esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Analysis and Results
\(H_{4}\)_Hypothesis_: A 3-phase process was performed using IBM AMOS 24. First, the effect of GPP on FP was analyzed. The modified model presents a good fit (CMIN = 14.638, DF = 7, \(\mathrm{CMIN}/\mathrm{DF}\) = 2.091, \(p\) = 0.041, \(\mathrm{RMSEA}\) = 0.049, \(\mathrm{CFI}\) = 0.995, \(\mathrm{GFI}\) = 0.989, \(\mathrm{AGFI}\) = 0.967, \(\mathrm{NFI}\) = 0.990). The explanatory power (R\({}^{2}\)) of GPP for FP was 55%. Path (GPP\(\rightarrow\)FP) indicates a significant and positive (\(\beta\) = 0.74, \(p\) < 0.01) effect. Later, the contemporary approach was performed by applying bootstrap [78]. Upon the introduction of EP, the obtained model presents a good fit (CMIN = 164.620, DF = 50, \(\mathrm{CMIN}/\mathrm{DF}\) = 3.292, \(p\) = 0.000, \(\mathrm{RMSEA}\) = 0.071, \(\mathrm{CFI}\) = 0.968, \(\mathrm{GFI}\) = 0.942, \(\mathrm{AGFI}\) = 0.909, \(\mathrm{NFI}\) = 0.955). While the explanatory power (R\({}^{2}\)) of GPP for EP was 53%, the explanatory power (R\({}^{2}\)) of GPP for FP increased to 73%. As path (GPP\(\rightarrow\)EP) (\(\beta\) = 0.73, \(p\) < 0.01) and path (EP\(\rightarrow\)FP) (\(\beta\) = 0.62, \(p\) < 0.01) were positive and significant, the \(\beta\) value decreased from 0.74 to 0.29 in the path (GPP\(\rightarrow\)FP) (\(\beta\) = 0.29, \(p\) < 0.01), a partial mediating effect can be mentioned [73]. In the final step, the model was re-run in a 95% confidence interval and on 5000 samples, and the significance of the partial mediating effect was confirmed [71].
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 5 Conclusions  
The design of a complete TRM is discussed in this paper, having extreme performances in terms of transmitted power (41.5 dBm), receiving amplification (up to 46 dB, with the lowest gain of 43 dB at 12 GHz), and a low noise figure (a minimum of 3.4 at low frequency and the highest of 3.9 dB at 12 GHz). The great electrical performances based on a compact and thermally efficient mechanical design demonstrated in this paper will allow for very high-resolution SAR images and large electronically steerable angles. Such performances are ensured in a wide band, thus making this TRM designed and manufactured within a European-funded program a reliable candidate to be included in the development of innovative SAR instruments for the next-generation satellite constellation for Earth observation. One of the main improvements that can be considered for future research is in terms of compactness for even better antenna steering performance, although this poses additional challenges in terms of denser package layout and heat extraction.  
Conceptualization, G.M. and A.F. ([PERSON]), M.A.; methodology, F.d.P., D.D.G. and M.A.; software, F.d.P., A.D.C., P.T. and A.O.; validation, F.d.P., G.M., D.D.G. and M.A.; writing--original draft preparation, F.d.P., A.O.; writing--review and editing, F.d.P., A.D.C., P.T., A.O., G.M., A.F. ([PERSON]), and M.A.; supervision, G.M. and A.F. ([PERSON]); project administration, A.F. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research is part of the ATOS (Advanced Technological Solutions for X-band Earth Observation Systems) project funded by the European Community under the H2020 Research and Innovation framework program, grant number 776350. [[https://cordis.europa.eu/project/id/776350](https://cordis.europa.eu/project/id/776350)]([https://cordis.europa.eu/project/id/776350](https://cordis.europa.eu/project/id/776350)). (Access date 25 September 2022).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: (**a**) Return loss at both ends of the RX chain. (**b**) Linear RX gain (S\({}_{21}\)) over temperature. (**c**) Noise figure over temperature.
