esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Equilibrium Isotherms
It can be noted that the \(R_{L}\) value is between 0.008045 and 0.02375 indicating that the equilibrium adsorption is favorable under the studied conditions. On the basis of the Freundlich isotherm model, the value of \(1/n\) is 0.1277 indicating that the adsorption process is favorable. For the Temkin isotherm model, A and B are 152.19 (L/g) and 92.29 (J/mol), respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Isotherms** & **Equations** & **Parameters** & **Values** \\ \hline \multirow{3}{*}{[PERSON]} & \(\frac{\zeta_{c}}{q_{c}}=\frac{1}{q_{c}K_{L}}+\frac{\zeta_{c}}{q_{m}}\) & \(K_{L}\) (L/mg) & 0.1370 \\  & \(R_{L}=\frac{1}{1+R_{L}\zeta_{0}}\) & \(q_{m}\) (mg/g) & 1000.00 \\  & \(R_{L}=\frac{1}{1+R_{L}\zeta_{0}}\) & \(R_{L}\) & 0.008045-0.02375 \\  & \(R^{2}\) & 0.9988 \\ \hline \multirow{3}{*}{Freundlich} & \(K_{f}\) (mg/g) & 488.65 \\  & \(1/n\) & 0.1277 \\  & \(R^{2}\) & 0.9809 \\ \hline \multirow{3}{*}{Temkin} & \(A\) (L/g) & 152.19 \\  & \(q_{c}=B\ln A+B\ln c_{c}\) & \(B\) (J/mol) & 92.29 \\ \cline{1-1}  & \(R^{2}\) & 0.9677 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The isotherm parameters for adsorption of Malachite Green (MG) onto nanoscale zerovalent zinc (nZVZ). Figure 17: The isotherms for adsorption of MG onto nZVZ (initial pH = 5, temperature = 25 \({}^{\circ}\)C and contact time = 120 min). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Contact Time** & **Adsorption Capacity (mg/g)** & **References** \\ \hline Cyclodext-based adsorbent & 120 & 91.9 & [1] \\ AMP a  & 120 & 335 & [57] \\ CCB beads b  & 120 & 435 & [58] \\ nZVZ & 120 & 1000 & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Adsorption capacity of MG for different adsorbents.
# Novel Service Efficiency Evaluation and Management Model
## 5 Conclusions  
The efficiency evaluation model of the multi-workstation service operation process proposed by this paper is to evaluate the service operation efficiency of each workstation  
Figure 3: Radar evaluation chart of service operation performance for multiple workstations.  
by means of the radar evaluation chart. The radar chart is a practical graphical analysis and management tool that can clearly see at a glance whether the point estimate of each indicator falls into the control block of the radar chart, so as to achieve the overall efficiency evaluation effect of multiple workstations. Additionally, the index value for each radar line represents the service operation efficiency of each workstation. In addition, the index is a unitless and easy-to-use evaluation and management tool that can concretely present the service operation efficiency of the workstation and provide a reference for improvement directions, in order to reduce the waste of management resources and time. After reviewing the multi-workstation service operation efficiency evaluation model proposed by this paper, we can list its advantages as follows:  
1. The service efficiency evaluation index of each workstation is not only convenient and easy-to-use, but it also has a one-to-one mathematical relationship with the performance achievement rate.
2. The point estimate of the indicator can be directly compared with the MV to determine whether the service operation efficiency has reached the required level. In this way, the advantage of simple and easy-to-use point estimate can be maintained, and the risk of misjudgment due to sampling errors can be reduced.
3. The MV is derived based on the upper confidence limit and the required value of the index, so it can reduce the risk of misjudgment caused by sampling errors.
4. The proposed novel service efficiency evaluation and management model can evaluate the efficiency of the multi-workstation service operation process as well as can directly monitor whether the service operation efficiency of each workstation needs to be improved at the same time, which will help the service industry move towards the goal of intelligent innovation management.
