esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization
#### 2.4.1 Principal Component Analysis  
PCA, an age-old yet increasingly relevant technique in the realm of sensors, functions as a data reduction strategy within multivariate statistics. This method aims to transform the initial N variables of a dataset into a new coordinate system of dimension N, while preserving the majority of the information contained in the original variables. Through this transformation, the variable with the highest variance is projected onto the primary axis, followed by the next most variant variable onto the second axis, and so forth. While originally designed to address unwieldy datasets, its application in the context of e-noses often centers around condensing a limited number of variables (typically 6-12) into two or three dimensions for visualization. This graphical representation facilitates qualitative classification, where distinctive clusters of points linked to specific gases become discernible. Such clusters offer an intuitive basis for classifying new measurements, assuming proximity to a relevant cluster. However, this approach leans heavily on human interpretation, making it subjective and vulnerable to biases. To enhance the objectivity of PCA analysis, alternative classification techniques have been developed, employing algorithmic autonomy to assign labels that denote the class to which a new data point belongs [83].
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis
### The Effect of Dropout Layer Type  
After using the second layer of the LSTM layers, the loss at first falls very quickly, and the accuracy is then much better than that of the single-layer LSTM. However, after the 10 th epoch, the accuracy improves slowly, and gradually trends toward overfitting, resulting in an increase in the loss.  
When using an ordinary dropout, the loss will fall slowly along with the increase in the forgetting rate; while the accuracy will decrease, the accuracy will also decline. However, there will be overfitting along with the decrease in the forgetting rate, which causes loss jitter.  
To solve this problem, the Spatial Dropout is used instead of the ordinary Dropout. As can be seen from Figure 7, the accuracy is thereby improved by about 3%.  
Figure 6: The impact of the LSTM layer number on classification accuracy.  
Figure 7: The classification accuracy of Spatial Dropout and ordinary Dropout.
## 1 Introduction
[PERSON] and [PERSON] [25] suggested that micropores increased as vitrinite increased and ash decreased. [PERSON] et al. [26] reported that micropores decreased as mineral matter content increased. [PERSON] and [PERSON] [27] observed that there was a certain correlation between maceral composition and specific surface area, and this correlation had an effect on the Langmuir adsorption volume (\(V_{\text{L}}\)). [PERSON] et al. [28] found that high-inertinite coal had a lower adsorption capacity compared with high-vitrinite coal. For semi-anthracites, there is a significant positive correlation between the amount of super-micropores and their methane adsorption capacity [18]. The fractal theory can be employed to characterize structural and surface irregularities, which directly affect the adsorption capacity of porous media [29; 30]. In general, a pore surface is more irregular and complex as the surface fractal dimension approaches the value of 3. The MIP, LP-NA, and NMR techniques can all be used to estimate the fractal dimension of pores [31; 32]. On the basis of the MIP test, [PERSON] et al. [16] classified pores into seepage pores and adsorption-diffusion pores. Their results showed that there was a negative correlation between the seepage pore fractal dimension and the coalification degree. Researchers believe that the surface fractal dimension may have a significant influence on the specific surface area according to the LP-NA test, whereas the structure fractal dimension may be more sensitive to ash yield (\(A_{\text{d}}\)), moisture content (\(M_{\text{ad}}\)), pore diameter, and micropore content [33; 34]. [PERSON] et al. [35] proposed multi-scale fractal dimensions and calculated the contributions of different pores to porosity and permeability. They found that a negative correlation exists between the porosity contribution of different pores and the coalification degree. In summary, previous studies have extensively examined the pore structure of anthracite. The comprehensive pore distribution of different macerals without the effects of anthracite geological structure and its effect on methane adsorption-diffusion, however, remain unclear. To determine them, anthracite samples from bright bands and dull bands of coal were obtained from No.3 coal seam, Hudi coal mine, in Qinshui Basin. For these samples, MIP, LP-NA, and LP-CA were used to characterize the comprehensive PSD, and isothermal adsorption tests were employed to describe the distinctive nature of the adsorption capacity.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.4.2 Comparison with Other Segmentation Techniques
Unlike these methods, our model performs segmentation on the localized RoIs, which limits the space of segmentation and uses the RoIAlign layer, which ultimately improves the accuracy of the segmentation result. Moreover, our method achieves comparable performance with [65] while using less computational resources.
