esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Until recently, chemical analysis for material characterization, particularly in the geosciences, has been possible only in the laboratory. For materials collected in the field, analysis tends to be limited in scope to a small number of samples that are subsequently analyzed in a laboratory setting using specialized instrumentation. This process is labor intensive, time-consuming, and often very costly because individual samples must be collected and packaged on-site in the field, transported to the laboratory, and then prepared in specific ways for analysis on what can be very expensive instrumentation. However, with the recent advent of portable single analyzer instrumentation for chemical analysis in the field, there is a technological opportunity to create a novel and unique characterization and forensic capability for the rapid, in-situ analysis of both soil and a wide range of other geological materials undisturbed in the field under ambient environmental conditions, particularly where the data from multiple analyzers can be combined for an enhanced interpretation through chemometric analysis.  
Laser-induced breakdown spectroscopy (LIBS) utilizes a pulsed laser to ablate an area of approximately 30 to several hundred microns in diameter on the surface of a material and determine its chemical composition through spectral analysis of light emitted from the high-temperature plasma generated by the ablation/evaporation process. It is particularly sensitive to light elements and is also capable of continuous depth profiling into a sample and microscale multielement mapping. Thus, it can also be used to 'clean' the surface of a sample so that underlying layers can be analyzed once any surface coating, crust, or corrosion is removed. Raman spectroscopy (RS) observes molecular character through monitoring the inelastic scattering of monochromatic laser light from the surface of a material to measure the energy of molecular vibrational and rotational modes and thereby acquire a structural fingerprint by which different substances can be identified. X-ray fluorescence spectroscopy (XRFS) is a technique for determination of bulk chemical composition that uses X-ray-induced fluorescence to compositionally interrogate an area of several millimeters in diameter on a material's surface and is particularly efficacious for transition metals and other heavy elements. Handheld models of these three types of analyzers are commercially available from multiple manufacturers.
# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
## 1 Introduction  
Driven by technological innovation and economic development, the field of robotics has integrated new achievements in various disciplines and could represent the frontier of research in human society. For example, the adoption of industrial robots in manufacturing environments has increased by 12% annually over the last decade, reaching more than 3.5 million operational units in 2023. Due to the difficulty in obtaining the dynamic parameter characteristics of the serial robot system, it is necessary to identify the parameters of the dynamic model in order to improve the system for further research. According to [1, 2], robotic parameter identification mainly considers the analysis and research of kinematic parameters, stiffness parameters, dynamic parameters, and other non-kinematic geometric parameters. In publications [3, 4, 5], the dynamic properties of the linkages and load of a serial robot will have an impact on the motion state and control effect, as the serial robot can constitute dual-arm and multiple robot systems during operation process. Therefore, in publications [6,7], in order to meet the different needs of operational tasks in diverse environments, identifying dynamic parameters of linkage and load of the robotis a necessary aspect that needs to be considered and studied. There are various methods for identifying the dynamic parameters of robots, such as the disassembly measurement method, CAD (computer-aided design) method, non-disassembly experimental identification method, and theoretical identification method. Furthermore, dynamic parameters can be combined differently based on the analysis of structural characteristics. According to [8], the advantage of the theoretical identification method is that it can obtain the overall dynamic model of the robot system and can also take into account some variable factors in the actual working conditions of the environment that can describe the dynamic properties of the identification function of the robot system.  
In the rest of this paper, based on the inertia parameter set of the dynamic model of a serial robot, dual quaternion algebra is used to derive the dynamic parameter identification model of a serial robot. This paper is organized as follows. Section 2 briefly introduces the related works of the dynamic parameter identification method for the robot system. Section 3 shows the derivation of the corresponding identification algorithm for a serial robot by using both the dual part and scalar part of the dual quaternion. Section 4 presents simulations and experiments of the dynamic parameters identification of linkage and load. The corresponding identification experiments are conducted on a dual-arm robot SDA5F. Section 5 concludes the paper.
