esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations and Future Research Direction  
We deployed an approach that is completely based on the knowledge acquired from data. In this case, we use a public data set where information is anonymized, e.g., comments, and thus domain expert's knowledge is prohibited. For instance, a system layout could help to increase plausibility regarding conclusions for the interdependencies found between components. In future research, the application of this approach in a use case where access to domain experts is feasible should be considered. In particular, the application of this framework requires to perform three steps: visual identification of interdependencies, the modeling, and the plausibility check (XAI) to assure and understand the existence of components; thus, this is not a straightforward process and needs user feedback (interaction). Moreover, the replicated approach considered the 24 h lags to catch long-terminformation and 3 h lags to catching short-term information. Results are acceptable, but the optimization of time lags could help to improve results further. Moreover, the SHAP approach proposed within the framework helps to increase the plausability of the achieved results in terms of demonstrating the impact of interdependencies on predictive models, although this is not a causal analysis (see [53] for an overview). Therefore, quantifying the impact of components altogether with the direction through causal analysis, such as causal impact analysis remains as future work. Last but not least, our approach considers visualization techniques such as the multi-line approach or the visual-based representation of Shapley values. This valuable concept could be integrated into an interactive dash-board that would further facilitate the exploration of the interdependencies with respect to interdependencies.
## 3 Results  
### Feasibility Testing of the PMS  
The PMS offers a rapid and efficient solution for the simultaneous detection of five veterinary drugs, significantly reducing the typically time-consuming process of residue detection in food samples [21; 22; 33; 34]. Without the need for chromatographic separation, the PMS demonstrates excellent potential for on-site applications, making it an invaluable tool for real-time food safety monitoring.  
To confirm the feasibility of the PMS for detecting multiple veterinary drugs simultaneously, a standard mixture (100 \(\upmu\)g mL\({}^{-1}\)) containing the five target analytes (sulfamethazine, thiabendazole, amantadine, clenbuterol, and ratcopamine) was analyzed. Additionally, individual analyses of each compound were performed to establish baseline spectra for comparison. This allowed us to ensure that co-elution or ion suppression did not occur when the compounds were tested as a mixture. The mass spectra of the single analytes were consistent with those observed in the mixture, with no significant shifts in \(m/z\) values or changes in fragmentation patterns, confirming that the presence of multiple analytes did not interfere with their detection. These results validate the compatibility of the PMS system for the simultaneous detection of the five target analytes (Figure 2), providing a reliable foundation for subsequent method validation.  
Figure 2: Representative mass spectra of sulfamethazine, thiabendazole, amantadine, ratcopamine, and clenbuterol.  
Specifically, sulfadimethoxine exhibited a quasi-molecular ion peak at \(m/z\) 279 in positive ion mode, with secondary fragment ions at \(m/z\) 260.7 and \(m/z\) 203. Thiabendazole displayed a quasi-molecular ion peak at \(m/z\) 202, along with secondary fragment ions at \(m/z\) 131.7, \(m/z\) 163.4, and \(m/z\) 175. Amantadine's quasi-molecular ion peak was identified at \(m/z\) 152.2, with secondary fragment ions at \(m/z\) 135. Ractopamine exhibited an excimer ion peak at \(m/z\) 302, with secondary fragment ions at \(m/z\) 283.9 and \(m/z\) 164. Clenbuterol's quasi-molecular ion peak was observed at \(m/z\) 277, with fragment ions at \(m/z\) 203 and \(m/z\) 259.8. The preliminary feasibility tests demonstrated that the PMS could simultaneously detect the five veterinary drugs (sulfamethazine, thiabendazole, amantadine, clenbuterol, and ractopamine) with distinct and stable spectra for each analyte. This confirms the system's capability to handle multi-analyte detection. These initial results establish a strong basis for the subsequent validation and optimization of the method. Additionally, the compact design and low weight of the PMS system (<30 kg) support its potential for on-site applications.
