esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
This paper presents a platform for the development stage of antenna arrays and beamforming algorithms. It allows us to test any antenna array with up to 16 feeds and unlimited algorithms without extensive reconfiguration. Also, it can directly feed conventional single antenna receivers and RF test equipment. With this system, it is not necessary to develop new hardware between design iterations or even for different systems, as only the software layer needs to be modified. With this, we aim to expedite the development stage of arrays and beamforming algorithms before producing dedicated solutions for industrial applications.  
Several tests were conducted to assess the functionality and performance of the implementation, as well as the potential impact of scaling the solution. It is worth noting that even when 20 beams are produced, the resource utilization in the ZCU216 remains far below the available capacity. In terms of software, execution speeds of 1.2 kHz were achieved for the four-beam multithread configuration, with room for more complex algorithms to be implemented. After calibration and when directing the main lobe towards \(\theta=45^{\circ}\) and \(\phi=90^{\circ}\), the highest phase difference between the computed and measured phase was 0.36\({}^{\circ}\). The average group delay measurement yielded 384.6 ns.  
In the future, system optimization will be performed. Live tests using the receiver feedback and an antenna array should also be performed. Stationary and moving tests should be carried out to assess the impact the system has on the receiver's performance.  
Conceptualization, D.G., D.B., H.D., P.M.M. and S.L.; methodology, D.G., D.B., H.D., P.M.M. and S.L.; software, D.G., D.B. and S.L.; validation, H.D. and P.M.M.; formal analysis, D.G., D.B. and H.D.; investigation, D.B. and H.D.; resources, D.B., P.M.M. and S.L.; data curation, D.G., D.B. and H.D.; writing--original draft preparation, D.G., D.B. and H.D.; writing--review and editing, D.B., H.D., P.M.M. and S.L.; supervision, P.M.M. and S.L.; project administration, D.B., H.D. and P.M.M.; funding acquisition, P.M.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by national funds, through the Operational Competitiveness and International Programme (COMPETE 2020) [Project n\({}^{\circ}\) 179491; Funding Reference: SIFN-01-9999-FN-179491].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Bosch Car Multimedia Portugal, S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Plant Height Estimation in Maize Populations
#### 3.1.2 Effect of AABB Enclosing Box Side Length on Height Estimation  
In order to study the effect of AABB enclosing box side length on the point cloud segmentation of maize monocots, this study conducted experiments with side lengths set to one, two, five, and the maximum value, respectively, and counted the extracted height data (Table 2). The results show that, as the side length decreases, the total number of extracted bounding boxes increases, and the maximum, minimum, and average height values decrease. Conversely, as the side length increases, the number of extracted bounding boxes decreases, and the height values increase. If the side length is too small, the original single-plant point cloud is over-split, resulting in low extracted heights. If the side length is too large, the multi-plant point clouds merge, leading to higher extracted heights.  
Figure 8 shows the histograms of maize height extracted using bounding boxes with edge lengths of one, two, and five. The histograms indicate that the extracted heights with different side lengths exhibit similar curve patterns, but the distribution and frequency of the values differ. When the side length is two, the range of extracted heights more closely matches the number of single plants in the field, as it can approximately encompass the single-plant point cloud. This results in extracted heights that better simulate the actual height of maize plants. Therefore, subsequent studies uniformly set the AABB enclosing box edge length to two to ensure the reasonableness and accuracy of the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bounding Box** & **Total** & **Maximum Values** & **Minimum Value** & **Average Value** \\ \hline
1 & 3319 & 1.6657 & 0.0367 & 0.6505 \\
2 & 889 & 1.7429 & 0.0864 & 1.0506 \\
5 & 165 & 2.1483 & 0.1770 & 1.3676 \\ greatest & 1 & 2.4706 & 2.4706 & 2.4706 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics of estimated corn plant heights using the point cloud extraction method.  
