esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 5 Discussion
Furthermore, SVR struggles to accurately estimate an appropriate kernel mapping when data is scarce, leading to reduced generalization capability. Improving model performance may require further adjustment of model parameters. In the feature importance analysis, the SHAP values derived from the Random Forest model reveal that the L* value has the highest mean SHAP score, indicating that lightness exerts the greatest influence on the model's predictive output and represents the most critical feature. This finding also indicates that increases in solid ink density lead to more pronounced changes in the lightness of the printed ink on the paper. Furthermore, the results obtained from implementing the model-generated optimal solid ink density values for process control and sample sheet printing indicate that density regulation can effectively assist practitioners in maintaining gray balance, as evidenced by the neutral gray chromaticity parameters. In the absence of press-specific requirements, the solid ink color patches on printed samples are generally expected to comply with the international standard ISO 12647-2:2013. However, in practical applications, the target colorimetric values are not strictly constrained by these standards and may be adjusted to accommodate specific production requirements. It is important to emphasize that this study is the first to apply the Random Forest algorithm to the prediction of solid ink density in offset printing. Compared with other machine-learning algorithms, the Random Forest method exhibits greater robustness against overfitting, especially when trained on small-scale datasets. The modeling approach proposed in this study is applicable not only to the offset printing conditions used in the experiments but also exhibits strong portability and scalability. Unlike traditional physical modeling approaches, the quantitative relationship between colorimetric values and ink density established through Random Forest is independent of equipment or substrate characteristics and is based entirely on measured data. As a result, practitioners are not required to account for how specific presses or materials affect ink color reproduction. With appropriate modifications to the input features, the proposed method can be extended to various paper and ink combinations, as well as to spot color printing scenarios. Furthermore, the model offers both data support and algorithmic foundations for the development of intelligent color control systems in printing environments, facilitating the transition from traditional printing workflows to a \"predictive control--closed-loop optimization\" paradigm. Therefore, the proposed approach holds significant promise for both industrial application and academic research.
## 3 Results
### GPP, \(R_{eco}\) and \(\mathbf{\mathrm{NPP}}_{ec}\)  
Carbon fluxes _GPP_, \(R_{eco}\) and \(\mathbf{\mathrm{NPP}}_{ec}\) followed the seasonal pattern of _NEE_. Cumulative fluxes (_NEE_, _GPP_, \(R_{eco}\), _NPP_) during the period of the study are shown in the Appendix A (Figure A5). During the dormant season, the amount of carbon fixed by photosynthesis (_GPP_) had daily values that are close to zero (Figure 7). During the early spring, with the development of leaves, _GPP_ started to rise rapidly and peaked in June. After a peak in early summer, _GPP_ started to decline slowly and dropped again to small values after leaf fall. \(R_{ECO}\) followed the yearly pattern of air temperature (Figure 8). The highest daily values of \(R_{ECO}\) were achieved in the summer, while during the dormant season the daily values of \(R_{ECO}\) were significantly lower. The yearly pattern of \(\mathbf{\mathrm{NPP}}_{ec}\) followed the yearly pattern of \(\mathbf{\mathrm{GPP}}\). The highest daily values of \(\mathbf{\mathrm{NPP}}\) were also achieved in June and the lowest during the dormant season (Figure 9). Negative \(\mathbf{\mathrm{NPP}}\) indicates that autotrophic respiration exceeded _GPP_. Such a situation occurred mainly during the cold part of the year. Annual sums of carbon fluxes are shown in Table 1. Both the annual _GPP_ and annual \(R_{ECO}\) do not exhibit a statistically significant trend with time. Similarly to annual _NEE_, the annual values of _GPP_ and \(R_{ECO}\) also do not correlate significantly with the average annual \(R_{\mathrm{g}}\), \(T_{a}\) and P. Furthermore, they do not correlate with \(\Delta\)_SWCMay-Sqp_ either.  
Figure 6: Annual _NEE_ with respect to main meteorological variables: (**A**) mean annual global radiation; (**B**) mean annual air temperature; (**C**) annual precipitation; and, (**D**) \(\Delta\)_SWCMay-Sqp_, the difference in the mean monthly soil water content between the months of May and September (n.s.—not significant, CI—confidence interval).  
