esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To the best of our knowledge, this paper presents the first source enumeration approach based on the machine learning algorithm using gaps of eigenvalues. It is shown that our proposed machine learning based clustering approach has fairly good performances, and it also reveals the strong feasibility to improve its performance when the appropriate learning data are sufficiently supported for the designated SNR range. 3. While in most existing literature, the performances of source enumeration approaches are evaluated with predefined fixed parameters (e.g., the number of sources and the arrival angles of the sources), which results in the eigenvalues of the covariance matrix being fixed. In this paper, the performances for the cases with a comprehensive number of sources and arrival angle of the sources are compared in this paper. It is shown that our proposed approaches have comparatively good performances in the various scenario conditions of signal sources. The remainder of this paper is organized as follows: Section 2 surveys related research studies on DOA estimation and source enumeration approaches. Section 3 presents our system model for the source enumeration. In Section 4, two source enumeration approaches based on the gap ratio criterion formula and threshold of eigenvalues gaps are proposed. Analyses through simulations are presented in Section 5, and conclusions are drawn in Section 6.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 5 Review on ECS Methods on WBAN  
The IEEE 802.15.6 standard allows for performing error correction on WBAN communication at the bottom two layers of OSI. After reviewing several error correction approaches that are offered in WBAN in the literature, it is found that the classification of ECS is based on error correction techniques: FEC, HARQ, including HARQ-I and HARQ-II, and modulation order. ECS methods that are introduced in the literature are classified based on use of OSI layer that is displayed on Table 4.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
###### Abstract  
Architectural elements--such as shapes, colors, and lighting--significantly influence how users emotionally respond to spaces. This study addresses the challenge of capturing unconscious and rapid emotional responses by employing a 32-channel electroencephalography (EEG) approach with 40 participants, who viewed multiple images of architectural spaces while real-time brain activity was recorded. Event-related potential (ERP) analysis focusing on N100, N200, P300, and late positive potential confirmed reliable differences in neural signals between preferred and non-preferred stimuli. Two convolutional neural network long short-term memory deep learning models were trained on the EEG data: one using all the ERP segments, and the other focusing on statistically significant ERP features. The first model achieved a high recall but a relatively lower precision, while the second improved accuracy and precision at the expense of recall. These findings suggest real-time, objective measures of users' emotional responses can inform early-stage architectural design and reduce reliance on subjective evaluations. By integrating EEG-based insights into smart architecture or virtual reality simulations, designers may optimize building features to align with user preferences and well-being, contributing to the development of effective and user-centric built environments.  
EEG; event-related potential; convolutional neural network long short-term memory; affective architecture; user preference prediction Article
## 3 Admittance Control for Active Training Mode
### Admittance Control  
Admittance control [21], which can improve the safety, comfort and compliance of rehabilitation training, is widely used in rehabilitation robots [22; 23]. Admittance control, which can establish the relationship between human-machine interactive force and position [24], is described as:  
\[M_{d}(\ddot{x}_{d}-\ddot{x})+B_{d}(\dot{x}_{d}-\dot{x})+K_{d}(x_{d}-x)=f_{ext} \tag{5}\]  
where \(x,\dot{x},\ddot{x}\) represent position, velocity, acceleration, respectively. \(f_{ext}\) is the human-machine interactive force, which is obtained in real time by the force sensor. Additionally, \(K_{d}\) (spring stiffness), \(B_{d}\) (damping), and \(M_{d}\) (mass) refer to the admittance parameters.  
Figure 3: Coordinate frame definition and schematic diagram for kinematic analysis.  