Figure 8: Histogram of estimated maize plant height using the point cloud extraction method for different sizes of wraparound boxes.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 1 Introduction  
The Golgi apparatus is an organelle found in most eukaryotic cells. Being part of the endomembrane system in the cytoplasm, it resides at the intersection of the exocytic and endocytic pathways, and works mainly in post-translational modifications and sorting of lipids and proteins. One unique characteristic of the Golgi is the multilayer stack that divides the Golgi membrane system into several sub-compartments known as _cis-_, medial, and _trans_-Golgi, each of which contains a set of glycosylation enzymes that sequentially remove or add various sugar monomers to proteins as they pass through the Golgi. To fulfill its function, the Golgi structure is highly dynamic, while Golgi structure and function are tightly regulated [1]. Similarly, the microenvironment of each sub-compartment is also under strict regulation in response to intracellular environmental changes.  
Generally, Golgi homeostasis requires not only the proper positioning and functioning of glycosylation enzymes and trafficking machineries, but also the regulation and maintenance of Golgi lumen pH, ion, and redox homeostasis, which have been generally reviewed previously [2]. Metal ions in the Golgi lumen, such as Ca\({}^{2+}\), Mn\({}^{2+}\), Zn\({}^{2+}\), and Cu\({}^{2+}\), are important cofactors of many Golgi-residing enzymes. Being an indispensable part of Golgi homeostasis, the maintenance of metal ion homeostasis involves the cooperation of ATPase pumps, channels, and metal ion binding proteins. Mutations in these regulators impair metal ion homeostasis, disrupt cellular activities, and cause human diseases (summarized in Table 1). Here we provide a more in-depth review specifically on the regulators involved in Golgi Ca\({}^{2+}\), Mn\({}^{2+}\), Zn\({}^{2+}\), and Cu\({}^{2+}\) ion homeostasis and their functions under physiological and pathological conditions.
## Materials and Methods  
### Winery Wastewater (WW) Composition and Analytical Procedures  
The WW used in this research was obtained from the local Grivas winery located in Agrinio city (western Greece), and derived from the washing of equipment and winery surfaces during the production of white wine. Following 3 hours of sedimentation (to remove large particles) the effluent was transferred into 4-liter bottles and stored at 4 \({}^{\circ}\)C in the dark for a maximum of three days before use. The effluent presented pH values of between 4-5, conductivity of about 300-700 \(\upmu\)S/cm, dissolved COD (d-COD) of 50,000-70,000 mg/L, and BOD\({}_{5}\) (Biological Oxygen Demand after five days) in the range of 22,000-31,000 mg/L. The raw wastewater was diluted with tap water at different rates leading to four initial WW concentrations of about 3500, 7500, 9000 and 18,000 mg d-COD/L (C\({}_{3500}\), C\({}_{7500}\), C\({}_{9000}\), C\({}_{18,000}\), respectively).  
d-COD and BOD\({}_{5}\) concentrations were determined following the methods described in 'Standard Methods for the Examination of Water and Wastewater' [45]. To determine d-COD the samples were initially filtered through 0.45 \(\upmu\)m-Millipore filters (GN-6 Metricel Grid 47 mm, Pall Corporation) and then measured following the closed reflux, colorimetric method using a Multiparameter Bench Photometer (HANNA C99). Controlled conditions of temperature, agitation, and light absence were applied for BOD\({}_{5}\) determination following the respirometric method (WTW OxiTop). Values of pH, temperature, conductivity and Dissolved Oxygen (DO), were measured using a HANNA HI9828 instrument. Inorganic ions (NH\({}_{4}\)+-N) were quantified by ionic chromatography using a Thermo Dionex ICS-5000 DC instrument coupled to a conductivity detector AERS 500 (4 mm) employing an IonPac AS23 (4 mm \(\times\) 250 mm) column for anions (the eluent comprised 4.5 mM Na\({}_{2}\)CO\({}_{3}\)/0.8 mM NaHCO\({}_{3}\) with a flow rate of 1.0 mL/min, a temperature of 30 \({}^{\circ}\)C, and an injection volume of 25 \(\upmu\)L), and an IonPac CS12A (2 mm \(\times\) 250 mm) column for cations (the eluent comprised 20 mN H\({}_{2}\)SO\({}_{4}\) with a flow rate of 1.0 mL/min, an ambient temperature and an injection volume of 25 \(\upmu\)L). Kinetic experiments in the trickling filters were performed in triplicate, and the results presented in all of the figures display the mean value \(\pm\) standard deviation (SD). To investigate statistically significant differences, a one-way between groups analysis of variance (ANOVA) at the 95% significance level was used to examine the effect of the design and operational parameters on the CWs. ANOVA was applied to compare the effluent concentrations of each unit. Post Hoc pair comparisons were also performed to test equal variations, using [PERSON]'s honestly significant difference test. Homogeneity of variance tests (Levene) were by-passed, since the number of data points for each group was the same. Statistical analyses were performed using SPSS Statistics 23.0 for Windows.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 3 System Model
### Problem Formulation  
In this section, we formulate our research problem. We aim to jointly consider the total charging time consumed by the AP and the wasted energy in the charged devices. Our purpose is to find an appropriate schedule of per-sector charging time with minimized energy overflow. Because the charging time and energy overflow have different measures, we normalize the values of time and wasted energy between 0 and 1. The \(N_{t}\) is the normalized time calculated using Equation (5). \(x_{j}\) is the charging time of sector \(j\), and the \(t_{\max}\) is the longest charging time of all sectors, i.e., \(t_{\max}=\max x_{j},1\leq j\leq S\).  
