esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Removal Effect of the Mixed Filter Material
#### 3.2.2 The NH\({}_{3}\)-N Removal Effect  
Figure 8 shows the NH3-N removal effect of single volcanic rock and coconut shell activated carbon filter media and their mixed filter media over 36 days. In the initial stage of operation, the mixed filter material with a mixing ratio of 1:3 (Reactor 5\({}^{\#}\)) had the best NH\({}_{3}\)-N removal rate compared with other mixed filter materials (Reactors 4\({}^{\#}\) and 6\({}^{\#}\)), and the removal rate reached 88.97 \(\pm\) 1.11%. It was found in a previous study that among single filter materials, Reactor 3\({}^{\#}\) had the best removal effect on NH\({}_{3}\)-N. Through comparison, it was found that the average removal rate of Reactor 5\({}^{\#}\) was 2.15 percentage points higher than that of Reactor 3\({}^{\#}\). Although the mixture of volcanic rock and coconut shell activated carbon filter material formed a multi-level pore structure, which enhanced the adsorption and retention effect and improved the removal efficiency of the filter material, this difference was masked by the standard deviation of the two. This indicates that the performance  
Figure 7: The turbidity removal effect of mixed filter media.  
boundary of the single filter medium was not significantly broken through. This is due to the fact that the porous structure of volcanic rock and the high specific surface area of activated carbon do not produce an effective complementary effect. After 26 days of operation of the BSF reactor, the NH\({}_{3}\)-N removal rate reached a stable state. Reactor 5\({}^{\#}\) has the best removal effect on NH\({}_{3}\)-N, which is 0.37 percentage points higher than that of Reactor 3\({}^{\#}\). Similarly, this difference is covered by the standard deviations of the two, indicating that it does not significantly exceed the performance boundary of a single filter material. This is due to the fact that the porous structure of volcanic rocks and the high specific surface area of activated carbon do not effectively complement each other, and also to the influence of the uniformity of microbial community distribution.
# Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
## 5 Results and Discussion
This indication might be anything between 0 and 1. ANNMOGOA-1 has very good R-value that is equal 0.98. ANNMOGOA-2 and ANNMOGOA-7 have \"poor performance\", whereas ANNMOGOA-1, ANNMOGOA-3, ANNMOGOA-4, and ANNMOGOA-5 have \"good performances\", according to the SI value. ANNMOGOA-6 has \"fair performances\". MBE illustrates ANNMOGOA-1, ANNMOGOA-5, and ANNMOGOA-6 overestimate the compressive strength while ANNMOGOA-2, ANNMOGOA-3, ANNMOGOA-4, and ANNMOGOA-7 underestimate that. Figure 15 indicates the estimated values of models against measured values. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ANN** & **RMSE** & **MAPE** & **MAE** & **MBE** & **SI** & **R** & **OBJ** & **Complexity** & **Structure** \\  & **(MPa)** & **(\%)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline ANNMOGOA-1 & 4.49 & 8.75 & 2.58 & 0.19 & 0.11 & 0.98 & 4.12 & 106 & 5-15-1 \\ ANNMOGOA-2 & 18.69 & 52.40 & 14.47 & \(-\)1.14 & 0.44 & 0.49 & 23.28 & 6 & 5-1 \\ ANNMOGOA-3 & 7.65 & 16.66 & 5.75 & \(-\)0.70 & 0.18 & 0.93 & 8.167 & 22 & 5-3-1 \\ ANNMOGOA-4 & 6.29 & 13.17 & 4.28 & \(-\)0.01 & 0.15 & 0.96 & 6.99 & 85 & 5-12-1 \\ ANNMOGOA-5 & 5.99 & 10.37 & 3.50 & 0.91 & 0.14 & 0.96 & 7.11 & 71 & 5-10-1 \\ ANNMOGOA-6 & 10.74 & 20.25 & 6.58 & 0.01 & 0.25 & 0.87 & 9.69 & 15 & 5-2-1 \\ ANNMOGOA-7 & 14.61 & 27.60 & 9.15 & \(-\)0.83 & 0.35 & 0.73 & 15.84 & 8 & 5-1-1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information of NDSs model. A bar chart is included in Figure 16 to allow for a clearer comparison. They have the highest and lowest values for OBJ in ANNMOGOA-2 the ANNMOGOA-1, respectively, according to the bar chart, indicating that ANNMOGOA -1 is the best accurate model overall. Figure 15: Predicted vs. experimental compressive strength. Future work is necessary to input further information in the processes of iteration for machine learning such as admixtures, different types of cement and supplementary cementitious materials. These materials are factors to change the result of calculation; however, it could not be always perfect due to the authors and journal publishers not being mandated to provide all data's or sometimes due to secrets of production from industry not being permitted to publish those data. However, the concrete/mortar printing is not only affected by concrete mix proportion but also affected by other factors while handling the printing process and after printing processes such as humidity, temperature and curing condition.
# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 2 Materials and Methods  
### Instrumentation  
LoRaWAN Outdoor Gateway (part number 102991154) and 4\(\times\) soil moisture and temperature sensors (part number 101990564) were procured from Mouser Electronic, Buckinghamshire, UK with the remaining 6 units procured from DigiKey, Ireland (Thief River Falls, MN, USA). A 4.5 dBi LoRa antenna, 868 MHz, was procured from Paradar, London, UK, while the antenna extension cable was procured from Radionics, Dublin, Ireland. The time-domain reflectrometer-based soil profiler, SoilVUE(tm) (parameters: \(\epsilon\), temperature, bulk electrical conductivity and \(\theta\)) and the CR 300 data logger were procured from Campbell Scientific, Loughborough, UK. The Sense Cap node consists of the sensing element (temperature, \({}^{\circ}\)C and \(\theta\), %) and the sensor node controller which houses the LoRa communication module, battery and low-power microcontroller (Figure 1). The data communication architecture relies on LoRaWAN gateways to provide the coverage for data collection from the nodes and data upload to the cloud via 4G or Ethernet (Figure 1). For this study, the data were retrieved from the Sense Cap portal and further archived into an SQL database.
## 4 Discussion
### Bunch Structure Measures  
The response of eCO\({}_{2}\) on bunch structure and the proportion of berry size groups showed differences between the two cultivars, Riesling and Cabernet Sauvignon. For example, bunch length was larger for Cabernet Sauvignon than for Riesling, whereas Riesling was more spread over all seven berry size classes compared to Cabernet Sauvignon. Differences between the three seasons were also detected, but less for the CO\({}_{2}\) treatment, even though all bunch parameters showed higher values under eCO\({}_{2}\) treatment for both cultivars. The higher single berry weight detected for both cultivars in 2015 and 2016 was not approved in proportion of berry size groups. Riesling showed the weakest CO\({}_{2}\) response for all bunch structure parameters in 2015, the warmest of all three seasons, compared to 2014 and 2016. In 2016, Riesling showed a significant increase in bunch length and weight, berry number, and total berry weight under eCO\({}_{2}\). The higher bunch length for eCO\({}_{2}\) in 2016 could be due to more berries per bunch, which led to an elongation of the bunch to preserve a similar bunch density. Contrary to Riesling, Cabernet Sauvignon did not show stronger responses under eCO\({}_{2}\) for berry parameters, particularly in berry and bunch weight, as well as berry number in 2015. [PERSON] and [PERSON] [56] already reported these differences in cultivar responses to climatic factors, especially for temperature. Overall, it was previously shown that the bunch compactness of Cabernet Sauvignon increased under eCO\({}_{2}\) whereas Riesling did not show differences between the two treatments [9]. As an additionally positive result from that previous study, no differences in incidence or frequency of _B. cinerea_ were detected for both cultivars and the three years under eCO\({}_{2}\), even though bunch compactness is known to relate to susceptibility to grapevine diseases [57] and to _B. cinerea_ in particular [58; 59; 60; 61].  
Regarding the proportion of the different berry size groups Riesling showed the highest proportion for berry size groups 5 (12.5-14 mm) and 6 (14-16 mm), which was in accordance with previous literature [19]. In contrast, Cabernet Sauvignon berries were almost entirely in classes 4 (10-12.5 mm), 5 and 6. As a consequence of this, the Riesling distribution was more spread out over all berry size classes compared to the Cabernet Sauvignon, indicating a higher intrinsic berry size variability for this variety that was--based on this early dataset--not significantly affected by elevated CO\({}_{2}\) conditions.  
