esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 2 Proposed Method
### ICP Fine Registration  
When the ICP algorithm registers the two point clouds \(P\) and \(Q\), it searches for the nearest neighbor point pairs that meet certain constraints, and calculates the optimal rotation matrix and translation matrix to minimize the error function. The larger the amount of data in the point cloud, the more time-consuming it is to query the point pairs. In our approach, we use the KD tree in the ICP algorithm, which can quickly search for neighbor point pairs and improve the registration efficiency [20]. The specific process of registration is as follows:  
Step 1, nearest neighbor points searching. Use the KD tree algorithm to search for the nearest neighbor \(q_{i}\) of the point \(p_{i}\) (\(p_{i}\in P\)) in \(Q\), and minimize the Euclidean distance \(|p_{i}-q_{i}|\).  
Step 2, optimal rotation matrix and rotation matrix solving. Use the least square method to solve the optimal rotation matrix \(R\) and translation matrix \(T\), the error function \(G(R,T)\) is minimized.  
\[G(R,T)=\frac{1}{N}{\sum}_{i=1}^{N}\left\|(Rp_{i}+T-q_{i})\right\|^{2} \tag{10}\]  
Step 3, source point cloud transforming. Perform \(R\), \(T\) transformation on the point cloud \(P\), and get the transformed corresponding points \({p^{\prime}}_{i}=\left\{{p^{\prime}}_{i}=Rp_{i}+T,p_{i}\in P\right\}\), then calculate the average distance of point pair \(({p^{\prime}}_{i},q_{i})\):  
\[d=\frac{1}{N}{\sum}_{i=1}^{N}\left\|{{p^{\prime}}_{i}-q_{i}}\right\|^{2} \tag{11}\]  
Step 4, threshold judging. Set the threshold \(\epsilon_{icp}\), if the conditions \(d_{k}-d_{k+1}<\epsilon_{icp}\) are met, end the iteration, otherwise iterate goes on.  
Step 5, registration completing. According to the finally obtained \(R\) and \(T\), the point cloud \(P\) is transformed into the coordinate system of the point cloud \(Q\) to complete the registration.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 3 System Model and Problem Formulation
### Energy-Efficient Deep Reinforcement Learning Allocation Framework  
We formulate the problem of (12) as an MDP and leverage DRL to solve it. In a DRL-based algorithm, an agent can make decisions in real time by interacting with the environment without the need for prior data sets. The agent reviews and updates its policy using a trial-and-error method. A correct description of the underlying process can be advantageous for a successful implementation of DRL-based algorithms, for example, when considering control strategies in wireless communications [44] or nonlinear power systems [45].  
A classical MDP is mathematically characterized by the tuple \(\{\mathcal{S},\mathcal{A},\mathcal{P},\Gamma,\mathcal{R}\}\). \(\mathcal{S}\) represents the state space, \(\mathcal{A}\) represents the action space, \(\mathcal{P}\) represents the transition probability from the current state \(s\) to the next state \(s^{\prime}\), \(\Gamma\) represents the discount factor to achieve trade-off between the current reward and the future reward, and \(\mathcal{R}\) represents the reward function.  
We formulate the contexts of a state, an action, and a reward within the framework of V2X with the RSUs network based on the PPO algorithm. Figure 3 illustrates the structure of the PPO-based allocation, consisting of a V2X environment and an agent. The agent uses system information states \(s\), such as computation and communication capacity, to determine the optimal allocation decision \(a\), resulting in a high reward \(r\) (indicative of low energy consumption), thus altering the condition of the system to \(s^{\prime}\). The V2X environment includes the connections to gNB, where the DRL framework is implemented. The DRL framework covers an area composed of several RSUs.  
In our 6G V2X Open RAN topology, vehicles are assigned to the infrastructure such as RSUs to process their tasks. The RSUs receive vehicle traffic and decide the amount of resources to be allocated to the vehicle to process its task and minimize energy consumption. The system control plane, which serves as the assignment agent, makes the assignment and allocation decisions. The assignment agent is deployed within an RSU. This learning agent requires environmental information and trains using prior experience to determine the best action. The prior experience has the form of state, action, next-state, and reward \((s,a,s^{\prime},r)\). The agent will learn to take the best action during the state of a particular environment.
