esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
First, the two critical node selection methods of Section 2.4 are compared by developing a random graph of \(N\) nodes and \(E\) edges. The edges in this scenario can link to a node using a probability \(p\). For simplicity in the calculation, parameter values \(N=200\), \(E=238\), and \(p=0.01\) were selected by drawing on previous work that used random graphs to model transportation networks and smart power grid networks [32]. The power requirements \(C_{P}(v)\) of each node \(v\) were created by sampling a uniform [0, 1] random variable. Table 1 lists the twenty most critical nodes using the combined metric ranking. Table 2 shows the twenty most important nodes of the same infrastructure employing the list of lists ranking. As discussed earlier, the \(z\)-_score_ normalization method was used for scaling the terms throughout our numerical results.  
Table 3 contrasts the results of the two critical node identification methods for an individual network. Observe the variations within each rank concerning the most critical nodes; the collection of nodes in the highest twenty possess \(\geq 80\%\) equivalent. Since both ranking methods produce comparable outcomes, the combined metric has been utilized for the rest of our study.
## 3 Results and Discussion
### Zeta Potential Analysis  
Figure 9 presents a zeta potential analysis conducted on the magnetic phase of the samples (Fe\({}_{3}\)O\({}_{4}\)), aimed at understanding the behavior of the surface charges of the magnetic nanoparticles and their interaction with the surrounding colloidal medium. This figure shows that at a pH very close to 7, the zeta potential is nearly zero, indicating low electrostatic repulsion and a propensity for agglomeration due to Van der Waals forces and magnetic interactions [63]. In this context, it is important to mention that the pH of the cell culture medium was 7.4, the same medium to which the magnetic nanoparticle treatments were added. Consequently, with this pH of 7.4 and a zeta potential very close to zero, it is possible that there is an agglomeration of the nanoparticles due to surface charges and magnetic interactions. This agglomeration could contribute to the lack of toxicity and cell death, as the agglomerated particles might be less reactive [64].  
Magnetic nanoparticles with a zeta potential close to zero tend to form agglomerates that are less toxic to cells. This is due to several factors: interactions between nanoparticles and cells are less intense when the particles are agglomerated, reducing the likelihood of cellular damage [65]. Additionally, cells can internalize individual nanoparticles more easily than large agglomerates, and the internalization of individual nanoparticles can trigger cytotoxic responses, whereas large agglomerates are less likely to be internalized [66; 67]. Furthermore, individual nanoparticles can release metal ions that are toxic to cells, but agglomeration reduces the exposed surface area and, consequently, the release of ions. In summary, the magnetic nanoparticles reported in this research, exhibits a zeta potential close to zero, which can lead to particle agglomeration, reducing their reactivity and cellular toxicity. However, further studies are necessary to fully understand the implications of agglomeration and nanoparticle reactivity in various biological contexts.
# Notch Fatigue Life Research Based on Critical Distance Theory
## 5 Experimental Results Compared with Theory  
According to the stress distribution at the arc notch measured by the static load combined with the finite element results according to Equations (1) and (7) to obtain each, and using Equations (8) and (9) to obtain the predicted fatigue life, the actual fatigue life obtained from the test and the calculated predicted fatigue life are combined in Table 5. The comparison results show that the data distortion is serious and the error is more than 65% at more than 10 million cycles when using the point method to predict the life of steel box girder cross-sectional notches. The line method is used to predict the life of steel box girder cross-sectional cutouts, and the error is basically within 20% except for model No. 3, which has a large dispersion of 29%. The error is inferred to be related to the size effect. Considering the mechanism of fatigue damage, the accumulation of fatigue damage of materials is determined by the joint influence of stresses in a certain region rather than a point at the root of the notch, and this influence is related to the stress-strain field in a certain range. The process of fatigue damage of a material is an irreversible change in itself at the microscopic level, and this change is mostly related to material deformation. In the interior of the material, the mutual forces between the grains prevent their own deformation, so it is relatively easy for that deformation to occur on the outer surface, i.e., the closer the region to the root of the notch, the greater the contribution to fatigue damage. If the calculation model for life prediction of full-scale specimens is established by the volume method, the fit of the conclusions obtained will be further improved.  
