esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Roughness  
To estimate surface roughness along the profiles we analyzed laser scanner data conducted with a RIEGL-LMSQ560 during the airborne survey in April 2018. The geo-located laser scanner point cloud (Latitude, Longitude, Elevation referenced to WGS84) was obtained using laser rangeand angle measurements, post processed GNSS trajectory (PPP solution using Waypoint 8.4) and attitude information (Inertial Measuring Unit: Honeywell Laseref V) within our processing scheme, following [PERSON] et al. (2016).  
Surface roughness, here defined as standard deviation of a high pass filtered laser scanner elevation model (ALS-DEM), was estimated at 1 km along track spacing. A series of 20% overlapping ALS-DEMs, each with a pixel resolution of 1 m and dimension of 1200 m \(\times\) 1200 m, were conducted from the geo-located laser scanner point cloud data using an inverse distance weighting interpolation scheme. The final ALS-DEMs were referenced to the EPSG:3413 coordinate system.  
High pass filtering (subtracting a smoothed version of the ALS-DEM using a 300 m kernel) was applied to remove large scale topographic features, as we were only interested in small scale roughness in the order of meters (e.g., sastrugis).
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 1 Introduction
### Contributions of This Study  
To address the aforementioned convergence and performance issues, we proposed an optimised VS process called Decoupled Hybrid Visual Servoing (DHVS). In the proposed method, two separate controllers combined together, one using 3D reconstruction of visual features and the other one directly using 2D information of image (detailed in Section 2.4). The main findings of the study are summarised as follows:  
* In terms of the convergence time and tracked distance, the proposed approach produces a more optimised trajectory in both the image-space and the joint space than other classical image-based, position-based and hybrid methods.
* The proposed approach produces more controllable trajectories (higher manipulability) for the robot than IBVS when tracking objects.
* In comparison to PBVS and HVS approaches, DHVS approach is less likely to lose the object from the camera Field of View (FOV).
* The VS process has been boosted by using adaptive gains.
* The effect of robot singularity is minimised by using Damped Least Square method (DLS); it helps to smooth out the discontinuities caused by the decoupling process of the image Jacobian and using adaptive gains.
* The functionality of the manipulator has been increased by defining the manipulability as secondary task.  
Figure 2 illustrates the contributions of the proposed hybrid VS approach in the image space, joint space and the interaction of these two. The proposed DHVS approach will be explored in-depth in Section 2.4, and its effectiveness will be compared with the other methods in both simulation and the real world (Section 4).  
The rest of this research is as follows: A brief overview of various visual servoing controllers has been investigated in Section 2. Section 3 explains the simulation and experimental setups designed to evaluate the performance of the proposed DHVS method. In Section 4, the behaviour and effective parameters of four VS methods have been investigated. Finally, this work is concluded in Section 5 and future works are discussed.
## 5 Data Preparation and Filtering
### Filtering of Raw Operational Data Techniques
#### 5.2.3 Fast Fourier Transform  
The Fast Fourier Transform (FFT) technique is commonly used in a wide variety of applications, including audio and image compression formats, among others. Most signals are highly compressible in the FFT domain, which represents transform scales as a function of frequency in the detection of noise to be removed. Furthermore, FFTs can accelerate the detection and filtering processes, making them useful in digital signal processing.  
