esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 3 Proposed System  
This paper considers efficient load balance for application data over multiple connections. The solution proposed in this work address are:  
* A general solution that is by making a fair distribution of application data over the available multiple connections in accordance with their own preferences.
* Attainment of a load balance solution without altering neither the hardware nor any of TCP/IP protocol suite.  
Load-balance techniques can be classified into equal cost load balance (ECL) and unequal cost load balance (UCL). The ECL is based on sending data in an equal distribution form either as per packet or session. The UCL is based on sending data unequally over all available connections.  
The load-balance technique uses scheduler modes, either as a round robin or a weighted round robin mode [40]. The round robin sends data as a packet per packet over all available connections in a rotational order. Weighted round robin does nothing different, but its distribution ratio depends on the bandwidth of the connections. The round robin mode has issues related to performance degradation of the TCP applications and multimedia applications. However, the weighted round robin techniques have proven to be relatively better.  
Our proposed model will provide utilization of the available network connections based on the weighted round robin mode. One primary issue with weighted round robin is that it depends on the connection bandwidth. The connection bandwidth is not the seldom attribute used to judge on the connection. One way to overcome this problem is by allowing the proposed model to be dependent on several attributes. The connection utility function describes the mechanism used to distribute the application data as a weighted round robin.
## 5 Conclusions  
In this paper, the results clearly demonstrate the effectiveness of using a PID controller combined with the PSO algorithm for frequency regulation in multi-area interconnected power systems, particularly in two-area and three-area systems. Simulation results highlight the ability of the PID-PSO method to regulate frequency fluctuations, significantly reduce frequency deviations, and shorten the stabilization time compared to the traditional PID-ZN method. In three-area systems, the PID-PSO method exhibits strong capabilities in maintaining frequency stability under continuously changing load conditions, further emphasizing the practical applicability of this study. These results underscore that integrating the PSO algorithm into PID parameter tuning not only enhances precision but also improves system resilience and performance under complex load fluctuation scenarios.  
Although the PID-PSO method has proven to be effective, there are still many potential directions for further development. These include comparisons with other algorithms, such as GWO or WOA, or the application of hybrid approaches to fully leverage the strengths of each algorithm. Additionally, integrating artificial intelligence, such as deep learning and reinforcement learning, promises to significantly enhance PID parameter optimization, especially in nonlinear systems or systems integrating renewable energy sources. Practical experiments on power systems, combined with research on smart energy management, and load forecasting will be key to improving system performance and reliability.  
Figure 13: Convergence of PSO for PID frequency control (scenario 3).  
**Author Contributions:** Conceptualization, V.H.P. and V.N.N.T.; methodology, D.H.T.; software, D.N.C.; validation, V.H.P. and D.H.T.; formal analysis, D.H.T.; investigation, D.H.T.; resources, D.H.T.; data curation, D.H.T.; writing--original draft preparation, V.N.N.T. and D.N.C.; writing--review and editing, V.H.P.; visualization, D.H.T.; supervision, V.H.P.; project administration, D.H.T.; funding acquisition, D.H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Materials and Methods
### Mass Spectrum Validation  
All the LC-MS analyses in this study were performed at the Centre National en Electrochimie et en Technologies Environmentales, Inc. (CNETE, Shawinigan, QC, Canada). To validate the presence of the compounds of interest, the samples were analyzed under the same LC conditions but used the MS detector, achieving this by diluting each sample 10 times in methanol at LC-MS grade containing 0.1% formic acid and injecting the samples into a Thermo Scientific UPLC Dionex Ultimate 3000 MS LTQ XL UPLC-MS system equipped with a ZORBAX Eclipse XDB-C18 at 80 A, 4.6 \(\times\) 250 mm, and 5 \(\upmu\)m (Agilent; PN.: 990967-902). The MS detector was set to analyze five scan events during the same period of analysis, taking one SIM event for each compound and one for large-scale TIC analysis (the event parameters are detailed in Appendix A, Figure A5). The analysis conditions used were the following: the temperature applied to the column was 30 \({}^{\circ}\)C and the isocratic mobile phase used comprised methanol 85% and formic acid 0.1%, mixed with 15% pure water at a flow rate of 0.4 mL per minute. The total run time of an analysis was 70 min per sample. The sample injection volume was 2.5 \(\upmu\)L. The events observed at 220 nm were analyzed for 5 main events via the MS spectra corresponding to each of the main cannabinoids, while fragmentation was compared to the corresponding standard and theoretical fragmentation using the software program Mass Frontier 7.0.