Overall, with no differences in 2014 and an increase in probable differences in the following years, it is reasonable to assume that, in grapevines, there is some kind of adaptation period to elevated CO\({}_{2}\) conditions. Hence, this dataset of three years allows a first glimpse of what we might expect in possible changing climatic conditions in the future.
## 1 Introduction
### Optimization of MHPP
Despite the elegance of the obtained analytical results, these simplifications limit the practical implementation, for example, in the (frequent) situation in which the river profile is irregular. Removing this kind of simplification improves the applicability of the developed methodologies at the cost of a dramatic increase in the problem complexity. This results in optimization problems that cannot be solved resorting to linear programming or convex optimization tools, and this is when machine learning techniques and metauristic optimization come into play. For example, ref. [19] presents a method to select an adequate turbine and to compute the optimal and penstock diameter based on Honey Bee Mating algorithm, ref. [20] introduces the application of a genetic algorithm to optimize the flow rate and number of generators in a multi-objective problem where generated energy and investment cost are the objective functions, ref. [16] develops a stochastic evolutionary algorithm to select the optimal turbine capacity and lengths/diameters of the penstock segments, and [11] presents an evolutionary algorithms to maximize power production or net economic profit by optimizing, the penstock diameter, and the type and configuration of the turbines. In all previous works, the intake and turbine locations are assumed to be given. On the contrary, some works include the penstock layout and the intake and turbine locations in the optimization problems. Some examples of these are presented in [21; 22], where arbitrary 2D river profiles are considered and integer programming and evolutionary methods are employed, respectively. The extension of these approaches to (discretized) 3D profiles is presented in [23]. Another interesting work presented in [24] makes use of three optimization modules and genetic algorithms to simultaneously determine the optimal intake location, penstock length and diameter, and turbine number, capacity, and discharge schedule. As a significant improvement with respect to previous approaches, this work proposes the optimization of the layout considering a continuous, 3D formulation of the problem, capable of dealing with an arbitrary terrain and river profile, using a Genetic Algorithm (GA).
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 4 Analysis of Simulation Results and Experimental Results
### Simulation Results
Since the model in this paper belongs to an electrically large-sized target, the calculation time using the traditional MOM method is too long and the requirements for the computer configuration are relatively high. Therefore, in this paper, the high-frequency approximation method RL-GO is selected to solve the target RCS to shorten the calculation time. (4) Mesh the model. The scale of mesh division is generally one-tenth of the wavelength. For the high-frequency approximation method, the division scale can be appropriately increased. In this paper, triangular mesh division is selected, and the number of divided meshes is 1753. (5) The script for importing the ISAR imaging calculation method in Equation (5) into FEKO is shown in Figure 7. Then, the calculation is performed and POSTFEKO is opened to display the ISAR imaging results. The ISAR imaging results are placed in the prediction set of CapsNet, and finally, the target recognition results can be obtained as shown in Figure 8.  
As can be seen from Figure 8, a radar ISAR imaging system based on CapsNet, as proposed in this paper, can accurately identify aircraft ISAR imaging.
# Remote Sensing for International Peace and Security: Its Role and Implications
###### Abstract  
Remote sensing technology has seen a massive rise in popularity over the last two decades, becoming an integral part of our lives. Space-based satellite technologies facilitated access to the inaccessible terrains, helped humanitarian teams, support complex emergencies, and contributed to monitoring and verifying conflict zones. The scoping phase of this review investigated the utility of the role of remote sensing application to complement international peace and security activities owing to their ability to provide objective near real-time insights at the ground level. The first part of this review looks into the major research concepts and implementation of remote sensing-based techniques for international peace and security applications and presented a meta-analysis on how advanced sensor capabilities can support various aspects of peace and security. With key examples, we demonstrated how this technology assemblage enacts multiple versions of peace and security: for refugee relief operations, in armed conflicts monitoring, tracking acts of genocide, providing evidence in courts of law, and assessing contravention in human rights. The second part of this review anticipates future challenges that can hinder the applicative capabilities of remote sensing in peace and security. Varying types of sensors pose discrepancies in image classifications and issues like cost, resolution, and difficulty of ground-truth in conflict areas. With emerging technologies and sufficient secondary resources available, remote sensing plays a vital operational tool in conflict-affected areas by supporting an extensive diversity in public policy actions for peacekeeping processes.  
