esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 3 Results
### Knowledge Base Analysis
The Gaofen and WorldView clusters are more relevant to image processing methods and algorithms, which indicates that the application of data acquired by these two satellites is still in its infancy. Both WorldView and Gaofen are (ultra) high-resolution commercial imaging satellites and carry advanced remote sensing instruments. WorldView was launched earlier than Gaofen and is more widely used. WorldView is considered the highest-resolution and most stable commercial imaging satellite; it also has a short revisit period (about one day). Gaofen is a high-resolution EO system that was developed by China as a priority; frequent launches of satellites in this series have taken place in recent years (Table S3). However, Gaofen EO data are not open-access. The users of Gaofen data are mainly from China, and this is an obstacle to this mission having a greater impact on the remote sensing community.
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 3 Results
### Determination of the Effective Dose of Veratraldehyde
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{
\begin{tabular}{c} **Conc.** \\ **\%** \\ \end{tabular} } & \multicolumn{4}{c}{**Repellency (\%, Mean \(\pm\) SB \({}^{\star}\)**} \\ \cline{3-6}  & **1 h** & **2 h** & **3 h** & **4 h** & **5 h** & **6 h** \\ \hline \multirow{4}{*}{Veratraldehyde} & 5 & \(97\pm 1.7\)a & \(98\pm 0.5\)b & \(83\pm 3.7\)b & \(76\pm 4.3\)a & \(75\pm 0.8\)c & \(64\pm 2.7\)b & \(1.3\) (0.68\(-1.99\)) \\  & 7 & \(100\pm 0\)a & \(100\pm 0\)a & \(89\pm 0.5\)a & \(89\pm 4.6\)bc & \(77\pm 4.3\)b & \(4.7\) (4.01\(-5.32\)) \\  & 10 & \(100\pm 0\)a & \(100\pm 0\)a & \(99\pm 0.8\)a & \(97\pm 3.0\)a & \(96\pm 1.1\)ab & \(96\pm 1.0\)a & \(5.3\) (4.68\(-5.99\)) \\  & 15 & \(100\pm 0\)a & \(100\pm 0\)a & \(100\pm 0\)a & \(91\pm 8.1\)a & \(99\pm 0.8\)a & \(98\pm 0.9\)a & \(4.3\) (6.8\(-4.99\)) \\ \hline \multirow{4}{*}{DEET} & 20 & \(100\pm 0\)a & \(100\pm 0\)a & \(100\pm 0\)a & \(99\pm 0.6\)a & \(100\pm 0\)a & \(99\pm 0.5\)a & \(5.3\) (4.03\(-6.64\)) \\ \cline{1-1}  & 20 & \(100\pm 0\)a & \(100\pm 0\)a & \(100\pm 0\)a & \(99\pm 0.6\)a & \(100\pm 0\)a & \(99\pm 0.5\)a & \(5.3\) (4.03\(-6.64\)) \\ \hline \hline \end{tabular}  
* Means with the same letter are not significantly different according to a Bonferroni multiple-comparison test (\(p=0.05\)).  
\end{table} 
Table 3: Repellency and CPT based on the concentrations of veratraldehyde against _Aedes albopictus_ females using the arm-in-cage test.
# Benchmarking Message Queues
## 6 Results
### Throughput  
The comprehensive results of the experiments for throughput are shown in Figures 2 and 3. Figure 2 shows the maximum throughput in terms of the number of events per second whereas Figure 3 shows the maximum throughput in terms of megabytes per second.  
Figure 3: Throughput in terms of megabytes per second.  
Figure 2: Throughput in terms of events per second.  
When it comes to measuring the throughput of message queues in terms of the number of events processed per second, Apache Kafka emerged as the clear winner among the four technologies. It demonstrated a remarkable performance, leaving the other three systems far behind. RabbitMQ, on the other hand, secured the second spot, but the gap between Apache Kafka and RabbitMQ was still significant. Redis secured the fourth spot. Finally, ActiveMQ Artemis ranked fourth with an even larger difference in performance when compared to the top three. A common trait noticed between all four technologies was that the number of events per second decreased when increasing the message size.  
