esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Solution
### Analysis of MCDA for EAR  
Working of the MCDA in a summarized form has already been presented in Section 2. In continuation of this here we present the analysis for highlighting the parameters that can be used for designing an energy-aware routing algorithm.  
Layer 1 nodes broadcast their node density value. The listener nodes of this broadcast among second layer nodes set a forwarding node table having node IDs in the precedence level of their node density. Table 2 shows the sample format of the same for a typical node, say node \"w\".  
The underlying node first selects the node from the \"\(Forwarding\ Node\ Table\)\" with highest nodal density. Since the network is homogenous and the energy consumed in cluster design is almost equal in all nodes, the energy delta \(\Delta E\) between the nodes is very small. Hence, all the three nodes of layer 1 (as shown in Table 2) have almost same the energy level. In view of this, the highest degree node is selected first as FN from this Forwarding Node Set (FNS). Higher node degree means more neighbors and so the a big FNS is related to it to share the load of each other and also more nodes to tolerate any expected node faults. More-over, the rotation of FN designation is also easy. For the process of FN rotation, we introduce a two-level threshold strategy. The first level, Load Balancing Threshold, triggers the load balancing process and the second level, Role Transfer Threshold, is for transferring the role of being FN to some other suitable node. We are using almost the same technique in the rotation of CHs in subsequent tiers of our multilayer clustered network architecture. The CHs of the first clustered layer (_i.e._, second layer of the network) forward their data packets to the selected nodes of the first layer. These collected packets from the second layer nodes are directly sent to the BS by the first layer nodes. In the same way, the CHs of the second clustered layer (third network layer) have decision maker (DM) nodes that are in their communication range. Data is sent to these nodes to further pass it on the way to a BS. The same data packet communication fashion is followed by all the CHs of subsequent layers in order to make the data packets reach the BS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decision Maker Candidate** & **Node ID from Layer 1 Nodes** & **Node Density** \\ \hline Y & 7 \\ U & 7 \\ Z & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forwarding Node Table at node “w”.
## 3 Database of Water Content and Hyperspectral Information
### Data Acquisition Method
#### 3.1.3. Input Coordinates to Set Shooting Range of Hyperspectral Camera and Drone Path  
The four-point coordinates previously acquired through GNSS surveying were input to the hyperspectral camera. Here, the hyperspectral camera was the Shark microHSI 410 (Corning, Corning, NY, USA), which is capable of measuring reflectance in units of at least 4 nm in the wavelength range of 400 to 1000 nm. The bands related to water absorption are generally known as the SWIR (Short Wavelength InfraRed, wavelength of  
Figure 4. Preparation of data extraction: (**a**) Number marking through spray lacquer; (**b**) Installation of calibration tarps.  
Figure 3. Preparation of data acquisition: (**a**) Determination of area; (**b**) GNSS survey.  
900-2500 nm) region, such as 930, 1450, 1950, and 2500 nm. However, according to [PERSON] and [PERSON] [24], in the case of materials made of irregular soil particles such as sand, there is no significant difference in the SWIR region when water is present, and the difference in reflectance with water content is clearly visible in the VNIR (Visible and Near-InfraRed) region of 400-1000 nm. Therefore, in this study, a hyperspectral camera in the VNIR region was used.  
The measurement principle is that when a hyperspectral camera exists within the input coordinate area, as shown in Figure 5a, imaging is carried out. However, if it is outside the range of the area, imaging does not proceed. Therefore, the drone's path must be set to allow only simple movement within the shooting coordinates, and takeoff, landing, departure, and turning points must not exist within the range.  
Takeoff, landing, departure, arrival, and turning points were determined by UGCS, the drone's automatic navigation program, as shown in Figure 5b. First, the drone takes off outside the shooting range and arrives at the starting coordinates so that it can pass through the center of the shooting range. Afterwards, a straight flight is performed up to the turning coordinate, and the drone moves to the same arrival coordinate as the departure coordinate. Finally, the flight ends after arriving at the set landing coordinates, and no problems occur even if the takeoff and landing coordinates are the same. Here, the turning point was set for round-trip imaging to check whether the hyperspectral information had the same value at the same measured location. The coordinate system was WGS-84, which most commonly uses latitude and longitude. Compared to the similar GRS80 coordinate system, the latitude and longitude of WGS-84 coordinates are easy to input into the UGCS program. The advantage is that accuracy improves depending on the number of decimal places.