Figure 11: The relation curve of \(\text{lg}\sigma_{\text{ave}}-\text{lg}N\).
## 4 Discussion
### Landslide Disasters in the Context of Future Climate Change  
Research indicates that with global warming, the frequency and intensity of extreme weather events have increased globally, and future climate changes are likely to lead to significant increases in extreme precipitation levels worldwide [11]. Studies have shown that various precipitation indices used to study Central Asia exhibit spatial diversity and heterogeneity, with a general trend toward increased moisture [94]. This trend is not limited to Central Asia: China Climate Change Blue Book (2021) reports that the average annual precipitation in China has shown an increasing trend over the past 40 years, exacerbating the risks associated with extreme weather events. Recent studies have clearly indicated that daily precipitation intensity in southeastern China is projected to increase significantly during 2041-2060 under both RCP4.5 and RCP8.5 emission scenarios, reflecting the region's heightened hydrological sensitivity to climate change [95]. Qin employed the RegCM4 regional climate model driven by GFDL_ESM2M to simulate changes in the frequency and intensity of extreme precipitation events across major river basins in China under the RCP4.5 scenario. Qin's findings suggest that, compared to the historical period, southeastern China will likely experience a substantial increase in extreme precipitation occurrences, with the annual total precipitation from very wet days (R95p) increasing by more than 10 mm and the maximum 5-day precipitation value (RX5 day) rising by over 4 mm [96]. These changes contribute to enhanced soil moisture accumulation and saturation, thereby increasing antecedent effective precipitation, which plays a crucial role in hydrological triggering mechanisms. Moreover, the rise in antecedent effective precipitation has been identified as a key contributing factor to shallow landslides, as increased precipitation elevates both surface and groundwater levels. Intense rainfall events can generate rapid runoff, alter slope stability, and significantly raise soil moisture content, thereby elevating landslide risks [97]. Additionally, climate change can cause the permafrost in plateau areas to melt, leading to ground subsidence and sliding and accelerating mountain snowmelt, which further increase soil moisture and slope instability, ultimately triggering landslides [98].  
To predict and manage landslide disasters accurately in the context of future climate change, enhancing the predictive capabilities of climate models and improving early warning systems for extreme weather events are crucial. In particular, integrating high-resolution climate projections under various shared socioeconomic pathway (SSP) scenarios can help assess the sensitivity of landslide-prone areas to changes in precipitation patterns. Moreover, coupling climate outputs with physics-based or machine learning-based landslide susceptibility models allows for a more comprehensive risk assessment, enabling targeted mitigation strategies and adaptive planning.
## 4 Results and Discussion
### Results
#### 4.1.6 Results on Vegetation Indices  
Eight vegetation indices were derived to enhance crop mapping. Here, their contribution to crop classification was studied. In the Dijon study area, five comparative experiments were conducted, using (1) the 4-band image, (2) the vegetation index image (VI), (3) both the 4-band and 6-band images (10-band), (4) both the 4-band and vegetation index images (4 + VI), and (5) both the 10-band and VI images (10 + VI), respectively. The classification accuracies of various combinations of features are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Crop**} & **WWT** & **WBR** & **WRP** & **WTT** & **SBR** & **CON** & **OA** \\ \hline \multirow{2}{*}{F1} & Inst & 0.8368 & 0.7389 & 0.8667 & 0.4481 & 0.5946 & 0.5904 & 0.8225 \\  & Type & 0.8470 & 0.7718 & 0.9154 & 0.6633 & 0.6766 & 0.6258 & 0.8467 \\ \hline \multirow{2}{*}{F1} & **Crop** & **SOY** & **SFL** & **GRA** & **AFF** & **GRS** & **FLW** & **F1 (all)** \\ \hline \multirow{2}{*}{F1} & Inst & 0.5249 & 0.5088 & 0.9610 & 0.6447 & 0.9406 & 0.5692 & 0.6943 \\  & Type & 0.5811 & 0.7130 & 0.9710 & 0.6645 & 0.9407 & 0.7405 & 0.7624 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy evaluation using instance-wise and type-wise contrastive learning. Inst and Type indicate instance-wise and type-wise contrastive learning, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **5-Day** & **10-Day** & **20-Day** & **30-Day** & **40-Day** & **60-Day** \\ \hline Cloud/shadow & 53.83\% & 29.10\% & 9.24\% & 5.53\% & 0.03\% & 0.00\% \\ OA & 84.21\% & 82.03\% & 81.17\% & 77.68\% & 77.44\% & 74.81\% \\ F1 & 0.7553 & 0.7089 & 0.6986 & 0.6401 & 0.6392 & 0.5834 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy comparison for time-series composition with different periods.
