esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Nutrient Disorders of _Camabis sativa_
## 2 Materials and Methods
### Micronutrient Disorders  
#### 3.2.1 Manganese  
Symptoms of Mn deficiency developed 6 weeks after treatment. Initially, the plants developed a bright yellow netted interveinal chlorosis on the upper and central foliage (Figure 4). This chlorotic netting initiated at the midrib of the leaflets and spread outward toward the leaf margin as symptoms progressed. In advanced symptoms, the interveinal netting became very distinct against the green veinal regions. Additionally, the interveinal regions developed small tan necrotic regions on the leaf surface (Figure 4).  
There were no statistically significant differences in the dry weights of the control plant and the Mn-deficient plants (Table 3). Despite the lack of statistical significance, foliar Mn concentrations were 74% lower in the Mn-deficient plants than in the controls. Deficiency treated plants contained 7.56 mg\(\cdot\)kg\({}^{-1}\) Mn, while the control plants contained 29.40 mg\(\cdot\)kg\({}^{-1}\) (Table 4). Foliar Mn concentrations in the control plants were slightly below the published sufficiency range of 41-93 mg\(\cdot\)kg\({}^{-1}\) Mn ([PERSON] et al., [9]).  
Figure 4: Nutritional disorders of manganese (Mn), boron (B), and copper (Cu) deficiency in _Cannabis sativa_ ’T1’ plants. These pictures display the symptomological progression of nutritional orders as they progress from initial, intermediate, and advanced.  
Foliar Mn concentrations were 53% higher in the Mn toxicity treatment plants than in the controls. Toxicity treated plants contained 47.88 mg\(\cdot\)kg\({}^{-1}\) Mn, while the control plants contained 31.13 mg\(\cdot\)kg\({}^{-1}\) (Table 4). Foliar Mn concentrations, in both toxicity treated and control plants, were within the published survey range of 41-93 mg\(\cdot\)kg\({}^{-1}\) ([PERSON] et al., [9]). While the toxicity treatments were higher, they were still within the accepted ranges for most crops. There were no statistically significant differences in the dry weights of the control plant and the Mn toxicity plants (Table 3). Higher concentrations of Mn will need to be used to further refine the threshold for toxicity symptomatology.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 4 Implementation and Evaluation Setup  
The localization system was implemented using a custom designed hardware board and a Contiki based software implementation. In Table 2 a full description of the hardware and software can be found. Both the hardware and software design are available open source [14].
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 3 Basic Methods
### K-Means Clustering  
An efficient, easily understandable, and profoundly interpretable method characterizes the K-means algorithm within the domain of clustering ([PERSON] and [PERSON], 2010). Due to its widespread and successful applications, this study adopts the K-means algorithm to cluster alarm sequences. The K-means operates on the concept that a centroid can effectively represent a cluster. Initially, \(k\) centroids are randomly chosen, and each document is assigned to a cluster using a distance metric. Subsequently, the \(k\) centroids are recalculated. The process is iteratively repeated until an optimal set of \(k\) clusters is achieved, guided by an objective function. In this algorithm, the choice of a suitable distance metric significantly influences the clustering performance.  
When alarms are transformed into vector representations using Word2 vec, the alarm sequences are represented as sequences of vectors. A method for measuring similarity involves the calculation of Euclidean distances between average vectors derived from the aggregation of alarm vectors across alarm sequences. Let two alarm sequences be represented as \(s=(a_{1},a_{2},\ldots,a_{n})\) and \(s^{\prime}=(a^{\prime}_{1},a^{\prime}_{2},\ldots,a^{\prime}_{n^{\prime}})\), respectively. After mapping them using Word2 vec, they become the corresponding vector sequences \(\textbf{s}=(\textbf{a}_{1},\textbf{a}_{2},\ldots,\textbf{a}_{n})\) and \(\textbf{s}^{{}^{\prime}}=(\textbf{  
Intuitively, the information of alarm vectors in the word embedding space is significantly compressed. As a result, the clustering performance obtained needs improvement.