## 4 Discussion
### Emission Dynamics of Methane and Carbon Dioxide  
The dynamics of methane and carbon dioxide (results shown) are a reflection of the decomposition of organic matter and microbial activities during composting [66]. The reduction of CH4 under the influence of OM decomposition resulted from the increase in moisture [67]. From the study, increasing the MC of the compost pile to 55% and 65% improved the rate of organic matter decomposition, while composting at a 45% level slowed down the decomposition of organic matter. The moisture content serves as a reactant during the decomposition of organic matter [40]. However, MCs below or above the optimal range inhibit aeration and microbial oxygenation, resulting in poor biodegradation of organic matter and compounds [68]. The temporally low emission trend of CH4 (results shown) is attributed to the continual aeration which was practiced [69]. In this study, an airflow rate of 0.4 Lmin\({}^{-1}\) (10 min on and 10 min off) was supplied to the reactors throughout the composting process. The aeration rate, which was maintained throughout the composting process, might have reduced the buildup of CH4 in the system and caused the microorganisms to discharge CH4 at a low rate. According to [PERSON] et al. [20], microorganisms have a lower demand for oxygen when the rate of organic matter decomposition decreases, causing them to excrete a lesser amount of CH4. Nevertheless, the initial high carbon content of the corn straw (results shown) contributed to the high rate of CH4 generation, as composting microorganisms waste more energy from high-carbon materials than from low-carbon materials [70].  
The dynamics of CO2 is an important measure for evaluating organic matter decomposition during composting. All treatments showed a sharp increase in CO2 on day 5 due to the high heat generated during the period [71]. The temperatures generated during the period may have been suitable to attract heat-tolerant microorganisms for organic matter biodegradation. The high level of CO2 losses in the lowest treatment (MC = 45%) could be explained by the insufficient water content in the compost pile which affected humification and mineralization processes. Composting is an amalgamated process that involves humification and mineralization [71]. Hence, insufficiently humidified and mineralized piles cause composting activity to emit more CO2 as a result of OM losses [72].
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 3 Analysis of the Research Object Maintenance Process
### Conclusions from the Analysis  
After analyzing the average airworthiness of the M-346 aircraft, it was concluded that the performance of long-term periodic maintenance has a significant impact on reducing the total airworthiness of military aircraft.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Duration [Days]:** \\ \hline Expected value & 10.7 \\ Median & 9.5 \\ Variance & 13.1 \\ Standard deviation & 3.5 \\ Minimum value & 5.0 \\ Maximum value & 19.0 \\ Gap & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duration of M-346 CPFI services performed in 2018–2020 in Polish Air Force—statistical parameters.  
Figure 1: Chart—summary of the duration of CPFI servicing.  
The technical documentation provided by the manufacturer of the M-346 aircraft does not specify the sequence of tasks to be performed during a given periodic maintenance. The user also has no information about the expected time of the completion of a specific task or its labor consumption. Therefore, it is not possible to precisely plan the course of the service based on the available technical documentation.  
Operational documentation used during the operation of the aircraft the M-346, developed by the A/C manufacturer, as well as the documentation developed by the user, do not allow recording the labor consumption of individual tasks during periodic maintenance. Therefore, data on the labor intensity of individual tasks are not collected.  
The current state of affairs does not allow the user to prepare a reliable schedule based on historical estimates. Furthermore, an analysis of the history of periodic maintenance performed in the PLAF reveals significant discrepancies in the total duration of individual maintenance tasks. Despite the relatively extensive experience in conducting semi-annual maintenance (CPFI), the times required to complete each maintenance cycle vary considerably.  
Based on the analysis, it was determined that there is a need to develop a dedicated tool--an IT system for acquiring data on the duration of individual tasks included in the considered service.
# Novel Beam Scan Method of Fabry-Perot Cavity (FPC) Antennas
## 4 Conclusions  
In this paper, we have proposed a novel beam-scan method in an FPC antenna providing relatively high gain with a low SLL obtained using a tapered PRS. In order to produce the desired tilted main beam, an AMC ground plane was newly introduced, which was easier and more practical to manipulate the reflection phase than modifying PRS cells. In that only changing the AMC's reflection property is enough to produce a scanned beam, our approach is innovative compared with previous works.  
To verify the proposed method, we have performed several simulations for different beam-scan directions and measured the fabricated prototype antenna. Accordingly, we have successfully proved that the proposed antenna indeed provides a well-directed beam in the desired target directions with reasonably high gain.  