The results regarding which message queue performed the best and worst were the same for throughput in terms of megabytes per second and throughput in terms of the number of events per second. This is expected since the throughput in megabytes per second is just another method of representing throughput, as these data were calculated by multiplying the maximum number of events by the message size. However, having throughput data in these two forms could be beneficial for the reasons stated in the Methodology section. We also noticed that the throughput increases with respect to an increasing message size for Apache Kafka, RabbitMQ, and Redis. However, for ActiveMQ Artemis, when increasing the message size, the throughput increases up to a certain point and then starts to drop.  
To summarize the results, Apache Kafka outperformed the other three message queues in terms of throughput by a significant margin. While RabbitMQ came in second place, the gap between RabbitMQ and Apache Kafka was still substantial. Redis lagged behind both Apache Kafka and RabbitMQ. ActiveMQ Artemis had the lowest throughput and lagged significantly behind the other message queues.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison
#### 5.4.4 Energy Consumption Analysis  
Energy efficiency is critical in IoT networks, where many nodes are battery-powered and operate in constrained environments. Figure 8 compares the average energy con  
Figure 7: Throughput comparison among [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40] (Mbps).  
sumption per node of the proposed framework against [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al [33]. as the network scales from 10 to 100 nodes.  
Our framework consistently consumes less energy than all baselines across the entire range. At 10 nodes, it achieves an average consumption of 0.22 J, representing a 26.7% reduction compared to [PERSON] et al. [33] (0.3 J), 26.7% compared to [PERSON] et al. [40] (0.3 J), and 56% compared to [PERSON] et al. [39] (0.5 J). This energy advantage persists and grows with network size.  
At 100 nodes, our framework uses 0.78 J per node, which is:  
* 40% less than [PERSON] et al. [33] (1.3 J)
* 40% less than [PERSON] et al. [40] (1.3 J)
* 35% less than [PERSON] et al. [39] (1.2 J)  
This reduction is primarily due to our lightweight trust evaluation computations and avoidance of cluster-based overheads inherent in some baselines. Additionally, our use of local, asynchronous consensus combined with efficient IOTA ledger operations minimizes redundant transmissions and retransmissions, which conserves battery life.
## 3 Results and Discussions
### Characterization of Fine Lateritic Materials
#### 3.1.3 Particle Size Distribution (PSD) and Atterberg Limits  
The PSD of lateritic soils in the study area is summarized in Table 2. Five (05) granulometric classes are given for these materials and include clays (40.0-61.1%), silts (11.3-27.9%), fine sands (5.7-22.3%), coarse sands (0.7-20.9%), and gravels (0-15.1%). According to Figure 7, PSD curves for the samples are very close to each other. Therefore, these lateritic materials are classified as well graded to poorly graded laterites. The results also indicate that the studied soil materials are found in the A-7-5 and A-6- classes in accordance with the AASHTO classification system. These soil materials display low to tolerable performance in road construction. These poor performances are linked to their high fine content greater than 35%, which is the maximum percentage recommended by [72] for pavements carrying a low volume of traffic.  
The liquid limit (LL) values of the studied fine laterites range from 48.2 to 71.8% (Table 2). These values are similar to those obtained on soil from Sangmelima-Mengong [3]. However, they confirm the abundance of silts and clays in all samples [41,77]. Therefore, the high values of the LL (>35%) indicate that the soil is susceptible to shrinkage or cracking [44]. Accordingly, these soils require improvement before an optimal use in subgrade construction.  
Figure 6: Water content variation and kaolinite proportions in the fine lateritic soils of Bangangté.  
