esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Optimization and Performance Test
### Experiment on Stability of Seeding Dropping Duration under Multi-Planting Frequency
#### 3.3.1 Test Method  
The rotational speed of the drive cam was changed by adjusting the rotational speed of the drive motor, and then the opening speed of the seedling carrying plate was changed, and the variations in the dropping duration of the pot seedlings under different rotational speeds were tested. Since the sliding process occurred before the pot seedlings fell, the test was only carried out at the No. 3 position, that is, only \(t_{3}\) was measured to avoid collision between the pot seedling body and the seedling cylinder wall.  
The motor speed adjustment method was referred to the previous section, and after the speed adjustment was completed, the pot seedlings were loaded for the dropping seedling test. The speed adjustment range was 60\({}^{\circ}\)-150\({}^{\circ}\)/s, corresponding to the planting speed of 50-125 plants/(min - row). The adjustment range was grouped at an interval of 10\({}^{\circ}\)/s for a total of 10 groups, with 10 tests per group. The frame in which the seedling transport plate started to rotate was the starting frame, while the frame in which the pot seedlings were completely dislodged from the outlet was the ending frame. At the end of the experiment, the duration of seedling dropping at each speed was obtained using high-speed photographic video, and then the mean and coefficient of variation of the seedling dropping duration were calculated and compared.
## 3 Proposed Approaches  
The UniTS model demonstrates a strong performance when handling multi-task time-series data. However, it exhibits limitations when applied to tasks involving ENF data: (1) the model lacks a specialized design for extracting periodic features from time-series data with significant periodicity, resulting in a suboptimal performance when capturing the periodic features of ENF. (2) Noise is easily introduced during the collection, transmission, and processing of ENF data. The UniTS model does not adequately consider noise resistance when processing such noisy data, consequently impacting classification accuracy. (3) ENF data encompass not only temporal information, but also rich spectral information. While the UniTS model primarily focuses on temporal features, its ability to extract and utilize spectral features is relatively limited.  
To address the limitations of the UniTS model in classifying frequency data and enhance its performance, this study implemented innovative enhancements. Firstly, the original gaussian error linear unit (\(GELU\)) activation function was substituted with the sinusoidal activation function (\(SAF\)) to leverage its periodic nature for the improved capture and representation of periodic features within input signals, thereby enhancing the processing efficacy of frequency data. Secondly, a spectral attention module was introduced to extract features and compute spectral attention weights in the frequency domain, enabling the identification and emphasis of crucial components of frequency data. This heightened the model's sensitivity to frequency domain information and classification performance. The experimental results demonstrate that the enhanced model achieves a peak classification accuracy of 97.47% on the OSF public dataset.
# An Algorithm for Nutrient Mixing Optimization in Aquaponics
## 3 Results
### Simulation Studies
#### 3.1.3 Resource Optimization and Additional Constraints on Tracking Accuracy  
The results of the previous simulation study show a high concentration of PO4\(-\)P and a minor lack of Mg2+ and NO3\(-\)N. Assuming that a lack of nutrients causes more problems than an oversupply (that does not violate any concentration constraints), this scenario aims to prevent too-low concentrations for each nutrient. For this, the bounds for the minimum ion concentrations in the post-mixing solution are set to the value seen in Equation (25). The new optimization results are shown in Figure 5 and Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Source** & \
\begin{tabular}{c} **Used Amounts for** \\ **Mixing in L** \\ \end{tabular} & **Fertilizer** &
\begin{tabular}{c} **Used Amounts for** \\ **Mixing in g** \\ \end{tabular} \\ \hline FW & 519.071 & Ca(NO3)2 & 0 \\  & & KNO3 & 292.758 \\ PW & 210.401 & K2 SO4 & 0 \\  & & NH4 NO3 & 181.379 \\ RW & 68.529 & MgSO4 & 46.181 \\  & & Fe-Chelat & 3.099 \\ TW & 0 & KH3 PO4 & 0 \\ \hline Total & 798 & Total & 523.417 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nutrient sources used when optimizing the reference error and the consumption of resources.  
Figure 4: Resulting normalized concentrations of nutrients in the solution when the reference error and consumption of resources are optimized.  
In Figure 5, it can be seen that the concentration of every nutrient in the solution is at least at 95% of the reference concentration. Thus, the algorithm was able to set the required minimum level and prevent a lack of any nutrient in the prepared solution. However, the higher concentrations of magnesium and nitrogen also lead to an increased consumption of fertilizers. Furthermore, the average relative error increases slightly to a value of 7.8%.
