esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Simulation and Analysis  
### Test Environment and Experimental Data  
The proposed CG-TSA was used to solve the problem of airport gate assignment. To test and simulate the scenario, 10 gates for 40 flights between 0:00 and 24:00 were used based on a typical airport flight schedule for a day. Gates 1-10 were numbered. Gate 1, Gate 6, Gate 7, and Gate 9 were the four big gates. The five medium gates were Gate 2-Gate 3, Gate 5, Gate 8, and Gate 10, and the one small gate was Gate 4. Gates 1-4 and Gates 6-7 were located near the bridge. Gate 5 and Gates 8-10 were located off the bridge. Large gates can be assigned to all types of aircraft, medium gates can be assigned to medium and small aircraft, and small gates can only be assigned to small aircraft. Table 7 contains information about the boarding gate (where \"1\" indicates that the gate is close to the boarding bridge and \"2\" indicates that the gate is far away from the boarding bridge). Table 8 contains details of flight information. The information described includes the flight number, the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fain No.**} & \multirow{2}{*}{**Dim**} & \multicolumn{2}{c}{**TSA**} & \multicolumn{2}{c}{**CNO**} & \multicolumn{2}{c}{**FBO**} &  
serial number of the flight number in order of arrival time, the aircraft type (represented by 1-8 in this document), the matching gate type (1-3 for small, medium, and large gates, respectively), and the landing runway (there are three landing runways represented by A, B, and C, respectively). The safe time between two adjacent flights at the same gate is 20 min. Table 9 contains information on fuel consumption. The described information includes the fuel-consuming reference models as well as the average fuel consumption per minute. Table 10 contains information on runway-to-gate distances. The distance between the aircraft number and each runway is specified (in this paper, runways A, B, and C).
## 3 Results and Discussion  
### Results of the 1 st Model (Evaluating the 1 st Model Independently)  
As shown in Figure 8, owing to the intrinsic stochasticity of the KNN-KDE probability model, a minor fraction of particles is inevitably assigned to geometrically occluded, physically inadmissible regions behind the mask wires. To rectify these artifacts, we implemented a dedicated projection algorithm (see Appendix A) that probabilistically maps each invalid sample to the nearest location compatible with the imposed geometric constraints. To address invalid predictions, each point is repositioned to a location more consistent with known physical behavior. These adjusted values are then combined with the original valid outputs to create a complete and coherent prediction set. Using a localized, probability-weighted sampling strategy, the approach effectively reconstructs missing or erroneous data in regions such as dead zones, ensuring both physical plausibility and alignment with the spatial particle patterns present in the training dataset. The reallocated results for the specific test sample (with an inlet pressure of 4 MPa, an inlet temperature of 800 \({}^{\circ}\)C, a substrate SOD of 20 mm, a mask SOD of 16 mm, a mask wire diameter of 0.46 mm, a mask opening size of 1.14 mm, and an open area of 51%) are illustrated in Figure 16.  
Figure 16 shows that the projection-reallocation step successfully recovers a particle arrival pattern that matches the expected flow behavior in a masked cold spray process. The mask wires block the particles, creating clear empty regions--dead zones--where no particles reach. Outside these zones, the highest particle density appears along the jet centerline and gradually decreases outward as the plume spreads. Overall, the corrected distribution (Figure 8 vs. Figure 16) respects physical boundaries and accurately captures the spatial behavior of particles, rather than simply filtering out noise.  
Figure 15: Optimization and weighting process for the weighted random forest model.
# Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine
###### Abstract  
A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.  