### Algorithm Development Data  
One of the most confounding challenges in the development of both empirical and semi-analytical algorithms is the lack of a large, comprehensive database containing a broad enough dynamic range in optical characteristics to be representative of the majority of the world's oceans, while also having realistic combinations of inherent optical properties, which are not guaranteed in large, synthetic, modeled datasets. NOMAD represents the first (and most recent, as of this writing) major effort to provide the ocean color community with such a dataset. It was aggregated and selected from all of the relevant field data submitted to the NASA SeaBASS archive ([[http://seabass.gsfc.nasa.gov/](http://seabass.gsfc.nasa.gov/)]([http://seabass.gsfc.nasa.gov/](http://seabass.gsfc.nasa.gov/))), and has been extremely useful to those in the ocean color algorithm community since its original publication in 2005 and update in 2008. However, NOMAD was not focused on CDOM. For example, while it contains about 3700 coincident radiometric and phytoplankton pigment observations, coincident radiometric and CDOM observations number just \(\sim\)1200. In part, this is because CDOM data collected using in situ instrumentation were excluded for various reasons discussed at greater length below. The remaining CDOM records--those measured from discrete water samples--were modeled spectrally at the preselected NOMAD wavebands after fitting field data to Equation (1), and do not extend into the UV where spectral shape can provide useful insights into the origin and photooxidation state of CDOM. NOMAD does not contain any DOC data observations.  
Using the methodology described in the next section, we extend the NOMAD approach to create a global ocean color algorithm development database better suited to DOC and its optical components, CDOM and CDOM spectral slope, ultimately including over 51,000 field observations of surface-averaged inherent optical properties. These are matched to between \(\sim\)8000 and \(\sim\)11,000 coincident estimates of sea surface reflectance made from in situ measurements as well as satellite imagery from SeaWiFS and MODIS Aqua and Terra instruments. The global ocean carbon algorithm database (hereafter Global Ocean Carbon Algorithm Database (GOCAD) records are split into independent sets of field stations for training/optimization (i.e., with in situ radiometry) and validation (i.e., with satellite imagery) of algorithms, as described in the Section 2. A basic overview of the most relevant aspects of the global dataset is presented in Section 3.1. In Section 3.2, empirical and SAA approaches to retrieval of DOC, CDOM, and CDOM slope are developed and discussed. Finally, algorithms are applied to global climatological satellite imagery and discussed in Section 3.3.
## 5 Experimental Setup
### Baselines  
To evaluate the performance of SaSLM, we compare SaSLM with the following baseline methods:  
* _matrix factorization on set rating_ (MFSet): This is the personalized baseline compared in the experiments of [5]. It assumes that if a user rates a set, he/she will give all contained items the same ratings. The MFSet is the matrix factorization (MF) method with same set ratings assigned to all items.
* _average rating model_ (ARM): The first method introduced by [5], where the set rating is predicted by the average of predicted scores of items in the set. We have introduced ARM in Equation (1).
* _variance offset average rating model_ (VOARM): The second method introduced by [5]. Aside from estimating the mean of ratings of items in the set, VOARM also estimates the variance. We have introduced VOARM in Equation (2).
* _extremal subset average rating model_ (ESARM): The third method introduced by [5], which learns the ratings of extremal subsets and aggregates the scores to predict set rating. We have introduced ESARM in Equation (3).