Assuming that the robots runs smoothly during active training, the change of velocity of rehabilitation robot's end-effector will be small and the acceleration term will be omitted. Thus, admittance control was simplified as:  
\[B_{d}(\dot{x}_{d}-\dot{x})+K_{d}(x_{d}-x)=f_{ext} \tag{6}\]  
where \((x,\dot{x})\) is the current state. We can obtain:  
\[\Delta\dot{x}=(f_{ext}+K_{d}(x-x_{d}))B_{d}^{-1} \tag{7}\]  
For the Jacobian \(J(\theta)\), the desired joint velocity \(\dot{\theta_{d}}\) can be obtained as:  
\[\dot{\theta}_{d}=J^{-1}(\dot{x}+\Delta\dot{x}) \tag{8}\]  
\[\theta_{d}=\int\dot{\theta}_{d}dt \tag{9}\]  
Then, the joint angle \(\theta_{d}\), integrated by the angular velocity \(\dot{\theta_{d}}\), can be obtained.  
As shown in Figure 4, the controller is composed of two control loops: the admittance control loop and the position control loop, where \(L\) represents the forward kinematics and \(\mathcal{T}^{-1}\) represents the inverse of Jacobian matrix, and \(B_{d}\),\(K_{d}\) are the admittance parameters. The program is implemented in C++ software (Microsoft Visual C++ 6.0) and the position command \(q_{1}\) and \(q_{2}\) is sent to the steps of each motor for rotation function. The control loop is updated every 20 ms [25].
# Optimization of Pile Driver Frame Based on Sensitivity Analysis
## 5 Results  
After calculation, the variable values, maximum stress, maximum displacement and structural mass of the optimization process can be obtained, as shown in Figure 9. The thickness of the design variables before and after optimization and the rounded thickness after optimization are shown in Table 5. The comparison results of mass, maximum stress and maximum displacement of the structure before and after optimization are shown in Table 6.  
Figure 9: _Cont._  
In practice, design variables are always subject to artificial influences that lead to uncertainty. Uncertainty often makes the optimal solution beyond the feasible range, and it eventually becomes infeasible. Moreover, the complex structure of the pile driver makes it difficult to directly design the dimensions of each design variable, and judge whether the design requirements are satisfied. As shown in Table 5, the values of each variable are well  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Maximum Stress**} & \multicolumn{2}{c}{**Mass**} & \multicolumn{2}{c}{**Maximum Displacement**} \\ \multicolumn{2}{c}{**/Mpa**} & \multicolumn{2}{c}{**/kg**} & \multicolumn{2}{c}{**/m**} \\ \hline Before & After & Before & After & Before & After \\ optimization & optimization & optimization & optimization & optimization \\
110 & 162 & 82,556.1 & 64,282.6 & 0.00201 & 0.00326 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of structural stress and displacement before and after optimization.  
Figure 9: The value of each variable in the optimization. (**a**) The value of R\({}_{1}\); (**b**) the value of R\({}_{3}\); (**c**) the value of R\({}_{4}\); (**d**) the value of R\({}_{5}\); (**e**) the value of R\({}_{6}\); (**f**) the value of stress; (**g**) the value of displacement; (**h**) the value of mass.  
optimized and satisfy the respective value ranges. Therefore, the method of constructing parametric modeling, building a mathematical model, and finding the optimal solution is to satisfy the expected effect of the design.  
As can be seen from Table 6, the maximum stress value of the large body skeleton is 110 MPa under pile compression before optimization and 162 MPa after optimization. With a safety factor of 1.34 and a maximum safe stress value of 175 MPa, the optimized stress value still has a large margin and thus meets the design requirements. The maximum displacement is increased to 3.26 mm before and after optimization, compared to 2.01 mm before optimization. However, there is still a large excess for the stiffness condition for large body structures. It can be seen that the optimized structure satisfies the design requirements. At the same time, the mass of the optimized large body structure was reduced from 82,556.1 kg to 64,282.6 kg before optimization, which is in line with the optimization goal of reducing the structural mass in this paper.