5. Compared with the model that only evaluates the overall performance of the process, the model proposed in this paper is relatively unlikely to miss opportunities for improvement. In addition to the performance evaluation of the multi-workstation service operation process, it is also applicable to the performance evaluations of other service operations.
## 3 Analysis Key Components
### Analysis of the Chain Drive Film-Soil Separation Mechanism
#### 3.2.2 Analysis of the Shaking Rollers  
The movement characteristics of the shaking rollers and chain link shaking directly affect the performance of the film-soil separation. The structure of a shaking roller is shown in Figure 7 and consists of a drive shaft, a shaking frame and a roller wheel. The shaking frame is composed of an equilateral triangle, and the rollers are mounted evenly on the shaking frame with a central circle \(\varphi\)150 at an angle of 120\({}^{\circ}\). The drive shaft and the shaking frame are fixed, and the rollers rotate on the shaking frame. When working, the active chain roller drives the drive shaft to rotate through the chain drive, driving the rotation of the shaking frame, and the roller wheel comes into contact with the chain of the chain drive film-soil separation mechanism, driving the chain to shake in a rolling manner.  
Based on the speed of the chain during operation, the speed of the jitter rollers is calculated according to Equation (5) as follows:  
\[n_{1}=60\times v/\text{L}=30\omega_{1}/\pi \tag{5}\]  
where \(v\) is the speed of the chain movement (m/s), \(\omega_{1}\) is the angular speed of the shaking roller (rad/s), \(n_{1}\) is the speed of the shaking roller (r/min), and L is the circumference of the shaking roller (mm). The theoretical speed of the shaking roller is calculated as 406.46 r/min.
## 3 Results and Discussion
### Effects of BGLSP on Textural Properties of CSB  
The textural characteristics of CSB serve as intuitive indicators of its quality and can be quantified using parameters such as hardness, chewiness, gumminess, springiness, and cohesiveness. Table 5 presents the textural properties of CSB with varying levels of BGLSP additions, as determined through textural profile analysis. The inclusion of 10% BGLSP resulted in a significant increase in the hardness, chewiness, and gumminess of CSB (\(p<0.05\)). Hardness is a critical parameter influencing the sensory quality of CSB and serves as a primary indicator of aging during storage [26]. BGLSP is rich in fiber, lipids, and protein components, which can interact with gluten and starch, thereby affecting the formation of the gluten network [1]. Similarly, [PERSON] et al. [10] reported that the addition of flaxseed flour led to an increase in the hardness of CSB. Cohesiveness reflects the strength of the internal bonds within the CSB, and lower cohesiveness may be preferred by certain consumers in China [26]. Springiness is typically used to describe the ability of an object to return to its original shape after deformation [27]. As demonstrated in Table 5, the incorporation of BGLSP resulted in a reduction in the cohesiveness and springiness of CSB compared to the control sample. This phenomenon may be attributed to the dilution of gluten protein, which is likely caused by the steric hindrance effects of fiber [12].
## 7 Conclusions  
This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 \(\times\) 6 linear series-fed microstrip patch antenna array, specifically optimized for 5G millimeter-wave (mm-wave) communication at 28 GHz. The antenna, fabricated on a low-loss Rogers RO3003 substrate, integrates a custom-designed, symmetric two-way microstrip power divider to ensure balanced excitation and phase consistency across array elements. The final prototype achieved a simulated peak gain of 11.5 dBi and a broad impedance bandwidth of 30.21%, making it highly suitable for mm-wave applications, including 5G NR bands (n257, n258). Measured results confirmed strong impedance matching, with a return loss better than \(-20\) dB, and they demonstrated excellent agreement with simulation data.  
The far-field radiation patterns validated the design's ability to produce a narrow, highly directive main lobe in the E-plane and acceptable side lobe levels in the H-plane. Importantly, the observed main beam tilting in the H-plane demonstrates the array's inherent capability for passive beam steering, achieved through careful feedline design and optimized inter-element spacing (\(\sim\)0.5\(\lambda\)). These characteristics affirm the array's potential for both fixed beamforming and directional beam control without the need for active phase shifting.  