Figure 8: Daily values of \(R_{ECO}\) in Jastrebarsko forest during 2008–2017.  
Figure 7: Daily values of \(GPP\) in Jastrebarsko forest during 2008–2017.  
Figure 9: Daily values of \(NPP_{EC}\) in Jastrebarsko forest during 2008–2017.  
The highest annual _GPP_ (1775 gC m\({}^{-2}\) year\({}^{-1}\)) was recorded in the year 2015. The highest recorded annual \(R_{ECO}\) (1402 gC m\({}^{-2}\) year\({}^{-1}\)) occurred in 2015 as a result of drought and high soil and air temperatures during the vegetation period. Despite a high radiated energy, the lowest recorded annual value of _GPP_ (1384 gC m\({}^{-2}\) year\({}^{-1}\)) occurred in 2017, while the lowest annual value of \(R_{ECO}\) (1117 gC m\({}^{-2}\) year\({}^{-1}\)) occurred in the year 2008. _NPP\({}_{EC}\)_ was highest in the year 2009 (937 gC m\({}^{-2}\) year\({}^{-1}\)), while the year 2017 had the lowest annual _NPP_ (632 gC m\({}^{-2}\) year\({}^{-1}\)). Temporal analysis showed a slightly negative (\(-\)10.7 gC m\({}^{-2}\) year\({}^{-2}\)) but not statistically significant (\(p\) = 0.303) trend in _NPP\({}_{EC}\)_.
## 4 Approval Model
### Prediction Improvement  
From Table 6, to improve the efficiency of the prediction, EPOC number, number of hidden nodes, number of hidden layers and the value of the sigmoid function are increased. There are four hypotheses, similar to those of the preference model. After proving the hypothesis above, the model predicting the results of approval is summarized below in Table 7.  
From the above table, it can be seen that the NN with full factors before the change was accurate at 48%. After improved prediction by four hypotheses, the best result was 55% with 1 input layer, 1 output layer and 1 hidden layer, 10 hidden nodes and 3 output nodes. NN with cutting factors by Pareto before the change had an accuracy of 26%. After improved prediction by four hypotheses, the best result was 40% with 1 input layer, 1 output layer and 1 hidden nodes and 3 output nodes. The NN with cutting factors by 50% before the change had an accuracy rate of 30 and MSE of 0.00774%. After improved prediction by four hypotheses, the best result was 30% and MSE was 0.03905% with 1 input layer, 1 output layer and 2 hidden layers, 18 hidden nodes and 3 output nodes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Prediction Results**} \\  & **Accuracy (\%)** & **MSE** & **EPOC** \\ \hline NN & 48 & 0.00765 & 500 \\ NN with cutting & & & \\ factors by Pareto (80:20) & 26 & 0.00814 & 500 \\ NN with cutting & & & \\ factors 50\% & 30 & 0.00774 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summarized models predicting the structure.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Nodes**} & \multicolumn{2}{c}{**Output**} & \multicolumn{1}{c}{**Hidden**} & \multicolumn{1}{c}{**Hidden**} & \multirow{2}{*}{**Acc**} & \multirow{2}{*}{**EPOC**} & \multicolumn{2}{c}{**Sigmoid**} & \multirow{2}{*}{**MSE**} \\  & & **Nodes** & & & & & & & \\ \hline NN with full factors & 55 & 3 & 1 & 10 & 55 & 3000 & 0 & 0.00956 \\ NN with cutting factors & 32 & 3 & 1 & 40 & 40 & 8000 & 0.2 & 0.03264 \\ by Pareto(80:20) & & 30 & 2 & 18 & 40 & 4000 & 0.2 & 0.03905 \\ by 50\% & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of model predicting the results of approval model.
