esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Eutectic Solvent Pretreatment and Green Separation of Lignocellulose
## 1 Introduction
Greater interaction results in a lower melting point of the mixture, leading to a larger \(\Delta T_{f}\). Mixtures at specific molar ratios will exhibit the lowest melting point, known as the eutectic point [27]. The low melting point and high fluidity of DESs, along with its capacity to induce numerous \(\beta\)-O-4 bond cleavages in lignin structure and partial carbon-carbon bond cleavages (such as \(\beta\)-\(\beta^{\prime}\), \(\beta\)-5\({}^{\prime}\)), render it an efficient solvent for delignification, which is important in lignocellulosic separation processes [28]. There have been more than 1000 studies on the application of DESs in lignocellulosic separation in the past decade and it has received increasing attention from researchers since 2018. The cellulose separated and purified from lignocellulosic is depolymerized into glucose and further converted into bio-based chemicals such as ethanol, furan dicarboxylic acid, caprolactam, sorbitol, levulinic acid, \(\gamma\)-valerolactone [29; 30; 31; 32], 5-hydroxymethylfurfural (5-HMF) [33; 34], 2-furfurfural (2-F) [33], and 2,5-Bishydroxymethylfuran (BHMF) [35], which become an important direction of value-added conversion of waste biomass. DES has been widely discussed and studied in recent years and has been applied in many practical applications as a new solvent. For example, [PERSON] et al. investigated the ability of hydrophobic DES to extract non-polar compounds [36]. [PERSON] et al. investigated the superiority of DES as an early sample preparation step for atomic absorption spectrometry [37]. This paper provides a comprehensive review of the research and application of deep eutectic solvent (DES) in lignocellulosic separation over the past decade (2014-2024). The review focuses on assessing the impact of DES on delignification efficiency and subsequent sugar conversion. Investigations delve into understanding the effects of DES composition, treatment conditions, and pretreatment measures on lignocellulosic separation. Furthermore, the recycling capabilities and environmental sustainability of various DES formulations are evaluated.
## 4 Conclusions  
In this study, some interesting results were obtained, describing how the nutritional compounds of strawberries react during storage at different temperatures with different storage times and treatments.  
* Higher amounts of nutritional compounds were detected in the WF compared to the DF.
* For preserving nutritional quality, the WF treatment was optimum, and it showed good results in \(-80\)\({}^{\circ}\)C storage compared to \(-20\)\({}^{\circ}\)C. This is an important indication for application in the laboratory analysis and processing industry.
* The anthocyanins' content in the WF seemed to not decrease during 7 months of \(-80\)\({}^{\circ}\)C storage, with some exceptions.
* Owen drying was not an ideal treatment for preserving vitamin C, almost completely degrading this compound in strawberry fruits.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Anthocyanins** & **Vitamin C** & **Folate** & **Phenolic Acids** \\ \hline Anthocyanins & 1.00 & 0.70 \({}^{*}\) & 0.04 & \(-0.03\) \\ Vitamin C & 0.70 \({}^{*}\) & 1.00 & 0.31 \({}^{*}\) & 0.52 \({}^{*}\) \\ Polate & 0.04 & 0.31 \({}^{*}\) & 1.00 & 0.27 \({}^{*}\) \\ Phenolic acids & \(-0.03\) & 0.52 \({}^{*}\) & 0.27 \({}^{*}\) & 1.00 \\ \hline \end{tabular}
\end{table}
Table 7: Correlation matrix among the nutritional parameters analyzed. \({}^{*}\) indicates a significant correlation for \(p\leq 0.05\).  
* In the WT and DF, the amount of folate increased during storage. More folate was detected in the WF.
* The different strawberry cultivars presented different amounts of nutritional compounds: the Arianna and Francesca fruits had more vitamin C, phenolic acids, and folates, but the Silvia fruit had more anthocyanins.
* At the end of storage (7M), there was more loss of vitamin C in the Arianna WF compared to the other two cultivars' WF.
* There was a slight increase in the amount of anthocyanin in the Arianna and Francesca WF after 7M of storage, but in the Silvia WF the amount of anthocyanin was retained.  
