esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### OLS Outlier Recognition Results  
All sample data exceeding the judgment benchmark are summarized, as shown in Table 5. The threshold values of the four methods used to judge abnormal data points for the data in this paper are as follows: the lever value is [+0.1553, \(-\)0.1553], the student residual is [+2, \(-\)2], [PERSON]'s value is [+0.039, \(-\)0.039], and DFITS value is 0.2606. First, considering leverage and studentized residuals, it can be observed from Figure 5 that the sample 82 data has the highest leverage value of 0.19 and a studentized residual value of 1.96. With further reference to the [PERSON]'s distance and DFFITS distance, the [PERSON]'s distance and DFFITS distance of the 82 nd sample were 0.11 and 0.96, respectively, both exceeding the threshold for identifying outliers. Due to the small data sample size, the  
The validity of the identified outliers is verified by the change in the parameter estimate after deleting a certain data point. If the parameter estimation changes greatly, then the data point is considered to have a greater impact on the parameter estimation, and the data point is judged to be an abnormal value or a strong influence point; it is easy to hide between the abnormal values, and a certain statistic may be unable to accurately identify outliers, so using the OLS again to verify is recommended. As shown in Figure 6, R \(-\) squared, Akaike information criterion (AIC) [60], and Bayesian information criterion (BIC) [61] are used as reference indicators. AIC is a statistic describing the degree of fit of the regression curve to the real data points. The closer the value is to 1, the better the degree of fit. AIC is a standard to weigh the complexity of the estimation model and the excellence of fitting data. AIC is defined as \(\text{AIC}=2k-2\ln(L)\), where \(k\) is the number of parameters and \(L\) is the likelihood function. BIC means that under incomplete information, subjective probability is used to estimate some unknown states, and then a Bayesian formula is used to modify the occurrence probability. Finally, the expected value and modified probability are used to make optimal decisions, which is defined as: \(\text{BIC}=\ln(n)k-2\ln(L)\), where \(k\) is the number of model parameters, \(n\) is the number of samples, and \(L\) is the likelihood function. The penalty of BIC is larger than that of AIC, and the \"penalty\" is imposed according to the number of independent variables. The smaller the values of AIC and BIC are, the better the model. The before and after indicators, which prove the effectiveness of outlier elimination, are shown in Table 6. R\(-\)square improved from 0.860 to 0.900, AIC decreased from 498.1 to 490.3, and BIC decreased from 519.2 to 511.3, demonstrating the effectiveness of eliminating abnormal samples. This ensures the stability and scientificity of the data for regression modeling, and improves the accuracy and stability of the regression equation.
###### Abstract  
Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-order information in many graph learning tasks. Inspired by the success of GNNs, we investigate improving semantic dependency parsing with higher-order information encoded by multi-layer GNNs. Experiments are conducted on the SemEval 2015 Task 18 dataset in three languages (Chinese, English, and Czech). Compared to the previous state-of-the-art parser, our parser yields 0.3% and 2.2% improvement in average labeled F1-score on English in-domain (ID) and out-of-domain (OOD) test sets, 2.6% improvement on Chinese ID test set, and 2.0% and 1.8% improvement on Czech ID and OOD test sets. Experimental results show that our parser outperforms the previous best one on the SemEval 2015 Task 18 dataset in three languages. The outstanding performance of our parser demonstrates that the higher-order information encoded by GNNs is exceedingly beneficial for improving SDP.  
s +
Footnote †: journal: Applied sciences  
Article  
Improving Semantic Dependency Parsing with Higher-Order Information Encoded by Graph Neural Networks
## 2 A Few Preliminaries  
The denoising problem can be expressed as  
\[\mathbf{x}(n)=\mathbf{s}(n)+\mathbf{v}(n), \tag{1}\]  
where \(\mathbf{x}(n)\) is the one-dimensional signal containing noise and sampled as \(n=0,\ldots,N-1\), \(\mathbf{s}(n)\) denotes the source signal, and \(\mathbf{v}(n)\) is the noise signal, which is customarily modeled as wGn with zero mean and variance \(\sigma^{2}\). The goal is to recover an estimated version \(\hat{\mathbf{s}}(n)\) as close as possible to the true signal \(\mathbf{s}(n)\). Since the denoising methods discussed/developed in this paper are essentially based on either EMD or VMD, this section presents a brief introduction to mathematical concepts in these decomposition tools.  