## 4 Earth Observation Products and Dissemination Platforms
Emerging African space agencies should aim at increasing their participation in reducing disaster risks and improving disaster response in various regions of Africa. In addition, EO and other Geospatial technologies should increasingly be exploited to support policy frameworks such as Africa Regional Strategy for Disaster Risk Reduction. Emerging space agencies should provide expertise, reference data and satellite datasets from their Earth observation missions and becoming more involved in the International Charter \"Space and Major Disasters\". More critically, capacity building efforts should be intensified to empower various end-users such as national disaster management centers and local governments in the use of Earth observation for disaster management. Other product dissemination platforms that provide Earth observation data products over Africa include the Global Human Settlements Layer (GHSL, [[https://ghsl.jrc.ec.europa.eu](https://ghsl.jrc.ec.europa.eu)]([https://ghsl.jrc.ec.europa.eu](https://ghsl.jrc.ec.europa.eu))) provided by the European Commission's Joint Research Council (JRC). Information on human settlements is critical because the state and growth of settlements and built-up infrastructure are closely linked to the economic and social status of nations. In fact, in addition to their functional role as places of habitation and safety, human settlements are a distinguishing feature of human civilization. On the downside, developing countries still face acute settlement challenges due to rapid population growth, urbanization and vulnerability to natural disasters. Urban sprawl and the proliferation of slums is a major problem  
Figure 7: Water-related information for supporting integrated water resources management in South Africa. (**a**) The National Seasonal Water Bodies Layer and (**b**) irrigated and non-irrigated fields for compliance monitoring to National Water Act No. 36 of 1998 and associated regulations. confronting African countries and other developing nations. The need for timely and consistent spatial information is a common thread in all the international treaties and national plans.
## 2 Materials and Methods
### Illumination Correction  
Since non-uniform illumination has been a common phenomenon of images, illumination correction becomes an indispensable pre-processing of object localization. Some studies performed more thorough research of this issue [15,16], properly increasing the local contrast of the major structures in the image, which could ultimately raise the accuracy of structure identification. In color retinal images, a green channel highlights the blood vessels and the OD region. Hence, a gray level image is obtained by extracting the green channel from the color retinal image.  
Non-uniform illumination is always one of the main influences of feature selection, subsequently making OD not the brightest region and the vessels unclear. The intensity of vessels would even be higher than OD. In order to decrease uneven background intensity, an illumination-correction process described in [17] is employed. It is defined as:  
\[I^{\prime}(x,y)=I(x,y)-\frac{\mu_{{}_{N}}(x,y)}{0.1+\sigma_{{}_{N}}(x,y)} \tag{1}\]  
Where \(I^{\prime}\) denotes the illumination-corrected image, and \(I\) is the original gray image. \(\mu_{N}\)(\(x\),\(y\)) and \(\sigma_{N}\)(\(x\),\(y\)) are respectively the mean and standard deviation of pixels within a \(N\times N\) window centered at the pixel (\(x\),\(y\)). Here, \(N\) is set as 35. The image after illumination correction is shown in Figure 2a.  
Figure 2: Images in gray level morphology processing, (**a**) the illumination-corrected image \(I^{\prime}\); (**b**) the vessel-removed image \(I_{vr}\); (**c**) the image \(I_{g}\) after morphological gradient; (**d**) the circular border of the optic disc.  
Figure 1: The flow chart of the fovea localization.
## 1 Introduction
The lateral impact performance of wire ropes was studied to understand the wire ropes' dynamic behavior under various conditions, including tension and side impacts [16]. In many studies, the wire rope was modeled as an Euler beam under tension. The vibration response of a tensioned Euler beam was theoretically solved under various boundary conditions [17]. The Euler beam-based distributed transfer function method has been developed to model the vibrational response of wire rope and cable structures [18]. The model included shear effect, tension, and hysteresis damping of helical strands. The tensioned cable was modeled as a Euler beam, and the axial force, bending stiffness, and rotational stiffness were investigated using its natural frequencies and modes [19]. The first few natural frequencies were used to investigate the axial force and bending stiffness in the cable model [20]. The transfer function method has been used to investigate dynamic characteristics for various structures. The dynamic properties of the complex structure beams were measured based on the transfer function method. To analyze the vibrations of sandwich beams of different core materials and a polymer beam, flexural wave propagations were analyzed using the Timoshenko beam theory and Euler beam theory [21]. In a recent study, a detailed mechanical analysis of a wire rope under tension was performed, and the experimental results were verified using a finite element model [22]. Structures are subjected to various tensions over time, and their bending deformation and vibration damping are important factors for safety and lifespan. In this study, the dynamic characteristics of the wire rope under tension were measured using the transfer function method. The flexural wave propagation was analyzed based on the Euler beam theory. A wire rope and steel beam were used in experiment. A universal testing machine applied a static load to the specimen, and a vibrating shaker excited random vibrations. The vibration response was measured using two accelerometers. The steel beam was used to measure the translational and rotational stiffness of the boundary conditions and compare the dynamic characteristics of the wire rope. The frequency-dependent bending stiffness and loss factor were measured with increasing tension. For the wire rope, the bending stiffness increased and the loss factor decreased as the tension increased. A regression model was employed to determine the correlation between tension and dynamic characteristics. Statistical validation was conducted to confirm the performance and significance of the regression model. The regression model can be used for predicting dynamic behavior for arbitrary tensions.