Generally, it can be concluded that oven drying is not a recommended technique to treat fruits for preserving nutritional quality during storage. Storage time did not greatly affect the nutritional quality in whole fruits, though they presented a higher amount when stored at \(-80\)\({}^{\circ}\)C.  
Conceptualization, L.M. and B.M.; methodology, R.Q.; software, R.Q.; validation, L.M., F.C. and B.M.; formal analysis, R.Q.; investigation, L.M. and B.M.; resources, B.M. and F.C.; data curation, L.M. and F.C.; writing--original draft preparation, R.Q.; writing--review and editing, L.M. and B.M.; visualization, R.Q.; supervision, B.M.; project administration, B.M.; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Partnership for Research and Innovation in the Mediterranean Area (PRIMA)'s 2019-2022 MEDBERRY project.  
Not applicable.  
The authors declare no conflict of interest.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 9 StarBurst  
### Mission Overview  
The StarBurst Multimessenger Pioneer is a highly sensitive and wide-field gamma-ray monitor designed to detect the prompt emission of gamma-ray bursts. StarBurst is designed as a SmallSat to be deployed to LEO as a secondary payload using the Evolved Expendable Launch Vehicle Secondary Payload Adapter (ESPA) Grande interface for a nominal 1-year mission starting in 2027 to coincide with LIGO's scheduled fifth observing run. StarBurst will utilize NASA's Tracking and Data Relay Satellites ([[https://www.nasa.gov/mission/tracking-and-data-relay-satellites/](https://www.nasa.gov/mission/tracking-and-data-relay-satellites/)]([https://www.nasa.gov/mission/tracking-and-data-relay-satellites/](https://www.nasa.gov/mission/tracking-and-data-relay-satellites/)) (TDRS) system to report possible electromagnetic counterparts to gravitational wave mergers with low latency  
Figure 14: The MDP (99%) for 1 s GRBs as a function of GRB fluence for the POLAR and POLAR-2 instruments. In addition, the sensitivity of the GAP, the first dedicated gamma-ray polarimeter, is indicated. For reference, the fluence of the very weak GRB 170817A is indicated with a dotted line.  
via the GCN system. StarBurst is among the first of NASA's new Pioneer-class missions, intended to perform compelling astrophysics science at a lower cost than missions in the Explorers Program. The Pioneer program provides opportunities for early-to-mid-career researchers to propose innovative experiments and lead space or suborbital science investigations for the first time.
## 4 Results
### Correlation of Spectral and Wavelet Analysis Results  
By correlating the results obtained from spectral analysis and wavelet analysis, we can gain a more comprehensive understanding of how corrosion influences the structural integrity and performance of the bucket wheel arm in the mining environment. The cumulative wavelet and spectral analyses are presented numerically in Table 4. This correlation allows us to assess the combined effects of corrosion on the natural frequencies and dynamic behaviour of the structure.  
Thus, according to the accumulated values in Table 4, the physical analysis of the mechanical system for the bucket wheel arm can be detailed as follows.  
If D = 0 mm, without corrosion:  
* The metal structure is in an original state, without any corrosion damage.
* The dominant frequency (\(\mathrm{f_{dom}}\)) measured is 0.3 Hz, indicating a specific dynamic behaviour for the initial structure.
* In comparison, the frequency measured by finite element analysis (\(\mathrm{f_{meas}}\)) is significantly higher at 1.384 Hz, suggesting greater stiffness or initial mass than originally estimated. If D = 3 mm, there is a moderate level of corrosion.
* Moderate damage of 3 mm leads to an increase in \(\mathrm{f_{dom}}\) to 1.0 Hz.
* \(\mathrm{f_{meas}}\) continues to be higher than \(\mathrm{f_{dom}}\) with a value of 1.376 Hz.
* This increase in \(\mathrm{f_{dom}}\) indicates a significant change in the dynamic behaviour of the structure, probably due to loss of material or stiffness caused by corrosion. If D = 4.5 mm, there is a high level of corrosion.
* With damage greater than 4.5 mm, \(\mathrm{f_{dom}}\) increases significantly to 1.2 Hz.