The EMD algorithm [6] breaks down a one-dimensional signal, \(\mathbf{x}(n)\) into a limited and frequently tiny number of fundamental oscillatory modes called IMFs. For the recovered signal to qualify as an IMF, two stopping requirements must be met [20]:  
1. There should be no more than one difference between the number of extrema and zero crossings.
2. The mean of the upper and lower envelopes should be zero.  
Finally, the original signal \(\mathbf{x}(n)\), decomposed via EMD into \(M\) numbers of IMFs plus residual, can be expressed as  
\[\mathbf{x}(n)=\sum_{i=1}^{M-1}\mathbf{d}_{i}(n)+\mathbf{r}(n), \tag{2}\]  
where \(\mathbf{d}_{i}(n)=[d_{0,i}(n),\ldots,d_{N-1,i}(n)]\) denotes the modes with \(i=1,\ldots,M\), and \(\mathbf{r}(n)=[d_{0,M}(n),\ldots,d_{N-1,M}(n)]\) is the residual.
# Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings
## 3 Numerical Results
### Prediction Models
#### 3.2.1 One-Day Prediction  
To further validate the model's training results, a training and validation loss is presented with the model loss over epochs. Training loss is an indication of how well a model is fitting training data, or how effectively it is learning patterns. Validation loss displays how well the model fits new, unseen data. As shown in Figure 7, both training and validation loss have an initial decrease. A decreasing trend for training loss indicates that the model is learning and showing improvement in the training data. A decreasing validation loss shows that the model is improving its predictions on unseen data. We can also infer that the model has an optimal fit because both losses decrease and they stabilize at a certain point. Figure 8 illustrates that the frequency of error for the one-day prediction has a high concentration of values surrounding zero, indicating high levels of model accuracy.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Testing Error (RMSE)**} & \multicolumn{1}{p{56.9 pt}}{} \\ \cline{2-4}  & **One-Day Prediction** & **Three-Day Prediction** & **Seven-Day Prediction** \\ \hline ARIMA & 244.09 & 182.37 & 194.16 \\ \hline SVR & 291.23 & 200.03 & 191.97 \\ \hline FNN & 63.07 & 61.37 & 195.93 \\ \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Testing Error (MAE)**} & \multicolumn{1}{p{56.9 pt}}{} \\ \cline{2-4}  & **1 day** & **3 days** & **7 days** \\ \hline ARIMA & 143.25 & 103.64 & 119.5 \\ \hline SVR & 206.59 & 103.4 & 107.13 \\ \hline FNN & 43.62 & 41.37 & 113.45 \\ \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Testing Error (nRMSE)**} & \multicolumn{1}{p{56.9 pt}}{} \\ \cline{2-4}  & **1 day** & **3 days** & **7 days** \\ \hline ARIMA & 0.163 & 0.110 & 0.066 \\ \hline SVR & 0.194 & 0.120 & 0.066 \\ \hline FNN & 0.042 & 0.037 & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing error comparison across three models.  
Figure 7: Training and validation loss for one-day prediction.  
Figure 9 depicts the actual versus predicted results of PV system output over a span of 24 h. The actual results are obtained from PVWatts, while the predicted output comes from the NN model. As depicted in the graph, the predictions shown are highly accurate, proving the effectiveness of the model.