## 5 Experimental Results
### Shortcomings and Future Improvements  
The proposed resonance module has only an on-off mode that maintains either a low stiffness or a high stiffness. Furthermore, the purpose of the study was to analyze the feasibility and the performance of the proposed system in a manually controlled mode. In future work, an automated magnetic field controller needs to be designed to generate a continuous magnetic field.  
Although the smart elastomer is effective in changing the stiffness, it has an inherent damping effect that limits high-resonance vibrations. There was an increase in the damping of the elastomer by 27% under a magnetic field, but this was a much smaller change than the stiffness change ratio, which was 152%. However, there should be a more in-depth study of the effect of the damping of this smart elastomer.
## 2 Materials and Methods
### Problem Context and Objectives  
The Diesel Particulate Filter (DPF) is a critical component in the emissions control systems of hybrid diesel buses, especially in urban environments characterized by frequent stops, low speeds, and prolonged idling. These operational conditions create suboptimal scenarios for DPF regeneration, a process that requires sustained high exhaust temperatures and engine loads to effectively burn off accumulated soot. Consequently, incomplete regenerations are common, leading to accelerated clogging and an increased need for unplanned maintenance interventions.  
In the fleet under study, the Electronic Control Unit (ECU) continuously monitors the Diesel Particulate Filter (DPF) saturation level and classifies it using an ordinal soot load scale ranging from Zone 0 to Zone 5. Zones 0 to 3 correspond to normal or near-normal operating conditions, while Zone 4 is classified as critical due to its activation of engine power limitations and the need for manual regeneration. If saturation continues unchecked and the vehicle reaches Zone 5, it must be immediately withdrawn from service to undergo a forced regeneration at the depot, a process that can only be triggered using an OBDII diagnostic scanner. This procedure operates under more aggressive cleaning conditions, which, while effective, may also accelerate the degradation of the DPF. It is worth noting that both manual and forced regenerations typically require 30 to 40 min and involve keeping the bus parked and the engine at high revolutions while injecting fuel into the exhaust system to generate the necessary temperatures for soot combustion. Table 1 summarizes the soot zones and their corresponding operational implications. From a maintenance perspective, Zones 3 and 4 are particularly critical.  
In addition to the problem itself, a major limitation in this context is the slow and incremental development of the dataset, particularly with respect to the moments of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Soot Level Zone in the DPF** & **Observations** \\ \hline
0 to 3 & Bus operability is not affected. \\
4 & Engine power is reduced. Manual regeneration is required. \\
5 & The bus must be returned to the depot. Forced regeneration is required. \\ \hline \end{tabular}
\end{table}
Table 1: Soot level zones and their impact on bus operation.  
Figure 1: After-treatment system of hybrid vehicles.  
This gradual accumulation of data requires methodological strategies specifically tailored to scenarios of data scarcity and delayed availability.  
Given this operational context, the main objective of this study is to develop a supervised learning model capable of predicting, in real time, the expected duration a vehicle will remain within Zones 3 or 4 after entering them. The target variable, duration within a soot zone, is calculated based on historical ECU data by measuring the time difference between the first and last timestamp of each uninterrupted zone occurrence. This predictive insight enables maintenance teams to assess whether a vehicle can safely complete its assigned route or requires immediate intervention.
## 4 Results--MIS for Police Centers Location
### Graphical User Interface (GUI)  
The main useful requirement in this MIS is friendly human-computer interaction and communication. The information flow begins with the upload of the file related to the crime database. Figure 3 illustrates the File screen, which is responsible for facilitating user access when importing data into the proposed MIS. From there, the user can view and format the data imported into the system.  
