esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The concentrations of dissolved Cu and Zn ions were determined using atomic adsorption spectroscopy iCE-3000 (Thermo Scientific, Waltham, MA, USA) in an air-acetylene flame using wavelengths of 324.8 nm and 213.9 nm respectively. Solutions containing ammonium and phosphate ions were immediately analysed using a DR-6000 UV-vis spectrophotometer ([PERSON], Loveland, CO, USA). Ammonium ions were determined using the silver nitrate titration with a chromate indicator (Mohr's method) [51], whereas phosphates were determined using the application of ammonium molybdate [52]. The results are presented in form of relationship between the equilibrium sorption capacity \(Q_{e}\) (mg/g) (\(Q_{e}\) = (\(C_{0}\) - \(C_{e}\)) V/m) and temperature (series I) and in form of the relationship between the equilibrium concentration in the solution (\(C_{e}\)) and the equilibrium sorption capacity \(Q_{e}\) (mg/g) (series II). In the calculations, \(V\) (L) is the solution volume, \(m\) (g) is the sample mass, and \(C_{0}\) (mg/L) is the initial concentration of the contaminant. The maximum adsorption capacity of reactive materials \(Q_{max}\) and the Langmuir equilibrium constant \(K_{L}\) (L/g) were determined by matching the [PERSON]'s linear form of Langmuir isotherm [53] to the obtained results with application of Solver in Microsoft Excel:  
\[\frac{C_{e}}{Q_{e}}=\left(\frac{1}{Q_{max}}\right)C_{e}+\frac{1}{Q_{max}\cdot K _{L}} \tag{1}\]  
Additionally, the results were used to determine parameters describing the thermodynamics of the processes. Enthalpy change \(\Delta H^{0}\) (J/mol) and entropy change \(\Delta S^{0}\) (J/mol\(\cdot\)K) were calculated in accordance with van't Hoff equation:  
\[lnK_{C}=\frac{-\Delta H^{0}}{R}\frac{1}{T}+\frac{\Delta S^{0}}{R} \tag{2}\]  
the dimensionless equilibrium constant \(K_{C}\) was based on parameters of the Langmuir isotherm [53]:  
\[K_{C}=M_{w}\cdot 55.5\cdot 1000\cdot K_{L} \tag{3}\]  
and the Gibbs energy change \(\Delta G^{0}\) (J/mol) was calculated from the relationship:  
\[\Delta G^{0}=\Delta H^{0}-T\Delta S^{0} \tag{4}\]  
where \(R\) (J/(mol\(\cdot\)K) is the universal gas constant (equal to 8.3144), \(T\) (K) the absolute temperature, \(M_{w}\) (g/mol) is the molecular weight of the adsorbate, and factor 55.5 is the number of moles of pure water per liter. [PERSON]'s correlation was used to identify the statistical significance of the relationship between the parameters of contaminant sorption capacity (\(Q_{e}\)) and temperature. Statistical significance was at the level of \(p\) < 0.05. Moreover, equations describing the relationship between the equilibrium sorption capacity and temperature were estimated by using the multinomial regression model and statistical significance was tested using Statistica (StatSoft, Tulsa, OK, USA).
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 2 Background
### BERT Model  
Bidirectional encoder representations from Transformers (BERT) [28] is a state-of-the-art pretrained language model developed by Google. It leverages a transformer architecture that reads entire sequences of words at once, allowing it to capture context from both directions (left-to-right and right-to-left) simultaneously. This bidirectional approach enables BERT to understand the nuanced meaning of words based on their surrounding context, making it highly effective for various NLP tasks.  
BERT is also known as an effective model for extracting key information, such as entity names and values, from text. BERT excels in this task due to its ability to understand the context of words in a sentence through its bidirectional training. Advantages of BERT for key information extraction are provided as follows:  
* Contextual Understanding: BERT can capture the nuanced meaning of words based on their context. This is crucial for key value extraction where the meaning of a word may vary depending on its surroundings.
* Pretraining and Fine-tuning: BERT is pretrained on a large corpus and can be fine-tuned on specific tasks, making it highly adaptable and accurate for extracting specific types of information from text.  
Figure 1: Example data graph showing the relationships between movie, actor, and producer.  
* Token Classification: BERT can be used with token classification tasks, such as Named Entity Recognition (NER), where each token (word) in the sentence is classified into predefined categories (e.g., entities, dates, and values).