Although we have shown only a fixed tilted beam, our method can readily expand to electrical beam-scanning applications by employing active RF components in the AMC cells. In addition, our work has also presented a good idea of separating bias networks from a crucial field-bouncing cavity area. Therefore, in the active scanning approach, the expected potential problems that the bias lines will disturb a sensitive cavity mode are already removed. Consequently, we expect our method can be utilized in many practical beam-scan applications that require beam-scanning as well as high gain.  
**Author Contributions:** Formal analysis, W.J. and D.K.; Funding acquisition, J.K.; Investigation, W.J., H.-j.M. and D.K.; Methodology, W.J., Y.-g.-j., H.-j.M. and D.K.; Supervision, D.K.; Validation, W.J., Y.-g.J. and H.-j.-M.; Writing--original draft, W.J.; Writing--review & editing, Y.-g.J. and D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been supported by a grant-in-aid of HANWHA SYSTEMS based on the Defense Challengeable Future Technology Program of ADD.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ref. \# 1**} & **Beam Scanning** & \multicolumn{2}{c}{**Beam Scanning**} & \multirow{2}{*}{**Gain**} \\  & **Type** & & **Angle 2** \\  & & [\({}^{\circ}\)] & **[dB]** \\ \hline
[11] & Continuous & 1–39 & 12.2–16.0 \\
[12] & Continuous & \(\pm\)20 & 4.67–8.53 \\
[13] & Discrete & 0, \(\pm\)10 & 11.9–12.0 \\
[14] & Discrete & 0, \(\pm\)15 & 11.7–16.3 \\
[15] & Discrete & 0, \(\pm\)22 & 9.6–10.4 \\ This work & Continuous & \(\pm\)41 & 10.6–13.0 \\ \hline \hline \end{tabular}  
* \({}^{1}\) “# means the reference number. 2 on the elevation angles.  
\end{table}
Table 3: Performance comparison of beam-steering FPC antennas.
## 8 Results
### Structural Model Testing
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Hypotheses**} & **Path Coefficient (a)** & **t Values (a)** & **f Square (b)** & **Conclusion** \\ \hline H1: System Quality \(\rightarrow\) System Use & 0.34 ** & 4.19 & 0.08 & Supported \\ H2: System Quality \(\rightarrow\) User Satisfaction & 0.26 ** & 4.85 & 0.10 & Supported \\ H3: Information Quality \(\rightarrow\) System Use & 0.39 ** & 4.78 & 0.10 & Supported \\ H4: Information Quality \(\rightarrow\) User Satisfaction & 0.40 ** & 6.81 & 0.21 & Supported \\ H5: Service Quality \(\rightarrow\) System Use & \(-\)0.05 & 0.98 & 0.00 & Rejected \\ H6: Service Quality \(\rightarrow\) User Satisfaction & 0.10 * & 2.57 & 0.03 & Supported \\ H7: System Use \(\rightarrow\) Net Impacts & 0.19 ** & 4.23 & 0.06 & Supported \\ H8: System Use \(\rightarrow\) User Satisfaction & 0.26 ** & 5.93 & 0.15 & Supported \\ H9: User Satisfaction \(\rightarrow\) Net Impacts & 0.70 ** & 18.39 & 0.90 & Supported \\ \hline \hline \end{tabular} Note. (a) Bootstrapping with 5000 samples (two-tailed test), * \(p<0.05\), ** \(p<0.01\); (b) f square \(<0.02\) no effect, 0.02–0.15 low effect, 0.15–0.35 medium effect, >0.35 large effect of the exogenous latent variable.  
\end{table} 
Table 6: Structural estimates (hypothesis testing). Figure 3: The revised CRISS success model; * \(p<0.05\), ** \(p<0.01\).