* _bundle collaborative filtering_ (BCF): The simple CF method that considers user-bundle interactions for bundle recommendation. BCF aggregates item embeddings to represent the bundle by simply adding all the embeddings. Therefore, the rating of user \(u\) on set \(s\) is given by: \[\hat{\rho}_{u}^{s}=\mathbf{p}_{u}^{\top}\sum_{i\in s}\mathbf{q}_{i}.\]
* _bundle graph convolutional network_ (BGCN): The state-of-the-art method for bundle recommendation [21], which relies on graph convolutional network (GCN) [42] for embedding aggregation. Note that for SPL, we do not use user-item interactions for training. Therefore, for BGCN we only construct a user-bundle graph for embedding propagation.
## 3 Results
### Energy Storage, Power, and COP during the Charging Process  
Figure 5 summarizes the results obtained during the charging process based on energy, average power, and the COP of the system. Figure 5a presents the energy stored as the module charges from 12 \({}^{\circ}\)C to lower temperature values. It is observed that at an average temperature of \(-4\)\({}^{\circ}\)C, TES\({}_{1}\) had stored the highest amount of energy despite containing the minimum amount of PCM due to the high stratification effect in TES\({}_{2}\) and TES\({}_{3}\) (seen in Figure 4). TES\({}_{1}\) shows the highest potential when operating at a narrow temperature range, which is evident in Figure 5a,b, due to its ability to charge the PCM homogeneously. To fully utilize the potential of the PCM (latent energy storage) in TES\({}_{2}\) and TES\({}_{3}\), it is evident in Figure 5b,c that the module should be charged to lower temperature values beyond \(-4\)\({}^{\circ}\)C. For instance, by allowing the PCM to charge until \(-10\)\({}^{\circ}\)C, the energy stored in the two modules was higher than that stored in TES\({}_{1}\), which is expected since the two modules contain more PCM. Considering the COP of the system while using the three modules (Figure 5d), TES\({}_{1}\) achieved the highest values, followed by TES\({}_{2}\). Therefore, for a fair comparison of the modules and to reduce errors arising from the melting fraction, the KPIs were evaluated after complete phase change was ensured.  
Figure 4: Temperature profiles of the charging process for (**a**) TES\({}_{1}\), (**b**) TES\({}_{2}\), (**c**) TES\({}_{3}\), and (**d**) position of temperature sensors.
###### Abstract  
This work evaluates the effect of high temperatures and cooling methods on the drillability of Prada limestone. Samples from boreholes drilled during the design stage of the Tres Ponts Tunnel in the Catalan south Pyrenean zone (Spain) were subjected to temperatures of 105, 200, 300, 400, and 600 \({}^{\circ}\)C, and then cooled at a slow rate or by quenching. Sievers' J-value (SJ) and brittleness (S\({}_{20}\)) were determined on thermally treated samples, and the drilling rate index (DRI) was calculated for each temperature. The results show that thermal treatment implied a sustained increase in the drillability of the rock of up to 40% at 600 \({}^{\circ}\)C and a change in the drillability category (from medium to high). At 600 \({}^{\circ}\)C, SJ and S\({}_{20}\) tripled and doubled, respectively, the initial values obtained for the intact rock. The results were inconclusive about the influence of the cooling method on the drilling performance of Prada limestone for the tested range of temperatures. The substantial improvement observed in the drillability of Prada limestone when heated, measured in terms of DRI, could help in the development of novel thermally-assisted mechanical excavation methods. Additionally, strong correlations between drillability variables (i.e., SJ and S\({}_{20}\)) and physical and mechanical variables of Prada limestone (i.e., P- and S-wave velocities, uniaxial compression strength, elastic modulus, and Poisson's ratio) are proposed. Correlations will help make preliminary predictions of drillability based on properties such as uniaxial compression strength and ultrasound wave velocities.  
