esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Robotics  
Agricultural activities involving robotics exhibit a high degree of technology and autonomy to perform tasks. This degree of autonomy between a simple IoT device and a robot differs. Currently, most common applications in agriculture are linked to harvesting and weeding, followed by diseases detection and seeding [14]. Technological advances in machine vision, GPS, laser technologies, actuators, and mechatronics have allowed the implementation of robotic systems in agro-industry [24]. Several robotic harvesting systems have been developed for cucumber, strawberry, tomato, asparagus, grain, lettuce, mushroom, and orange crops, among others. Additionally, the integration of IoT, cloud computing and robotics is developing rapidly. Several sensors are applied in robotics, machine vision being one of the most important. Unfortunately, the number of applications completely developed which are used in real scenarios is still limited [12].
## 3 Results
### Scanning Electron Microscopy (SEM)  
Figure 6a-p show the morphologies of the fractured surfaces of the MWNT/NS-incorporated cement composites observed by SEM. To provide the further morphological data, supplementary SEM images are presented in Figure S1. C-S-H, which is a flat and broad hydration product, was more visible when the NS content was greater [13, 36]. In most of fractured surfaces, NS particles were not found, and this is likely due to that NS already underwent pozzolanic reactions. In the MWNT-incorporated composite types, most of MWNTs were not agglomerated and were distributed in a separated form. It was observed that diameter of MWNT was approximately 10 nm and length was several micrometers. In addition, it was found that the separated MWNTs were anchored in hydration products (e.g., Figure 6f-p) and MWNTs played the role of bridge (e.g., Figure 6f), which contributed to enhancements in mechanical strength. On the other hand, agglomerated MWNTs were detected in the C1.0N5 composite, which was prepared with relatively large amounts of MWNTs and NS. Unlike individual CNT strands, which led to bridging and packing effects in the cement hydrate or interface, the agglomerated CNTs did not yield these positive effects, but rather they acted similarly to pores in the microstructure of the cement composites [25, 37].  
Figure 5: The porosity results of the MWNT/NS-incorporated cement composites: (**a**) NS content varied and (**b**) MWNT content varied.  
Figure 6: _Cont._  
Figure 6: Scanning electron microscopy (SEM) images obtained from the fractured surfaces of each composite type, (**a**) CON0, (**b**) CON1, (**c**) CON3, (**d**) CON5, (**e**) C0.3N0, (**f**) C0.3N1, (**g**) C0.3N3, (**h**) C0.3N5, (**i**) C0.6N0, (**j**) C0.6N1, (**k**) C0.6N3, (**l**) C0.6N5, (**m**) C1.0N0, (**n**) C1.0N1, (**o**) C1.0N3, (**p**) C1.0N5.
###### Abstract  
This paper investigates the stability problem of the feedback active noise control (ANC) system, which can be caused by the modeling error of the electro-acoustic path estimation in its feedback mechanism. A stability analysis method is proposed to obtain the stability bound as a form of a closed-form equation in terms of the delay error length of the secondary path, the ANC filter length, and the primary noise frequency. In the proposed method, the system's open loop magnitude and phase response equations are separately exploited and approximated within the Nyquist stability criterion. The stability bound of the proposed method is verified by comparing both the original Nyquist stability condition and the simulation results.  