After the insertion of data about the crime's occurrence, particularly georeferenced data, the proposed MIS includes the points on a map and allows their interactive viewing. The user obtains more detailed information about the types of recorded occurrences. It is possible to carry out a descriptive analysis of the data and extract information about the different types of crimes; the period in which the crime was committed; if the victim officially registered what happened with the police units; and the victim's personal data, such as gender and address, among others.  
The data analysis module was implemented with interactive interface features, such as the graduated slide bar, which the user can manipulate to obtain different scenarios as it changes the number of centroids to be considered. At this stage, crimes are grouped into clusters based on the similarity of the region where crimes occur most frequently. It is possible to find the centroid of each cluster with the proposed MIS and work with this information as a candidate location to allocate a police facility. Figure 4 shows the cluster analysis visualization screen and highlights the centroid for each cluster.  
Figure 4: Data analysis visualization screen; k-means recommendation.  
Figure 3: File screen of proposed MIS.  
Figure 5 shows the screen that indicates the candidate locations for police facilities. This graphic resource enables a detailed analysis of the crimes covered by the calculated radius in a given area of activity. Thus, the module has four simultaneous analysis options, which are (i) Maxima Coverage, (ii) Minimum Uncovered, (iii) Average Distance, and (iv) Average Deviation, which allow the user to obtain a broad understanding of the problem that is faced and provide more information for the decision-making process.  
In the next step, the coordinates selected to install a police unit from the coordinates of the candidate locations are presented based on the information provided by the user regarding the radius and the number of facilities to be allocated. This potential allocation maximizes the number of crimes covered. The results of the analyses are composed of numerical information on the number of facilities, the geographical coverage radius, the average distance between police facilities, the number of crimes not covered, and various percentages in relation to the database used. These data are organized on the preprint reports screen.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 2 Construction of Longitudinal PID Controller for a Vehicle
### PID Control
However, using proportional control alone may lead to steady-state errors, meaning that the vehicle's speed may never fully reach the target value. To eliminate this steady-state error, integral control (I) is introduced in this study, which adjusts the system based on the accumulated error over time. When the vehicle deviates from the target speed for an extended period, the integral controller gradually increases the output until the error is eliminated. The control rule is  
\[I=k_{i}\int e(t)dt \tag{9}\]  
where \(k_{i}\) is the integral coefficient, and \(\int e(t)dt\) represents the accumulated error. Additionally, to reduce overshoot and improve system stability, derivative control (\(D\)) is used in this study. Derivative control adjusts the system based on the rate of change in the error, predicting the trend of the error to mitigate excessive fluctuations in vehicle speed. The control rule is  
\[D=k_{d}\frac{de(t)}{d(t)} \tag{10}\]  
where \(k_{d}\) is the derivative coefficient, and \(k_{d}\frac{de(t)}{d(t)}\) is the rate of change in the error. Figure 2: PID control principle diagram. By combining the proportional, integral, and derivative components, the PID controller achieves fast, accurate, and stable speed tracking control. This method ensures that the vehicle's longitudinal speed follows the target speed as quickly as possible while maintaining stability. The overall control formula is  
\[u(t)=k_{p}e(t)+k_{i}\!\!\int e(t)dt+k_{d}\frac{de(t)}{d(t)} \tag{11}\]  
The PID controller's advantage lies in the combination of proportional control's fast response, integral control's ability to eliminate steady-state errors, and derivative control's overshoot suppression, making longitudinal speed control smoother and more precise.