###### Abstract
In recent years, deep learning has shown remarkable potential in advancing the field of medical imaging, offering powerful tools for automated detection, segmentation, and classification across diverse imaging modalities such as CT [8; 9], MRI [10; 11; 12], and X-ray [13; 14]. Convolutional neural networks (CNNs), in particular, have revolutionized object recognition tasks and are widely employed to detect and characterize pathological structures. While many of these efforts have focused on disease detection or classification [15; 16; 17; 18; 19], there is an increasing need to leverage deep learning for more efficient image preprocessing and visualization workflows. In the context of coronary imaging, deep learning can alleviate the high volume of manual CCTA post-processing by identifying, tracking, and segmenting coronary arteries with minimal user input [20]. However, the issues of variability in imaging protocols, scanner hardware, and patient populations persist, underscoring the necessity for robust, generalizable algorithms that integrate seamlessly into clinical workflows. Recent works leveraging U-Net and Mask R-CNN for coronary segmentation underscore the complexity of arterial geometry and potential misregistration near branching points [21; 22; 23; 24; 25]. In contrast, our bounding box strategy focuses on localizing vessels without merging distinct structures. In this work, we aim to address these challenges by focusing on an object-detection approach for identifying coronary arteries in cross-sectional CCTA images. Rather than relying solely on labor-intensive segmentation, we propose bounding box-based detection to streamline the visualization and interpretation process. This approach may reduce operator dependence by more quickly highlighting relevant vessel segments. Crucially, achieving accurate detection in CCTA entails addressing complex anatomical structures, variable image quality, and potential motion artifacts--factors that can affect both model training and real-world performance. In summary, our contributions are as follows:We propose an annotation method that defines bounding boxes for major coronary arteries (RCA, LCA-LAD, and LCA-CX) in cross-sectional CCTA slices, enabling systematic and reproducible labeling. 2. We develop and evaluate a deep learning pipeline using object detection techniques, integrating conventional metrics (e.g., Average Precision) with novel criteria (e.g., localization error) to capture the distinct ways detection failures manifest. 3. We provide a comprehensive framework that highlights the most significant sources of detection error, offering insights into potential improvements in automated CCTA preprocessing. This framework can be further extended to advanced visualization strategies or 3D reconstructions, promoting more standardized and reliable coronary vessel identification. Through these efforts, our study offers a new perspective on streamlining CCTA interpretation. By systematically detecting coronary vessels and elucidating error patterns, we aim to reduce the manual burden in clinical workflows.
## 3 Results and Discussion
### Fatty Acids Profile of C. reinhardtii  
In this study, total fatty acid (TFA) profile of _C. reinhardtii_ as well as _Chlorella_ and _Spirulina_ was analysed using GC-MS (Table 2). _C. reinhardtii_ contained the highest amount of TFA compared with _Spirulina_ and _Chlorella_, which is consistent with its higher concentration of the total lipids. The fatty acid profile of _C. reinhardtii_ is marked by a high amount of \(\alpha\)-linolenic (ALA) (C18:3 _n_-3), accounting for 42.4% of its TFA. Palmitic acid (C16:0) was the second predominant fatty acid with 23.8%, followed by oleic acid (C18:1) with 14.7% of its TFA content. In contrast to _C. reinhardtii_, the fatty acid profile of _Chlorella_ showed that linoleic acid (LA) (C18:2 _n_-6) was the dominant fatty acid (31.4% of the TFA), followed by ALA (23.4% of the TFA) and palmitic acid (22.2%). Despite this clear difference, the proportion of unsaturated fatty acids (USFAs) to saturated fatty acids (SFAs) was nearly the same in both _C. reinhardtii_ and _Chlorella_ with USFAs constituting above 70% of TFAs. _Spirulina_, on the other hand, showed a different trend, where SFAs, mainly palmitic acid (57.88% of the TFA) outweighed the USFAs. Unlike _C. reinhardtii_ and _Chlorella_, gamma-linolenic (GLA) (C18:3 _n_-6) (19.45% of the TFA) is the main USFA in _Spirulina_ followed by LA (18.9% of the TFA). Fatty acid profile is used in many studies as a chemotaxonomic marker to define taxa in algae generally. For example, eukaryotes, including green microalgae, produce ALA and PUFAs as major fatty acids stored as acyl lipids located mainly in the chloroplasts [48]. Likewise, cyanobacteria or photosynthetic bacteria, as well as non-photosynthetic bacteria, exclusively produce saturated and monounsaturated fatty acids. However, some genera like _Spirulina_, can synthesis GLA which is usually linked to the galactolipids [49]. Several studies investigated the potential of cultivating _Spirulina platensis_ under various growing conditions to produce GLA which can reach up to 32.6% TFA with the advantage of being easy to purify
# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
###### Abstract  
Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.  