## 3 System Design
### Tracking
#### 3.7.1 Tracking in High-Sensitivity Mode  
When tracking Galileo E1b/c signals in high-sensitivity mode, the receiver initiates signal tracking with synchronization to the pilot's secondary code. This synchronization is achieved through the acquisition process, which computes the CAF using a circular cross-correlation with the complete Galileo pilot tiered code, including both the primary and secondary codes. Consequently, the tracking process can commence with an extended CI time that surpasses the duration of the PRN code. The receiver is configured to track the pilot signals, which have predictable tiered codes. This configuration enables the utilization of CI times that exceed the duration of a single data bit in the tracking process. These capabilities are essential for tracking weak signals, addressing the challenges posed by potential bit errors in the demodulated data due to low \(C/N0\) conditions [36; 37]. To effectively track weak signals, it is necessary to employ an extended CI time. However, compared to signal acquisition, a shorter CI time can be utilized for the tracking process, due to the fundamental differences between tracking and acquisition within signal processing. Tracking is an estimation problem that benefits from prior knowledge about the code phase and the carrier phase. This information not only enhances the sensitivity of the tracking loops but also allows for refined estimations of these parameters. In contrast, acquisition is primarily a detection problem, focused on initially identifying the signal's presence without the benefit of prior accurate information. In line with this, the receiver was tested using a tracking CI time of 40 ms, which is smaller than the CI time used in acquisition (100 ms). For more details on the configuration of the tracking loops, refer to Appendix A.  
The current implementation of the tracking algorithm does not include any type of multipath mitigation. The high sensitivity tracking mode is mainly designed to track weak GNSS signals.
## 2 Materials and Methods
### Calculating SIF\({}_{y}\)  
SIF signals incorporate both the physiological (photosynthetic) response of vegetation and the physical (absorbed and scattered light). SIF normalized by the absorbed photosynthetically active radiation (APAR) has been used to better approximate the physiological photosynthetic response of vegetation more independently of the absorbed and scattered light [40; 44; 58]. OCO-2 SIF\({}_{\rm d}\) and MODIS fPAR data were used to calculate a satellite-based approximation of _relative_ SIF yield (SIF\({}_{\rm y}\)). An estimated SIF\({}_{\rm y}\) at the satellite level was calculated according to the following equation:  
\[SIF_{y}=\frac{SIF_{d}}{cos(SZA)fPAR} \tag{2}\]  
where SZA is the solar zenith angle for the associated OCO-2 SIF measurement, and fPAR is the MODIS fPAR measurement with high-quality flag most closely matching the OCO-2 SIF measurement date.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.3 Supervised and Unsupervised  
[PERSON] et al. [58] presented a model that can be trained with and without supervision and that belongs to the supervised and unsupervised categories. The supervised setting uses reconstruction, diversity, and classification as loss functions, whereas the unsupervised setting uses reconstruction and diversity as loss functions.
## 7 Results
### Thermal Analysis  
In subpayload 1, PCO gathered temperature data for the sCMOS, power supply, and camera during every image capture. A temperature sensor was fixed to the PCO body, and the readings were registered by subpayload 2's Raspberry Pi in addition to its own internal temperature. The operational temperature of the payload components was a fundamental aspect of the payload design due to the expected ambient temperature of \(-80\) \"C. Figure 10 displays temperature readings for different components of the payload and the external environment during the payload's operational phase of the flight. This work is based on observations with the CNES temperature sensor under a balloon operated by CNES, within STRATO-SCIENCE 2023 and in the framework of the CNES/CSA Agreement.  
Figure 9: The distribution of time delays between images during high-resolution 2048 \(\times\) 2048 imaging, with the frequency of occurrences on the Y-axis and the time difference in milliseconds on the X-axis. The mode (460 ms), median (470 ms), and mean (593.28 ms) are indicated by the red dashed, green solid, and orange dash-dotted lines, respectively.
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 2 BeiDou Timing Group Delay Bias from Precise Products
### BeiDou Broadcast Ephemeris Error
If we separate the BDS-2 and BDS-3 satellites into two different groups, the average differences of \(\alpha\cdot R-Clk\) between BDS-2 and BDS-3, i.e., \(\text{Average}(\alpha\cdot R-Clk)_{BDS-3}-\text{Average}(\alpha\cdot R-Clk)_{BDS-2}\), can be derived, being 1.36, \(-\)1.90, and 7.61 ns at B1I, B3I, and B1I/B3I, respectively. However, it should be noted that except for the significant bias between BDS-2 and BDS-3, the \(\alpha\cdot R-Clk\) bias also is dispersed by satellite. Moreover, the time variation of the bias on each satellite is rather stable, with average STD values of 0.57, 0.63, and 0.83 ns at B1I, B3I, and B1I/B3I, respectively. This indicates that this bias can be well-calibrated.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Comparison with Existing Approaches
#### 2.3.2 Machine Learning for Predictive Maintenance  
[PERSON] et al. [15] introduced a hybrid CNN-LSTM framework for predictive maintenance, leveraging time series data to anticipate machine failures in Industry 4.0 settings. [PERSON] and [PERSON] [16] proposed a predictive maintenance system utilizing IoT data and machine learning algorithms such as Random Forest and XGBoost for real-time fault detection in manufacturing environments. [PERSON] et al. [17] presented a machine learning-based approach that emphasizes the comparative analysis of various models, including deep learning techniques such as LSTM, for fault prediction using real-world datasets. While these studies focus primarily on fault prediction, our research integrates fault prediction with SMT-based job scheduling optimization, enabling simultaneous fault anticipation and resource management tailored for dynamic production environments.  