* \(\mathrm{f_{meas}}\) continues to decrease, indicating a further loss of stiffness or mass of the structure.
* This significant change in \(\mathrm{f_{dom}}\) indicates a pronounced change in the dynamic behaviour of the structure and can be associated with a significant reduction in its performance and safety.  
The results suggest that corrosion profoundly affects the dynamic behaviour of a metallic structure, leading to significant changes in its dominant frequencies. These changes can be used to monitor the health of the structure and assess the need for corrective or preventive interventions to prevent damage and maintain the performance and safety of the structure.
## 4 Payload Development
### Field Campaigns  
Multiple field campaigns were conducted to acquire starfield images with the optical systems. On some occasions, short component-level tests focusing on a particular imaging system took place, while on others, full end-to-end tests, including downlink capability, were carried out. These campaigns aimed to calibrate the optical system based on the brightest stars in view. Innisfil, Ontario, was the site of one of the system-level tests. The sky brightness in Innisfil was measured at 20.18 mag./arc s\({}^{2}\), ranking it as five on the Bortle Dark Sky Scale [30], in contrast to a sky brightness of 17.80 mag./arc sec\({}^{2}\) that is in typical urban cities, ranking nine on the same scale. Figure 4 illustrates a series of images captured by the PCO camera during the Innisfil field campaign, featuring an RSO sequence. These images have been enhanced using the Zscale algorithm, which highlights images near the median [31]. This algorithm is beneficial in situations where data has extreme outliers, such as astronomical images that include stars. Additionally, multiple system-level tests were conducted in Timmins, Ontario, with a sky brightness ranked as class 4 on the Bortle Dark Sky Scale prior to the launch window. These campaigns were essential in acquiring datasets from various locations, in addition to those from the proposed flight.
## Appendix B Parameter Estimation for Distribution Functions
### Determining Parameter Dependency Levels  
Once the dependency matrix is constructed, parameters are assigned a dependency level through an iterative algorithm, ensuring all dependencies are accounted for. Algorithm steps are as follows:  
1. Initialize all parameters with a dependency level of 0 (unresolved).
2. Iterate through parameters until all have an assigned level: \(\odot\) Skip parameters already assigned a level. \(\odot\) For unresolved parameters, check dependencies: If any dependencies are unresolved, skip the parameter. If all dependencies are resolved, assign a dependency level one higher than its highest dependency.
3. Continue until every parameter has an assigned dependency level.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction  
Most human body functions such as perception, motor control and cognitive behaviour are linked to the nervous system [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. They are controlled by neural cell networks that generate and propagate electrochemical signals, in great part through the release and diffusion across synaptic junctions of neurotransmitters that mediate the exchange of those signals between the neural cells. A synapse involves a presynaptic neuron and a postsynaptic neuron that release and bind neurotransmitters, respectively.  
As neurotransmitters are essential components in the nervous system, and in order to understand how the brain works and identify neurological diseases, multiple approaches have been proposed for neurotransmitter detection and monitoring. Depending on the used technology, the regularly used detection methods can be classified into five categories: (1) Nuclear medicine tomographic imaging, including positron emission tomography and single-photon emission computedtomography; (2) Optical sensing, including surface-enhanced Raman spectroscopy, fluorescence, chemiluminescence, optical fiber based biosensors and colorimetry; (3) Electrochemical detection, including voltammetry and amperometry; (4) Analytical chemistry techniques, including high performance liquid chromatography; and (5) Microdialysis. In practice, two or more techniques may be combined for better detection accuracy. However, the detection of neurotransmitter is still challenging due to their sparsity in the brain and their mixture with other molecules. Thus, for accurate detection and monitoring, a neurotransmitter has to be localized and identified among other biomolecules and other neurotransmitters, as they can have similar behaviours.  
In order to qualify as a neurotransmitter, a molecule must meet the following criteria, according to [PERSON] et al. [PERSON] et al. (2015):  
* It must be synthesized and released by the same neuron and stocked at the presynaptic terminal;
* Its release induces a specific behavior on the postsynaptic neuron;
* Exogenous administration must generate the same effect;
* Its induced action on the postsynaptic cell can be stopped by a specific mechanism.  
