esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 3 Method
### Data Mining-Decision Tree Algorithm  
Decision trees are a type of supervised machine learning process where the data is continuously split as per the prescribed parameters. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes, and the decision nodes are placed where the data is split. It can be divided into two types: Yes/No type and Continuous data type. It is one of the best independent variable selection algorithms that make a model with logistic (or linear) regressions or with neural networks, which reduce the number of variables by selecting only the relevant ones: use decision trees. The process is fast as compared to the calculation of simple correlations with the target variable, and it also takes into account the interactions between variables. Decision trees are non-parametric means and no specific data distribution is necessary, and easily handle continuous and categorical variables.  
The main importance of decision trees is that they identify subgroups, in which each terminal or intermediate leaf can be seen as a subgroup/segment of your population. It delivers high-quality models, which are able to squeeze pretty much all information out of the data, especially if you use them in ensembles. In this study, this concept is used to detect the islanding state of a DGS.  
The data from the 13-bus system is extracted into the tree model by using the Classification and Regression Tree Algorithm (CART). The CART algorithm uses the predictive modeling for designing the decision tree. CART was first developed by [PERSON] et al. in the 1980s [75], and was first introduced in the Power System by [PERSON] [76; 77]. The CART model of the DGS is obtained by recursively partitioning the data space and fitting a simple prediction model within each partition, due to which partitioning can be represented graphically as a decision tree [78; 79]. CART uses the empirical impurity reduction known as Gini Index for splitting the node and selects the split that maximizes the decrease  
Figure 2: Block diagram of the grid-connected DGS.  
in impurity and generates the sequence of sub-trees by growing a large tree and pruning back until only one root node is left [80]. The Gini index is given by:  
\[\text{Gini (t)}=1-\Sigma\left[\text{p(j|t)}\right]^{2}, \tag{11}\]  
where p(j|t) is the relative frequency of class j at node t.  
The decision tree model generated using the CART algorithm is tested in Python software using tested and validated test data obtained from the auto-grounding method.
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 1 Introduction
In this paper, we propose a crew overboard behavior detection network based on a dense residual block, tailored to the characteristics of crew overboard behavior. We use a sparse sampling method to extract video frame sequences, which form the input of the model and can improve the computational efficiency of the model. DR-Net is constructed as a backbone feature network to extract the behavioral features and convolutional splitting is used to reduce the number of network parameters. In DR-Net, dense blocks are utilized to fully extract the behavioral features in the shallow network and residual blocks are employed to extract the deep features to reduce overfitting. In addition, we introduce CTAM to efficiently extract key behavioral features and adopt the LeakyReLU activation function to enhance the robustness and generalization of the model, which can improve the detection performance of the network.
## 4 Experiments and Results
### Data Preprocessing and Augmentation  
We randomly split the 131 CT scans from the LiTS dataset into 91 for training, 20 for validation, and 20 CT scans for testing our proposed G-UNETR++. The 20 CT datasets from 3D-IRCADb and the other 20 CT datasets from Sliver07 are used for cross-validation to assess the robustness and generalization of the proposed method. The Hounsfield unit values of all the datasets are adjusted to the range of \([-200,200]\) to enhance the contrast between the liver and other tissues and remove irrelevant tissues. Figure 5 shows the original CT slice and the preprocessed CT slice. We resize the CT slices from \(512\times 512\) to \(256\times 256\) to reduce computational cost. The same data augmentation operations used in UNETR++, such as rotation, scaling, mirroring, and intensity shifting, are applied in our method.
