esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Link Flooding Attacks and Detection Methods  
LFAs exploit vulnerabilities in topology by generating legitimate traffic nearby the target area to congest key links. If all target links are congested, the target area cannot receive requests from an external network, and the service in the target area is therefore unreachable. LFAs comprise two kinds of attacks, i.e., Coremelt attacks and Crossfire attacks, where the former represents having a single target link, while the latter represents having multiple target links. For example, Reference [19] uses end-to-end and hop-by-hop network measurement techniques; Reference [20] tests node-to-node traffic variation to capture abnormal links through bandwidth usage, delay time, and packet loss rate to detect an attack in traditional networks; Reference [21] calculates traffic density to find target links and then monitors these target links' utilization; References [22; 23] builds a centralized network architecture and periodically observes the target area network traceroute packet to eliminate malicious traffic before an attack occurs.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 3 Consistency Processing Method  
### Color Consistency Framework  
To address the problems of uneven lighting and color inconsistency, a color consistency processing method for the HY-1C CZI images of Antarctica is proposed. This processing framework consisted of two parts: single-view consistency processing and multi-view consistency processing. The overall  
Figure 3: The statistics of two adjacent images randomly selected from the dataset. (**a**) Mosaicked image. (**b**) The upper image. (**c**) The lower image. (**d**) Histogram of the three color channels of the upper image. (**e**) Histogram of the three color channels of the lower image. (**f**) The mean and standard deviation values.  
processing framework is shown in Figure 4. First of all, the color, texture, and spatial distribution information was calculated to eliminate the uneven light and abnormal color existing in a single image, and we obtained an image with unified visual color perception. Then, on the basis of the previous step, a normalizing operation based on a color transfer method was performed between the different images. The light and/or color differences between images were thus eliminated, especially on both sides of the mosaic lines.
## 4 Stable _V/f_ Control with Constant Power Factor Loop  
\(V/f\) control is a form of scalar control maintaining a constant ratio between stator voltage and required stator frequency. It is a good alternative in applications where highly dynamic control performance is not required, such as heating, ventilation, and air conditioning (HVAC), fans, or pumps. The \(V/f\) ratio is usually calculated from the nominal values of the PMSM. It provides an opportunity to control the PMSM machine without the mechanical sensor on the rotor shaft. The position sensor is not demanded, because the required \(\alpha\beta\) voltages are calculated with a transformation angle obtained by integrating the required speed.  
By employing the \(V/f\) control, the high-performance MCU needed in the FOC scheme is not required. It is a very simple control strategy composed of fewer functions than sensorless FOC, as evident from Table 2. For example, a single PI controller in \(V/f\) control instead of six in sensorless FOC avoids the time-consuming controller gains calculation. The sensorless components in the stabilizing and CPF loop are illustrated in the next sections. However, coming with this simplicity, there are also some drawbacks such as:  
* Instability of the system after exceeding a specific applied frequency;
* Low dynamic performance;
* Poor fault protection against stall detection and overcurrent.  
Nevertheless, the first issue can be solved by employing the stabilizing loop to prevent undesired rotor oscillations. The second disadvantage may not be a problem in several applications, where high dynamic performance is not required.  
The block diagram of the \(V/f\) control with stabilization and constant power factor loop is shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Control Structure** & **PI** & **I** & **Park** & **Clarke** & **Atan** & **Filters** \\ \hline Stable \(V/f\) control with constant power factor loop & 1 & 1 & 2 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stable \(V/f\) control with constant power factor loop–complexity.  
Figure 13: Linearized version of the tracking observer with PLL mechanism.
