esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Application of Geochemical Modeling for the Performance Assessment of Radioactive Waste Disposal
The model does not contain parameters to modify the standard state or consider independent reactions among chemical species. Its application is currently limited to low-temperature and low-pressure conditions. The Pitzer equation has been used in several geochemical simulation software packages. It is often used for the analysis of environmental problems involving high-concentration solutions. (3) The thermodynamic equilibrium constants of a reaction must be further supplemented and improved. Currently, the equilibrium constants of various minerals and soils for surface complexation models of radionuclides are scant. Hence, experts should compile and evaluate data in order to determine the recommended values of such constants. The composition of soil is complex and varies according to the place of origin, which imparts strong regional characteristics to the equilibrium constants of surface complexation models. Therefore, it is likely that researchers will need to customize their databases to their local conditions. (4) Surface complexation should be included in chemical transport modeling in order to evaluate the safety of nuclear waste disposal. Empirical methods, such as the isothermal linear model (Kd method) [67; 68; 69; 70], were previously used to calculate the groundwater and radionuclide transport in the performance assessment of radioactive waste disposal. Although the Kd method can describe the heterogeneous interface behavior of radioactive species at specific pH and Eh, as well as the composition of the ions in the surrounding water, it is not suitable for situations involving changing geochemical conditions. On the other hand, a surface complexation model can account for changes in hydrochemistry and mineral ion composition with time, and thus better represents the effects on migration of radionuclides under varying geochemical conditions [71]. Surface complexation should be included in chemical transport models in order to better support the reactive chemical transport of radionuclides between water and minerals. Thus, it provides a more powerful simulation basis for heterogeneous interfaces in geochemical reactions. (5) The chemical field (C) of a geochemical model can be coupled with the thermal-hydraulic-mechanical stress (THM) model to form a fully coupled THMC model. Examples like the PHREEQC model have been successfully coupled with the COMSOL model [72]; additional examples of model coupling include the COMSOL-IPHREEQC model [73], the coupled thermal-hydraulic-chemical-geomechanical model [74], THM-GeoC [75], and interface COMSOL-PHREEQC (iCP) [76].
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 1 Introduction
Academician [PERSON]'s team derived a combined system error model, taking the ADS-B system delay into account, and solved it using generalized least squares (GLS) [22], both of which require the linearization of the nonlinear model to be solved. [PERSON] proposed the use of the SICP algorithm to estimate the radar systematic error. This method does not need to establish the radar measurement error model, and only realizes error estimation through the translation transformation matrix between two tracks. However, this method is sensitive to measurement noise and too dependent on data correlation results [23]. [PERSON] proposed a radar systematic error estimation scheme, based on the probability hypothesis density (PHD). Although it can achieve target error estimation without prior correlation information, it also has an approximation to nonlinear equations, meaning the radar calibration effect was affected [24]. To solve the above problems, this paper first studies the radar calibration method, based on the cooperative target, and establishes the integrated radar measurement error model by referring to the position information of the cooperative aircraft in the radar  
Figure 1: Radar measurement error. range. On this basis, an error estimation method based on the improved sparrow search algorithm (ISSA) is proposed to transform the nonlinear model solving problem into the multidimensional multi-peak optimization problem, so as to avoid the introduction of deviation in the linearization process of the nonlinear system. At the same time, a variety of strategies are used to improve the convergence speed of the sparrow search algorithm (SSA), so that the radar systematic error estimation can be both accurate and in real time, so as to improve the radar calibration effect and the target track accuracy.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 2 Materials and Methods  
A common harvesting system is a rectenna, which consists of an antenna to collect ambient energy, a matching network, and a rectifier to transform the signal from AC to DC (Figure 1). In this paper, only the antenna component of a rectenna is studied and optimized, in regard to its ultra-wideband properties, specifically broadening its bandwidth to include 5G NR, as well as maintaining all other characteristics that make an antenna suitable for rectenna applications, like omnidirectionality, etc.  
The initial antenna design, presented in [1], represents an ultra-wideband patch antenna with slots, stubs, and partial grounding, capable of harvesting RF energy signals from 1.7 to 2.7 GHz, while in [2] an optimized version of this antenna is proposed that operates in the same bandwidth, but with many fewer degrees of freedom than the initial structure. This research constitutes an extension of the initial effort in [2] to optimize the structure presented in [1].  