## 4 Results
### Lstm  
Our sixth experiment explored the Long Short-Term Memory (LSTM) model, which is ideal for dealing with sequential data. Figure 12 depicts the training curves for both the training and validation datasets. While LSTMs excel with sequential data, our model's performance deteriorated owing to dataset limitations. The considerable volatility in data distribution, as well as the imbalanced class representation, caused challenges.  
The model most likely ignored other classes in favor of predicting the one that predominated in the imbalanced dataset. This emphasizes how crucial it is to deal with  
Figure 11: Training history curve of Inception Network on training and validation data. (**a**) Accuracy, precision, and AUC. (**b**) Loss.  
Figure 12: Training history curve of LSTM model on training and validation data. (**a**) Accuracy, precision, and AUC. (**b**) Loss.  
data imbalances prior to training subsequent models. The data variance prevented the cost function from fully converging, even if the graphs showed that it was heading in the direction of the minimum. Comparing this to earlier versions, the performance was not as good. While the training accuracy increased to 70-75%, other parameters, such as precision and AUC, continued to decline, averaging 58-64% (Figure 12a). There were notable fluctuations (20-30%) in the loss values as well (Figure 12b). The difficulties in using LSTMs on imbalanced, highly variable datasets are highlighted by these findings. Although the LSTM model works well with sequential data, our dataset's constraints made it less effective in this experiment. Improving the effectiveness of LSTMs in this particular situation may require addressing data imbalance and maybe investigating methods to handle data volatility.
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
## 4 Discussion
Assessment of metal toxicity in soils and their accumulation in plants throughout 17 sites across Northeast Italy revealed measurements of 20-3975 \(\mathrm{\SIUnitSymbolMicro g}\)/g even though limits for industrial sites are set to 600 \(\mathrm{\SIUnitSymbolMicro g}\)/g [49]. Plant accumulation of Cu in dandelions (_Taraxacum officinal_ Weber [PERSON]. 1780) and willows (_Salix caprea_ L., _Salix eleaagnos_ Scop., and _Salix purpurea_ L.) was much lower than that in the soil at 16-81 \(\mathrm{\SIUnitSymbolMicro g}\)/g [49]. This short review of environmental Cu levels is used to illustrate that the current LIF method would only be applicable for detecting Cu in the most extreme cases, such as the 1900 \(\mathrm{\SIUnitSymbolMicro g}\)/g of moss near the Murgul copper mine [48]. The lowest threshold determined in our experiment, 340 mg/m\({}^{2}\), is one to two magnitudes higher than levels found in urban areas. These results show that this preliminary experiment needs further improvements to the optical system, e.g., adjustment of laser intensity or gain controls on the camera, which could be made in future studies. For now, we conclude that the analysis techniques using histogram differences and DTW single-color comparison and red vs. green color plots are in good agreement and appropriate for Cu detection. The technique shows potential for in situ monitoring where collected moss images could be evaluated against uncontaminated controls. However, applications of in situ imaging using LIF may still encounter possible challenges, such as the natural response of moss to environmental conditions in the field instead of the controlled grow tent used for testing. Moss growth, like many plants, can be affected by a number of factors that can impact LIF and chlorophyll behavior including temperature, water content, nutrient availability, and light [50]. Another aspect of this study that needs to be explored further is the underlying cause of image color changes. We hypothesized that the shift in decimal code to the \"darker\", lower end may be linked to a change in chlorophyll content in response to metal toxicity that results in either a shift from chl-a to chl-b or a reduction in the chl-a-to-chl-b ratio [1; 9; 17]. It is proposed that the addition of measuring the chl-a and chl-b levels through experiments extracting chlorophyll content may help confirm that hypothesis. At this time, the exact mechanism(s) responsible for change in moss fluorescence have yet to be determined. Independently of the underlying physiological cause, however, LIF as applied here demonstrated reproducible and convincing results, showing that it can detect Cu contamination in moss samples.
# Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
## 3 Materials and Methods
### Barriers to Maintenance 4.0
#### 3.1.6 Operational Main Criteria  
* Legacy equipment incompatible with digitalization: One of the barriers to Maintenance 4.0 applications is old equipment and machines that are not compatible with digitalization. The technological tools used to collect data in Maintenance 4.0 applications are very difficult to integrate into old equipment and machines [1; 13].