## 3 Hierarchical Control System
### General Work Cycle of the Implemented System  
The general work cycle of the presented system consists of several steps (see Figure 1) which lead to complete treatment of effluent. A block diagram of the described work cycle is shown in Figure 4. The most important factor in the proposed solution is the step-feed strategy. This approach assumes the step-feed of sewage and the periodic aeration of the content of the SBR which allows to efficiently remove the nitrogen compounds without using any external source of carbon. The quantity of the refills during each cycle is determined through the asymmetric filling strategy alongside the changing number of biological (aerobic, anaerobic) phases during each refill. Each cycle is divided into a series of sequences depending on the intensity of wastewater inflow and the level of contamination. The capacity limit determines the specific volume of the last filling, regardless of the intensity of sewage inflow [29].  
Figure 3: Structure of the hierarchical control system: \(Q_{air},n_{b},x_{b},e_{DO},e_{Q},N_{tot},P_{tot}\), COD, NO\({}_{3}\), \(NH_{4}\) are the air flow [m\({}^{3}\)/h], blower speed [rpm], state of blower: on/off [-], error of DO [g O\({}_{2}\)/m\({}^{3}\)], error of air flow [m\({}^{3}\)/h], total nitrogen [g N/m\({}^{3}\)], total phosphorus [g P/m\({}^{3}\)], chemical oxygen demand [g O\({}_{2}\)/m\({}^{3}\)], nitrate concentration [g N/m\({}^{3}\)], ammonia concentration [g N/m\({}^{3}\)], respectively. The abbreviation for reference trajectory is designated as _ref_.  
Biological reactions phase is the key factor in the whole wastewater treatment process. It has the most significant impact on the quality of treated sewage as well as energy consumption over the cycle. The number and duration of sequences (aerobic plus anaerobic), as well as the order of single reactions (aerobic, anaerobic), are discussed in further sections of this paper.  
The management of the sedimentation is dependent on the total suspended solids (TSS), which is monitored in the top and mid layer of the SBR as well as minimal sedimentation time. Setting the correct time of this stage is very important in terms of efficient wastewater treatment process. Too long sedimentation phase may lead to the return of discharge of phosphates and as a result leave the sewage partly untreated. The decantation stage can start after reaching the minimal sedimentation time and when two conditions are fulfilled: TSS in the top layer is below assumed value and it decreases in the middle layer. This stage ends each time the volume of wastewater and active sludge reaches its minimal level or a specific amount of solids is detected in the wastewater.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 4 Discussion
It is impossible to obtain fast and reasonable response results based on the changes of these uncertainties even depending on a priori knowledge. How to enable multi-robots to timely self-adjust to dynamic task changes and obtain reasonable operational tasks or operational task sequences through real-time interaction with dynamic environments is an urgent problem for multi-robot task assignments in agriculture. The path of agricultural multi-robot operations is designed to accomplish dynamic operational tasks, and the robot's travel rules are usually fixed. The global path of multi-robot offline planning only considers fixed travel rules, such as the point-to-point method and image method, which can avoid static obstacles smoothly, but cannot be extended to be applied to similar agricultural scenarios. In particular, if the dynamics of the agricultural environment change rapidly (e.g., weeds are growing in the field after the rainy season) and the agricultural information is not fully known (e.g., the constructed mapping usually does not contain dynamic obstacles), fixed path planning cannot meet the needs of complex tasks (e.g., weeds are not on the planned path). Therefore, how to perform reliable path planning for multiple robots based on operational tasks with distinct temporal characteristics is a problem that needs to be solved for multi-robot path planning in agriculture. Flexible and robust formation control  
Multi-robot formation control currently focuses mostly on robot swarms walking steadily along a straight line in a fixed formation. However, when a multi-robot system encounters unexpected events, such as robot failure, communication failure, or stopping travel due to dynamic obstacles, how to mitigate the impact on other robots, respond quickly, and adjust the robot formation shape to continue the task is a concern for agricultural multi-robot formations. Although some studies have shown that multiple robots can be selectively controlled based on time or event drivers, or by replacing the \"leader\" in the queue, none have been applied in real production. Communication system based on plant characteristics  
Communication is the basis of multi-robot collaboration in agriculture, whether it is multi-robot positioning, collaborative control, or remote supervision, communication is indispensable. The agricultural environment lacks communication infrastructure construction, and most of them directly used industrial communication systems do not consider the relationship between outdoor plant growth and communication signals, and their communication range and signals will be attenuated to different degrees in the agricultural environment. Therefore, the construction of a communication system adapted to the agricultural multi-robot operating environment is a problem that needs to be solved for multi-robot communication.
