esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Requirements and Challenges of the Smart Grid
### Reliability, Robustness and Noise Resilience  
Electronic devices may become inoperative or fail because of the presence of very high electromagnetic disturbances nearby as explored in [21]. As a result, a malfunctioning power asset can be not identified as such, provoking partial (isolated) outage or overall system failures which can affect larger regions and a higher number of consumers due to the cascading effect [22]--as it happened in the aforementioned European blackout. Therefore, the endurance of the WSN devices and a prompt detection of the failing ones is a must to prevent malfunctioning power assets from impoverishing the capacity of the power network. In this regard and from a global perspective, the correct operation of the whole information network must be guaranteed during the entire lifespan, providing specific mechanisms to resist and/or recover from harsh propagation conditions, severe weather conditions, strong electromagnetic interferences, equipment degradation, _etc._  
From a technological point of view, it is a common denominator of any power network the presence of high electromagnetic disturbances which strongly deteriorate the performance of any wireless network and, in particular, of a WSN deployment. These high electromagnetic disturbances along with the absence of unlimited power sources are two of the most important concerns for a WSN operating in a power grid environment [21; 23]. Our SENSED-SG is engineered having taken these effects into account, thus offering better network performance in comparison with other wireless technologies (e.g., Wi-Fi or Wi-MAX).  
The harsh propagation conditions found in power grid environments are mainly caused by:  
* Electromagnetic interferences caused by current lines and power assets in general.
* Multiple metallic structures that may reflect and distort waves and cause noise-cancellation phenomena.
* Multiple objects blocking line of sight.
* Other communication systems working in the same frequency such as Wi-Fi, Bluetooth, Cordless Phones, _etc._ (mainly in substations found in industrial areas).  
Although many current systems have some mechanisms to deal with these phenomena (DSSS techniques, MIMO-antennas, _etc._), further efforts are needed to improve communication quality in such harsh environments. SENSED-SG makes use of many of the well-known techniques (noise-tolerant modulation techniques, timed-out retransmissions, use of the least-utilized frequency channel, _etc._) and implement other novel solutions as explored in the following sections.  
Another aspect to consider is the failure of a particular WSN node. Under these circumstances, the network topology may change causing neighboring nodes of the faulty node to seek alternative routes to the central station. Consequently, the routing protocol must provide explicit mechanisms to refresh and discard any possible corrupted route.
## 3 Results
### Osmotic Dehydration of PEF Pre-Treated Tomatoes
#### 3.2.3 Objective Firmness and Color of PEF Treated Tomatoes during OD  
The evolution of objective firmness and total color difference \(\Delta E\) of untreated and PEF-treated samples during OD is presented in Figure 3c,d, respectively. Firmness values are expressed as relative firmness compared to the untreated sample at 0 min of osmotic dehydration. Although water is lost during OD and pectic substances form gels with calcium ions from the osmotic medium, both untreated and PEF treated samples exhibited a decrease in objective firmness during OD. For a treatment at 300 pulses and an OD time exceeding 60 min, firmness has decreased by 50% compared to the untreated sample. Color difference is calculated with regards to the untreated sample at OD time 0 min. A perceivable color difference is achieved for the untreated sample as OD progresses, attributed to the increase in redness (_a_ parameter) due to water loss. PEF-treated samples exhibited an almost constant color difference immediately after processing, which is also maintained during OD, regardless of processing intensity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{\(D_{\textit{eff}}\)} & \multicolumn{2}{c}{\(D_{\textit{eff}}\)} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**DEI**} \\  & **Water Loss** & & & & \\  & **(m\({}^{2}\)/s \(\cdot\) 10\({}^{-10}\))** & & & & \\ \hline Control & 5.17 \(\pm\) 0.69 \({}^{\text{a}}\) & 0.942 & 3.82 \(\pm\) 0.42 \({}^{\text{a}}\) & 0.899 & 1.35 \(\pm\) 0.03 \({}^{\text{a}}\) \\
