esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposal Experiment and Attack Models
### Attack Models
Hence, the Each-To-Each Events Comparison technique is the first detection mechanism designed to detect event-based attacks, and it is utilised in Modes B and C. The All-To-All-Events Comparison technique is the second detection mechanism, and was designed to detect behaviour-based attacks; it is utilised in Mode B. Additionally, due to the immutability of stored trusted events, the accuracy of the detection mechanisms was high. Figure 9: Typical sequence of events in a drive-by-download attack [43]. Based on the above-mentioned attacks' background information, we also propose two attack models in order to measure our proposal's efficiency in achieving its main goal of attack detection, as well as test the validity of having immutable data in these kinds of systems. The first attack model is the event-based model, which represents the attacks that may affect the existing event or event details. For example, if the 10 th event occurring in system A indicates that Method A sent two values to Method B, then the event-based model attacks could affect the event by deleting it, inserting a different event, or changing all/some of the event(s) details, such as method names or number/types of variables, etc. The second attack model is the behaviour-based model, which represents the attacks that affect the system behaviour layout. For example, if System B has 20 events occurring in one sequence, then the behaviour-based model attacks could affect the number of events or the structure of the event sequence. Therefore, we injected the proposal with both of these attack models during runtime to perform our validation testing. The preliminary results show that the proposal was able to detect both attack models based on the selected detection modes related to the detection mechanisms. As mentioned in the previous section, we proposed three detection modes that utilise two proposed detection mechanisms. Hence, the Each-To-Each Events Comparison technique is the first detection mechanism designed to detect event-based attacks, and it is utilised in Modes B and C. The All-To-All-Events Comparison technique is the second detection mechanism, and has been designed to detect behaviour-based attacks; it is utilised in Mode B. Additionally, due to the immutability of stored trusted events, the accuracy of the detection mechanisms was high.
# An Efficient Explainability of Deep Models on Medical Images
## 4 Results
### Medical Opinion  
In order to validate the results, it was crucial to obtain feedback from healthcare professionals, in this case three doctors, who are directly involved in the interpretation of these medical images on a daily basis. To structure this evaluation, we opted for the 5-points Likert explanation satisfaction scale developed by [PERSON] [17]. This evaluation framework is based on five essential criteria for the explainability of AI models: comprehension, satisfaction, sufficiency of detail, completeness and accuracy. Each criterion is rated on a scale of 1 to 5, where 1 indicates low satisfaction and 5 indicates maximum satisfaction.  
Each doctor independently assessed the results on the basis of these criteria, enabling us to gather their qualified opinions. The scores awarded by each doctor for each criterion were then used to calculate the \"Average Opinion Score\", which represents the doctors' average satisfaction with our results.  
Table 4 shows the mean Opinion Score results for the 5 different methods. Details of these evaluations, including individual scores for each criterion and each doctor, are available in the appendix to this brief.  
From the mean Opinion Score presented in the Table 4, it is clear that the GradCAM, LIME and, to some extent, Vanilla Gradient explainability methods were preferred by doctors, as evidenced by the higher scores they received. Specifically, for comprehension, LIME scored 4, GradCAM 3.83 and Vanilla Gradient 2.83. For satisfaction, GradCAM scored 4, LIME 3.67 and Vanilla Gradient 3.33. For sufficient detail and completeness (Exhaustiveness), GradCAM scored the highest with 4.17, Vanilla Gradient 3.17 and 2.83, respectively, while LIME 3.5 and 3.67, respectively. Finally, for accuracy, GradCAM also scored highest with 4, Vanilla Gradient 3.17 and LIME 3.33. In contrast, the SmoothGrad and Integrated Gradient methods received generally lower scores on all evaluation measures. This indicates that these methods may be perceived as less intuitive or useful to physicians.