## 2 Materials and Methods
### Co-Immunoprecipitation  
COS-1 cells were seeded in six-well plates at a density of 2 \(\times\) 10\({}^{5}\) cells per well. The following day, cells were transfected with 0.5 ug of pCMV-FLAG-ER\(\alpha\), and either 1 ug pEGFP-PARP7, 0.8 ug pEGFP-PARP7\({}^{\text{H532A}}\) or 0.1 ug of pEGFP using Lipofectamine 2000. Various amounts of pcDNA3.1 was used to reach a total amount of 1.5 ug DNA. Cells were treated with E2 for 24 h. For the truncated ER\(\alpha\) variants, cells were transfected with 0.5 ug of pCMV-3 xFLAG-ER\(\alpha\) ABC, pCMV-3 xFLAG-ER\(\alpha\) ABCD, or pCMV-3 xFLAG-ER\(\alpha\) CDEF, and 1 ug of pEGFP-PARP7. The following day, cells were harvested and lysed in lysis buffer (200 mM NaCl, 20 mM Hepes, 1% Nonidet P-40). 10% of the lysate was kept as an input control, and the remaining lysate was incubated with 2 \(\upmu\)g of anti-FLAG (Sigma-Aldrich; M2) and 20 \(\upmu\)L of Dynabeads(tm) Protein G (Thermo Fisher Scientific) with constant rotation for 2 h at 4 \({}^{\circ}\)C. The beads were washed five times with wash buffer (200 mM NaCl, 20 mM Hepes, 0.1% Nonidet P-40) and finally eluted in 2\(\times\) Laemmli sample buffer supplemented with 10% \(\upbeta\)-mercaptoethanol. Samples were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were incubated with anti-poly/mono-ADP-ribose (Cell Signaling Technology, Danvers, MA, USA; E6F6A), anti-FLAG (Sigma-Aldrich; F7425), and anti-GFP (Clontech Laboratories; JL-8). The 10% totals were incubated with anti-FLAG, anti-GFP and anti-\(\upbeta\)-actin. After incubation with appropriate secondary antibody, bands were visualized with SuperSignal(tm) West Dura Extended Duration Substrate (Thermo Fisher Scientific). Quantification of modified FLAG-ER\(\upalpha\) was done in ImageLab(tm) (BioRad) by normalizing to \(\upbeta\)-actin of the respective samples.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions
### Average Energy Consumption Comparisons  
Similarly, the overall power or energy consumed by the network is minimized, as low power is required to transmit a single bit from source to destination, as depicted in Figure 9. Furthermore, BP reduced load congestion occurring in the path and further reduced the device drain rate. As the device speed increases, the energy consumption of devices increases. Energy consumption increases from approximately 56.43 mAh to 57.42 mAh for MP-OLSRv2, from 56.18 mAh to 57.13 mAh for MEQSA-OLSRv2, and from 55.86 mAh to 56.62 mAh for MBMQA routing schemes when device speed increases from 10 m/s to 60 m/s. As seen from Figure 9, it can be observed that MBMQA can consume less battery energy compared to MP-OLSRv2 and MEQSA-OLSRv2 in all device speed scenarios because the source device forwards traffic flow towards intermediate devices which have the highest energy level and lowest drain rate.  
Figure 6: Packet delivery ratio for various device speeds.  
Figure 7: Packet Drop for various device speeds.  
Figure 8: Energy cost packet for various node speeds.  
Figure 9: Energy consumption for various device speeds.