20 pulses & 6.16 \(\pm\) 0.67 \({}^{\text{ab}}\) & 0.945 & 4.10 \(\pm\) 0.35 \({}^{\text{ab}}\) & 0.855 & 1.46 \(\pm\) 0.04 \({}^{\text{b}}\) \\
50 pulses & 6.75 \(\pm\) 0.52 \({}^{\text{bc}}\) & 0.972 & 4.64 \(\pm\) 0.39 \({}^{\text{b}}\) & 0.896 & 1.50 \(\pm\) 0.01 \({}^{\text{b}}\) \\
100 pulses & 7.91 \(\pm\) 0.62 \({}^{\text{c}}\) & 0.982 & 4.52 \(\pm\) 0.40 \({}^{\text{b}}\) & 0.820 & 1.62 \(\pm\) 0.02 \({}^{\text{c}}\) \\
300 pulses & 7.40 \(\pm\) 0.54 \({}^{\text{c}}\) & 0.951 & 4.58 \(\pm\) 0.43 \({}^{\text{b}}\) & 0.871 & 1.75 \(\pm\) 0.03 \({}^{\text{d}}\) \\ \hline \end{tabular} Different superscript letters indicate significant differences between means \(D_{\textit{eff}}\pm\) standard deviation and \(DEI\pm\) standard deviation as calculated by [PERSON]’s multiple range test for a significance level of \(p=0.05\).  
\end{table}
Table 1: Effective moisture diffusion (\(D_{\textit{eff}}\) for Water Loss) and solids diffusion (\(D_{\textit{eff}}\) for Solids Gain) coefficients, and dehydration efficiency index (\(DEI\)) for different PEF treatment conditions as calculated from fitting [PERSON]’s law to the experimental data.
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 2 Materials and Methods
### Workflow Design  
We implemented the necessary functions in the management system following the standard field geographic information collection process. Additionally, we designed four modes for data collection and verification to allow users to flexibly assign personnel and complete tasks. Figure 4 illustrates the detailed workflow of field data collection supported by our platform.  
Figure 3: XML toolkit structure.  
As shown in Figure 4, the platform's workflow is divided into six steps: task configuration, task allocation, field data collection, field data review, backend data quality review, and data visualization and analysis. During the task configuration phase, we use the XML toolkit and XML schema to enable users to quickly customize collection pages based on specific scenarios. In the task allocation phase, we implemented a fast task area division and allocation method using the Geohash coding algorithm. This method divides the task area into appropriately sized vector tiles based on the selected partition level. The detailed process is as follows:  
Firstly, this method obtains the minimum bounding rectangle of the task area. Secondly, according to the chosen partition level, the size of the vector tile corresponding to the Geohash encoding is confirmed. Thirdly, obtain all vector tiles covering the minimum bounding rectangle. After eliminating the tiles that do not intersect with the task region, the remaining tiles are the task sub-regions. Finally, assign the collectors to each tile in order.  
For the field data collection and field data review phase, we have designed multiple flexible working modes, including unrestricted collection, partitioned collection, partitioned review, and synchronized collection and review. The management system will decide whether to execute the collection or verification process and how to assign task personnel according to different modes. In the data visualization and analysis phase, we use spatial indexing technology and the Geohash algorithm in the database to facilitate rapid access to spatial data. We employ the Geohash clustering algorithm, along with window size, lower-left corner coordinates, and current scale, to enable fast filtering and clustering of the displayed data.
## 2 Methods  
Standard K-means clustering is a method of vector quantization that aims to partition \(n\) observations into \(k\) clusters in which each observation belongs to the cluster with the nearest distance (cluster centers or cluster centroid) [27]. In the design of the satellite beam layout, the task is also to partition \(n\) objects (LEO satellites) into \(k\) clusters (spot beams). However, the evaluation criterion is not only to minimize the within-cluster variances of squared Euclidean distance, but also balance the number of objects in each beam [28; 29]. It is crucial to mitigate the traffic jam induced by overprovisioned beams with overload objects, or the waste of resources caused by unprofitable beams with few requirements.  
