esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 5 Performance Analysis
These comparisons, for scenarios implementing the densest fat-tree topologies, are shown in Table 3. The resources available for the ns-3 environment are the presented in Section 4.2.2, i.e., 30 CPUs and 244 GB of RAM. On the other hand, the scenarios 2\(2\)1 to 8\(8\)1 from Sibyl were executed on a cluster of 22 VMs, each with 2-core vCPUs and 8 GB of vRAM, while the scenarios 10_10_1 to 16_16_1 were executed on a cluster composed of 160 VMs, each with 4-core vCPUs and 8 GB vRAM. It is worth noting that our environment use a single CPU, this is due to the nature of the ns-3 simulator with DCE. Additionally, in addition to the information provided in the table, we observed a RAM saturation in the largest scenario performed. This can explain the gap between the execution time presented in the last of the simulated scenarios in ns-3. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Proposed Feature** & **Execution Time** & \
\begin{tabular}{c} **Memory** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **Execution Time** \\ **Improvement** \\ \end{tabular} \\ \hline None & 2:50.140 & 705 MB & - \\ Disable IPv6 & 1:44.284 & 705 MB & +38.71\% \\ Reduce simu time & 2:18.452 & 705 MB & +18.62\% \\ Without zebra & 1:29.786 & 403 MB & +47.23\% \\
**All together** & **1:18.833** & **403 MB** & **+53.67\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of execute the scenario 6\(6\)6_link-failure-spine-tof after applying performance improvements. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Number** \\ **of Nodes** \\ \end{tabular} & \
\begin{tabular}{c} **Execution Time** \\ **in ns-3** \\ \end{tabular} &
\begin{tabular}{c} **Execution Time** \\ **in Sibyl** \\ \end{tabular} \\ \hline
2 2 2 1 node-failure-leaf & 20 & 0:59 & 2:2 \\
4 4 4 1 node-failure-leaf & 80 & 4:12 & 3:4 \\
6 6 6 1 node-failure-leaf & 180 & 10:41 & 6:25 \\
8 8 8 1 node-failure-leaf & 320 & 21:16 & 20:49 \\
10 10 1 node-failure-leaf & 500 & 36:31 & 14:18 \\
12 12 1 node-failure-leaf & 720 & 1:22:40 & 37:3 \\
14 14 1 node-failure-leaf & 980 & 2:41:23 & 1:5:31 \\
15 15 1 node-failure-leaf & 1125 & 7:21:43 & - \\
16 16 1 node-failure-leaf & 1280 & - & 1:44:58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of execution times between the environment in ns-3 and the sibyl framework for the failure of a leaf node in fat-trees multi-plane.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs
### Boolean Overlay  
Although various MCDA techniques in which GISs are employed are useful for site selection for solar power plants, several studies have also been performed to find suitable sites by considering constraints using Boolean overlay operators.  
The potential of large-scale CSP has been mainly studied in Africa, which has strong solar energy [24; 25]. Conditions such as solar radiation, proximity to transmission lines, terrain, land cover, and hydrology can be considered to select a suitable area for CSP plant installation. Although the detailed constraints are different for each study, the suitable area for large-scale PV plant installation can be analyzed using similar approaches [26; 27]. The constraints such as population areas and infrastructures are also important for evaluating suitability as well as the geographic factors such as slope, aspect, and land cover. Some studies have used GHI and DNI to analyze both PV and CSP [28]. After selecting a suitable area, the installation capacity or power generation of the area can be analyzed. This Boolean overlay method has the advantage that it is the simplest method for solar site evaluation, but it has a disadvantage that it cannot quantify the relative suitability for the appropriate region.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 2 Motivation and Design of the S3B Tandem Phase
### Timeline of the Sentinel-3B Tandem Phase  
The Sentinel-3B satellite was launched on 25 th April, 2018. After early operations, the satellite was put on a drifting orbit. The Sentinel-3B instruments were activated during the month of May 2018, and the satellite reached its tandem position on 7 th June, 2018. Both satellites followed the same ground track (within the across-track control range of \(\pm 1\) km) at a 30 s interval. During the tandem phase, the operational mode of the SRAL-B instrument was changed several times for sensitivity analyses. On 16 th October, 2018, Sentinel-3B was placed on a drifting orbit to reach its nominal operational position, forming an orbital angle of 140\({}^{\circ}\) with Sentinel-3A. As mentioned above, the drift rate was kept low until 24 th October and increased during the following month. The nominal orbital position was reached on the 23 rd November 2018. Figure 1 below provides a schematic overview of the main events of the Sentinel-3 tandem operations. A detailed mission activity timeline is available from the website of the Sentinel-3 tandem for climate study [12].  