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 2 Materials and Methods
### Soil Thermal Diffusivity Estimation Methods
#### 2.2.2 Arctangent Method and Logarithmic Method  
For climate-controlled greenhouses, indoor air temperatures are maintained at specific set points. Therefore, the greenhouse air temperature is not a true sine function as is assumed for open fields. Instead, the upper boundary condition is more closely described by a Fourier series [10]:  
\[T(z,t)=T_{ave}(z)+\sum_{i=1}^{n}A_{i}(z)\sin(i\omega t+\varphi_{i}) \tag{11}\]where \(A_{i}\) is the amplitude of the temperature harmonic \(i\)  
\(\varphi_{i}\) is the initial phase shift of series i  
This equation indicates that the variation in the soil temperature consists of two parts: the constant temperature \(T_{ave}\) at depth \(z\) and \(n\) superimposed sine waves. Using the trigonometric function transformation, the equation can be written as:  
\[\mathrm{T}(z,t)=T_{ave}(z)+\sum_{i=1}^{n}(a_{i}\cos(i\omega t)+b_{i}\sin(i \omega t)) \tag{12}\]  
where \(a_{i}=A_{i}(z)sin\varphi_{i}\) and \(b_{i}=A_{i}(z)cos\varphi_{i}\), are the parameters of the Fourier series. Then,  
\[A_{i}(z)=\sqrt{{a_{i}}^{2}+{b_{i}}^{2}}\]  
\[\varphi_{i}=\arctan\left(\frac{a_{k}}{b_{k}}\right)\]  
where \(a_{k}\) and \(b_{k}\) are the shorter sides of triangle k.  
Applying the Fourier series method shown in Equation (12), [PERSON] et al. (1983) used the first four terms (n = 2) to describe an upper boundary condition at the soil depth \(z\)(15). Using an arctangent transformation, the soil temperatures at depths \(z_{1}\) and \(z_{2}\) measured every 6 h during a 24 h period are \(T_{1}\), \(T_{2}\), \(T_{3}\), and \(T_{4}\) and \(T_{1}^{\prime}\), \(T_{2}^{\prime}\), \(T_{3}^{\prime}\), and \(T_{4}^{\prime}\), respectively. Thus, the apparent thermal diffusivity (\(k_{AR}\)) can be calculated as (16)  
\[k_{AR}=\frac{\omega(z_{2}-z_{1})^{2}}{2\left(\arctan\left(\frac{(T_{1}-T_{3}) (T_{2}^{\prime}-T_{4}^{\prime})-(T_{2}-T_{4})(T_{1}^{\prime}-T_{3}^{\prime})}{ (T_{1}-T_{3})(T_{1}^{\prime}-T_{3}^{\prime})+(T_{2}-T_{4})(T_{2}^{\prime}-T_{4 }^{\prime})}\right)\right)^{2}} \tag{13}\]  
Using the logarithmic method, [PERSON] (1979) showed that the apparent thermal diffusivity (\(k_{LG}\)) can be calculated as (17)  
\[k_{LG}=\left\{\frac{0.0121(z_{2}-z_{1})}{2 ln\left[(\frac{(T_{1}-T_{3})^{2}+(T_ {2}-T_{4})^{2}}{(T_{1}^{\prime}-T_{3}^{\prime})^{2}+(T_{2}^{\prime}-T_{4}^{ \prime})^{2}}\right]}\right\}^{2} \tag{14}\]
## 2 Analytical Models
### Evaluation of Discharge Coefficient  
The above-mentioned correlations provide different methods to predict both the carry-over factor and the flow coefficient. Both of those quantities allow us to properly describe the flow phenomena occurring inside a labyrinth cavity and hence, they give an estimation of the mass flow rate across the seal as a function of the available thermodynamic conditions. However, since the goal of the flow check tests is the characterization of the labyrinth seal, a non-dimensional discharge coefficient has been calculated as the ratio between the measured (or predicted) mass flow \(\dot{m}\) and the ideal mass flow rate \(\dot{m}_{is}\) coming from an isentropic expansion in a nozzle with the same cross-section area:  
\[C_{d}=\frac{\dot{m}}{\dot{m}_{is}} \tag{10}\]  
where the ideal mass flow rate was evaluated by means of the total temperature \(T_{0}\) at the seal inlet, the overall pressure ratio \(PR\) across the seal calculated as \(p_{0}/p_{N}\) and the tip clearance area \(A\):  
\[\dot{m}_{is}=\frac{p_{0}A}{\sqrt{T_{0}}}\cdot\sqrt{\frac{2\gamma}{R(\gamma-1)} \left[\left(\frac{1}{PR^{*}}\right)^{2/\gamma}-\left(\frac{1}{PR^{*}}\right)^{ (\gamma+1)/\gamma}\right]} \tag{11}\]  
The value of \(PR^{*}\) used in the equation is taken as the minimum between the actual PR across the seal and the critical one (\(\approx\)1.893, since air is used), as it is considered that the flow can reach, at most, sonic conditions at the tip clearance section, where \(A\) is evaluated.  