It is worth noting that K-means with a fixed number of cluster members was studied previously [30; 31]. The data samples were clustered by standard K-means first, and the number of group elements was adjusted locally by employing a greedy strategy. In that method, the predefined fixed number of cluster members is necessary to guide the following recruitment or discard process on each cluster. The situation is partially different from the beam spot allocation process since the multi-beam satellite system has a certain amount of tolerance on the variance of object number per beam. The optimization process  
Figure 1: Schematic of a GEO data relay satellite covering the target LEO satellite constellation with spot beams. The LEO satellites are located within a field of view (FOV) of approximately \(\pm 11^{\circ}\). The number in the center of each circle indicates the objective LEO satellites in the coverage of the certain beam (Rearth is the radius of the Earth).  
should also take the size of the cluster into account, which determines the uniformity of the physical beam width. In other words, we should not only chase the uniformity of beam occupation, but also balance the uniformity of the beam width. That is why we cannot offer a certain predefined fixed number of cluster members before the clustering process. In this work, we propose a modified K-means algorithm to solve the aforementioned problem with introduced parameters to guarantee the obligatory minimum number per cluster and globally optimize the number of objects in each beam without locally implementing recruitment or the discard process. The tradeoff between the uniformity of the beam occupation and the uniformity of the beam width is also considered and discussed in the following sections.  
Figure 2 illustrates the step-by-step process proposed in this paper. The whole process is introduced in detail in the following subsections.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
## 2 Numerical Setup
### Mesh Verification and Validation  
Figure 2 presents the mesh details of the rotor stage. A multi-block structural grid was generated for the computational domain using the commercial software ICEM CFD 16.1. The grids near the wall were refined to meet the requirements of the RNG \(k\)-\(\varepsilon\) turbulence model. Three kinds of mesh density in all were used to carry out the mesh sensitivity study and confirm that the results were not dependent on the mesh size. Figure 3 shows the variation of the mass flow rate and stage torque with the element number. It can be seen that the results with 3.67 million elements coincided with the results with 7.30 million elements. Therefore, the rest of the computations were conducted with 3.67 million elements. Furthermore, Figure 4 shows the comparison of the isentropic Mach number distribution between the numerical results using the RNG k-\(\varepsilon\) turbulence model and the experimental data. The isentropic Mach number \(Ma_{is}\) is defined as:  
\[Ma_{is}=\sqrt{\left(\left(\frac{P_{t,0}}{P_{s}}\right)^{\frac{\gamma-1}{\gamma} }-1\right)\frac{2}{\gamma-1}}, \tag{2}\]  
where \(P_{s}\) is the static pressure at each location and \(\gamma\) is the ratio of specific heats (\(\gamma=1.4\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Values** \\ \hline Solution type & - & Transient \\ Working fluid & - & Air ideal gas \\ Inlet total pressure, \(P_{t,0}\) & kPa & 243.5 \\ Inlet total temperature & K & 420 \\ Outlet static pressure, \(P_{s,1}\) & kPa & 101.325 \\ Turbulence intensity & \% & 4 \\ Wall temperature & K & 302 \\ Rotational speed, \(n\) & r/min & 10000 \\ Eccentric ratio (y-direction), \(e/t_{m}\) & - & 0.1, 0.3, 0.5, 0.7 \\ Timestep & s & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the boundary conditions.  
Figure 2: Mesh details of the computational domain.  
It can be seen that the numerical results agree well with the experimental data at most streamwise locations. Additionally, the results here are consistent with that in [PERSON] et al. [22], which is also based on the same turbine rig. Therefore, the numerical method with the RNG \(k\)-\(\varepsilon\) model is validated as reliable.