###### Abstract  
Long-term groundwater level analysis, which is usually based on traditionally defined hydrological years is essential in an era of global warming and other climatic and environmental changes, especially in urban areas. A complex interplay of multiple factors influencing the groundwater level makes the investigation of their interdependencies a challenge. Based on multiple data sets and a long time series available as well as specific geological and hydrological conditions, a semi-urban district of Wroclaw/Poland was selected as a case study for investigating these dependencies. This paper presents an interdisciplinary approach to the analysis of groundwater level fluctuations by combining mathematics, signal processing, hydrogeology, and meteorology. Applying well-known methods from disciplines other than hydrogeology, the authors investigated seasonal behavior and similarity of groundwater level fluctuations during 15 hydrological years. Based on segmentation and agglomerative clustering (AHP), five classes of groundwater levels fluctuations for predefined hydrologic years and the corresponding seasons were identified and compared to the classification scheme by [PERSON]. Additionally, the relationship between precipitation and groundwater level was investigated using Pearson, Kendall and Spearman correlations. This led to the identification of \"typical\" and \"untypical\" seasons for the correlation between the cumulative precipitation sum and groundwater levels. The results presented here will be used for further investigations of groundwater level fluctuations using additional factors and statistical methods. These aim to identify periods that describe similarities better than the commonly used hydrological year.  
Keywords:hydrogen analysis; hydrogeology; detrending; clustering; geodata +
Footnote †: journal: Applied Statistics and Data Mining Techniques
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.2 Global Precipitation and Extreme Events
The data acquired from PMWI sensors are crucial for understanding the dynamics of snowstorms, facilitating better forecasting and response strategies to mitigate their impact [65]. Additionally, data from TRMM TMI further enhance the ability to analyze storm characteristics. Utilizing resources and datasets from the National Snow and Ice Data Center (NSIDC), these sensors offer valuable insights into storm behavior, aiding in predicting and mitigating severe weather events [65; 66].
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
###### Abstract  
This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. Machine learning models can recognize key knowledge from a dataset without any preliminary knowledge about the dataset. Hence, machine learning methods have been utilized for solving power grid-related problems. Two sets of historical data were used herein: Local weather data and power grid outage data. First, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather and outages to characterize the heavy rain events. The analysis results show that multiple weather effects are significant in causing power outages, even under heavy-rain conditions. Furthermore, this study proposes a cost-sensitive prediction method using a support vector machine (SVM) model. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the state of the grid. The developed model was evaluated using G-mean values. The proposed method was verified via actual data of a heavy rain event that occurred in South Korea.  
Keywords:power outage; heavy rain; machine learning; power big data; grid resilience +
Footnote †: journal: Applied sciences
## 5 Results
### Visual Search Outcome  
#### 5.1.1 Number of Correct Responses  
We summarized the results of correct responses in Table 1, averaged among all participants in each group. The participants completed most of the search tasks correctly, and the highest average number of correct responses was recorded when the target-distractor similarity was low and the amount of demonstrated visual information was relatively small. The mistakes appeared comparatively more often when the target differed from the distractors by 15% of the line thickness. The statistical analysis using the Wilcoxon signed rank test proved the significant effect of target-distractor similarity (\(p<0.001\)), but not set size (\(p=0.33\)). Moreover, no statistically significant difference was reached in the average correct response rate when comparing the performance between any two experience adjacent groups using the Wilcoxon rank sum test (\(p=1.00\)).
## Data Set
### Preprocessing Dataset  
As Web of Science categorizes each article based on a multitude of categories, first of all, we had to choose the relevant ones. To simplify the categorization, we summarized Web of Science categories to sections. Figure 5 illustrates all sections (In addition, the left side of Figure 6 illustrates the distribution of sections for the search string predictive maintenance.) on one hand and the ones identified as being relevant on the other. Based on the relevant Web of Science categories, we filter articles utilizing the _white list principle_, i.e., we keep an article in the data set, if it matches at least with one of the relevant categories. As can be obtained from Figure 5, we excluded a lot of Web of Science categories for the search string _predictive maintenance_. First, we calculated an article density for sections (cf. Figure 6, right side). The density is defined as _(number of articles in section)/(number of categories in section)_. If the density for a section is low, the section is excluded.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Query** & **Entries** & **Oldest** & **Typical Category** & **Articles from 2016** \\ \hline PDM \({}^{a}\) & 4742 & 1970 & EEE \({}^{*}\) & 311 \\ CM \({}^{b}\) & 144,758 & 1956 & EEE \({}^{*}\) & 8416 \\ SM \({}^{c}\) & 1447 & 1970 & EEE \({}^{*}\) & 109 \\ LM \({}^{d}\) & 1299 & 1967 & ND \({}^{**}\) & 74 \\ RCM \({}^{e}\) & 588 & 1978 & EEE \({}^{*}\) & 27 \\ PA \({}^{f}\) & 717 & 1996 & EEE \({}^{*}\) & 119 \\ \hline \hline \end{tabular} \({}^{*}\) Engineering Electrical Electronic. \({}^{*}\) Nutrition Dietetics. \({}^{a}\) Predictive Maintenance. \({}^{b}\) Condition Monitoring. \({}^{c}\) Smart Maintenance. \({}^{d}\) Lean Maintenance. \({}^{e}\) Reliability-centered Maintenance. \({}^{f}\) Predictive Analytics.  
