esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.  
image enhancement; particle swarm optimization; gamma transform; fractional order; coal mine environment +
Footnote †: journal: Medical Image Analysis
## 2 Data Analysis and Features Extraction
### Battery Experimental Data Analysis  
This study uses battery aging cycle test data from the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland for validation. The batteries selected for the experiment are labeled (CS2-35, CS2-36, CS2-37, and CS2-38). The battery dataset was collected using a (constant current constant voltage, CCCV) charging protocol, with a constant charging current of 0.5 A and a cutoff voltage of 4.2 V. The charging process continued under constant voltage until the charging current decreased below 0.05 A. The LIBs cells feature a nominal capacity of 1.1 Ah with lithium cobalt oxide (LiCoO\({}_{2}\)) as the cathode material. The batteries provide complete charging data following the CCCV process. The charging process initiates with constant current charging at a 0.5 C rate until the terminal voltage reaches 4.2 V. Once this voltage is reached, the system switches to constant voltage charging, maintaining 4.2 V until the charging current drops below 0.05 A. The operational state of the batteries is divided into two stages: charging and discharging. Due to variability in usage patterns and operating conditions, the discharging stage presents issues in extracting stable input HFs. Therefore, in this study, input HFs are extracted from the more consistent charging phase.
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 5 Conclusions  
In this work, we proposed a tool that allows users to obtain segmentation labels by picking a few pixel points on the organ. The tool cannot segment without giving a pixel point but has the advantage that learning requires less labeled data than previous studies and makes data collection easier. This tool calculates the kernel density for all pixels, finds areas equal to or greater than a specific threshold value, and demonstrates its performance through experiments. However, the proposed method has a constraint that some non-target regions nearby a target organ could be classified as the same organ when the pixels have similar visual characteristics to a target organ. Furthermore, if an organ has various textures or multiple regions, our algorithm may miss some regions of the organ. Even though these constraints can be mitigated by carefully adjusting the threshold value, we should improve our algorithm in future work.  
Conceptualization, Y.K. and J.L.; methodology, Y.K. and J.L.; software, J.L. and J.Y.; validation, J.L. and J.Y.; formal analysis, M.J.; investigation, S.Y.; data curation, J.Y. and M.J.; writing--original draft preparation, S.Y. and M.L.; writing--review and editing, all authors; visualization, J.L.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gaussian Kernel** & **Feature Kernel** & **Dice Score** & **Hausdorff Distance** \\ \hline O & O & 0.7590 & 23.31 \\ \hline O & X & 0.3795 & 31.08 \\ \hline X & O & 0.7370 & 23.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study for kernels.  
Figure 8: Ablation Study: Importance of the Gaussian Kernel. The first and fifth columns are CT images. The second and sixth columns are ground truth segmentation labels. The third and seventh columns are predictions generated by the model without the Gaussian kernel. The fourth and last columns are predictions generated by our model (Gaussian kernel + feature similarity kernel).  
**Funding:** This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (Ministry of Science and ICT (MSIT)) through the Artificial Intelligence Convergence Research Center (Hanyang University ERICA), under Grant 2020-0-01343, and in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (MSIT) under Grant 2020R1G1A1011471.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Algorithms for Validation and Ranking
### Feature Extraction
The average of the curve distances \(d\), introduced with Equation (5), is calculated for all possible combinations. The following equation is an example, defining the feature for the three valid measurements \(\mathbf{m},\mathbf{n},\mathbf{o}\in M\) per sensor/analyte pair. \[f_{6}(\mathbf{m})=f_{6}(\mathbf{n})=f_{6}(\mathbf{o})=\frac{d(\mathbf{m}, \mathbf{n})+d(\mathbf{m},\mathbf{o})+d(\mathbf{n},\mathbf{o})}{3}\] (13)
7. Dynamic Range \({}^{-1}\) It is beneficial for the later integration of the read-out electronics that the sensor work in a low dynamic range. Therefore, the span of the analyte segment of the measurement can be extracted as a feature. \[f_{7}(\mathbf{m})=\max\mathbf{m}_{\mathrm{a}}-\min\mathbf{m}_{\mathrm{a}}\] (14)