In FFT, the signal was transformed from time-series to frequency domain represented as power spectral density (PSD) [62]. This PSD represents the signal's intensity magnitude where the PSD's peak represents the noise to be filtered out as depicted in Figure 14. To denoise, a threshold is set based on the optimal response to be retained. The PSD could be applied to ship operational data such as the fuel consumption recorded by mass flowmeters as the sensor measurement noise may vary irregularly, making denoising challenging, as simple filtering techniques do not perform well.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 1 Introduction
The periodic exchange of control messages for cluster formation and maintenance is termed as cluster overhead. This overhead consumes radio resources (e.g., channel bandwidth) and wastes the UAVs' energy. The lifetime of the cluster is a parameter, among others, that is used for evaluating the performance of a clustering model. The longer the lifetime of the cluster, the less will be the cluster maintenance overhead and more effective the model will be. The lifetime of the cluster depends upon the efficient selection of CHs. The performance of a network using clustering depends upon its clustering algorithm. In the literature, artificial intelligence techniques have been used to divide networks into clusters. These techniques produce more optimal results, but, the main disadvantage of these techniques is that they have high computational complexity. They take too much time to converge towards an optimal solution. In the highly dynamic environment and with the limited processing power of UAVs, computationally expensive techniques are also not suitable for clustering in FANETs. Selection of transmission power also plays a vital role in the energy consumption of UAVs. There is direct relationship between transmission power and energy consumption. Selecting a transmission power above or below an optimal value will result in more energy consumption. An optimal transmission power must be high enough to maintain good connectivity with neighbors yet low enough to avoid the wastage of energy [8]. In this paper, we propose the Energy Aware Link-based Clustering (EALC) model. EALC uses a variant of the K-Means Density [9] clustering algorithm in order to elect CHs. K-Means Density is used along with K-means for initial selection of centroids. Original K-Means Density uses only one parameter, i.e., the degree of the neighborhood, whereas EALC adds two other parameters--energylevel and distance--to the neighbors for the election of an optimal CH. EALC enhances the cluster lifetime and improves the energy consumption. EALC also saves the nodes' energy by efficiently selecting the transmission power of nodes according to the operational requirements. EALC outperforms the Ant Colony Optimization- and Grey Wolf Optimization-based clustering models in term of cluster building time, cluster lifetime and energy consumption. The rest of the paper is organized as follows: in Section 2 a review of existing clustering models is presented, Section 3 provides a detailed description of the proposed EALC, while discussion and results are given in Section 4 and in Section 5 we conclude the paper.
# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 4 Experiment and Analysis
### Training  
Experiments were conducted to partially augment the data by creating two sets of invalid triples, one set replacing the head entities in the triples with invalid entities and the other set replacing the tail entities in the triples with invalid entities. We randomly drew the same number of invalid triples from these two sets to ensure robust performance in detecting head and tail entities. The experimental initial embedding used TransE-generated entity and relation embeddings to initialize our embedding.  
The experiment adopted the training process of encoder-mapper-decoder. Firstly, the multi-hop triple information was fed into the graph attention network for encoding, and the encoding of entities and relations of the knowledge graph was obtained. Then, the entities were mapped according to the space of relations, and the mapped entity-relationship encoding was obtained. Finally, the bi-directional convolutional decoder was trained to perform the relation prediction task.  
Different parameter settings were used for different dataset experiments. (1) For the WN18 RR dataset, we used Adam to optimize all parameters, the coding layer epoch was 3600, the initial learning rate of the coding layer was set to \(5\times 10^{-6}\), the decoding layer epoch was 200, the initial learning rate of the decoding layer was set to \(1\times 10^{-5}\), the decoding layer \(batch\_size\) was 64, and both entity, and the relation embedding of the last layer was set to 200. (2) For the FB15k-237 dataset, all parameters were also optimized using Adam, the epoch of the coding layer was 3000, the initial learning rate of the coding layer was set to 0.00001, the epoch of the decoding layer was 200, the initial learning rate of the decoding layer was set to 0.000001, the \(batch\_size\) of the decoding layer was 64, and both the entity and relational embeddings of the last layer were set to 200.