## 3 Results
### Transmitted Signal Estimation
well as between the true transmitted signal and the estimated transmitted signal, for each signal in our dataset. Subsequently, we calculate the average energy of the residuals for both cases. Figure 7a illustrates the relationship between the SNR and the energy of the residuals. As anticipated, an increase in the SNR corresponds to a decrease in the energy of the residuals. Furthermore, the figure demonstrates that the average energy of the residuals between the true transmitted and estimated transmitted signal is significantly lower than the average energy of the residuals between the true transmitted and received signal. This observation strongly suggests that the good performance of the estimation leads to reduced residuals. Figure 7b presents the average energy of the residuals in relation to the phase offset. Consistent with previous discussions, we observe that the estimation performance is notably poor when the phase offset is \(\pm 45\) degrees. Furthermore, upon examination of this figure, it becomes evident that the scenario depicted in Figure 6b represents an outlier case. As previously discussed, the presence of outliers does not significantly impact the overall performance of our system. Lastly, Figure 7c illustrates the relationship between the average energy of residuals, specifically those between the true transmitted signal and the estimated transmitted signal, and the absolute residual CFO. The absolute residual CFO is computed as the absolute difference between the true CFO and the estimated CFO. As  
Figure 7: Average energy of residuals with respect to RF impairments. (**a**) Average energy of residuals with respect to SNR. (**b**) Average energy of residuals with respect to phase offset. (**c**) Average energy of residuals with respect to absolute residual CFO. anticipated, a clear direct relationship is observed: as the absolute residual CFO increases, the average energy of the residuals also increases.
## 2 Materials and Methods
### Volatile Compounds  
The extraction of the volatile compounds from the tomato samples was carried out using the headspace solid-phase micro-extraction (HS-SPME) method. A sample of 5 g of ground tomatoes was placed on a 50 mL vial with a magnetic bar, with polypropylene caps and a polytetrafluoroethylene/silicone septum. After equilibration time of 5 min at 40 \({}^{\circ}\)C, a 30/50 \(\upmu\)m fiber of a length of 2 cm (Supelco) consisting of DVB/CAR/PDMS (Divinylbenzene/Carboxen/Polydimethylsiloxane) was exposed to the vial headspace at 40 \({}^{\circ}\)C, with continuous agitation (600 rpm) in a magnetic stirrer (IKA C-MAG HS 4, IKA--Werke GmbH & Co. KG, Staufen, Germany). After 50 min of exposure, fiber was introduced into the injector of a gas chromatograph for analysis [13].  
The isolation and identification of the volatile compounds were performed using a gas chromatograph, Shimadzu GC-17A (Shimadzu Corporation, Kyoto, Japan), coupled with a Shimadzu mass spectrometer detector (GCMS QP-5050A). The chromatograph was equipped with a column Restek Rxi-1301 Sil MS (Restek Corporation, Bellefonte, PA, USA), fused silica, 30 m \(\times\) 0.25 m \(\times\) 0.25 \(\upmu\)m film thickness. Analyses were carried out using He as carrier gas at a flow rate of 0.6 mL min\({}^{-1}\), a split ratio of 1:5, with scan mode (from 45 \(m/z\) to 400 \(m/z\), and scan speed = 1000), and the following program: (a) initial temperature80 \({}^{\circ}\)C; (b) rate of 3.0 \({}^{\circ}\)C min\({}^{-1}\) up to 210 \({}^{\circ}\)C and hold for 1 min; (c) rate of 25 \({}^{\circ}\)C min\({}^{-1}\) up to 300 \({}^{\circ}\)C and hold for 5 min.  
The volatile compounds were identified using 3 methods: (i) retention indexes (RI), (ii) GC-MS retention time of the chemical compound, and (iii) comparison of the compound mass spectrum with those of databases (NIST05 and Adams 2012 spectrum library). The retention indexes were calculated using a mix of aliphatic hydrocarbons (Supelco), soluble in methanol and ranging between C-6 and C-22. Analyses were run in triplicate (\(n=3\)).