rock drillability; drilling rate index (DRI); thermal treatment; temperature; limestone; correlations Vol. 2021, 11, 3286. [[https://doi.org/10.3390/app11073286](https://doi.org/10.3390/app11073286)]([https://doi.org/10.3390/app11073286](https://doi.org/10.3390/app11073286))
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 4 Evaluation Metrics
### Basic Evaluation Metrics  
Basic metrics are characterized by their simplicity and are often combined to produce more sophisticated metrics. They are defined as follows [20]:  
* **True positives** are correspondences that have been correctly found by an automatic ontology matching tool. **True Positives (TP) \(=\mathcal{A}\cap\mathcal{R}\)**
* **False positives** are correspondences that have been falsely found by an automatic ontology matching tool. **False Positives (FP) \(=\mathcal{A}-\mathcal{R}\)**
* **False negatives** are correct correspondences that have not been found by an automatic ontology matching tool. **False Negatives (FN) \(=\mathcal{R}-\mathcal{A}\)**
* **True negatives** are false correspondences that have been correctly discarded by an automatic matching tool. **True Negatives (TN) \(=(E\times E^{{}^{\prime}})-(\mathcal{A}\cup\mathcal{R})\) \(E\) and \(E^{{}^{\prime}}\) = number of entities in the input ontologies \(\mathcal{O}_{1}\) and \(\mathcal{O}_{2}\). (\(E\times E^{{}^{\prime}}\)) = set of all possible correspondences between \(\mathcal{O}_{1}\) and \(\mathcal{O}_{2}\).  
Based on such definitions, the set of automatically identified correspondences comprises true positives and false positives (\(\mathcal{A}=TP+FP\)), and the set of expected correspondences is composed of true positives and true negatives (\(\mathcal{R}=TP+TN\)). Evidently, false positives and false negatives reduce the matching accuracy. Therefore, an efficient ontology matching system aims to minimize both of them.
## 1 Introduction
The water shortage and complex topography are major reasons for sparse vegetation coverage, and hence a more fragile ecological environment [23]. Hitherto, the estimations of ET and GPP in the Upper Heihe watershed were isolated. [PERSON] [24] utilized micro-lysimeters to test the performance of four energy balance methods for ET estimation in the meteorological station of Qilian Mountains, and found that the FAO-56 Penmen-Monteith provided the highest accuracy. [PERSON] [25] established the relationship model between four stations' eddy-covariance-based GPP and the leaf area index (LAI), with vegetation indexes extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) in the Heihe River basin. [PERSON] [20] utilized a remote sensing-based light use efficiency model (MODIS MOD_17) to simulate the GPP of the Upper Heihe watershed, and foundthat temperature and vapor pressure deficiency had a positive relationships with GPP. These researches lack the description of an ecological water and carbon-coupled mechanism, neither considering the effect of rhizosphere soil water on carbon assimilation rate, nor considering the feedback of the soil carbon pool, biological carbon pool and vegetation growth on the ecosystem water cycle. [PERSON] [26] compared the performance of two land surface models--Community Land Model 4.0 (CLM 4.0) and Dynamic Land Model 1.0 (DLM 1.0)--in the simulation of soil organic carbon of the Upper Heihe watershed, and found that these land surface modes had low accuracies for soil moisture estimation in this cold alpine region. The coupling of the distributed-hydrological and traditional biological models is needed for the watershed scale electrophysiological simulation, because it considers not only the microclimate and topographically controlled soil water movement, but also vegetation-unique ecophysiological characters. This study utilized a process-based ecohydrological model (DLM-Ecohydro) to simulate two ecophysiological processes, evapotranspiration and photosynthesis, in the cold alpine region. We will test how the model performed after considering topographically controlled lateral water movement and the coupling of the water cycle and carbon assimilation at daily time steps. Through the study of spatiotemporal differentiation of ET and GPP in the Upper Heihe watershed, we will also explore the driving mechanism of ecosystem water and carbon flux variations.
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 1 Introduction
This allows the network to better utilize learned temporal features, reducing the blurriness of predicted frames and accelerating the prediction of future video segment positions. * Model stability in position prediction: To further enhance the stability and practicality of video position prediction, this paper modifies the GRU's gating mechanism from linear operations to convolutional ones. The Convolutional Gated Recurrent Networks (ConvGRU) learns long-term spatiotemporal dependencies, ensuring the continuous transfer of feature information and reducing the loss of spatiotemporal features during prediction. This guarantees consistency in spatiotemporal information throughout the sequence, improving the model's prediction accuracy and training speed. The remainder of this paper can be summarized as follows: Section 2 describes the related work on video prediction, Section 3 proposes a new prediction architecture, followed by experimental results and analysis presented in Section 4. Finally, conclusions are drawn in Section 5.