## 2 Materials and Methods
### Radiation Biophysics Beamline Setup and Experimental Procedures
#### 2.1.1 Scattering Chamber  
The apparatus (Figure 1) mainly consists of:  
1. A disk-shaped scattering chamber, in whose centre it is possible to mount a specially designed aluminium target-holder, provided with two beam collimators (3 and 5 mm diameter) and a frame-holder on which there is a beam scattering Au foil (10 mm diameter, 1 mm thickness, Goodfellow Cambridge Ltd., London, UK). The target holder can be moved in the vertical direction to remove it from the beam path. From the centre of the chamber depart several radial channels, by 15\({}^{\circ}\) steps in the forward direction, and some backwards channels.
2. An SSBD (Si BU-011-05-300, useful area 0.50 cm\({}^{2}\), 300 mm of minimum depletion depth, Ametek Ortec, Oak Ridge, TN, USA) for the beam monitoring, placed at the end of the forward channel at 60\({}^{\circ}\).
3. A channel, placed at the end of the forward channel at 15\({}^{\circ}\), equipped with a gate valve and ending with a window (13 mm diameter); next to the window, it is possible to mount, alternatively, a second SSBD, identical to the one described above, or a CR39 SSTD (13 mm diameter and 0.5 mm thickness, Mi.AM Srl, Piacenza, Italy) for beam monitoring and flux calibration, respectively. During the irradiation of the biological samples, the detectors were replaced by the sample holder assembly (see Section 2.1.2); hence, in what follows, we shall refer to this channel as _cell channel_. A knife-edge collimator (Figure 1) is mounted on the outlet of the scattering chamber, (8 mm internal diameter, 12 mm external diameter) to collimate the scattered beam on the solid angle intercepted by the cell sample or by the detectors placed at the bottom of the channel.
4. A Faraday Cup (FC), placed at the end of the 0\({}^{\circ}\) forward channel, which allows to measure the current intensity of the beam.  
The irradiation of the cell sample was achieved by inserting the Au foil at the centre of the chamber, thus diverting the particles through Rutherford scattering. The presence of a silicon detector placed at a different angle with respect to the cells, allows to carry out live-time dosimetry (see Section 2.2), thanks to the known angular dependence of the Rutherford scattering. Such an experimental procedure was chosen to de-focus the primary beam using a very short distance between the target and the biological samples (less than 40 cm). This, in turn, allows to use higher currents, which guarantees more stable operating conditions and radiobiologically meaningful dose rates (i.e., in the order of Gy/min).
## 3 Results
### Capacity  
For evaluation of steganography methods, [PERSON] and [PERSON] [7] define several variables related to capacity, i.e., the number of bits that can be encoded into the carrier MIDI file referred to as embedding capacity \(N_{c}\), the total number of embedded bits divided by the size of the carrier MIDI file before encoding referred to as the embedding rate \(E_{r}\), the total number of embedded bits divided by the total number of events in the carrier MIDI file referred to as the number of bits/event \(N_{e}\), and the absolute change of the carrier MIDI file before and after encoding divided by the size before encoding referred to as the file-size change rate \(F_{t}\). Following their initiative, the suggested method is evaluated according to these key performance indicators and parameters and compared to the corresponding values of other methods. It is assumed that the full embedding capacity is used for encoding. See Table 3 for a comparison of a selection of steganography methods. As Table 3 shows, the Velody 2 strategy does not limit the number of available events until the \(N_{e}\) approaches 6 bits/event, and even then the reduction is small.  
