esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Liquid Product  
#### 3.3.1 GC-MS Analysis of the Liquid Product  
A GC-MS analysis was performed to identify the depolymerization products present in the liquid phase derived from the DES-assisted KL depolymerization conducted in ethanol at 190 \({}^{\circ}\)C for 8 h. Initially, 80 compounds were identified; however, among these, the 10 most representative ones were selected to compare the performance of various DES systems. The identified products primarily included aromatic monomers derived from syringyl and guaiacyl units. In the liquid fraction of the non-DES-treated bio-oil (Figure 7), several phenolic derivatives were detected, including guaiacol (5.59 wt%), 2-methoxyophenol (2.54 wt%), vanillin (7.77 wt%), acetovanillone (2.2 wt%), ethyl vanillate (1.81 wt%), homovanillic acid (2.01 wt%), and 1,2-benzenedi-4-methyl (1.6 wt%). Notably, these compounds originate from the cleavage of \(\beta\)-O-4 linkages in the guaiacyl units of lignin [13]. In addition to these, a substantial amount of isoeugenol (0.43 wt%) was detected. Interestingly, following treatment with the (ChCl:ZnCl\({}_{2}\)) DES, the yields of all detected compounds increased considerably in the liquid fraction. This suggests that the (ChCl:ZnCl\({}_{2}\)) DES enhances the alkylation, hydrogenation, and decarboxylation reactions of the unsaturated and oxygenated monomers in the liquid fraction, thereby increasing the yields of the resulting compounds.
## 3 Results  
### Influence of pH in TC Adsorption by Bentonite Materials  
The results presented in Figure 1 indicate that natural bentonite exhibits a high adsorption capacity for tetracycline (TC) at pH 2.5, with a progressive decrease in adsorption as pH increases. This trend is consistent with previous studies on tetracycline adsorption onto natural clays [9]. At low pH, the bentonite surface is predominantly protonated, acquiring a positive charge due to the adsorption of H\({}^{+}\) ions. Simultaneously, tetracycline is primarily present in its cationic form (TC\({}^{+}\)), which enhances electrostatic interactions with the negatively charged sites on the clay. Under these conditions, cation exchange becomes the dominant adsorption mechanism, contributing to the high adsorption capacity observed at acidic pH. Additionally, the formation of hydrogen bonds between the protonated functional groups of TC and hydroxyl groups on the bentonite surface may further promote adsorption [2; 10; 11].  
As pH increases, the surface charge of bentonite becomes less positive, and the ionization state of tetracycline changes. At intermediate pH values (4-7), TC predominantly exists in its zwitterionic form (TC\({}^{0}\)), reducing electrostatic attractions with the clay surface and leading to a gradual decline in adsorption. At higher pH levels (above 7), tetracycline primarily exists in its anionic form (TC\({}^{-}\)), while the bentonite surface acquires a net negative charge due to the deprotonation of hydroxyl groups [5; 6]. This resultsin electrostatic repulsion between the adsorbent and the adsorbate, further decreasing adsorption efficiency.  
The linear correlation observed between adsorption capacity (q\({}_{\text{e}}\)) and pH, as indicated in Figure 1, suggests that pH-dependent electrostatic interactions play a dominant role in the adsorption process. However, secondary interactions, including van der Waals forces and surface complexation, may also influence the overall adsorption behavior [11; 12; 13].
## 3 Artificial Neural Network Model and Training Results
### The Case of Having Two Scatterers  
When the number of scatterers is 2, after generating 10,000 full-view data for artificial neural network learning and testing, 9500 of them were divided into training data set and 500 are divided into test data set to proceed the learning. That is, the ratio of training data and test data were set to 95:5. Each time the artificial neural network was trained five times, the mean square error between the output data from each artificial neural network and the ground truth data were measured and graphed as shown in Figure 4a-c In this case, the \(x\)-axis is the number of learning, the red line is the mean square error for the validation data, and the green line is the mean square error for the training data. As shown in the figure, it can be seen that the mean square error measured using the training data and the validation data are similar as the learning progressed in the three artificial neural networks. As a result of calculating the mean square error with 500 test data after the learning was finished, as shown in Figure 4d, about 88% is distributed between 0 and 0.02, and the error is found to be very small because the maximum error is 0.07.  