The number of known neurotransmitters molecules exceeds 100 [PERSON] et al. (2015) and, based on their chemical structure, they are generally classified as amino acids, monoamines, neuropeptides, purines and gastrocnemius among others. Figure 1 shows the main locations of the brain where representative neurotransmitters are found in the highest concentrations.  
Although all neurotransmitters are essential to the good functioning of the central and peripheral nervous system, only a small group is involved in the majority of known neuropathies. As a result, the existing detection techniques are usually optimized for specific neurotransmitters, of which those of the monoamine and amino-acid groups and others such as acetylcholine. A summary description of those neurotransmitters follows:
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 4 Perceptual Evaluation
### Test Methodologies
#### 4.1.1 Simple Scenes  
The first listening test assessed the perceptual effect of AIO in binaural Ambisonic rendering for simple scenes. The base stimulus was a one second burst of monophonic pink noise at a sample rate of 48 kHz, windowed by onset and offset half-Hanning ramps of 5 ms, with half a second of silence between each burst. Test sound locations \(\psi\) were chosen as the central points of the faces of a dodecahedron, to avoid test sound locations coinciding with loudspeaker locations. To reduce the total number of trials, symmetry was assumed leaving only locations in the left hemisphere or on the median plane. Table 1 displays the spherical coordinates of each test sound location.  
The simple-scenes listening test followed the multiple stimulus with hidden reference and anchors (MUSHRA) paradigm, ITU-R BS.1534-3 [54]. The reference was a direct HRIR convolution, the medium anchor was a low-pass filtered version of the reference with an \(f_{c}=7\) kHz, and the low anchor was the monophonic base stimulus low-pass filtered at an \(f_{c}=3.5\) kHz. The other 6 stimuli were binaural Ambisonic renders for three Ambisonic orders \(\{M=1,M=2,M=3\}\), without and with AIO, totaling 9 test stimuli per trial. For each trial, the listener was asked to rate the 9 stimuli with a score between 0 and 100 in terms of overall perceived similarity to the reference, in accordance with the Spatial Audio Quality Inventory (SAQI) [55] whereby increased similarity would be rated higher. Each trial was repeated once, giving a total of 16 trials. Stimuli and trial ordering was randomized and presented double blind.
## 4 Orbit Determination Results
### Data Processing Strategies for Autonomous Orbit Determination  
In data processing for autonomous orbit determination in this paper, necessary correction of observation was first carried out. Then, time reduction processing was carried out according to the predicted orbit of the current epoch. Afterwards, gross errors in observations were detected and eliminated and high-quality observations were acquired. Finally, the autonomous orbit determination was conducted and the data processing flow is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**PRN** & **Minimum** & **Maximum** & **Mean** \\ \hline  
Mean & 0.74 & 4.41 & 0.99 \\ \hline \end{tabular}
\end{table}
Table 2: The statistical situation of PDOP values for the BDS-3 satellites.
## 4 Conclusions  
The following conclusions can be drawn from the results of this study considering the proposed dosages.  
1. The use of 30% or lower amounts of RCA does not significantly influence on total porosity and compressive strength. After 30% of RCA in the mixture, the total porosity increases and compressive strength decreases significantly.
2. Corrosion of steel bars initiates more quickly in RAC than in conventional concrete. The more RA the RAC contains, the earlier corrosion in the steel begins.
3. The corrosion of steel in conventional concrete and RAC accelerates with time. The more RA the RAC contains, the more rapidly the steel corrodes.
4. The use of RA introduces more interfaces in concrete, which accelerates the steel corrosion process because the porosity increases and the electrical resistivity decreases.
5. Steel corrosion and the electrical resistivity in concrete are not significantly influenced by replacing a maximum 30% of coarse aggregate with RCA.  
Steel corrosion and the electrical resistivity in concrete are not significantly influenced by replacing a maximum 20% of fine aggregate with RFA.
* Within the limitations and scatter of the present experiments, the degradation of durability parameters occurs with all RA specimens but is significantly greater where the RCA replacement is >30% and where the RFA is >20%.