The tandem phase provided the opportunity to compare various operational modes for the surface topography mission: synthetic aperture radar (SAR) mode in \"open\" or \"closed\" loop tracking, low-resolution mode (LRM). The timeline of the joint S3A/S3B altimetry operations is provided in Figure 2. This point is further addressed in Section 3.  
Figure 1: The main phases of the Sentinel-3B tandem operations.  
Figure 2: Sentinel-3A and -3B tandem phase altimetry chronology.
## 4 Discussion  
### The Necessity of Comparing Different Research Methods  
Different model training methods have their strengths and limitations. Selecting the most appropriate algorithm based on specific research objectives is essential. Linear regression models exhibit limited predictive performance, as they struggle to effectively capture nonlinear features in the data. As shown in the training results in Section 3.1, the XGBoost and MLPRegressor models delivered the best performance. Since most parameters related to SIF can be derived from satellite observations, the types of input features used in the model are relatively consistent, and complex feature engineering is not required. Neural network models, due to their complex structure, demand higher computational resources and have lower training efficiency. However, they are advantageous for large and complex datasets where deep nonlinear relationships exist among features. Most current studies adopt neural network algorithms for regional downscaling [21], striking a balance between accuracy and efficiency. For global SIF downscaling tasks, tree-based models offer advantages such as faster training and simpler parameter tuning [19]. Overall, compared to other algorithms, XGBoost maintains excellent predictive performance while demonstrating superior training efficiency. Furthermore, while interpretability techniques like SHAP analysis are applicable to many models, including neural networks, their application to tree-based models like XGBoost is particularly computationally efficient and direct [51]. Therefore, by comprehensively considering its high prediction accuracy, computational efficiency, and practical, efficient interpretability, this study ultimately selects XGBoost as the optimal prediction model.  
In addition, the HRSIF product generated in this study is based entirely on machine learning algorithms, whereas the SIF\({}_{\text{Diveiller}}\) product is derived from a semi-empirical approach that relies on predefined equations and parameter fitting, which may introduce uncertainties due to the assumptions inherent in those formulations. Compared to SIF\({}_{\text{Diveiller}}\), the HRSIF product developed in this study offers several notable advantages. First, HRSIF employs a fully data-driven machine learning method that avoids dependence on prior assumptions from semi-empirical models, thereby reducing the risk of biases caused by model simplification. Second, by incorporating land cover classification data, the HRSIF approach can better adapt to the complex environmental characteristics of different regions. As shown in Section 3.1, the model performs well across various land cover types, which enhances its applicability to diverse ecosystems and allows for a more accurate representation of GPP, as demonstrated in Section 3.4. Moreover, this method provides flexibility to incorporate additional remote sensing variables into the training process, further improving its capacity to characterize the spatiotemporal variability of SIF.