Surface current distribution analysis further confirmed efficient power delivery, balanced excitation across elements, and constructive interference, validating the power divider's critical role in achieving stable, high-performance radiation. Overall, the integrated antenna design not only simplifies fabrication and maintains a compact footprint but also supports high-gain beamforming and passive beam steering critical for next-generation 5G mm-wave systems, point-to-point wireless backhaul, automotive radar sensing, and beam-steerable phased array platforms.  
Methodology, M.A.S.; Formal analysis, M.A.S. and M.A.; Writing--original draft, M.A.S.; Writing--review & editing, M.A.S.; Supervision, A.O.N. All authors have read and agreed to the published version of the manuscript.  
The research paper is supported by University of Greenwich and by the Brain Korea 21 FOUR Project (Dept. of IT Convergence Engineering, Kumoh National Institute of Technology).  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.
# Type Hierarchy Enhanced Event Detection without Triggers
## 4 Experiments and Evaluation
### Ablation Study  
In our proposed method, we undertake an ablation study to evaluate the effect of the word-level and context-level representations and type hierarchy concepts. Firstly, since we perform type hierarchy concept enhancement for each event type, we report our framework's performance without a hierarchy concept and concept confidence. In particular, an average score will replace concept confidence when removing it. Table 5 illustrates that our model is not affected by sparsity, and confidence measures the similarity of the hierarchy concept to the event type. Additionally, the results show that hierarchical concepts have a more significant impact on the model, which proves the effectiveness of our proposed method.  
Secondly, we verify the effect of word-level representation, context-level representation, superordinate concepts and subordinate concepts on the model. For example, if we only use word-level representation, the hierarchy concept remains unchanged. As Table 6 shows, all of the modular components display significant benefits to event detection. Specifically, we can see that using a single representation of a sentence will cause a significant drop in results, which demonstrates the effectiveness of two-level representation.  
To further verify the effect of the hierarchy concept, we replace it with the prototype triggers mentioned in the previous work [9]. Taking the event type Contact:Meet as an example, we finally represent it as Meet _meet reunited retreats_. Table 7 shows experimental results. From the table, we observe that the hierarchy concept achieves better performance,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline only word-level & 76.6 & 68.2 & 72.2 \\ only context-level & 76.7 & 69.0 & 72.6 \\ only sup-concept & 77.7 & 69.0 & 73.1 \\ only sub-concept & 77.4 & 68.6 & 72.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the effect of word-level representation, context-level representation, superordinate concepts and subordinate concepts on the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline without hierarchy concept & 77.2 & 68.0 & 72.3 \\ without confidence & 77.3 & 69.3 & 73.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of framework without hierarchy concept and hierarchy concept confidence.  
which is understandable as prototype triggers may be ambiguous triggers for event detection, such as \"meet\". However, compared with other ablation experiments, the results show some improvement, indicating that semantic enhancement of event type is effective for event detection.