## 6 Case Study of a Wind Power Plant Connected to a Weak Grid
### Stability Analysis
#### 6.1.1 Short Circuit Ratio (SCR) Variations for \(X_{g}/R=20\) and \(X_{c}/X_{g}=0\)  
In this scenario, the SCR is changed from 1.25 to 1.5, \(X_{g}/R=20\) and \(X_{c}/X_{g}=0\) (without series compensation). Some poles of the system are shown in Table 1. As it can be seen, there is an unstable pole for SCR = 1.25.  
Nyquist criterion-based stability analyses for SCR = 1.25 and SCR = 1.5 are shown in Figure 9. When the SCR = 1.25, there is an encirclement of the point \(-1\), which shows that the system is unstable.
# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and Vital Signs Monitoring  
The monitoring of vital signs in newborns, such as respiratory and heart rates and body temperature, is one of the key elements of modern neonatal care. For example, an imbalance in vital signs is one of the early signs of infectious diseases, which represent one of the main causes of mortality and morbidity worldwide. To monitor these parameters in newborns, sensors are placed in direct contact with the skin, potentially causing skin injury, stress, and discomfort for babies. Non-contact measurement methods (i.e., camera recordings, laser Doppler vibrometry, and others) have recently been developed to continuously record vital parameters and avoid such complications in fragile patients [76; 77]. Advanced algorithms are needed to work on the images deriving from automatic camera-basedmeasurements of vital signs. The tracking of regions of interest (ROIs) and signal fusion for vital signs extraction are important steps in the performance of camera-based temperature monitoring. Deep learning-based approaches may lead to robust, real-time assessment of vital parameters, such as body temperature alterations, as suggested by [PERSON] et al. [78]. In their work, clinical data for neonates were used to train a model based on deep learning in order to achieve real-time extraction of surface temperature. The analysis of the results was challenging for several reasons (e.g., the parameters and data related to the changes in the positions of infants' bodies during the recording). However, the authors showed the feasibility of low-cost, embedded GPUs for real-time temperature monitoring of neonates, and further studies are expected to expand this method in clinical practice.
# Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
###### Abstract  
The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite-ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system's reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40 \(\times\) 10\({}^{-15}\) and 2.30 \(\times\) 10\({}^{-15}\) for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.  
inter-satellite link; timekeeping algorithm; low-orbit constellation; parameter estimation 2024 164092.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 3 Materials and Methods
### Classification Architectures
#### 3.2.3 GRU  
The GRU is a more recent recurrent network initially created for machine translation tasks. This model is similar to the LSTM because it can capture long-term dependencies. However, unlike the LSTM, this model does not require internal memory cells, reducing complexity. A GRU unit combines the forgetting gate and the input gate into a single update gate; it also integrates the cell state and the hidden state, solving local minima stalling and gradient descent problems. A main advantage of the GRU over the LSTM is that it requires less computational resources by having a less complex structure.
# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
## 7 Algorithm Complexity  
To analyze the Weighted-CAPIC performance, we conducted several tests and also explored it in terms of algorithm complexity. We used the Time Complexity parameter to measure the complexity of the algorithm. Measurement of complexity based on time is carried out for a particular input [18]. However, the processing time is highly dependent on various factors such as the type of machine used, the parallel processing performed, and so on. Big-O notation is used to represent it to avoid biased algorithm complexity values.  
Weighted-CAPIC, according to the pseudocode in the algorithm 1, can be written as the input correlation of total class content D, equal to 5D+2, so that the Big-O notation is O(D). From the previous research, Dynamic-CAPIC caching algorithm as input correlation can be written mathematically as 4D+2, and its Big-O notation is O(D) [9]. This means that the complexity of the Weighted-CAPIC algorithm is the same as Dynamic-CAPIC but provides a better cache hit ratio (CHR) than Dynamic-CAPIC for the priority class and the overall network.  
Figure 9: Network cache hit ratio for various request rate difference.