## 3 Results
### Physical Properties
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & **OM** & **BD** & \(\mathbf{K_{sat}}\) & **MET** & **VM** & **N** & **K** & **P** & **RD** & **LA** & **CHL** & **AY** & **BY** & **DM** & **OIL** & **HI** \\ \hline OM & & \(-0.87\) & 0.96 & \(-0.84\) & 0.91 & 0.64 & 0.83 & 0.47 & 0.78 & 0.16 & 0.54 & 0.75 & 0.38 & 0.90 & 0.59 & \(-0.03\) \\ BD & \(-0.84\) & & \(-0.79\) & 0.80 & \(-0.87\) & \(-0.57\) & \(-0.72\) & \(-0.50\) & \(-0.86\) & \(-0.09\) & \(-0.71\) & \(-0.77\) & \(-0.27\) & \(-0.99\) & \(-0.37\) & \(-0.09\) \\ Ksat & 0.84 & \(-0.67\) & & \(-0.83\) & 0.85 & 0.69 & 0.84 & 0.46 & 0.73 & 0.17 & 0.52 & 0.73 & 0.48 & 0.82 & 0.56 & \(-0.12\) \\ MET & \(-0.91\) & 0.87 & \(-0.77\) & & \(-0.77\) & & \(-0.73\) & \(-0.85\) & \(-0.51\) & \(-0.77\) & \(-0.27\) & \(-0.66\) & \(-0.92\) & \(-0.23\) & \(-0.81\) & \(-0.34\) & \(-0.25\) \\ VM & 0.88 & \(-0.64\) & 0.95 & \(-0.78\) & 0.36 & 0.59 & 0.16 & 0.87 & 0.14 & 0.41 & 0.57 & 0.35 & 0.88 & 0.27 & \(-0.15\) \\ N & 0.66 & \(-0.67\) & 0.87 & \(-0.76\) & 0.76 & & 0.79 & 0.77 & 0.31 & 0.20 & 0.56 & 0.79 & 0.48 & 0.61 & 0.57 & 0.13 \\ K & 0.92 & \(-0.83\) & 0.86 & \(-0.95\) & 0.84 & 0.82 & & 0.72 & 0.67 & 0.23 & 0.79 & 0.93 & 0.12 & 0.75 & 0.69 & 0.32 \\ P & 0.31 & \(-0.57\) & 0.32 & \(-0.49\) & 0.23 & 0.55 & 0.57 & & 0.16 & \(-0.09\) & 0.66 & 0.71 & 0.16 & 0.52 & 0.71 & 0.35 \\ RD & 0.61 & \(-0.80\) & 0.41 & \(-0.75\) & 0.33 & 0.58 & 0.59 & 0.37 & & 0.25 & 0.67 & 0.71 & \(-0.02\) & 0.82 & 0.13 & 0.19 \\ LA & 0.91 & \(-0.81\) & 0.81 & \(-0.94\) & 0.85 & 0.79 & 0.94 & 0.58 & 0.62 & & \(-0.07\) & 0.21 & \(-0.09\) & 0.17 & 0.02 & 0.37 \\ CHL & 0.81 & \(-0.75\) & 0.58 & \(-0.83\) & 0.60 & 0.53 & 0.87 & 0.65 & 0.52 & 0.84 & & 0.85 & \(-0.15\) & 0.65 & 0.35 & 0.44 \\ AV & 0.75 & \(-0.73\) & 0.81 & \(-0.80\) & 0.70 & 0.87 & 0.90 & 0.73 & 0.57 & 0.86 & 0.81 & & 0.01 & 0.76 & 0.45 & 0.49 \\ BY & 0.57 & \(-0.45\) & 0.48 & \(-0.62\) & 0.52 & 0.43 & 0.75 & 0.68 & 0.09 & 0.68 & 0.84 & 0.69 & & 0.32 & 0.25 & \(-0.75\) \\ DM & 0.81 & \(-0.98\) & 0.62 & \(-0.82\) & 0.62 & 0.61 & 0.75 & 0.54 & 0.77 & 0.78 & 0.70 & 0.66 & 0.38 & & 0.46 & 0.06 \\ OHL & 0.60 & \(-0.63\) & 0.67 & \(-0.66\) & 0.68 & 0.78 & 0.66 & 0.62 & 0.50 & 0.81 & 0.55 & 0.75 & 0.41 & 0.68 & & 0.01 \\ HI & 0.29 & \(-0.49\) & 0.18 & \(-0.28\) & 0.05 & 0.23 & 0.12 & \(-0.06\) & 0.76 & 0.14 & 0.01 & 0.15 & \(-0.46\) & 0.52 & 0.18 \\ \hline \end{tabular} Critical values of correlation \(p<0.05\) and \(p<0.01\) are 0.65 and 0.85, respectively. The coefficients above the diagonal line are related to the first year and the numbers below the diagonal line are related to the second year. Ksat: saturated hydraulic conductivity, VM: volumetric moisture, N: nitrogen concentration, P: phosphorus concentration, K: potassium concentration, RD: root depth, CHL: chlorophyll content, LA: leaf area, DM: day to maturity, BY: biological yield, OIL: achene oil percentage, HI: harvest index.  