time series prediction; extreme learning machine; adaptive weight; online learning Article
## 2 Materials and Methods
### Transwell and Competitive Exclusion Experiments  
Transwell experiments were conducted based on the method presented by [PERSON] et al. [47]. Strain NH and _C. jejuni_ were cultured in separate serum bottles, each containing 20 mL Bolton broth (EMD Millipore, Billerica, MA, USA), and placed in a 7 L Anaerobic chamber (Mitsubishi Gas Chemical, Tokyo, Japan). This was followed by the addition of pressurized microaerobic atmosphere (10% CO2, 5% O2, 85% N2) to the box through a custom-designed valve, with subsequent growth occurring for 18 h at 42 \({}^{\circ}\)C with 120 RPM orbital shaking, as described previously [46]. Briefly, the cultures were washed twice in maximum recovery diluent (MRD, 0.1% peptome, 0.85% NaCl, pH 7.0), enumerated by dilution at 1:2 with 10% sterile buffered formalin (Sigma), and counted with a thin-body Petroff-Hauser counter and a phase-contrast microscope at 1000\(\times\). The washed cultures were then added at a density of 1 \(\times\) 10\({}^{6}\) cells/mL in 2 mL Bolton broth per chamber of wells in a 6-well transwell plate (Corning, Kennebank, ME, USA). The cultures were either added to the same side or opposite sides of the transwell membranes, as described in Section 3. Control wells contained either strain NH or _C. jejuni_ alone. The inability of strain NH to pass through the membrane of the transwell inner chamber was confirmed in the control wells. Transwell plates were placed inside a sealable bag and the bag was inflated with a microaerobic atmosphere (10% CO2, 5% O2, 85% N2). The inflated bag was placed inside a glass dish and placed in a rotating incubator at 42 \({}^{\circ}\)C with 120 RPM shaking. Each chamber of each well was sampled by the removal of 0.23 mL at 0, 10, 24, and 30 h post-inoculation. Removed samples were serially diluted with MRD and plated on modified charcoal cefoperazone deoxycholate agar (mCCDA) consisting of Blood Free Campylobacter Agar Base (HiMedia, Mumbai, India), 1.5% agar, and 33 ug/mL cefoperazone sodium, and on tryptic soy agar (TSA). Preliminary experiments confirmed that strain NH failed to grow on mCCDA under a microaerobic atmosphere, while _C. jejuni_ failed to grow aerobically on TSA. The mCCDA plates under an microaerobic atmosphere were thus used to quantify the growth of _C. jejuni_ in the transwells, while aerobic TSA plates were used to quantify the growth of strain NH in the transwells. Identical experiments were conducted to investigate the competitive exclusion of _L. monocytogenes_ by strain NH, except that the medium for the growth of _L. monocytogenes_ in transwells was tryptic soy broth (TSB, EMD Millipore, Billerica, MA, USA). The transwell plates, for _L. monocytogenes_, were incubated in sealable bags inflated with ambient air from an aquarium air pump, and the enumeration of _L. monocytogenes_ was performed on Oxford medium (Hardy Diagnostics, Santa Maria, CA, USA) with _Listeria monocytogenes_ selective supplement (Oxoid, Basingstoke, UK).
## 7 Conclusions  
To tackle the fault-tolerant problem in a many-to-one stream model in a smart healthcare monitoring scenario, we have designed a proactive resilient data transmission mechanism with the principle of random linear network coding. This fault-tolerant scheme includes two aspects: the greedy grouping of nodes and coding tree construction. As long as the gateway receives a sufficient number of linearly independent encoding combinations, original packets can be recovered quickly even when some errors occur. Additionally, a scalable simulation framework with encoding and decoding functions has been devised to verify the performance of three different fault-tolerant mechanisms. Based on this platform, a number of experiments have been conducted comparing transmission reliability, costs of resource consumption, latency performance, and transmission efficiency. Numerical statistics have shown that this elastic transmission mechanism outperforms traditional network protection mechanisms in improving the successful delivery ratio and the useful throughput ratio, and reduces the degree of resource redundancy and average end-to-end delay. The performance analysis offers some guidelines for effectively and efficiently designing survivability protection methods under different traffic patterns. In the future, this proactive network-coding-based elastic-transmission mechanism will be implemented in a real-time healthcare monitoring systems.  
The preliminary work of this paper was presented at the International Conference on Computer Science and Software Engineering (ICCSSE) 2013. The work was supported by the National Natural Science Foundation of China (61501105, 61471109, 61471110), the National Key Research and Development Program of China (Grant No. 2016 YFC0801607), the China Postdoctoral Science Foundation (2013M541243), the Fundamental Research Funds for the Central Universities (N150404018, N130304001, 150404015, N150401002, N161608001), and the Postdoctoral Science Foundation of Northeast University (20140319).  
[PERSON] performed the experimental part, modelled, analyzed data and reported the results. [PERSON] and [PERSON] contributed to the experimental part and result analysis. [PERSON] and [PERSON] guided the whole research and supported the structure of the paper.  
The authors declare no conflict of interest.  
Figure 14: Comparison of useful throughput ratio.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 3 Results  
### CYGNSS Observations  
Figure 3 shows the CYGNSS SR measurements in the main affected cities in Henan from 20 July 2021. Figure 4 shows the CYGNSS measurements after quality control obtained through Section 2.1.2. The data are the points sampled by CYGNSS along the satellite tracks. And for the CYGNSS data for our study area, we generated a grid (approximately \(3\times 3\) square kilometers) and applied linear interpolation. Figure 5 shows the gridded data. As can be seen from the figure, the gridded data are more sensible than satellite tracks representation.  
Figure 4: CYGNSS sampling points after quality controls on 20 July 2021.  
Figure 3: CYGNSS sampling points along the satellite tracks on 20 July 2021.