In the literature, a variety of antennas for RF energy harvesting applications are proposed, including almost all possible types of antenna. In [4], a square, electromagnetically coupled, two-layer antenna, capable of harvesting energy from three bands, namely at 2.1 GHz, 2.4-2.48 GHz, and 3.3-3.8 GHz, is proposed. The authors in [5] suggest a slotted fractal patch with partial grounding, operating from 2.15 to 2.9 GHz. Then, in [6], a cross-dipole slotted antenna is presented, harvesting from 1.8 to 2.5 GHz. The broad-band 1 \(\times\) 4 quasi-Yagi-Uda antenna array in [7] harvests from 1.8 to 2.2 GHz, while, the 16-port wideband dual-polarized patch antenna presented in [8] operates from 1.74 to 2.57 GHz. The authors in [9] propose a compact broad-band slotted patch antenna operating from 2.1 to 3.5 GHz, for harvesting energy from the LTE band. In Table 1, a comparative study of the optimal antenna proposed and broad-band antennas from the literature in the same frequency band is displayed.  
It is obvious that the proposed antenna outperforms the previously suggested antennas for RF energy harvesting for this frequency band, as it has a greater operational bandwidth and is the only one capable of harvesting from all the vastly used wireless telecommunications technologies, i.e., GSM (1.8 GHz), UMTS (2.1 GHz), Wi-Fi (2.4 GHz), LTE (2.6 GHz), and 5G NR (3.5 GHz). This makes the proposed design one of the most suitable antenna components for rectennas that aim to collect as much energy as possible in modern telecommunications networks, as its huge frequency band of operation can cover the majority of RF energy harvesting scenarios.  
Figure 1: Rectenna components.
## 5 Validity of Experiment
### Limitations  
Several limitations must be acknowledged in this study. First, the realism of the simulation environment is a concern, as our research relies on simulations run within the EdgeSimPy framework. These simulations offer a secure and controlled testing environment, but it is possible that they do not accurately represent the complexity and unpredictability of real-world edge cloud deployments. Real-world performance may differ from simulation results due to a variety of factors, including hardware failures, network congestion, and ambient factors. Utilizing simplified workload scenarios to assess resource management algorithms has another drawback. Although these scenarios are meant to illustrate typical edge computing use cases, they might not fully capture the variety, dynamic nature, and unpredictability of real-world workloads. More research into increasingly complicated and variable workload circumstances may be necessary to gain a more thorough grasp of algorithm performance. In addition, the scope of algorithm comparison places limitations on our study. While additional possibly pertinent algorithms were not taken into consideration, we evaluated four widely used resource management algorithms in edge cloud situations. Future studies could incorporate a wider range of algorithms in this comparison, providing a more comprehensive analysis. This study recommends that future research look into dynamic adaptive mechanisms and sophisticated computational tools to address these limitations. The incorporation of these novel methodologies may augment the robustness and flexibility of resource allocation schemes, rendering them more appropriate for the heterogeneous and dynamic terrain of edge computing.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Grid Mode for Cluster Head Transmission Power Adjustment  
Since the UAV moves at a high speed, if the transmission power of the cluster head is changed due to the distance between the UAVs, the cluster head needs to calculate the required transmission power all the time, wasting resources. We designed a grid mode-based transmission power adjustment method, as shown in Figure 3. The transmission power of cluster heads was set discretely, and each grid has different colors corresponding to the different sizes of transmission power. The advantages of this design are: (1) avoiding the frequent change in transmitting power and thus reducing the energy consumption caused by its internal calculation; and (2) predicting the moving trajectory, so that the cluster head can analyze the trend of cluster members and accurately adjust its transmitting power.
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 1 Introduction
### Paper Contributions
The paper proposes the development of an intelligent, personalized, and specialized monitoring system for the Azure infrastructure to prevent medical complications in the elderly. Unlike existing solutions, this work integrates a multidimensional anomaly detection algorithm with cloud infrastructure, ensuring efficient data processing and medical alerting. The system contributes to proactive healthcare management by identifying physiological deviations before they escalate into critical conditions. The paper structure is as follows: The methodology is presented in Section 2, where the novel elements are introduced, along with the description of the dataset, the proposed system, the analyzed algorithms, and the architecture and communication of the proposed IoT infrastructure. Section 3 focuses on HADA's results in detecting anomalies, monitoring elderly individuals' data, and performing IoT infrastructure speed tests. In addition, HADA was generalized to outline its use in real-world scenarios. The discussion of the results is depicted in Section 4, and the conclusions are detailed in Section 5.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 5 Experimental Results
### Accuracy Analysis  
The TUM dataset provides the ground truth of the real trajectory for the convenience of comparison with the experimental results. The indicators to evaluate the accuracy of DS-SLAM implemented in this paper are ATE (Absolute Trajectory Error) and RPE (Relative Pose Error). ATE is the direct difference between estimated pose and real pose, which can directly reflect the accuracy of the algorithm and the global consistency of the trajectory. RPE is used to evaluate the drift of the system. In this paper, we present the values of Root Mean Squared Error (RMSE), Mean Error, Median Error, and Standard Deviation (S.D.). The experimental results are shown in Tables 4 and 5, comparing the accuracy of ORB-SLAM, DS-SLAM software algorithm and hardware implementation DS-SLAM proposed in this paper under different data sequences. Figure 8 shows ATE and RPE plots from ORB-SLAM2 and hardware implementation DS-SLAM in high dynamic sequence (fr3_walking_xyz) respectively. The ATE results show that black is the true value of the trajectory, blue is the estimated trajectory, and red is the error between the two.  
