esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Additionally, many research papers had determined that the result range of the SQI is changeable depending on the laboratory and RS-derived sub-parameters [17; 18]. Another study addressed the topic by using multiple components such as vegetation, and Soil Quality and applied the MEDALUS method on which our study also took its basis: in research that was carried out for near real-time yield forecasting on cotton areas of Jarkurgan district, Uzbekistan, during the timeline from April through August 2019, used geospatial analyses of RS-based multi-temporal vegetation and multi-source SQ indices such as soil salinity, soil texture, slope gradient, soil depth, drainage condition, the content of total calcium carbonate in soil, and soil organic matter rate [19; 20]. For this exact purpose, the MEDALUS method for indicating the SQI was used, which consisted of both RS and laboratory data. As a result, the correlation between those indices and crop yield had been confirmed with a 96% validity rate. Although the method suggested by the researchers was relatively simpler and more apprehensible to apply for yield forecasting purposes, it was not quite accessible due to the need for field information such as laboratory results for indicating SQI. However, unlike the Mamatkulov approach, we estimated those indices only using RS data and GIS methods without applying any field information such as laboratory-derived SQI indicators. In this context, the main purpose of the study is to forecast the yield of the cropland which we lack information on the productivity of prior years, without the need for any field research or regression models. Unlike the other RS-based empirical-statistical yield estimation methods which require multiple-year trends and RS data to develop regression models, in this method, only the combination of bare soil, sprouted field, and elevation frames are needed. In addition to that, contrary to the other RS-based Yield estimation methods, in this article possibility of estimating yield forecast without the need for the regression models based on the previous yield reports was tested. To conclude everything that has been stated so far, for this research,the applicability of the satellite-based data and calculations on indicating SQI elements and Yield forecasts were tested. The study paper is organized as follows: Section 2 describes the study area and several experimental data such as Sentinel 2 mosaic datasets and SRTM Digital Elevation Model (DEM) data. Section 3 describes the methodology used in this study. Section 4 discusses the test results and discussion. Section 4 presents the major conclusions of the study.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 4 CubeSat Scheduling Model
### Restrictions
#### 4.3.3 Power-On Constraints  
In order to rationalize the initial start-up problem of tasks, all tasks must satisfy the step-by-step start-up constraints and the start-up sequence constraints.  
\[\phi_{j,t}\geq\phi^{\prime}_{j,t} \tag{21}\]  
\[\sum_{t=1}^{T}\phi^{\prime}_{j,t}=1,\forall j \tag{22}\]  
\[\sum_{l=1}^{t}\phi^{\prime}_{j,l}=\begin{cases}1,\left(\begin{array}{c}if \sum_{l=1}^{t}\phi_{j,l}\geq 1,\forall t\in\{1\dots T\}\\ 0,\left(\begin{array}{c}if\sum_{l=1}^{t}\phi_{j,l}\leq 0,\forall t\in\{1\dots T\} \\ \end{array}\right)\end{array}\right) \tag{23}\]  
Constraints (21) to (23) establish an auxiliary decision variable \(\phi^{\prime}_{j,t}\) that ensures that the auxiliary decision variable \(\phi^{\prime}_{j,t}\) can detect the first start of job \(j\). \(\phi^{\prime}_{j,t}\) takes the value of \(1\) only when job \(j\) is first started and is \(0\) for the rest of the time.  
\[\sum_{j=1}^{l}\phi^{\prime}_{j,t}\leq 1,\forall t \tag{24}\]  
Constraint (24) is aimed at the initial start-up of the CubeSat and is used to constrain the initial start-up timing of tasks so as to avoid the situation that multiple high-priority tasks are powered on at the same time.  