8. Power Consumption \({}^{-1}\) The MOX sensors contain a heating element that needs to be heated up to a specific temperature. As mentioned before, the operating temperature has a big influence on the response and the power consumption of the sensor. A goal could be to minimize the power consumption by still maintaining a feasible response. The feature is the average of the heater voltage during the entire measurement. Let \(\mathbf{v_{m}}\) be the heater voltage values for measurement \(\mathbf{m}\), if available. \[f_{8}(\mathbf{m})=\bar{\mathbf{v}}_{\mathbf{m}}\] (15)
9. Signal to Noise Ratio (SNR) To compare different sensors to each other, the ratio of signal strength to its baseline noise is a good indicator. To obtain the signal strength, the reaction phase of the measurement is segmented into rolling mean-valued windows of size 41 samples. Depending on the reaction type, the signal strength is then calculated with:  
\[p_{\mathrm{s}}(\mathbf{m})= \begin{cases}\max\mathbf{a}-z&\text{if } \mathbf{m}_{\mathrm{a}}\text{ has a positive-slope reaction},\\ z-\min\mathbf{a}&\text{else},\end{cases}  \tag{16}\] \[\text{with}\quad z=\overline{\mathbf{m}_{\mathrm{b}}\langle| \mathbf{m}_{\mathrm{b}}|,40\rangle}\quad\text{and}\quad\mathbf{a}[i]= \overline{\mathbf{m}_{\mathrm{a}}\langle i,20\rangle},\,i\in\mathbb{N},\,1\leq i \leq|\mathbf{m}_{\mathrm{a}}|\]  
Finally, the SNR is calculated as:  
\[f_{9}(\mathbf{m})=\frac{p_{\mathrm{s}}(\mathbf{m})}{s\Big{(}\mathbf{m}_{ \mathrm{b}},\,\widehat{\mathbf{m}_{\mathrm{b}}}\Big{)}} \tag{17}\]  
Let \(\mathbf{m}_{k}\in M\) be the measurement with the corresponding index \(k\in\mathbb{N}\) for which is claimed \(1\leq k\leq|M|\). With the features defined in this section, a feature vector \(\mathbf{g}_{j}\) for each feature is calculated. \[\mathbf{g}_{j}[k]=f_{j}(\mathbf{m}_{k}) \tag{18}\]  
For further use, the features are min-max normalized and inverted, if needed. The final feature vector \(\mathbf{f}_{j}\) for each feature is defined as:  
\[\mathbf{f}_{j}[k]= \begin{cases}\mathbf{g}_{j}^{\prime}[k]&\text{if feature }j\text{ does not need to be inverted},\\ 1-\mathbf{g}_{j}^{\prime}[k]&\text{else}.\end{cases}  \tag{19}\]
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 5 Case Study and Experiment Variation
### Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline \(H_{0}\) & 970 & mm & \(L_{d}\) & 640 & mm \\ \(L_{r}\) & 181 & mm & \(B\) & 1280 & mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values of the designed tractor. Thus, the designed tractor can level itself on a side slope whose angle is less than \(15^{\circ}\) while ensuring the comfort of tractor operators. The effect of active input on the value of the lateral overturn indicator is shown in Figure 7b. The lateral instability limit angle (\(\theta_{lim}\)) can be calculated using Equation (29). Bigger active rotation angles result in a rapid increase in overturn stability. In the initial state, when the rotation angle of the rear wheel \(\alpha=0\)deg, \(\theta_{lim}\) is \(33.42^{\circ}\); when the active rotation angle obtains its maximum value, \(\theta_{lim}\) is \(44.87^{\circ}\). The lateral instability limit angle, \(\theta_{lim}\), increased \(34.26\%\). Overall, for the designed tractor, with the increase in active adjustment angle (\(\alpha\)), the tractor can level its configuration to enhance the comfort of the operators; on the other hand, the lateral instability angle increases. Thus, the designed tractor can increase its overturn stability by controlling the active input of the adjustment mechanisms.
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
###### Abstract  
In modern automotive engineering, accurate vehicle sideslip angle estimation is crucial for enhancing vehicle safety, performance, and driver comfort. This paper addresses the challenge of estimating sideslip angle, an essential parameter for advanced driver-assistance systems (ADAS) and autonomous driving technologies. This study introduces a combined dynamic-kinematic extended Kalman filter (DK-EKF) approach that leverages the strengths of both kinematic and dynamic models while mitigating their individual limitations. The proposed DK-EKF enhances observability in low yaw rate conditions, a common issue with kinematic models, and improves the robustness of dynamic models against parameter uncertainties. A validation is conducted through extensive experimental tests, demonstrating the DK-EKF's superior performance in various driving scenarios. The results confirm the efficacy of the proposed method in providing reliable sideslip angle estimation.  