## 2 U-Net and Compressed Sensing
### Principle of CS  
Compressed sensing (CS) is a method of compressed sampling. It realizes data sampling at much lower than the Nyquist sampling frequency [16]. CS requires the signal to be sparse, and the measurements of signal are obtained through sparse and dimensionality reduction of original signal. Measurements can retain most of the information in the original signal with a very small amount of data. Equation (1) expresses the process of obtaining measurement \(\mathbf{y}\in\mathbb{R}^{M}\):  
\[\mathbf{y}=\mathbf{A}\mathbf{x} \tag{1}\]  
where \(\mathbf{x}\in\mathbb{R}^{N}\) is the original signal, \(\mathbf{A}\in\mathbb{R}^{M\times N}\), (\(M<<N\)) is the measurement matrix. The core problem of CS is to reconstruct the original signal \(\mathbf{x}\) from measurements. But it needs to make constraints on \(\mathbf{x}\). The most common one is that the original signal \(\mathbf{x}\) is sparse [16], but it is not the only assumption [21,22]. The original signal \(\mathbf{x}\) can be recovered from measurements by solving the optimization problem through a reconstruction algorithm [26,27] when measurement matrix \(\mathbf{A}\) satisfies the Restricted Isometry Property (RIP). However, the general signal is only approximately sparse, which may degrade the quality of the reconstructed signal. Therefore, much research [21, 22, 23] used neural networks to constrain the signal structure instead of the sparsity constraint. The measurements of signal were used to define loss. These algorithms effectively solved problems caused by signal sparse in CS. At the same time, they proved that measurements can be used as an optimizable target to train the model and improved the quality of signal reconstruction. Based on the method of CS using generative models in [22], without relying on sparsity constraint, we define CS loss function using measurement matrix, clean speech and enhanced speech generated by the proposed U-Net model (detailed in Section 3.3). The aim is to further improve speech enhancement performance.
## 2 Basic Theory
### Basic Theory of SAE
At this time, the loss function is consistent with the traditional AE loss function, and the optimization function is as follows:  
\[(\theta^{*},\theta^{*^{\prime}})=argmin\frac{1}{n}\sum_{i=1}^{n}L(x_{i},\hat{ x}_{i})=argmin\frac{1}{n}\sum_{i=1}^{n}L(x_{i},g_{\theta^{\prime}}[f_{ \theta}\left(\widetilde{x}_{i}\right)]) \tag{6}\]  
The cost function is:  
\[J_{DAE}(W,b)=\frac{1}{m}\sum_{i=1}^{m}\left(\frac{1}{2}\left\|h_{W,b}( \widetilde{x}_{i})-H_{i}\right\|^{2}\right) \tag{7}\]  
where \(h_{W,b}(\widetilde{x}_{i})\) is the activation value of neurons in the hidden layer. The combination of DAE is stacked into a deep learning hierarchical structure, that is, multiple DAE are cascaded to complete the task of feature extraction layer by layer, and representative features with lower dimension are obtained. Its essence is to take the damaged information with noise as the input signal, so that the reconstructed signal has a certain robustness to the noise in the input signal. The stacked denoising autoencoder (SDAE) takes the hidden layer output of each layer as the input of the next layer, and obtains the parameter sum of each layer. In this paper, [PERSON]'s layer by layer greedy learning algorithm is used to construct a SDAE network. The main idea of the algorithm is to train only one layer of the network at a time, that is, to train a DAE with only one hidden layer at a time. When the DAE of this layer reaches the optimization, the next DAE will be trained. Build the three-layer SDAE model, the input layer nodes is \(n\), hidden layer nodes is \(m\), the original input data \(x\) is de-noised according to the proportion coefficient \(\lambda\) to get \(\widetilde{x}\). According to Equations (1)-(6), the hidden layer \(H\) of the first layer is obtained, and the hidden layer output of the first layer network is obtained according to formula (7) of the minimized cost function, that is, the model parameters \(\theta_{1}=(W,b)\) are obtained. According to the layer by layer greedy training algorithm, the hidden layer outputs of the second and third layer networks are obtained in turn. The model parameters \(W\) and \(b\) are obtained by updating the weight once in each iteration using the gradient descent method. The process is as follows:  
1. Let \(\Delta\ W_{1}=0,\Delta\ b_{1}=0\)
2. Calculate \(\
abla_{w(l)}L(x,\hat{x})\), \(\
abla_{b(l)}L(x,\hat{x})\)
3. \(\Delta W_{l}=\Delta W_{l}+\
abla_{w(l)}L(x,\hat{x})\)
4. \(\Delta b_{l}=\Delta b_{l}+\
abla_{b(l)}L(x,\hat{x})\)
5. \(W_{l}=W_{l}-\alpha(\frac{1}{m}\Delta\ W_{l}),b_{l}=b_{l}-\alpha(\frac{1}{m} \Delta\ b_{l})\), where \(\alpha\) is the learning rate.