\[\sum_{l=1}^{t}\phi^{\prime}_{j,l}\leq\sum_{l=1}^{t}\phi^{\prime}_{j,l^{\prime} },t\in\{1\dots T\}\qquad\text{ \ \ \ \ \ \ \ }if\ u_{j_{1}}\leq u_{j_{2}} \tag{25}\]  
Constraint (25) is used to constrain the start-up sequence between multiple tasks. The start-up sequence is in the order of task priority; tasks with higher priority are started first, and tasks with lower priority are started later. No timing constraints are imposed on tasks with time window restrictions.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 3 Evaluation Results and Discussion
### Evaluation Metrics and Reference Methods  
To evaluate the performance of the proposed and other reference methods, three evaluation metrics are used, including raw pitch accuracy (RPA), raw chroma accuracy (RCA), and overall accuracy (OA).  
\[RPA=\frac{N_{TP}}{N_{VF}} \tag{20}\]  
\[OA=\frac{N_{TP}}{N_{FR}} \tag{21}\]  
where \(N_{TP}\), \(N_{VF}\), and \(N_{FR}\) are the number of true positives (correctly estimated voiced frames), voiced frames and total frames. RCA is similarly calculated as RPA, but the octave errors are ignored.  
The three reference methods are the on-line melody extraction from polyphonic audio using harmonic cluster tracking (HCT) [11], the predominant-F0 estimation using Bayesian harmonic waveform models (BHWM) [10], and the melody extraction based on the modified Euclidean algorithm (MEA) [12]. For detailed information of these three reference methods, please refer to Section 1 and the related literature.
## 3 Ecotoxicity
### Biodegradability  
Biodegradation tests have been widely performed to determine the impact of these substances in the environment. Most of the tests used methodologies in accordance with the OECD 301 D guidelines for the evaluation of aerobic biodegradability [101].  
Cholinium-based solvents have been determined to be readily biodegradable [108], showing more than 60% biodegradability in a time lapse between 7 and 14 days. Compounds tested by [PERSON] et al. [61] reached 96% biodegradability for ChCl:oxalic acid, 84% for ChCl:glyecline, and 66% for ChCl:glucose. The individual components of mixtures were also classified as readily biodegradable according to the OECD criteria. In the case of reported DESs, it was found that low toxicity is related to higher biodegradability [61; 87].  
For ILs, the alkyl chain and the lipophilicity of the compound influence its biodegradability, although it has been seen that some compounds show worse biodegradability profiles due to the presence of a larger number of carbons [109]. The effect of hydroxyl groups also improves biodegradability.  
[PERSON] et al. determined the biodegradability of eight DESs combining two cholinium salts with four HBDs. It was found that ChCl-based DESs presented a higher biodegradability than ChAc-based ones. However, in this test, only ChCl/urea and ChCl/acetamide compounds could be considered to be readily biodegradable [87].  
In other works, the pure DES, glycerine (Gly), reline (Rel), and ethaline (Etha) were considered to be readily biodegradable, but when these mixtures were combined with water, the biodegradability of Gly and Etha decreased, while Rel reached a final percentage of biodegradation notably higher than that of the pure mixture [84]. Since there is not yet any proven explanation as to how the introduction of water into the system affects these compounds' biodegradability, only assumptions can be made. To obtain a full understanding of the influence of water on biodegradability, it is necessary to know how water affects the physicochemical and, therefore, the toxicological and biodegradable properties of these chemicals. DESs are formed via intermolecular hydrogen bonding between the individual components of the mixture. When water is introduced, these intermolecular bonds are broken, and new ones are formed with water [110]. Higher concentrations cause a reduction in inter- and intramolecular interactions, so dissociation can be more easily observed [111]. The dissociation of the mixture compounds could be one of the causes of variation in biodegradability. In general, no clear trend has been reported to date.
# Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
## 3 Results
From this result, it can be observed that the penetration loss of the D-band in thin glass is not very large. The loss of the D-band signal as it passes through the 5 cm-thick slate is about 20 dB in Figure 8d, indicating that the D-band millimeter wave signal can hardly penetrate the 5 cm-thick slate. However, in the four cases, under the same obstacle, the penetration loss does not change much with the increase in the signal frequency. It can be observed that the penetration loss of the D-band is independent of frequency.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
###### Abstract  
This study investigates the impact of response time on ability estimation within an Item Response Theory (IRT) framework, introducing time-sensitive formulations to enhance student assessment accuracy. Seven models were evaluated, including standard 1 PL-IRT and six response-time-adjusted variants: TP-IRT, STP-IRT, TWD-IRT, NRT-IRT, DTA-IRT, and ART-IRT. Three optimization techniques--Maximum Likelihood Estimation (MLE), full parameter optimization, and K-fold Cross-Validation (CV)--were employed to assess model performance. Empirical validation was conducted using data from 150 students solving 30 mathematics items on the \"TestYounself\" platform, integrating response accuracy and timing metrics. Student abilities (\(\theta\)), item difficulties (b), and time-effect parameters (\(\lambda\)) were estimated using the L-BFGS-B algorithm to ensure numerical stability. The results indicate that subtractive models, particularly DTA-IRT, achieved the lowest AIC/BIC values, highest AUC, and improved parameter stability, confirming their effectiveness in penalizing excessive response times without disproportionately affecting moderate-speed students. In contrast, multiplicative models (TWD-IRT, ART-IRT) exhibited higher variability, weaker generalizability, and increased instability, raising concerns about their applicability in adaptive testing. K-fold CV further validated the robustness of subtractive models, emphasizing their suitability for real-world assessments. These findings highlight the importance of incorporating response time as an additive factor to improve ability estimation while maintaining fairness and interpretability. Future research should explore multidimensional IRT extensions, behavioral response-time analysis, and adaptive testing environments that dynamically adjust item difficulty based on response behavior.  
adaptive testing; item response theory; response time; adaptive testing; K-fold cross-validation; speed accuracy; TestYounself platform; mathematics assessment Article
## 8 Retrieval Performance
### Wind Speed  
The methods of retrieving wind speed have been proposed using the LES and TES and waveform area by [7; 8], which are all to develop empirical mapping function between wind speed and the observable above. As shown in Figure 14, it is clear that wind speed has inversely proportional relationship with PW. In this paper, the analytic links between the wind speed and the observable are replaced by the neural network [34], in which the hidden layer has three neurons, neuron functions \(f_{1}(x)\) in the hidden layer is Sigmoid and \(f_{2}(x)\) in the output layer is purelin as  
\[f_{1}(x)=\frac{1}{1+e^{-x}} \tag{23}\] \[f_{2}(x)=kx \tag{24}\]  
To compare the performance of the single and multiple-parameter observation, two cases are adopted.  
1. case 1: the single observable of the delay waveform, such as PW, is the input of neural network;
2. case 2: the three observable are all considered as the input of the neural network.  
When developing retrieval models of wind speed, 1000 sets of simulated data are divided two groups. One group are considered as training set and the other as test set.  
Table 4 gives comparisons of retrieved wind speed using retracked and non-retracked waveform, from which it is summarized that (1) the result that retrieved wind speed using retracked waveforms have better precision than the ones using non-retracked waveforms, which indicates that the proposed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**1 ms [m]**} & \multicolumn{2}{c}{**1 s [m]**} & \multicolumn{2}{c}{**3 s [m]**} \\ \cline{3-6}  & & **re.** & **non-re.** & **re.** & **non-re.** \\ \hline std & 4.66 & 6.19 & 40.98 & 11.49 & 125.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard Deviations of Estimated sea surface height for Retracked and Non-Retracked Waveform. re. and non-re. is the Shorthand of Retracking and Non-Retracking.  
Figure 14: Relationship between wind speed and peak waveform.  
retracking method is valid; (2) the less the TRP is, the higher the retrieval results using retracked waveform are, hence, to obtain the better precision of retrieved wind speed, higher tracking refresh rate on-board is needed; (3) combining all observable waveforms to retrieve wind speed could significantly improve measurement performance, hence, multi-parameters observation which could uses more feature information of the waveform is a trend to improve retrieval precision.