Through applying a series of benchmark test functions (including unimodal, multimodal functions, and CEC 2015 and CEC 2017 test function sets), the researchers carried out the test on the performance of the WaOA, and compared it with ten other well-known meta-heuristic algorithms, like the genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), etc., fully demonstrating its superior exploration and development capabilities. In addition, the algorithm was also utilized to solve engineering design problems and practical engineering optimization problems in the CEC 2011 test function set. This paper conductsa comparison involving WaOA, the sand cat swarm optimization algorithm (SCSO), the ant lion optimization algorithm (ALO), and the seagull optimization algorithm (SOA), and 12 standard functions are selected for testing. Two nonparametric statistical methods, the Friedman test and the Wilcoxon rank sum test, serve to guarantee the reliability of the evaluation results. The test results show that the WaOA performs well in most benchmark test functions and has the best performance. Based on the field-measured temperature dataset of C40 concrete of a dam in the downstream area of Jinsha River, this study comprehensively considers the combined impacts of the fluctuation in external environmental temperature and water-cooling effect, and applies the WaOA to the inversion analysis of the thermal parameters of the dam concrete. By formulating specific engineering optimization problems, the performance of the WaOA is evaluated in depth, and it offers a reference for the inversion of thermal parameters of arch dam concrete.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 5 Current Advances in Biotechnological Remediation
### Advances in Bioremediation  
1. Genetic and Metabolic Engineering: Techniques such as CRISPR-Cas9 and recombinant DNA technology have been used to engineer strains of _Pseudomonas_, _Burkholderia_and _Alcanivorax_ with enhanced degradation efficiency and stress tolerance [100,101]. These engineered microbes can degrade persistent compounds like PAHs and PCBs even under harsh marine conditions. * Synthetic Biology: Engineered microbial consortia now include synthetic operons, quorum-sensing circuits and pollutant-responsive promoters, which enable coordinated and pollutant-triggered degradation [102,103]. Moreover, biosensors developed from synthetic biology tools can monitor contaminant levels and remediation performance in situ [103]. * Nanotechnology: Nanoparticles such as iron oxides, carbon nanotubes and silver nanoparticles are used to improve adsorption and delivery of microbial inocula. These are often embedded in hydrogel matrices made from marine-compatible polymers like alginate and chitosan, which protect microbes from environmental stress while enhancing pollutant interaction [104,105]. * Bioelectrochemical Systems (BESs): BES technologies such as microbial fuel cells (MFCs) combine microbial activity with electron flow mechanisms to enhance the oxidation and reduction of organic pollutants. These systems are especially effective for degrading petroleum hydrocarbons by boosting electron transfer between microbes and electrodes, hence improving reaction rates and system control [106,107]. * Metagenomics and Bioinformatics: High-throughput metagenomic sequencing and bioinformatic analyses allow for the discovery of novel pollutant-degrading genes, the design of custom microbial consortia and the monitoring of microbial succession and pollutant breakdown in real time [99].  
These microbial innovations support precise and scalable remediation strategies with reduced ecological risk. Table 3 summarizes several key studies documenting the application of these technologies.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
###### Abstract  
Hyper-redundant space manipulators (HRSMs), with their extensive degrees of freedom, offer a promising solution for complex space operations such as on-orbit assembly and manipulation of non-cooperative objects. A critical challenge lies in achieving stable and effective grasping configurations, particularly when dealing with irregularly shaped objects in microgravity. This study addresses these challenges by developing a segmented hybrid impedance control architecture tailored to multi-point contact scenarios. The proposed framework reduces the contact forces and enhances object manipulation, enabling the secure handling of irregular objects and improving operational reliability. Numerical simulations demonstrate significant reductions in the contact forces during initial engagements, ensuring stable grasping and effective force regulation. The approach also enables precise trajectory tracking, robust collision avoidance, and resilience to external disturbances. The complete non-linear dynamics of the HRSM system are derived using the Kane method, incorporating both the free-space and constrained motion phases. These results highlight the practical capabilities of HRSM systems, including their potential to grasp and manipulate obstacles effectively, paving the way for applications in autonomous on-orbit servicing and assembly tasks. By integrating advanced control strategies and robust stability guarantees, this work provides a foundation for the deployment of HRSMs in real-world space operations, offering greater versatility and efficiency in complex environments.  