# Advances in Random Fiber Lasers and Their Sensing Application
## 2 RFLs Based on Various Optical Fibers  
It is quite remarkable that RFLs based on kinds of optical fibers would have distinct output properties, owing to corresponding features for different optical fibers. A variety of optical fibers, such as PCF, single-mode fiber (SMF), active optical fiber (AOF), dispersion compensation fiber (DCF), polarization-maintaining fiber (PMF), multimode fiber (MMF), tapered optical fiber (TOF), and true wave fiber (TWF), can be applied to rather different families of RFLs, which could be helpful for better understanding the mechanism of random lasing in the disordered system and even greatly broaden the application of RFLs. Therefore, investigations on the RFLs based on different kinds of optical fibers are expected to be very necessary.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 3 Network Model and Problem Formulation
### Problem Formulation
Therefore, by utilizing Equation (9) we ensure that \(\alpha\) is greater than the link utilization of each individual link within all time slots. \[\sum_{k\in P_{j,i}}\sum_{e\in E}x^{i,j}_{k}l^{ij}_{k,e}d^{i}_{e}\leq r_{j}, \quad\forall j\in F,\forall i\in\mathcal{T} \tag{1}\]0  
Finally, taking into account both the number of path handovers and network load balancing, the ILP formulation of our problem is:  
\[\sum_{k\in P_{j,i}}\sum_{e\in E}x^{i,j}_{k}l^{ij}_{k,e}d^{i}_{e}\leq r_{j}, \quad\forall j\in F,\forall i\in\mathcal{T} \tag{1}\]1 (10) \[\sum_{k\in P_{j,i}}\sum_{e\in E}x^{i,j}_{k}l^{ij}_{k,e}d^{i}_{e}\leq r_{j}, \quad\forall j\in F,\forall i\in\mathcal{T} \tag{1}\]2 (1)-(9)  
So far, we have successfully modeled the routing optimization problem for TS flows in LEO satellite networks as an integer linear programming (ILP) model. While solving this model can provide the optimal routing solution for TS flows, the complexity of solving ILP models is exponential, which is not feasible for large-scale LEO satellite networks. Furthermore, it can be easily proven that this problem is an NP-complete problem since even when simplifying the problem to a single time slot scenario, it remains NP-hard [27]. To effectively solve this problem, we propose an approximate algorithm called the longest continuous path (LCP), which strikes a good balance between solution speed and performance. The details of the LCP algorithm will be discussed in the next section.
## 1 Introduction
MID can detect the modulation components in a first-order modulation signal, but it still cannot accurately detect the carrier components and the corresponding modulation components in a multi-harmonic modulated vibration signal. Therefore, a hybrid demodulation approach has been proposed for fault diagnosis in a multi-stage gearbox in this work, which combines the VMD and MID techniques and has some complementary advantages in the demodulation of a complex signal. This article is organized as follows: the VMD theory and its reduced frequency aliasing effects are introduced in Section 2. The hybrid demodulation technique is proposed in Section 3. Its effectiveness is first demonstrated using a simulated signal combined with MID associated with the EMD technique. The proposed approach is then applied to detect four types of tooth fault in a multi-stage gearbox in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Materials and Methods
### Phenology-Aware Module (PAM) Module
#### 3.3.4 Spatiotemporal Integration  
Although temporal features are crucial, spatial context should also be utilized, as crops grow in continuous fields and exhibit spatial patterns. After obtaining the multi-scale temporal features for each location and time, PAM integrates spatial information through a 2D convolution layer. In practice, the output of the temporal convolution stage is a sequence of feature maps. Specifically, a 3 \(\times\) 3 2D convolution kernel (128 filters, stride 1,'same' padding) is used to extract local spatial features (see Formula (10)):  
\[F_{PAM-spatial}^{t}=Conv_{3\times 3}\left(F_{fused}^{t}\right)\cdot\beta_{t} \tag{10}\]  
where \(\beta_{t}\) is the time attention weight (to be generated in subsequent steps), used to dynamically adjust the contribution of each time step. Through this process, temporal information is effectively integrated with spatial information, thereby enhancing the model's performance in complex scenarios.
