DEMOenhancement/index.html](http://dayanaribas.vivolab.es/DEMOenhancement/index.html)) (accessed on 1 September 2022)).  
[PERSON] designed the SE systems and performed the set of experiments, took an important role in the analysis of the results, and she wrote the manuscript. [PERSON] designed and implemented the deep structured network. [PERSON] and [PERSON] helped to revise the manuscript and approved it for publication. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Grant 101007666; in part by MCIN / AEI/10.13039/501100011033 and by the European Union \"NextGenerationEU\"/PTRR under Grants PDC2021-120846-C41 and PID20201-126061 OB-C44, and in part by the Government of Aragon (Grant Group T36_20R).  
Figure 5: Quality metrics (PESQ, STOI, CBAK and COVID) for enhanced noisy speech averaging throughout all noises.
# A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
## 5 Conclusions  
Power control is an important and complex problem, and merely reducing sending power cannot perfectly control interference and achieve optimal capacity. In this article, the novel algorithm, DCGAPC, was proposed. The method fully considered the channel capacity, network radius, and the optimization of the transmitting power of the nodes. It used the DCGAPC layer design model, power control module including SNR computing, interference calculation, and power control. The modeling indicated that MAXPCA has the maximum capacity and the lowest energy efficiency, and MINPCA has optimal energy efficiency, the lowest capacity, and the largest delay. However, it has been shown that the DCGAPC algorithm suggested in this article can regulate sending power via the condition of the channels. Therefore, its interference is lower than both the MAXPCA and MINPAC methods.  
[PERSON] designed the measurement scheme, carried out the simulations, and wrote the paper; [PERSON] supervised the work, arranged the architecture, and contributed to the writing of the paper; and [PERSON], [PERSON], and [PERSON] performed the field test and analyzed the data.  
Figure 10: Comparison of energy efficiency in the three algorithms.  
Figure 9: Capacity comparison of the three algorithms under different service loads.  
**Funding:** This research was financially supported by the Project for the National Natural Science Foundation of China (61402069), the 2017 Project for the Natural Science Foundation of Liaoning province (20170540059), and the General project of the National Social Science Fund (2019 AG00482).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Designed 1D CNN-BiLSTM-AdaBoost Model
### Detailed Model Description  
#### 2.2.1 Design of the 1D CNN  
The core components of the 1D CNN include convolutional layers, pooling layers, flattening layers, and fully connected layers [20]. The basic principle is illustrated in Figure 2. In the figure, T represents the length of the input data time window, and \(V\) denotes the number of variables across all sampled points. The convolution kernel \(f\) slides across a specific dimension of the time domain to extract dynamic features, enabling the extraction of high-level abstract feature representations from each convolutional layer. The feature \(\mathbf{h}_{j}^{l}\in\mathbf{R}^{c_{1\times 1}}\) can be expressed as follows:  
\[\mathbf{h}_{j}^{l}=\sigma(\mathbf{H}^{l-1}*\mathbf{f}_{j}^{l}+\mathbf{b}_{j}^{ l}) \tag{1}\]  
where \(\mathbf{h}_{j}^{l}\) refers to the \(j_{th}\) feature at the \(l_{th}\) layer; \(\mathbf{H}^{l-1}=\left[\mathbf{h}_{1}^{l-1},\mathbf{h}_{2}^{l-1},\cdots, \mathbf{h}_{N}^{l-1}\right]\in\mathbf{R}^{C_{l-1}\times N_{l-1}}\) represents the \(l-1_{th}\) convolutional layer; \(\mathbf{f}_{j}^{l}\) is the \(j_{th}\) convolution kernel at the \(l_{th}\) layer; \(b_{j}^{l}\) represents the network bias; \(C\) is the feature dimension; \(N\) indicates the number of  
Figure 1: Structure of the 1D CNN-BiLSTM-AdaBoost model.  
convolution kernels; \(\sigma(\cdot)\) is the nonlinear activation function; and \(*\) denotes the convolution operation.  
The 1D CNN layer is adjusted through multiple iterations, ultimately selecting three different convolution kernel sizes (the convolutional layer group consists of three convolutional layers). The convolutional layers use ReLU as the activation function. The pooling layer reduces the input data size while retaining essential feature information, using max pooling. The dropout layer is employed to mitigate overfitting of neurons. Through experimentation, the dropout probability is set to 0.5.  