In addition to predictive maintenance, adaptive scheduling under uncertainty has also been approached through reinforcement learning techniques. The next section explores these methods.
## 3 Preliminaries
### Reward-Based Payoff Strategy  
The RBPS is designed to address the limitations of traditional client selection strategies by incorporating a combination of accuracy, battery level, and privacy into a single, unified framework. In RBPS, the payoff for each client is determined by a weighted sum of the following components:  
* Accuracy: the local model's accuracy on the client's data.
* Battery Level: the remaining battery percentage of the device.
* Privacy: the privacy level of the data, which is impacted by the client's ability to maintain confidentiality.  
The overall payoff function for client \(i\) in the RBPS framework is defined as follows:  
\[\text{Payoff}_{i}=\omega_{A}\cdot\text{A}_{i}+\omega_{B}\cdot\text{B}_{i}+ \omega_{P}\cdot\text{P}_{i} \tag{3}\]  
where  
* \(A_{i}\) is the contribution to model accuracy (e.g., historical validation performance);
* \(B_{i}\) is the normalized battery level;
* \(P_{i}\) is the privacy preference or sensitivity score (e.g., set by user or device type);
* \(\omega_{A}\), \(\omega_{B}\), and \(\omega_{P}\) are weights that adjust the importance of each factor based on the client's characteristics.  
The weights \(\omega_{A}\), \(\omega_{B}\), and \(\omega_{P}\) are dynamically adjusted based on the current state of each client. This allows the strategy to better reflect the practical constraints and priorities of devices during training. For example, if a client's battery level drops significantly, the system can increase \(\omega_{B}\) to reduce the likelihood of selecting that device, helping to preserve its energy. If a client contributes consistently to model improvement, \(\omega_{A}\) may be increased to prioritize its selection. Similarly, when stronger privacy preferences are detected, \(\omega_{P}\) can be raised to give more consideration to privacy concerns. This dynamic adjustment makes the selection process more adaptive and context-aware. Rather than relying on fixed priorities, the system responds to real-time information from each client, promoting a balanced trade-off between model accuracy, energy efficiency, and privacy protection.
## 4 Summary and Future Directions
### Monitoring Cattle Behavior and Pasture Utilization  
Most animal behavior studies have been performed in small pastures. However, cattle in small, familiar, and intensively managed pastures may behave differently than in extensive rangelands [38]. Cattle behavior characteristics and pasture utilization will be assessed more precisely using global positioning system (GPS) and geographic information system (GIS) technologies within the larger production-scale pastures (P13-P16). Lightweight GPS collar receivers will be used to monitor cattle positions at short intervals. This data will allow us to assess the effect of several factors such as how landscape features, forage composition and availability, location of shade/water/supplements, and environmental conditions affect cattle behavior and movement, and use of pasture areas.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 1 Introduction
Traditional GPP remote sensing products (such as MODIS GPP) also have certain shortcomings, as they do not take into account the direct impact of atmospheric CO\({}_{2}\) on light use efficiency (LUE), underestimating the CFE [2]. Recently, some emerging remote sensing GPP products have overcome these shortcomings. Examples are the NIRv GPP dataset, which is estimated by a new type of vegetation index, near-infrared reflectance of vegetation (NIRv) based on remote sensing data [30], and EC-LUE GPP corrected for LUE based on eddy covariance tower flux data [31]. Based on the advantages of these datasets, several studies initially attempted to use multiple regression models to estimate the response of GPP to atmospheric CO\({}_{2}\) increases [15; 32]. However, compared with traditional regression models, machine learning algorithms (such as the random forest algorithm) are more flexible. They can further integrate the nonlinear influence of each explanatory variable on GPP change and the interaction between variables. Hence, using machine learning algorithms can potentiate the use of these datasets to quantify the response of GPP to changes in the external environment [33]. In this study, we used NIRv GPP and EC-LUE GPP combined with a random forest algorithm to explore the relationship between CFE and stand age, stand type, and site condition in the Heilongjiang region of China from 1982 to 2010. Our objectives were to (a) identify the variation of CFE with stand age, (b) compare the differences in CFE among the different stand types, and (c) compare the differences in CFE among the different site conditions.
