esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison between Proposed Method with Several Other Methods  
The proposed method is compared against several other algorithms: least mean square (LMS) [28], recursive least square (RLS) [29], random forest (RF), support vector machine (SVM) and artificial neural network (ANN), in terms of the ANC performance. Generally, the LMS algorithm is simple and easy to implement with low complexity. But it has the disadvantages of a slow convergence rate and poor tracing performance. Recursive least square (RLS) algorithm has better convergence speed and tracing performance with higher computation complexity compared with LMS algorithm. Figure 12 shows the de-noising learning curves of proposed method, LMS, RLS, RF, SVM and ANN for 10 types of noise datasets with 500 steps iteration.  
It can be seen from Figure 12 that the learning curve of the method proposed in this paper begins to converge at iteration step 75 which is much smaller than that of LMS algorithm. For the RLS algorithm, the convergence iteration step is 48. Although the convergence speed of the proposed method is not as fast as that of RLS, its maximum value of the learning curve is 8.08 dB, which is much less than that of RLS, 29.66 dB. This demonstrates that the proposed method has better stability compared with LMS and RLS. In addition, the convergence iteration steps for the learning curve of RF, SVM and ANN methods are 145, 58 and 80 respectively, which are not very different than that of the proposed method. However, the maximum value of the learning curve of RF, SVM and ANN methods is 26.23 dB, 29.51 dB and 28.25 dB respectively, which is much larger than the value of the proposed method. In addition, the steady-state attenuation result of the proposed method is \(-\)25.6 dB\(\sim\)\(-\)27.25 dB, and results of the LMS, RLS, RF, SVM and ANN methods are \(-\)17.8 dB\(\sim\)\(-\)24.08 dB, \(-\)17.5 dB\(\sim\)\(-\)24.52 dB, \(-\)22.47 dB\(\sim\)\(-\)26.57 dB, \(-\)22.05 dB\(\sim\)\(-\)25.83 dB and \(-\)23.37 dB\(\sim\)\(-\)26.61 dB, respectively. This shows that the proposed  
Figure 11: The influence of maximum depth and number of estimators on the GBDT regression performance.  
method has better noise attenuation using the 10 types of noise datasets. The reason for this is that the proposed method constructs a corresponding regressor according to the clustering results of different noise types, which greatly improves the noise attenuation effect.
# An IoT Surveillance System Based on a Decentralised Architecture
## 3 Proposed Architecture
### Architecture Elements
#### 3.4.2 Unmanned Ground Vehicle (UGV)  
In this section, we will show the configuration of each drone that is used to patrol areas inside the architecture. Each drone has been assembled using common components available on the market. A schematic representation is shown in Figure 4. In the following, we will describe how the components, depicted in the figure, work. The controller used for wheeled ground drone was Arduino Uno [43] and the mobility was ensured by four 9V electric engines connected to the control board _Motor Driver L298N_. The L298 was an all in one integrated circuit. It was designed to accept standard transistor-transistor logic (TTL) levels and drive inductive loads such as relays, solenoids, DC and stepping motors [44]. This kind of board allowed the separated management of only two engines and for this reason they have been connected in series. In this way it was possible to manage simultaneously two engines connected with the wheels on a side of the drone. The control board also manages a line following sensor bar that is needed to follow the circuit for the patrolling drone. The drone behaviour can be described by a simple finite state machine (FSM) with four states: charging, patrolling, warning patrolling, alarm patrolling. The default state of the drone was the patrolling state during which the drone simply follows the path-marks to move inside the patrolling area. During this state the drone was always searching for anomalies or suspicious objects. For a computer vision task, an Arduino Board did not possess the computational capabilities to run computer vision tasks. For this reason a Raspberry Pi 3 [45] was also equipped on board to run all the heavy tasks. When an anomaly is detected by the on-board algorithm the state changes in Warning Patrolling. During the warning patrolling state the drone slowed down to gather a more stable video stream and to allow a human operator to evaluate the situation. If no anomaly was detected or the human operator manually switched the state returns to patrolling. If the human operator detects a possible dangerous situation the state was changed to alarm patrolling. During the alarm patrolling, the drone was fully driven by a human operator to allow some very complex operations. From the alarm patrolling state the drone can switch to the warning patrolling or simple patrolling. The transition to the alarm patrolling state can also be triggered from a message from another smart object to notify the drone the area of interest to reach. The FSM schema is depicted in Figure 5.  
