esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Development of the New LMO Monitoring Strategy for LM Cotton Survey in Natural Environments  
The \"Transboundary Movement, etc., of Living Modified Organisms Act\" gives seven ministries within the Korean government individual national authority over the use of LMOs. Accordingly, the MOE has monitored domestic LMO transport/use since 2009. However, the project procedures and management systems of the old LMO monitoring initiative need to be strengthened, in view of advancements in analysis techniques and performance efficiency. To enable efficient conservation of the natural ecosystem in South Korea, a new monitoring scheme was proposed. First, the new scheme covered South Korea nationwide, instead of conducting sample surveys in only some areas. Second, each suspicious sample was collected and individually analyzed, although multiple samples were collected from the same site. This enabled the calculation of the number of LMOs at each collection site. Third, event-specific PCR was performed to identify the introduced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Name** & **Sequence (5’-3’)** & **Product Size (bp)** \\ \hline \multirow{3}{*}{GHB119} & GHB119-F & CCAGTACTAAAATCCAGATCATGCCA & \multirow{3}{*}{327} \\  & GHB119-R & ACTGATGCTCAAACGGTTACA & \\  & GHB614-F & GCAGGCATGCAAAGCTTTAAA & \\  & GHB614-R & TCAAGCCACAGTTTGATTGCA & 226 \\  & MON88913-F & GCCTTTGCTACTTAAAGAGACT & \multirow{3}{*}{299} \\  & MON88913-R & GGCCTCCATATTTGGCTTATTTC & \\  & MONN15985-F & GTTCATGATGGCGGATATCC & \\  & MONN15985-R & CAGCTGGCGCATTCTT & 282 \\  & LLCOTTON25-F & CAGATTTTTGTGATTGGAATTC & \\  & LLCOTTON25-R & TCAGAACTTCTCACATTGGC & 231 \\  & T304-40-F & CCTAGATCTTGGGATAACTTTGAAAGA & \\  & T304-40-R & GATCGTTCAAACATTTGCCA & \\  & MON1445-F & GGAGTAAGCAGATTCAGATCAAACAC & \\  & MONN1445-R & CGCTGGCTTCTATGCCGAGATT & \\  & 281-3006 & 281-3006-R & CCTTGTGTGCTACCTGATGATT & \\  & 281-3006-R & GCCTGATGATGGCGCGCGT & \\  & MON531-F & GGTACGATGATGGCCTACTCTT & \\  & MONN531-R & GTAGCCTTCTCACCTGACAGACTCTAACC & \\  & _Adh_C-F & TCCAGAGCTCCCACTTGAT & \\  & _Adh_C-R & CCCACCCTTTTTTGTTAGC & 178 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oligonucleotide primers used in this study.  
LMO gene, event name, and manufacturer of each confirmed LMO sample. This approach helped to estimate the putative risk to the receiving environment, including the soil fungal community and insect populations. Finally, classification of the LMO collection site was performed for further selection of LMO monitoring sites and post-management of released LMOs in the following year (Figure 1).
## 2 Background
### Concept-Drift Types  
The change in concept over a period of time is known as concept drift. This concept change may happen in four ways, giving four types of CD categories, defined as follows:  
1. There is a concept change within a short duration of time (Sudden).
2. An old concept is gradually replaced by a new concept over a period of time (Gradual).
3. A new concept is incrementally reached over a period of time (Incremental).
4. The old concept reoccurs after some time (Reoccurring).
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Computational Overhead  
To evaluate the computational overhead introduced by SNNL, we compare the baseline WGAN and its SNNL-enhanced version on the CSE-CIC-IDS2017 dataset using identical experimental settings. Each configuration was run three times, and the reported values reflect the average across runs. Table 8 summarizes the results.  
These results show that integrating SNNL introduces a moderate computational cost: the average training time per epoch increases by approximately 25.5%. In contrast, GPU memory usage remains nearly identical, suggesting that the added cost is computational rather than memory-bound.  
Figure 3: F1-score comparison by majority class on CSE-CIC-IDS2018 dataset.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **WGAN** & **WGAN+SNNL** & **Overhead** \\ \hline Avg. Training Time per Epoch (s) & 102.26 & 128.41 & +25.5\% \\ Peak GPU Memory Usage (MB) & 1822.77 & 1823.23 & \(\approx\)0\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average computational overhead of WGAN vs. WGAN+SNNL on CSE-CIC-IDS2017.  