## 4 POD with Ambiguity Resolution
### Results
#### 4.2.1 Ambiguity Resolution Results  
Ambiguity resolution performance is evaluated in terms of the residual and fixing rate. Ambiguity residual is the difference between the float ambiguity and its nearest integer. After removing the satellite and receiver FCBs, the float SD WL and NL ambiguities should be close to the integers. For DD ambiguities, the FCBs of the receiver and satellite are cancelled, which leads to an integer nature. As illustrated by [PERSON] [67], the distribution of the ambiguity residuals is non-Gaussian. It is symmetric and the point of symmetry is the origin, which implies that the mean of the ambiguity residuals is zero. The distribution will become more peaked when the estimated float ambiguity is more precise. Figure 5a,b are residual distributions of SD WL and NL ambiguities while Figure 5c,d are distributions of DD ambiguities. Blue bars in the figures are percentages of the related residuals. The mean values of SD and DD WL and NL residuals are all less than 0.01 cycles and verify the symmetry of distribution. The standard deviations (STDs) of SD WL and NL residuals are 0.10 cycles and 0.07 cycles and validate the high quality of the estimated float ambiguities. For DD WL and NL ambiguities, the STDs are both 0.06 cycles. The smaller STDs of DD ambiguity residuals confirm the effectiveness of the DD operation to cancel the receiver and satellite FCBs.  
Figure 5: Residual distributions of **(a)** SD WL, **(b)** SD NL, **(c)** DD WL, and **(d)** DD NL ambiguities of GRACE-FO satellites.  
The ambiguity fixing rate is the number of fixed ambiguities divided by the total number of ambiguities. Figure 6 illustrates the daily ambiguity fixing rates of SD and DD ambiguities. Figure 6a is for SD ambiguities while Figure 6b is for DD ambiguities. The upper and lower panels of Figure 6a are results of GRCC and GRCD satellites. Blue and red dots in Figure 6 are fixing rates of WL and NL ambiguities. For the GRCC satellite, one year average fixing rates of SD WL and NL ambiguities are 98.5% and 95.8%. A similar performance is observed for the GRCD satellite; the fixing rates are 98.4% and 96.5%. The excellent fixing rates confirm the good quality of applied phase bias products. For the DD case, higher fixing rates are obtained for WL and NL ambiguities, which are 99.1% and 96.7%. Gaps in day of year (DOY) 32-51, 146-149, 205-211, and 299-302 are because of data missing.
## 4 Results and Discussion
### Developed Models
#### 4.1.2 BPNN  
As shown in Table 7, the optimal BPNN model has one neuron in the hidden layered network. Thus, having an architecture [7-1-1] which means seven input parameters and one neuron in the hidden layer, and an output layer. This is because it has the lowest MSE value on test datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Covariance Functions**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}  & \(R\) & **MSE** & \(R\) & **MSE** \\ \hline Matérn 3/2 & 0.9961 & 0.0798 & 0.9986 & 0.0903 \\ Matérn 5/2 & 0.9978 & 0.0452 & 0.9956 & 0.1546 \\ Squared exponential & 0.9978 & 0.0458 & 0.9942 & 0.1812 \\ Rational quadratic & 0.9978 & 0.0458 & 0.9942 & 0.1812 \\ Exponential & 1.0000 & 0.0000 & 0.9850 & 0.3008 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the Five different GPR Models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Number of Neurons**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}  & **in Hidden Layer** & \(R\) & **MSE** & \(R\) & **MSE** \\ \hline
7-1-1 & 1 & 0.9929 & 0.1453 & 0.9924 & 0.1714 \\
7-2-1 & 2 & 0.9956 & 0.0902 & 0.9909 & 0.2085 \\
7-4-1 & 4 & 0.9680 & 0.6452 & 0.8312 & 3.8234 \\
7-5-1 & 5 & 0.9247 & 1.4831 & 0.9699 & 0.5622 \\
7-6-1 & 6 & 0.9995 & 0.0105 & 0.4489 & 156.8569 \\
7-7-1 & 7 & 1.0000 & 0.0007 & 0.9830 & 0.3294 \\
7-8-1 & 8 & 1.0000 & 0.0002 & 0.9536 & 0.9092 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of BPNN for Different Architectures.