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 3 Materials and Methods
### Second-Order Section Decomposition  
Higher-order digital filters often encounter numerical challenges, such as coefficient quantization sensitivity and overflow, which can degrade performance in low-precision or fixed-point implementations. By decomposing the filter into multiple second-order sections (SOS), these numerical issues are localized and thus more effectively mitigated. Each SOS is represented as a biquad transfer function:  
\[H(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}}{1+a_{1}z^{-1}+a_{2}z^{-2}}. \tag{2}\]  
This corresponds to the time-domain difference equation:  
\[y[n]=-a_{1}y[n-1]-a_{2}y[n-2]+b_{0}x[n]+b_{1}x[n-1]+b_{2}x[n-2]. \tag{3}\]  
Figure 1: Step response and transient time of an exemplary 4 th-order low-pass Butterworth filter (sampling frequency Fs-1 kHz; cutoff frequency Fc-50 Hz).  
A higher-order IIR filter of order \(N\) is typically realized by cascading \(M=\left[\frac{N}{Z}\right]\) second-order sections, with an optional first-order section for odd-order filters:  
\[H_{total}(z)=\prod\
olimits_{i=1}^{M}H_{i}(z). \tag{4}\]  
The decomposition into SOS format is commonly performed using polynomial factorization of the filter's numerator and denominator.  
SOS, with its fewer coefficients and simpler pole-zero structure, is less susceptible to instability and rounding errors. This modular design facilitates targeted optimization, as each second-order section can be tuned independently for optimal performance, reducing the overall computational effort. From a resource perspective, SOS decomposition lowers memory usage and power consumption, making it particularly valuable for real-time and embedded systems. It also enhances flexibility by allowing sections that handle more critical frequency components to use higher-precision resources as needed. Moreover, the ability to add or remove sections without overhauling the entire filter design increases scalability--an important benefit in adaptive or time-varying scenarios where filter parameters may need to change rapidly. Ultimately, SOS decomposition provides a robust framework for achieving high filter performance, efficient resource utilization, and straightforward optimization in higher-order digital filter designs.  
In this paper one can find an extension of the benefits of using SOS decomposition in terms of a proposal to introduce time-varying coefficients into a finite horizon to improve the transient time of the whole design.
## 2 Materials and Methods
### SEM and EDS Investigations of the Implant Surface  
The implant fixtures and surrounding tissue collected during the resection surgery were immediately placed in a mixture of 2.5% glutaraldehyde (GA) in 0.1 M of phosphate buffer (PB) after slight irrigation. The implant fixtures underwent coating before SEM examination (JSM-7800F Prime(r); Jeol Ltd., Tokyo, Japan). The examined regions were the implant surface and attached bone tissue at the upper, middle, and apical parts of the fixture. The surface was scanned thoroughly under 500\(\times\) magnification and areas with representative features were chosen for micrography and element analysis (Figure 4). The EDS points were designed as \"patient number-EDS number-position of the analyzed point on the fixture\" (T: thread, U: upper one-third of the fixture, M: middle one-third of the fixture, A: apical one-third of the fixture). The SEM was operated at 10 kV and 65\(\times\), 500\(\times\), 1000\(\times\), 2500\(\times\), 5000\(\times\), 10,000\(\times\), and 20,000\(\times\) micrographs were acquired.  
The element composition of the specimens was accessed by using an EDS instrument (XFlash(r) 6, Bruker, Berlin, Germany) connected to a microscope detector and the ESPRIT(r) analysis software (Bruker, Berlin, Germany). The EDS method involved qualitative and semi-quantitative microanalysis, including element distribution mapping. In the distribution maps, provided relative concentrations were indicated by color density. The representative point of each region was chosen and analyzed under a magnification of 10,000\(\times\). The mass concentration (C) ranges in element concentration determined from EDS were classified as: major: C > 0.1 mass fraction (greater than 10% mass), minor: 0.01 \(\leq\) C \(\leq\) 0.1 (1% mass to 10% mass), and trace: C < 0.01 (below 1% mass).