###### Abstract  
The drying process is one of the oldest methods used to obtain food products that could be stored for a long time. However, drying is an energy-intensive process. Additionally, convective drying, due to the high temperature used during the process, results in loss in bioactive substances as well as nutritional value. Thus, in this research, apple slices were dried in a combined hot air-infrared air dryer with four different drying strategies and drying kinetics, internal and external mass transfer (Crank and Dincer models), and then the energy parameters were investigated. The first, second, third, and fourth strategies, respectively, include one-stage drying with a hot air (HA) or infrared energy source (IR), one stage but with two sources of hot air and infrared (HA-IR), and then there are two stages of first hot air and then infrared drying (HA+IR) and vice versa (IR+HA). According to the results, the highest effective moisture diffusion coefficient of the two Crank and Dincer models was equal to 1.49 \(\times\) 10\({}^{-9}\) and 1.55 \(\times\) 10\({}^{-8}\) m\({}^{2}\)/s, obtained in the HA70-IR750, and the lowest effective moisture diffusion coefficient was equal to 1.8 \(\times\) 10\({}^{-10}\) and 2.54 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s, obtained in IR250+HA40. The maximum (10.25%) and minimum (3.61%) energy efficiency were in the IR750 and HA55-IR250 methods, respectively. Moreover, the highest drying efficiency (12.71%) and the lowest drying efficiency (4.19%) were obtained in HA70+IR500 and HA40-IR250, respectively. The value of specific energy consumption was 15.42-51.03 (kWh/kg), the diffusion activation energy was 18.43-35.43 (kJ/mol), and the value of the specific moisture extraction rate (SMER) was in the range of 0.019-0.054 (kWh/kg). Compared to the other strategies, the second strategy (HA-IR) was better in terms of drying time and mass transfer, and the third strategy (HA+IR) was more efficient in terms of energy efficiency and drying efficiency. The infrared drying in the first strategy was better than that in the other methods in the other strategies in terms of retention of bioactive compounds.  
apple; infrared; combined dryer; drying strategy; mass transfer; bioactive compounds; energy efficiency +
Footnote †: journal: Applied Sciences
## 1 Introduction
This is, though, a very complex decision to be made by human operators, because itinvolves analyzing several network parameters for all IP flows traversing the network and then selecting the flows in a timely manner to balance the network properly [1]. In parallel to traffic-driven lightpath provisioning, much effort is being put into investigating the best approaches to establishing lightpaths and then monitoring them once they are in service so that QoS is assured with minimum cost and sufficient efficiency. This is especially the case in all-optical networks where the IP layer (or in general any digital information) is only present and the endpoints of a lightpath, which results in impairment accumulation and in major challenges to traditional performance and fault management.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction
### State-of-the-Art Forest Disturbance Monitoring with Optical Data
Such approaches have been tested for forest disturbance mapping in the Republic of the Congo [14; 43] and for mapping forest fire-affected areas in Indonesia [44]. A recent research study at a tropical forest site in Bolivia showed that the Sentinel-1 time series data can provide much more timely detections than Landsat and ALOS PALSAR-2 data [45]. Most of the studies for detecting disturbances from Sentinel-1 data assume that forest disturbances are necessarily characterized by a decrease in C-band backscatter within the disturbed area, which does not always seem to be the case [46]. Therefore, a new method that uses the geometric effects of SAR shadowing to detect forest change areas from Sentinel-1 SAR data has been proposed by [47]. Depending on viewing geometry, SAR shadowing occurs at forest edges and new forest edges thus result in new SAR shadows that can be easily detected in the time series. Ascending and descending orbit data are needed to detect new shadows on two sides of the forest disturbance patch. The entire disturbed area is then reconstructed from the newly detected shadows using a convex envelope boundary operator. This new method is used for the SAR based forest disturbance detection in this study and Section 4 describes it in more detail.
## 3 Results and Discussion
### Investigation of Peptide Coupling Using UV-Visible Spectroscopy
#### 3.1.5 Quantitative Determination of DPP-IV/CD26 Activity  
Results of the Au NP assay revealed that the color change was directly proportional to the DPP-IV activity present. The assay provided a quantitative estimation of the total DPP-IV activity in the range of 10-30 U/L. The ratios of spectral absorbance (A\({}_{750}\)/A\({}_{525}\)) of the reacted Au NP solutions were plotted as a function of the corresponding standard activities of DPP-IV (U/L). These two absorbance values, A\({}_{750}\) and A\({}_{525}\), were chosen to represent the relative amount of aggregated and dispersed Au NPs, respectively. The increase in the absorbance at 750 nm indicates the formation of the Au NPs clusters/aggregates, while the peak at 525 nm represents the \(\lambda_{\rm max}\) of suspended C/G Au NPs [30,31]. Figure 5 shows that the absorption ratio (A\({}_{750}\)/A\({}_{525}\)) of C/G-Au NPs increased with the activity of DPP-IV/CD26 from 10 to 30 U/L.  
Figure 4: UV-visible absorption spectra of C/G-capped Au NPs after incubation with different activities of DPP-IV/CD26. The DPP-IV/CD26 activity ranged from 10 to 30 U/L. Lower DPP-IV/CD26 activities did not induce spectral changes.  