Figure 4: To the left: Power curve of the binomial test of deviance from the value 0.5 of the parameter \(\pi=P\) (a respondent cannot tell apart the stego MIDI file from the carrier MIDI file.). To the right: Power curve of the \(\chi^{2}\)-test of deviance from the binomial distribution with parameters \(n=10\) (since there are 10 questions in the experiment) and \(\pi\). For both tests, the power depends on the sample size, i.e., the number of respondents in this case. Here, the sample size was 30 as indicated by the red curves, but had it been 10 the curves would have been as indicated in green and had it been 100 the curves would have been as indicated in blue.  
It can also be seen that the \(E_{r}\) is high compared to other techniques. While the Velody 2 strategy most likely does not outperform the work of [PERSON], [PERSON] and [PERSON] [8] when it comes to the deviation in average velocity, this is of little practical effect since it according to the statistical experiment seems hard to detect this when listening and that there exist tools that creates exactly this type of deviation as a part of the music production process. The suggested strategy from [PERSON] and [PERSON] [7] achieves a fairly good \(N_{e}\), but this comes at the cost of a low \(E_{r}\) value due to the file size expansion introduced by the strategy. The method proposed by [PERSON], [PERSON] and [PERSON] [3] achieves a good \(E_{r}\) value with no inflation, performance effects, or obvious artefacts. However, the optimal performance of this elegant strategy is still outperformed by the averaged performance of the Velody 2 method.
## 3 Results
### Assessment of the Models' Level of Detail and Feature Recognizability  
Further to the quantitative results above reported, looking at the models obtained, they also differ in other aspects that are not easy to quantify and that can be only described qualitatively. In general terms, they are strictly related to the noise level and to the model resolution, both influencing the \"level of detail\" (LoD) of the obtained models. While noise level mainly depends on the TLS system (as well as on the algorithms applied in post-processing), resolution is strongly dependent on acquisition speed. Therefore, an improved working mode in order to enhance the obtainable LoD can simply be to slow the walking speed in the proximity of objects of special interest.  
As already reported in Section 2.6.2, some objects such as:  
* planar black and white targets,
* spheres, and
* architectural details can be used to evaluate and compare the recognizability of features in the three clouds.
## 4 Simulation Results and Discussion
### Experiment in a Real Environment  
To test the proposed APF-IRRT* algorithm for mobile robot path planning, we performed it on an open robotic platform: the Mecanum wheel omni-directional mobile robot U-car, as shown in Figure 11. The main parameters of the U-car are the maximum linear velocity (4.5 m/s) and the maximum angular velocity (3.5 rad/s).  
Figure 11: The Mecanum wheel omni-directional mobile robot U-car which was used in the experiment.  
Figure 10: The effect of complexity and size of grid map in search time of four algorithms. (**a**) shows the effect of two different complexity grid maps in search time of four algorithms. The red and blue curves are test environment M5 and M2, respectively. (**b**) shows the effect of two different area grid maps in the search time of four algorithms. The red and blue curves are relatively larger area map and smaller area map, respectively.  
The experiments were performed in a real laboratory environment considering the test environment M3 with three static obstacles. The size of the experiment area was set as 5 m \(\times\) 5 m. The start point and the target point were set to (0, 2.5) and (5, 2.5), and the obstacles were set to (1.5, 3), (1.5, 2), (3, 2.5), respectively. For testing purposes, the linear velocity and the angular velocity of the omni-directional mobile robot were set to 1.0 m/s and 1.5 rad/s, respectively. The proposed APF-IRRT* algorithm planned a path to provide a sequence of motion commands to the mobile robot from the start point to the target point. The test results are shown in Figure 12, with a set of images of the mobile robot which show that the mobile robot drove at different positions. From Figure 12, we can see that the Mecanum wheel omni-directional mobile robot executes the path planning satisfactorily in a real test environment.  