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 2 Materials and Methods
### General Procedure
#### 2.2.3. Tributyl(vinylbenzyl)phosphonium Docusate ([P\({}_{444\text{vB}}\)][AOT])  
Sodium docusate ([Na][AOT]) (0.015 mol, 6.70 g) was dissolved in 30 mL of dichloromethane (DCM). The [Na][AoCT] solution was added dropwise to a separate round-bottom flask containing [P\({}_{44\text{vB}}\)][Cl] (0.015 mol, 5.35 g) dissolved in 15 mL of DCM. The reaction mixture was stirred for 12 h at room temperature. The resulting suspension was then filtered through Celite. The filtrate was evaporated under reduced pressure and dried under high vacuum (200 m Torr) for 2 h to produce [P\({}_{44\text{vB}}\)][AOT] as a viscous pale-yellow liquid (8.78 g, 78.99%). **1H NMR** (400 MHz, CDCl\({}_{3}\)) 8 7.43-7.25 (m, 4H, H\({}_{14}\), H\({}_{15}\), H\({}_{16}\), H\({}_{17}\)), 6.67 (ddd, = 17.6, 11.0, 2.1 Hz, 1H, H\({}_{18}\)), 5.84-5.70 (m, 1H, H\({}_{19}\)), 5.33-5.22 (m, 1H, H\({}_{20}\)), 4.18 (dd, = 11.4, 3.6 Hz, 1H), 4.08-3.84 (m, 5H, H\({}_{21}\), H\({}_{22}\), H\({}_{31}\)), 3.27 (dd, J = 17.5, 11.4 Hz, 1H, H\({}_{30}\)), 3.14 (dd, J = 17.5, 3.7 Hz, 1H, H\({}_{30}\)), 2.29-2.13 (m, 4H, H\({}_{1}\)), H\({}_{5}\)), 1.99 (s, 1H, H\({}_{23}\)), 1.60 (q, J = 5.7 Hz, 1H, H\({}_{32}\)), 1.56-1.19 (m, 18H, H\({}_{2}\)), H\({}_{3}\), H\({}_{6}\), H\({}_{7}\), H\({}_{10}\), H\({}_{11}\), H\({}_{26}\), H\({}_{27}\), H\({}_{28}\), H\({}_{24}\), H\({}_{35}\), H\({}_{36}\), H\({}_{37}\)), 1.25 (s, 7H, H\({}_{24}\), H\({}_{8}\), H\({}_{36}\), H\({}_{37}\)), 0.96-0.76 (m, 17H, H\({}_{4}\), H\({}_{8}\), H\({}_{12}\), H\({}_{25}\), H\({}_{29}\), H\({}_{34}\), H\({}_{38}\)). **FT-IR: v/cm\({}^{-1}\)**= 2931 (m,sh), 1630 (w,sh), 1512 (w,sh), 1731 (s,sh), 1323 (wsh), 1158 (s,sh), 853 (s,sh), 990 (s,sh) 908 (s,sh). **MS** (+ESI) (Acetonitrile): m/z 319.2548 [C\({}_{21}\)H\({}_{36}\)P] calc. 319.2555. (\(-\)ESI) (Acetonitrile): m/z 421.2267 [C\({}_{20}\)H\({}_{37}\)O\({}_{7}\)S] calc. 421.2260.
## 3 The Main Parameters and Indices of MOT
### Application Requirement Analysis
#### 3.3.1 Miniaturization  
In the early stages of development, atomic gravimeter research focused on improving sensitivity and stability, as well as exploring basic physics [52], without considering the device size. In approximately 2013, to conduct research on the detection of the equivalence principle and gravitational wave measurement, Stanford University and the Wuhan Institute of Mathematics at the Chinese Academy of Sciences developed a 10 m high fountain-type atomic gravimeter [53; 54]. This device significantly increases the area of atomic interference and evolution time and improves the measurement sensitivity. In addition to basic physics research, gravity-related information also holds significant application value in inertial navigation, resource exploration, geophysics, and other fields [55; 56]. In response to these application needs, the development of miniaturized and mobile atomic gravimeters has gradually become an important direction for atomic gravimeter research. Various experiments have been conducted on elevator measurements, vehicle measurements, ship measurements, and airborne measurements, and the adaptability of atomic gravimeters in complex environments has become increasingly important.  
The miniaturization of other devices is also necessary. For example, small atomic clocks can offer highly precise time references for mobile devices to ensure accurate data transmission [57; 58]. In the military sector, miniaturized atomic gyroscopes can enhance the performance of inertial navigation systems, improving the navigation and attitude control accuracy of space vehicles [59; 60; 61]. Furthermore, miniaturized atomic gravity gradiometers can provide gravity compensation data for inertial navigation devices, reducing the impact of vertical line deviation and supporting the high-precision long-endurance navigation of underwater carriers, such as submarines [62; 63].