# Content-Adaptive and Attention-Based Network for Hand Gesture Recognition
## 5 Conclusions  
In this work, we present a network based on content-adaptive and attention mechanisms for hand gesture recognition. The proposed network is a pure self-attention mechanism-based architecture, which does not have any convolution or recurrent operations. The self-attention mechanism has the advantage of both shortest maximum path length and minimum sequential operation sequential operation. The properties of the self-attention mechanism give the network the ability to classify gestures by focusing on information from the entire video sequence. To improve the sampling of the model and reduce the input of gesture-free frames, we introduced a content-based adaptive sampler. We use a sliding window for gesture detection on the incoming frames to determine the starting sampling frame for the encoder. We evaluated the proposed network on two publicly available benchmark datasets, NVGesture and EgoGesture. The extensive comparative experimental results demonstrate that the proposed hand gesture detection and recognition method outperforms the existing approaches based on CNNs or RNNs. In the future, we plan to further improve the recognition accuracy of the attention-based model on gesture recognition tasks. The study of uncertainty in the attention-based model will also be our future work.  
Conceptualization, Z.C.; methodology, Z.C. and Y.L.; software, Z.C.; validation, Z.C. and Y.L.; investigation, Z.C. and Y.L.; writing original draft preparation, Z.C.; writing review and editing, Z.C., Y.L. and B.-S.S.; supervision, Y.L. and B.-S.S.; project administration, B.-S.S.; funding acquisition, B.-S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00594, Morphable Haptic Controller for Manipulating VR:AR Contents), and in part by the China Scholarship Council.  
Not applicable.  
Figure 6: The confusion matrix of the gesture recognition results using our method on two datasets: (a) NVGesture dataset (25 gesture actions); (b) EgoGesture dataset (83 gesture actions).
## 3 Designing Control Strategy and Model
### Joint Control Model of Wind and Storage Based on TPA-BiGRU
#### 3.2.1 Bidirectional Gated Recurrent Unit  
GRU replaces the forgetting gate and input gate in LSTM with an update gate, so the GRU model has fewer parameters and a more straightforward structure, and the performance of the two is comparable. The specific calculations are as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{2}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{3}\]  
\[\widetilde{h}_{t}=\tanh((W_{h})\cdot[r_{t}\cdot h_{t-1},x_{t}]) \tag{4}\]  
\[h_{t}=(1-z_{t})\cdot h_{t-1}+z_{t}\cdot\widetilde{h}_{t} \tag{5}\]  
where the symbol \(\sigma\) represents the sigmoid activation function \(\sigma(z)=\frac{1}{1+e^{-z}}\); \(x_{t}\) is the current wind farm data; \(h_{t-1}\) is the output of the upper hidden layer; \(r_{t}\) and \(z_{t}\) are the reset door and the update door, respectively; \(\widetilde{h}_{t}\) is the candidate hidden gate state; \(W_{r}\), \(W_{z}\), and \(W_{h}\) are the network parameter matrices; [ ] is vector splicing; and \(\cdot\) is the multiplication of the matrices by elements.  
Figure 2: Joint control model of wind and storage based on TPA-BiGRU.  
To better obtain the mapping relationship between the input data and the energy storage regulation, this paper chooses the BiGRU network to capture the relationship between the two in a bidirectional time series. It can be seen from Figure 2 that the output of the BiGRU model contains both historical and future information of the input data, which can avoid the lack of information and improve the prediction accuracy when dealing with rolling optimized data for wind power and energy storage.