* Difficulties in process control and performance optimization: The success of Maintenance 4.0 applications is based on stable and optimized processes. If the process is unstable and not optimized, it is not possible for digital maintenance systems to work efficiently and correctly [1; 13].  
* Scheduling and planning optimization challenges: Difficulties in optimizing scheduling and planning directly negatively impact the goal of Maintenance 4.0 to transition from reactive to proactive and strategic maintenance. Therefore, not only technology, but also operational processes need to be digitalized and optimized [13; 46].
* Challenges in estimating remaining useful life: The use of continuously updated algorithms with high-quality data to accurately estimate remaining useful life is essential for the success of Maintenance 4.0 applications [53].
* Need for accurate predictive maintenance models: This is an important barrier because cost reduction, business continuity, and efficiency increases, which are among the main goals of Maintenance 4.0, can be achieved with correct predictive maintenance models [53].
* Lack of a structured framework specific to Maintenance 4.0 implementation: To effectively implement Maintenance 4.0 applications, a structured framework is needed for a proper digital transformation process, the integration of technologies, and performance monitoring and continuous improvement processes [13].
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 2 Background  
The advancement of data center and virtualization technologies has led cloud storage. The concept of the cloud computing paradigm delivers through online network accesses shared resources that are configurable as well as flexible resources. Thereby, load-balancing protocol and its approaches are significant in cloud servers. The load-balancing protocols are mainly designed with the consideration  
Figure 1: Data center image of application.  
of three types of flows (static, dynamic proactive, and dynamic reactive) and the granularity [7]. The static flow is then allocated to the available paths through applying the fixed conditions by the use of the hash for each packet header. Conversely, this technique is inflexible to the scenarios of throughput-oriented flows, while it is designed to the same path. In such a situation, the flows cannot move to other less utilized paths. Meanwhile, the dynamic reactive flow is able to move across any of the available paths subject to available bandwidth. The dynamic reactive flow provides a significant result. However, it increases higher complexity in measuring link utilization, flow counting, and estimating the best flow. On the other hand, the dynamic proactive allows the flows to become assigned to the available paths in a fixed assignment, where the initial assignment is mainly performed according to the available bandwidth. This approach is better compared to static flow and dynamic reactive flow in terms of implementation overhead, flexibility, and performance. Four kinds of granularity are observed such as per packet, per flow, flowlets, and per flow cell.
* Per packet: Can be used for the best load balance; however, due to the higher reordering, there are chances to attain insignificant reordering.
* Flowlets: Due to the variation of the candidate path latencies, the flowlet's size changes enthusiastically. If the latency rates are higher, the flowlets become large. It is obvious that it applies per packet and per flow technique. Therefore, the load balancing is then fine as well as coarse-grained. As a result, the flowlets become promising in load balancing for asymmetric paths. Nevertheless, the flowlets causes a small flow reordering, which can interrupt the execution times.
* Per flow cell: It uses the fixed size with the consideration of tens of packets. The most positive effect of per flow cell is that it uses simplified load balancing to reduce the possible reordering of small flows. However, it rises the reordering for larger flows, which can be fragmented into several flow cells.
Microdosimetric quantities are preferable to non-stochastic metrics, such as LET, because they account for energy-loss straggling and the finite range of \(\delta\)-rays [15, 25, 26, 27]. Calculations of these quantities are typically carried out by Monte Carlo track structure simulations (MCTSs). However, among other things, such simulations require significant processing power, and they are often time-consuming for direct microdosimetric calculations in PBT planning and patient-specific optimization. Therefore, various (semi) analytical microdosimetry methods have been proposed as alternatives to full-scale MCTSs [27, 28, 29, 30, 31]. In this study, an established analytic microdosimetry model is utilized to calculate the dose-averaged lineal energy (\(y_{\text{D}}\)) and the dose-averaged linear energy transfer (\(\text{LET}_{\text{D}}\)) of monoenergetic proton beams with energies from 1 to 250 MeV. The calculated \(\text{LET}_{\text{D}}\) and \(y_{\text{D}}\) values are used for the estimation of RBE for five different cell types based on nine empirical RBE models and two biophysical RBE models (TDRA and MKM). Comparisons between an analytic approach to estimate \(y_{\text{D}}\) and \(\text{LET}_{\text{D}}\) and the fast algorithm used in the MCDS code are presented.