Figure 5: Calibration curve of A\({}_{750}\)/A\({}_{525}\) versus the different activities DPP-IV incubated with C/G-capped Au NPs. Error bars represent the standard deviation (n = 3).
## 2 Definitions and Conditions for the Solution Types
For this type, angle \(\theta_{k}\) between the two links (\(a_{k}\) and \(a_{k+1}\)) is equal to \(0\) or \(\pi\)\([rad]\), i.e.,: \(\theta_{ks}=0;\ \pm\pi;\pm 2\pi;\pm 3\pi;\ldots\)_  
_The relative singularity configurations between two links are denoted with \(5_{0}\) if \(\theta_{ks}=0\); \(\pm 2\pi;\pm 4\pi;\pm 6\pi;\ldots\)--fully stretched hand, and with \(S_{\pi}\) if \(\theta_{ks}=\pi;\ \pm 3\pi;\pm 5\pi;\pm 7\pi;\ldots\)--fully folded hand._  
**Property 1**.--**Symmetry. : _If \(P_{k-1}\) and \(P_{k+1}\) are two points in the plane where the axes \(k-1\) and \(k+1\) of the robot are located, the lengths of the links are respectively \(a_{k}=const\
eq 0\), \(a_{k+1}=const\
eq 0\) and the existence of left- and right-handed solutions for both links is possible, then these two configurations for the links are symmetric with respect to a line passing through points \(P_{k-1}\) and \(P_{k+1}\), and the angles \(\theta_{kR}\) and \(\theta_{kL}\) are equal in magnitude and have opposite signs i.e., \(\theta_{kR}=-\theta_{kL}\) (Figure 5)._  
This property can easily be proved using the equality of triangles \(\Delta P_{k-1}P_{kk}P_{k+1}\equiv\Delta P_{k-1}P_{kL}P_{k+1}\). **Property 2**.--**Uniqueness. : _If the distance \(d\) between the two points \(P_{k-1}\) and \(P_{k+1}\) is_  
\[d_{P_{k-1},\ P_{k+1}}=\left|\sqrt{\left(P_{k+1,x}-P_{k-1,x}\right)^{2}+\left(P _{k+1,y}-P_{k-1,y}\right)^{2}}\right|=a_{k}+a_{k+1}\] (  
configuration or vice versa is possible only by passing through a relative singularity configuration. When the robot has joint constraints and/or obstacles in its workspace, situations are possible for which there are different types of solutions, but the transition between them cannot be realized. This fact is important for planning robot movements. We are looking for an algorithm to find the inverse kinematics of a planar robot that can be represented by the equivalent diagram from Figure 2b, and it is necessary to determine the type of solutions according to definitions 1 to 3 and take into account the joint constraints. As an addition, it can be checked for collisions of the robot links with obstacles. For the implementation of the algorithm to make sense, the robot must have at least two rotational joints. The solution to this problem is useful for the following purposes:  
* Detect and classify multiple inverse kinematics solutions for a single position and orientation of the gripper;
* Generate IK solutions for a planar trajectory in the workspace with or without changing the gripper orientation;
* Generate the configuration space and regions of it corresponding to Definitions 1-3;
* Generate the workspace and regions of it corresponding to Definitions 1-3.
## 2 Methodology
### Case Study Selection and Expert Panel  
This study was conducted at a factory in South Korea that has been utilizing quadruped robots since April 2022. The plant is densely populated with complex pipelines and equipment, with many narrow spaces and complex passageways that require frequent robot-human interaction. The quadruped robots are being used for safety patrols, equipment inspections, and monitoring of hazardous areas. Figure 2 is an easy-to-understand illustration of the case study environment.  
Figure 1: Framework for conducting research.  
Figure 2: Case study illustration.  
This factory faces unique environmental challenges, such as dense pipeline networks with elevated passages, narrow spaces inaccessible to wheeled robots, uneven surfaces and steps, and areas requiring complex maneuver. For these reasons, quadruped robots were adopted, as they can efficiently address tasks that traditional wheeled robots struggle with. This environment presents unique safety challenges for human-robot collaboration, particularly in confined spaces where robots must maintain stability while interacting with workers--conditions that differ significantly from traditional industrial settings.  