\end{table}
Table 1: Overview of the gathered data set.  
Figure 4: Published Articles for _Predictive Maintenance_.  
For example, in the section medicine (cf. Figure 6), the density is very low, meaning that the average number of articles per category is small. The reason we exclude such sections is based on the assumption that predictive maintenance solely plays a side role in these sections.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Description of the Uvsq-Sat NG Satellite and Its Payloads
Table 4 provides the Uvsq-Sat NG external area usage on each satellite side, which enables the identification of satellite elements not depicted in Figure 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Satellite Side** & **Element** \\ \hline X\(+\) & 8 Azur Space solar cells \\  & One hold-down-and-release mechanism of the Z\(-\)/X\(+\) deployable panel \\  & One Fine Sun Sensor and one Uvsq-Sat NG photodiode \\ \hline X\(-\) & 8 Azur Space solar cells \\  & One hold-down-and-release mechanism of the Z\(-\)/X\(-\) deployable panel \\  & One Fine Sun Sensor and one Uvsq-Sat NG photodiode \\ \hline Y\(+\) & One Star Tracker aperture \\  & One Uvsq-Sat NG photodiode \\ \hline Y\(-\) & 4 Azur Space solar cells \\  & One Uvsq-Sat NG photodiode \\ \hline Z\(+\) & Uvsq-Sat NG NIR Spectrometer aperture \\  & Uvsq-Sat NG NanoCam aperture \\  & 2 Earth Radiative Sensors \\  & S-band patch antenna \\  & One Uvsq-Sat NG photodiode \\ \hline Z\(-\) & The Z\(-\)/X\(+\) deployable panel with 16 Azur Space solar cells \\  & The Z\(-\)/X\(+\) deployable panel with 16 Azur Space solar cells \\  & One GNSS patch antenna \\  & One deployable Uvsq-Sat NG magnetometer \\  & One Fine Sun Sensor and one Uvsq-Sat NG photodiode \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Uvsq-Sat NG external area usage and number of solar cells employed on each satellite side. Figure 2: General ADCS control loop of the Uvsq-Sat NG CubeSat. Earth-Centered Earth-Fixed (ECEF) is a coordinate system that defines positions in relation to the Earth’s center while considering the Earth’s rotation. It provides a fixed reference frame for navigation and positioning calculations. Uvsq-Sat NG will integrate the Damocles new electronic board, which is designed to deactivate Uvsq-Sat NG's power sources at the conclusion of the satellite's operational lifespan. The incorporation of this technology is of paramount importance, as it will isolate the satellite's power sources and discharge its batteries (Panasonic NCR18650B batteries) to mitigate the potential for in-orbit explosions. The development of Damocles is aimed at mitigating the proliferation of space debris. It also serves as a function to comply with the French Space Operations Act. One benefit of the CubeSat approach lies in the accessibility of readily available commercial off-the-shelf (COTS) components, which serves to lower expenses and expedite development timelines while being reliable.
## 3 Results and Analysis of the Filed Measurements
### Discussion  
In general, the indoor thermal environment of the commercial kitchen is complex, with high temperatures and high humidity. A proper ventilation system in a typical commercial kitchen is necessary to remove waste heat and oil fume pollutants. For commercial kitchens without air conditioning systems, especially in the cooking zone, about 82.92% of the working hours exceed the acceptable range in summer. Therefore, air conditioning systems are always needed to improve the indoor environment of commercial kitchens [21]. A personalized air conditioning system is worth recommending. It can not only ensure the chef's thermal comfort but also has a certain energy -saving potential [43].  