HRSM; impedance; multi-point contact; hybrid control Article
## 2 The Experimental Data
### Validating the Sound-Speed Estimations through Caustic Amplitude Modeling  
A second way to estimate \(K\) is by using caustic amplitude modeling. As shown in the previous section, the acoustic-seismic rays may bend significantly while propagating through the water and sub-bottom layers. Due to bending, a range of rays may at some point focus and intersect (Figure 6), causing a significant increase in sound intensity (i.e., forming a caustic). The formation and shape of such a caustic is sensitive to the velocity gradient. Thus, identifying such caustics in the experimental data and matching them with simulation results can constrain the value of \(K\). For this study, we carried out preliminary caustic amplitude modeling for select sets of \(c_{0}\), \(K\), and \(H_{2}\), in order to validate the estimations that were obtained from our travel-time-based geoacoustic inversion. Section 4.5 in [36] presents an analytical formula for \(K\), which makes use of the caustic's position.  
In the following model, the sound speed in the water is constant and equals the topmost sound speed at the seafloor. In the sub-bottom layer, the sound speed is linearly increasing with depth as \(c_{b}=c_{0}+Kz\). For such a model, the linear positive sound-speed gradient can be expressed as  
\[K=8c_{0}\frac{2H_{1}-z_{s}-z_{c}}{r^{2}}, \tag{7}\]  
where \(z_{s}\) and \(z_{c}\) are the depths of the sound source and caustic, respectively, \(r\) is the range between them, and \(H_{1}\) is the depth of the seafloor. For the more complex case of a vertically varying water column and a specific sound speed at the seafloor, the position and intensity of such a caustic can be numerically calculated via the modeling of a broadband sound-field propagation, for instance, by using a parabolic equation approach. The parabolic equation is a powerful approach for solving inseparable wave propagation problems, for which: (1) the properties of the medium vary gradually in range (in the horizontal directions), but may have strong vertical variations; and (2) energy that propagates outward in range dominates energy that scatters back toward the source [58; 59]. In comparison to the conventional approaches of Ray Theory and Normal Modes, the Parabolic Equation method combines computation efficiency and accuracy when the depth of the model (in the order of kilometers in our case) is much bigger than the wavelength and many radiation angles are considered. Parabolic Equation approaches do not consider very small angles of incidence, which can be neglected for high ranges (order of kilometers) due to high attenuation. Thus, here we use the Parabolic Equation approach to model a synthetic source gather for its consequent comparison with the experimental one (Figure 4c). To approximate the experimental wavelet of Figure 3 we used a Ricker wavelet with a similar spectral content (Figure 9; [60]).  
For a more robust estimation of \(c_{0}\), \(K\), and \(H_{2}\), one can perform a full geoacoustic inversion of the intensity and arrival time of the caustic, using a weight function like the one described in the previous section (Equations (5) and (6)).
## 3 Results and Discussion
### Electronic and Optical Properties  
Having analysed in detail the structural properties of the ordered and disordered kesterite crystal structures, here we focus on the electronic and optical properties. Remembering the weighted averages over the 10 symmetry inequivalent cation arrangements to obtain the structural properties of the disordered kesterite crystal structure, the same can be applied to the calculation of the density of states (DOSs) and optical properties. Figure 5a shows the DOSs of the ordered (upper panel) and the disordered (lower panel) kesterite crystal structures, thereby showing the total DOSs as shaded grey backgrounds, and the partial DOSs belonging to Cu (4d), Sn (2b), and Se (8i) as red, black, and green solid lines, respectively.  
Figure 4: Diffraction patterns for the ground state crystal structures of the ordered (black) and disordered (red) kesterite crystal structures, simulated using VESTA [21] and employing wavelengths according to (**a**) X-ray and (**b**) neutron diffraction. (**c**) Distribution function analyses for selected bond lengths, again for the ordered (black) and disordered (red) kesterite crystal structures, respectively.  