In addition, to identify the similarity between the ground truth data and the output data obtained through the trained model, using the image map (imshow) function of Python, output data were separated into the real-part and imaginary-part to be visualized, respectively, like Figure 5. Looking at the figure, it can be seen that the distribution patterns of the two data are similar to each other. That is, since the error between the ground truth data and the output data through the learned model is small and the distribution pattern is similar, it can be predicted that the results obtained by imaging using the two data will be similar.  
Figure 3: Schematic diagram of an artificial neural network.  
Figure 4: Cases with two scatterers, Mean square error (**a-c**) for each group of test data and distribution of mean square error of test data (**d**).  
Figure 5: Visualization of the real-part and imaginary-part of ground truth data and output data when there are two scatterers. (**a**) ground truth data (real-part); (**b**) output data (real-part); (**c**) ground truth data (imaginary-part); (**d**) output data (imaginary-part).
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
## 4 Results and Analysis
### Evaluation by IGS Final Products
These results confirm that there is a significant decrease in RMS and absolute bias values in multi-system combined  
Figure 8: The ZTD differences of single- and multi-system solutions with respect to IGS products during DOY 140–150, 2019 (**top panel**: DGAR; **bottom panel**: PTGG). solutions. Comparing the combined solutions to single-system solutions, ZTD estimates can be more accurate and reliable, and certain outliers appearing in single-GNSS solutions can be eliminated. These prove the significant potential of multi-system combination mesoscale atmospheric research and Numerical Weather Prediction models.
## 3 Results and Discussion
### Optimisation of the Extraction Performance
In support of such a hypothesis were results on naproxen interactions with \(\beta\)-CD, where increasing pH was demonstrated to provoke instability on the inclusion complex, a fact ascribed to lower affinity of charged drug for the hydrophobic \(\beta\)-CD cavity [20]. Although accurate determination has shown that pK\({}_{\text{a}}\) may lie well above 7 for several substituted phenolics [21], for some flavonoids such as quercetin, which are frequently encountered in plant tissues, pK\({}_{\text{a1}}\) may vary within 5.06 to 7.36 [22]. In any case, at pH \(>\) 5, even limited dissociation of the most acidic phenolic hydroxyls could occur, provoking a significant increase in polyphenol polarity. This issue was also addressed in previous studies on polyphenol extraction with water/ethanol mixtures [23; 24; 25], where optimal extraction pH for total polyphenols was always \(<\)5. In these cases, increased extraction yield was ascribed to higher solubility of non-dissociated polyphenols in ethanol-containing solvents and increased polyphenol stability at acidic pH. Variations in \(C_{\text{CD}}\) concentration within the limits tested for \(\beta\)-CD and HP-\(\beta\)-CD were shown to exert non-significant impact on Y\({}_{\text{TP}}\), but it was not clear whether the presence of any CD used could affect Y\({}_{\text{TP}}\). To examine this, extractions were performed with each CD under optimised conditions, as well as with aqueous solutions under the same R\({}_{\text{L/S}}\) and pH, without the addition of CD (Table 6). In every case, it was demonstrated that addition of CDs provoked significantly higher Y\({}_{\text{TP}}\), highlighting the importance of the CDs used as aqueous extraction boosters. The highest difference in Y\({}_{\text{TP}}\) was found for m-\(\beta\)-CD (22.06%), followed by HP-\(\beta\)-CD (19.32%) and \(\beta\)-CD (11.28%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extraction Medium** & **Y\({}_{\text{TP}}\) (mg GAE g\({}^{-1}\) dw)** & \multicolumn{3}{c}{**Extraction Conditions**} \\ \hline  & & \(C_{\text{CD}}\) (_w/v_, _%_) & R\({}_{\text{L/S}}\) (mL g\({}^{-1}\)) & pH \\ \hline \(\beta\)-CD & 45.75 \(\pm\) 1.11 & 0.88 & 93 & 3.75 \\ Buffered dH\({}_{\text{2}}\)O & 40.59 \(\pm\) 1.01 & - & 93 & 3.75 \\ HP-\(\beta\)-CD & 50.52 \(\pm\) 1.19 & 1.40 & 100 & 3.90 \\ Buffered dH\({}_{\text{2}}\)O & 40.76 \(\pm\) 1.02 & - & 100 & 3.90 \\ m-\(\beta\)-CD & 54.25 \(\pm\) 1.35 & 1.40 & 94 & 4.54 \\ Buffered dH\({}_{\text{2}}\)O & 42.28 \(\pm\) 1.05 & - & 94 & 4.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The effect of each of the CDs tested on the extractability of polyphenols from _S. fruticosa_, compared with equally buffered deionized water, under optimal R\({}_{\text{L/S}}\).