## 3 Results and Discussion
### Microbiological Quality of Artisanal Suero Costeno  
Table 2 shows the aerobic mesophilic counts of the raw milk and some microbiological quality parameters of the artisanal _suero costeno_ produced in the department of Cordoba (Colombia). Approximately 75% of the milk used to produce _suero costeno_ complies with Colombian regulations, which establish a maximum value of aerobic mesophilic bacteria of 7 \(\times\) 10\({}^{5}\) CFU/mL, while only 43.8% complies with the EU regulation (EC853/2004), which establishes a maximum value of aerobic mesophilic bacteria of 1 \(\times\) 10\({}^{5}\) CFU/mL. The high mesophilic load found in raw milk in some producers (CO1, CO8, C1, S2, S3, SP1, SP2, and SP3) may result from the lack of good hygiene practices during milking, storage, and transportation, which mostly affects the small producers of artisanal _suero costeno_. To improve the hygienic quality of raw milk, it is necessary to comply with good milking practices [37], maintain the cold chain, and improve the hygiene standards of transport and storage tanks [38].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Municipality**} & \multicolumn{3}{c}{**Milk**} & \multicolumn{3}{c}{_Suero Costeno_} \\ \cline{3-7}  & **Artisanal** & **Aerobic** & **Mold and** & & & _Staphylococcus_ \\  & **Producer** & **Mesophilic** & **Yeasts** & & & **Coagulase-** \\  & & **Bacteria** & & & & **Positive** \\ \hline \multirow{7}{*}{Cienaga de Oro} & CO1 & 4.92 \(\pm\) 3.61 \({}^{f}\) & 5.03 \(\pm\) 4.45 \({}^{d}\) & 5.18 \(\pm\) 4.42 \({}^{g}\) & 4.41 \(\pm\) 3.56 \({}^{e,f}\) & 0.00 \(\pm\) 0.00 \({}^{e}\) \\  & CO2 & 6.70 \(\pm\) 5.82 \({}^{b}\) & 6.28 \(\pm\) 4.48 \({}^{c,d}\) & 5.87 \(\pm\) 4.72 \({}^{f}\) & 5.76 \(\pm\) 5.05 \({}^{c,d,e}\) & 4.87 \(\pm\) 4.65 \({}^{d,e}\) \\ \cline{1-1}  & CO3 & 3.65 \(\pm\) 3.18 \({}^{f}\) & 5.66 \(\pm\) 5.23 \({}^{d}\) & 4.76 \(\pm\) 3.30 \({}^{g}\) & 0.00 \(\pm\) 0.00 \({}^{f}\) & 0.00 \(\pm\) 0.00 \({}^{e}\) \\ \cline{1-1}  & CO4 & 5.80 \(\pm\) 5.25 \({}^{d,e}\) & 5.38 \(\pm\) 4.85 \({}^{d}\) & 5.86 \(\pm\) 4.60 \({}^{f}\) & 5.7 \(\pm\) 5.06 \({}^{c,d,e}\) & 4.32 \(\pm\) 4.05 \({}^{e}\) \\ \cline{1-1}  & CO5 & 4.45 \(\pm\) 3.48 \({}^{f}\) & 5.88 \(\pm\) 5.21 \({}^{d}\) & 4.73 \(\pm\) 3.90 \({}^{g}\) & 0.00 \(\pm\) 0.00 \({}^{f}\) & 0.00 \(\pm\) 0.00 \({}^{e}\) \\ \cline{1-1}  & CO6 & 5.65 \(\pm\) 4.75 \({}^{d,e}\) & 7.09 \(\pm\) 5.75 \({}^{a}\) & 5.81 \(\pm\) 4.87 \({}^{f}\) & 4.92 \(\pm\) 4.26 \({}^{d,e,f}\) & 6.71 \(\pm\) 5.83 \({}^{a}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Microbiological parameters of milk and artisanal _suero costeno_ (log ufc/g).  
The samples of artisanal _suero costeho_ showed an absence of _Salmonella_ spp., consistent with findings reported for _suero costeho_ produced in the Bolivar department, Colombia [17], and Bongo, an artisanal fermented product from Uganda [39].