From the DOSs in Figure 5a three main conclusions can be drawn: (1) the DOSs for the disordered kesterites are smoother compared to the ordered kesterites, which can be traced back to the averaging over 10 supercells with symmetry inequivalent cation distributions and slightly different internal coordinates, but their overall values are quite similar; (2) there is no change in the contributions of partial DOSs at the valence band maximum and conduction band minimum, i.e., the orbital character of the DOSs at the band edges does not change due to disorder effects; (3) the conduction band minimum of the disordered kesterite is shifted towards smaller energies, resulting in a band gap lowering of about 182 meV, which is in line with experimental and theoretical investigations.  
Lastly, for each of the 10 symmetry inequivalent cation arrangements we also calculated the optical properties, i.e., the real and imaginary parts of the dielectric functions, as described in some earlier works [25, 26]. However, they have been used to calculate the absorption coefficient \(\alpha\), which is depicted in Figure 5b for the ordered (black lines) and disordered (red lines) kesterite crystal structures. It can be seen that similarly to the structural properties and DOSs, there is a smoothening of the absorption coefficient for the disordered kesterite due to the already mentioned averaging, and a clearly visible shift of the onset in the absorption towards lower energies, indicating the already observed band gap lowering due to the Cu-Zn disorder, respectively.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 4 Discretization Error Characteristics
### Numerical Dissipation Characteristics  
To assess the dissipation error control method using Equation (41), we performed a numerical experiments, which is plane wave propagation in a long duct, as shown in Figure 6. The duct has 400 m length with 0.05 m width. We calculated the plane wave propagation up to 1.0 s with FE mesh of \(h=0.0125\) m and \(\Delta t=1/59,224\) s. The sound speed was set as 340 m/s. The plane wave incidence with the waveform of Gaussian pulse was considered at the tube inlet. Waveforms were calculated at receivers located at \(x=10\)-330 m and were converted into frequency responses via discrete Fourier transformation. In this examination, to avoid numerical error occurring near the source, numerical dissipation error after \(x\) m propagation, \(e_{\text{n}}(x)\), was evaluated as  
\[e_{\text{n}}(x)=L_{\text{n}}(x)-L_{\text{n}}(1), \tag{43}\]  
where \(L_{\text{n}}(x)\) represents the numerical sound pressure level at point \(x\) m apart from the source. Then the numerically calculated error \(e_{\text{n}}(x)\) was compared with the theoretically evaluated dissipation error by Equation (41) as shown below.  
\[e_{\text{reference}}(x)=C_{\alpha}-C_{\alpha}x \tag{44}\]  
Figure 7a,b respectively present comparisons of dissipation error between \(e_{\text{n}}(x)\) and \(e_{\text{reference}}(x)\) and absolute errors between \(e_{\text{n}}(x)\) and \(e_{\text{reference}}(x)\). The results demonstrate that theoretically estimated values have good agreement with the numerical values. As in Figure 7b the discrepancy increases at larger propagation distances and at higher frequencies. This increase is attributable to the contribution of truncated higher order terms in \(c^{h}\) on theoretical analysis. However, the theoretical estimation gives acceptable accuracy with maximum absolute error below 0.5 dB. As the results above showed, one can assess a recommended time interval value easily using Equation (41) in advance.  
Figure 5: Theoretical dissipation error characteristics of the proposed method: (**a**) anisotropic characteristics in terms of sound propagation directions at 2 kHz and 3 kHz and (**b**) convergence of dissipation error relative to time resolution.  
Figure 6: Long numerical duct model with 400 m length.
