esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The result is a maximum accuracy of 90%, depending on the relevant threshold value [27]. Furthermore, in another work [28], average accuracy rates were 99.28% and 99.97% using features extracted directly from acoustic emission (AE) signals to train the KNN classifier in a pipeline system. Another study compared different prediction methods regarding the valve internal leakage rate [29]. In that paper, factor analysis was also introduced to realize the dimensionality reduction in the AE signals of a valve. Their accuracy rates were between 74.5% and 98.8%. In other research [30], k-means clustering was used to contribute to constructing the cascade support vector data description (Cas-SVDD) and improving the accuracy of pipeline leak detection. In addition, a combination of CNN and the depiction of underwater acoustic signals in the time-frequency range were studied [31] to provide a new understanding of underwater pipeline error recognition methods, with the recognition accuracy validated via a pipeline leakage rate of 95.5%. In another work [32], applied convolutional neural networks (CNN) were used to classify whether images (CCTV frames) are of different classes of natural gas leaks in onshore wellheads. Those experimental results showed an accuracy rate of 99.78%. Two studies [33, 34] implemented a feature-selection method based on SVM classifiers to identify leak severity levels of gas pipelines for a real-time monitoring system. The corresponding accuracy rates were 95.6% and 99%. In this paper, the F-OE method is suggested to increase the accuracy of natural gas data level detection. The main contributions of this research are that we propose a novel factor analysis method and OE-based ML method to predict NG leakage risk levels in open data in an unsupervised mode. Here, after we performed a ten-scale logarithmic transformation, the overall content distribution pattern of the original data did not change significantly until the k-means clustering. Therefore, our method is suitable for the early prediction of NG leaks in the air. It presents higher accuracy and higher AUC scores than the techniques. The model is evaluated for its accuracy, mean squared error (MSE), F1-score, and receiver operating characteristic curve (ROC).
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
###### Abstract  
The objectives of the study were to determine the spatial rate of CO\({}_{2}\) flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive--cross polarization) and VV (Vertical Transmit/Vertical Receive--like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrae Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (\(\sigma^{\circ}\)) calculated from Sentinel-1 data showed that LAI dominates the influence on \(\sigma^{\circ}\) when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R\({}^{2}\) = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The \(\sigma^{\circ}\) VH and \(\sigma^{\circ}\) VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (\(\sigma^{\circ}\) Sentinel-1 VH, \(\sigma^{\circ}\) Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R\({}^{2}\) = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrae wetland habitat types. Eventually, emissions of CO\({}_{2}\) to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.  
NEE; backscattering coefficient; LAI; soil moisture +
Footnote †: journal: Remote Sensing
# Analysis of the Dynamic Behavior of Multi-Layered Soil Grounds
## 2 Materials and Methods  
### Soil Properties  
The weathered soil used in this study was sourced from a cut slope at a construction site in Ulju-gun, Ulsan Metropolitan City, and underwent comprehensive physical property analyses. These analyses included specific gravity, grain size distribution, Standard Proctor, and relative density tests. The results indicated a specific gravity of 2.69, with maximum and minimum dry unit weights of 18.27 kN/m\({}^{3}\) and 12.43 kN/m\({}^{3}\), respectively. The optimum moisture content was determined to be 12.5%, and Attereberg limit tests classified the soil as non-plastic (NP) with a plasticity index (PI) of 0. The maximum and minimum void ratios were found to be 1.123 and 0.443, respectively. The soil had a fines content of 10.8%, resulting in a Unified Soil Classification System (USCS) designation of SW-SM. For the dynamic model testing, a specimen was prepared from soil that passed through the No. 4 sieve, with approximately 1% of the original sample being retained on the No. 4 sieve. Table 1 presents the geotechnical index properties of the weathered soil utilized in the 1 g shaking-table test.  
The silica sand investigated in this study displayed specific characteristics: a specific gravity of 2.65, a friction angle measuring 38 degrees, and maximum and minimum void ratios of 1.06 and 0.64, respectively. Its coefficient of curvature (Cc) stood at 1.03, while the coefficient of uniformity (Cu) was measured at 1.76. This silica sand is classified as SP in the Unified Soil Classification System and has a median particle size (D50) of 0.235 mm. The reason for using weathered soil and silica sand is that the characteristics of the two are quite different, allowing the dynamic characteristics of the site to be more clearly observed. Table 2 presents the geotechnical index properties of the silica sand used in the 1 g shaking-table test.
# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 3 Materials and Methods
### Defect Types  
The dataset consists of seven distinct classes: Dif_Large_Hole (5997 samples), Large_Hole (6299 samples), Misaligned_Large (4494 samples), Misaligned_Small (4200 samples), No_Defect (11,996 samples), No_Hole (4196 samples), and Small_Hole (6300 samples). These classes are categorized based on their defect characteristics, aligning with the objective of clearly identifying defect presence or absence rather than specific defect nuances alone. Specifically, the Large_Hole and Small_Hole classes represent properly positioned holes with diameters of 10 mm and 5 mm, respectively. The Large_Hole category captures samples with large holes exhibiting irregular shapes or dimensions. Classes Misaligned_Large and Misaligned_Small comprise components where holes of 10 mm and 5 mm diameter, respectively, are incorrectly positioned. No_Defect samples are characterized by standard reference holes of 7 mm diameter, ensuring consistency in baseline quality, while No_Hole contains components entirely lacking holes.  
The dataset distribution and each defect class are presented in Figures 4 and 5 across seven distinct classes used for defect detection. The classes and their respective sample counts are visually represented in a bar graph, clearly highlighting dataset imbalances. Specifically, the No_Defect category has the highest number of samples (11,996), ensuring a robust baseline for quality assessment, while classes such as No_Hole (4196 samples) and Misaligned_Small (4200 samples) represent minority classes. This graphical representation underscores the need for balanced training approaches, such as data augmentation or synthetic data generation, to enhance model performance across all categories and ensure effective generalization in real-world scenarios.  
Figure 3: Experimental setup for real-time defect detection, showcasing the imaging system and associated hardware configuration for capturing and processing images of metal components.
## 3 Multi-Hop State-Aware Routing Strategy Based on Traffic Flow Forecasting
### Routing Strategy Based on DDQN  
It is mentioned in Section 3.1 that an agent is modeled as \(\{S,A,P,R\}\) in deep reinforcement learning. As the WSN routing strategy is combined with the deep reinforcement learning, the model \(\{S,A,P,R\}\) is constructed as the Table 3.  
where the source node is \(v_{s}\), the destination node is \(v_{d}\), and the current NST at the enhanced node. \(v_{next}\) represents the next hop decided by the enhanced node. For example, if a packet needs to be routed from \(v_{s}\) to \(v_{d}\), the reward \(r\) is expressed as  
\[r=\left\{\begin{array}{lr}r_{success}&v_{next}=v_{d}\\ -\Big{(}\alpha\cdot r_{off}+\beta\cdot r_{low}+\gamma\cdot r_{congestest}\Big{)} &v_{next}offline/low/congested\\ -{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{ }}{}{}{}{}{}{}{}{}{  
are the weight coefficients of the three events. The training process of DDQN is referred to [21].  
Since the state, energy, and traffic load are considered in DDQN as the input parameters, the proposed routing strategy can respond to the network topology changes. The workflow of MHSA-TFF is shown in Algorithm 3.
```
1: For each enhanced node, do the prediction of the traffic between nodes.
2: Evaluate node states and update the NST
3:if the NST changes then  Interacts with other enhanced nodes to update the network topology endif
4: Load DDQN at the enhanced node
5: If an ordinary node has the routing requirement, input the \([v_{s},v_{d},NST]\) to the corresponding enhanced node.
6: The enhanced node outputs the next-hop \(v_{next}\) and sends to the ordinary node
7: The ordinary node transmits the packet to \(v_{next}\)
```  
**Algorithm 3** The Workflow of the MHSA-TFFDDPG algorithm  
MHSA-TFF takes the multi-hop node state information as the input to the routing decision process. which is partially updated. The extensive wireless sensor network is divided into several small pieces by the setting of enhanced nodes. Each enhancement node is responsible for gathering information within the multi-hop region and making routing decisions within this multi-hop region.
## 4 Results and Discussion  
### Evaluation Metrics  
Prediction results are evaluated using mean absolute error (_MAE_), root mean square error (_RMSE_), and absolute error (_AE_). The following formulas are presented:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert x(i)-\hat{x}(i)\rvert \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(x(i)-\hat{x}(i)\right)^{2}} \tag{15}\]  
\[AE=\left\lvert T_{RUL}-\hat{T}_{RUL}\right\rvert \tag{16}\]  
where \(n\) is the number of predicted cycles, \(x(i)\) is the real value, \(\hat{x}(i)\) is the predicted value, \(T_{RUL}\) is the number of cycles at the end of battery life in the actual case, and \(\hat{T}_{RUL}\) is the predicted value of RUL.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 4 Weather Detection
### Training Data Generation  
The training data were generated using denoised backscatter data and weather phenomenon (presence/absence) data. For precipitation, the precipitation sensing value of AWS was used. For fog, AWS and visibility data were used to indicate whether fog occurred. As shown in Table 9, the backscatter coefficients of all heights were used for AWS hourly data.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 3 Analysis of Numerical Simulation Results
### Beam Expansion  
When a laser propagates through atmospheric turbulence, beam expansion occurs, which is typically described by the effective spot radius relative to the centroid of the spot [29].  
\[W_{eff}^{2}=2\iint r_{1}^{2}l(x,y)\mathrm{d}\mathrm{x}\mathrm{d}y\Big{[}\iint I (x,y)\mathrm{d}\mathrm{x}\mathrm{d}y\Big{]}^{-1} \tag{27}\]  
In this paper, to further explain the beam expansion, we introduce the relative vacuum expansion factor, which is the ratio of the beam expansion to the expansion in the vacuum. Its expression is given by the following:  
\[K_{W}=W_{eff}/W_{eff0} \tag{28}\]  
where \(W_{eff}\) is the effective beam radius in turbulence, and \(W_{eff0}\) is the effective radius in vacuum.  