To test the ability of avoiding dynamic obstacles for APF-IRRT* algorithm in a real environment, we performed the experiment with two Mecanum wheel omni-directional mobile robot U-cars, robot 1 was used as a main robot, robot 2 was used as a dynamic obstacle. To distinguish the two robots, the robot 1 was set to move backward, and its linear velocity and angular velocity were set to 0.4 m/s and 1 rad/s, respectively. Robot 2 was set to move forward, and its linear velocity and the angular velocity were set to 0.2 m/s. A set of images of the test results, which show how the robot moves at different positions, is shown in Figure 13. As shown in Figure 13, we can see that the main robot can avoid dynamic obstacles well.  
Figure 12: Experiment of the proposed APF-IRRT* algorithm in a real scenario.
## 5 Spatial Footprint of Coulomb Stress Changes
the radiated seismic energy as \(M=\frac{2}{3}logE\), the cumulative distribution function \(N\)(\(>\)\(M\)) of earthquake magnitudes \(M\) can be deduced as [73]:  
\[\frac{N(>M)}{N}=\left[1-\left(\frac{1-q_{m}}{2-q_{m}}\right)\left(\frac{10^{M}}{ \alpha_{m}^{\frac{3}{2}}}\right)\right]^{\frac{2-q_{m}}{1-q_{m}}} \tag{5}\]  
where \(q_{m}\) is the entropic index and \(\alpha_{m}\) a model parameter that expresses the proportionality between the seismic energy and the size of the fragments. In [74] updated the derived equation to include the minimum earthquake magnitude \(M_{0}\) in a seismic catalog, which now reads as:  
\[\frac{N(>M)}{N}=\left[\frac{1-\left(\frac{1-q_{m}}{2-q_{m}}\right)\left(\frac {10^{M}}{\alpha_{m}^{\frac{3}{2}}}\right)}{1-\left(\frac{1-q_{m}}{2-q_{m}} \right)\left(\frac{10^{M_{0}}}{\alpha_{m}^{\frac{3}{2}}}\right)}\right]^{ \frac{2-q_{m}}{1-q_{m}}} \tag{6}\]  
The derived model, in the form of Equation (6), has extensively been used to describe the earthquake dynamics of local and regional seismicity, (e.g., [75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86]). In comparison to the Gutenberg-Richter (G-R) scaling law, the FA model provides a reasonable explanation of recorded earthquake magnitudes over a wider range of scales, while the \(b\)-value may be obtained as a special case for values over a certain threshold magnitude [75.86] as:  
\[b=\frac{(2-q_{E})}{(q_{E}-1)}\]  
The result of the application of the NESP model (Equation (6)) to the observed cumulative distributions \(N\)(\(>\)\(M\)) of earthquake magnitudes for the foreshock and aftershock sequences of the Mw = 6.0 mainshock, as well as for the NE and SW spatial clusters, are shown in Figure 15. The model provides a good fit to the observed distributions over the entire range of magnitudes, for the model parameters referred to in Table 6 and Figure 15. The greater \(q_{m}\) value for the SW cluster indicates greater tectonic instability in this region where the mainshock and the major aftershocks occurred. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(N\) & \(M_{c}\) & \(a_{m}\) & \(q_{m}\) & \(\tau_{0}\) & \(q_{\tau}\) \\ \hline Foreshocks & 410 & 2.8 & \(3005\pm 734\) & \(1.46\pm 0.02\) & \(732.2\pm 47.9\) & \(1.72\pm 0.11\) \\ Aftershocks & 4465 & 2.5 & \(384\pm 99\) & \(1.50\pm 0.01\) & \(69.2\pm 12.3\) & \(1.78\pm 0.09\) \\ (both NE and SW clusters) & 1815 & 2.5 & \(518\pm 84\) & \(1.43\pm 0.01\) & \(204.9\pm 12.4\) & \(1.81\pm 0.12\) \\ SW cluster & 2431 & 2.5 & \(409\pm 101\) & \(1.53\pm 0.01\) & \(61.8\pm 4.3\) & \(2.16\pm 0.17\) \\ \hline \end{tabular}
\end{table} 
Table 6: Parameter values for the foreshock and aftershock sequences in Arkalochori, as well as for the NE and SW aftershock clusters. \(N\) is the number of events (with \(M\geq M_{c}\)). Mc the magnitude of completeness, \(a_{m}\), \(q_{m}\) the parameters of the NESP model (Equation (6)) and \(\tau_{0}\), \(q_{\tau}\) the parameters of the \(q\)-exponential function for the inter-event time distribution (Equation (10)).