# The Impact of Antenna Height on 3D Channel:
## 3 Results
### Discussion  
Based on the simulation results in Sections 3.1-3.3, some insights and observations regarding the 3D wireless channels are given as follows. On the one hand, to obtain the maximum and minimum values of the AS and the DS, BS antennas need to be located at half of the building's height under three typical street scenarios. On the other hand, the PDFs of EAoD and EAoA are approximated as double-sided exponential functions. Meanwhile, the parameters of the double-sided exponential function are linearly correlated with the antenna height under the straight street scenario, but change dramatically when the BS antenna height is half of the buildings height under forked road and crossroad scenarios.  
Furthermore, from Figures 3, 7, and 10 we can find that most numbers of EAoD and EAoA concentrate at 0 radians, even though we ignore the direct component. Generally, the path distance of a single reflection angle is smaller than the multi-path which makes the weighting value higher. Thus, we can conclude that the single reflection path plays a leading role for researching the distributions of EAoD and EAoA.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 3 SPE in Food Analysis--Main Fields of Applications
### Mycotoxins
Presurized liquid extraction is a convenient method for coupling with an online SPE and detection system. Online automated SPE with a C18 cartridge in addition to offline traditional SPE ensures improved precision and sensitivity of determination of mycotoxins in trace amounts in the abovementioned matrices [43]. For the analysis of mycotoxins in food (corn flour, beer, milk powder, peach seed), a new approach based on the use of dispersive micro-SPE (d-\(\upmu\)-SPE) with zirconia nanoparticles as a dispersant has recently been used [38]. d-\(\upmu\)-SPE enables carrying out a single separation with an enrichment stage for the extraction and determination of aflatoxin B1, fumonisin B1, ochratoxin A, ochratoxin B, T-2 toxin, and zearalenone with crucial advantages such as reduced time, lower amounts of organic solvent, and less sorbent wear when compared to traditional SPE. Moreover, the desirable extraction yield causes high selectivity, acceptable reproducibility and, as a consequence, low limits of detection (LOD) [38]. Cereal grains can be readily contaminated by fungi responsible for toxins synthesis. The most dangerous mycotoxins for human health present in infected corn or wheat are fusarium toxins, aflat, and ochratoxins, which are formed under proper conditions (moisture, temperature) [35]. The majority of the commercially available SPE cartridges are not adequate for the clean-up of various types of mycotoxins and often special columns are designed for specific sets of toxins. An SPE stationary phase (multi-walled carbon nanotubes) has been used for the extraction of fungal toxins. The main advantage of a newly applied SPE absorbent is its suitability for fast one-step purification for a wide range of mycotoxins. In addition, pre-treatment of the sample by multi-walled carbon nanotubes SPE cartridges has satisfactory recovery and, together with the use of UHPLC-MS analysis, forms a rapid, reliable and sensitive approach for the determination of a broad spectrum of mycotoxins [44].
## 3 Results and Discussion
### Corrected Shape Coefficient with a V-Shaped Notch  
The \(K_{\rm I}\) at the deepest point of the crack under different conditions can be obtained by the FEM, and then the corrected shape coefficient \(F_{\rm I}\) of type I SIF with a V-shaped notch was calculated by Equation (5). Figure 9 shows the data calculated according to the crack shape coefficient expression. Under different ratios of \(R/r\), the \(F_{\rm I}\) curve is numerically fitted by the least square method by changing the ellipticity ratio \(a/c\) and the relative crack depth \(a/t\).  