## 2 Data Overview
### Data Preprocessing  
The condition monitoring data of the concrete pump truck studied in this paper are time series data collected by sensors. Due to factors such as the timing error of sensors or poor communication conditions, certain data are missed in the data set. For the four types of data, such as engine speed, system pressure, pumping capacity, and reversing frequency, the missing data may be very close to the data uploaded the previous time due to the high data collecting frequency, so the nearest complement method is adopted to fill in missed data. The cumulative working time is accumulated data; it can be assumed that the changing of the cumulative working time is slow and uniform, so the linear interpolation method is adopted to fill the missed data [25]. The original data of the engine speed in a certain period of time is shown in Figure 3, and the processed data is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Data Type** & **Unit** & **Scope** & **Meaning** \\ \hline Engine Speed & integral & RPM & 0–2000 & The speed of pump truck engine \\ System Pressure & integral & MPa & 0–32 & pump hydraulic pressure of pumping system \\ Pumping Capacity & integral & \% & 0–100 & The percentage of pump truck in its maximum value \\ Reversing Frequency & integral & times/minute & 0–30 & Pump cylinder reversing times per minute \\ Cumulative & floating & hour & \(\geq\)0 & Cumulative working time of pump truck \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meanings of condition monitoring data of the concrete pump truck.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 2 Methodology  
### Research Question Formulation  
A crucial step in conducting an SLR is to formulate clear and focused research questions that guide the entire review process. The following research questions were defined to address specific aspects of diffusion algorithms, network simulators, and their performance in IoT and Space IoT configurations:  
* Research Question 1: General What are the key diffusion algorithms and network simulators utilized in the literature to study network diffusion in IoT and Space IoT configurations, and how do these algorithms perform in various scenarios?
* Research Question 2: Network Simulators Which network simulators are commonly employed in the literature to simulate and study network diffusion in IoT and Space IoT configurations, and what are their features and limitations?
* Research Question 3: Network Diffusion Algorithms What are the key characteristics, strengths, and weaknesses of network diffusion algorithms used in studies focusing on network diffusion in IoT and Space IoT configurations?* Research Question 4: Network Architecture How does the choice of network architecture impact the performance of network diffusion algorithms in IoT and Space IoT configurations?
* Research Question 5: Network Diffusion Performance Evaluation What performance metrics are commonly used to evaluate the effectiveness of network diffusion algorithms in IoT and Space IoT configurations, and how do these algorithms perform under various scenarios?
* Research Question 6: Performance evaluation of the network simulator How do existing network simulators perform when simulating network diffusion in IoT and Space IoT configurations, and what are the key metrics used to evaluate their performance?  
These research questions are designed to guide this literature search and review, ensuring that the existing literature relevant to the study's objectives is comprehensively analyzed.
# Analytic Form Fitting in Poor Triangular Meshes
## 2 Literature Review
### Mesh Segmentation Fitting Methods  
In these methods [6; 7; 8], the priority is to segment the input (point-normal vector set or point graph). The fitted analytic form is a by-product. These methods create input clusters using diverse strategies: fitting-segmentation iteration, grouping using X-means or mean shift (separate segmentation), or classification by ranges of additional information (scalar or vector fields) such as color, depth, abrupt dihedral angle, etc. These methods have limitations as they need at least one of the additional pieces of information mentioned above and also require dense datasets.  