The values of the plasticity index are presented in Table 2, with both lowest and highest values being observed in the BG1 soil profile, with respective values of 19.5 and 35.4% for samples BL26 and BL23. According to [42], these values are greater than 15%, which is the maximum percentage recommended for the base layer. With the exception of samples BL23 and BL13, all the other samples had plasticity index values less than 30%, which corresponds to the maximum value required for the base layers of pavements. Furthermore, the high plasticity of these samples can be linked to the high content of the clay fraction as well as the clay minerals [15,78]. Based on the Casagrande plasticity chart, BL26 can be designated as inorganic clays of medium plasticity, and the rest of the samples as inorganic silts of high compressibility and organic clays (Figure 8). With regard to the [59] classification, all the studied soil samples belong to the A class of the A\({}_{2}\) and A\({}_{3}\) groups. According to [79], these materials display low to tolerable performance in road construction. Their consistency index (CI) varies from 0.92 to 2.28, which is an indicator of the firmness of the soil. As the value increases, so does the shear strength.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 2 Materials and Methods
### Satellite Based Crop GPP and Yield Modeling
#### 2.2.3 Crop Production and Yield Estimation  
Crop yield was calculated as the product of the estimated cumulative 8-day crop GPP for the growing season between April and September for each year of the 2008 to 2015 study period and an empirical harvest index (HI) specific to each of the seven MT crop types. Crop production was obtained by integrating the estimated yield over the area planted for each crop type at both county and MT state levels. Modeled crop production (P\({}_{\text{M}}\)) was compared with respective MT county-level annual production values for each crop type reported by the USDA. The range of HI values for each crop type was assembled from the literature and summarized in Table 3; this range includes HI values used to convert aboveground biomass, GPP or net primary production to crop yield. However, in this study, we only consider HI as the conversion ratio from GPP to crop yield (HI\({}_{\text{GPP}}\)). The Monte Carlo Markov chain (MCMC) method was used to calibrate HI\({}_{\text{GPP}}\) (Table 3) by minimizing the root mean square error between modeled crop production (P\({}_{\text{M}}\)) and two-thirds of the NASS reported county scale crop production (P\({}_{\text{N}}\)) records for each crop type in MT.  
We also compared the 30-m model crop yield estimates (Yield\({}_{\text{M}}\)) against independent field-scale crop yield observations (Yield\({}_{\text{F}}\)) obtained from 10 m by 10 m plots at four different MT farms. The Yield\({}_{\text{F}}\) observations were obtained from GPS linked yield monitors on combine harvesters and represent four different crop types, including barley, peas, spring wheat and winter wheat. Here, the collocated spatial mean Yield\({}_{\text{M}}\) results and Yield\({}_{\text{F}}\) observations for individual crop types were compared over coarser 90 m by 90 m windows to reduce potential geolocation errors for each year of record from 2008 to 2015.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Crop Type** & **HI from Literature \({}^{\star}\)** & **Calibrated HI\({}_{\text{GPP}}\)** \\ \hline Alfalfa & 0.07 to 0.18 & 0.55 \\ Barley & 0.30 to 0.62 & 0.42 \\ Maize & 0.25 to 0.58 & 0.44 \\ Durum Wheat & 0.31 to 0.43 & 0.22 \\ Peas & 0.33 to 0.59 & 0.28 \\ Spring Wheat & 0.31 to 0.53 & 0.24 \\ Winter Wheat & 0.33 to 0.53 & 0.35 \\ \hline \hline \end{tabular} \({}^{\star}\)HI from the literature includes HI values derived for converting biomass, gross and net primary production to crop yield.  
\end{table}
Table 3: Estimated annual harvest index (HI) values for the seven major MT crop types.
