esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 4 Mine Operation Using GIS
### Ore Haulage Operation Optimization
A GIS database was established for underground haulage road networks; then, the average travel speed of the diesel vehicles on each road was estimated to calculate the carbon dioxide emission factor through the field survey. The results revealed that the amount of carbon dioxide emissions related to truck haulage operations could be calculated by considering the carbon dioxide emission factor and travel distance of diesel vehicles determined by the GIS-based network analysis. However, this study did not consider that the carbon dioxide emission factor of each road could change according to the road gradient, load capacity of vehicles, and emission standards. [PERSON] et al. [33] followed the new carbon dioxide emission factor suggested by the European Environment Agency. This study constructed a vector network of underground haulage roads, which includes data detailing the speed of the diesel vehicle, road gradient, and load capacity. [PERSON] et al. [34] developed the Bluetooth Beacon-Based Underground Navigation System to display optimal haulage route and truck location data on mobile devices in real time. A GIS-based network analysis was adopted for optimal haulage routing. After determining the optimal haulage route, the route information is updated to the web server. Moreover, GIS-based spatial visualization was used to display the installation locations of wireless sensors, which identify the dump truck locations.
## 2 H\({}_{2}\)O Collisional Broadening Measurements
### H\({}_{2}\)O Air Broadening and Temperature Dependence at 1.37 \(\mu m\)  
#### 2.2.1 H\({}_{2}\)O Air Broadening  
For the air broadening measurements, the water bath was removed and the gas cell in Figure 1 was replaced with a gas cell with an optical path length of 20 cm. For the spectroscopic measurements using this new setup and probing the H\({}_{2}\)O line at 7299.43 cm\({}^{-1}\), a H\({}_{2}\)O sample was prepared in the gas cell by manometric mixing, and after equilibration, the total gas pressure (stability: better than 0.1 hPa) was increased in steps (while measuring the absorption signals and recording the total gas pressure and gas temperature) by adding air into the gas cell. The uncertainty of the air pressure was 0.2% relative.  
With knowledge of \(\gamma_{\rm H2O}\) and \(n_{\rm H2O}\), and using Equation (1), the H\({}_{2}\)O air broadening coefficient at gas temperature \(T\) can be derived using Equation (4),  
\[\Delta v_{L}-2\cdot p_{\rm H2O}\cdot\gamma_{\rm H2O}\cdot\left(\frac{T_{0}}{ T}\right)^{n_{\rm H2O}}=2\cdot\gamma_{air}\cdot p_{air} \tag{4}\]  
Figure 5 depicts \(\Delta v_{L}-2\cdot p_{\rm H2O}\cdot\gamma_{\rm H2O}\cdot\left(T_{0}/T\right) ^{n_{\rm H2O}}\) as a function of \(2\cdot p_{air}\). The H\({}_{2}\)O partial pressure \(p_{\rm H2O}\) was determined using the H\({}_{2}\)O amount fraction derived from the measured TDLAS spectra similar to [7] and employing the line strength value for the H\({}_{2}\)O line at 7299.43 cm\({}^{-1}\) derived in a separate experiment [18]. A GLR was applied (with uncertainties in the x- and y-axis, estimated as being the uncertainties of the respective quantities) to the data in Figure 5, resulting in the slope value \(\gamma_{\rm air}\) of (0.1034 \(\pm\) 0.0017) cm\({}^{-1}\)/atm at (295.04 \(\pm\) 1) K. The uncertainty 0.00168 cm\({}^{-1}\)/atm of the air broadening coefficients correspond to a relative uncertainty of 1.6 % (\(k=1\)).  
Figure 5: A GLR linear regression applied to measured data to determine the air broadening coefficient (yair) of the H\({}_{2}\)O line at 7299.43 cm\({}^{-1}\) at (295.04 \(\pm\) 1) K. RMS error: 1.04 \(\times\) 10\({}^{-16}\). The correlation coefficients between the x- and y-axis are assumed to be unity.