## 1 Introduction
* Considering the characteristics of both large and small flows, we integrate active congestion control and real-time dynamic scheduling methods, and originally divide the traditional network into two sub-nets, performing network transmission according to the characteristics of different streams and different stages of scheduling. The two sub-nets can adjust the bandwidth allocation ratio V(\(\alpha\)) on each link according to different transmission tasks and topological structures. * A heuristic algorithm is designed for the MFLBS problem and tested on two highly versatile network topologies: the partial mesh model and the three-layer non-blocking fully populated network model. * A simulation experiment is designed for the cloud data center, and the MFLBS algorithm is compared with two other algorithms: the dynamic scheduling algorithm one-hop DLBS [17] and the static load-balanced scheduling algorithm FCFS [18]. The result proves that our algorithm can significantly improve the throughput and effectively reduce the average delay of the flow. The remaining content of the article is as follows. Section 2 reviews related work, and Section 3 details the network model of the data center used in this article and formulates the MFLBS problem. The scheduling mechanism of the two sub-nets and our MFLBS algorithm is described in Section 4. Subsequently, designs of the simulations are put forward in Section 5 to test the MFLBS algorithm and compare the MFLBS algorithm with other scheduling algorithms. Finally, Section 6 includes the summary of our research and future work.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 4 Experimental Results
### Comparison with Recognition Methods Based on Commonly Used Features  
In this section, the performances of commonly used features such as kurtosis, sum energy of IMFs (SEI), energy entropy of wavelet packet coefficient (EEWPC), spectral centroid (SC), and mean of MFCC (MMFCC) are compared with the proposed method to illustrate its superiority in this paper. The relevant outcomes are shown in Figure 13 and Table 2.  
For traditional methods, the training accuracy and test accuracy of energy entropy of wavelet packet coefficient is best, reaching 93.10% and 0.9286, respectively. It means that the recognition accuracy of the time-frequency domain features is significantly higher than that of time domain features and frequency domain features, which is because coal and gangue's vibration signals are non-stationary, and neither time domain features nor frequency domain features can fully demonstrate the subtle difference of collected signals. However, the time-frequency domain features can solve such a problem, thus, achieving higher recognition accuracy. In addition, the mean of MFCC shows good performance in identifying coal and gangue, reaching a recognition accuracy of 81.03%, which means advantages of auditory in recognizing coal and gangue signals. It further verifies the effectiveness of applying the  
Figure 13: Recognition results of different feature extraction methods.  
AN response model to recognize coal and gangue. However, as compared with the mean of MFCC, a higher recognition accuracy is achieved based on auditory spectrum features, which can be explained by the consideration of more auditory characteristics in the AN response model. Although the above features show excellent performance for identifying coal and gangue, their recognition accuracy is still less than that of the auditory spectrum-based features, which means such features extracted by the AN response model is more advantageous.
## 3 Experimental Details
### Evaluation of Photocatalytic Activity  
The photocatalytic activity was measured by the degradation of methyl orange during light illumination. To assess the adsorption of methyl orange, 10 mg of sample powder was added to 50 mL of 10 ppm methyl orange under magnetic stirring at 500 rpm. After that, the mixed solution was placed in the dark for 20 min in order to provide an adsorption-desorption equilibrium. Then, a fluorescent lamp, used as a light source, was activated to examine the photocatalytic degradation for 1 h. After light illumination, 2 mL of a mixed solution was collected at specified time intervals and was filtered through a polytetrafluoroethylene filter (PTFE, pore size: 0.2 \(\upmu\)m). Then, the absorption spectra of the collected solution were investigated by UV-visible spectrometry.