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method  
This section introduces a feedback deep neural networks-based algorithm for MIMO-NOMA detection. Initially, we establish the fundamental structure of the MIMO-NOMA system and subsequently present the FDNN algorithm designed to address signal detection challenges.  
Figure 1: Structure of the DNN model.
## 4 Discussion
This quality makes it valuable for authorities aiming to stay proactive rather than reactive. When examining which variables matter most (see Figure 8), the usual factors dominate: natural population change, unemployment, net salary, and trade balance. Their impact is both practical and understandable--financial stress and reduced opportunities tend to increase crime risk. Tourist arrivals and weather also significantly influence crime, especially during busy summer months or holidays. The application of SHAP analysis provided a robust framework for identifying dominant factors influencing crime risk predictions. When analyzing the'very high' crime risk class, as illustrated in Figure 11, features such as 'unemployed people', 'natural population change', and 'trade balance' emerged as consistently impactful drivers of crime risk, underscoring their profound influence on the model's output. This global perspective is important for strategic policy-making, enabling authorities to focus on long-term systemic factors. Locally, SHAP explanations for specific periods demonstrated how dynamic shifts in these and other variables, often in combination, contributed to particular crime risk predictions. For example, during the COVID-19 lockdown, the model's elevated crime risk predictions could be directly attributed to the increased weight of 'unemployed people'. This granular level of interpretability allows police forces to understand the specific drivers behind forecasted risks and allocate resources more judiciously. By providing this explainability, the ARIMA-ANN becomes a transparent analytical instrument, fostering greater trust and actionable insights for crime prevention and resource optimization. In summary, the hybrid ARIMA-ANN model stands out for several reasons. It effectively combines regular, predictable trends with sudden shifts that occur in real life. Clearly defined risk classes prevent it from overlooking rare but important events. The model adapts well to rapid changes, such as policy shifts or economic shocks. It remains grounded in data, relying on real economic and social variables to make predictions. However, while the model shows promise for Galati County, it should be tested in other regions or countries with different patterns before it can be deemed a universal solution. Future research should also explore new data sources--perhaps real-time data feeds or better connections with police systems--to enhance its predictions further. Overall, the hybrid ARIMA-ANN approach offers more than just improved accuracy. It provides a well-rounded, adaptable framework that helps public safety teams identify potential issues early and allocate their resources wisely. With further research and testing, it could set a strong benchmark for crime risk prediction, not only in Romania but anywhere detailed and timely risk classification is essential.
## 2 Results and Discussion
### Volatile Compounds
#### 2.2.1 Volatiles Derived from Lipids  
Volatile flavour compounds may be formed from lipids via several different pathways. The primary pathways include mainly \(\beta\)-oxidation, and oxidation via lipoxygenase enzymes. Linolenic acid is metabolized via the lipoxygenase catalyzed oxidation producing 2-(_E_)- or 3-(_E_ or _Z_)-hexenal which in turn are reduced to 2-(_E_)- or 3-(_E_ or _Z_)-hexenol, responsible for the formation of respective hexenyl esters.  
Linoleic acid, via the same pathway produces hexanal which is reduced to hexanol, responsible for the formation of hexyl esters. Through \(\beta\)-oxidation, linoleic acid produces hexanoic acid which may be converted to butanoic or pentanoic acid, responsible for the formation of the respective esters (hexanoate, pentanoate, butanoate, butyl and pentyl). The widest variety of flavour compounds formed from lipids arises via lipoxygenase activity. Many of the aliphatic esters, alcohols, acids, and carbonyls found in fruit are derived from the oxidative degradation of linoleic and linolenic acids. [PERSON] and [PERSON] [26] reported that only the 9- and 13-hydroperoxides are formed (95:5 ratio, respectively) via enzymic action. Later on, [PERSON] _et al._[27] showed that only the 13-hydroperoxide makes the greater contribution to flavour. Acids and ketones as well as other intermediates in the oxidation process are readily converted to alcohols, aldehydes, and esters by other enzyme systems in the plant. Both \(\alpha\)- and \(\beta\)-oxidation pathways have been demonstrated to exist thereby providing a wide range of volatiles for further conversion to flavour compounds [28].  