Given the improved minority-class detection observed in prior sections, this overhead is acceptable in practice--particularly in offline training scenarios where detection performance is prioritized over minimal runtime.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 2 Game Design
### Attacker Maneuverability  
Figure 3 shows the simulated attacker lost rate versus attacker maneuverability \(\alpha_{e}\), with the kill probability of interceptor, \(p_{k}=0.6\) and \(0.7\), respectively. The attacker lost rate is defined as  
\[A_{c}=100\times\frac{M_{c}}{M_{0}}\% \tag{4}\]  
where \(M_{c}\) is the number of attackers hit by interceptors, and \(M_{0}\) is the total number of attackers dispatched in one game. At a given \(\alpha_{e}\), \(200\) games are played, with the defense stations randomly distributed in the deployment area. It is observed that \(A_{c}\) has a minimum value at \(\alpha_{e}=0.5\), and the value of \(A_{c}\) at \(p_{k}=0.6\) is lower than its counterpart at \(p_{k}=0.7\). Larger \(\alpha_{e}\) implies an attacker may turn sidewise or even backwards to evade an interceptor. Once the intercept point is lost, the attacker will resume its original target via a straight path. The interceptor may capture the attacker on its resumed course later.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 3 Local Search Maximum Likelihood Parameter Estimator
### ML Fine Estimator  
After TF coarse estimation, ML fine estimation is performed, the previous result \(\left(f^{\prime}_{0},\ k^{\prime}\right)\) is used to narrow the search scope of the compressed likelihood function of observation \(y(n)\). It is worth noting that \(\left(f^{\prime}_{0},\ k^{\prime}\right)\) is estimated by DnCNN's output \(y^{\prime}(n)\), however the final estimation results of the ML fine estimator is based on \(y(n)\), we will analyze the estimation accuracy of \(y^{\prime}(n)\) and \(y(n)\) later.  
Consider an observation \(y(n)\) with additive Gaussian white noise, as shown in Equation (6).  
\[y(n)=A\exp\left[j2\pi\left(f_{0}n+\frac{k}{2}n^{2}\right)\right]+w(n) \tag{6}\]  
where the parameters, chirp rate \(k\), and initial frequency \(f_{0}\), are unknown. The data model expressed in (10) can be written in matrix form as Equation (7).  
\[y=\mathbf{H}A+\mathbf{w} \tag{7}\]  
where the signal vector \(\mathbf{y}\) is given by \(\mathbf{y}=\left[y(0),\ldots y(N-1)\right]^{T}\), the noise vector \(\mathbf{w}\) is given by \(\mathbf{w}=\left[w(0),\ldots w(N-1)\right]^{T}\), \({}^{T}\) denotes the transpose. The matrix \(\mathbf{H}\) is defined as Equation (8).  
\[\mathbf{H}=\left[\begin{array}{c}\exp\left(j2\pi\left(f(0)+\frac{k}{2}(0)^ {2}\right)\right)\\ \vdots\\ \exp\left(j2\pi\left(f(N-1)+\frac{k}{2}(N-1)^{2}\right)\right)\end{array}\right] \tag{8}\]  
Under the ML principle, the ML estimate of \(y(n)\) is obtained by maximizing the compressed likelihood function, which describes as Equation (9).  
\[\left[\widehat{f_{0}},\hat{k}\right]=\underset{i,k}{\operatorname{argmax}} \left[y^{H}\left(H\left[H^{H}H\right]^{-1}H^{H}\right)y\right] \tag{9}\]  
\({}^{H}\) denotes the conjugate transpose. It can be observed that obtaining \(\left(\widehat{f_{0}},\hat{k}\right)\) will require a multidimensional grid search over the two-parameter vectors, which is of high computational load. To avoid that, we resort to the local search method by limit the  
Figure 5: WVD of chirp signal with an initial frequency \(f_{0}=12\), chirp rate \(k=6\). (**a**) is TF distribution of the signal, (**b**) is frequency distribution at discrete time \(0.5\), \(0\) and \(-0.5\) in (**a**).  