###### Abstract  
Permanent magnet synchronous motors (PMSM), which are with the advantages of high torque-to-weight ratio and high efficiency, are widely applied in modern industrial systems. However, existing approaches may fail to accurately track the speed trajectory because of the load disturbances. This paper proposes an equivalent and combined control strategy to mitigate the slow time-varying load disturbances and decrease the overshoot for PMSM in full speed range. First, a state observer is proposed to reconstruct the current variables and speed state in the d-q axis. Hence, one can get the speed and position information without the sensors. Then, the disturbance and the load are estimated by the estimating law. Thus, it can reduce the effect of load and disturbances. Further, the PD control is introduced to weaken the overshoot. As a result, the speed trajectory can be more effectively hold both in high speed and low speed. Finally, numerical examples are presented to demonstrate the validity and effectiveness of the proposed estimation scheme and its robustness under different conditions.  
Keywords:state observer-based control; PD control; permanent magnet synchronous motors (PMSM); speed control +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
This manuscript analyzes and elaborates on the principle of doppler shift positioning for SOPs in LEO communication satellites. It derives and calculates the PDOP for the proposed doppler shift positioning method, drawing on the PDOP concept from GNSS. Due to the asynchronous nature of LEO communication satellites, the relative positions of satellites to users change in real time. Therefore, we have extended the PDOP concept to \"single satellite PDOP\", which reflects the degree of satellite position change. In conclusion, the following points can be made:  
(1) When there are less than three observable satellites, according to the impact of a single satellite on positioning accuracy in the third part of the article, the observable time and elevation angle of the satellite should be comprehensively considered to select the satellite for positioning.  
(2) When more than three satellites are visible, it is crucial to consider not only those with high elevation angles and significant changes in orbital position but also the PDOP values of various satellite permutations.  
Furthermore, the impact of the algorithm proposed in this paper on the time to first estimation is noteworthy. The algorithm calculates the position and velocity information of satellites based on the TLE of the satellite and completes satellite selection on the computing platform prior to positioning. Based on the current simulation results, the satellite selection algorithm does not significantly affect the first localization estimation. For instance, with Starlink, the observable time of satellites is currently on the order of minutes, whereas the processing time of the algorithm proposed in this paper is on the level of seconds. We will explore the impact of the algorithm on the time to first estimation in future research.  
Conceptualization, Y.T. and Y.G.; methodology, Y.T.; validation, Y.T., S.W., and Z.Z.; formal analysis, Y.T.; writing--original draft preparation, Y.T. and Y.G.; writing--review and editing, S.W., C.Y., and Z.Z.; project administration, Y.G.; funding acquisition, Y.G. 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, grant number 62073335, and the Science Fund for Distinguished Young People in Shaanxi Province, China, grant number 2022 JC-42X.  
Not applicable.  
The original data presented in the study are openly available at [[[http://celestrak.org/NORAD/elements/index.php?FORMAT=tle](http://celestrak.org/NORAD/elements/index.php?FORMAT=tle)]([http://celestrak.org/NORAD/elements/index.php?FORMAT=tle](http://celestrak.org/NORAD/elements/index.php?FORMAT=tle))], accessed on 1 January 2025.  
The authors declare no conflicts of interest.
# An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines
## 2 Mathematical Model
The initial conditions are given by \(v_{w}(0)=0\), \(L_{e,0}=L_{T}-x_{0}\), and \(p_{1,0}^{*}=p_{atm}^{*}=101325P_{a}\). The upstream boundary condition is defined by \(p_{1,0}^{*}\), representing the air pocket's initial condition. At the downstream end, the boundary condition is given by \(p_{atm}^{*}\), corresponding to the free release of water into the atmosphere. * Gravity-related term: The gravity term (\(z_{1}/L_{e}\)) is presented in Equation (4) as: \[\Delta z_{1}/L_{e}=\sin(\theta),\] where \(\Delta z_{1}\) is the elevation difference. The resulting system can thus be expressed as:  
\[\frac{dv_{w}}{dt} =\frac{p_{1}^{*}-p_{atm}^{*}}{\rho_{w}L_{e}}+g\sin(\theta)-\frac {fv_{w}|v_{w}|}{2D}-\frac{R_{v}gA^{2}v_{w}|v_{w}|}{L_{e}}\] (4) \[\frac{dL_{e}}{dt} =-v_{w}\] (5) \[p_{1}^{*} =\frac{p_{1,0}^{*}(L_{T}-L_{e})^{k}}{(L_{T}-L_{e,0})^{k}},\] (6) with initial conditions \[v_{w}=0,\ L_{e,0}=L_{T}-x_{0},\text{ and }p_{1,0}^{*}=p_{atm}^{*}=101325\text{ Pa}.\] (7) This mathematical model was previously developed by the authors [5].