autonomous navigation; orchard; GNSS/INS integration; LiDAR; agricultural robot IEEEexample:BSTcontrol
# Influence of Approaching Excavation on Adjacent Segments for Twin Tunnels
## 4 Numerical Analysis Results and Comparisons
### Influence of Approaching Excavation on Adjacent Tunnel  
Figure 9 shows the evolutions of internal forces in the measured segment during the approaching excavation. The negative values indicate that the forces are in compression and moment anticlockwise. When the second tunnel is far from the measured cross-section (at Cross-section A), the profiles of normal force and bending moment are asymmetric with respect to the curved tunnel axis, which is mainly affected by the overcutting in the curved tunnel. After that, the asymmetric characteristics become more striking due to the approaching excavation. As the second tunnel approaches and keeps away from the measured cross-section (i.e., successively approaches cross-sections B and C), Figure 9a shows the gradual increase in the normal force. The increments at the tunnel springline near the second tunnel (\(\alpha=0^{\circ}\)) and the tunnel crown (\(\alpha=90^{\circ}\)) are larger than those at the tunnel springline far from the second tunnel (\(\alpha=180^{\circ}\)) and the tunnel invert (\(\alpha=270^{\circ}\)), respectively. When the second tunnel approaches the measured Cross-section B, Figure 9b shows the decrease in the absolute magnitude of the bending moment. Nevertheless, when approaching Cross-section C, the absolute magnitude of the bending moment increases as compared to that when approaches Cross-section A. The changes of bending moment between the cases of far from and passed over the measured cross-section occur at the regions near the second tunnel (from \(\alpha=-90^{\circ}\) to \(\alpha=90^{\circ}\)) are more obvious than those at the regions far from the second tunnel (from \(\alpha=90^{\circ}\) to \(\alpha=270^{\circ}\)). This result is consistent with the results obtained by [PERSON] et al. and [PERSON] et al. (2015); [PERSON] et al. (2016), who reported that the maximum increment of bending moment in an existing tunnel is located at the regions near a new tunnel.  
Figure 9: Internal forces in the measured segment along the periphery during the approaching excavation: (**a**) normal force; and (**b**) bending moment.
## 3 Results and Discussions
### Comparative Assessment of DTR and GBR during the Forecasting Process
However, the RMSE of 0.338 exhibited by the DTR is high compared to the GBR. Henceforth, both GBR and DTR were efficient during the prediction of the sorption level of the BNF, and the highest efficiency was showcased by the GBR model. Moreover, the RMSE values for GBR and DTR models are 0.109 and 0.116, which is again very low, meaning both models performed very efficiently during the prediction of the desorption level in the BNF. However, these values are lower than the ones obtained during the prediction of the sorption level. The value of R\({}^{2}\) was equal to 0.999 for both GBR and DTR during the prediction of both sorption and desorption levels of the BNF. It is noteworthy to recall that R\({}^{2}\) is a statistical measure that defines how well the regression predictions approximate the real data points [64]. An R\({}^{2}\) of approximately 0.999 is exceptionally high, indicating that nearly 100% of the variability in the selected output is explained by the inputs used in both models. An R\({}^{2}\) value of 1 indicates that the model explains all of the variance in the data, while an R\({}^{2}\) of 0 indicates that the model explains none of the variance. That can be explained by the importance/significance of the input variables on the output [24]. Previously, a study using GB to predict the compressive strength obtained R\({}^{2}\) of 0.97 and 0.96 during the training and testing, respectively. This shows the high performance of the GBR model during the prediction of the unconfined compressive strength of stabilized soil [65]. The similarity can be due to the unconventional nature of the matrix used in both studies: earth-based matrix. However, during the prediction of concrete's compressive strength, both GBR and DTR displayed lower R\({}^{2}\) (0.83 and 0.88, respectively) [32]. It follows that the input parameters and data points utilized to run the model have a direct impact on the models' performance. That means that the sorption rate of the BNF is highly dependent on the temperature, humidity, duration of exposure, and mass. The results obtained from both models demonstrate an excellent fit of the models to our primary dataset.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 5 Simulation Results and Analysis
### Simulation Results of Large Network  
Another simulation in a larger data center shown in Figure 8 was conducted to analyze the effect of the number of VMs on the data rate in the physical network. It was assumed that at most two PMs could be connected to each leaf switch. When more resources were required as the number of VMs was increased, the data center network was extended dynamically by adding additional switches and PMs. For simplicity, we assumed the same resource requirements for all VMs: two CPU cores, 4 GB of  
Figure 7: Aggregate data rate in physical network for different traffic patterns between VMs: (**a**) mean values for the Gaussian distribution; (**b**) standard deviations for the Gaussian distribution; and (**c**) maximum values for the uniform distribution.  