## 7 Conclusions  
We presented a systematic method to determine the optimum aspect ratio and showed that an aspect ratio of 416 \(\times\) 416 has higher mAP for both 5000 and 10,000 steps. By  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **SSD Mobilenet V2** & \multirow{2}{*}{**EfficientDet-Lite0 (\%)**} & **Mobilenet-SSD (\%)** \\  & **FPNLite (\%)** & & \\ \hline Blue cube & 34.02 & 22.85 & 8.12 \\ Blue cylinder & 27.9 & 6.83 & 18.81 \\ Red cube & 54.95 & 13.27 & 28.52 \\ Red cylinder & 42.4 & 36.48 & 36.63 \\ Yellow cube & 54.39 & 4.70 & 22.64 \\ Yellow cylinder & 38.54 & 23.66 & 29.41 \\ Overall mAP & 42.03 & 17.96 & 24.02 \\ \hline Comparison & Nil & \(-\)24.07 & \(-\)18.01 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Mean Average Precision of lightweight detectors.  
increasing the RGB saturation level of the images, we gain a 7% increase in mean Average Precision (mAP) when compared to the control group and a 20% increase in mAP when compared to the COCO2017 validation dataset of 22.2%. We showed that Learning Rate 0.08 with Edge TPU Standard provided a high detection score of 97%, as compared to Learning Rate 0.16 with Edge TPU Max. By combining the enhancement level and variation of distance, we proved that the optimum approach distance of 45 cm was able to obtain the maximum detection scores. The results are validated by comparing the performance with other SOTA embedded controllers--EfficientDet-Lite0 and Mobilenet-SSD.  
Furthermore, our mAP for SSD Mobilenet V2 is 46.63%, whereas the mAPs of previous studies such as [PERSON] et al. [10] and [PERSON] et al. [32] are 45% and 23.4%, respectively. This demonstrates how our research has led to improved object detectors.  
In the future, we plan to continue to develop a machine learning model with practical data preparation for embedded devices. Our goal is to further improve the inference time and Average Precision so that it can be used in applications such as the tightening of bolts and holes and the alignment of shipping containers. The use of machine learning models for pick-and-place applications on Raspberry Pi using SSD MobileNet V2 FPN320-Lite is relatively new and will provide useful insights toward developing vision systems that can perform reliably on real-world images.  
Conceptualization, E.K. and J.J.C.; methodology, E.K., Z.J.C. and M.L.; validation, E.K., Z.J.C. and M.L.; formal analysis, E.K., Z.J.C. and M.L.; investigation, E.K.; writing--original draft preparation, E.K.; writing--review and editing, E.K. and J.J.C.; supervision, Z.J.C.; funding acquisition, J.J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data that support the findings of this study are included within the article.  
The authors declare no conflicts of interest.
###### Abstract  
Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt-var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.  
Voltage unbalance; volt-var curve control; smart PV inverter; multiagent proximal policy optimization
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 4 Semi-Active Control of Suspension Systems
### Modern Control Strategies
#### 4.2.2 Model Predictive Control (MPC)  
Although linear quadratic optimal control can directly obtain the optimal feedback matrix, it fails to account for system constraints, thereby limiting its practical application. In contrast, MPC considers system dynamics constraints when solving the optimization problem within a finite time horizon, making the control more practical for real-world use. MPC gained widespread application in the control field during the 1980s [60]. The fundamental steps of MPC include predicting future system states, optimizing the open-loop system, and applying the optimization results to the system. With the rapid advancement of science and technology, the online solving capability of controllers has significantly improved. This progress has catalyzed extensive research and rapid development of MPC by scholars.  
[PERSON] and [PERSON] were among the pioneers in applying the concept of MPC to analyze the impact of different prediction horizons on the control performance of semi-active suspension systems in the frequency domain [15]. [PERSON] et al. conducted comparative studies on the effects of constrained optimal control, unconstrained optimal control, and MPC [61]. They found that when the control time horizons were set to 1, MPC achieved the same performance as constrained optimal control. Furthermore, MPC's performance progressively improves as the control time horizons increase. [PERSON] et al. enhanced MPC by utilizing system affine theory, thereby reducing the online solution time of the MPC algorithm [62]. [PERSON] and [PERSON] validated the control efficacy of MPC (shown in Figure 11b) using a half-vehicle suspension model (shown in Figure 11a) across various frequency bands [63; 64]. [PERSON] et al. applied MPC to the semi-active suspension control of passenger vehicles, demonstrating favorable ride comfort results through simulation analysis [65].  
Figure 11: A preview controller of semi-active suspension based on a half-car model proposed in [63]: (**a**) the control-oriented semi-active half-car model; (**b**) the preview control scheme.
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 3 ChatGPT for K-GEC
### Types of Validation  
To analyze the proficiency of LLMs in the K-GEC task, a holistic metric is not sufficient; an in-depth analysis per error type is imperative. Therefore, we evaluate across a total of 21 error categories. Table 1 shows descriptions of the categories under scrutiny. **Spacing Error**: Breaching Korean spacing standards can happen, and just like in deep learning models, such mistakes might arise from individuals typing rapidly or from certain habits. **Punctuation Error**: Incorrect punctuation usage can alter the meaning of a sentence. This mistake might be made by deep learning models, particularly when confronted with unfamiliar terms. **Numerical Error**: There can be a mix-up between cardinal and ordinal numbers. For example, (The second)' could be mistakenly typed as (The two)'.  
