esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks  
[PERSON]  
1 Faculty of Electrical Engineering, Iqra National University, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan; [PERSON]]  
[PERSON]  
2 Faculty of Electrical Engineering, CECOS University, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Electrical Engineering, City University of Science and Information Technology, Peshawar 25000, Pakistan; [PERSON]]  
[PERSON]  
4 Faculty of Electrical Engineering, Gublam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan; [EMAIL_ADDRESS]  
[PERSON]  
5 Department of Electrical Engineering, Preston University, Kohat, Khyber Pakhtunkhwa 25000, Pakistan; [EMAIL_ADDRESS]  
[PERSON]  
6 School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea  
6 Correspondence: [EMAIL_ADDRESS]; Tel.: +82-52-259-1401646  
underwater communication systems [3]. As chromatic dispersion (CD) is a severe problem in WDM systems because different wavelengths travel through the same fiber, the long-haul transmission gives rise to the accumulated CD in a wavelength-dependent setup [4], which can be compensated by using slope compensation; however, it causes an increase in nonlinear distortions[5]. As compared to linear accumulation of CD, the PMD accumulates stochastically along the fiber length and causes distortion and reduction in peak power for long-haul fiber-optic transmission [6]. For this reason, optical fibers with smaller values of CD are preferred in long haul OCNs. Due to inter symbol interference (ISI) and spectral extension in the transmitted optical pulses caused by the channel effects in OCNs, nullifying the effects of CD and PMD have been topics of great interest for the researchers. To mitigate the distortion effects caused due to the spectral extensions in the transmitted optical pulses from CD and PMD, various techniques such as optical compensation technique and electronic compensation procedure have been studied previously [5]. These can be explained as follows.
# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
## 4 Conclusions  
In this research, a predictive system was developed for predicting the weld bead geometry in three welding processes. We carried out experiments for SMAW, MIG, and TIG welding processes to obtain the experimental data. From these data, we applied the regression technique and a statistical method for generating the mathematical models showing the relationship between the welding process parameters as the input information and the weld bead geometry as the response. From these mathematical models, the main factors affecting the weld bead shape were determined. These main factors were used as the input parameters for the input layer in the deep learning artificial neural network. The predictive system with four modules was developed using the Python platform. The SMAW, MIG, and TIG modules were implemented using deep learning neural networks for SMAW, MIG, and TIG processes. These results will be applied in developing a virtual welding process system for education carried out by the University of Transport and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Welding Process** & **Weld Bead Geometry** & **Experimental Results** & **Predicted Results** \\ \hline \multirow{3}{*}{TIG welding process} & Weld bead width (K\({}_{\text{T}}\)) & 4.4 \(\div\) 12.9 & 5.87 \(\div\) 10.6 \\ \cline{2-4}  & Reinforcement (R\({}_{\text{T}}\)) & 0.3 \(\div\) 1.4 & 0.5 \(\div\) 1.27 \\ \cline{2-4}  & Penetration (P\({}_{\text{T}}\)) & 1.0 \(\div\) 2.6 & 1.38 \(\div\) 1.9 \\ \hline \multirow{3}{*}{MIG welding process} & Weld bead width (K\({}_{\text{M}}\)) & 7.1 \(\div\) 12.4 & 7.31 \(\div\) 12.03 \\ \cline{2-4}  & Reinforcement (R\({}_{\text{M}}\)) & 2.3 \(\div\) 4.2 & 2.72 \(\div\) 3.79 \\ \cline{2-4}  & Penetration (P\({}_{\text{M}}\)) & 1.3 \(\div\) 2.8 & 1.53 \(\div\) 2.43 \\ \hline \multirow{3}{*}{SMAW welding process} & Weld bead width (K\({}_{\text{S}}\)) & 7.4 \(\div\) 11.3 & 8.81 \(\div\) 9.63 \\ \cline{2-4}  & Reinforcement (R\({}_{\text{S}}\)) & 0.6 \(\div\) 2.0 & 1.2 \(\div\) 1.44 \\ \cline{1-1} \cline{2-4}  & Penetration (P\({}_{\text{S}}\)) & 1.3 \(\div\) 2.9 & 1.91 \(\div\) 2.12 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of the values of the weld bead geometry between experiment and prediction.  