## 2 Literature Review
The workday of each vehicle is a sequence of routes where each route starts and ends at the depot and we call it a tour. Multiple routes can be performed by a vehicle during one day, and these routes are collectively called a tour. These routes are denoted by the set \(R\) and the maximum number of routes in any tour (for any vehicle) is fixed in our model. Each vehicle can be configured to use one, two, or three wagons giving it different capacities. The configuration of each vehicle must stay the same on all trips. Next, we define some of the mathematical model's decision variables. The decision variable \(s_{ir}^{k}\) denotes the time that the vehicle \(k\) starts to service customer \(i\) in route \(r\). If the vehicle \(k\) does not service customer \(i\) in route \(r\), \(s_{ir}^{k}\) has no meaning; consequently, its value is considered irrelevant. Variable \(x_{irj}^{k}\) is one if vehicle \(k\) drives directly from customer \(i\) to customer \(j\) and zero otherwise in route \(r\). Variable \(z_{n}^{k}\) is used to determine how many wagons vehicle \(k\) needs, where \(n\) is in \(\{1,2,3\}\). If \(z_{2}^{3}=1\), vehicle 3 has 2 wagons. Therefore, \(z_{1}^{3}\) and \(z_{3}^{3}\) must be 0, which means vehicle 3 does not have 1 or 3 wagons. Moreover, finally, \(q^{k}\) is the \(k^{\text{th}}\) vehicle's capacity, depending on the number of wagons attached. \[x_{irj}^{k}= \begin{cases}1&\text{if vehicle $k$ drives directly from vertex $i$ to vertex $j$ on route $r$}\\ 0&\text{otherwise}\end{cases} \]  
\[z_{m}^{k}= \begin{cases}1&\text{if the $m$ wagons are attached for vehicle $k$}\\ 0&\text{otherwise}\end{cases} \]  
We assume \(a_{0}=0\) and therefore \(s_{0r}^{k}=0\), for all \(k\) and \(r\). The goal is to design a set of routes that minimizes the total distances of all routes and  
* Each customer is serviced exactly once;
* Every route starts at vertex 0 and ends at vertex \(n+1\);
* The time windows of the customers are satisfied;
* The total demand on a route can not exceed the capacity of the vehicle, which depends on the number of wagons attached to it (1, 2, or 3);
* Total number of the wagons used should be less than \(|W|\);
* A vehicle is assigned only one configuration;
* Each vehicle must leave the depot 0 ;
* All vehicles must return to the depot \(n+1\);
* The start time of the next route by the same vehicle should be after the finishing time of its previous route;  
An unused vehicle is modeled by driving the empty route \((0,n+1)\). The mathematical model is described next. \[\min\sum_{k\in V}\sum_{r\in R}\sum_{i\in N}\sum_{j\in N}d_{ijr}^{k}x_{ijr}^{k}\] (1) s.t.
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 4 Results and Discussions  
### Dataset  
In this paper, we adopt the publicly available ISCX VPN-nonVPN dataset (IS-CXVPN2016) to demonstrate the performance of our proposed method [26]. The dataset was established by the Canadian Institute for Cybersecurity by defining sets of tasks. It is one of the most used datasets in the field of Internet traffic identification and has been widely applied in VPN traffic identification, encrypted protocol classification, abnormal traffic detection, etc. This paper adopts it as the dataset for unknown Internet traffic identification to evaluate the performance of our proposed model. Moreover, the use of public datasets also allows other researchers to make comparisons and improvements based on our experiments to prompt progress in the area of unknown Internet traffic identification. The dataset contains 21 different Internet protocols of 7 types, which are web, email, chat, streaming, file transfer, VoIP, and P2P. In our experiment, we divide the dataset into three scenarios that are different in the types and quantities of Internet protocols. Table 1 outlines the details of the dataset.  
To demonstrate the robustness and effectiveness of our proposed model, we adopt three scenarios to make the verification. The types of Internet traffic included in each scenario are as follows:  
* Scenario A: facebook_audio, aim_chat, facebook_video, facebook_chat, email.
* Scenario B: hangouts_chat, netflix, gmail_chat, hangouts_video, ftps, hangouts_audio, icq_chat.
* Scenario C: skype_chat, sftp, scp, skype_file, skype_video, youtube, vimeo, skype_audio, spotify, voipbuster.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 2 Shielding Grounding Optimization Method
### Related Work
The contact impedance between the cable shield and the connector is also often overlooked. Therefore, this method can be improved from the perspective of modeling accuracy in the future. Combined with the analysis model proposed by other researchers, the influence of external devices such as circuit boards and connectors on cables and the entire system could be taken into account. Finite element analysis software was used to simulate and analyze the influence of these factors, and the results were imported into CST, to establish a co-simulation for multiple pieces of software and to improve the simulation accuracy.