The kitchen exhaust fume system plays an important role in ensuring a comfortable indoor environment and removing pollutants. However, the test results show that only considering the exhaust hood information to design the exhaust fume system is unreasonable, which may lead to fumes overflowing from the exhaust hood during cooking. Based on this, our research group reported in another paper [44] on how select a suitable method for designing an exhaust fume system. The results show that it is necessary to combine the characteristics of commercial kitchens and comprehensive consideration of various factors such as kitchen civil engineering information, stove information, and exhaust hood information to design an appropriate exhaust fume system.  
Figure 16: Relationship between monthly total electricity consumption and monthly average instantaneous power of oil fume exhaust system.
# Failure Response of Simultaneously Pre-Stressed and Laser Irradiated Aluminum Alloys
## 3 Results and Discussion
### Failure Behavior
Since Al-7075 material has relatively high mechanical properties, but low thermal conductivity and diffusivity values, the combination of low laser power density (1.1 kW/cm\({}^{2}\)) and various loading speeds did not cause any noticeable change except softening and re-crystallization during the fracture process; while for higher laser power densities (Curves 2-4) in Figure 2a-d, the fractured process was shortened with a sharp drop in the specimen's yield strength with the increase of irradiated laser power densities. Again, as for Al-6061, Al-7075 has a similar strengthening mechanism, but different micro-structural states, so the same explanation for the rapid drop in stress along increasing laser power density is applicable. Fractured and yielding processes are thermally-activated and stress-driven movement, and this movement effectively depends on the strain rate (in our case, loading speed) and temperature. In the thermally-activated processes, an energy barrier should be removed for the movement development. Figure 1: Failure behavior of Al-6061 specimens for various laser power densities under the fixed preloading speeds. (**a**) 0.5 mm/min; (**b**) 1 mm/min; (**c**) 2 mm/min; (**d**) 4 mm/min. The preloaded stress with various speeds acts to reduce the barrier when it is in the direction of movement and to elevate it when it opposes the movement.
# High-Intensity Conditioning for Combat Athletes: Practical Recommendations
## 6 Physiological Targets of High-Intensity Conditioning
### Defining Training Type  
Precise definitions of high-intensity conditioning strategies have yet to be agreed. However, there is some acknowledgement that training types can be classified into the following categories: sprint interval training (SIT), HIIT, speed endurance training (SET) and muscle buffer training (BUFF) [3; 5; 6; 7; 8; 43].  
Whereby:  
* SIT: <30 s maximal exertion RPE 10/10--example 30 s maximum sprints [44].
* SET: from 30 to 60 s prescribed as ISO RPE 9/10--example 30 s \(\times\) 8 with 3 min recovery [45].
* BUFF: intensities that elicit blood lactate concentrations between 8 and 12 mmol/L ISO-RPE of 8/10--example 6 \(\times\) 2 min with 3 min recovery [8].
* HIIT: intervals from 2 to 20 min prescribed as an ISO-RPE of 9/10, 90% maximum heart rate, or 90-95% VO\({}_{\text{2 max}}\)--example 4 \(\times\) 8 min with 2 min recovery between intervals [46].  
Physiological targets for high-intensity conditioning reside on a continuum, rather than in discrete areas and a target is never exclusive to a single physiological adaptation. Figure 1 provides examples of the typical high-intensity conditioning sessions and position on the high-intensity continuum. It is generally considered that peripheral targets include the neuromuscular system and myocytes and central targets include the cardiovascular system [5; 6].
## 5 Experimental Results
### Tests in Ideal Static Scenarios
#### 5.3.2 Scenario 1
One can see that before the first _REQUEST_, \(\rm SU_{1}\) typically has a completion rate equal to \(0.98\), while \(\rm SU_{2}\) and \(\rm SU_{3}\) achieve slightly smaller values (equal to \(0.97\) and \(0.93\), respectively). Hence, in this scenario, all the SUs can complete the reception before the second _REQUEST_ with a high probability. Figure 14: PMF of the archive reception time in Scenario 1. Figure 13: Average number of _DATA_ chunks requested and average number of _DATA_ messages sent following a _REQUEST_, in Scenario 1.