## 3 Results
### Characteristics of Spatial and Temporal Evolution of Vegetation GPP on the Qinghai-Tibet Plateau  
#### 3.2.1 Characterization of Temporal Variation in GPP of Vegetation on the Qinghai-Tibet Plateau  
As illustrated in Figure 4, the interannual trend of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau from 2001 to 2022, estimated based on MODIS-PSN simulations, exhibits an overall fluctuating upward trajectory. The mean interannual GPP value is 476.69 gC/m\({}^{2}\)/year, with an annual growth rate of 2.9894 gC/m\({}^{2}\)/year. Notably, the highest interannual GPP values occurred in 2006, 2010-2013, 2015-2016, and 2018-2022, all exceeding the interannual average. The peak value of 505.082 gC/m\({}^{2}\)/year was recorded in 2020, while the lowest interannual value of 428.055 gC/m\({}^{2}\)/year was observed in 2003. Periods of consistent increase in GPP were identified in 2001-2002, 2003-2006, 2008-2011, 2014-2015, and 2017-2020. The results indicate that the vegetation GPP on the Qinghai-Tibet Plateau exhibited a significant fluctuating growth trend (\(p<0.05\)), suggesting that policies such as the conversion of pasture to grassland, along with other ecological protection measures, have positively contributed to vegetation restoration and environmental improvement in the region [39; 41].
## 4 Simulation Results and Discussions
### Simulation Results at Low Speed  
In this subsection, the reference speed is set as \(N_{r}=300\) r/s, then it can be obtained that \(\omega_{m}=\frac{\pi}{30}N_{r}\). The load and disturbance are set as \(T_{L}=10\) N\(\cdot\)m and \(\delta=2\sin(500t)\), respectively.  
To obtain better speed response, the gain parameters are set as follows. The parameters of sliding mode controller are set as \(\varepsilon=200\), \(q=300\), and \(c=70\). The PD controller parameters are set as \(K_{p}=10\) and \(K_{D}=0.07\). The observer parameters are set as \(L_{1}=\left[\begin{array}{cc}10&0\\ \end{array}\right]\) and \(L_{2}=\left[\begin{array}{cc}0&10\\ \end{array}\right]\). The estimator gain is set as \(K_{1}=\left[\begin{array}{cc}0.01&10\\ \end{array}\right]\), \(K_{2}=\left[\begin{array}{cc}0&0\\ \end{array}\right]\). In addition, the correction parameter \(\theta=0\). The simulation results are shown in Figures 2-4.  
Figure 3 shows the estimation results of load and disturbance. It can be seen that the estimate signal has some slight errors with the actual signal before t = 0.02s and can track the actual signal well after t = 0.02s. This means that the proposed method can well suppress the load disturbance caused by load change. Also, one can find out that the estimate signal has some noise. The reason of the phenomenon is that the numeral model is simplified from Simulink model. Some uncertainties are ignored and operating states are in ideal condition.  
Figure 3: The actual load and disturbance with their estimates in low-speed condition.  
Figure 2: The speed control and estimator blocks.  
The Table 3 can be obtained from Figure 6. It also shows that the control effect of the proposed combined controller is very good. The decrease of overshoot is due to the addition of PD controller based on sliding mode controller. The anti-torque capability is enhanced because the state observer is used to estimate the torque disturbance, and then compensate it into the combined controller.  
Figure 5: The actual load and disturbance with their estimates in high-speed condition.  
Figure 6: The speed response results without compensation and with compensation in high-speed condition (the blue line indicating the speed response results with compensation, and the red line indicating the speed response results without compensation).  
\begin{table}
\begin{tabular}{c c c} \hline
**Items** & **Combined Controller** & **Traditional SMO** \\ \hline Rise time/ms & 6.509 & 7.609 \\ Setting time/ms & 4.376 & / \\ Overshoot/\% & 0.498 & 38.184 \\ Lowest speed/rpm & 989.1 & 886.8 \\ Recovery time/ms & 0.05 & 0.28 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation comparison performance (\(N_{r}=1000\) r/s).