Given the above, one may question the importance of these pathways since there is very little lipid in fruits. To address this question, one must recognize that many aroma compounds have low sensory thresholds (ppm). It takes only a small amount of precursor to yield ppm quantities of aroma compounds. Second, there is a significant quantity of linoleic and linoleic acid in plant chloroplasts. As fruits ripen, they lose their green colour due to chloroplast degradation, which then releases membrane lipids which are rich in these key aroma precursors. These precursors then enter the pathways described above to form a large variety of esters and carbonyls that characterize the aroma of many fruits. Based on ANOVA analysis it is shown that volatile compounds that come from fatty acid oxidation promoted by lipoxygenase are significantly more abundant in the Skeena cherry cultivar followed by the Ferrovia cultivar.
## 3 Results and Discussion
### Study of the ILs Structure  
Ionic liquid anion and cation play an essential role in extraction processes; due to this fact, a study about the influence of ILs structure is performed.  
The results of the extraction efficiencies for the two studied anions (NTf\({}_{2}\) and Nf\({}_{2}\)) are shown in Table S3 and Figure 1. As it is possible to observe in this figure, the extraction efficiencies are higher than 90% for all the initial concentrations of phenolic compounds. Therefore, in this work, the experimental results showed that there are not significant differences between [1P1 Mpyr][NTf\({}_{2}\)] and [1P1 Mpyr][Nf\({}_{2}\)].  
However, there are other works where the anion structure of the ILs' effect in the extraction process is important. For instance, [PERSON] et al. studied ILs based on three different anions ([NTf\({}_{2}\)], [BF\({}_{4}\)], and [PF\({}_{6}\)]) for the extraction of 14 phenolic compounds, and it was observed that the best results were obtained with the [BF\({}_{4}\)] anion, and no significant differences were found between the other two anions [8].  
With the aim of comparing the effect of the cation structure into extraction efficiencies, the results obtained with [1P1 Mpip][NTf\({}_{2}\)], [1P3 Mpy][NTf\({}_{2}\)], and [1P1 Mpyr][NTf\({}_{2}\)] were analyzed. These two ILs have the same number of carbon atoms in the cation ring, but only one of them presents aromatic character ([1P3 Mpy][NTf\({}_{2}\)]). The experimental results show that the aromaticity of the cation does not increase the extraction efficiencies (Table S3, Figure 2A). Similar results were observed by [PERSON] et al. [18], who studied the capability of imidazolium and pyrrolidinium based ILs to remove phenol, o-cresol, and resorcinl from water, obtaining better results with the non-aromatic one (pyrrolidinium).  
Regarding the non-aromatic cation, the best results were obtained when the [1P1 Mpyr][NTf\({}_{2}\)] ionic liquid is used in all studied cases, increasing these differences when the results for o-cresol extraction were compared (Figure 2B).  
In previous work carried out for this research group, where the influence of the alkyl chain of the cation structure in the extraction process was studied, it was observed that an increase in the alkyl chain promotes an increases in the extraction efficiency. [PMim], [BMim], and [HMim] cations with the same anion [NTf\({}_{2}\)] were used to extract the same phenolic compounds studied in this work, and best result were obtained using [PMim][NTf\({}_{2}\)] IL [4]. In other work [7], 1-ethyl-3-methylimidazolium bis(fluorosulfonyl)imide ([C\({}_{2}\)mim] FSI) ionic liquid was studied as the extraction agent for phenolic compounds. The results obtained in these two articles are compared with those obtained with the studied ILs in this work (Figure S1). As it is possible to observe in this figure, the results obtained with the eight ILs are comparable, obtaining extraction efficiencies above 95% for all systems.