## 3 Results and Discussion  
### Metal Concentrations in Aqueous Samples  
The concentrations of the selected studied elements in tap water and concoctions and the comparisons with drinking water quality guidelines are summarized in Table 1. The results show that most of the metal concentrations were within the acceptable limits for human consumption, except for Ni, Cr, As, Cd, and Pb. The consumption rates for these five metals should be controlled because of their known adverse effects on human health. The two alkali metals (Na and K) had concentrations significantly higher in the concoctions than in tap water in all samples, while the alkaline earth metals (Ca and Mg) were significantly higher in 47 and 76% of the samples, respectively. These samples are listed in Table S2 in the Supplementary. The samples with concentrations that were significantly higher in the concoctions than in tap water for Fe (76%), Al (59%), and Zn (29%) are listed in Table S2. The concentrations of Ca, Mg, Na, and K were similar to those reported in other herbal concoctions within South Africa [30], with one recent study recording maximum values between 10 and 30 mg L\({}^{-1}\) for Ca and Mg and 50-60 mg L\({}^{-1}\) for Na and K [21]. The concentrations of Fe and Zn were similar to those reported for potable water in Nigeria, where the concentrations were as high as 0.3 and 3 mg L\({}^{-1}\), respectively [29]. This was an indication that the presence of these metals in concoctions was due to other components of the concoctions in addition to the tap water.  
The trace metals (Ni, Cr, Co, As, Cd, and Pb) were found in concentrations higher than the permissible limit, which raises some health concerns. While this is generally higher than the concentrations reported in most aqueous herbal mixtures, some of the metals have also been reported to have concentrations above permissible limits [8; 29]. For example, in a recent study in Nigeria, the concentration of Pb in aqueous herbal samples was as high as 3.01 mg L\({}^{-1}\), while the concentration of Co (0.266 mg L\({}^{-1}\)) was similar to the current study [29].
#### 2.3.2 Measurement Acquisition  
Initially, measurements were to be acquired by using a single Wenner-Schlumberger array with \(n=3.5\), and 12 individual resistivity measurements acquired per sounding (6 red and 6 green measurements) (where \(n\) is the dipole separation factor). However, after the second survey, it was decided to acquire measurements by using two Wenner-Schlumberger arrays, one with \(n=1.5\) and one with \(n=3.5\), thereby doubling the number of individual resistivity measurements per sounding to 24 (12 red and 12 green measurements). To prevent adverse effects of electrode charge-up on data quality [79], the individual  
Figure 5: Photographs of the survey equipment.  
resistivity measurements were acquired in decreasing number of current electrodes. The sequence of electrode combinations designed for the acquisition of a red or green series of measurements with both Wenner-Schlumberger arrays is presented in Supplementary Materials Table S1.  
Each 2-D survey was acquired as a series of 6 to 12 fully collinear soundings (electrodes on a same straight survey line at all times) with a 50 m spacing between the sounding mid-points (Supplementary materials Figure S2). This spacing allowed soundings to largely overlap each other, and this enable good lateral resolution in the true cross-sections to be obtained. Each individual resistivity measurement was acquired by using alternating positive/negative current pulses of 0.5 s duration each, with a targeted minimum voltage of 20 mV, and with 4 to 16 current pulses stacked to achieve a standard deviation of resistance (noted SD) below 1% (the resistance is noted as \(R\) and is calculated from the sent current, \(I\), and the induced voltage, \(U\), as \(R=U/I\)). The general sequence of acquisition and the measurement parameter settings designed for the 2-D VES surveys are summarised in Supplementary Materials Figure S3.