The expert panel consisted of four main groups, with the composition and qualifications of each group shown in Table 1.  
The entire expert cohort was surveyed anonymously via email, with 61 responding to a total of 161 requests in the first round, for a response rate of approximately 37%. In the subsequent round, 19 of the 61 participants from the previous round responded, for a response rate of approximately 31%. In more detail, the responses by the group were Group 1: quadruped robot operator, 5 out of 8 (approx. 62.5%); Group 2: safety management expert, 5 out of 24 (approx. 21%); Group 3: quadruped robot developer, 4 out of 12 (approx. 33%); and finally, Group 4: academic expert, 5 out of 12 (approx. 41.7%). Although the response rate and sample size may appear low by general survey standards, they are considered acceptable within the context of the Delphi method. Delphi studies prioritize qualitative insights and consensus-building among experts over large sample sizes. According to [PERSON] and [PERSON] (2004), the size of a Delphi panel is determined more by group dynamics and the ability to process results than by statistical power, with panels of 10-18 experts often producing valid outcomes [23].
## 4 Analytical Prediction of the Suppressive Impact of MRCFM on Sideband Constituents
### Analysis and Prediction of Sideband Vibration Suppression Effects  
To forecast the suppressive effect of MRCFM on vibro-acoustics, this paper develops a Simulink and JMAG multi-physics field co-simulation model. The calculation process is shown in Figure 11. Firstly, the electromagnetic force on the inner surface of the stator tooth is calculated by the two-dimensional electromagnetic model of the motor, and the nodal force is mapped into the structural model as an excitation. Finally, the vibration response of the prototype is calculated by the modal superposition method [32]. The equation of the modal superposition method can be expressed as  
\[\{F\}=[M]{\sum_{i}^{N}}\{\Phi_{i}\}\ddot{y_{i}}+[C]{\sum_{i}^{N}}\{\Phi_{i}\} \dot{y_{i}}+[K]{\sum_{i}^{N}}\{\Phi_{i}\}y_{i} \tag{15}\]  
where \([M]\), \([C]\) and \([K]\) denote the mass, damping and stiffness matrices, respectively. \(N\) denotes the superimposed modal order. \(\Phi_{i}\) denotes the \(i\)-th order modal vibration mode. \(y_{i}\) denotes the displacement in the modal coordinate system of the node.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modal Order** & **Measured Result** & **Simulation Result** & **Relative Error** \\ \hline
2 & & & 2.9\% \\  & & 1396.5 Hz & 1437.4 Hz & \\ \hline
3 & & & 0.4\% \\  & & 2047.1 HZ & 2055.3 Hz & \\ \hline
4 & & & 1.0\% \\  & & 2891.2 Hz & 2861.5 Hz & \\ \hline
5 & & & 4.6\% \\  & & 3645.7 Hz & 3475.6 Hz & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental validation of modal parameters.  
Figure 11: The multi-physics field analysis process for electromagnetic noise of PMSM.  
The sideband vibration response spectra of the prototype with different PWM strategies are shown in Figure 12, which shows that the unoptimized PWM strategies have obvious order characteristics. Figure 13 shows the sideband vibration response spectra of the PWM strategies after the introduction of the MRCFM method. After the introduction of the MRCFM method, the PWM strategies no longer have significant main-order components, and the sideband vibration response amplitude is smaller and uniformly distributed. The peak sideband vibration responses are reduced by 36.7%, 39.5% and 42.0% for DPWMMIN/MAX, DPWM0/2 and DPWM1/3, respectively.
## 3 Machine Learning-Based GNSS Residual Error Modeling Methodology  
The flowchart for the monitoring scheme determination is outlined in Figure 1. Using measurements from the receivers, we compute the GNSS measurement residuals observed with the receivers. We used the SBDD residuals as the ZBDD underestimates the noise, and the detrending of the carrier phase overestimates noise. As the SBDD residuals have effects of both the multipath and noise, the effect of the components should be separately examined. The resulting residuals are each modeled using machine learning regression. Finally, the estimated residuals from the models are used to compute the thresholds for carrier phase residual monitoring.