\[N_{t}=\frac{\sum_{j=0}^{S}x_{j}}{S\times t_{\max}} \tag{5}\]  
The \(N_{e}\) is the normalized energy overflow for \(D\) devices calculated using Equation (6). \(e_{i}^{av}\) denotes the wasted energy of device \(i\) and \(e_{max}\) is the most wasted energy among all devices, i.e., \(e_{max}=\max e_{i}^{av},1\leq i\leq D\). The value of \(e_{i}^{av}\) is calculated in Equation (7), where device \(i\) is in sector \(j\) and \(\beta\) is the battery capacity of a device.  
\[N_{e}=\frac{\sum_{i=0}^{N}e_{i}^{av}}{S\times e_{\max}} \tag{6}\]  
\[e_{i}^{av}=B_{i}+(P_{r}^{i}\times x_{j})-\beta \tag{7}\]  
Our object function is formulated as Equation (8):  
\[\min N_{t}\times N_{e},\ \text{subject to} \tag{8}\]  
\[x_{j}\geq 0,e_{i}^{av}\geq 0,\forall i\in D,\forall j\in S \tag{9}\]  
\[B_{i}+P_{r}^{i}\times x_{j}\geq\beta,\forall i\in D,\forall j\in S \tag{10}\]  
Figure 3: Sector model. (**a**) The First Sector (**b**) The Second Sector  
This paper aims to yield a battery-charging schedule with minimized energy overflow. Basically, a better solution has smaller values of the \(N_{t}\) and \(N_{p}\). The constraint in Equation (9) ensures that both values of harvesting time and wasted energy are positive. The constraint in Equation (10) requires that each device must be fully charged.
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 4 Simulation and Results
### Performance Validation
In addition, because the wind sample contains more high  
Figure 8: Simulation result using PDF-MPPT method and conventional MPPT method for the turbulent wind speed with changed average value: **(a)** rotor speed; **(b)** electrical power generated; **(c)** cumulative generation of using PDF-MPPT method compared with using the conventional MPPT method. The green line with the legend of “ideal”, the blue line with the legend of “conventional” and the brown line with the legend of “PDF-MPPT” in **(a–c)** means the corresponding performance index in ideal operation state, using the conventional MPPT method and using the novel MPPT method, respectively. frequency components, and the wind turbine tracks the change of wind speed by adjusting the rotor speed, which leads to some sharp profiles in the simulation results of Figure 8a,b, the fluctuation of rotor speed and electrical power is much weaker than that of wind speed. Figure 8c shows the changes in cumulative power generation of the system under the conventional MPPT control and the novel MPPT control in the period of 50-250 s. Under high wind speed, as of 170 s, the cumulative power generation of the system under the two controls is 56.1863 kw-h and 56.2231 kw-h respectively. Later, when the wind speed is low, the energy capture efficiency and the corresponding cumulative power generation of the system under the novel MPPT control decreases. As of 250 s, the cumulative power generation of the system under the two controls is 66.2629 kw-h and 66.2252 kw-h respectively. It can be found that the cumulative power generation of the system controlled by the novel MPPT method is always higher than that of the conventional method, indicating that the energy accumulated at high wind speed can still make up for the energy loss at low wind speed. The effectiveness of the novel MPPT control is verified by analyzing the changes in relevant indicators in Figures 6 and 7.