Breaching Korean grammar and spelling standards is commonplace. Such mistakes are categorized into primary and secondary, which can occur concurrently. The primary error is as follows: **Remove Error**: Certain words or their endings may be omitted, resulting in this error type. Such omissions are a recognized error category in Korean. **Addition Error**: This includes the unnecessary repetition of words, overlooking postpositions, or erroneously adding endings, which can stem from rapid typing or a lack of grammatical understanding. **Replace Error**: Errors like substituting one word with another or misarranging syllables within words often arise from fast typing. **Separation Error**: Separating the consonants and vowels within characters is frequent, often because the space key is typically employed for word separation. **Typing language Error**: These errors occur when typing while the keyboard mode is not set to Korean. For instance, (\") might be typed as 'dkcla'. **Foreign word conversion Error**: Korean has its set standards for foreign word pronuncations. Deviations like writing (\") instead of (\") result in errors.  
The secondary error is as follows: **Consonant vowel conversion error**: Errors in non-verbal units can occur, like writing  
the context or situation. **Auxiliary predicate Error**: Misuse of auxiliary verbs, which are essential for constructing Korean sentences. **Dialect Error**: Utilizing non-standard regional language variations. Determining if something is an error depends on the intention of the speaker or writer. **Polite speech Error**: Using an inappropriately formal or informal expression, indicative of Korean cultural nuances. **Behavioral Error**: Assigning an action to a subject incapable of it, such as 'the banana eats the orange'. **Coreference Error**: Erroneous references in sentences can produce unintended outcomes. **Discourse context Error**: Producing sentences that are inconsistent with previous content or context. **Neologism Error**: Incorporating words or spellings that are not acknowledged in standard grammar. Determination of an error, similar to dialects, depends on the user's intent.
## 4 Results and Comparison
### RUL Estimation for Test Instance Bearings
#### 4.1.4 RUL Estimation for Test Instance Bearing1-7  
The similarity evaluation for the test instance Bearing1-7 is performed based on the 2 nd PC feature sequence using Bearing1-2 as the learning instance. From the segment of [0, 825] (time cycles) of Bearing1-2, the nine synthetic feature sequences are generated with the scaling factor \(\alpha=2.40\), 2.50, 2.60, 2.70, 2.80, 2.90, 3.00, 3.10, and 3.20, as shown in Figure 14. The duration \(\Delta\) is equal to 45 (time cycles). Table 5 presents the results of similarity evaluation between each synthetic sequence and the degradation segment of [0, 1500] (time cycles) of Bearing1-7. Selecting the seven synthetic sequences of \(\alpha=2.50\), 2.60, 2.70, 2.80, 2.90, 3.00, and 3.10, the final RUL estimate can be computed as \(RUL_{e}=822\) (time cycles) using the proposed ensemble method. As the actual RUL of Bearing1-7 is known as \(RUL_{a}=757\) (time cycles), the relative error of estimation can be calculated as \(E_{r}=-8.61\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(\alpha\) & 3.40 & 3.60 & 3.80 & 4.00 & 4.20 & 4.40 & 4.60 & 4.80 & 5.00 \\ \(d_{min}(\alpha)\) & 0.2689 & 0.2371 & 0.2145 & 0.2009 & 0.1963 & 0.2000 & 0.2109 & 0.2264 & 0.2444 \\ \(RUL(\alpha)\) & 2031 & 2187 & 2342 & 2497 & 2652 & 2806 & 2959 & 3111 & 3263 \\ \hline \end{tabular}
\end{table}
Table 4: Similarity evaluation for RUL estimation of Bearing1-6 using Bearing1-2.  
Figure 13: Similarity evaluation for RUL estimation of Bearing1-6 using Bearing1-2.
## 3 Experiments and Analysis
### Evaluation Indicator  
In this experiment, mAP (mean average precision), FPS (frames per second), and ModelSize are used as model performance evaluation metrics, which are defined as shown in Equations (1)-(3).  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[AP=\int_{0}^{1}Precision(Recall)d(Recall) \tag{3}\]  
\(T\) and \(F\) denote the number of samples classified correctly and incorrectly, respectively, and \(P\) and \(N\) denote the number of samples predicted to be positive and negative, respectively. \(TP\) indicates the number of samples for which the sample is positive and which the classifier predicts to be positive. \(FP\) indicates the number of negative samples predicted to be positive using the classifier. \(FN\) indicates the number of samples that are positive and predicted to be negative using the classifier.