active noise control; feedback ANC; stability; secondary path; closed-form equation; Nyquist stability criterion
## 3 Results
### Transition Frequency and Correlation Between Meteorological Drought and Ecological Drought
Furthermore, as presented in Table 4, the relationship in regions experiencing land use shifts demonstrate unique characteristics. The correlation of drought characteristics between grassland and cultivated land was positive, with a drought frequency of 0.68 and drought durations of 0.93 and 0.92, respectively. This suggests a fundamental consistency, indicating that the alteration in surface vegetation type had minimal impact on the occurrence and transition of drought. The drought intensity increased somewhat upon the conversion of grassland to cultivated land, measuring 0.51, which suggests that the drought resistance of cultivated land is somewhat inferior to that of grassland. The correlation of drought features was positive when cultivated land and grassland were transformed into bare land, and negative when bare land was changed into cultivated land and grassland. This phenomenon was intricately linked to the multifaceted impacts of plant cover on soil water retention, evapotranspiration regulation, soil structure enhancement, regional climate alteration, anthropogenic activities, and ecosystem resilience. The reduction in vegetation cover diminishes the soil's water retention capacity and the ecosystem's regulatory ability, hence elevating the likelihood of drought incidence and transition. The augmentation of vegetation cover can significantly alleviate the incidence and transition of drought by boosting these functions. The transformation of uncultivated land into agricultural and grassland can significantly diminish the incidence and transition of drought. The augmentation of grassland vegetation cover can enhance soil's water retention capacity and water usage efficiency, while diminishing soil water evaporation. Although arable land is more effective in alleviating the onset and transition of drought, it necessitates increased human oversight and may diminish its drought resilience due to soil deterioration. Prioritizing the transformation of barren land into grassland in the research area may provide a more efficacious strategy for ecological restoration and drought resilience. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Correlation**} & \multirow{2}{*}{**Cropland**} & \multicolumn{4}{c}{**Grassland**} \\ \cline{3-8}  & & \begin{tabular}{c} **Low** \\ **Vegetative** \\ **Coverage** \\ \end{tabular} & \begin{tabular}{c} **Medium** \\ **Coverage** \\ \end{tabular} & \begin{tabular}{c} **Medium-to-** \\ **Coverage** \\ \end{tabular} & \begin{tabular}{c} **High** \\ **Coverage** \\ \end{tabular} & \begin{tabular}{c} **High** \\ **Coverage** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Drought \\ frequency \\ Drought \\ duration \\ \\ \end{tabular} & 0.71 & 0.73 & 0.72 & 0.69 & 0.66 & 0.38 \\
\begin{tabular}{c} Drought \\ intensity \\ \end{tabular} & 0.96 & 0.95 & 0.93 & 0.91 & 0.84 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation between meteorological and ecological droughts in stable land uses.
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 2 Beam Dynamics Design
### Rfq Full-Length Model Simulation  
Table 6 shows the final RF simulation results of full-length RFQ, including electrode modulation, undercuts, PSLs, and tuners. CST and HFSS software are used to verify each other. The simulation results show that the cavity power consumption of the RFQ is 54 kW. The specific shunt impedance is about 511 k\(\Omega\)-m at a conductivity of \(5.8\times 10^{7}\) S/m, the final mode separation is almost 6.5 MHz, and the Q value is about 17,000.  
Table 7 shows the RF power consumption distribution of different parts of the RFQ. The results can be used for the design of water-cooling solutions. The electrode part consumes the most power, and the maximum surface power density of the RFQ is 12.7 W/cm\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **HFSS** & **CST** \\ \hline TE210 (MHz) & 81.527 & 81.248 \\ TE110 (MHz) & 88.012 & 87.727 \\ TE111 (MHz) & 92.730 & 92.451 \\ TE211 (MHz) & 85.397 & 85.126 \\ Mode separation (MHz) & 6.485 & 6.479 \\ (TE110) & 6.485 & 6.479 \\ Q (\(\sigma=5.8\times 10^{7}\) S/m) & 16,908 & 17,749 \\ Power loss (kW) & 54 & 54 \\ Specific shunt impedance (k\(\Omega\)-m) & 511 & 511 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulations of the full-length model with vane modulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Part** & **Power Consumption (kW)** & **\%** \\ \hline Wall & 19.82 & 36.70 \\ Vane & 28.57 & 52.91 \\ PISL & 3.23 & 5.99 \\ Tuner & 2.38 & 4.40 \\ All & 54 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power consumption distribution.  
Figure 13: Analysis of tuners.  
Table 8 lists the main parameters of the BISOL and the international CW RFQs for heavy-ions acceleration [31]. It can be seen that the 4-vane type has a higher Q value and less power consumption compared to the 4-rod type. For the 4-vane type, FIRB RFQ adopted the variable voltage design, which can shorten the RFQ length, but its Kp is larger than that of BISOL RFQ while maintaining the voltage constant. The design parameters of BISOL RFQ and LEAF RFQ are closest, with the former having a slightly larger Kp value and shorter length than the latter.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 2 Related Work  
Different waste management models have been developed using certain IoT devices. [PERSON] et al. [34] introduced a stage of municipal waste management using the collection of recycled waste data based on IoT innovation. Several waste management activities, such as waste collection, transport, and recycling, were also presented. However, an active optimization technique was proposed by [PERSON] et al. [35] for efficient waste recycling. An active selection that presents descriptions by identifying factors was developed. It compares the characteristics of wastes in a container on a day-to-day basis.  