\end{table} 
Table 3: Correlation coefficients among some physicochemical properties of soil and morphological traits of sunflower.
# Apollonian Packing of Circles within Ellipses
## 2 Materials and Methods  
### The Descartes Formula  
#### 2.1.1 The Descartes Formula  
The Descartes theorem [7] applies to any configuration of four mutually tangent circles in which the interiors of all circles are disjoint; in such case, the four circles are said to be arranged in a so-called Descartes configuration.  
The theorem states that any Descartes configuration of four mutually tangent circles with curvatures \(k_{j}\) satisfies  
\[\sum_{j=1}^{4}(k_{i})^{2}=\frac{1}{2}\left(\sum_{j=1}^{4}k_{j}\right)^{2}, \tag{1}\]  
where \(k_{j}=1/r_{j}\) (\(j=1,2,3,4\)) are the curvatures of the circles and \(r_{j}\) are the radii of the circles.  
The theorem in its complex form [6] states that any Descartes configuration of four mutually tangent circles with curvatures \(k_{j}\) and centers \(z_{j}=x_{j}+iy_{j}\) satisfies  
\[\sum_{j=1}^{4}(k_{i}z_{i})^{2}=\frac{1}{2}\left(\sum_{j=1}^{4}k_{j}z_{j} \right)^{2}. \tag{2}\]  
As stated in [7], the theorem may be extended to include the case in which three of the circles are internally tangent to the fourth one by defining the concept of oriented circle with signed values of radius and curvature and by characterizing the fourth circle, which encloses the other three, with negative values of such parameters. In this approach, the theorem is applicable to any oriented Descartes configuration defined by either one of the following statements: (i) the interiors of all oriented circles are disjoint; (ii) the interiors of all oriented circles are disjoint when all orientations are reversed.  
By directly solving the quadratic equations that constitute the thesis of the theorem [7], we obtain the formula expressing the curvature of the fourth circle in terms of the curvatures of the remaining circles in the Descartes configuration.  
\[k_{4\;,1,2}=k_{1}+k_{2}+k_{3}\pm 2\sqrt{k_{1}k_{2}+k_{1}k_{3}+k_{2}k_{3}}\;. \tag{3}\]  
The two algebraic solutions, corresponding to the two options for the \(\pm\) symbol, refer to the two possible solutions of the geometric problem, i.e., it is possible to construct up to two distinct circles that are tangent to three mutually tangent circles in a Descartesconfiguration. By means of a similar procedure, starting from Equation (2), it is possible to obtain a corresponding formula for circle center \(z_{4}\).  