Communications, Vietnam. This developed system enables the prediction of the weld joint quality.  
Formal analysis, V.-H.B.; methodology, N.-H.T.; software, V.-T.H.; validation, N.-H.T., V.-H.B. and V.-T.H.; writing--original draft, N.-H.T., V.-H.B. and V.-[PERSON]; writing--review and editing, N.-H.T., [PERSON]-H.B. and [PERSON]-T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vietnam Ministry of Education and Training, grant number: B2021-GHA-01.  
Not applicable.  
The authors declare no conflict of interest.
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 4 Discussion
### Influence of Initial Conditions  
Initial conditions, such as contact conditions and TacTip materials, are crucial for accurate results, as they significantly influence the deformation of TacTip.  
Regarding contact conditions, the accuracy of the simulated results decreases notably when the object and TacTip are in close contact. As the object presses into the sensor, the strain on the TacTip skin increases significantly, thereby invalidating Hypothesis 1 of the algorithm, which assumes the strain on the TacTip skin is negligible. However, in practical applications, the TacTip would not typically be used in tight contact with objects, as feedback from shallow contact is sufficient to obtain object information. Additionally, tight contact may risk damaging the TacTip skin.  
To simulate the deformation of the TacTip with different materials accurately, the algorithm's parameters are highly tunable, including the joint angle limit between two chain sections, the total number of chain sections, the scale of the tip, and the shape of the tip, which can also be applied to a semispheroid. This tunability makes the algorithm generalizable to different variants of the TacTip with varying materials and dimensions. For this research, dimensions were adjusted to align with the behavior of the basic TacTip.
## 2 Testing Environment  
Benchmarks tests were launched on two actual PDAs, a Compaq iPAQ3970 and an HP hx2790. HP Hx2790 is more powerful than the other so that we can generalize how further technology improvements can modulate the results. Compaq iPAQ3970 uses an Intel XScale PXA250 processor at 400 MHz. It runs Linux Familiar operating system. On the other hand, HP Hx2790 uses an Intel XScale PXA270 processor at 624 MHz. It runs Windows Mobile operating system. Specifications of both platforms are detailed in Table 1.  
Tests were conducted with the support of a PC, a DELL-DCNE with a 2.8 GHz Intel processor and 512 MB of RAM, and which runs Ubuntu Linux. Tests and required libraries have been built in the PC using cross-compilers (see Table 2) and have been installed and launched on the PDA through a serial port connection. Security-related libraries can be downloaded from KISON Research Group web page: [[http://kison.uoc.edu](http://kison.uoc.edu)]([http://kison.uoc.edu](http://kison.uoc.edu)).  
The performance study has evaluated two aspects of different PDA processes: temporal cost and consumed energy. Time delays have been measured through the implementation of temporal monitors in test applications. We have used the function times of the C Standard Library to control the elapsed CPU time for every process. The energy costs have been estimated from the battery status information provided by the battery drivers. In particular, in the Linux Familiar operating system, we have used the asic driver for the management of the battery which outputs status information in the system file /prov/asic/battery. In Windows Mobile we have used the GetSystemPowerStatusEx2 function from the Windows MSDN library that retrieves information of the battery status.