## 3 Experimental Results
### Strain Results under Thermal Load  
#### 3.3.1 Track Slab Strain  
From the processing of the data collected by the fiber grating strain gauges, the variation law of the track slab temperature-strain curve with the number of thermal loading cycle was obtained, as shown in Figure 11. It can be seen from Figure 11a,b that at the fixed end section (with shear slots) and at the mid-span section, the temperature-strain plot of the track slab follows the increase of the thermal loading cycle and gradually shifts toward the strain increase direction. The distribution of the temperature-strain plots is relatively dense in the initiation and stabilization stages of the interlayer gap. In the expansion stage, the distribution of the temperature-strain plot is relatively sparse. From Figure 11c, it can be seen that at the sliding end section (without shear slots), the increase of the thermal loading cycles has no effect on the temperature-strain plot of the track slab; the curves substantially coincide.  
According to Figure 11, the variation of the track slab strain with the thermal loading cycles can be drawn under the three temperature rising conditions of 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, as shown in Figure 12. Under the same temperature load, the track slab strain increases gradually with the increase of the thermal loading cycles. Over the three stages of initiation, expansion and stabilization, the strain increase rate of the track slab exhibits a \"slow-fast-slow\" change law. From the comparison analysis of Figure 12a-c, it can be seen that at the end of the 18 th thermal loading cycle, the increase amplitude of the track slab strain at the fixed end section is relatively large (approximately 100%), followed by that at mid-span (approximately 35%), while the strain increase amplitude at the sliding end section is relatively small (within 5%).  
Figure 10: Curve of the gap length varying with the cycles of temperature loading action.
# The Effectiveness of an Authentic E-Learning Environment for Deaf Learners
## 2 Background
### Authentic Learning and Tasks  
Learning from real-world situations, or authentic learning, is one common teaching approach that was used in different learning fields, and studies showed that including real-world scenarios in the assignments increases the engagement of foreign language learners [37, 38, 39]. Authentic learning by involving students in the learning subject increases motivation among learners when adopting tasks and raises the opportunity for students to develop their problem-solving skills [33]. To encourage students to use the target language in their communication, authentic tasks by immersing learners in authentic scenarios prepare them for real-world challenges [34]. [PERSON] et al. [39] believed that authentic learning allows learners to engage in the learning process and the cognitive domain is increased. This kind of learning, also called learning from real-world situations [12], prepares the learning settings by using authentic tasks to achieve something in a meaningful teaching environment. Significantly, learning from outside of school enhances their learners' memory and problem-solving skills, which are applicable in their lives.  
[PERSON] et al. [2] believed in the value of using real-world situations for designing learning environments and highlighting the learner's value concerning what they are taught in real life. [PERSON] et al. [35] described this approach as the common teaching method and said authentic learning is the idea that the tasks should somehow have a connection to the real-world situation, which increases the learners' practice in real-life phenomena.  
Adopting real-world situations to design learning environments and tasks are key factors in increasing motivation among learners [40]. Collaboration as an important factor in creating active learning settings is increased when real-world situations apply to learners. Authentic learning highlights the use of real-life problems to increase the problem-solving skills of learners by fostering learners to find their solutions by completing their tasks. Tasks at the core of authentic learning encourage learners to share their ideas, which mirror authentic challenges, and develop critical thinking and creativity skills [41]. The goal of learning through an authentic learning approach is to encourage learners to work together and increase their motivation for preparing their final products [42].  
Despite research to explore the effects of authentic learning in different educational contexts, the lack of research specifically addressing deaf learners through e-learning is notable. This leads to the current study being designed and formulating research questions to fill this gap in the current literature.