## 3 Results and Discussion  
### Experiment Result  
The experiment results are shown in Figure 3: samples with different overhanging lengths warped in the first few printing layers. The two groups of experiments with lengths of 4.5 mm and 5.0 mm needed to stop the forming process by deleting the corresponding length\(-\)processing files due to excessive warping when the formation reached seven layers. For the two groups of parts with lengths of 3.5 mm and 4 mm, the first 20 layers warped seriously. However, this did not hinder the continuation of the manufacturing process. When the fabricated height was greater than 0.6 mm (20 layers), the warping of the parts was too large to continue processing and had to be stopped.  
As shown in Figure 4, the overhang bottoms of all the formed parts are not flat. As the distance increased from the solid support area, the height gradually decreased. Figure 4 shows a partially enlarged detail of the fourth group (2.0 mm \(\times\) 1.8 mm), which demonstrated a better forming result than the other groups. The actual forming height of the sample was shorter than expected, as is the length of the overhang structure, and a large amount of granular powder occurred at the bottom. The actual forming bottom is inclined.  
Figure 5a shows the deformation of the top area of the part. With the increase in the forming height, the warping of the top area gradually improved and finally tended toward 0 mm. Compared with the experimental group with a longer overhanging length, the deformation of the top area with a shorter overhanging length decreased faster. Figure 5b shows the actual forming lengths of different combinations. Figure 5a shows that the actual manufacturing length of the samples with lengths of 2.0 mm and 2.5 mm are capable of meeting the design requirements. As the overhanging length continued to increase, the actual lengths of C, D, and E, shown in in Figure 5b, demonstrated a large deviation. For group C, which has an overhanging length of 3.0 mm, the actual length was less than the design length when the forming height was less than 0.6 mm (20 layers). When the overhanging height was 1.8 mm (60 layers), the actual length was greater than the design length.  
Figure 3: Different heights and lengths of overhang structure specimens.  
Throughout the experiment, it was found that the forming defects mainly consisted of dorsal formation and warping in the printing process. The dorsal formation mainly appeared at the bottom of the overhang layer, while the warping mainly appeared at the edge of the overhang layer. In order to further explore the forming process of the defects, the LPBF process was simulated.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 8 Related Works
It is worth noting that this study differs from ours as it measured communication latency in a commonly used 1 Gbit/s network environment rather than a high-performance network environment. In our paper, we propose a solution to reduce deep learning overhead by empirically analyzing communication time on a large GPU cluster. We replicate the collective communication architecture in various multi-GPU environments, including bare metal and containers, using Linux shell and PyTorch. We measure latency and perform experimental comparative analysis on the gradient and parameter communication process. This analysis aims to help reduce deep learning overhead by providing insights into communication time for each library and detailed characteristics such as communication speed and scalability. Our focus is on understanding the actual communication overhead arising from direct function calls during deep learning workload runs.
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 4 Results and Discussion
The analytical and simulation results are given in Figure 7. It is assumed that the maximum transmitting power allowed in each channel is fixed, which ranges between 20 and 30 dBm, and the maximum power constraint of each cognitive radio sensor node is 30 dBm. The average capacity increases as the maximum allowed transmitting power increases. However, the average capacity reaches the saturation point when the total transmission power reaches the maximum power constraint. Last, but not the least, we evaluate the reliability performance. The reliability in terms of PDR against the bit error rate is shown in Figure 8. SACRB-MAC and CRB-MAC have better reliability performances. This is because more receivers are participating in the data forwarding process, due  
Figure 6: Average capacity performance against network density. Figure 7: Average capacity performance against the maximum allowed transmit power of each channel. to the receiver-based mechanism. Therefore, we can draw the conclusion that SACRB-MAC shows resiliency to channel quality variations and provides high reliability.
## 4 Experiments
### Benchmark Performance
#### 4.3.3 Computational Resource Consumption Analysis  
To comprehensively evaluate the performance of the proposed model, this experiment presents insights into memory consumption and runtime, comparing them with methods such as AA-RMVSNet [8], CasMVSNet [6], and CIDER [34], as depicted in Figure 7.  