Compared with ORB-SLAM2, the introduction of semantic information in DS-SLAM can eliminate the interference of moving objects and significantly improve the accuracy in thr dynamic environment. The accuracy of our work is in the same order of magnitude as DS-SLAM, but it is slightly reduced because of model quantization. We have made a trade-off between accuracy and energy consumption, sacrificing a little precision to improve the energy efficiency of the system, providing a solution for the actual deployment of DS-SLAM on mobile robots.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 2 Foundations and Evolution
### Historical Evolution of Computer Vision in Robotics  
The field of computer vision (CV) has undergone a profound evolution, transitioning from the early heuristic-based image processing in the 1980s to the sophisticated deep learning-driven perception systems today. Initial robotic vision focused on basic tasks such as edge detection, object localization, and obstacle avoidance using monocular cameras [20].  
Advances in sensors, notably RGB-D cameras and LiDAR, enabled three-dimensional scene understanding and real-time spatial mapping [21; 22]. Simultaneously, breakthroughs in algorithmic foundations, such as convolutional neural networks (CNNs) and simultaneous localization and mapping (SLAM), transformed perception capabilities [21; 23; 24]. In collaborative robotics, CV has evolved from a passive sensing modality to an active, predictive component that is essential for real-time scene understanding, human activity recognition, and adaptive task execution [25].
# Location-Aware Point-to-Point RPL in Indoor IR-UWB Networks
## 2 Related Works
The cooperative positioning algorithms by using multiple nodes can improve the positioning accuracy problem caused by the channel environments [8; 11; 18; 19]. The Inertial Navigation System (INS) provides a methodology for tracking the position, direction, and speed (direction and movement speed) of a mobile device by using motion sensors (accelerometers) and rotation sensors (gyroscopes). Related research experiments have shown that the combination of cooperative positioning and INS algorithms results in accurate mobile node tracking and network-wide positioning performance improvement [18; 20; 21; 22]. To reduce the cost associated with the P2P-RPL route discovery overhead, we use the UWB-based cooperative multi-hop positioning with smartphone INS tracking by particle filtering for the exact self-localization of each node. From the accurate location information for each network node, Location-Aware P2P-RPL algorithm is proposed with limiting the route discovery packet forwarding region adaptively based on the location information of the source and the destination nodes.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Ablation Experiments  
This section validates the effectiveness of different optimization modules through ablation experiments. We sequentially added the AOD-Net dehazing preprocessing module, the CSPCM++ module, the LSKNet attention mechanism, the FocalGIoU loss function, and the DWConv module to the baseline model YOLOv5s, creating several improved models. These improved models were then compared using the same test data.  
The experimental results are shown in Table 4. Among these, Model A refers to the baseline YOLOv5s model. Model B uses the dataset after applying AODs de-fogging. Models C through H all involve de-fogging using the AODs model. Specifically, Models C, D, and E incorporate the CSPCM++ module, the LSKNet attention mechanism, and the FocalGIoU loss function, respectively. Models F, G, and H are combinations of these modules, with Model H representing the final model proposed in this paper. The table displays that the baseline model YOLOv5s achieves a [EMAIL_ADDRESS] of 80.80% and a [EMAIL_ADDRESS]-0.95 of 54.30%, while our AODs-CLYOLO model reaches 83.80% and 59.80% for these metrics, respectively. Analyzing the detection samples reveals that the CSPCM++ module and the LSKNet attention mechanism significantly contribute to the improvement in accuracy. Our method effectively integrates multi-level features by combining global features, which positively impacts the detection of foggy industrial scenes. By adding four optimization modules, the AODs-CLYOLO model (Model H) achieves the best performance, with [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 improving by 3.7% and 10.1%, respectively, compared to the baseline model (Model A).  