## 2 Materials and Methods
### Experiments Methodology  
#### 2.1.1 Materials  
To study the effect of EW on heavy oil recovery, an oil sample from a heavy oil field in West Kazakhstan was collected with a reservoir depth is 362-376 m and an average reservoir temperature of 26-36 \({}^{\circ}\)C [35]. The oil flashpoint is 108 \({}^{\circ}\)C, and the pour point is \(-17\)\({}^{\circ}\)C. Table 1 shows the oil properties. The oil is highly resinous (25.8%) and contains 1.4% of paraffin, 4.1% of asphaltenes, and a significant amount of sulfur (2.51%) [36]. The acid number of crude oil is equal to 4.77 mg/KOH.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Dynamic Viscosity (mPa-s)** & **Density (g/cm\({}^{3}\))** \\ \hline
20 & 967.36 & 0.938 \\
30 & 442.49 & 0.932 \\
40 & 225.97 & 0.925 \\
50 & 125.58 & 0.919 \\
60 & 75.28 & 0.912 \\
70 & 47.88 & 0.905 \\ \hline \end{tabular}
\end{table}
Table 1: Oil properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Core Plug** & **Porosity, \%** & \
\begin{tabular}{c} **Absolute** \\ **Permeability (md)** \\ \end{tabular} &
\begin{tabular}{c} **Effective** \\ **Permeability (md)** \\ \end{tabular} \\ \hline N1 & 17.53 & 35.66 & 18.59 \\ N2 & 17.75 & 34.18 & 25.01 \\ N3 & 17.88 & 31.23 & 17.31 \\ N4 & 17.75 & 23.56 & 15.58 \\ \hline \end{tabular}
\end{table}
Table 2: Core plugs properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tong** & **Formation** & \begin{tabular}{c} **Caspian** \\ **Searwater** \\ **(CSW), ppm** \\ \end{tabular} & \begin{tabular}{c} **5\(\times\) Diluted** \\ **CSW with** \\ **CSW (LSW), ppm** \\ \end{tabular} & \begin{tabular}{c} 5\(\times\) Diluted** \\ **CSW with** \\ **PSm** \\ \end{tabular} & \
\begin{tabular}{c} 5\(\times\) Diluted** \\ **CSW with** \\ **2 Ca * 2 SO\({}_{4}\)** \\ **Ions (EW1),** \\ \end{tabular} &
\begin{tabular}{c} 5\(\times\) Diluted** \\ **CSW with** \\ **2 Ca * 2 SO\({}_{4}\)** \\ **Ions (EW2),** \\ **ppm** \\ \end{tabular} \\ \hline Sodium (Na\({}^{+}\)) & 54,500 & 3300 & 660 & 660 & 660 \\ Potassium (K\({}^{+}\)) & 0 & 155 & 31 & 31 & 31 \\ Calcium (Ca\({}^{2+}\)) & 9450 & 360 & 72 & 144 & 72 & 144 \\ Magnesium & 1450 & 740 & 148 & 148 & 296 & 296 \\ (Mg\({}^{2+}\)) & & & & & \\ Chloride (Cl\({}^{-}\)) & 105,000 & 5400 & 1080 & 1080 & 1080 \\ Sulfate (SO\({}_{4}^{2}\)) & 0 & 3050 & 610 & 1220 & 1220 \\ Total & 170,400 & 13,005 & 2601 & 3283 & 3359 & 3431 \\ concentration & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Composition of formation water, seawater, LSW, and EW samples.
## 3 Results
### Performance of RP-MPSK Modulation  
We considered RP-BPSK, RP-QPSK, RP-8-PSK, and RP-16-PSK. Going beyond 16-PSK results in closely spaced constellation points. The angular spacing for 16-PSK modulation is \(22.5^{\circ}\), therefore a maximum phase error of \(11.25^{\circ}\) in a symbol is permissible on the receiver  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of Symbols, \(N_{s}\) & 100,000 \\ No. of Monte Carlo Trials, \(N_{t}\) & 25, 100 \\ No. of Samples per symbol period, \(N_{p}\) & 3, 6, 7, 16 \\ Order of Modulation, \(M\) & 2, 4, 8, 16 \\ Bit Energy to Noise Ratio, \(E_{b}/N_{o}\) & 0–10 dB \\ Interference level, \(\mu\) & 0.3 (low), 0.9 (high) \\ \hline \hline \end{tabular}
\end{table}
Table 1: RP-MPSK Simulation Parameters.  