# A Novel Approach for a Inverse Kinematics Solution of a Redundant Manipulator
## 3 Inverse Kinematic Model and Computing Algorithm
### Kinematic Singularities Avoidance Task  
The kinematic singularities avoidance task plays a significant role during numerical computing. Around the singular positions, the manipulator loses its manipulability and the numerical computation slows down until it fails [30]. Kinematic singularities represent the problem with the mapping of task space to joint space. This problem occurs when the determinate of the Jacobian matrix equals zero, that is, \(\mathrm{detJ(q)}=0\). There are many methods dealing with these computing problems. In this study, the damped least squares method arises from the objective function \(\mathrm{H}\). The damped least squares method was used for the first time in 1986 by [PERSON] [31] and also independently by [PERSON] [32]. \(\mathrm{H}\) is given as:  
\[\mathrm{H}=\left\|\dot{\mathrm{q}}-\dot{\mathrm{x}}\right\|^{2}+\left\|\rho \dot{\mathrm{q}}\right\|^{2} \tag{8}\]  
where the first term provides primary task solution and the second term deals with kinematic singularities by suitable choice of non-zero positive parameter \(\rho\). The vector of joint velocity \(\dot{\mathrm{q}}\) is derived by \(\frac{\mathrm{dH}}{\dot{\mathrm{d}}\dot{\mathrm{q}}}=0\):  
\[\dot{\mathrm{q}}=\mathrm{J}^{\mathrm{T}}\left(\mathrm{J}\mathrm{J}^{\mathrm{ T}}+\rho^{2}\mathrm{I}\right)^{-1}\dot{\mathrm{x}} \tag{9}\]  
where \(\mathrm{I}\in\mathrm{R}^{\mathrm{m}\times\mathrm{m}}\) is a square diagonal unit matrix with the dimension of end-effector task space.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
###### Abstract  
Although there are many methods in the literature to eliminate noise from images, finding new methods remains a challenge in the field and, despite the complexity of existing methods, many of the methods do not reach a sufficient level of applicability, most often due to the relatively high calculation time. In addition, most existing methods perform well when the processed image is adapted to the algorithm, but otherwise fail or results in significant artifacts. The context of eliminating noise from images is similar to that of improving images and for this reason some notions necessary to understand the proposed method will be repeated. An adaptive spatial filter in the wavelet domain is proposed by soft truncation of the wavelet coefficients with threshold value adapted to the local statistics of the image and correction based on the hierarchical correlation map. The filter exploits, in a new way, both the inter-band and the bandwidth dependence of the wavelet coefficients, considering the minimization of computational resources.  
adaptive spatial filter; wavelet domain; noise reduction; medical imaging; hierarchical correlation map; wavelet functions Article
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.4 CNN-BiLSTM  
There are four convolutional layers present within the proposed CNN-BILSTM model. The max-pooling layer and a dropout layer follow each of these layers. The neural network input is a vector of 96 MFCC features. There are 64 filters present within the four CNN layers along with a kernel size of 3 \(\times\) 3 that are included in a 3-pixel stride setting. For the convolution layer, a lower resolution version was generated by the max-pooling layer, and 2 \(\times\) 2 pool size was applied. A dropout layer, of ratio 20%, was also included later, helping to avoid overfitting. Lastly, an entirely connected layer was included, along with a dense layer that maintained an activation function, Softmax.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of Man-in-the-Middle (MiTM)
#### 5.3.1 Analysis  
Under normal traffic conditions, the communication between an IoT device and an AMQP server begins with a TCP three-way handshake: the IoT device sends an SYN request, the server responds with an SYN/ACK, and the device acknowledges with an ACK. Once this connection is established, data exchange proceeds with correct flags and window sizes.  