parameter vectors' scope to a small area, this operation is based on the result of the TF coarse estimator, which describes as \(\text{f}\in[\text{f}_{0}{}^{\prime}-\sigma_{1},\text{f}_{0}{}^{\prime}+\sigma_{1}]\) and \(\text{k}\in[\text{k}^{\prime}-\sigma_{2},\text{k}^{\prime}+\sigma_{2}]\), where \(\sigma\) is limitation width. We can see that the accuracy of the TF coarse estimator and the scope of \(\sigma\) directly affect the performance of the proposed estimator, we will analyze it later.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Deep Neural Network
#### 8.3.5 Overfitting Problem of LSTM  
With the deep learning algorithms, overfitting of the model is a common problem. Overfitting occurs when the model achieves very high accuracy while training but classifies the data inadequately or sometimes drastically fails when exposed to classify new test data. If the training losses decrease but the validation losses increases, that is a sign of overfitting.  
Overfitting can be avoided by simplifying the network design. Adding more hidden layers does not always improve the efficiency of the model. Finding a fair tradeoff between the number of hidden layers used, the number of neurons in each hidden layer, and adding the drop-out layers with piecewise dropping of neurons and reducing the learning rate every 10 epochs facilitated better performance of the LSTM network. Another approach is early stopping. If the validation losses start rising and there is a drop in the learning accuracy, it is better to stop network training early. In the Figure 12 training is early stopped on completion of 34 epochs yielding an accuracy of 98.89%.
## 4 Discussion
### Study Limitations  
This work has demonstrated that there was a significant PD severity group difference in the gait parameters recorded using insole sensors. The effective use of this technique for monitoring the progress of the PD will require further investigation, considering a greater number of subjects in each group, the effect of factors such as age, gender, and fatigue, and labeling the dominant side. It is also essential to develop the framework for the selection of the device and the measurement protocol to ensure the reproducibility of the recordings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gait** & **CON (\%)** & **PD1 (\%)** & **PD2 (\%)** & \(p\)**-Value** \\ \hline Stance interval & 63.85 \(\pm\) 2.23 & 67.0 \(\pm\) 3.08 & 67.76 \(\pm\) 5.21 & 0.0295 \({}^{*}\) \\ Swing interval & 35.63 \(\pm\) 1.73 & 33.04 \(\pm\) 3.80 & 32.23 \(\pm\) 5.2 & 0.0492 \({}^{*}\) \\ Double-support interval & 44.10 \(\pm\) 3.58 & 49.20 \(\pm\) 6.3 & 53.22 \(\pm\) 8.15 & 0.0021 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\) significance \(<\) 0.05.  
\end{table}
Table 4: Percentage of stance, swing, and double-support intervals when CON, PD1, and PD2 groups are compared.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Workflow of Parallel Experiments
#### 2.5.1 Hardware  
The GPU used in most of the experiments is an AMD Radeon R9 270, and it was programmed using OpenCL. The clock speed of the GPU is of 925 MHz, and the communication with the CPU is made through a PCI Express x16 slot that transmits about 16 GB/s.  
The architecture of the GPU is designed for parallel operations that require no synchronization [36]. The 1280 ALUs of the GPU can work independently, but not in a coordinated manner. If the program requires synchronization, the available resources are limited. The maximum number of synchronized work-items, also called the maximum work-group, is of 256 parallel work-items [37].  
The CPU used to provide instructions to the GPU, image preprocessing, random number generation, and displaying results is a DELL XPS 8700 with an Intel Core i7-4790. The base frequency  
Figure 3: Workflow of programs BEE and NO BEE. The random number generation is only used in program BEE. Parallel NCC is specific to program NO BEE and Parallel HSA is specific to program BEE.  
Figure 2: The original HSA defined a smaller search region around each recruiter. The proposed parallel version defines a unique region that contains the recruiters. (**a**) Original; (**b**) New proposal.  
of the processor is 3.60 GHz. We use OpenCV for image manipulation. The CPU also has 15.6 GiB of RAM available.  
The experiments of Section 3.7, which tested state of the art trackers, requiered a second GPU with the following characteristics: NVIDIA GeForce GTX 1050 with 640 cores and 1354 MHz clock speed.