In this section, we set the weights to 0.00, 0.26, and 0.45. Figure 6 shows the variation of the effective beam radius \(W_{eff}\) and the relative vacuum expansion factor \(K_{W}\) for four typical single aberration conditions as the turbulence strength changes from \(2.5\times 10^{-16}m^{-2/3}\) to \(10^{-13}m^{-2/3}\).  
From Figure 6, we can clearly see that stronger turbulence results in more severe beam expansion. The influence patterns of the weight coefficients of different aberrations on the beam expansion characteristics in the far field are distinct: (1) When the weight coefficient of \(Z_{4}\) aberration is higher, the effective beam radius in the far field is larger, and the relative vacuum expansion factor also increases, particularly in stronger turbulence conditions. Compared to the state without aberrations, \(Z_{4}\) aberration always has a larger effective beam radius. In weak turbulence conditions, its relative vacuum expansion factor is consistently smaller, but as the turbulence strength increases, \(Z_{4}\) aberration begins to enhance the beam expansion effect, especially under higher weight coefficients. (2) As the weight coefficient of \(Z_{7}\) aberration increases, the effective beam radius and the relative vacuum expansion factor both increase. Compared to the state without aberrations, \(Z_{7}\) always exhibits more severe beam expansion, particularly under higher weight coefficients. (3) The beam expansion phenomena of \(Z_{11}\) and \(Z_{25}\) aberrations are similar, in that both the effective beam radius and the beam expansion become larger as turbulence strength or the weight coefficient increases. However, compared to the state without aberrations, \(Z_{11}\) aberration has a little greater promoting effect on beam expansion.  
Figure 6: The variation of \(W_{eff}\) and \(K_{W}\) with \(C_{n}^{2}\) for no aberration and four typical aberrations (\(Z_{4}\), \(Z_{7}\), \(Z_{11}\), \(Z_{25}\)) at different \(a_{i}\), respectively. (**a,b**) \(Z_{4}\); (**c,d**) \(Z_{7}\); (**e,f**) \(Z_{11}\); (**g,h**) \(Z_{25}\).
## 1 Introduction
Ideally, to maximize request processing efficiency, the number of concurrently processed tasks should be reduced when computational resource utilization is high and increased when resource utilization is low, thereby achieving optimal processing speeds. In the classical token bucket algorithm, task shaping is achieved by filling the token bucket with tokens at a specific rate, making it challenging to determine the optimal token fill rate. Consequently, under high-concurrency conditions, the average task processing delay increases and the service rate decreases. To address these challenges, this paper proposes an adaptive task traffic-shaping algorithm designed for environments with limited resources. This study is an improvement of the traditional token bucket method, mainly addressing the limitations of fixed-rate token bucket algorithms in single-server GIS platforms by proposing an adaptive task traffic-shaping framework that dynamically adjusts the token fill rate based on real-time system load. The following are the main contributions of this research: (1) A task request queueing model is put forward to formulate the relationship between the arrival rate of queue-expected tasks and the current system task processing rate. This model serves as a link between theoretical queueing dynamics and practical system behavior, thereby establishing a connection that enables the translation of theoretical concepts into real-world system operations. (2) A hybrid analytical framework,which integrates the M/M/1 queueing theory and Hidden Markov Modeling (HMM), is developed to predict the optimal token rates. The predicted token rates are utilized to dynamically regulate the arrival rate of queue-expected tasks. Subsequently, based on the adjusted arrival rate, the token fill rate is adaptively adjusted, ensuring the system's adaptability to changing task loads. The proposed approach eliminates manual rate tuning, enhances resource efficiency, and ensures stability under heterogeneous workloads, offering a tailored solution for resource-constrained GIS systems. The remainder of this paper is organized as follows: Section 2 introduces the principle of token bucket algorithm, which is an important foundation and theoretical basis for the proposed methodology, then reviews the relevant research on traffic-shaping methods and summarizes the shortcomings of the existing methods; Section 3 elaborates the methodology for the adaptive traffic-shaping algorithm based on resource loading; Section 4 reports the results and performance analysis; Section 5 discusses the advantages, application prospects, and limitations of the proposed method; and Section 6 presents the conclusions of the study.