Different methods exhibit varying memory usage on the DTU test set, as illustrated in Figure 7a. Through comparison, it is found that our method consumes only 4.53 GB of GPU memory, significantly less than other methods. Meanwhile, among all compared methods, our approach achieves an overall error reduction to 0.315, demonstrating excellent performance. Figure 7b shows the time required for depth map prediction across different methods on the \"Tanks&Temples\" dataset. Our method computes in just 1.53 s, significantly faster than CIDER. Additionally, our method achieves an F-score of 58.60, demonstrating excellent overall performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **F-Score** & **Family** & **Francis** & **Horse** & **Lighthouse** & **M60** & **Panther** & **Playground** & **Train** \\ \hline ACMH [17] & 54.82 & 69.99 & 49.45 & 45.12 & 59.04 & 52.64 & 52.37 & 58.34 & 51.61 \\ COLMAP [45] & 42.14 & 50.41 & 22.25 & 25.63 & 56.43 & 44.83 & 46.97 & 48.53 & 42.04 \\ ACMP [1] & 58.41 & 70.30 & 54.06 & 54.11 & 61.65 & 54.16 & 57.60 & 58.12 & 57.25 \\ MVSNet [5] & 43.48 & 53.96 & 34.69 & 25.07 & 50.09 & 55.90 & 50.86 & 47.90 & 28.55 \\ CasMVSNet [6] & 56.42 & 76.36 & 58.45 & 46.20 & 55.33 & 53.96 & 54.02 & 58.17 & 46.56 \\ DDR-Net [47] & 54.91 & 76.18 & 53.36 & 43.43 & 55.20 & 55.57 & 52.28 & 56.04 & 47.17 \\ UCS-Net [28] & 54.83 & 76.09 & 53.16 & 43.03 & 54.00 & 55.60 & 51.49 & 57.38 & 47.89 \\ AA-RMVSNet [8] & 61.51 & _77.77_ & 54.90 & 51.53 & 64.02 & 64.05 & 59.47 & 60.85 & 46.65 \\ Fast-MVSNet [27] & 47.39 & 65.18 & 39.59 & 34.98 & 47.81 & 49.16 & 46.20 & 53.27 & 42.91 \\ UniMVSN [41] & 64.36 & 81.20 & 66.43 & 53.11 & 63.46 & **66.09** & **64.84** & **62.23** & 57.53 \\ CVP-MVSNet [46] & 54.03 & 76.50 & 47.74 & 36.34 & 55.12 & 57.28 & 54.28 & 57.43 & 47.54 \\ PatchmatchNet [29] & 53.15 & 66.99 & 52.64 & 43.24 & 54.87 & 52.87 & 49.54 & 54.21 & 50.81 \\ Effi-MVS [43] & 56.88 & 72.21 & 51.02 & 51.78 & 58.63 & 58.71 & 56.21 & 57.07 & 49.38 \\ DA-PatchmatchNet [44] & 54.79 & 68.10 & 54.60 & 45.65 & 57.32 & 53.43 & 48.21 & 57.64 & 53.33 \\ ET-MVSNet [48] & **65.49** & **81.65** & **68.79** & **59.46** & **65.72** & **64.22** & 64.03 & 61.23 & **58.79** \\ Ours & 58.60 & 78.23 & 57.19 & 48.36 & 58.25 & 59.58 & 56.60 & 60.36 & 50.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results on the Tanks&Temples dataset—intermediate dataset (F-score, higher is better). F-score is the average across all scenes, and the best and second-best results are highlighted in bold and underlined, respectively.