Ref. [6] describes a hierarchical segmentation of dense point clouds. This method generates the k-nearest graph by merging edges of the input connectivity graph. This merging uses as criteria a penalty function related to the quality of fitting of the point clusters to a given primitive (one of sphere, cone, plane, torus). Ref. [7] applies segmentation-fitting iterations seeking to simultaneously segment the full input set and to fit quadratic analytic primitive options (plane, sphere, cylinder, ellipsoid, paraboloid) to the support subsets. It executes an initial segmentation of the input dataset before the iterative process. Ref. [7] does not provide detailed information on such a preprocessing. Ref. [8] executes a color-based segmentation of indoor 3D depth map scenarios. Then, a matching is conducted between the generated patches and a template database of predefined 3D indoor furniture models. Finally, the user refines the segmentation. The authors train a matching algorithm of random-regression forest to obtain the desired result. The process of matching the support set \(S\) to various reference models is an expensive task and is out of our scope since we do not need to train any learning algorithm. Ref. [9] executes a rough segmentation of a dense good quality point cloud or triangular mesh. This segmentation seeks to find neighborhoods of the input data which present _slipage_ compatibility, i.e., which can slide onto themselves (e.g., spheres, cylinders, planes). The segmentation is carried out by using a greedy clustering, based on the local slippage compatibility. This reference aims for an approximate segmentation. It does not determine the \(g()\) analytic form, nor presents execution times or complexity information.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion  
This section presents the experimental results for this proposal. The proposed method was tested on 1000 color images extracted from the COCO dataset [30], which contains images of complex everyday scenes with common objects in their natural context. The experimentation was performed in terms of imperceptibility and robustness. To provide clarity in the testing, the results are reported as \"PSO-DFT\" and \"Conventional DFT\" labels in each figure, to show the improvements of performance obtained using the PSO algorithm in 2D DFT-based invisible watermarking. For conventional 2D DFT-based invisible watermarking, the values of pair of radiuses \(r_{1}\) and \(r_{2}\) are fixed at 50 and 150, respectively, and the value of the watermark strength \(\alpha\) is 1.5, accordingly to our previous proposal reported in [6].
# Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
## 6 Experimental Study
### Constant Force Grinding Experiment  
According to the geometric model of the workpiece surface, the Cartesian space trajectory is planned, the specific grinding pose of the robot end effector is deduced via interpolation with the given feed speed, and the grinding trajectory of the joint space is obtained online with the inverse differential kinematics of the robot. According to the on-site machining requirements, the surface quality of the workpiece should be below Ra 0.3 \(\upmu\)m, the reference grinding force is 5 N, and the force fluctuation is \(\pm\)0.2 N. Table 4 shows the grinding process parameters. The CIC, AVIC, and DPAVIC control parameters are set to the same as the flat force control parameters.  
As can be seen from Figure 16, the force error in the CIC force tracking control method is far more than \(\pm\)0.2 N, and the force control result of AVIC is slightly better than that of CIC, while the error of the DPAVIC method stays within \(\pm\)0.2 N. This proves the ability of the method to improve the precision of force control.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feed Rate** **(mm/s)** & **Grinding Tool Diameter** **(mm)** & **Sandpaper** **(grit)** & **Grinding Tool Speed (rpm)** **(N)** \\ \hline
15 & 50 & 100 & 3000 & \(-5\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grinding process parameters.  
Figure 16: Contact force signal during grinding.  
Figure 15: Force control results of large curvature surface.  
Figure 17 shows the robot grinding process and the surface quality of the workpiece after grinding. To verify the effectiveness of the proposed method for improving machining quality, a JiTai TR200S roughometer was used to measure the surface roughness of the workpiece, a Dino-Lite microscope was used to photograph the grinding surface morphology, and a Mitotoyo Roughness Meter SJ-210 was used to measure surface waviness. As shown in Figure 17b, 10 points on the workpiece surface are selected and the average of three measurements is taken.  
Figure 18 shows the measured roughness of the workpiece. Compared with the CIC method, the average surface quality of the workpiece is improved from Ra 0.5 \(\upmu\)m to Ra 0.218 \(\upmu\)m. Figure 19 shows the influence of the three force control methods on the workpiece surface waviness. Figure 19a shows that when using the CIC method, the workpiece surface waviness fluctuates with the grinding force oscillation. Figure 19b shows a slight reduction in waviness with AVIC compared with the CIC method. Figure 19c shows the ability of the DPAVIC method to reduce the workpiece surface waviness, which has the lowest waviness compared with the other two methods. Therefore, the proposed method has the ability to suppress the external disturbance and reduce the force tracking error and fluctuation.  
Figure 17: Workpiece grinding quality.  
Figure 18: Workpiece surface roughness.