## 3 Experiment  
### Study Area  
The study area is located in the eastern part of the Ordos Basin, in the southwestern region of Shenmu City, as shown in Figure 2A. During the late Early Ordovician period, the Caledonian orogen caused the overall uplift of the Ordos Basin, exposing the land surface to prolonged weathering and erosion. In the Late Carboniferous Benxi period, the Ordos Basin began to subside gradually, with seawater transgressing from east to west, leading to sediment deposition [31,32]. The Benxi Formation consists of three stratigraphic members, arranged from bottom to top: the Hutian, Pangou, and Jinci Members (Figure 2B) [33]. The Hutian Member corresponds to the lowstand systems tract (LST) and comprises ferruginous-aluminous rocks and black mudstone. The Pangou Member is equivalent to the transgressive systems tract (TST) and is characterized by dark-gray mudstone, Pangou sandstone, limestone, sandstone, and minor coal seams. The Jinci Member, corresponding to the highstand systems tract (HST), contains a thick basal sandstone unit, followed by dark-gray mudstone and shale with thin coal seams, with the stable No. 8 coal seam at the top marking the boundary between the Taiyuan and Benxi Formations.  
In the eastern part of the basin, the Taiyuan Formation developed in delta plain, delta front, and shallow marine subtidal environments. The lithological assemblages associated with these different sedimentary environments, along with the coal seams of the Benxi Formation, form distinct reservoir-seal combinations, such as coal-sandstone, coal-mudstone, and coal-gray mudstone combinations. Their spatial distribution is controlled by the coal lithology of the Benxi Formation and the sedimentary environments of the Taiyuan Formation [34].  
Figure 3 presents the seismic profile of the study area, showing that hor11 marks the top boundary of the Taiyuan Formation, while hor19 represents the zero-crossing point of the 0-phase seismic reflection of the No. 8 coal seam in the Benxi Formation. This can be roughly considered the central position of the No. 8 coal seam in the vertical direction. Regionally, the target layer is the No. 8 coal seam in the Shenmu area, with a burial depth  
Figure 2: Geological background of the study area: (**A**) a map showing the location of the study area; (**B**) a stratigraphic column of the study area.  
exceeding 2200 m. The coal seam thickness ranges from 8 to 12 m, with an average of 9 m, and it gradually thins from northeast to southwest [35]. The coal seam has significant thickness and stable distribution. The dominant seismic data frequency is approximately 40 Hz. The deep burial and low resolution present considerable challenges for coal seam identification and prediction.
###### Abstract  
Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.  