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
## 2 Materials and Methods
### Microorganisms and Media  
Three MTBE-degrading strains (_Stenotrophomonas_ L1, _Pseudoxanthomonas_ M3, and _Splingobacterium_ M10) used in this study were isolated and activated from the culture preserved in a \(-80\)\({}^{\circ}\)C refrigerator in the laboratory [23]. The mineral salt medium (MSM)used for microorganism growth and MTBE degradation in this study was contained as follows (g/L): KH\({}_{2}\)PO\({}_{4}\) 0.7, K\({}_{2}\)HPO\({}_{4}\) 0.85, (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) 1.23, CaCl\({}_{2}\):H\({}_{2}\)O 0.03, FeSO\({}_{4}\):7H\({}_{2}\)O 0.001, and 5 mL trace element solution containing the following trace elements (mg/L): H\({}_{3}\)BO\({}_{3}\) 60, CoCl\({}_{2}\):6H\({}_{2}\)O 40, ZnSO\({}_{4}\):7H\({}_{2}\)O 20, MnCl\({}_{2}\):4H\({}_{2}\)O 6, NaMoO\({}_{4}\):2H\({}_{2}\)O 6, NiCl\({}_{2}\):6H\({}_{2}\)O 4, and CuCl\({}_{2}\):2H\({}_{2}\)O 2. The membrane-filter sterilized solution of each of the metal salts was prepared by dissolving the metal with a calculated concentration and added to sterile MSM medium. Different concentrations of MTBE were added into MSM as a carbon and energy source. The Luria-Bertani (LB) formulation included the following components (g/L): Tryptone 10, yeast extract 5, and NaCl 10. The pH value of mediums was adjusted to 7.0 by adding either the NaOH or HCl solution. All apparatus and medium were autoclaved at 121 \({}^{\circ}\)C for 20 min in advance.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 5 Conclusions  
We have presented a comprehensive framework and a set of algorithms for error-bounded scientific data compression, which are broadly applicable to tensor datasets with moment constraints. Tucker decompositions, product quantization, and error-bounded compression are combined with an autoencoder (AE) in the framework. An AE with the Tucker decomposition can learn efficient global representations, and the reconstruction errors are bounded by selective residual post-processing with an error-bounded compressor.  
Figure 7: Running the AETMC framework with test data: The AE (**AETMC\({}_{400}\)**) is trained at timestep 400 and tested at timesteps 500, 600, and 700 with comparisons against separately trained **AETMC** models.  
This leads to high levels of compression with bounded errors. Furthermore, the constraint satisfaction step enables us to reduce the errors of the QoI, and therefore the QoI are preserved during compression. The application to XGC and comparison with MGARD has shown that we can achieve compression ratios in excess of 500 while satisfying QoI error bounds. We also show that a pretrained AE model shows good performance across nearby timestep data points in the XGC simulation. Therefore, the framework can compress multiple timestep data efficiently by omitting retraining the AE model for each timestep. Immediate future work will focus on improving machine efficiency with amortization across time, and end-to-end learning machines which integrate latent space entropy estimation and constraint satisfaction. Non-linear QoI that cannot be simplified need further investigation as well. Finally, we plan to develop methods for other scientific applications (such as climate) which require error-bounded compression.  
Conceptualization, S.R. and S.K.; methodology, J.L. and A.R.; software, J.L., Q.G. and J.C.; validation, J.L., T.B. and Q.G.; formal analysis, J.L.; investigation, J.L.; resources, S.K.; data curation, J.C. and J.L.; writing--original draft preparation, J.L., A.R. and S.R.; writing--review and editing, J.L. and A.R.; visualization, J.L.; supervision, A.R.; project administration, S.R. and S.K.; funding acquisition, S.R. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by DOE DE-SC0022265 and DOE DE-SC0021320 RAPIDS2.  
Not applicable.  
The authors acknowledge the DOE (Grant No. DE-SC0022265) and DOE RAPIDS2 (Grant No. DE-SC0021320) for funding this project.  
The authors declare no conflict of interest.