## 3 Results and Discussion
### Analysis Inputs
#### 3.3.1 Coal:  
Carbon and hydrogen content in the fuel are the two most impactful input parameters (see Table 5). Together, they have a minimum impact of 64.67% for the 'gross power' output and a maximum impact of 90.35% for the 'adiabatic flame temperature'. The next two important input parameters are 'evaporator steam outlet temperature' and 'LHV' for the outputs 'gross power' (respectively, 12.86% and 8.69%), 'gross efficiency' (respectively, 12.86% and 8.75%),'steam flow' (respectively, 15.94% and 6.57%) and 'CHP efficiency' (respectively, 10.64% and 11.24%). The impact of the other input parameters for this outputs is lower than 3%. The 'LHV' input is also impactful for all the other parameters ('steam pressure': 7.90%, 'adiabatic flame temperature': 8.75%, 'effective radiation temperature': 8.86%, 'FEGT': 8.66%). The'steam pressure' also depends on'superheater 3: gas/air side  
Figure 12: Effective radiation temperature. Coal: mean = 1655 \({}^{\circ}\)C, standard deviation = 200 \({}^{\circ}\)C. Biomass: mean = 1489 \({}^{\circ}\)C, standard deviation = 166 \({}^{\circ}\)C.  
convective heat transfer coefficient adjustment factor' (3.96%), while the ash percentage is more impactful for 'FEGT' (5.59%). All the other inputs have an impact lower than 3%.  
In general, the plant performance indicators mainly depend on the carbon and hydrogen content of the fuel and its LHV; also, the evaporator outlet temperature has an impact on these outputs. The steam quality indicators are influenced by the carbon and hydrogen content of the fuel and the live steam flow rate output also by the evaporator outlet temperature. The furnace operation indicators are also mainly influenced by the carbon and hydrogen content of the fuel and its LHV. Of course, it is expected that the carbon and hydrogen content and the LHV of the fuel would have a big impact, but it is surprising that the influence is that big compared with all the other input parameters. The evaporator steam outlet temperature is the only other parameter that has a reasonable impact on the output. This temperature ultimately determines the final energy content of the steam to the steam turbines; the lower this temperature is, the lower the final steam temperature will be, which explains why this input parameter impacts only the plant performance indicators and the live steam pressure.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 3 Study Area and Data Collection
### Model Evaluation  
We compared the results using two metrics: mean absolute error and the coefficient of determination (\(R^{2}\)). The mean absolute error is the mean of the sum of all the differences between the predicted and the actual value, and it is the most widely used metric for regression problems. \(MAE\) is defined by mathematical expression shown below:  
\[MAE(y,\hat{y})=\frac{\sum_{i=0}^{n}|y_{i}-\hat{y}_{i}|}{n} \tag{3}\]  
where, \(y_{i}\) is the actual value for sample \(i\), \(\hat{y}_{i}\) is the predicted value for sample \(i\), \(n\) is the total number of samples.  
The coefficient of determination (\(R^{2}\)) is the square of the correlation between the predicted and the actual value. The reason for selecting this metric was due to it being used by most of the previous studies in the field of retro-reflectivity degradation prediction. The coefficient of determination (\(R^{2}\)) is defined as:  
\[R^{2}(y,\hat{y})=1-\frac{\sum_{i=0}^{n}(y_{i}-\hat{y}_{i})^{2}}{ \sum_{i=0}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\] \[\textit{where}\ \bar{y}=\frac{\sum_{i=0}^{n}y_{i}}{n} \tag{5}\]
## 4 Methodology
### Activation Fine-Tuning  
In experiments, we show that our threshold fine-tuning (TFT) can produce optimally thresholded attribution maps with significantly higher attribution quality than the original attribution maps. However, one downside is that TFT has to be performed on every input, which can be costly for providing an AI-based inference service with input attribution.  
Therefore, we propose an activation fine-tuning (AFT) strategy that uses TFT to generate auxiliary data to adjust the activations of a target CNN so that they will be better suited for creating attribution maps. The idea is that since the optimal masks \(M_{\tau^{+}}\) contain the information of class-relevant regions in activations, we can use the masks as new data to fine-tune the activations. Figure 2 illustrates our AFT strategy, which takes activations(at a specific layer where we generate input attribution) and uses a tuner network to fit the shape of activations with multiple channels to the masks with no channel information.