# Extending Cooling Rate Performance of Fast Scanning Chip Calorimetry by Liquid Droplet Cooling
## 4 Discussion
The heating curves of PBN in Figure 10b can be interpreted following an analysis scheme provided previously [39], using the Mettler Toledo Flash DSC. Compared to [39], a higher maximum cooling rate was achieved with the same sensor, but by using it in combination with self-made FSC electronics [20,30]. PBN crystallization is a multi-step process involving the intermediate formation of a liquid-crystalline phase, transforming into crystals. With increasing cooling rate, first crystallization and then also liquid-crystal formation is suppressed; at even higher rates, crystal nucleation is also inhibited. Depending on the frozen-in state of the structure, when cooling to below the (similar) glass transition temperatures of the melt and the liquid-crystalline phase (around 370 K), on subsequent heating, formation of liquid crystals (slightly above \(T_{\text{g}}\)) and crystals from the  
Figure 11: Comparison of the cooling rate dependence of the crystallization peak maximum temperatures of HDPE, cooled using standard FSC with a smallest sensor at \(T_{\text{t}}\) = 83 K and with the droplet cooling on a medium-sized sensor at room temperature (\(T_{\text{t}}\)). The sensors are shown on the right. Figure 12: Temperature gradients in polymer vs. metal systems, caused by different thermal contact resistances at the interfaces involved. Thermal resistance between sensor, sample and droplet are designated R\({}_{\text{S-S}}\) and R\({}_{\text{D-S}}\), respectively. liquid-crystalline phase are observed. More details, although not the subject of the present study, can be found in earlier works [40, 41]. The strong cooling rate dependence shown in Figure 10b highlights the need for an extended cooling rate range, simultaneously with a detailed analysis of the consecutive heating scan at a constant, much lower scanning rate.
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 3 Phytomicrobiome and Pesticide Biodegradation
This is because the microorganisms are not very mobile in the soil, and hence, they always show different antagonistic/mutual relationships with other microbes, especially nematodes, creating a double critical problem when spreading the target microbes around the root zone [92]. In this context, inoculum density and inoculum methods are important to obtain desired results. Seed coating, seed priming, root colonization, foliar application, and soil drenching are the popular methods of microbial inoculation. [PERSON] et al. (2022) observed that prolonged seed coating could efficiently boost the colonization of the endophytic bacterium _C. pinensis_ in cereal crops [93]. On the contrary, Inoculation of _Orthobactrum_ spp. strain DF-1 with soil resulted in complete nitenpyram insecticide degradation in both sterile and non-sterilized soil within 14 days of incubation [94]. Similarly, soil inoculation of _Tepidobacillus decaturensis_ strain ST1 degraded 200 mg kg\({}^{-1}\) of imidalcoprid insecticides completely with a half-life of 12.95 days in non-sterile soil and 18.77 days in sterile soil [95]. Therefore, proper inoculation techniques lead to the development of higher root colonization of degradable microbes, which can provide a niche for pesticide-degrading microbes in the plant microbiome.
# T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM
## 3 Technology of the System
### Discretization of the Model  
The position and orientation recognition of the free-form surface workpiece is an important function provided by the intelligent CMM. The specific requirement is to be able to quickly recognize the different free surface workpieces in the detection space, including their position and orientation. The unified model established in Section 3.1 is discretized to reduce the storage space required. A point cloud is generated based on the T-spline surface formula. The recognition is realized by registration of the generated point cloud and extracted point cloud. The center of gravity coordinates and eigenvector direction are aligned via principle component analysis (PCA). Point cloud registration is achieved using the ICP algorithm.  
This paper uses the T-spline surface formula to generate the point cloud whose adjacent distance is not larger than a specified value. The conversion between the two coordinate systems is achieved via point cloud registration. After the registration, if the maximum distance (\(D_{max}\)) between the corresponding points is less than a certain value, the measured workpiece and the surface model are considered to be the same.  