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
###### Abstract  
In Aerial Manipulation, the motion of the robotic arm can cause unwanted movements of the flying base affecting the trajectory tracking capability. A possible solution to reduce these disturbances is to use a free revolute joint between the flying base and the manipulator, thus reducing the torque applied to the base from the manipulator. In this paper, a novel approach to solve the inverse kinematics of an aerial manipulator with a free revolute joint is presented. The approach exploits the Generalized Jacobian to deal with the presence of a mobile base, and the dynamics of the system is considered to predict the motion of the non-actuated joint; external forces acting on the system are also included. The method is implemented in MATLAB for a planar case considering the parameters of a real manipulator attached to a real octocopter. The tracking of a trajectory with the end-effector and a load picking task are simulated for a non-redundant and for a redundant manipulator. Simulation results demonstrate the capability of this approach in following the desired trajectories and reducing rotation and horizontal translation of the base.  
aerial manipulation; Unmanned Aerial Vehicle; mobile robotics; inverse kinematics; multi-body systems Article
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 2 Related Works
### Free Space Segmentation  
Image segmentation is an essential and widely researched topic in computer vision and image processing, and it can provide the necessary information to separate free space [29]. Image segmentation aims to partition the image into meaningful regions, preserving the boundaries and properties of the objects in the scene. Free space segmentation is one of its applications, referring to the process of using a computer vision model to divide images into traversable space and occupied space (obstacles) [29; 30; 31; 32]. The model's input can be monocular or stereo images; the output is pixel-wise segmentation.  
One of the limitations of free space segmentation is that it cannot produce geometrical representations of the free space, which are often required by collision-checking algorithms or trajectory optimization techniques in autonomous navigation. Free space segmentation algorithms typically produce a binary or pixel-wise map separating free space from the occupied space. However, this information alone may not be sufficient for local planning as it is challenging to mathematically model the safety constraints from non-geometrical forms. Another limitation is the possibility of false segmentation, particularly in challenging environments such as urban scenes and natural landscapes. The algorithm's output needs to be verified to ensure that it is accurate and reliable.
## 2 Materials and Methods
### Parameter Selection  
In the proposed method, \(\gamma\), \(\omega\), \(\lambda\), and _Maxiter_ should be set in advance. The following experiments use various values to investigate the parameters.  
The measure of image information contains two parameters, \(\gamma\) and \(\omega\). \(\gamma\) is used to adjust the uniformity and content sensitivity of the seeds. As shown in Figure 3, with the increasing values of \(\gamma\), the value of performance metrics changes significantly because small \(\gamma\) values imply the seeds are too sensitive to the image content and concentrated in complex areas while ignoring other areas. When \(\gamma\) becomes too large, the distribution of seeds tends to regular grid seeds, and the content sensitivity is ignored. \(\omega\) is used to adjust the proportion of the color term in Equation (7). In practice, we truly use their product, \(p=\omega\cdot\gamma\). Approximately, the values of \(l(i)\) fall in the interval \([0,p]\). As shown in Figure 4, when \(p\) increases, more detailed information is considered and the resulting superpixels are more irregular.  
Figure 3: The performance metrics of our approach with different \(\gamma\) values.  
\(\lambda\) represents the weight parameters of the distance measure in the stage of minimum spanning tree clustering. Particularly, when \(\lambda\)\(\rightarrow\)\(\infty\), the generated superpixels are regularly grid only related to the position of the initial seeds. When \(\lambda\) = 0, the pixels with similar colors are clustered to generate irregular superpixels. The trend is shown in Figure 5. Figure 6 shows the average values of BR, ASA, and USE on the BSDS 500 for different \(\lambda\) values when K = 400. Overall, the performance metrics becomes worse as the value of \(\lambda\) increases.  
Figure 4: Superpixel segmentation results with different \(p\) values.  
Figure 5: Superpixel segmentation results with different \(\lambda\) values.  
The number of iterations _Maxiter_ can balance time cost and performance. The metrics with _Maxiter_ are shown in Figure 7. It is easy to find that our algorithm can quickly converge after two iterations. More iterations will only increase time consumption. Also shown in Figure 2, the superpixels became more compact after iterations.  
From the discussion above, we select \(\gamma=6\) and _Maxiter_\(=3\), where achieve the best performance. Considering the accuracy and compactness of superpixels, we empirically select \(\lambda=20\) and \(p=3\).