## 4 Structure of the Network and Training Scheme
### Training Scheme  
In previous investigations of DL-based CSI feedback, the majority of papers either did not specify the training scheme [21; 23] or simply employed a fixed learning rate [14; 18; 20]. Nevertheless, to achieve the ideal performance, we need to tailor distinct training schemes for different networks since the learning rate is one of the most performance-impacting hyperparameters. The fixed learning rate is easy to implement, but selecting the appropriate learning rate is difficult and the model may converge slowly. In contrast, a specified training scheme, although more complex to implement, provides a more flexible and adaptive learning process that typically improves model performance and training efficiency.  
We discover that the warm-up cosine training scheme is preferable to a fixed learning rate for CSI feedback tasks. As shown in Figure 3, the learning rate increases linearly at the early stage, serving as a warm-up period. During the warm-up period, the relatively lower learning rate at the beginning ensures good convergence of the network and the larger learning rate later helps expedite the training speed. When performing the gradient descent algorithm, the learning rate should become smaller as the global minimum of the loss function approaches. Consequently, the cosine function is used to diminish the learning rate. The warm-up and cosine decay stages are described respectively as  
\[\gamma=\frac{t}{T_{\text{wu}}}\gamma_{\text{max}},0<\ t\leqslant T_{\text{wu}} \tag{7}\]  
\[\gamma=\gamma_{\text{fin}}+\frac{1}{2}(\gamma_{\text{max}}-\gamma_{\text{fin}} )\left(1+\cos\left(\frac{t-T_{\text{wu}}}{T-T_{\text{wu}}}\pi\right)\right),T_ {\text{wu}}<t\leqslant T \tag{8}\]  
where \(\gamma\) and \(\gamma_{\text{fin}}\) represent the current and final learning rates, respectively. The number of epochs at the corresponding moment is denoted respectively as \(t\) and \(T\). The learning rate and epoch at the end of the warm-up stage are denoted as \(\gamma_{\text{max}}\) and \(T_{\text{wu}}\). It is obvious that the decay of the learning rate in the cosine training scheme is continuous, which makes the training more stable. In order to make the warm-up cosine training scheme perform best, suitable hyperparameters need to be set, such as the length of the warm-up period, the peak learning rate, and the period and amplitude of the cosine function.  
Figure 3: The trend of learning rate when utilizing the warm-up cosine training scheme and a constant learning rate.
## 5 Experimental Results and Discussions
### Results Analysis  
We have evaluated the performance of our proposed model with the NYU depth dataset [22], which consists of numerous categories of different indoor scenes that include scenes of bedrooms, dining rooms, classrooms, conference rooms, etc. These images were preprocessed using fuzzy colour stacking to remove the background noise. These pre-processed images are then segmented using colour-based segmentation techniques and K-means clustering to solve the issues of spatial variance and layout. These segmented images are then fed to a pretrained DenseNet for feature extraction. These extracted features are then given to an attention module utilising World Cup optimisation for feature selection. These selected superior features are classified using a deep LSM (liquid state machine) classifier [49]. Figure 5 shows the input scene of a bedroom image and the corresponding preprocessed image. The foreground picture quality is improved by reducing the background noise using the fuzzy colour stacking technique [48]. Figure 6 shows the normalised histogram plot for the sample input image and its corresponding stacked image.  
Figure 5: Sample input image of a bedroom and its corresponding preprocessed image.  
Figure 6: Normalized histogram plot for sample input image of a bedroom and its corresponding preprocessed image.  
Figure 7 shows the segmented results of some sample images, preserving the semantic cues and spatial information of our sample indoor scene.  
We have also evaluated the accuracy of our segmentation module by performing a pixel-wise comparison between our segmented image and its corresponding ground truth. We have estimated the similarity measure by calculating the dice coefficient (F1 score) and Jaccard's index (intersection over union). Figure 8 shows the plot of the dice coefficient, and Figure 9 shows the Jaccard's coefficient. Both plots show a maximum co-efficient value of 1, which indicates good accuracy for our segmentation module.  