The following formula provides the mapping of surface points to control points  
\[E_{map}(\mathrm{p}_{i})=S(s_{i},t_{i})-\mathrm{p}_{i},i=1,2,3\cdots \tag{7}\]  
where (\(s_{i}\),\(t_{i}\)) are the coordinates of the pre-image point in the parameter field to the corresponding control point and \(p_{i}\) is the surface mapping point. \(p_{i}\) is solved for using the least squares method to minimize the energy function.  
As shown in Figure 5, a knot is inserted based on the quad-tree split. When the distance between the surface mapping points, which correspond to knots \(P_{1}\)'\(P_{2}\)'\(P_{3}\)'\(P_{4}\)' in the parameter domain, is greater than a certain value, the parameter space is split and knots \(P_{5}\)'\(P_{6}\)'\(P_{7}\)'\(P_{8}\)'\(P_{9}\)' are inserted. Then, the corresponding surface mapping point and adjacent distance are calculated. When the adjacent distance is less than a certain value, the quad-tree division is stopped, and finally, all of the mapping points are saved as the final matching point cloud.  
The adjacent distance of the generated points is less a certain value, which ensures a certain degree of accuracy and saves storage space by releasing dynamic data. Compared with other methods based on software, such as Unigraphics NX (Siemens PLM software, Plano, TX, USA) generating the point cloud, this method can set the \(D_{max}\) to the nearest point, the overall generation, without human intervention, reducing the software storage space required for the entire system.  
Figure 5: According to the distance in real space the point cloud is generated.
## 3 Numerical Simulation Analysis
### Algorithm Accuracy  
This section focuses on comparing the reconstruction error of the different algorithms in the absence of noise. Legendre polynomials are a complete set of orthogonal polynomials that describe a rectangular surface. The first to the 90 th Legendre polynomials are used as the basis functions to simulate the wavefront. In each simulation process, the simulated wavefront is represented by only one Legendre polynomial with a coefficient of 1. The reconstruction range of the entire region is a square region with dimensions ranging from 1 to 1 mm in the \(x\) and \(y\) dimensions, discretized into a 100 \(\times\) 100 sub-aperture grid. The reconstructed wavefront \(W\) contains the following reconstruction errors:  
\[\Delta W=W-W_{0}, \tag{15}\]  
where \(W_{0}\) is the ideal wavefront value. The relative root-mean-square reconstruction error \(R\) is applied to evaluate the algorithm error. \(R\) is defined as  
\[R=\sqrt{\frac{\Delta W^{2}}{{W_{0}}^{2}}}. \tag{16}\]  
Figure 5 shows a comparison of the reconstruction errors between the proposed method (FA) and traditional wavefront reconstruction methods (PA, BA, ZA) without considering noise. Due to the relatively simple surface shape represented by the first 15 terms of the Legendre polynomial expansion, the three improved algorithms (FA, BA, ZA) have similar relative reconstruction errors \(R\). However, beyond the 15 th term, the FA, based on truncated Taylor expansion, has a smaller truncation error, resulting in higher reconstruction accuracy. From the simulation results, it can be seen that, compared with existing hybrid reconstruction methods or the multi-directional reconstruction algorithm (BA), the relative reconstruction error value \(R\) of the proposed subregion multi-directional slope and curvature mixed wavefront reconstruction method (FA) is reduced by about 4 orders of magnitude. Compared with the ZA, it also has significant advantages in accuracy for certain types of reconstructed surfaces, and the reconstruction accuracy for other surface types is at least comparable to that of the existing algorithms.  
Figure 5: Comparison of relative reconstruction errors among methods for 1–90 th term Legendre polynomials in the ideal case.
## 4 Conclusions  
This paper presented a laboratory study on the rheological, spectroscopic, and chemical characterization of asphalt binders modified with GP and PCM. Different binders--control, PG 70-28, 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS binders--were assessed using a viscometer, a dynamic shear rheometer (DSR), Fourier transform infrared spectroscopy (FTIR), environmental scanning electron microscopy (ESEM), a thermogravimetric analyzer (TGA), and differential scanning calorimetry (DSC). A few conclusions and recommendations were drawn from the experimental results:  
* The maximum evaporation temperature of the PCM obtained from the TGA analysis was low, which made it difficult to maintain the quantity of PCM in the asphalt mix to achieve its anticipated benefits. Adding PCM to HMA was not applicable. Alternatively, to mitigate the risk of evaporation of the PCM during the production of asphalt mixes, it would be more beneficial to use the PCM with warm and half-warm mix asphalts (WMA and HWMA) or cold mix asphalt (CMA).