conflict resources monitoring; disease control and prevention; human rights; genocide tracking; human rights violation; geopolitics 2021 13 439 10.3390/rs13030439
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 3 Vision-Based Line Marking Detection and Condition Assessment
### Segmentation  
Based on the extracted features, this module applies segmentation on the planar images. Segmentation, also known as grouping or fitting [41], aims to generate distinctive and informative summary representations of pixel groups, which are individual line markings in this case. Single markings are selected and exported as separate image files. To locate pavement marking instances, Canny [42] is used for edge detection, whose outcomes are later fed into a Hough line Transform algorithm. Hough Transform [43] has been intensively used for line detection, which tests links between any random point pairs and select ones with higher votes than the user-defined threshold. To further eliminate outliers, lines with unreasonably large slopes are removed, and those belonging to non-marking areas are also filtered out according to their spatial positions. A grouping method is then applied to determine individual line marking areas. The proposed segmentation method was also tested on the same set of sample images. Figure 4 presents the results.  
Figure 4: Test of the segmentation method on pavement images. **(a)** Planar images; **(b)** results of Canny edge detection; **(c)** results of Hough line transform and grouping.  
Figure 3: Test of the feature extraction method on pavement images. **(a)** Original images; **(b)** results of inverse perspective transform; **(c)** results of feature extraction.
## 3 Results  
The valgus group comprised 10 subjects (age: (20.7 \(\pm\) 1.6) year. ; height: (154.9 \(\pm\) 5.4) cm; weight: (54.1 \(\pm\) 8.6) kg), and the varus group comprised seven subjects (age: (22.0 \(\pm\) 2.5) year; height: (161.8 \(\pm\) 4.6) cm; weight: (51.7 \(\pm\) 4.5) kg). Maximum knee valgus angles in the valgus group were 11.5 \(\pm\) 6.1\({}^{\circ}\), and -17.8 \(\pm\) 9.5\({}^{\circ}\) in the varus group. The within-subject coefficient of variation was 23.5% and the standard deviation for the knee valgus and varus angles was 1.6\({}^{\circ}\). The average vGRF impulse, joint angular impulse, and mechanical work values of the lower extremities in the valgus and varus groups are shown in Table 1. The ratios (expressed as percentages) of negative mechanical work at the lower extremity joints to total negative mechanical work (i.e., the sum of hip, knee, and ankle joint negative mechanical work) were 5.9%, 47.1%, and 47.0%, respectively, in the valgus group, and 17.6%, 36.1%, and 46.6%, respectively, in the varus group. The average kinetic and kinematic results at the instant of the maximum knee flexion indicated that the vGRF in the valgus group was significantly greater than that in the varus group ((1.65 \(\pm\) 0.36) N/kg vs. (1.24 \(\pm\) 0.46) N/kg, \(p=0.049\), ES = 0.23). Hip flexion ((51.4 \(\pm\) 12.6)\({}^{\circ}\) vs. (64.7 \(\pm\) 19.5)\({}^{\circ}\), \(p=0.10\), ES = 0.06), knee flexion ((72.8 \(\pm\) 9.2)\({}^{\circ}\) vs. (83.5 \(\pm\) 16.3)\({}^{\circ}\), \(p=0.10\), ES = 0.17), and ankle dorsiflexion angles, at the instant of the maximum knee flexion ((35.1 \(\pm\) 1.60\({}^{\circ}\) vs. (37.8 \(\pm\) 8.8)\({}^{\circ}\), \(p=0.36\), ES = 0.16), did not differ significantly between the valgus and varus groups. Moment variables in joints at the instant of the maximum knee flexion showed significantly larger hip extensor moments ((0.49 \(\pm\) 0.23) N\(\cdot\) m/kg\(\cdot\)m vs. (0.79 \(\pm\) 0.27) N\(\cdot\) m/kg\(\cdot\)m, \(p=0.03\), ES = 0.28) and smaller knee extensor moments ((1.17 \(\pm\) 0.28) N\(\cdot\)m/kg\(\cdot\)mvs. (0.72 \(\pm\) 0.34) N\(\cdot\) m/kg\(\cdot\)m, \(p\) = 0.01, ES = 0.37) in the valgus group than those in the varus group. There was no significant difference in the ankle plantar flexor moment ((1.27 \(\pm\) 0.3) N\(\cdot\) m/kg\(\cdot\)m 1 vs. (1.04 \(\pm\) 0.45) N\(\cdot\) m/kg\(\cdot\)m, \(p\) = 