## 3 Introduction of the Experiments  
### Experiment on CFST Arches under In-Plane Loads  
[PERSON] [31] conducted a experiment of nine parabolic single tube CFST arch models with different rise-span ratios and load models. Here, the arch under uniform distributed vertical loads was chosen and abbreviated to arch-A below. Arch-A was 9 m in span, 1:6 in rise-span ratio, 159 mm in diameter, and the steel tube was 4.5-mm thick. The yield stress, elastic modulus of steel and cubic compressive strength, and elastic modulus of concrete were 376.2 MPa, 204 GPa and 41.60 MPa, and 31.3 GPa, respectively. Five loading devices were set at equal intervals along the arch span, respectively, as five-point loads to simulate the process of in-plane uniformly vertical loads. As shown in Figure 2, displacement meters and strain gauges were arranged at _L_/12, _L_/6, _L_/4, _L_/3, _L_/2, _2_L_/3, _3_L_/4, _5_L_/6, and 11_L_/12 spans of the arch model, as well as at the arch bases, to monitor displacements and strains. Each section set four measuring points from \(\varepsilon_{1}\) to \(\varepsilon_{4}\). Loads were applied in steps during the loading process.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 2 Background
### Contribution  
In order to overcome the limitations of sequential data processing methods, we propose a fusion system architecture for multiple LiDAR sensors that considers multiprocessing for multiple clients with multithreading within a server process. The proposed system can reduce the processing time for multiple LiDAR sensors by maximizing the resource usage of the dual core. In Figure 2, the left figure illustrates sequential data processing, in which the client receives the data of LiDAR sensors through the socket, sends it to the server after processing, and then receives the next data from the LiDAR sensors and processes them. The sequential data processing method sequentially accesses each port input from LiDAR sensors in a single process, and then collects, converts, and transmits data to the server. In this process, the processing of a LiDAR sensor must be completed to process the next data of the LiDAR sensor. This makes it difficult to ensure performance in real time in the complete system because a delay proportionate to the number of connected LiDAR sensors occurs. In Figure 2, the right side shows the processing method that can ensure concurrency through multiprocessing by creating multiple clients and sockets to receive data from multiple LIDAR sensors simultaneously. The proposed processing method can ensure performance in real time because it processes data at a fast processing time, but the efficiency varies as per the scheduling technique. We consider a scheduling method that is suitable for processing data from multiple LiDAR sensors.
## 4 Development and Validation of CFD/RBD Coupled Solvers
### Verification of RBD/CFD Co-Simulation  
This paper verifies the validity of CFD/RBD coupling simulation by comparing the short-period poles that come from system identification based on RBD/CFD time domain simulation data with the short-period poles calculated by AVL's nmode function. The time-domain simulation data involved in system identification include the AOA response curves to the elevator step input at four Mach numbers, as shown in Figure 9. The comparison of pole distribution is shown in Figure 10. It can be seen from the figure that the short-period pole distribution obtained by RBD/CFD co-simulation is similar to that obtained by AVL calculation, especially the natural frequency and the variation trend with Mach number are consistent, and there are only some errors in damping ratio.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 4 Simulation Results  
In this section, we assess the influence of neural network hidden layer structure and training batch size on performance. Subsequently, we present BER performance under varying \(\Delta_{SNR}\)dB between users. Finally, a performance comparison is made between FDNN receivers and SIC receivers, and the training parameters are detailed in Table 1.  
Consider a two-user uplink NOMA system using QPSK modulation. Due to varying distances between users and the BS, a near-far effect induces channel gain and phase differences. A learning rate \(\alpha\) of 0.02 and regularization parameter \(\lambda\) of 0.01 were chosen for optimal performance after preliminary testing. The hidden layers, trainng sample size, and batch size will be discussed in the subsections below.
## 4 Results and Discussion
### Detailed Analysis of Model Performance
#### 4.7.4 Four Sensors with 50% Overlapping Data  
In this configuration, both the TCN and GRU models achieve a high test accuracy of **0.9762**. However, analysis of their confusion matrices reveals that the TCN model outperforms GRU in terms of misclassification rates across all activity classes.  
**Confusion Matrix Insights:**  
* TCN Model: Fewer misclassifications overall, particularly in challenging stationary activities, indicating better discrimination of subtle patterns.
* GRU Model: More misclassifications in certain classes, suggesting difficulties in distinguishing similar activities with overlapping data.  
**Why TCN is the Best Model:**  
* Superior Class-wise Accuracy: TCN minimizes misclassifications across all activities, enhancing robustness and reliability critical for ITS applications.
* Effective with Overlapping Data: TCN's dilated causal convolutions capture long-term dependencies more effectively than GRU.
* Better at Distinguishing Similar Activities: TCN excels in identifying subtle differences, reducing misinterpretation risks in real-world scenarios.  
**Conclusion:** Despite identical overall metrics, the **TCN model is the superior choice** due to its lower misclassification rates and better class-wise performance. This underscores the importance of evaluating models beyond aggregate metrics to ensure reliable and effective HAR systems in ITS applications.  
The **LSTM** model also performs well with a test accuracy of **0.9760** but does not surpass TCN in class-wise accuracy. Traditional models show improvement but still do not match the performance of deep learning models.