## 2 Materials and Methods
### Ion Analysis  
Isocratic ion chromatography on an ICS-1100 instrument (Thermo Scientific, Sunnyvale, CA, USA) equipped with an auto-sampler (AS-DV Thermo Scientific, Sunnyvale, USA) was used for the determination of the concentrations of ions (Na\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\), NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), PO\({}_{4}\)\({}^{3-}\), and SO\({}_{4}\)\({}^{2-}\)). Separations were performed using an Ion Pac AS22 and analytical column CS16 for anions and cations, respectively. The separations of samples (25 \(\mu\)L injection volume) were performed with a flow rate of 1.2 mL min\({}^{-1}\) of the mobile phase. Separated ion concentrations were determined after electrochemical suppression using AERS 500 and CERS 500 (Thermo Scientific, Sunnyvale, CA, USA) suppressors for anions and cations, respectively. Calibration was performed against external standards (Dionex Combined Seven Anion Standard II and Dionex Combined Six Cation Standard-II) diluted from stock solutions supplied by Thermo Scientific. The detection limits (DL) for selected ions were evaluated in triplicate analysis of standard deviation analysis for blank field samples and their minimum concentrations were detected in the ranges 0.05-0.14 \(\mu\)g/m\({}^{3}\) and 0.001-0.07 \(\mu\)g/m\({}^{3}\), for anions and cations, respectively. Details of the analytical procedure are presented elsewhere [27].
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
## 3 Results
### Microstructure Analysis
#### 3.1.3 Morphological Analysis  
The morphologies of OS, OSC500, OSC700, and OSC900 are displayed in Figure 3. The OS surface is smooth and dense, with no observable pores in it (Figure 3a-c). After carbonization at 500 \({}^{\circ}\)C, the OSC500 surface becomes rough and develops a flocculent network structure with tiny pores (Figure 3d-f). When the carbonization temperature is increased to 700 \({}^{\circ}\)C, the surface roughness further increases, and the pore diameter enlarges (Figure 3g-i).  
Upon completion of ZnCl\({}_{2}\) gasification at 700 \({}^{\circ}\)C, ZnCl\({}_{2}\) molecules were impregnated into the carbon interior, serving as a backbone for the carbon polymer deposited during carbonization. Subsequent washing with hot water removed Zn\({}^{2+}\), producing a large number of new pores, increasing defects in the C network, and enhancing the specific surface area, thereby contributing to the overall surface roughness of the particles [44; 45; 46]. Figures 4 and 5 also illustrate the synergistic binding of Zn with the C matrix. EDS tests were performed on the OSC700 surface Figure 4a, and the results are shown in Figure 4b-e.  
Figure 3i also reveals that the deposited C combines with the original surface in a networked and flocculent manner, forming numerous bumps, pits, and wrinkles that provide additional adsorption sites for the adsorption process. However, carbonization at 900 \({}^{\circ}\)C (Figure 3j-l) significantly reduces the number of surface pores and increases the pore size. This phenomenon occurs because some inorganic minerals begin to decompose at 900 \({}^{\circ}\)C, causing the collapse of pore channels and the enlargement of pore sizes. The EDS diagram of OSC700 is shown in Figure 4. It can be seen that OSC700 is mainly composed of Si and O elements. Doping of Zn elements and deposition of C elements, i.e., porous carbon, appeared on its surface after calcination at 700 \({}^{\circ}\)C with the addition of ZnCl\({}_{2}\). It can be seen from (b) and (e) that the appearance of the C element and the Zn element are two simultaneous appearances, which indicates that the nascent carbon is combined with the compounds of Zn.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SSA(m\({}^{2}\)/g)** & **Total Pore Volume (cm\({}^{3}\)/g)** & **Mean Pore Diameter (nm)** \\ \hline OSC500 & 10.4 & 0.0289 & 14.50 \\ OSC700 & 10.95 & 0.0318 & 11.63 \\ OSC900 & 6.43 & 0.0233 & 11.11 \\ \hline \end{tabular}
\end{table}
Table 2: Surface structural parameters of OSC500, OSC700, and OSC900.  
Figure 4: EDS image of OSC700 (a), corresponding to C element (b), O element (c), Si element (d), Zn element (e).