## 1 Introduction
Although past research seems to have established the seasonal correlation of GPP with EVI at individual flux tower sites, questions still remain whether or not the relationship is consistent across sites, whether it is superior to the other existing satellite metrics of vegetation abundance and structure (NDVI, LAI, and FPAR), and whether it lends itself to estimating annual GPP. Although there are now a number of process-based models for GPP estimation, the use of a single satellite-derived metric still has great appeal for ease of application, assessing the effects of uncertainties caused by poor quality input data, and simplifying the exploration of the underlying mechanisms of vegetation responses to climate change and variability. In this study we analyzed and compared how well four MODIS vegetation products relate to short-term and annual GPP estimated at a network of ideal-quality FLUXNET sites.
###### Abstract
In this type, the aim is to detect changes made to an image and to decide whether the image is the same as the original. Although our work involves proving the watermarking ownership type, it mainly deals with the authentication type of watermarking. When devising a blind image authentication algorithm it must take into account that the center of the image contains more important data than the edges. Watermark embedding may affect important parts of an image in a negative way. The present study seeks to reduce the effect of watermark embedding on important parts of the image, while making sure that the image does not undergo a malicious change. It is better to determine the genuineness of an image without having to obtain an original copy from a trusted party. In this sense, blind image authentication is a more desirable watermarking method than a non-blind method. A blind authentication and proof of ownership image watermarking algorithm that may resist certain innocent image operations, while detecting malicious alterations to an image, is proposed. The proposed method will make it possible to determine which parts of a received image have been maliciously altered by an attacker.
# Heterologous Production of Acrylic Acid: Current Challenges and Perspectives
## 2 Heterologous Production of 3-HP, a Frequent Intermediary from the AA Pathway  
The 3-HP building block, besides being a precursor of AA, can be used to produce several important chemicals, such as 1,3-propanediol, acrylamide and malonic acid, among others. Therefore, the US Department of Energy considered it as a \"Top Value Added Chemical from Biomass\" in 2004 [5, 6]. Since then, 3-HP heterologous production has been thoroughly explored.  
3-HP can be naturally produced as an intermediate or an end product in low levels using different pathways [58] in _Lactobacillus_ sp. [59, 60], _Chloroflexus aurantiacus_[61], _Metallosphaera sedula_[62] and _Klebsiella pneumoniae_[47], among others. Since the identification of these pathways in these hosts, the heterologous production of 3-HP has been reported in different hosts (_E. coli_, _Saccharomyces cerevisiae_, _Corynebacterium glutamicum_, _Pseudomonas denitrificants_ and _Bacillus subtilis_, among others) and optimized in endogenous producers (e.g., _K. pneumoniae_) using mostly three different routes: glycerol (CoA-independent), \(\beta\)-alanine and malonyl-CoA. These routes only need two enzymes to convert these precursors to 3-HP (Figure 3). Other routes less explored include propionyl-CoA, oxaloacetate and CoA-dependent glycerol routes [63, 64, 65]. This section does not intend to give an exhaustive review of 3-HP heterologous production (for that, please check [64, 66]) but instead provides some useful insights into AA biosynthesis.  
Figure 2: Main acrylic acid (AA) bio-based production routes. AA can be produced using glycerol, malonyl-CoA and \(\beta\)-alanine routes, and 3-hydroxypropionic acid (3-HP) may or may not be produced as an intermediary. The routes that do not produce 3-HP as an intermediary also share enzymatic steps with the routes that produce it.
# Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
## 2 Background
### Task Delegation  
A task delegation scenario involves an agent (_delegator_) who aims to achieve a goal \(g\) but cannot perform a task \(\tau\) necessary to accomplish \(g\) by itself. Thus, to achieve its goal, the delegator needs to delegate \(\tau\) to a partner (_delegate_) capable of completing \(\tau\) from the delegator's perspective [2,9]. In this scenario, the delegator will achieve \(g\) only if the delegatee successfully completes \(\tau\)[14]. In particular, we divide the task delegation process into three phases (i.e., offer request, partner selection, and execution):  
**Offer request:** A delegator notifies its partners of the intention to delegate a task \(\tau\) (i.e., a task request). After receiving such a notification, partners send their offers to the delegator, providing their performance estimations for \(\tau\). Performance estimates are made based on the task criteria (e.g., the cost and time required to perform \(\tau\)).  