A specific monitoring system was proposed by [PERSON] et al. [35] to monitor garbage bins and trucks using Radio Frequency Identification (RFID) and information and communication technology. In addition to cameras, GPS is utilized in this system [35].  
Garbage bins used for different types of waste are placed outside doors. Homogenous and heterogeneous trucks are used to collect waste [10; 34; 35].  
In [36], bin collection is recorded to enhance the waste collection process. Figure 1 shows an example of small devices/gadgets with a web-based system that allows the integration of the IoT as a new generation technology.  
The architecture of the WMS consists of waste resource buildings to solve and manage various waste problems. Smart bins are used to improve waste collection and provide information for statistical analysis. Waste collection trucks are used to improve IoT-based Smart WMS in a smart city. The transportation of collected waste, workers, supervisors, and waste management centers are connected together, as interaction between WSNs and MANETs with IoT form the WMS. Figure 2 shows the proposed architecture.  
Figure 1: IoT based Waste Management System.  
This section reviews the existing genetic-based routing algorithm. Considerable research on genetic-based routing algorithm field has been performed. The information from the related literature has been intensely reviewed to help motivate the methodology of the present study.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.3 Bio-Inspired Clustering Scheme for FANETs (BICSF)  
[PERSON] et al. [50] proposed a fresh routing technology named bio-inspired clustering method (BICSF). BICSF contains three mechanisms, as shown in Figure 10. In terms of cluster formation, a GSO inspired clustering formation algorithm based on residual energy of UAV and luciferin level is proposed. Within the cluster, nodes are divided into two functions: cluster head and members. The nodes are divided according to the fitness value. Fitness values were calculated based on luciferin level and residual energy level.  
\[\begin{split}& L_{i}(t)=(1-\rho)L_{i}(t-1)+\gamma F(p_{i}(t)),\\ &\mathit{ResidualEnergy}=\Sigma_{i}^{n}(\mathit{initial\ energy\ level}(i)-\mathit{current\ energy\ level}(i)).\end{split} \tag{9}\]  
Figure 10: Phases of Bio-Inspired Clustering Scheme for FANETs (BICSF).  
where for every glowworm \(i\), \(L_{i}(t-1)\) represents the previous luciferin value of \(i\) and \(\lambda\) is the luciferin improvement fraction. \(F(p_{i}(t))\) is the objective function of glowworm \(i\) at current position of glowworm \((p_{i})\).  
Generally, the node with the highest fitness value is regarded as the cluster head. In order to better manage, a cluster management mechanism inspired by krill herd (KH) was proposed, and biological genetic methods such as mutation operator and crossover operator are used to solve the optimal position of the flying node. As for communication in cluster, this paper proposes a route selection scheme based on path detection calculation.  
\[path\ detection\ function=\frac{\omega 1\times residual\ energy}{(\omega 2\times N _{i})(\omega 3\times distance)}. \tag{10}\]  
where \(\omega 1,\omega 2,\omega 3\) are the weights for residual energy, number of neighboring UAVs, and distance between UAVs, respectively, and \(\omega 1+\omega 2+\omega 3=1\).  
During the cluster maintenance process, the method of monitoring the energy level of the flying nodes is used to effectively judge the status of the nodes and find a solution in time.  
**Advantages:** (1) This algorithm improves the efficiency of the protocol by optimizing the formation of clusters. (2) The management of the krill cluster ensures minimal misalignment of the cluster nodes. (3) An efficient cluster maintenance mechanism ensures that the cluster adapts to the changes in the environment and node migration.  