memory, and 50 GB of storage. Furthermore, we assumed the same resources were available on all PMs: 25 CPU cores, 50 GB of memory, and 700 GB of storage. At most twelve VMs can be placed on each PM due to the resource constraints of each PM.  
Figure 9 shows the resulting data rates in the physical network and in the entire data center network (the sum of the data rates in the physical and virtual networks) as a function of the number of VMs. The result shows that the data rate in the physical network is almost same as the total data rate in the data center when the GP scheme is used for VM placement. However, in comparison, the SP algorithm considerably reduces the data rate in the physical network as the number of VMs increases. Specifically, when there are ten VMs, all VMs are placed on a single PM, the data rate in the physical network is 0 regardless of whether SP or GP is applied. However, as the number of VMs increases, data rate in physical network increases rapidly when the GP scheme is used to place the VMs because GP does not consider the traffic dependency between VMs.  
In addition, we evaluated the performance of the SP algorithm in terms of amount of data traffic in the physical network, which can be computed by  
\[T_{p}=\sum_{i=1}^{n}\sum_{j=1}^{n}w_{i,j}\cdot h_{i,j}, \tag{9}\]  
where \(w_{i,j}\) is the data rate between \(v_{i}\) and \(v_{j}\), and \(h_{i,j}\) is the number of physical links between \(v_{i}\) and \(v_{j}\). The amount of data traffic in the physical network with respect to the number of VMs is shown in Figure 10. When the number of VMs is less than 30, the amount of data traffic in the physical network is less than 0.5 Tb for both SP and GP. However, it increases to almost 40 Tb for GP and to around 30 Tb for SP when the number of VMs is increased to 200. This results indicate that it is important to consider the traffic dependency between the VMs in order to reduce the data traffic in the physical network.  
Figure 8: Large data center network topology with more VMs.  
Figure 9: Data rate in the physical network with respect to number of VMs.
## 2 Geometric and Operational Parameters for the Numerical Runs
### Boundary Conditions  
The scheme of the employed boundary conditions for the compact modeling is shown in Figure 2a, together with the established coordinate system (placed at the center of the cross-section at the outlet of the screen, \(z=0\)). The tangential velocity is defined as having an origin around the \(x=0\) and \(y=0\) points.  
The non-slip condition was fixed for the walls, and an outflow condition was fixed at the outlet cross-section of the domain. For the velocity at the inlet, different constant values of axial and tangential velocities were imposed for each of the 81 cases from Table 2. The constant values of axial and tangential velocity for each case are such that the mean axial velocity (\(V\)) and the swirl number (\(S\)) conditions at the inlet coincide with the values shown in Table 2, and they are the same values employed by [PERSON] et al. [17].  
The turbulence intensity also must be fixed as conducted by [PERSON] et al. [17]. Thus, at the inlet for all cases, the turbulence was fixed through the hydraulic diameter (\(D_{h}\)) of the section (\(D\), as it is a square section), and a constant turbulence intensity of 50%. In that work, it was explained that changes in the values of turbulence intensity at the inlet did not produce important changes in the flow pattern downstream of a screen and, therefore, this inlet turbulence modeling strategy was validated.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 2 Description of Satellite Data Transmission
### Conflict in Ground Resource Utilization
#### 2.2.1 Sgvtc  
SGVTC refers to the conflicts that arise when two or more satellites are present within the acquirable range of the same ground station during a specific time period. As depicted in Figure 2, this can occur when a ground station has satellites 1 through 3 entering its visible range within a particular timeframe, each possessing a distinct visible time window. The figure illustrates that the visible times of satellite 1 and satellite 3 overlap during the t1 \(-\) t2 timeframe, resulting in an SGVTC. The ground station's receiving system must devise strategies to address this conflict or alternatively cancel one of the tasks.