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh
#### 5.1.1 One-Day Lead-Time Forecast
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **Time Window Size** & \multicolumn{2}{c}{**One**} & \multicolumn{2}{c}{**Two**} & \multicolumn{2}{c}{**Three**} & \multicolumn{2}{c}{**Four**} & \multicolumn{2}{c}{**Five**} \\ \hline \hline \multicolumn{1}{c}{**Scenarios**} & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** \\ \hline \multirow{10}{*}{Hybrid} & MEAN & 1.607 & 0.938 & 1.470 & 0.947 & 1.407 & 0.952 & 1.366 & 0.954 & 1.335 & 0.956 \\ \cline{2-11}  & MEAN-SUN & 1.446 & 0.950 & 1.346 & 0.956 & 1.295 & 0.959 & 1.262 & 0.961 & 1.238 & 0.962 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.355 & 0.955 & 1.276 & 0.960 & 1.234 & 0.963 & 1.207 & 0.964 & 1.187 & 0.965 \\ \cline{2-11}  & MEAN-HUM-RAIN & 1.383 & 0.954 & 1.294 & 0.959 & 1.251 & 0.962 & 1.222 & 0.963 & 1.203 & 0.964 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.324 & 0.957 & 1.249 & 0.962 & 1.209 & 0.964 & 1.182 & 0.966 & 1.162 & 0.967 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.309 & 0.958 & 1.241 & 0.962 & 1.203 & 0.965 & 1.175 & 0.966 & 1.157 & 0.967 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.289 & 0.960 & 1.223 & 0.963 & 1.185 & 0.966 & 1.159 & 0.967 & 1.143 & 0.968 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.280 & 0.960 & 1.216 & 0.964 & 1.179 & 0.966 & 1.153 & 0.967 & 1.137 & 0.968 \\ \hline \multicolumn{11}{c}{**MEAN**} & 1.605 & 0.938 & 1.463 & 0.948 & 1.396 & 0.952 & 1.382 & 0.955 & 1.318 & 0.957 \\ \cline{2-11}  & MEAN-SUN & 1.438 & 0.950 & 1.329 & 0.957 & 1.274 & 0.960 & 1.238 & 0.963 & 1.212 & 0.964 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.344 & 0.956 & 1.258 & 0.961 & 1.212 & 0.964 & 1.182 & 0.966 & 1.161 & 0.967 \\ \cline{2-11}  & MEAN-HUM-RAIN & 1.371 & 0.954 & 1.272 & 0.960 & 1.226 & 0.963 & 1.193 & 0.965 & 1.173 & 0.966 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.311 & 0.958 & 1.229 & 0.963 & 1.185 & 0.966 & 1.155 & 0.967 & 1.133 & 0.968 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.295 & 0.959 & 1.220 & 0.964 & 1.178 & 0.966 & 1.147 & 0.968 & 1.127 & 0.969 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.273 & 0.960 & 1.199 & 0.965 & 1.158 & 0.967 & 1.129 & 0.969 & 1.111 & 0.970 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.263 & 0.961 & 1.192 & 0.965 & 1.151 & 0.967 & 1.123 & 0.969 & 1.105 & 0.970 \\ \hline \end{tabular}
\end{table} 
Table 1: Conf.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Analysis and Insights from Performance Metrics
#### 5.8.4 New Insights  
Quiet Eye Metrics as Strong Predictors  
The high accuracy and F1 scores validate the importance of visual attention and gaze dynamics in predicting shot accuracy in badminton.
## 2 Results
### Characterization of the GGP-CLEAS Biocatalyst  
#### 2.2.1 Effect of the pH Value on the Activity/Stability of Free and GGP-CLEAS Biocatalysts  
Figure 2 shows the free enzyme activity/pH profile, with a clear optimal at pH 6 and a drop in activity using lower or higher pH values (to 43% at pH 5 and 40% at pH 7). The immobilization of the enzyme via CLEAS technology permits a great increase in the activity in the acid pH region value (although the optimal pH remained 6, the activity at pH 5 was 93%). The activity of the immobilized enzyme remained over 50% even at pH 3 when the free enzyme maintained under 20% of the maximal activity at this pH value. The resistance to changes in the pH values after immobilization of the GPP activity at a pH over 6 was smaller than in the acid pH values, with just over 50% of the activity at pH 6 showing at pH 7. However, at pH 9, the immobilized enzyme retained more than 30% of the maximum activity, while the free enzyme only maintained 6% of the activity at pH 6.  
Considering that the specific activity of the immobilized enzyme was higher than the free enzyme at pH 6, these results mean that the specific activity of the immobilized enzyme surpassed that of the free enzyme in all the pH ranges; this increase in activity thus becomes an advantage of the immobilized biocatalyst. This higher activity retention at extreme pH values could be caused by a higher stability of the enzyme in all the studied pH ranges.  