The flattening layer transforms the local features from the three convolutional layers into a single vector by merging and integrating all relevant elements. These features are then passed to the BiLSTM model and subsequently to the fully connected layer for further processing.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 5 Satellite Precipitation Products  
The development of satellite SPPs is sensor-dependent, particularly those within the GPM constellation. During the initial phase, most products (e.g., GPCP and CMAP) are released on coarse spatiotemporal resolutions (2.5\({}^{\circ}\), monthly). The launch of the TRMM and the improvements in satellite sensors thereafter have enabled the quasi-real-time generation of high-resolution (e.g., better than 0.25\({}^{\circ}\), 3 h) global precipitation datasets. The High-Resolution Precipitation Products (HRPs) such as TMPA, CMORPH, GSMaP, PERSIANN, and IMERG were subsequently generated. Currently, precipitation data are available at various spatiotemporal resolutions to cater to diverse user requirements. Figure 3 provides an overview of the major satellite precipitation products currently accessible.
# Grain Sieve Loss Fuzzy Control System in Rice Combine Harvesters
## Material and Methods
### Hardware and Software System of the Test Bench  
The hardware circuit of the control system mainly comprises the power circuit, working parameters' knob-adjusting circuit, the displacement sensor signal-acquiring circuit, the motor revolution speed-controlling circuit and grain sieve loss, fan revolution speed, the return plate, and the sieve vibration frequency-acquisition circuit. The EPLAN 8.23 software (EPLAN, [PERSON], Germany) was used to design the corresponding circuit, integrate the above-mentioned circuits, and connect the electrical components according to the input/output (I/O) distribution to construct the hardware system. The software system is composed of the Human-Machine Interface (HMI) and the lower computer program. We selected the SIEMENS S7-1200 PLC, SM1231 Al8 analog input module, SM122 DQ16 digital output module, CM1241 RS485 communication module, frequency converter G120C (SIEMENS, Munich, Germany), and some transmitters to build the lower computer hardware system. Ladder language was utilized to complete the system compilation. The HMI was programmed by LabVIEW 14.0 software (National Instruments, Austin, TX, USA) to complete the information display and storage functions, and send instructions to the lower computer. Modbus-TCP communication protocol was utilized to fulfill the task of information exchange between the lower computer system and the HMI. The control strategy can be programmed in LabVIEW 14.0; once the control strategy is activated, the inherent algorithm selects the current grain sieve loss and current working parameters as input, and the target value of the ideal working parameters can be calculated out. Then, they were transferred into the lower computer system through communication protocol to finish process of adjusting the working parameters. The designed hardware structure of the operating monitoring and control system is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Range** \\ \hline Length of return plate/mm & 900/1100/1300 \\ Return plate vibration frequency/HZ & 4–9 \\ Sieve vibration frequency/HZ & 4–9 \\ Fan speed/rpm & 0–1500 \\ Grain auger speed/rpm & 800 \\ Tail auger speed/rpm & 1000 \\ Sieve opening/mm & 20–30 \\ Guide plate I angle/\({}^{\circ}\) & 8–45 \\ Guide plate II angle/\({}^{\circ}\) & 13–45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adjustment range of the working parameters.  
Figure 3: Working principle of the sieve-opening adjusting system. 1. Displacement sensor, 2. Push rod, 3. Direction converter, 4. Joint bearing, 5. Connector, 6. Active sieve.
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
## 3 Sierpinski Resonators
So far, resonance frequencies close to the desired ones were obtained with the help of electromagnetic simulations and experimental verifications. In this case, the lengths of the edges for the triangles were 4 mm and 6 mm, respectively, corresponding to resonances around 26 GHz and 20 GHz approx.