**Disadvantages:** As the density of nodes increases, there will be some new nodes enter the cluster or some expired nodes exit the cluster, and then the lifetime of the cluster is gradually shortened due to frequent topology changes.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 5 Simulations
### Background  
The presence of an incoherent background affects the analysis of the SCS, it being up to several orders of magnitude below the incoherent background, and it may disappear into it. To assess the sensitivity of the SPA, backgrounds of 1.0 \(\times\) 10\({}^{-5}\), 1.0 \(\times\) 10\({}^{-4}\), and 1.0 \(\times\) 10\({}^{-3}\) cm\({}^{-1}\) were added to the experimental data. In this case, a power law with the exponent fixed to the theoretical value of \(-4\), as predicted by the Porod approximation of sharp interfaces, was considered using commercial software. The SCS with an added background of 1.0 \(\times\) 10\({}^{-4}\) cm\({}^{-1}\) along with the fit of the Guinier approximation is shown in Figure 6.  
The fit of the Porod approximation is shown in Figure 7.  
The results are shown in Table 3; the values obtained by the SPA with an added background can be compared to those obtained without an added background, shown in Table 1.  
Figure 6: Guinier approximation of the simulated SCS by using the SDF with \(\sigma\) = 0.4 and an incoherent background of 1.0 \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). The points used for the Guinier approximation are shown with black symbols, while the others are red. The fit is shown with a red line.  
Figure 7: Porod approximation of the simulated SCS by using the SDF with \(\sigma\) = 0.4 and an incoherent background of 1.0 \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). The points used are shown with black symbols, while the others are red. The fit is shown with a red line.  
Figure 8 demonstrates the validity of the proposed SPA, the reconstructed SDFs being with and without the additional backgrounds, in good agreement with the original SDF. Surprisingly, the SDF of the case with the highest background (1.0 \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)) is very close to the SDF reconstructed without any additional background. Indeed, the SPA is able to reconstruct the SDF.
## 3 Microwave Photonics Applications of DFT  
As it enables unique one-to-one mapping relation between frequency and time of a stretched broadband optical pulse, DFT has been widely applied in a variety of microwave photonics systems, for signal generation, measurement and processing applications. In this section, we discuss the recent development in employing DFT technique for various microwave photonics applications, such as real-time spectroscopy, microwave arbitrary waveform generation, microwave spectrum sensing, and photonic analog-to-digital conversion.
## 3 Results  
To evaluate the performance of the proposed system for dexterous robotic grasping, we compared the LSTM, GP, and Transformer models with the YOLOv8-n-seg model for classifying and grasping the list [Apple, Banana, Ball, Bottle, Box, Lemon, Orange, Strawberry, Tomato]. The experimental setup consisted of an Intel RealSense D4351 RGB-D camera for the vision system, a 6-DoF Annin Robotics AR4 robotic arm, and a Beijing Inspire Robot Dexterous Hand with integrated force sensors. The computing hardware included an Intel Core i7 CPU, 16 GB RAM, and an NVIDIA RTX 4070 8 GB GPU.  
The proposed method demonstrated superior grasping performance compared to conventional approaches. The Transformer model achieved a 95.6% success rate, surpassing LSTM and GP (91.3%). Unlike rule-based methods, which rely on predefined force thresholds, our approach dynamically adjusts grasp forces using real-time sensor feedback, ensuring adaptability across various object geometries. While Gaussian Processes effectively quantified uncertainty, they struggled with rapid force variations, making them less suitable for highly dynamic interactions. The Transformer model excelled in force prediction, enabling smoother modulation and more precise grasping. The grasp configuration system further enhanced stability in two- and three-finger grasps, particularly for delicate objects like strawberries and lemons. These results highlight the effectiveness of deep learning-driven grasp adaptation, creating a robust and flexible strategy for robotic manipulation.
## 3 Results and Discussion
### Larvae Growth  
About 2000 hatchlings of _Hermetia illucens_ were introduced in a growing tray containing a substrate made of chicken feed and water mixture. The growing tray was placed in the chamber unit with Sensors S4 and S5 submerged in the substrate. The example climate control application introduced in Section 2.2.5 was used as the application running on the control unit. A temperature set point of 33 \({}^{\circ}\)C was used to regulate the air temperature inside the chamber. Cyclic controller for ventilator was selected with an ON and OFF time of 600 s and 1200 s, respectively, to regulate the CO\({}_{2}\) concentration. The data collected using the designed system is presented in Figure 13 for a period of 40 h.  