## 2 Building Blocks
### EfficientNet  
EfficientNet [16] is a convolutional neural network (CNN) architecture and scaling method that scales the network dimensions (depth, width, resolution) using a compound coefficient. The basic building block of the network is the inverted bottleneck residual block (previously introduced with MobileNetV2 [15]), a custom convolutional module that provides a good compromise between performance and memory footprint. The EfficientNet family of models is specifically designed for cases where computational resources are limited. However, even with a limited number of parameters, the network can still provide great performance. EfficientNet reaches state-of-art transfer accuracy on multiple benchmark datasets with one order of magnitude fewer parameters. EfficientNet has been used in applications from different domains. Although some of such domains were completely unrelated to Earth Observation (e.g., path prediction in autonomous driving, image classification in the mobile framework), a handful of researchers started also using this family of models for Remote Sensing. Because of their efficiency and capability of extracting highly discriminative features, EfficientNet models have been widely employed as Remote Sensing scene classifiers [17; 18; 19]. For example, in [PERSON] et al. [17], [PERSON] et al. [19], EfficientNet-B3 networks are used for scene classification. [PERSON] et al. [18] enriched the EfficientNet-B3-based model by adding an Attention module to further increase the classification performance. [PERSON] et al. [20] used EfficientNet-B3 to map satellite images to census data in order to characterize vulnerable communities at the residential block level and, therefore, localize poor areas where poverty reduction policies can be implemented. According to the published papers, no instance of EfficientNet used as encoder for a segmentation model has yet been proposed.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 3 System Architecture
### Spectrum Broker  
In channel allocation for dynamic radio spectrum sharing, an accurate prediction of network performance is important. In this architecture, a central spectrum broker is responsible for allocating radio spectrum channels to cognitive unlicensed users as shown in Figure 2. A centralized sensing mechanism is performed for obtaining detailed information on unused frequencies although a decentralized sensing mechanism is also used. The transceiver on each IoT sensor device scans the spectrum availability and forwards the information of these spectrum holes to the spectrum broker via base station (or access point) in the respective network. The radio spectrum channel is allocated to the cognitive user on the IoT device depending on the number of unoccupied channels.
## 1 Introduction  
Nowadays, many large-scale data applications in various fields require massively parallel operations to obtain results efficiently. For massively parallel computations, we can use well-known system-level programming platforms including CUDA (Compute Unified Device Architecture), OpenCL (Open Computing Language), and ROCm (Radeon Open Compute). Recently, programmers in the massively-parallel computation field found that the platform-independence and the high-level abstraction are also important, in addition to the cost-effectiveness [1; 2; 3; 4].  
This paper focuses on the new parallel computing implementations of SYCL (C++ Single-source Heterogeneous Programming for OpenCL) [5; 6], which are actually C++ template-level implementations of high-level massively parallel computation libraries. Since SYCL can be implemented over the existing standards of OpenCL and/or CUDA, we can indirectly achieve the platform-independence. The programmers can interact with abstracted SYCL function calls, and SYCL will do complex jobs with CUDA and/or OpenCL. Additionally, C++ template features provide the high-level abstractions to the programmers.  
Although the Khronos Group introduced this well-designed standard of SYCL [5; 6], they only provide the official specifications, without reference implementations. These days, the SYCLprogrammers can use various SYCL implementations from different vendors. Unfortunately, an efficient SYCL implementation requires deep understanding of C++ templates, CUDA, OpenCL, and the parallel computation itself, and current SYCL implementations show diverse performances.  
We will represent how heterogeneous parallel computing was historically implemented before the SYCL. We will compare the characteristics of existing SYCL implementations among various vendors. Finally, we compare the performance of those SYCL implementations by measuring execution times for well-known massively parallel computation problems.  
Since most of the big-data computation and the big-data visualization applications need typical operations provided by SYCL (and underlying OpenCL and/or CUDA), this comparison and analysis provides fundamental information on the abstract-level cost-effective implementations of massively parallel computing, especially for big-data applications. This work is the first on the performance of SYCL implementations to the best of our knowledge.