* The evaporation temperature for the GPCM was higher than the PCM. Hence, the GPCM could be used in HMA.
* The viscosity analysis showed that the viscosity of PCM- and GPCM- modified binders significantly decreased compared to the control binder. This reduction indicated that PCM and GPCM additives have a softening effect on the asphalt binder.
* The observations obtained from the ESEM indicated that adding PCM and GPCM resulted in an increase in the width of the fibril sizes of the modified binders, which indicated that the additives had a softening effect on the asphalt binders.  
Figure 14: Black Space curves for all binders.  
* The DSC results showed that the 7%GPCM binder released heat flow less than that of the 5%PCM binder, which could negatively impact the thermal properties of the PCM in the asphalt binder. This could be attributed to the low thermal conductivity of the GP.
* For the DSR rheological test analysis, it was concluded that 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM, and 7%GPCM exhibited similar overall properties to the control binder. However, the results demonstrated that the GPCM significantly impacted the stiffness of the modified binder at intermediate temperatures.  
Conceptualization, H.A. and H.B.; methodology, H.A. and H.B.; formal analysis, H.A.; investigation, H.A.; resources, H.B.; data curation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, H.B.; visualization, H.A.; supervision, H.B.; project administration, H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) [NSERC Discovery Grant--RGPIN-2022-03743].  
Not applicable.  
The authors declare no conflict of interest.
# Simultaneous Prevention of Rotational and Translational Slip for a Humanoid Robot
## 1 Introduction
Furthermore, most of these previous strategies, such as inverted pendulum model or inverted pendulum flywheel model [12; 13; 14; 19], utilize the simplified model. In these models, a humanoid robot is regarded as a mass point so that the influences of the swing leg are ignored. However, the effect of the swing leg is a significant matter in slip prevention. In order to overcome the above deficiencies, this paper presents a simultaneous prevention for rotational and translational slip during humanoid walking. The main contributions are as follows:  
1. A three mass model is utilized to improve the accuracy of the dynamic model for slip prevention. It takes into account the effect of swing leg on robot slip. 2. A bionic walking pattern generator by mimicking the yaw moment compensation mechanism of humans is presented for rotational slip prevention. It effectively reduces the yaw moment with the optimized trajectories of swing arm and rotational waist based on the three mass model. 3. A novel reaction force ratio reduction control using CoM (center of mass) acceleration compensation is proposed for translational slip prevention. It rapidly makes the ratio of tangential force to normal reaction force smaller than the coefficient of the ground friction. The rest of the paper is organized as follows. Section 2 introduces the dynamic model of slip prevention. Section 3 analyzes the yaw moment compensation mechanism of human walking and then presents a human-inspired walking pattern generator for rotational slip prevention. Section 4 provides a novel reaction force ratio reduction controller for translational slip prevention. Section 5 demonstrates the effectiveness of the proposed slip prevention methods with simulations and experiments. Section 6 concludes the paper.
### Contribution  
To our knowledge, this work is unique in achieving a significantly better prediction of the Signal-to-Noise Ratio (SNR) of EHF signals using Liquid Time-Constant Networks. Explainability and interpretability play an important role in machine learning [28]. This work is also novel in applying Symbolic Regression to fit a mathematical expression to SNR values of a simulated EHF wireless system for interoperability. The core research contributions can be summarized as follows:  
* Quantitative demonstration of substantial improvement in multi-link prediction for mmWave wireless communication systems using Liquid Time-Constant Networks (LTC) over conventional methods such as using Long Short-Term Memory.
* Interpretation of the SNR values of mmWave signal using Symbolic Regression.  
The rest of the paper is organized as follows. Section 2 details the approach followed for the experiments. It briefly explains the concepts, the dataset used, how it was generated, and the setup for Symbolic Regression. Numerical experiments, including the hyperparameter settings, and results are discussed in Section 3. Section 4 discusses the results from the experiments and the conclusions are included in Section 5.