The air temperature inside chamber is maintained at the set 33 \({}^{\circ}\)C, as shown in Figure 13a. The PID controller compensating the variation in temperature due to the ventilation can be seen in the peaks of the control signal \(u_{T}\). It can be noticed that the substrate temperature (Sensor S4) gradually increases and reaches a peak temperature of 45.5 \({}^{\circ}\)C at 35 h. Similarly, the variation in the CO\({}_{2}\) and O\({}_{2}\) concentrations increases gradually also reaching the peak around the same time step. These variations in substrate temperature and gas concentrations can be concluded to be results of larval metabolism. Correlating these three measurements, it can be stated that increase in temperature increases larval metabolic activity and thus the dependency of the larval growth on temperatures can be concluded. Another observation that can be made from the humidity measurement (Figure 13d) of air is that, after the time step corresponding to the peak metabolic activity, humidity gradually falls. Moisture in substrate is gradually lost through evaporation and consumption by the larvae. It can be stated that decrease in air humidity can be explained by the substrate drying up. It is however inconclusive from this experiment if the fall in larval metabolic activity is due to the dry substrate.  
Figure 13: Larvae growth experiment: (**a**) temperature variation in growing medium, chamber unit and outside; (**b**,**c**) CO\({}_{2}\) and O\({}_{2}\) concentration variation due to ventilation and larval metabolic activity; and (**d**) humidity variation inside and outside the chamber unit. E1 corresponds to measurements made outside the chamber using a reference sensor and data represented by \(u\) correspond to actuator state.
## 3 Problem Definition
### Definition of Terms  
#### 3.2.1 Max-Out-Of-Orderness  
Max-Out-Of-Orderness defines the maximum amount of time that an element(s) for a particular window of timestamp \"t\" is allowed to be late, by at most \"n\" milliseconds, after the earliest arriving element of that window, before being ignored in the computation of a final result for said window. As an example, the maxOutOfOrderness is set to value 6000, meaning that elements are allowed to be late for a maximum of six seconds while not being ignored in the final results as shown in Figure 4.
## 3 Results and Discussion
### Prediction Model of Part Shrinkage Varying with the Time-Averaged Packing Pressure  
Three illustrations were used to demonstrate the correlation between part shrinkage and melt temperature, packing pressure, and packing time. The influence of melt temperature on the cavity pressure in the middle of the cavity is shown in Figure 13. A higher melt temperature allows for easier pressure transfer from the gate to the cavity. Generally, the higher the melt temperature, the longer the crystallization time and the packing pressure value, resulting in a higher averaged pressure integral (Table 7). The influence of the packing pressure and time on the cavity pressure profiles is demonstrated in Figures 14 and 15, respectively. Notably, higher packing pressure and time lead to higher cavity pressure and increased pressure duration. As a result, the averaged pressure integral values are higher. The results of Table 7 indicate that packing pressure affects the pressure integral most significantly. Packing time also has a secondary impact on the pressure integral, and its influence increases with packing pressure. The observed results are consistent with previous results [11].  
Figure 11: Maximum warpage versus cooling time.  
Figure 12: (**a**) Response chart and (**b**) contour plot of crystallization time prediction model.  
Figure 14: Pressure and temperature curves at MID molded under different packing pressures.  
Figure 13: Pressure and temperature curves at MID molded under different melt temperatures.  
Using Equation (2), the averaged solidification cavity pressure can be calculated based on the pressure profiles at different locations. The averaged values are listed in the end column of Table 7. The measured and calculated data of time-averaged pressure and shrinkage were used to fit the three shrinkage prediction models (Equations (4)-(6)) that were previously described). After conducting ANOVA and residual analysis using Minitab software [30; 31], the adjusted and non-adjusted coefficients of determination for the three models are shown in Table 8. Based on this result, a second-order function was chosen, and the results are as follows:  
\[S_{V}=1.910-0.004115\times P_{Sarg}+6\times 10^{-6}\times P_{Sarg2} \tag{9}\]  
The model-predicted values versus the measured shrinkage are given in Figure 16.  
Average solidification pressure V.S. shrinkage ratio  
Figure 16: Variation in the shrinkage ratio with the time-averaged solidification pressure.  
Figure 15: Pressure and temperature curves at MID molded under different packing times.