## 2 Research on Anti-Skid Mechanisms of Asphalt Pavement
### Factors Affecting the Skid Resistance of Asphalt Pavements
#### 2.2.2 Influence of Gradation Type of Mix on Skid Resistance of Asphalt Pavements
[42] investigated the skid resistance of three types of asphalt mixture pavements, namely Open-Graded Friction Course (OGFC), Stone Mastic Asphalt (SMA), and Antiskid Asphalt Mixture (AK-16A), and concluded that OGFC pavements have the best skid resistance, but the fatigue resistance of OGFC pavements is poor, and AK-16 asphalt pavements are average across all performance categories. The findings of [PERSON] et al. [43] also support this perspective. The SMA-16 asphalt mixture pavement exhibits satisfactory skid resistance during its early service period. The results of the research conducted by [PERSON] et al. [44] on intermittent-graded asphalt mixtures for road performance demonstrate that these mixtures exhibit excellent resistance to putting at high temperatures, excellent water stability, and superior durability. Moreover, they exhibited optimal macroscopic texture structure depth. Consequently, intermittent-graded asphalt mixtures are recommended as a superior surface material for roads [45; 46; 47]. In practice, the asphalt mixture of interrupted gradation pavement construction is susceptible to segregation. To address this issue, [PERSON] et al. [48] developed a novel gradation based on the CAFV method and interrupted gradation design theory [40; 49].
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 4 Measurement of Wireless Coverage and Quality
### Testing Mobile Base Stations  
Mobile base stations provide coverage info through the main communication method in sparsely populated areas, such as mountainous areas and the open sea. Mobile base stations have a vehicle or a backpack type, and coverage inspection should measure frequency intensity (RSRP) while moving based on the coverage boundary provided in the mobile station equipment specification [23; 26]. The test area should be selected for mountainous areas, urban areas (big-, small-, and medium-sized cities), and empty sites considering the diffraction, refraction, reflection, etc., of electric waves. Testing for data communication, voice, and video PTT functions should be conducted in terms of interoperability, call quality, and coverage through an efficient operation of mobile base stations and independent network configuration. Therefore, detailed test criteria of the mobile base station are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Test Content** & \multicolumn{2}{c}{**Test Criteria**} \\ \hline \multirow{7}{*}{Convenience} & & - & Availability of \\  & & & self-organization \\  & & & network feature \\  & & - & Subscriber registration and \\  & & & group creation \\  & & - & Required time for service \\  & The ability to move the mobile & & provision \\  & base station quickly and provide & - & Convenience of antenna \\  & immediate service in the field & & installation and \\  & & & appropriateness of antenna \\  & & & type for each wireless \\  & & & environment \\  & & - & Expansion of call capacity \\  & & - & MCPTT function based on \\  & & & 3 GPP Rel13 \\ \hline \multirow{3}{*}{Coverage} & \multirow{3}{*}{Coverage} & \multirow{3}{*}{
\begin{tabular}{c} Coverage by wireless \\ environment \\ \end{tabular} } & Effective coverage measurement \\  & & & (downtown area, empty site, and \\  & & & mountainous areas) \\ \hline \multirow{7}{*}{Interoperability} & Handover testing (RAN sharing, & - & Availability of S1/X2 \\  & etc.) & & handover \\  & - & Mobile station (normal type) & - & Availability of \\  & & \(\leftrightarrow\) Fixed station (PS-LTE) & - & intra/inter-RAT handover \\  & - & Mobile station (general type) & - & Availability of idle mode \\  & & \(\leftrightarrow\) other communication & - & Parameter applicability for \\  & & networks/LTE-R/LTE-M & - & RAN sharing (neighbor list, \\  & & & PLMN, etc.) \\ \hline \multirow{3}{*}{Call quality} & - & Individual call quality & \\  & & (voice/video) & Call service based on quality \\ \cline{1-1}  & - & Group call quality & verification indicators \\ \cline{1-1}  & & (voice/video) & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mobile base station test items.  
The test environment for mobile base stations (generic vehicle and generic portable types) is shown in Figure 11.