# Design of Direct Current Microgrid Converter with Cost-Effective Low-Voltage Battery Storage System
## 6 Battery
### Balancing Circuit  
Lithium-ion batteries are sensitive to an even voltage distribution between cells connected in series. To avoid overvoltage damage while charging, an external balancing circuit is required.  
There are two main types of balancers regarding small power scale depending on topology.  
A passive balancing circuit mostly compensates for small voltage differences, and an active circuit topology is used for big voltage differences. A large-capacity power bank usually uses an active system due to the high power flow needed.  
Passive systems are based on the burning energy of cells to a dummy load, so they are not very effective. Their advantages are cost and minimal possible voltage differences for starting to balance.  
The active systems require isolated converter topologies and usually start working only if the voltage difference between cells reaches the threshold value.  
A passive balancing circuit looks like a good cost-effective solution on a small power scale. However, in our case of using 18,650 recycled cells from laptop batteries, the cells are  
Figure 6: (a) Current slope for different cases; (b) converter working intervals (active parts are red).  
not in the same condition. These differences can be reduced by pairing cells with capacity and equivalent series resistance parameters.  
Despite this context, voltage differences can still be high when using only a passive balancer. Our proposed solution uses a cheap active system balancer based on energy flow only between two adjacent cells (Figure 7). This eliminates the need for isolated topology because isolated magnetic components are large and expensive.  
High-frequency switching can also eliminate the size and price of components. In our design, we use a factory-produced balancer IC ETA3000. It provides a high switching frequency in built-in switching components. Only passive external components are needed. Therefore, this solution is cheap, small, and suitable for recycled cells with bigger parameter dispersion. The average value of the balancing current is set to 1 A. This means there is a battery capacity of 0.1 (10 Ah).  
The disadvantage of this solution is that it balances only between adjacent cells. For our battery voltage using three cells in a series topology, we need to use two balancer circuits. In practice, there may be a need to transfer energy between two non-adjacent cells. In this case, the middle cell is used to transfer energy between two balancing circuits (Figure 8). This can reduce the life of the middle cell.
## 3 Results
### Running Initiation Key Events of Interest/Running Initiation Performance
#### 3.3.3. Participant Scale  
The sample scale figure is completed by Figure 6, which shows the three key initiation events in VE and RWE for each participant. At the sample scale, the participant-scale analysis demonstrates that the three key events exhibit a similar pattern for every participant with the wave initiation that occurs first, followed by the gaze release action that occurs in second place and the foot lift action that occurs in third place. However, although the pattern is maintained, the participant-scale analyses reveal differences among participants in the relative timing of these events.  
For example, the average timings of P6 and P9 in the RWE condition are very different for the three key events, especially for the wave initiation. P6 initiates their wave much later than P9 (\(-\)2285 \(\pm\) 300 ms vs. \(-\)4581 \(\pm\) 300 ms). However, despite the differences between participants, the behavior of most participants seems to be maintained throughout the VE condition. The only participants with obvious behavioral differences are P1, P4, and P8, with P1 and P4 being the only ones to show a visible difference over the whole running initiation, i.e., over the three key events. The mixed ANOVA (repeated measures: conditions and events; fixed factor: participants) confirmed the previous description showing no significant behavioral difference between the VE and RWE conditions. However, all other comparisons are significant, whether single effects or interactions. The timings of the events are significantly different (F (1,85) = 3760.165; \(p<0.001\); \(\eta_{p}^{2}=0.978\)), the participants' behavior also differs significantly (F (11,84) = 40.219; \(p<0.001\); \(\eta_{p}^{2}=0.840\)), and a double interaction of participants \(\times\) conditions \(\times\) events is also significant (F (11,86) = 6.290; \(p<0.001\); \(\eta_{p}^{2}=0.452\)). Intra-participant post hoc comparisons with Bonferroni correction were made in order to compare each key event's timing occurrence in both conditions (i.e., VE and RWE). The results show that for 8 of the 12 participants, there was no significant timing difference for any of the key events. Nevertheless, significant differences were found  
Figure 5: Averaged within-participant variability for each event (wave initiation shown in blue, gaze release in red, and foot lift in green) in real-world environment (RWE) and virtual environment (VE). Error bars represent standard deviation of each individual variability for each event in both conditions. Clouds of points represent individual variability of each participant for each event in both conditions. *** \(p<0.001\).  
for P1, P4, P8, and P10 for at least one of the three events (P8--wave initiation: \(p<0.05\), \(d\) = 2.905; P4--gaze release and foot lift: \(p<0.05\) and \(p<0.05\), \(d\) = 2.857 and \(d\) = 2.505; P1--gaze release: \(p<0.01\), \(d\) = 2.28; P10--gaze release: \(p<0.05\), \(d\) = 2.536).