Figure 5: Performance evaluation of RP-MPSK modulation and demodulation using Q-RPW Model.  
to correctly detect the symbol. In case of 32-PSK, the margin for random phase error is slightly higher than \(5^{\circ}\). Heavy multipath environments, and the errors rendered by PLL are prone to more errors because of small allowable phase error. The performance of further higher orders in MPSK is even worse [25].  
In our case, to demonstrate the superior performance of RP-MPSK, BERs of RP-BPSK, RP-QPSK, RP-8-PSK, and RP-16-PSK are compared with theoretical BER of BPSK where second-order diversity is exploited as a function of \(\alpha=E_{b}/N_{o}\) (Figure 6). The rationale for this comparison is that RP-MPSK also exploits second-order diversity. Sampling rate of only three times the RPW frequency \(\left(N_{p}=3\right)\) is chosen. This choice of sampling rate is quite logical for preliminary validation. Since the final decision on symbol estimation in RP-MPSK is based on the most frequently occurring symbol, RP-MPSK cannot perform correct decision if \(N_{p}=2\) is used. Significant diversity gain is achieved using RP-MPSK instead of BPSK. The BERs of RP-BPSK, RP-QPSK, and RP-8-PSK are remarkably improved compared to BPSK. In case of RP-BPSK and RP-QPSK, an ample improvement of about 8 dB in \(\alpha\) is observed to achieve a small BER of 1%. Following this trend, an improvement of 10 dB or more in \(\alpha\) is anticipated to achieve BER of 0.1%. It is indeed interesting to note that RP-16-PSK performs equivalent to BPSK with a sampling rate slightly above the Nyquist rate \(\left(N_{p}=3\right)\). This improvement can be attributed to the fact that the three samples thus obtained come from three different polarizations. A few more common observations can also be made. For example, the performance of RP-BPSK and RP-QPSK is equivalent, and they offer the lowest BER. RP-8-PSK outperforms RP-16-PSK.
## 1 Introduction
As mentioned in [57], some remarks should be taken into consideration when interpreting the product. Firstly, since no direct water stress factor is implemented in the DMP algorithm, the retrieved values should be considered as optimal values. The CGLS-DMP model is only partially water limited through the sensitivity of fAPAR to vegetation water stress [37]. For the C-Fix model, [PERSON] _et al._[48] therefore proposed to limit RUE by using estimates of soil moisture content and water vapour deficit which required several empirical coefficients tuned to local vegetation conditions [58]. [PERSON] _et al._ (2009) successfully introduced a water stress index to the C-Fix model, which in turn enabled simulations of the gross and net carbon fluxes of Mediterranean forest ecosystems [54]. Secondly, although RUE values differ between biome types, it is considered as a global constant for all land cover types. Thirdly, the temperature stress factor was parameterized for European forests, and reflects neither the difference between C\({}_{3}\) and C\({}_{4}\) plants nor the differences within one plant type. Fourthly, although the CO\({}_{2}\) level increases each year, it is considered as a global constant. Finally, the AR is a linear function of daily mean temperature and is assumed biomass-independent [59]. The average value of the original AR fraction is around 0.7 for Europe and is an overestimation compared to other values found in the literature [1; 42; 60; 61; 62]. We hypothesise that the relative importance of the stress factors strongly contributes to explaining regional differences in biomass production and yield. An accordingly modified DMP product could therefore serve as a better proxy for crop yield than the currently available CGLS-DMP, and perhaps also outperform other simpler satellite-derived biophysical proxies such as fAPAR and NDVI. The present study aims to parameterise the dry matter productivity approach specifically for wheat and maize and to compare this modified DMP model with the original CGLS-DMP and with other BPs derived from satellite imagery. The value of this improvement is evaluated in the context of arable productivity at the regional administrative or agro-ecological level. The model performance is assessed using statistical metrics based on the comparison between regional cumulated BPs and regional crop yield statistics of silage maize and common wheat for selected sites in Belgium, France and Morocco during the period 1999-2012.