## 1 Introduction
### Related Work
Examples of these local area underwater networks are fish farms or permanent underwater observatories where there is the need to sense environmental variables and send these data to a ground station in real time. Examples of this interest are works like [10; 11; 12; 13] where the use of acoustic communication is the base of the multimodal network. Another use of multimodal communications is to control and supervise underwater robots. Nowadays, the base of these multimodal communication systems is changing from acoustic to visual light; this is happening mainly because of recent improvements in the robustness of this new technology, examples of which can be found in [14; 15]. As mentioned above, network congestion control is critical when dealing with cooperative robots. In ref. [16], the authors present an open-source underwater simulator designed to simulate wireless communications, with a specific focus on acoustic communications. This simulator provides valuable support for underwater robotics researchersin developing robotic applications that take into account low-bandwidth and fluctuating network conditions. Another example of network congestion control for cooperative robots was reported in [17], where the authors developed a mixed reality human-robot interface with adaptive communication congestion control for the teleoperation of mobile redundant manipulation in a hazardous environment--in this case, the large hadron collider at CERN.
## 4 Conclusions  
The purpose of this paper was to study the trajectory-tracking problem for WIPRs and propose a hierarchical sliding mode controller with a nonlinear perturbation observer to achieve accurate control of the reference trajectory and maintain the pendulum stability during motion. A nonlinear disturbance observer was designed to make the system more robust to unknown external disturbances. The underdriven coupling of WIPRs was addressed by dividing the system into two subsystems through the decoupling of its control state variables. The hierarchical sliding mode control method with an improved convergence law was then applied to control the system and suppress the \"jitter\" phenomenon. Finally, the Lyapunov function was chosen to verify the stability of the system mathematically.  
The feasibility of the control system was verified using simulation software. However, considering the complexity of the real-world environment and external uncertainty, future work will focus on building a hardware system for the robot to study the real effects of the control method of WIPRs in the real world.  
All of the nominated authors initially made significant contributions to the paper. Conceptualization, M.H. and X.Z.; methodology, X.Z.; software, X.Z.; validation, X.Z., D.C. and M.H.; formal analysis, M.H.; investigation, M.H.; resources, X.Z.; data curation, X.Z., D.C. and Z.X; writing--original draft preparation, M.H.; writing--review and editing, X.Z., D.C. and Z.X.; visualization, D.C.; supervision, M.H.; project administration, M.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61971048) and the Key Incubation Project of Beijing University of Information Science and Technology (5212110927).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to internal laboratory confidentiality agreements.  
Figure 13: Tracking of circular trajectories.
## 5 Analytical Study and Model Calibration
### Validation Against Shear-Critical Experimental Data  
In order to rigorously assess the predictive capability of the fiber-based FE model, an extensive validation exercise against four independent experimental datasets have conducted that specifically address shear-critical phenomena in RC beam-column joints. Table 4 below encapsulates the experimental values reported in the literature. Studies such as [PERSON] et al. [67], [PERSON] et al. [10], [PERSON] et al. [8], and [PERSON] [30] have consistently measured critical shear stresses in the range of 5.0-5.2 MPa. Conversely, the analytical model presented in Section 5.1 revealed a critical shear stress value of 5.16 MPa.  
In conclusion, the advanced calibration of this fiber-based model against these six experimental datasets significantly bolsters its predictive accuracy. This comprehensive validation not only corroborates the simulation results but also reinforces the study's claims regarding the superior seismic performance achieved via the TnT BRB retrofit strategy. The detailed comparison with experimental data thus serves as a critical benchmark, ensuringthat the fiber-based model reliably captures both the global dynamic behavior and the localized shear-critical phenomena that dictate the seismic resilience of RC structures.