Figure 1: SDS-PAGE of soluble GGP and GGP-CLEAS. Lane 1: molecular weight marker (220 kDa), lane 2: soluble GGP and lane 3: GGP-CLEAs. Other specifications may be found in Methods section.  
The free enzyme and GGP-CLEAS were incubated at 25 \({}^{\circ}\)C for 5 days, and the remaining activity and half-lives were determined (Table 1). The free enzyme maintained 100% of the activity at pH 3 and 4, retaining a lower percentage of initial activity when the pH increased (very slightly at pH 5-7) to reach 11% of residual activity at pH 9. That way, it was not possible to determine the half-lives of the free enzyme at pH 3 and 4, but the values were over 57 h at pH 5-7, 10.5 h at pH 8, and only 8.25 h at pH 9. The immobilized enzyme also maintained 100% of the initial activity at pH 3 and 4, showing similar decreases at pH 5-7, and the lowest activity retained was found at pH 9. The calculated half-lives were pretty similar to those of the free enzyme. At this temperature, it was not possible to detect a significant difference in the stability of the enzyme before and after immobilization. This shows that the low activity of the free enzyme, compared to the immobilized enzyme at a pH under 6, is not caused by a destabilization of the enzyme but should be related to some conformational change induced by the pH that produces a less efficient (and that way less active) but more stable enzyme form.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 6 Conclusions  
In this paper, a model of the dynamics of cells trapped by the optical trap generated by holographic optical tweezers is analyzed, and constraints are added to ensure that the cells will not be trapped outside the optical trap. On this basis, a multi-cell cooperative control method is designed, and a multi-cell cooperative controller based on the FBEL neural network is proposed. The controller controls the holographic optical tweezers to generate multiple optical traps to capture the cells, thereby manipulating the cell movement. After a series of cell manipulation simulation experiments, it has been successfully verified. Compared with other previous controllers, the cell cooperative controller proposed in this paper is able to capture the cells' properties of the cell movement.  
Figure 14: The cell trajectory of the vertical formation (\(\beta\)/k = 0.09).  
Figure 15: The cell trajectory of the triangular formation (\(\beta\)/k = 0.09).  
paper has higher control accuracy, faster convergence speed, and good robustness. There will be no confusion about the trajectory of the captured cells, which greatly improves the safety of multi-cell manipulation experiments. It provides a new idea for efficient and precise cell manipulation automation using holographic optical tweezers.  
The obstacle avoidance problem of captured cells in complex environments, however, has not been considered as it pertains to the cell cooperative controller proposed in this paper. At the same time, there are still some unknown disturbances in cell manipulation experiments in reality, and it is necessary to further increase the compensation controller to improve the robustness of the system. These issues will continue to be studied and optimized in future work.  
Methodology, J.Z. and D.-H.W.; Software, J.Z. and P.-S.Z.; Investigation, J.Z. and H.H.; Resources, J.Z.; Writing--original draft, J.Z., H.H. and P.-S.Z.; Writing--review & editing, J.Z. and Y.-K.Y.; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian Province Science and Technology agency under grant number [2020]01285, 2022]05285].  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
This work was supported in part by the Natural Science Foundation (2020)01285, 2022]05285) of the Science and Technology Agency, Fujian, China.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BP & Back Propagation \\ FBEL & Fuzzy Brain Emotional Learning \\ FBELNN & Fuzzy Brain Emotional Learning Neural Network \\ FCMNN & Fuzzy Cerebellar Model Neural Network \\ PI & Proportional-Integral controller \\ PID & Proportional-Integral-Derivative controller \\ RBF & Radial Basis Function \\ RBFNN & Radial Basis Function Neural Network \\ RMSE & Root Mean Square Error \\ SLM & Space Light Modulator \\ \end{tabular}
## 5 Results and Discussion
### Aluminum Alloys  
Aluminum alloys, especially hardened structural grades like Al-2024 and Al-6061 (UNS A92024 and UNS A96061), are representative low attenuation materials, whereas commercially pure grade Al-1100 (UNS A91100), annealed Al-6061, and some Al-7000-series are slightly more attenuating [44; 45]. Two Al-1100-H18 plates (G2a and G2b in Table 1), Al-6061-T6 plate (G3a), and Al-7475-H18 (UNS 97475) sheet (G4) were used. Their thicknesses were 2.06, 6.38, 12.7, and 2.72 mm, respectively. Note that H18 and T6 suffixes indicate fully cold-worked and precipitation-hardened conditions, respectively. All the Al sheets and plates were obtained many decades ago and their source information is unavailable, except for Al-7475 (Kaiser Aluminum Research Lab, Pleasanton, CA, USA).