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 4 Results and Discussion
### Results
#### 4.3.5 Mean Intersection over Union  
The Mean Intersection Over Union (mIoU), as presented in Figure 9, is an evaluation metric that is frequently used for semantic image segmentation. The Intersection Over Union, also known as the Jaccard index, is mainly used to measure the percentage of overlap between the prediction and actual output. The Dice coefficient is used in training as a loss function, and this is closely associated with this evaluation metric. The equation below presents a formula to calculate the Intersection ver Union (IoU).  
\[mIoU=\frac{1}{n}\sum_{c=1}^{n}TP/(TP+FP+FN) \tag{11}\]  
First, with the help of the above formula, we calculated the IoU for all the classes and then computed the mean of all the values to provide a global value. Here, \(n\) represents the total number of classes, \(TP\) represents the true positives, \(FP\) represents the false positives, and \(FN\) represents the false negatives.  
Figure 8: Size graph of each model.  
Figure 9: Mean Intersection Over Union of each model.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 10 Discussion
This expectation is based on the fact that all the image parameters employed in the process of gamma/hadron separation and energy reconstruction are independent of the event direction. Although the inferences that can be made from the results hold true for Monte Carlo simulated events and would be necessary to revalidate these conclusions when they are extrapolated to different data samples (such as real data from observations made with actual Cherenkov telescopes), this study may however allow for the elimination of some of the less effective methods. The methods under investigation all make use of a space of image parameters that is well suited to Monte Carlo scenarios: real data are influenced in ways that cause this space to be distorted. When using Cherenkov telescopes, the night sky background changes during observation. Additionally, the atmospheric conditions can vary greatly, causing inevitable detector changes and malfunctions. None of these distortions have been the focus of our investigation. In light of these results, the Stacking Ensemble methods identified in this study hold promise for improving the performance of gamma/hadron discrimination and energy estimation in Cherenkov telescope observations. However, further research is needed to validate these findings on real data and to explore the potential of both temporal parameters (e.g., arrival time gradient and RMS of the images [27]) and other Machine Learning methods (e.g., Deep Learning models [28] such as Convolutional Neural Networks, Recurrent Neural Networks, or Generative Adversarial Networks, as well as unsupervised learning techniques [29] like clustering or dimensionality reduction) that were not considered in this study. The ultimate goal is to integrate new high-performing Machine Learning techniques into the official data reduction and analysis pipeline of the ASTRI project. By doing so, we aim to offer the most advanced tools for reconstructing real events acquired by the ASTRI Mini-Array. Finally, although the simulated data samples considered in this work are specific to the ASTRI Mini-Array, it is reasonable to think that some of the results that we have obtained can also be generalized, at least in qualitative terms, to other Cherenkov telescope arrays, in particular the CTAO small-sized telescopes (SSTs), which share many of the technical characteristics and scientific performance response with the ASTRI telescopes.
## 5 Simulation and Discussion  
### Experimental Data and Model Parameters  
The strategy proposed in this paper is validated through a case study of a typical single-node IES located in Nanchang, Jiangxi, during the summer season. The optimization model was implemented in MATLAB R2024a utilizing the YALMIP R20230622 toolbox and solved using the GUROBI 11.0.3 solver. All simulations were executed on a computer equipped with 16 GB of RAM and an AMD Ryzen 7 5800H processor (Advanced Micro Devices, Inc., Santa Clara, CA, USA) running at 3.20 GHz.  
The configuration parameters and operation cost parameters of each device in the IES are shown in Table 2. The parameters for the stepped carbon trading mechanism are shown in Table 3, the specific data for time-of-use electricity and gas prices are given in Table 4.  
As described in Section 3.1, 1000 scenarios were initially generated using LHS. Subsequently, the optimal number of clusters was determined to be eight using K-means clustering and the elbow method. Figure 4 illustrates scenarios for the WT and cooling load, serving as representative examples for the RES and load-side scenarios, respectively. Table 5 lists the probabilities and the cost distribution of these eight typical scenarios, illustrating the variations among them.