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
## 3 Results
### Physicochemical and Thermal Characterization of Sediment Samples Treated with Phosphogypsum
#### 3.1.3 pH and Conductivity  
The conductivity and pH of sediment samples treated with phosphogypsum are shown in Table 4. The conductivity was 2430 \(\upmu\)S/cm for sample F-5 and 2990 \(\upmu\)S/cm for sample F-3. This suggests that the lower phosphogypsum content of F-3 may increase ion mobility, possibly due to the higher presence of dissolved salts or ions. The pH values are similar: for sample F-5, it was 6.38, and for F-3, it was 6.50, within the neutral to slightly acidic range. This indicates that the addition of phosphogypsum does not significantly affect the pH of the sediment. While conductivity varies between samples, indicating differences in ion content, pH remains stable, suggesting that phosphogypsum has minimal effect on sediment acidity or alkalinity, which is important for geochemical stability [1; 40].  
The first weight loss that occurs below 100\({}^{\circ}\) or nearly 100\({}^{\circ}\) in all samples corresponding to an endothermic reaction (Figure 3). This first mass loss (between 25 and 200 \({}^{\circ}\)C) is primarily attributed to the dehydration of the samples. Also, the significant peak of DTA curves at 135-150 \({}^{\circ}\)C probably corresponding to the dehydration of gypsum is observed for samples with phosphogypsum (PG) but also in sediment without PG with reduced intensity. This is emphasized in the case of both samples with PG, especially in the case of the sample with 5% PG. As can be seen, a slightly larger amount of lost mass in this interval is observed at samples with PG compared to a sediment sample, but the difference is not significant (Table 5).  
In the second interval, 200-400 \({}^{\circ}\)C, a broad exothermic peak associated with the thermal degradation of organic matter was observed in the sediment sample (Figure 3). Keeping in mind that this broad peak was not observed in the samples with PG (3 or 5%), it can be assumed that organic matter was reduced or protected in this way.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 6 Conclusions  
In this paper we propose ICENET--Information Centric protocol for sEnsor NETworks, capable of dealing with big data in WSNs. Big data gathering enables several emerging IoT applications, but presents challenges involving huge volumes of data, resource-constrained devices, and network instability. Current IoT applications require not only periodic data sending, but also the forwarding of queries and commands to nodes. The proposed protocol is a more efficient and suitable solution than protocols adopting traditional approaches, due to several factors. Compared to CoAP, a popular protocol designed to IoT environments, the proposed solution presents significantly less overhead during the query propagation phase, while collecting a larger amount of data. Moreover, our solution is more scalable, since the overhead reduces as the number of nodes increases. In addition, the ICN approach allows users to interact dynamically with the network. Lastly, an efficient soft-state mechanism makes the protocol robust to dynamic environments. This is an important feature, since message loss is a common event in wireless networks and sensor nodes are prone to failures. For the continuation of this work, we intend to propose an aggregation strategy to send requested data to clients and to determine the ideal values of the configurable parameters of the proposed protocol.  
Conceptualization, R.L., M.E.P. and E.J.; Investigation, R.L., M.E.P. and E.J.; Methodology, M.C.P.; Software, E.J.; Supervision, M.E.P.; Validation, G.B., G.M. and R.D.S.; Writing--original draft, R.L. and M.C.P.; Writing--review and editing, G.B., G.M. and R.D.S.  
This research was supported by CNPq under grant numbers 401710/2016-5 and 308240/2016-2.  
The authors declare no conflict of interest.