###### Abstract  
In wireless body area networks, temperature-aware routing plays an important role in preventing damage of surrounding body tissues caused by the temperature rise of the nodes. However, existing temperature-aware routing protocols tend to choose the next hop according to the temperature metric without considering transmission delay and data loss caused by human posture. To address this problem, multiple research efforts exploit different metrics such as temperature, hop count and link quality. Because their approaches are fundamentally based on simple computation through weighted factor for each metric, it is rarely feasible to obtain reasonable weight value through experiments. To solve this problem, we propose an enhanced mobility and temperature-aware routing protocol based on the multi-criteria decision making method. The proposed protocol adopts the analytical hierarchy process and simple additive weighting method to assign suitable weight factors and choose the next hop while considering multiple routing criteria. Simulation results are presented to demonstrate that the proposed protocol can efficiently improve transmission delay and data loss better than existing protocols by preventing the temperature rise on the node.  
Keywords:temperature-aware routing; multi-criteria decision making (MCDM); Analytic Hierarchy Process (AHP); simple additive weighting (SAW) +
Footnote †: journal: Applied Sciences
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 2 Related Work
### Sequence Model  
Recurrent Neural Network (RNN) [15; 16] is one of the methods used to represent sequential information and handle a variable-length sequence. However, RNN has gradient vanishing problems in a long length sequence [17]. _Long Short-Term Memory_ (LSTM) was proposed in [12], to capture short- and long-term memory. LSTM consists of three separated gates to update hidden and cell states. The process of updating states is given as follows:  
\[\begin{split} i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t}-1+b_{i})\\ f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t}-1+b_{f})\\ o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t}-1+b_{o})\\ \tilde{c_{t}}=tanh(W_{c}x_{t}+U_{c}h_{t-1})\\ c_{t}=f_{t}\odot c_{t}-1+i_{t}\odot\tilde{c_{t}}\\ h_{t}=o_{t}\odot tanh(c_{t})\end{split} \tag{1}\]  
where \(x_{t}\) is the current input of time step \(t\), and \(\sigma\) is sigmoid function. Each \(i_{t},f_{t}\), and \(o_{t}\) denotes the input, forget and output gates, and bias for \(b\), respectively. These gates control the exposure of memory and assist in preserving short and long memory. After the success of LSTM, _Gated Recurrent Unit_ (GRU) was proposed in [18] as another type of recurrent unit. RNNs achieved remarkable performance where sequential information was needed, such as _Natural Language Processing_ (NLP) and time-series domain.  
In the vessel fuel consumption prediction, sequence models are essential as data acquisition occurs consecutively during sailings. However, the irregularity of time interval made it challenging to apply sequence models [7]. This is because RNNs share parameters regardless of the irregular time interval. It means that RNNs equally update information in the sequence, even when the time interval is not equal. Other researchers applied LSTM in the vessel domain, but they did not consider irregularity of time [9; 11]. This implies the necessity of a sequence model addressing irregular time intervals.
## 2 Materials and Methods
### Trajectory Prediction  
In the proposed trajectory approach, GP is applied to build the motion state transitions of the flight trajectory, which is also a machine learning-based flight trajectory prediction algorithm. The DNN architecture is applied to enhance the representation of the nonlinear features, and further improve the prediction performance [23]. The following rules indicate how the GP is applied to predict the flight trajectory in this work:  
1. Data pairs with respect to the flight time \(t\) and motion states in historical trajectories (\(Tr\)) are regarded as the known training data, i.e., \((\mathbf{x},\mathbf{y})\), to optimize the DGP parameters;
2. Basically, the time instants in the prediction period serve as the input observation, i.e., \(Z\). Consequently, the mean and standard variation of the predicted Gaussian distribution at different instants illustrate the distributed possibility of trajectory positions in the 3D space;
3. The predicted deterministic trajectory connecting the means of all prediction instants is called the nominal flight trajectory (NFT);
4. The covariance matrix indicates the probabilistic distribution of the flight position at a different instant by a sequence of Gaussian distribution with different parameters.  
Figure 3: Graphic model of an extended DGP.  
In this section, three models are designed to address the common trajectory prediction issues in air traffic studies. The paradigm for different flight operations is shown in Figure 4.