Figure 7: Sample images of (**a**) bedroom, (**b**) bathroom, (**c**) dining room, and (**d**) kitchen with input image (**left**) and segmented image (**right**).  
The dataset used in the proposed model consists of a total of 1920 images that belong to 20 different indoor classes. We have divided 80% of the dataset into training sets and 20% into testing phases. Therefore, we have used 1536 images for the training phase and 384 images for the testing phase. Out of the 1536 images in the training set, we used 20% for validation, and the remaining images were used for training. We have trained our model using the Adam optimizer with the gradient threshold set to '1'. The model was trained with a learning rate of '0.01' that drops by a factor of '0.2'. We have trained and evaluated the performance of our model using a mini batch size of '128' for 500 iterations. Figure 10 shows the plots of accuracy and loss for the training and validation processes of our proposed model.  
Figure 8: Plot of Dice coefficient of the segmented images and their ground truth.  
Figure 9: Plot of Jaccard’s index of the segmented images and their ground truth.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 5 Conclusions  
In this study, we proposed a fall detection solution based on Recurrent Neural Networks, specifically utilizing bidirectional GRU layers. The developed models were rigorously evaluated using two commonly applied validation strategies: Leave-One-Subject-Out and 5-Fold Cross-Validation. The experimental results demonstrate that the proposed approach achieves better results for LOSO compared to existing methods reported in the literature attaining an accuracy of 98.99% and close to the best results for 5-Fold Cross-Validation achieving 99.82%.  
While the UniMiB-SHAR dataset has become a widely adopted benchmark in fall detection research, it was originally intended to complement other representative datasets in the field. As such, one potential direction for future work is to evaluate the model in real-world scenarios, where the characteristics of actual falls may differ from the simulated falls present in available datasets.  
Furthermore, although fall detection is a crucial step toward enhancing the safety of vulnerable individuals, particularly the elderly, it does not provide a complete picture of a person's health status. Falls may not always indicate a critical condition but can serve as early warnings of underlying medical issues. Therefore, future research could focus on the integration of additional physiological signals such as electrocardiogram (ECG), heart rate, or blood pressure to improve the reliability and clinical relevance of automated health monitoring systems.  
Such multimodal solutions could be particularly valuable in care environments, such as nursing homes or assisted living facilities, where early detection and real-time alerting of potential health threats could significantly enhance the responsiveness and quality of care provided to residents.  
Conceptualization, R.S.; methodology, N.B., M.G., K.K., M.K. and R.S.; validation, N.B., M.G., K.K. and R.S.; writing, N.B., M.G. and K.K.; writing--review and editing, R.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available in the UniMiB SHAR Dataset repository, [[http://www.sal.disco.unimib.it/technologies/unimib-shar/](http://www.sal.disco.unimib.it/technologies/unimib-shar/)]([http://www.sal.disco.unimib.it/technologies/unimib-shar/](http://www.sal.disco.unimib.it/technologies/unimib-shar/)) (accessed on 12 March 2025). Source codes are available on the GitHub platform at [[https://github.com/rsusik/fall_detection_rnn](https://github.com/rsusik/fall_detection_rnn)]([https://github.com/rsusik/fall_detection_rnn](https://github.com/rsusik/fall_detection_rnn)) (accessed on 12 March 2025).  
The authors declare no conflicts of interest.
## 3 Experimental Results and Discussion  
In order to assess the effectiveness of the proposed algorithm, performances of CAMRF-FLI are analyzed and compared with MLC, MV, MRF, OBV, and GS methods through two experiments.  
Figure 1: Flowchart of the proposed CAMRF-FLI method.  