Figure 8: Plot of object detection results for this paper’s algorithm and YOLOv5s. The first and third rows show the detection results of YOLOv5s, while the second and fourth rows show the detection results of the proposed model.  
To validate the effectiveness of the CSPCM++ module, we conducted comparative experiments using four different modules: Convmixer, Convmixer++, CSPCM, and CSPCM++. The results are presented in Table 5. The data indicate that, compared to the original baseline model, adding the improved CSPCM++ module to the detection head significantly enhances the mAP (0.5-0.95), reaching 58.80. This demonstrates the effectiveness of the detection head proposed in this study.
# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 4 Reputation-Based Collaborative User Recruitment Algorithm
### Cost for Tasks  
Generally speaking, in MCS, for any sensing tasks, there will be different constraints. For example, air quality sensing needs sensing data in different periods at the same loca tions, and autonomous driving will arrange the route in advance based on real-time traffic conditions. In order to meet the various requirements of the tasks, we assume that each task in each timeslot will recruit one user to sense and collect data, which can achieve the goal of more effective sensing and maximum coverage.  
No matter what kinds of nodes, the mobile users or the edge nodes, the participants who are recruited by the cloud platform \(P\) execute the whole sensing process and complete the tasks, which would have a cost, such as time and energy loss. Actually, when the users or edge nodes perform the tasks, the longer the execution time is, the more energy will cost. If the tasks need longer sensing time, the users or edge nodes will spend more time and energy executing the tasks. Although participants are volunteers, they will ask for a reward to cover their costs. In our system model, we assume that when each participant acquires the information about task \(\theta_{j}\), there will be a cost representing the loss which is proportional to the time they will spend performing the tasks. \(C_{u_{i}}^{\theta_{j}}\) is the cost of the user \(u_{i}\) for task \(\theta_{j}\) and \(C_{s_{n}}^{\theta_{j}}\) represents the sensing cost of the edge node \(S_{n}\) for task \(\theta_{j}\). For every task, it will have a reward for the participants to encourage their contributions when they complete the tasks and collect these expected data. \(R\omega_{\theta_{j}}\) is described as the reward of the task \(\theta_{j}\).  
In order to keep the budget balanced for each task, the cloud platform \(P\) will recruit participants whose costs are below the reward of the task. Therefore, we can attain the relationship between the costs of the participants and the tasks as follow:  
\[C_{u_{i}}^{\theta_{j}}\leq R\omega_{\theta_{j}}, \tag{11}\]  
\[C_{s_{n}}^{\theta_{j}}\leq R\omega_{\theta_{j}}. \tag{12}\]
# Neutralization Dynamics of Slow Highly Charged Ions in 2D Materials
## 2 Experimental Results on Highly Charged Ion Transmission
### Our Work
#### 2.2.2 Single Layer Graphene
The inset shows a fast-fourier transform of the image indicating a single crystallographic orientation of the SLG in this image. Residual contaminations are visible as varying grey contrast probably as a result of sample transport in air to the microscope. 180 keV Xe\({}^{40+}\) were applied and a several \(10^{4}\) nm\({}^{2}\) area was screened at atomic resolution. Note that ion fluencies between 100 and \(10^{4}\) ions/\(\upmu\)m\({}^{2}\) can be applied at maximum and (S)TEM measurements at atomic resolution demand typically FoVs of \(50\times 50\) nm\({}^{2}\) or smaller. Hence, many microscopy images were taken from different samples to gain statistical certainty about the absence of HCI induced pores. Irradiations were performed over several hours and even days, because of low ion fluxes provided by the HCI sources. Since we do not see gradual changes in the transmission over days, we conclude that additional contaminations from the background gas in our experimental chambers have a minor effect compared to contaminations from the (wet-chemical) transfer procedure. Figure 6: (Color online) (**a**) 2D charge state spectrum of 180 keV Xe\({}^{20+}\) after transmission through a freestanding single layer of graphene with different charge state positions marked by white lines and the scattering angle on the abscissa. (**b**) Integrated charge state spectrum (**a**) for scattering angles between \(-0.25\)\({}^{\circ}\) and \(+0.25\)\({}^{\circ}\) (please note the log-scale). (**c**) Relative neutralization \(\left(\frac{Z-Q_{\text{exit}}}{Z}\right)\) deduced from the mean of the exit charge state distributions (e.g., see (**b**)) as function of ion velocity. The equilibrium charge state \(Q_{\text{eq}}\) is given according to [PERSON] [40].