It should be noted that Equation (10) is the well-known definition of the discharge coefficient and it should not be confused with the \"discharge coefficient\" employed by [PERSON] inside his model (see Section 2.2.3).
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 2 Materials and Methods  
### Materials  
Base bitumen of 70/100 penetration grade commonly used in the Netherlands was provided by Nynas (Stockholm, Sweden). The specification of the base bitumen is shown in Table 1 according to EN 12591. The SARA (saturated hydrocarbons, aromatic hydrocarbons, resins, asphaltenes) analysis of base bitumen was done by Iatroscan TLC-FID (Bechenheim, Germany). The four-component proportions are 7% for saturates, 51% for aromatics, 22% for resins, and 20% for asphaltenes, respectively. The ambient grinding CRM from waste truck tires has particle sizes ranging from 0 to 0.5 mm. The gradation of CRM particles is shown in Table 2. The basic properties and composition of CRM are shown in Table 3. The morphology of CRM and base bitumen was obtained through environment scanning electron microscopy (ESEM) in Figure 1. The ESEM images confirmed that rubber particles from ambient grinding have an irregular shape and porous appearance. The surface of bitumen is very smooth and homogenous except for some impurities. This study included two types of non-foaming warm mix additives, the wax-based product W and the chemical-based product C. Additive W is a synthetic microcrystalline wax that is free of sulfur and other impurities. Additive C is a liquid blend of chemicals, such as surfactants and polymers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Test Description** & **Unit** & **Value** \\ \hline Consistency at intermediate service temperature & Penetration at 25 \({}^{\circ}\)C & mm/10 & 80 \\ \hline Consistency at elevated service temperature & Softening Point & \({}^{\circ}\)C & 46 \\ \hline Resistance to hardening at 163\({}^{\circ}\)C & Change in mass & \% & 0.72 \\  & Refuined penetration & \% & 52 \\  & Softening point after hardening & \({}^{\circ}\)C & 51 \\ \hline  & Density at 25 \({}^{\circ}\)C & g/cm\({}^{3}\) & 1.027 \\ Technical characteristic & Kinematic viscosity at 135 \({}^{\circ}\)C & mm\({}^{2}\)/s & 265 \\  & Solubility & \% & \(>\)99.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of Nynas 70/100.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sieves (mm)** & **Passing (\%)** \\ \hline
0.710 & 100 \\
0.500 & 93 \\
0.355 & 63 \\
0.180 & 21 \\
0.125 & 9 \\
0.063 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle size distribution of crumb rubber modifier (CRM).
## 4 Discussion
### Real-Life Applications  
Beyond the classification outcomes, comparable results have been observed to be achieved within the validation cohort, even with a reduction to half the feature set. Expanding the sample size posits the potential for significant enhancement of these results through a more comprehensive database. Frequency domain variables have shown superior efficacy in elucidating relationships with depression and potentially other mental and behavioral disorders. This methodological framework can be extrapolated to investigate anxiety and manic episodes, and stratify individuals into various gradations of severity of depression. The findings indicate that this approach is not only innovative but possesses considerable promise. With the current level of precision, this method could function as a diagnostic algorithm in psychiatry, allowing healthcare practitioners to confirm and classify individuals with up to 92% certainty regarding the presence or absence of depression. Further refinements in accuracy could facilitate integration into ubiquitous devices such as smartwatches, smartphones, or earphones, capable of capturing the requisite signals to offer real-time preventive diagnostics of an individual's mental state. Ultimately, this can precipitate the development of an alarm system based on the physiological recognition of mental states, targeted toward individuals with suicidal tendencies. This system could be used as an evaluation instrument in psychiatric evaluations, providing an auxiliary verification mechanism for healthcare professionals to corroborate or negate a depression diagnosis. In addition, there is potential for incorporation into wearable technology, which functions as an early warning apparatus for those susceptible to suicidal behaviors instigated by depression. This analytical method has the potential to be extended to a variety of mental illnesses and different severity levels. Providing quantification of phenomena traditionally subjected to human interaction may offer substantial advantages in the prevention, diagnosis, and management of socioeffective disorders, which present significant challenges for psychologists, psychiatrists, and other healthcare providers.