\[z_{4\;,12}=\frac{z_{1}k_{1}+z_{2}k_{2}+z_{3}k_{3}\pm 2\sqrt{z_{1}z_{2}k_{1}k_{2}+z_ {1}z_{3}k_{1}k_{3}+z_{2}z_{3}k_{2}k_{3}}}{k_{4}}\;. \tag{4}\]  
In order to achieve correct results in the implementation of the solution formulas expressed in Equations (3) and (4), attention should be paid to the multivalued character of the square root function in the complex plane. The choice of the correct complex square root value must be carefully carried out in the eventuality of the branch-cut crossing of the square root argument in Equation (4).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Frequency Domain
#### 4.1.1 Frequency Division Multiple Access (FDMA)-Based MAC Protocol  
The FDMA technique in UWSNs splits the available frequency band into sub frequency bands and assigns each sub frequency band to a single communication node, responsible for packet data exchange [64]. As a result, the bandwidth of FDMA sub channels used in between specific nodes in the network, is smaller than the overall bandwidth of the original transmission channel. This means that we can assign different bandwidth to same node in different time for different application but within the allowed limit of the total bandwidth of the whole channel [46]. Some FDMA-based MAC protocols proposed by different research groups are discussed in [80].
# Portable and Easily-Deployable Air-Launched GPR Scanner
## 3 Results
### Testing of Antennas Exhibiting Large Displacement of the Phase Center
Thus, phase compensation is conducted as proposed in Equation (2):  
\[E_{scatt,comp}(f,x,y,z)=E_{scatt}(f,x,y,z)\exp\Bigl{(}+jk_{0}(f)d_{offset}(f) \Bigr{)}, \tag{2}\]  
where \(E_{scatt,comp}(f,x,y,z)\) is the measured field at the position \(x\),\(y\),\(z\) after applying the phaseshift given by \(k_{0}(f)d_{offset}(f)\)). Validation of the proposed method is shown in Figure 16, where a 20 cm diameter metallic box was buried at a depth of 17 cm (Figure 16a). Cuts of the recovered reflectivity are plotted in Figure 16b-d, where the buried metallic box is located at an apparent depth of 40 cm. Thus, the estimated true depth, given a relative permittivity within the [5.0, 7.0] range, was: 40 cm/([7.0, 5.0])\({}^{1/2}\) = [15.1, 17.9] cm. From Figure 16c,d) it can be noticed that the range resolution is worse than when using the Vivaldi antennas, as the LPA bandwidth is 800 MHz. This means that these antennas are not suitable for detecting shallow targets, as the air-ground reflection can mask the reflection of the buried target. However, since they work at lower frequencies, they are useful for detecting targets buried in wet soils, since the attenuation becomes too high at higher frequencies. Figure 16: Picture of the metallic box buried at 17 cm depth (**a**). Reflectivity normalized amplitude, in dB, calculated after applying the LPA phase center compensation. Cut \(z\) = \(-\)140 cm (**b**), cut \(x\) = 40 cm (**c**), cut \(y\) = \(-\)42 cm (**d**). The \(z\) = 0 position is defined at the plane where the LPA antennas are attached to the payload. Next, the metallic box was buried at a depth of 25 cm, aiming to assess if it can be detected using the LPA. Results are plotted in Figure 17a-c, where the echo appears at an apparent depth of 60 cm. With respect to the previous test, where the metallic box was buried at a depth of 17 cm, the amplitude of the reflectivity decreases from -10 dB to -13 dB. As a reference, when using the Vivaldi antennas for this case, the amplitude of the reflectivity was found to be around -18 dB. Besides, as the metallic box was buried deeper, the distance between the air-ground interface and the buried target is greater resulting in better identification of the target even though the range resolution provided by the LPA is worse than the one achieved with the Vivaldi antennas due to the narrower bandwidth. Finally, to illustrate how phase center compensation improves SAR images, Figure 17d-f shows the same cuts of Figure 17a-c without LPA phase center compensation. It can be noticed that the buried target cannot be identified, due to the high level of clutter present in the image.