# Neural Multivariate Grey Model and Its Applications
###### Abstract  
For time series forecasting, multivariate grey models are excellent at handling incomplete or vague information. The GM(1, N) model represents this group of models and has been widely used in various fields. However, constructing a meaningful GM(1, N) model is challenging due to its more complex structure compared to the construction of the univariate grey model GM(1, 1). Typically, fitting and prediction errors of GM(1, N) are not ideal in practical applications, which limits the application of the model. This study presents the neural ordinary differential equation multivariate grey model (NMGM), a new multivariate grey model that aims to enhance the precision of multivariate grey models. NMGM employs a novel whitening equation with neural ordinary differential equations, showcasing higher predictive accuracy and broader applicability than previous models. It can more effectively learn features from various data samples. In experimental validation, our novel model is first used to predict China's per capita energy consumption, and it performed best in both the test and validation sets, with mean absolute percentage errors (MAPEs) of 0.2537% and 0.7381%, respectively. The optimal results for the compared models are 0.5298% and 1.106%. Then, our model predicts China's total renewable energy with lower mean absolute percentage errors (MAPEs) of 0.9566% and 0.7896% for the test and validation sets, respectively. The leading outcomes for the competing models are 1.0188% and 1.1493%. The outcomes demonstrate that this novel model exhibits a higher performance than other models.  
GM(1, N); neural ordinary differential equations; adjoint sensitivity algorithm; energy prediction
# Model-Free Method for Damage Localization of Grid Structure
## 2 Basic Principle of Structural Damage Localization Based on Displacement Difference  
There are many displacement-based damage identification methods [18; 19; 20], but none of them can explain the physical connection between the position of damage and the displacement. Generally, considering a structure with \(n\) degrees of freedom, the displacement under the action of static loads should satisfy the following equation:  
\[Ku=l \tag{1}\]  
where \(K(n\times n)\) is the overall stiffness matrix of the undamaged structure and \(u\) is the displacement vector under the static load vector \(l\). Equation (1) can be expressed as:  
\[u=Fl \tag{2}\]  
where \(F\) is the flexibility matrix of the undamaged structure, that is, \(F=K^{-1}\). Structural damage can result in a decrease in the stiffness and increase in flexibility. The model matrix of the undamaged structure is related to the model matrix of the damaged structure as follows:  
\[F_{d}=F+\Delta F \tag{3}\]  
\[K_{d}=K-\Delta K \tag{4}\]  
The displacement \(u_{d}\) produced after structural damage can be expressed as:  
\[u_{d}=F_{d}l \tag{5}\]  
Therefore, the displacement change \(\Delta u\) can be expressed as:  
\[\Delta u=u_{d}-u=\Delta Fl=\left[(K-\Delta K)^{-1}-K^{-1}\right] \tag{6}\]In order to further develop Equation (6), the following SMW formula is used:  
\[\left(K+XY^{T}\right)^{-1}=K^{-1}-K^{-1}X\left(I+Y^{T}K^{-1}X\right)^{-1}Y^{T}K^{-1} \tag{7}\]  
\(\Delta K=\Delta Kl^{T}\), using Equation (7) to simplify Equation (6) can obtain Equation (8):  
\[\Delta u=K^{-1}\Delta K\left(I-K^{-1}\Delta K\right)^{-1}K^{-1}l \tag{8}\]  
In order to express the relationship between the change in displacement and damages, Equation (8) can be rewritten as:  
\[\Delta u=\delta d_{i} \tag{9}\]  
\[d_{i}=K^{-1}\Delta K \tag{10}\]  
\[\delta=\left(I-K^{-1}\Delta K\right)^{-1}K^{-1}l \tag{11}\]  
The physical meaning of Equation (9) is very important. This equation indicates that displacement change \(\Delta u\) of the structure before and after damage was a linear combination of characteristic displacements. According to Equation (10), the characteristic displacement \(di\) was obtained by applying the non-zero column vector in \(\Delta K\) as a static load to the structure, and these non-zero column vectors in \(\Delta K\) can be defined as characteristic forces. The characteristic force and characteristic displacement are different for different structure types. The characteristic forces and displacements of the beam structure and rigid frame structure have been discussed in detail in literature [17]. In the following chapters, we will discuss the characteristic force and displacement of grid structures in detail and obtain the basic principle of the damage localization of the grid structure.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.5 Hydraulic Conductivity Zone  
The aquifer hydraulic conductivity is variable with the aquifer types. Multiple polygon zones were used to define the hydraulic conductivity. Since there is no reliable recorded data of hydraulic conductivity data in the area, it was estimated during calibration with the PEST automatic parameter estimation tool for the zones created based on geo-morphology of the area.