Compared with the Graph Convolutional Network (GCN) method, the proposed framework achieves a 27.28% improvement computational efficiency. 2. A graph-structured, multidimensional feature construction module has been designed. By integrating geometric features with complex network indicators, this module significantly enhances the robustness of the registration results. 3. A lightweight graph neural network, EKNet, has been developed. With an overlap rate of 20%, EKNet achieves a registration accuracy that is 80.66% higher than that of the sub-optimal method. 4. Open-source GPCR, a geometric point cloud registration dataset for building structures, is now available. This dataset is distinguished by its extensive scale and diversity, providing substantial support for advancing research and development in the field.
## 5 Conclusions  
An EANSG method was proposed to reduce the natural frequency of an optical platform and improve its low-frequency vibration isolation performance. Nano-resolution laser interferometry sensors were proposed and developed to monitor and feedback the micro-vibration of the optical platform, and precision electromagnetic actuators were developed to counteract the positive stiffness of pneumatic springs by actively generating negative-stiffness using a relative displacement feedback strategy. The principle and design of the EANSG are detailed as presented, and the effectiveness of the method was validated by experiments. The spectrum analysis of the impact response experimental results indicate that the natural frequency of the optical platform decreased from 2.00 to 1.37 Hz. The time domain displacement and velocity experimental results indicate that the of vibration displacement decreased from 1.28 to 0.69 \(\upmu\)m, the root mean square of the vibration velocity decreased from 14.60 to 9.33 \(\upmu\)m/s. Therefore, it can be concluded that the proposed method can effectively enhance the low-frequency vibration isolation performance of optical platforms, and can be applied in the fields requiring better low-frequency vibration isolation performance, such as advanced manufacturing, cutting-edge scientific research. Our future work will focus on the nonlinear feedback control of EANSG.  
Conceptualization, Y.Z. and J.C.; funding acquisition, J.C.; methodology, Y.Z. and J.C.; project administration, J.C. and L.Z.; software, Y.Z. and J.Z.; validation, J.Z., X.B.; writing--original draft, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51675139, L1924059 and the Fundamental Research Funds for The Central Universities.  
The authors declare no conflict of interest.
## 2 Methods
### Bayesian Networks  
Bayesian Networks (BNs) represent unclear causal links between variables contained in nodes by fusing graph theories with probability theories [40]. In risk analysis applications, there are often many unknown variables and many different pieces of evidence, some of which may be interrelated [41]. In the network, arrows start from the \"parent nodes\" and point to the \"child nodes\". The directed connections between parent and child nodes explicitly express causal relationships or influence mechanisms, while the conditional probability tables configured for each child node quantify the specific probabilistic manifestations of this dependency. Together, these elements form the dependency structure of the nodes in the graph, providing a complete description of conditional independence, as shown in Figure 4.  
From Figure 2, C1 points to B1, which means that C1 is the parent node of B1 and that B1 is the child node of C1; C1 and C2 have no parent node to point into, so they are called root nodes, which have corresponding prior probability distributions. Non-root nodes have corresponding conditional probability distributions. The Bayesian network formula is as follows:  
\[P(x_{1},x_{2},...x_{n})=\prod_{i=1}^{n}P(x_{i}|Parent(x_{i})) \tag{10}\]  
In the formula, \(Parent(x_{i})\) denotes the probability of the parent node \(x_{i}\) occurring; \(P(x_{i}|Parent(x_{i}))\) denotes the conditional probability value of occurrence of the child node; and \(P(x_{1},x_{2},...x_{n})\) denotes the system failure probability.  
The key difference between fuzzy Bayesian networks (FBNs) and traditional Bayesian networks (BNs) lies in the assignment of probability values to nodes. In BNs, probability values for nodes are generally calculated using statistical data. In contrast, fuzzy logic is widely employed in cases where data are missing or uncertain. FBNs combine fuzzy logic and Bayesian reasoning, introducing fuzzy numbers on the basis of the traditional Bayesian network architecture. This incorporation allows the probability values of nodes to vary  
Figure 4: Schematic of a simple Bayesian network.  
within a certain interval, thereby more accurately capturing the uncertainties inherent in real-world scenarios. The computation of FBNs involves two main steps: initially, fuzzy calculations are performed using Equations (1)-(9), followed by a Bayesian inference carried out with Equation (10).