## 6 Fully-Distributed Clock Synchronization Algorithms
### System Model  
In a fully-distributed clock synchronization algorithm, the synchronization is not performed pairwisely, but simultaneously among all of the sensor nodes. Therefore, instead of estimating the clock offset difference and the relative clock skew between two nodes, we aim to find the clock offset and skew for each individual sensor node. A two-way message exchange between nodes \(i\) and \(j\) in the \(n\)-th round of their message exchange is modeled in Figure 4. Following the same message exchange procedure described in Section 2.2, we can write:  
\[\frac{1}{f_{j}}[c_{j}(t_{n}^{2})-\theta_{j}]=\frac{1}{f_{i}}[c_{i}(t_{n}^{1})- \theta_{i}]+d_{ij}+X_{ij} \tag{45}\]  
\[\frac{1}{f_{j}}[c_{j}(t_{n}^{3})-\theta_{j}]=\frac{1}{f_{i}}[c_{i}(t_{n}^{4})- \theta_{i}]-d_{ji}-X_{ji} \tag{46}\]  
where \(d_{ij}\) and \(d_{ji}\) denote the fixed delays from node \(i\) to node \(j\) and from node \(j\) to node \(i\), respectively, and \(X_{ij},X_{ji}\) stand for the random delays. If only the effect of clock offset is considered, _i.e._, \(f_{i}=f_{j}=1\), and the network topology is assumed stationary, _i.e._, \(d_{ij}=d_{ji}=d\), the system model in Equations (45) and (46) reduces to the more simplified model:  
\[c_{j}(t_{n}^{2})-\theta_{j}=c_{i}(t_{n}^{1})-\theta_{i}+d+X_{ij} \tag{47}\]  
\[c_{j}(t_{n}^{3})-\theta_{j}=c_{i}(t_{n}^{4})-\theta_{i}-d-X_{ji} \tag{48}\]
## 2 Field Experiment
### Sand Accumulation Observation and Analysis  
The sand-accumulation data along the road were measured by UAV (unmanned aerial vehicle) oblique photography which has advantages of high efficiency, non-contact, and large range and can realize accurate measurement of dune movement and three-dimensional morphology [29; 30]. The UAV was a DJI M300 RTK multi-rotor UAV and the camera was Ruibo lens (Figure 5). Limited by experimental funds and site conditions, the observation time was from winter 2020 to summer 2021 and a total of three photography operations were conducted.  
Figure 4: Sand drift potential of each season in the study area.  
Figure 5: UAV for sand accumulation air survey and camera lens.  
Highway surface sand deposition distribution was observed, during which the wind direction was dominated by southwest and the angle between the wind direction and observed path angle was not less than 50\({}^{\circ}\); as shown in Figure 6, the wind direction was from the top down. As time goes on, the dunes gradually move to the highway surface and accumulated sediment increases. In winter, road area sand is scattered covered on the road surface, the morphology of sand sedimentation is more dispersed and the thickness of sand accumulation on the highway surface is less than 2 cm. In spring and summer the morphology of sand accumulation is aginated forming sand burial and the maximum thickness of sand accumulation on the highway surface is more than 50 cm; specific road area sand sedimentation statistics are shown in Table 1. The main reason for the difference in sand-accumulation morphology in different seasons may be that the overall wind speed in winter in the study region is weak and the sandy air current is easy to saturate when it meets tall dunes, so that grits cannot cross the windward side of the dunes to form sediment, so only a small amount of the grit crossing the dunes disperses to the highway surface. In spring, the wind speed gradually increases and the sandy air current is not easy to saturate so as that the sand-carrying wind has a great push effect on the dune making the dune move slowly and form sand burial on the highway surface. After that, the wind is still strong in summer, and the dune continues to move along the dominant wind direction, resulting in the deterioration of sand burial.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 4 Numerical Application  
The numerical application is performed by using a cable trough straight section, which is shown in Figure 2a. The straight section is a structural component that is used in its field application as a horizontal support for communication cables and it is considered as a hollow straight beam of uniform section and uniformly distributed load. The amount of the cable load and the site environmental vibrations are the principal failure mechanism. The component operational load is 30 Lb. In addition, its relevant data are, length \(L\) = 72 in, width \(W\) = 4 in, height \(H\) = 4 in, and thickness \(t\) = 0.125 in. The component is made of thermoplastic ABS with a yield strength \(S_{y}\) = 4350 Psi, ultimate strength \(S_{ut}\) = 7250 Psi, and an endurance limit \(S_{end}\) = 3625 Psi [22].  
The component has, due to its static functional application, two fixed supports, and a uniform load, see Figure 2b, and due to the dynamic environmental load, it is submitted to random vibration stress, see Figure 2c.  
Next, the analysis of the stress induced by the random vibration is performed.