## 1 Introduction
[PERSON] and [PERSON]'s five- and six-sample [27] algorithms effectively eliminate the phase shift nonlinearity by the extended averaging method. [PERSON] et al. [28] developed a six-sample algorithm insensitive to the second harmonic component. Other phase-shifting algorithms can be designed using Fourier representation [26], data-sampling windows [29; 31], or the theory of characteristic polynomial [30; 34; 38; 39]. [PERSON] [30] developed the \(2N-1\) algorithm by using characteristic polynomial theory, which can compensate for both coupling errors and phase shift miscalibration. However, the \(2N-1\) algorithm did not have the ability to compensate for the nonlinearities of the phase shift error. [PERSON] et al. designed a 19-sample algorithm with an ability to compensate for the coupling error [33; 35]. A new three-step phase-shifting algorithm with a faster speed than the traditional one was presented by [PERSON] et al. [32] by changing the arctangent function to a simple intensity ratio function. To reduce errors and eliminate ghosts, [PERSON] et al. [37] presented a new algorithm that can improve the quality of reconstructed images by applying parallel four-step phase-shifting holography to the hologram. We designed the \(5N-4\) algorithm [38] by placing the fifth multiple roots on the complex-unit circle. The \(5N-4\) algorithm has the ability to compensate for the third-order nonlinearity of phase shifts, but the errors occurring from the residual phase shift errors and coupling errors were observed clearly in the generated surface profile. To obtain more accurate measurements, a new algorithm with a stronger ability to compensate for the high-order nonlinearity of phase-shift errors and coupling errors should be developed. In this research, a novel \(10N-9\) phase-shifting algorithm was derived. The new algorithm utilizes the Discrete Fourier Transform function and polynomial window function. The proposed algorithm enables the measurement of silicon wafers with high reflectivity [39]. The characteristic of the new \(10N\) - \(9\) algorithm is represented in the frequency domain by Fourier description [40]. The phase errors were calculated, and the results indicate that the new \(10N-9\) algorithm has the smallest value of phase error, compared with other conventional algorithms. Finally, the repeatability measurement error by calculation across \(20\) experiments was \(2.045\) nm, which indicates that the novel \(10N-9\) algorithm outperforms the conventional phase-shifting algorithm.
## 2 Related Work  
Nonlinearity, model uncertainty and external disturbance have already become the key problems of UAV control systems. Development of single nonlinear control algorithm is generally able to handle one or two of them. However, hybrid control strategies could make full use of the advantages of each algorithm.  
Single control method could also obtain good results, especially after optimization. In [29], a robust MPC algorithm was employed to deal with external disturbance, the effectiveness of the proposed method was proved by several flight experiments. A SMC technique was presented to estimate external disturbances and consider the nonlinearity of flapping dynamics, simulation results showed the control system designed based on a linearized model was effective [30]. The authors of [31] came up with an improved adaptive control algorithm for an unmanned helicopter in the presence of model uncertainties, and the hardware-in-loop simulations were designed for generating measurement noises and validating the control method. However, limited by the characteristics of single control method, it is difficult to meet distinct demands of all those problems mentioned above.  
Hybrid control scheme is very popular in the design of flight control systems when facing multiple challenges. In [32], a neural network based observer was developed to estimate external disturbances and model uncertainties, while the control scheme of the UAV system was built with the backstepping control method, and only simulation results were provided in this paper. SMC combined with a nonlinear disturbance observer were studied to improve the tracking performance while the quadcopter was under disturbances and subjected to model uncertainties, simulation results were presented to demonstrate the effectiveness of the controller [33]. In [34], three categories were utilized to achieve the trajectory tracking of a small-scale helicopter, the first one was the linearization of the mathematical model by applying the feedback linearization technology, then, a disturbance observer was presented to estimate the total disturbances, which included matched ones and mismatched ones, at last, H-infinity control method was proposed to design the controller based on the linear model. Although there have been various studies on this topic, there still exist some unresolved problems, for example, flapping dynamics are barely considered in the control system, and controllers designed based on multiple algorithms bring too many parameters, which are difficult to adjust in flight experiments.  
Most of the strategies mentioned above were only verified by simulation tests. In this paper, the flapping dynamics of a helicopter are taken into account. Nonlinearities, model uncertainties and external disturbances are totally handled through the proposed Backstepping-ADRC. Additionally the control structure is not too complicated, which makes it a little easier to deploy in the embedded system than most other schemes. Moreover, the parameters of the control law are reasonable, which makes parameter adjustment much simpler in the flight test.