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Massive MIMO and Beamforming
The 5G NR specification also decreases the energy loss and greenhouse radiation of next-generation wireless networks in such a way that it contributes to the envisioned direction of information and communication technology sustainability. Besides other features, massive MIMO presents an exceptional obstacle in the ultra-dense scenario and introduces crucial challenges [280]. Defined CSI is a prerequisite to exploiting the MIMO system. After training the model for ML-based channel estimation and prediction, the complexity is extremely low compared to other models (Wiener filtering model/Kalman filtering-assuming model). Without previous information, ML-based techniques can find out the implicit characteristics from channel data throughout the training phase [281]. Moreover, a trained ML model predicts the performance and complexity while having user mobility information that identifies channel fluctuations (fast/low) in information between the user and gNB. More complex ML-based algorithms require more data to make decisions precisely in wireless communications. Following the guidelines in [282], Figure 6 shows resource blocks and resource elements in OFDM symbol time versus the subcarrier domain. Index modulation (IM) is also a noteworthy approach for enhancing energy and spectral efficiency. Limited power consumption and transmitter complexities can be achieved in MIMO-OFDM through IM. Generally, index modulation exploits subcarriers to deliver the available data to a receiver, whereas in OFDM-MIMO, IM performs a similar function in a perpendicular multiplexing system [283]. Thus, it acts as an additional resource of information. Nevertheless, the addition of IM brings detector complexity and an inflated system overhead. In [284], the authors proposed the concept of non-iterative detector design by using DL knowledge to tackle these challenges and exploit IM for 5G architecture. In contrast to traditional MIMO-OFDM, simulations proved that the addition of IM in MIMO-OFDM improves the BER performance, limits the inter-channel problems, and decreases the prerequisites for inter-antenna synchronization [285].
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 4 DRL-Driven Offloading Scheme Based on DT
### Digital Twin Simulated MDP  
In reinforcement learning, the DRL agent learns the current state from the environment and takes actions that lead the system to the next state. The environment gives the agent a reward based on the action. In order to solve the problem (8), the system first establishesan MDP, i.e. \(MDP=\langle\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma\rangle\), here \(\gamma\in[0,1]\) is the discount factor. From Figure 3, the current state \(s_{t}\) is constructed by the digital twin and output to the DRL agent.  
The current state \(s_{t}\in\mathcal{S}\) is expressed as  
\[s_{t}=\left\{\sum_{n=1}^{|NOT|}DT_{n}(t),G^{DT}(t),B\right\} \tag{19}\]  
In (19), \(DT_{n}(t)\) represents the DT model of the corresponding physical device. \(G^{DT}(t)\) represents the current state of the network, including the available resources of the nodes and links. \(B\) is the wireless channel bandwidth, which is a constant.  
The action space describes the DRL agent's strategy for resource allocation. We define the action space as \(a_{t}=\left\langle a_{t}^{off},a_{t}^{rsrc}\right\rangle\). In the tuple, \(a_{t}^{off}=\left\{a_{i,f}^{off}(t)\in[0,1]\middle|i\in\mathcal{N},f\in \mathcal{F}\right\}\) denotes whether task \(f\) has been offloaded at node \(i\). \(a_{t}^{rsrc}=\left\{c(t),p(t),b(t)\right\}\) indicates the resource utilization in the network, where \(c_{i}(t)\in c(t)\) represents the computation resources of edge servers, \(p_{n}(t)\in p(t)\) represents the transmit power distribution of the node at the current time, and \(b^{k}(t)\in b(t)\) represents the bandwidth allocated to the current subchannel.  
Through DT, an immediate reward \(r_{t}\) is returned, and the system is transferred to a new state \(s_{t+1}\) with state transition matrix \(\mathcal{P}\). The goal of the DRL agent is to find an optimal policy that can maximize the long-term cumulative reward \(\mathcal{R}\). Our goal is to minimize the total end-to-end delay. Therefore, the immediate reward function is defined as  
\[r_{t}=-\sum_{n,j\in\mathcal{N},f\in\mathcal{F}}D_{n,j,f}^{total} \tag{20}\]  
Figure 3: Structure of the DDPG agent.  
The reward described in (20) is the delay criteria; the smaller the processing delay, the higher the reward value. If the service delay of a task exceeds its maximum delay bound, the task will be discarded. In this case, we set the punish reward \(D_{n,l,f}^{total}=\tau_{f}\).