**Partner selection:** After receiving offers from its partners, the delegator selects its delegates. This selection involves evaluating each partner, considering their availability, effectiveness in performing \(\tau\), and accuracy in meeting estimates (competence).  
**Task execution:** Delegatees execute tasks, while delegators evaluate their performance. A delegator \(\alpha\) assesses a delegatee, \(\beta\), based on the outcome produced for task \(\tau\). This outcome, a tuple similar to an offer, reflects \(\beta\)'s actual performance. If \(\beta\) successfully delivers an outcome, \(\tau\) is complete or partially complete, depending on how accurately \(\beta\) met its performance estimates. Otherwise, \(\beta\) fails to perform \(\tau\).  
**Definition 1**.: _An offer is a four-tuple \(\langle\alpha,\,\beta,\,\tau,\,V\rangle\), where_  
* \(\alpha\) _is the agent who requests the offer (delegator);_
* \(\beta\) _is the agent who produced the offer (partner);_
* \(\tau\) _is the task that_ \(\alpha\) _intends to delegate to_ \(\beta\) _if_ \(\beta\) _is selected as its delegatee;_
* \(V=[(c_{1},\tau_{1}),...,(c_{k},\tau_{k})]\) _is a vector that represents the performance estimations made by_ \(\beta\)_, where each value_ \(v_{i}\in[0,+\infty]\) _is a_ \(\beta\)_'s estimation for a_ \(\tau\)_'s criterion_ \(c_{i}\)
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 2 Related Work
### Post Quantum Digital Signature Scheme
#### 2.3.3. Merkle-Signature Scheme  
The main disadvantages of the one-time signature schemes (OTS) are (i) large public key size and (ii) the need for a new public key generation for each message. The Merkle-Signature Scheme (MSS) addresses the flaw of OTS by generating one public key to sign more than one message. The number of messages should be a power of 2 such that \(\mathrm{M}=2^{n}\). It generates the public \(X_{i}\) and private keys \(Y_{i}\) such that \(Y_{i}\) is within \(1\leq i\leq 2n\) and hash function is \(h_{i}\)= \(H(Y_{i})\). It creates a hash tree with leaf level \(i=0\) and the root level \(i=n\)[31].
# Investigation of Rain-On-Snow Floods under Climate Change
## 3 Results and Discussion  
### Model Calibration and Validation  
As a first step of the study the CemaNeige CR6J model was calibrated and then validated using historical data. The RMSE results for the calibration period were 3.1, 2.6, and 1.5 mm for the Soca, Savinja, and Gradascica case studies, respectively. The RMSE results for the validation period were 2.6, 1.9, and 2.0 mm for the Soca, Savinja, and Gradascica case studies, respectively. The NSE results for the calibration period were 0.77, 0.42, and 0.75 for the Soca, Savinja and Gradascica case studies, respectively. The NSE results for the validation period were 0.77, 0.44, and 0.77 for the Soca, Savinja, and Gradascica case studies, respectively. According to the NSE criterion results, the best modelling performance for the calibration period was obtained for the Soca River catchment. A similar performance was obtained also for the Gradascica River case study, whereas a worse performance according to the NSE criterion for the calibration period was obtained for the Savinja River case study. For the validation period, the NSE criterion results show a similar situation as for the calibration period, whereas RMSE revealed the best performance for the Savinja and Gradascica rivers case studies and slightly worse results for the Soca River. Other results for the Savinja River catchment are not shown, based on the suggestion of [PERSON] et al. [34], who argued that NSE values below 0.5 using the daily time step can be regarded as an unsatisfactory modelling result. An example of runoff modelling for the calibration period for the Soca River case study is presented in Figure 2. Visual inspection of modelling performance using the 30-day rolling mean, the non-exceedance probability, and a scatter plot showing simulated and observed flow data (e.g., lower panel of the Figure 2) for the Soca and Gradascica case studies returns satisfactory results.