In the case of an AMQP MiTM attack, the attacker uses ICMP echo requests containing big-endian (BE) and little-endian (LE) sequence numbers (e.g., 32,487 and 59,262). The AMQP server responds with ICMP echo replies, often containing similar endianness indicators to ensure proper data interpretation. Following this, the attacker spoofs the IP and MAC addresses of the target, allowing interception of the communication.  
Key observations during the attack include the following:  
* The binding IP and MAC addresses are identified as spoofed once a response is received.
* If no response is obtained, the packet is considered dropped.  
Figure 7: Analysis of AMQP MiTM packets.  
* The syslog indicates warnings from the network manager regarding IP conflicts during the attack, further confirming malicious activity.
# A 2D Lithospheric Magnetic Anomaly Field over Egypt Using Gradient Data of Swarm Mission
## 4 Conclusions  
The current work makes use of 8 years (from January 2014 to August 2021) of magnetic field data observed by two lower-altitude satellites (Swarm A and C) of the Swarm constellation to model the lithospheric magnetic anomaly field over Egypt and the surrounding area. The data selected according to our criteria of quiet-time conditions and within \(\pm\)2.5 km of the altitude of 440 km have been chosen to overcome the altitudinal variation of the field. To make use of the geometrical configuration of the two lower-altitude satellites, the gradients of the field in the N-S and the E-W directions have been calculated. The observed E-W gradient data, derived from the differences between Swarm A and C, exhibited a noisier map than did the N-S gradient data. This could be attributed to the different environmental conditions surrounding the two satellites (Swarm A and C), as the satellites are separated by \(\sim\)150 km distance in longitude.  
In order to reduce the noise of the observed data, a weighted damped least-squares fit technique of a 2D model has been adopted. In this model, we have implemented the Legendre polynomial to model the gradient of the lithospheric magnetic anomaly data. To solve this model, we have expanded the field in terms of the Legendre polynomial from degree 7 to degree 56. These limits of expansion degrees have been chosen according to the spectrum of the model at the best damping parameter. The best damping parameter at  
Figure 8: The lithospheric magnetic anomaly field of the \(B_{x}\) component (**a**) observed by Swarm A, (**b**) observed by Swarm C, (**c**) gradient in the E–W direction, and d) gradient in the N–S direction.  
the knee of the trade-off curve for our solved model equals \(10^{-4}\). The weighted damped least-squares fit method of the 2D model has greatly reduced the effects of ionospheric and magnetospheric noise sources in both the N-S gradient and E-W gradient directions. The anomaly model derived starting from gradient data is of higher quality than the one reported by [27], which is based on non-gradient data.  
This research received no external funding.  
Swarn magnetic data has been downloaded from the following website: ([[https://swarm-diss.eo.esa.int/](https://swarm-diss.eo.esa.int/)]([https://swarm-diss.eo.esa.int/](https://swarm-diss.eo.esa.int/))) accessed date is 31 December 2021, and the magnetic activity data has been downloaded from the following website: ([[https://omniweb.gsfc.nasa.gov/form/dx4.html](https://omniweb.gsfc.nasa.gov/form/dx4.html)]([https://omniweb.gsfc.nasa.gov/form/dx4.html](https://omniweb.gsfc.nasa.gov/form/dx4.html))) accessed date is 31 December 2021.  
The authors declare no conflict of interest.