s +
Footnote †: journal: Applied sciences  
_Article_
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 3 Game Design  
We extend the model in our previous work [16]. To begin, it is necessary to determine a variable in the model that relates the agents' actions to time and the most obvious variable for consideration will be the task completion time, \(t^{c}(a)\). If it is possible to influence the variable in such a way that the temporal constraints are reflected in the task completion time, then explicit alteration of the reward structure for the game model will not be required. (i.e., If the task completion time is determined in such a way that the temporal constraints are always satisfied, then the agent's reward will be reduced if the chosen action cannot provide a feasible solution since the task will be incomplete.) In essence, a task allocation problem with both spatial and temporal coupled constraints can be considered to be two sub-problems--an allocation and a scheduling problem. The game model for allocation penalizes agents for actions that violate spatial constraints, while the scheduling algorithm eliminates possible rewards for actions that violate temporal constraints.  
To reflect the temporal relationships between the tasks, a temporal matrix \(\mathcal{T}\) is introduced. This temporal matrix differ from the matrix in CCBBA [8] in that the entries in the matrix do not represent time value restrictions but instead describes the explicit relationships between tasks using a coded variable in Table 2 for simplicity purposes. This difference is trivial as the temporal matrix and the proposed algorithm can be adapted to consider time relationships similar to CCBBA if required.  
While CCBBA describes six types of temporal coupled constraints, only five types of constraints need to be considered, less the _between_ coupled constraint. Recall that the _between_ coupled-constraint states that task \(A\) must begin after task \(B\) ends and end before task \(C\) begins. Therefore, it is possible to decompose a _between_ coupled constraint into a _before_ and an _after_ constraint as seen in Figure 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Code** & **Relationship** \\ \hline
0 & \(p\) is independent of \(q\) \\
1 & (_Simultaneous_) \(p\) must begin at the same time with \(q\) \\
2 & (_Before_) \(p\) must end before \(q\) begins \\
3 & (_After_) \(p\) must begin after \(q\) ends \\
4 & (_During_) \(p\) must begin while \(q\) is in progress \\
5 & (_Not During_) \(p\) must either end before \(q\) begins, or after \(q\) ends \\ \hline \hline \end{tabular}
\end{table}
Table 2: Code for Temporal matrix entry \(\mathcal{T}_{q,p}\).  
Figure 1: Decomposition of _between_ coupled constraint.
## 2 Materials and Methods
### The Methodology of the Experiments and Testing and Evaluation of the System's Efficiency  
Testing and evaluating the system effectiveness is quite a complex and ambiguous issue. Undoubtedly, this topic and process requires more in-depth study. The authors emphasize that this work is one of the first on the method of complex application of augmented reality for the maintenance of oil pumps, and accordingly, testing will be greatly simplified. When the work continues and the final product is developed, the approach to testing should be more complicated, a special application for testing should be written, and algorithms at all levels of the system structure should be run (Figure 1).  
To test the suitability of the developed structure, the following methodology was used:  
1. An application for installation with multiple units of equipment (up to 10 parameters and 10 3D models of units of equipment) was developed;  
2. The performance of the main channels was assessed, and the result of test signal transmission was recorded. The following main characteristics were measured: CPU, used memory, peak memory, and power;  
3. An application for 3 settings (each with up to 20 parameters and 10 3D models) was developed to identify the location of installations in 3-dimensional space with the possibility of changing the position by the user's action;  
4. The main characteristics were measured by the protocol;  
5. An application for five installations under the conditions of paragraph 5 were developed;  
6. The main characteristics were measured by the protocol;  
7. Analysis of the test protocols with a conclusion on the feasibility of collaborative application of the augmented reality system and dynamic model was conducted.  
Additionally, to evaluate the developed solution, the augmented NASA Task Load Index (NASA-TLX) test was used to evaluate the cognitive-psychological factors when working with the application [36; 37].  
To implement the test, 5 scenarios were simulated for the same task: \"Vibration sensor detected pump failure\". The task was to put the pump out of operation:  
1. The testers had only a smart shield and the option of calling a person with access to the equipment;2. The testers had a smart shield and a Supervisory Control and Data Acquisition (SCADA) system where the values of all system promoters could be viewed;
3. The testers had a smart shield and documentation with information on how to connect the system;
4. All interaction was with the augmented reality system only, and commands were only entered manually;
5. All interaction was with the augmented reality system only, and commands were entered by voice and manually.  
To conduct the test, we selected a group of test takers and asked them to complete the test. The test subjects were asked to answer the following questions:  
* Assess whether you felt threatened, discouraged, or irritated.