0.22, ES = 0.09). Moments and power curves over time, in lower extremity joints of the valgus and varus groups during DVJ landings, are represented in Figures 2 and 3. The scatter plots in Figure 4 show the negative mechanical work in lower extremity joints and knee valgus and varus angles for each participant. There was a significant positive correlation between negative mechanical work at the hip and knee valgus angles (R = 0.59, \(p\) = 0.01). By contrast, negative mechanical work at the knee had a significant, but negative, correlation with knee valgus angles (R = \(-\)0.55, \(p\) = 0.02). However, there was no correlation between negative mechanical work at the ankle and the knee valgus angles (R = \(-\)0.36, \(p\) = 0.15).
## 3 Testbed Applications Representing Various Parallel Processing Paradigms
### Divide-and-Conquer--Numerical Integration  
The implemented divide-and-conquer paradigm follows computations from the root towards leaves of a balanced binary tree and then back towards the root. This scheme starts with a single process and an original and successively generated problems are recursively partitioned into two subproblems. Upon partitioning, rightmost subproblems are passed to another process for parallelization. In the leaves of the tree, custom computations can be performed and results are then merged into a 2-times-smaller number of processes at each level up to the root and the original process. Actual implementation tested in this work sorted function \(f(x)=\frac{1}{1+x}\) over range \([100,200]\) using a parallel merge sort.  
Other potential applications following this paradigm might include [9]: recursive searching for a Region of Interest (ROI) within a large image, minimax, \(\alpha\beta\) search, etc.
## 5 Simulation Results
### Dynamic Response Characteristic Experiment  
The performance of the three controllers is compared in a steady atmospheric environment. The altitude channel is independent. Refer to the attitude system and adopt the same control method for the altitude channel. Set the flying height of the quadrotor UAV at 20 m, and the flight time is 15 s. The simulation results are shown in Figure 6. The three attitude angles are turned to the desired angle value \(\left[\begin{matrix}\theta_{d}&\theta_{d}&\psi_{d}\end{matrix}\right]^{T}= \left[\begin{matrix}0.5&0.5&0.5\end{matrix}\right]^{T}\), and they return to the initial state after being held in the air for 5 s. In the tenth second of the experiment, the step signal is added as a sudden disturbance, which makes the simulated flight environment of UAV more realistic. The aircraft keeps hovering in the air after being stabilized by the controller. The simulation results are shown in Figure 7.  
Figure 6 shows the altitude channel flight test effect. The maximum overshoot, rise time, settling time and recovery time after being disturbed are used as the performance indicators to judge the control system. After measurement and simple calculation, the detailed values of these index parameters are shown in Table 3. It is not difficult to see that there is no overshoot in FA-DIC and FNN-DIC. However, under the premise of ensuring system stability, FNN-DIC performs better, which is attributed to its shorter settling time  
Figure 5: Three-dimensional curve diagram of the control parameters: (**a**) \(k_{1}\), (**b**) \(k_{2}\).  
Figure 6: Experimental results of three controllers on altitude channel.  
and recovery time. Although the speed of the aircraft reaching the expected altitude in the PD-DIC is faster than FA-DIC, there will be a slight error between the actual flying altitude and the expected altitude. In addition, the overshoot is large, resulting in its adjustment time is much higher than that of the other two control methods.  
Similarly, in the control effects of the roll channel, pitch channel and yaw channel, only PD-DIC has an overshoot. Table 4 shows the comparison of performance indexes and parameters of the attitude system in three control modes. Generally speaking, the quadrotor under FNN-DIC has obviously better flight performance.