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 3 Results
### Case 2: Cruise Speed of 60 m/s  
For Case 2, the optimization was conducted at a cruise speed of 60 m/s using the same methodology as the one applied for 30 m/s. The Extra Trees Regressor was identified as the best surrogate model, with the performance metrics presented in Table 5.  
For a cruise speed of 60 m/s, from 9000 combinations, the flutter constraint was active only 0.322% of the time. The design variables for which the flutter constraint was active fell within the following specific ranges: AR of 15.2-15.4; \(\Delta\) of 10.4-11.3 degrees; \(G\) between 1.62 \(\times\) 10\({}^{6}\) and 1.70 \(\times\) 10\({}^{6}\) N.m\({}^{2}\)M; and \(\alpha\) of 6.86-6.95 degrees.  
Nevertheless, the optimization algorithm found 180 optimal solutions where the flutter constraint was never active. These solutions differed in design variables from those where the flutter phenomenon was a concern. A subsequent, more detailed analysis aimed to reduce the relative error to under 5% for all outcomes.  
The results in Table 6 show that all percentage errors were below the threshold after a deeper exploration of the design space and an increase in the optimization parameters, as was implemented for the 30 m/s analysis.  
However, this intensive optimization resulted in a 491% increase in computational time. Nevertheless, the potential for greater accuracy seems to justify the additional computational effort. Comparing the Pareto fronts in Figure 6, an improvement was evident, especially at higher \(C_{L}/C_{D}\) values. In these cases, the advanced optimization provided better aerodynamic efficiency with a lower mass increase. However, for some design choices, there was a significant overlap in results between the initial and advanced optimizations, indicating that increasing iterations and population sizes do not always lead to different results.  
Figure 5: Pareto fronts for Case 1 (cruise speed of 30 m/s).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **RMSE (\%)** & **R\({}^{2}\) (-)** \\ \hline Flutter speed & 7.360 & 0.913 \\ Mass & 0.377 & 0.999 \\ \(C_{D}\) & 0.0034 & 0.947 \\ \(C_{L}\) & 0.013 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Final RMSE and R\({}^{2}\) metrics for Case 2 (cruise speed of 60 m/s).
# A Reinforcement Learning Approach to Speech Coding
## Appendix D Voice Activity Detection/Comfort Noise Generation (VAD/CNG)  
We only classify the input speech as Voiced, Unvoiced, or Silence. Both the Unvoiced segments and Silence segments are coded using CNG in the same way. The segments declared to be Voiced speech are passed through the reinforcement learning processing steps as shown in Figure 1.  
To make the decision about voice activity, the VAD uses the future Hamming windowed 128 samples. If the block is declared Silence, those same 128 samples are used to calculate the eight linear predictor coefficients used in CNG. These coefficients are different than the ones used in the Perceptual Weighting Filter (PWF). The VAD flag and the Silence Insertion Descriptor (SID) information is then sent to the Decoder. When no voice activity is detected, the gain or step size adaptation is halted and the gain at the beginning of the VAD/CNG frame is held fixed until voice activity is detected again. A VAD flag and the Silence No Update (SNU) frame is sent to the Decoder.  
Voicing classification is performed using the total power of the 128 samples, the power in the high frequency band above 1.75 kHz, and a low power threshold. If the total power is less than the power threshold, Silence is detected. Otherwise the ratio of the power in the high frequency band to the total power is compared to a threshold. If this high frequency band power to total power ratio is greater than a threshold, the speech is classified as Unvoiced. Unvoiced speech is coded using the same CNG method used for Silence. If the speech is not classified either as Silence or Unvoiced speech, it is classified as Voiced and coded with the reinforcement learning algorithms. The VAD/CNG provides a substantial reduction in bit rate but incurs a noticeable drop in the PESQ-MOS value and the perceptual quality. This VAD/CNG method is outside of the reinforcement learning processing and will need to be improved to produce the highest quality speech attainable when combined with the reinforcement learning architecture.