P RISMA; Sentinel-2; early-season crop mapping; machine learning; deep learning Article  
Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases  
16 May 2024  
19 June 2024  
1
## 2 Materials and Methods
### Sensory Analysis  
The sensory evaluation of the layer chip was approved by the Institutional Review Board of Changwon National University (IRB No. 7001066-202211-HR-071). The sensory evaluation (preference test) of RFIS for the layer chip was conducted on 48 participants including undergraduate students, graduate students, and faculty members of the Department of Food Science and Nutrition at Changwon National University using the ranking test method (1 point for the most preferred; 4 points for the least preferred). In addition, the overall acceptance of the samples (the layer chip and RFIS) was evaluated by 13 panelists using a 9-point scale, and 10 of them were then used to conduct a quantitatively descriptive analysis of the samples. The sensory panelists were given training on the definition, principles, and methods of QDA, as well as the concepts of the types and intensities of sensory attributes. The training was conducted for about 2 months, three times a week. Next, each sensory evaluation panelist was asked to suggest their sensory descriptive language for the samples. After discussion, if more than half of the panelists agreed, the sensory descriptive language was determined. Finally, 6 odor profiles were selected for RFIS, including baked potato, savory, sweet, nutty, meaty, and fish sauce. The standard substances for each odor profile were selected as follows: baked potato-like odor was 3-methylthiopropanal (Sigma-Aldrich Co., St. Louis, MI, USA), sweet was ethylmaltol (Anhui Jinhe Ind. Co. Ltd., Chuzhou, China), and savory, nutty, meaty, and fish sauce were butter-roasted squid (Keumhan, Gangneung, Gangwon, Republic of Korea), roasted almond (Dongwoonongsan, Gwangju, Republic of Korea), bacon (Chungjungone Co., Seoul, Republic of Korea), and fish sauce product (Daeyoung Fishery Food, Changwon, Republic of Korea), respectively, which were purchased from a local market. The strength of each odor was evaluated by comparing it to the standard substance using a 9-point scale (1 point: very weak, 9 points: very strong).
## 2 Geometry, Material Properties, and Assumptions
For the extra loss coefficient for the skin effect k\({}_{\rm s}\) it is assumed that it depends only on the power system frequency f, but not on harmonic frequencies of higher orders. This assumption makes sense because the lengths of bus bar conductors are significantly shorter than the lengths of the conductors in electricity transmission and distribution lines, where the effects of harmonic frequencies of higher orders on the skin effect are very small. The values for the coefficient k\({}_{\rm s}\) are taken from [22] or estimated based on available data.
## 5 Conclusions  
In this contribution, the numerical model of the single-stage FLTD with 24-separate columns is established by using the field-circuit coupled time-domain finite integration theory. Combined with the nonuniform grid and first-order SIBC, the pulsed discharge process of the FLTD cavity is calculated with accuracy and efficiency. Applicability of the field-circuit coupled TD-FIT for performance analysis of FLTD is verified by experiments. For the first time, we have demonstrated that, due to the influence of mutual flux linkage among neighboring discharging bricks, equivalent inductance of the discharging brick will increase by \(\sim\)35 nH when all of them are triggered synchronously. The 24 separate columns have negligible impact on the output performance of FLTD and can replace the traditional outer cylindrical wall. With the \(\pm\)80 kV initial capacitor charge voltage and 0.05 \(\Omega\) load resistance, the rise time and amplitude of load current are \(\sim\)110 ns and \(\sim\)0.95 MA, respectively. Electromagnetic characteristics of the discharging bricks and FLTD induction cavity have also been revealed. In the near future, we will focus on the accurate modeling of spatial multi-scale and nonlinear structures (such as magnetic cores ribbons, film capacitors) in the FLTD induction cavity and electromagnetic-thermal-mechanical coupled analysis during the discharging process.  
Conceptualization, H.Q. and S.W.; data curation, H.Q., X.J.; formal analysis, H.Q., N.Z., and Z.W.; funding acquisition, S.W. and F.S.; investigation, H.Q., X.J., H.J., and X.H.; methodology, H.Q. and S.W.; project administration, S.W., N.Z., and F.S.; software, H.Q., N.Z., S.W., and F.S.; supervision, S.W. and F.S.; validation, X.J., H.J. and X.H.; visualization, H.Q., N.Z., and Z.W.; writing--original draft preparation, H.Q., S.W., F.S., and Z.W., writing--reviewing & editing, H.Q., S.W., N.Z., F.S., Z.W., and S.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51790521; the China Postdoctoral Science Foundation, grant number 2019M653635 and 2017M620456; the Natural Science Basic Research Plan in Shaanxi Province, grant number 2019 JM-308 and 2019 JC-229; the Young Talent Fund of University Association for Science and Technology in Shaanxi, grant number 20200113; the Scientific Research Program of Shaanxi Provincial Education Department, grant number 20 JC003.  
The authors thank the anonymous referees for their valuable comments and suggestions.  
The authors declare no conflict of interest.