In the two experiments, the parameter \(\beta\) for MRF is set as 1, and the iteration is set at 100 for MRF and CAMRF-FLI. For OBV method, eCognition 8.9 software is used to obtain the segmentation map, and the related parameters are scale = 10, wcolor = 0.3. The window sizes for MV, GS, and CAMRF-FLI are changed from 3 \(\times\) 3 to 19 \(\times\) 19. The study areas cover grass, water, building, tree, bare soil, road, and shadow. The reference image is acquired by visual interpretation based on the well rectified image, 20% of the reference data is used as the train samples through the stratified random sampling method, and the remainder of the reference data is used as the testing samples. All algorithms were developed in Matlab 2013b. The Producer's Accuracy, Overall Accuracy (OA), and Kappa coefficient (\(\kappa\)) (based on the confusion matrix) are used to evaluate the classification performance. Considering computational simplicity, MLC is applied to obtain the posterior probability and initial class label for every pixel in experiments. Each algorithm is run ten times and the average classification accuracy, and the optimal classification results are derived.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 1 Introduction  
### Background of the Study  
At present, large-scale LEO satellite constellation projects are developing in the direction of networking [1; 2; 3; 4] and intelligence. ISLs are also increasingly being used in the design of LEO satellite constellations, including Starlink [5], Telesat [6], and several others. ISLs are critical in decoupling ground station control, boosting constellation operating autonomy, and realizing the satellite network's high reliability, timeliness, and self-organizing networking. Additionally, the use of an LEO satellite network with ISLs can reduce the latency compared to optical-fiber-based terrestrial networks [5]. The network nodes are not visible to each other at all times, due to the massive number of network nodes and their constant motion in orbit; thus, the selection for building ISLs is diverse and varies with time. Especially for inter-plane satellites, due to the relative motion among adjacent orbital planes, such that visibility cannot be maintained at all times, the links needs to be switched frequently and the topology varies dynamically. The disconnection of ISLs or frequent ISL switching will result in decreased constellation network performance. Therefore, optimized satellite network ISL-building planning is of great significance for realizing efficient satellite constellation networking.  
ISL-building planning does not determine the number of satellites or antennas, and can be figuratively analogized to the process of building highways among a specified group of cities. After the satellite constellation configuration and the ISL antenna design are determined, it plans the optimal network topology in order to realize the construction of the constellation network communication infrastructure. After completing the topology construction, there is a network routing algorithm to realize the choice of data routing, which has been extensively studied and is beyond the scope of this paper. As the number of antenna terminals carried by a satellite is limited, it is required to choose links among the several visible ISLs while not exceeding the number of terminals carried. There are certain factors to be considered, in order to improve network topology performance. The optimization goals of building network topology include longer ISL duration, better topology stability, and lower network delay. Longer ISL duration and better topology stability effectively reduce the network route switching frequency and the possible packet loss during the ISL switching process, while lower network delay generally involves lower average and maximum network delays. The two indicators are essential metrics for describing network performance and providing a timeliness guarantee for inter-satellite data transmission.
## 2 Geometrical Details and Mathematical Models
### Boundary Conditions  
The boundary conditions selected in this paper are as close as possible to the experiments. The Reynolds numbers at the jet inlet are 1000, 1750, and 2500, respectively [27]. Therefore, the boundary condition of jet inlet is set to velocity inlet and the velocity is 0.05 m/s, 0.0875 m/s, and 0.125 m/s corresponding to three different Reynolds numbers. The turbulent intensity \(I\) = 0.01, which is due to a low level of turbulence at the inlet and the turbulent length scale, is taken as the height of the jet inlet (\(L\) = 0.02 m). The turbulent kinetic energy and dissipation rate at the jet inlet are calculated from the following assumed forms: \(k=2/3\left(\overline{U_{i}}I\right)^{2}\) and \(\varepsilon=C_{\mu}^{0.75}k^{1.5}/(0.07L)\). The no-slip boundary conditions (\(\overline{U_{i}}=0\)) are imposed along the solid walls. The boundary conditions for turbulent kinetic energy and dissipation rate at the solid walls are summarized in Table 3[28; 32; 33; 34; 35; 36]. The CFD outlet is set to the pressure outlet boundary condition with a static pressure of 0 Pascals.  
Figure 2: (a) Computational grid distribution; (b) grid in xy plane.