# AI Somatotype System Using 3D Body Images:
## 5 Conclusions  
The purpose of this study was to develop an artificial intelligence (AI) somatotype system capable of predicting the three body types proposed by [PERSON]'s somatotype theory using 3D body images collected using a 3D body scanner. The following conclusions were drawn from this study. First, the AI somatotype model showed approximately 91% training accuracy and approximately 72% validation accuracy. The corresponding loss values were 0.26 for the training and 0.69 for the validation set. Second, the performance of the trained model was validated using test data, resulting in accurate predictions for 18 out of the 21 new data points, achieving approximately 85% classification accuracy, whereas three predictions were incorrect. These findings serve as foundational data for subsequent studies aiming to predict 13 detailed body types from the initial three body types. Additionally, in practical applications, anyone with a personal phone camera can identify various body types based on captured images and develop programs for smartphones and websites in the form of a user interface (UI) to predict obesity, diseases, etc. However, the 3D images used in this study had limitations due to the restricted attire of the subjects, and there are certain constraints to providing services that can be utilized by anyone. Nevertheless, despite these limitations, it is deemed significant that a model for classifying body types more conveniently than existing methods has been developed in this research. It is hoped that the results of this study will evolve to be utilized in various contexts in the future.  
Conceptualization, J.Y.; methodology, S.-Y.L. and J.-Y.L.; software, J.-Y.L.; validation, S.-Y.L. and J.-Y.L.; formal analysis, J.Y.; investigation, J.Y.; resources, J.Y. and J.-Y.L.; data curation, S.-Y.L.; writing original draft preparation, J.-Y.L.; writing--review and editing, S.-Y.L. and J.-Y.L.; visualization, J.Y.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1G1A1094776).  
**Institutional Review Board Statement:** This study was conducted after obtaining approval from the Korea National University of Physical Education Institutional Review Board (1263-202304-HR-009-01).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in this study.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Monitoring of Atmospheric Corrosion of Aircraft Alloy AA2024 by Acoustic Emission Measurements
## 2 Experimental Investigation
### Atmospheric Corrosion Experiment--Setup and Procedure
The salts used in the saturated solutions with the corresponding equilibrium RH at 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C are listed in Table 1.  
The experimental procedure and the variation of the RH, which simulated the potential operating conditions of an aircraft in the glass chamber, were as follows. The experiment started with the deposition of the droplet. The controlled variation of the RH was conducted by adding or exchanging two small Petri dishes containing the saturated solutions in a predefined sequence, as shown in Table 2. For the exchanging of the saturated solutions, the glass chamber was just opened as little and for as short a time as necessary to avoid affecting the prevailing environment inside. At the very beginning of the experiment, a high RH was used to avoid the evaporation of the deposited droplet on the specimen (step 1). After that, there was a slow decrease to a low RH to induce the gradual evaporation of the droplet (steps 1-5). This low RH was held until the droplet evaporated and the specimen was completely dry (step 5). Then, the RH was slowly increased to force rewetting by the deliquescence of the NaCl crystals left after the water evaporated from the droplet (steps 6-10). This rewetting only occurred on the NaCl crystals; no wetting was observed elsewhere on the specimen. Finally, after a high RH was reached again, an abrupt decrease to a very low RH was induced using silica gel instead of a saturated salt solution to dry out and preserve the corroded specimens (step 11). The small Petri dishes were exchanged when the RH approached and only slightly deviated from the expected equilibrium values presented in Table 2. These exchange procedures were the only interruptions to the continuous AE DAQ. Videos were not recorded during these phases. The temperature and RH prevailing in the glass chamber were continuously (also during the exchange of the saturated salt solutions) recorded using a data logger (type RS PRO RS-172 TK).
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Deployment
#### 5.1.1 Preparatory Phase  
In the preparatory phase, a particular network scenario is created and corresponding configurations are inputted through Web UI. Researchers need to decide the network architecture, the number and the type of nodes, node parameters, as well as link establishment strategies. According to user demands and settings, the main controller calculates connection relationships and link characteristics between nodes, as well as network parameters of emulation nodes. Calculations are stored in the database for subsequent use. In service modeling, a specific network service is determined. Researchers decide which network protocol to be used, thus defining the size of packets and the scheme of data communications. Data collection configurations should also be set at this phase, covering measurement modes and data capture modes.