* How difficult was it for you to do your job?
* How successful were you in your task?
* How quickly did you complete the task?
* How physically demanding was the task?
* How difficult was the task as a whole?
## 4 Performance Evaluation
### End-to-End Transmission Rate  
In this subsection, we vary node density from 25 to 200 (\(nodes/range\)) and show its effect on the transmission rate. We use networks of 2,500 nodes and set the number of ARQ retransmission to 10, since ARQ with a limited number of retransmissions is normally the practical choice for implementation. Transmission rate is the percentage of data packets sent by the source node and received by the destination.  
In general, the transmission rate of GF-HOP is low (Figure 8(a)). We observe that the transmission rates of GF-HOP with CBF and GF-HOP with DRDT are also low at density 25 due to the local minimum problem [25, 26]. However, in the case of GF-HOP with CBF and GF-HOP with DRDT, the transmission rate increases more as the density increases compared to GF-HOP. Especially, GF-HOP with DRDT has the best performance compared to GF-HOP with CBF because DRDT uses helper nodes more effectively than CBF does as the same reason that we explain in subsection 4.2. As shown in Figure 8(b), the transmission rate of GF-ETX is above 90%. However, it does not guarantee a 100%  
Figure 7: End-to-end transmission cost.  
transmission rate until the density is 150, since the number of retransmissions exceeds 10 over links with low PRR. This is due to transmission failures that could still occur in terms of greedy disconnections. We observe a slight performance improvement brought about by DRDT in this case.
# Design of a 3-Bit Circularly Polarized Reconfigurable Reflectarray
## 2 Materials and Methods  
### Extraction of Equivalent Circuit Model of PIN Diode  
In this study, the reconfigurable method of the proposed reconfigurable reflectarray was loading PIN diodes. In order to realize the reflection phase control of multi-bit circularly polarized reconfigurable reflectarray, it is necessary to load many PIN diodes on one reflector element. The circularly polarized reconfigurable reflectarray designed in this study works in the S-band, so the PIN diode SMP1340-040 LF produced by SKYWORKS was selected, which has the advantages of low loss and low cost. In order to accurately extract the equivalent circuit model of the PIN diode and ensure the consistency of the sample performance of the antenna after fabrication with the simulation results, this work adopted the following method: a vector network analyzer combined with TRL components to calibrate the PIN diode, and the frequency range of the test was from 1 GHz to 6 GHz.  
In this work, the vector network analyzer was used to measure the S-parameter of the PIN diode. Since the calibration device of the vector network analyzer can only move the calibration reference plane to the coaxial port of the cable, the test results are the combination of the microstrip line and PIN diode. In order to obtain the measurement results of PIN diode, the test circuit needs a to be de-embedded. Considering the simplicity and accuracy of the measurement method, the TRL calibration method was used to embed the test circuit. The TRL calibration required for testing is shown in Figure 1. A PIN diode was attached to the TRL calibration circuit, and then the whole circuit was tested by vector network analyzer.  
After obtaining the S-parameter of the PIN diode by using the vector network analyzer and TRL calibration method, it is necessary to fit the S-parameter of the equivalent circuit model of the PIN diode with the measurement results and finally optimize the value of resistance, capacitance, and inductance in the equivalent circuit. The equivalent circuit model of the PIN diode is shown in Figure 2, where \(L_{\mathrm{s}}\) is the lead inductance, \(R\) is the forward resistance, \(C_{\mathrm{j}}\) is the reverse-biased junction capacitance, and \(R_{\mathrm{s}}\) is the reverse-biased resistance. In consideration of the operating performance of the PIN diode, when the DC bias voltage is 0 V, it is marked as the off-state, and when the DC bias voltage is 0.9 V, it is marked as the on-state. Then, the circuit model under the on-state and off-state of the PIN diode is established in ADS to achieve the fitting of S-parameters of the two circuits under the same state. Finally, the values of resistance, capacitance, and inductance in the equivalent circuit are obtained. The specific parameter values are \(L_{\mathrm{s}}\) = 0.45 nH, \(R\) = 2 \(\Omega\), \(C_{\mathrm{j}}\) = 0.2 pF, and \(R_{\mathrm{s}}\) = 10 \(\Omega\).  
Figure 1: Photograph of the TRL calibration parts.