## 3 Results
### Ablation Experiments and Model Training Details
#### 3.1.3. BFFDC-YOLOv8-Seg Detection and Segmentation Effect  
This study tested the BFFDC-YOLOv8-seg model using reserved test set images to verify its actual detection and segmentation effects. A normalized confusion matrix for each weed category was created, as shown in Figure 9, and results were assessed based on the classification accuracy of the weeds. The matrix results indicate that the model performs uniformly in detection, achieving 100% accuracy for _Amaranthus_, _Cirsium_, _Digitaria_, _Eleusine_, and _Portulaca_, 96% for _Platostoma_, _Coperus_, _Sema_, and _Mollugo_, 92% for _Euphoria_, and 89% for _Ipomoea_. During testing, the model exhibited numerous errors in detecting _Euphoria_ and _Ipomoea_, missing some Euphoria instances and misidentifying some _Ipomoea_ as _Cirsium_ or _Sema_. This issue could be mitigated by collecting more data to enhance the model's ability to distinguish similar physical features such as the shape, texture, and color of leaves.  
Figure 8: Performance evaluation of the BFFDC-YOLOv8-seg model during training and validation. **(a)** The upper part of the figure displays four loss metrics on the training set, including Box Loss (box_loss), Segmentation Loss (seg_loss), Classification Loss (cls_loss), and Direction/Flow Loss (dfl_loss), shown as actual values (blue dots) and smooth curves (orange dashed lines). The lower part corresponds to the variation of the same loss metrics on the validation set. **(b)** The figure sequentially displays the curves for precision, recall, mAP50, and mAP50-95 of the segmentation Mask.  
Figure 9: Normalized confusion matrix for detection accuracy of various weeds.  
By comparing masks, the study found that the original model (YOLOv8-seg) failed to adequately segment weed stems and edges and missed small targets, as shown in Figure 10. The analysis suggests that the original network's insufficient convolution flexibility and limited ability to perceive irregular edges resulted in the omission of stems and edges; inadequate feature fusion also led to the nondetection of small targets. The weighted bidirectional multiscale feature fusion in BFFDC-YOLOv8-seg model enhances the network's ability to perceive small targets; DSConv, with its more flexible convolution kernels, has a stronger ability to detect stems and irregular edges, improving edge segmentation accuracy. Experiments demonstrate that in complex agricultural environments, our model outperforms the original in extracting small target features and segmenting irregular edges. Although our model demonstrates superior segmentation capabilities for irregular small targets, some gaps caused by overlapping leaves are still present within the mask. These gaps can lead to ineffective laser burning, thereby reducing overall work efficiency. Consequently, there is still room for improvement in edge segmentation.
## 4 Use of the Controller-Driven OppNet Architecture to Manage Congestion
### Network Congestion Prediction  
The controller uses the optimal congestion interval along with the calculated \(o_{t+n}\) to determine its prediction of the network's congestion. The network's congestion status falls into three states: _UNDER_USED_, _OPTIMAL_, and _CONGESTED_. The controller uses the following function to sentence which is its prediction of the network's congestion state (\(c_{t+n}\)):  
\[c_{t+n}(o_{t+n})=\begin{cases}\textsc{UNDER\_USED}&\text{if }o_{t+n}<o_{min}\\ \textsc{OPTIMAL}&\text{if }o_{min}\leq o_{t+n}<o_{max}\\ \textsc{CONGESTED}&\text{if }o_{t+n}\geq o_{max}.\end{cases} \tag{6}\]
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
###### Abstract  
In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators' subjective Quality of Experience (QoE). The human-centered QoE determination commonly requires human subject experimentation, which we combine with Electroencephalography (EEG) measurements to move towards automatized and generalized possibilities of determining the QoE. We evaluate the prediction performance for spherical/immersive images displayed with a mobile device VR viewer (Spherical Virtual Reality (SVR)) with the help of only four-position EEG data gathered at the forehead, which correlates well with practical applicability. We find that QoS levels can be predicted more reliably (directly with \(R^{2}=0.68\) or based on profiles with \(R^{2}=0.9\)) than the QoE, which exhibits significant error levels. Additional comparison with previous approaches for the Spherical Augmented Reality (SAR) QoE indicates better predictability in AR scenarios over VR.  
Keywords:virtual reality; quality of experience; quality of service; electroencephalography; image quality +
Footnote †: journal: Future Internet