## 3 Performance Analysis Framework  
In the following, we will derive the average SER of our proposed system model. We will follow the standard MGF-based approach [22]. The overall signal-to-interference-plus-noise ratio (SINR) \(\gamma\) can be obtained [26], by using Equation (2), as:  
\[\gamma=\frac{E_{s}|h_{1}|^{2}|h_{2}|^{2}}{|h_{2}|^{2}\sigma_{1}^{2}+E_{i}|h_{2 }|^{2}|h_{3}|^{2}+\frac{\sigma_{2}^{2}}{G^{2}}} \tag{6}\]  
Alternatively, Equation (6) can be written as:  
\[\gamma=\frac{|h_{1}|^{2}|h_{2}|^{2}}{|h_{2}|^{2}+\frac{E_{i}}{\sigma_{1}^{2}}|h _{2}|^{2}|h_{3}|^{2}+\frac{\sigma_{2}^{2}}{\sigma_{1}^{2}G^{2}}}\frac{E_{s}}{ \sigma_{1}^{2}} \tag{7}\]  
On substituting \(C=\frac{\sigma_{2}^{2}}{\sigma_{1}^{2}G^{2}}\) in Equation (7), we finally obtain:  
\[\begin{array}{ll}\gamma&=\frac{|h_{1}|^{2}|h_{2}|^{2}}{|h_{2}|^{2}+\frac{E_ {i}}{\sigma_{1}^{2}}|h_{2}|^{2}|h_{3}|^{2}+C}\frac{E_{s}}{\sigma_{1}^{2}}\\ &=\frac{|h_{1}|^{2}|h_{2}|^{2}}{|h_{2}|^{2}+\frac{E_{i}}{\sigma_{1}^{2}}|h_{2 }|^{2}|h_{3}|^{2}+C}\overline{\gamma}\end{array} \tag{8}\]  
where \(\overline{\gamma}=\overline{\gamma}_{1}=\frac{E_{s}}{\sigma_{1}^{2}}\), and \(\overline{\gamma}_{int}=\overline{\gamma}_{3}=\frac{E_{i}}{\sigma_{1}^{2}}\) are the per hop average signal-to-noise-ratio (SNR) and interference-to-noise-ratio (INR), respectively.
## 4 SLIPI-Scan for Extinction Coefficient (\(\mu_{\mathrm{e}}\)) Mapping  
### Description of the SLIPI-Scan Technique  
The SLIPI-scan technique is used to extract the \(\mu_{\mathrm{e}}\) by imaging the sample in a \"bread slicing\" manner by means of a laser sheet with sinusoidal intensity modulation (see Section 2.1). For each slice (layer), three sub-images with a 120\({}^{\circ}\) phase shift are acquired and the corresponding SLIPI image is constructed according to Equation (2). In order to correct for the light extinction between the laser sheet and the camera, also known as signal attenuation, a scanning procedure starting from the outside of the spray towards its center is performed [25, 26]. By stepwise acquisition of SLIPI images at different depths in the sample, one obtains a stack of 2D SLIPI images taken at different planes (\(m\)) of the sample, as is illustrated in a top view in Figure 6. At plane \(m=0\), i.e., before the introduction of the spray, the light sheet is incident on the reference cuvette only. The amount of fluorescence signal emitted towards the camera by the reference solution is captured in the first image of the stack. The acquired image at this plane \(m=0\) serves as a reference to measure the intensity incident on the spray for all subsequent planes. At plane \(m=1\), the lower part of the laser sheet is incident on the outermost edge of the spray. In the corresponding SLIPI image, both fluorescence signals from the edge of the spray, as well as from the reference cuvette, are now being detected. One notices that the recorded intensity from the reference cuvette has decreased at the bottom part at a height corresponding to the height where the laser sheet interacts with the spray. Following this manner, the spray is scanned in a total of 31 planes, separated by 500 \(\upmu\)m each, until the middle of the spray is reached.  
Figure 5: (**a**) The calibration curve of the SLIPI-LIF/Mie ratio plotted against droplet mean diameters, D\({}_{32}\) and D\({}_{30}\). Both experimental data and calibration fit are given; (**b**) the calibration curve for the calibrated SLIPI-LIF/Mie ratio (SMD or D\({}_{32}\)) plotted against droplet extinction cross-section. The droplet extinction cross-section is deduced from the Lorenz–Mie theory using the droplet size distribution histograms given in Figure 4e; (**c**) the calibrated SLIPI-LIF/Mie ratio showing the droplet SMD (for \(Z=15\) mm) is extracted by the calibration curve given in (**a**); (**d**) the droplets extinction cross-section map is extracted from the calibration curve shown in (**b**).  
Figure 6: Illustration of the SLIPI-scan technique in top view (**left column**), SLIPI-LIF image of the spray (**middle column**), and corresponding light transmission images of the reference cuvette (**right column**). Each row depicts a different plane m of the SLIPI-scan.