As shown in Figure 9, the corrected shape coefficient \(F_{\rm I}\) decreases with increasing ellipticity \(a/c\) and increases with increasing crack relative depth \(a/t\) under combined tensile stress and bending loads. The law of the corrected shape coefficient of the crack in this paper agrees well with that of the root crack with the T-shaped welded joint studied by [PERSON] et al. [37]. For the ampoule bottle with a V-shaped notch, its corrected shape coefficient increases significantly after \(a/t=0.6\). By binomial fitting of the curves shown in Figure 9, the expressions between the corrected shape coefficient of type I SIF and the crack relative depth can be obtained and are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{_a/t_} & \multicolumn{6}{c}{_a/c_} \\ \cline{2-9}  & \multicolumn{2}{c}{**0.2**} & \multicolumn{2}{c}{**0.4**} & \multicolumn{2}{c}{**0.6**} & \multicolumn{2}{c}{**0.8**} \\ \cline{2-9}  & \(|K_{\rm I}/K_{\rm I}|\) & \(1K_{\rm I}/K_{\rm I}|\) & \(|K_{\rm I}/K_{\rm I}|\) & \(1K_{\rm I}/K_{\rm I}|\) & \(|K_{\rm I}/K_{\rm I}|\) & \(1K_{\rm I}/K_{\rm I}|\) & \(|K_{\rm I}/K_{\rm I}|\) & \(1K_{\rm I}/K_{\rm I}|\) \\ \hline
0.2 & 821.02 & \(+\infty\) & 1000.00 & \(+\infty\) & 35.71 & \(+\infty\) & 25.71 & \(+\infty\) \\
0.3 & 77.50 & \(+\infty\) & 40.00 & \(+\infty\) & 1500.00 & \(+\infty\) & 50.26 & \(+\infty\) \\
0.4 & 51.65 & \(+\infty\) & 38.60 & \(+\infty\) & 56.25 & \(+\infty\) & 1351.35 & \(+\infty\) \\
0.5 & 49.29 & \(+\infty\) & 34.67 & \(+\infty\) & 39.23 & \(+\infty\) & 60.52 & \(+\infty\) \\
0.6 & 43.88 & \(+\infty\) & 29.11 & \(+\infty\) & 30.66 & \(+\infty\) & 32.96 & \(+\infty\) \\
0.7 & 34.67 & \(+\infty\) & 30.16 & \(+\infty\) & 25.21 & \(+\infty\) & 22.73 & \(+\infty\) \\
0.8 & 28.57 & \(+\infty\) & 29.23 & \(+\infty\) & 23.33 & \(+\infty\) & 21.53 & \(+\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ratios of \(|K_{\rm I}/K_{\rm I}|\) and \(|K_{\rm I}/K_{\rm III}|\) at the deepest point of the crack.  
Figure 9: Corrected shape coefficient of type I SIF with a V-shaped notch. (**a**) \(R/r=1.2\). (**b**) \(R/r=1.4\). (**c**) \(R/r=1.6\). (**d**) \(R/r\)=1.8.
# On the Estimation of the Moving Mass of a TMD Installed on a Lively Structure
## 4 Validation Example
### Isolated System  
To obtain a precise estimation of the SDOF system properties, it is first placed on a rigid floor (isolated from the structure) and, by means of an impact hammer, a force is applied to the moving mass. Both the input force and the acceleration response of the moving mass are synchronously recorded at a sample rate of 400 S/s. A total of five impacts are recorded and processed by means of the tfestimate function of MATLAB to estimate the frequency response function (\(H_{p}\) estimator). A rectangular window (i.e., with a constant weighting function equalling 1) with a length of \(N_{t}=32,768\) points, or 81.92 s, is used to divide the time series data into segments, so each resulting segment encompasses one single impact. The estimated accelare (frequency response function between an input force and an acceleration response), which is computed between 0.7 and 4.5 Hz with a frequency resolution of 0.01 Hz, is shown in Figure 3 (gray line). The vertical  
Figure 2: (**a**) Picture of the SDOF system used to illustrate the application of the methodologies, where A is the moving mass, B is the frame, C are the elastic members and D is the mangetic damper; and (**b**) the structure on which it is installed.  
lines between 1.5 and 2 Hz correspond to very small variations around \(\pi\) rad which arise due to estimation errors and noise. Since the phase plot shows data in the range \(\pm\pi\) rad, phase values over \(\pi\) rad or under \(-\pi\) rad are shifted by \(2\pi\) to enter the plotting range, leading to vertical lines joining them. To estimate the SDOF system properties, the obtained complex series of data in the frequency domain is fitted to the accelerance SDOF expression shown in Equation (10), which is obtained by deriving the receptance function \(h(\omega)\) in the frequency domain twice, equivalent to multiplying it by \((i\omega)^{2}=-\omega^{2}\)[20].  
\[a(\omega)=-\omega^{2}h(\omega)=\frac{-\omega^{2}/m_{t}}{-\omega^{2}+j2\zeta_ {t}\omega_{t}\omega+\omega_{t}^{2}} \tag{10}\]  
Figure 3 also shows the fitted frequency response function (black line) and the corresponding estimated properties are summarized in the first row of Table 1, associated to the case \"Rigid floor\", which is assumed to be the reference scenario in terms of error estimation. Note that the fitted line near \(\pi\) rad (under 2 Hz) is actually close to the experimental data at \(-\pi\) rad due to the \(2\pi\) phase shift mentioned above.