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.8. Hydrus  
Hydrus is a Windows-based 2D/3D finite element model for simulating the flow and transport of multiple species in differently saturated, heterogeneous, porous media. The model enables the optimisation of soil and hydrogeological parameters. One of the functions of Hydrus is to define good agricultural practice in relation to water management. Other environmental applications include risk assessment of contaminant plumes from landfills, infiltration of wastewater, and the interaction between groundwater and surface water. Since many environmental problems cannot be solved without insightful analyses of the fate of pollutants and their interactions, the hydrogeochemical model PHREEQC has been integrated into the basic version of Hydrus. The coupling of PHREEQC with Hydrus leads to a considerable extension of the functionality with regard to reactive transport, and enables the simulation of surface complexation, sorption, precipitation, and dissolution, as well as other mixed equilibrium and kinetic biogeochemical reactions. For the application of Hydrus in hydrocarbon transport studies, we refer to the work of Casey and Simunek [672], Ngo et al. [673], Mallants et al. [674]. The latter study reproduced the fate and transport of biocides in soil as a result of their accidental release. The simulation included the degradation chain of the biocide bronopol, the delay, and the convective-dispersive transport of the biocide bronopol and its degradation products.
## 3 Results
### Index Finger Operation Area  
All 15 sets of experimental results are provided in the Figure 7a (P10*5, P50*5, P90*5). The shape of dying areas does not present an apparent similar feature, but it indicates that there are thresholds in both vertical and depth directions for the index finger operation, as shown in Figure 7b. The Shapiro-Wilk normality test was performed before using ANOVA analysis, and the results were consistent with normality. Table 2 shows the ANOVA results of the index finger. There are statistical significances in the upper limit and maximum depth across different percentiles, while the lower limit and minimum depth do not show statistical significance.  
Figure 7: (**a**) Experimental results of IFOA; (**b**) results in IFOA. The upper endpoint of the joystick was set as the origin, based on which a Cartesian coordinate system was established. (**c**) Results distributions are shown through box-and-line plots with significant differences labelled. * indicates \(p<0.05\); ** indicates \(p<0.01\).  
Based on the ANOVA results, we conducted post hoc Tukey HSD tests with different percentile groupings for variables that showed significant differences, as the results illustrated in Figure 7c. Specifically, for the upper limit, there is a statistical significance between P10 and P50 (\(p<0.05\)) and a higher level of significance between P10 and P90 (\(p<0.01\)), whereas there is only a low assumption for the difference between P50 and P90 percentile (\(p<0.1\)). Regarding the maximum depth, there is a similar significance trend to the upper limit across different percentiles with a statistical significance between P10 and P50 (\(p<0.05\)), a higher level of significance between P10 and P90 (\(p<0.01\)), and a trifling significance between P50 and P90 (\(p<0.1\)).
# Large Scale Implementations for Twitter Sentiment Classification
## 6 Results and Evaluation
### Our Datasets for Evaluating MapReduce versus Spark Framework
#### 6.1.3 Space Compression  
As stated and above, the Bloom filters can compact the space needed to store a set of elements, since more than one object can be stored to the bit vector. In this subsection, we elaborate on this aspect and present the compression ratio in the feature vectors when exploiting Bloom filters (in the way presented in Section 4.2) in our framework. The outcome of this measurement is depicted in Table 5.  
Concerning MapReduce implementation, in all cases, the Bloom filters manage to minimize the storage space required for the feature vectors by a fraction between 15%-20%. On the other hand, for Spark implementation, the Bloom filters manage to marginally minimize the storage space required for the feature vectors (up to 3%) and, in one case (multi-class hashtags), the decrease in the required space is significant (almost 9%). According to the analysis made so far, the importance of Bloom filters in our solution is twofold. They manage to both preserve a good classification performance, despite any errors they impose, and compact the storage space of the feature vectors.  
There are two reasons for these small differences. First of all, in each Bloom filter, we store only one feature (instead of more) because of the nature of our problem. Secondly, we keep in our minds a Bloom filter object instead of a String object. However, the size that each object occupies in the main memory is almost the same (Bloom filter is slightly smaller). Since the size of our input is not very big, we expect this gap to increase for larger datasets that will produce significantly more space-consuming feature vectors. Consequently, we deduce that Bloom filters can be very beneficial when dealing with large-scale sentiment analysis data that generate an exceeding amount of features during the feature vector construction step.