Figure 4: Unmanned ground vehicle (UGV) boards components and logic connections.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 3 Basic Algorithms
### APF Method
#### 3.2.2 Repulsive Potential Field (RPF)  
The RPF is generated by obstacles and is used to prevent the robot from colliding with them. A common RPF function is expressed as:  
\[U_{rep}(q)=\left\{\begin{array}{l}\frac{1}{2}k_{rep}(\frac{1}{d(q_{obs})}- \frac{1}{q_{0}})^{2},d(q,q_{obs})<q_{0}\\ 0,d(q,q_{obs})\geq q_{0}\end{array}\right. \tag{8}\]  
In Equation (8), \(q_{obs}\) means the obstacle position, \(k_{rep}\) is the repulsive coefficient, \(q_{0}\) is the threshold of the obstacle influence range, and \(d(q,q_{obs})\) is the distance between the robot CP and the obstacle position. The repulsive force is the negative gradient of the RPF function:  
\[F_{rep}(q)=\left\{\begin{array}{l}k_{rep}(\frac{1}{d(q_{obs})}-\frac{1}{q_ {0}})\frac{1}{d^{2}(q_{obs})}\frac{(q-q_{obs})}{d(q_{obs})},d(q,q_{obs})<q_{0} \\ 0,d(q,q_{obs})\geq q_{0}\end{array}\right. \tag{9}\]  
The repulsive force will increase rapidly as the robot approaches the obstacle.
## 5 Bioactive Properties of COS
### Effect of COS Composition on Anti-Inflammatory Properties  
The role that inflammation plays in the course of various pathologies, such as cancer and autoimmune diseases, is well reported [82]. The anti-inflammatory activity of three COS mixtures, enriched in _fd_COS, _pa_COS and _fa_COS, respectively, was assayed by our group to assess the influence of COS nature on the activity [83]. This was investigated by measuring the decrease in the levels of TNF-\(\alpha\) (tumor necrosis factor) in murine macrophages (RAW264.7) after stimulation with a mixture of lipopolysaccharideosaccharides (LPS). The amount of TNF-\(\alpha\) was measured at different times upon incubation with 10 ng/well of LPS. TNF-\(\alpha\) reached its highest concentration after 6 h post-stimulation with LPS (data not shown).  
The secretion of TNF-\(\alpha\) decreased during the first 6 h in macrophages treated with the three COS mixtures in comparison with macrophages stimulated with LPS only (Figure 7). These results confirmed the inhibitory effect of COS against inflammation. Figure 7 shows the results using 250 ng of COS per well, which was the best of the assayed concentrations.  
According to [PERSON] et al., the attenuation of secretion of TNF-\(\alpha\) induced by LPS after incubation with COS was regulated at the transcription level [84]. Previous studies on the effect of COS composition on their anti-inflammatory activity focused mostly on the molecular size [85; 86] rather than the degree of deacetylation. Regarding the effect of DD on anti-inflammatory behavior, [PERSON] et al. demonstrated that highly deacetylated COS showed higher anti-inflammatory activity than COS with an average of 50% N-deacetylation (representative of paCOS) [87]. In general, low MW and high DD seems to increase the anti-inflammatory properties [86].  
Figure 7: Effect of fully deacetylated (_fd_COS), fully acetylated (_fa_COS) and partially acetylated (_pa_COS) (at 250 ng per well) on tumor necrosis factor (TNF-\(\alpha\)) concentration from cells stimulated with lipopolysaccharideaccharides (10 ng/well). The pictures show the amount of TNF- \(\alpha\) at 2 and 6 h after stimulation. Adapted with permission from [83].
## 3 Results and Discussion
### Intensity of Gamma Rays Underground  
Besides the ENPEMF measurement, the gamma-ray flux was also monitored at the Geophysical Observatory with the gamma detector placed about 100 m underground in the borehole. Inside the borehole, the flux of gamma rays does not depend on weather conditions and possesses the necessary stability for revealing the anomalies caused by an earthquake preparation process. This explains the essential advantage of borehole radiation monitoring over on-surface measurements. Our studies [33; 37] have shown that precipitations, such as rain, snow, and hail, together with other meteorological factors, can significantly impact gamma-ray flux in the near-surface atmosphere [38; 39; 40].  
Monitoring of gamma radiation in the energy range of 250-500 keV was carried out for the period starting from 1 February and up to 13 March 2024, and the result is presented in Figure 6. In the measurement, the output pulse signals coming from the gamma detector were continuously counted during succeeding 10 s long time intervals, and then the momentary counting rate was defined as the number of pulses arriving per second (p.p.s.).  
It is clearly seen in the plot of Figure 6 that 9 days before the 4 March 2024 earthquake, a cove-like depression has appeared in the variation record, such that the gamma flux has returned to its background only one day till the main shock. The peak discrepancy between the values of the counting rate at the minimum of gamma radiation intensity and the level of average background exceeded one standard deviation.  
Figure 5: A comparison of the ENPEMF time series measured simultaneously with the MGR-02-16 devices installed at different distances from the epicenter of the 4 March 2024 \(M6.1\) earthquake and with the magnetic antennas directed north–south (**top panel**) and West–East (**bottom panel**). Vertical lines (\(EQ\)) mark the earthquake moment. The counting rate of ENPEMF pulse signals is presented in one-second time normalization (p.p.s.).  
Statistical significance of the observed cove-like effect may be estimated through a comparison of the background record of gamma-ray flux registered in the time from 1 February up to 24 February with the data corresponding to the period of the anomalous effect between 24 February and 4 March. Using the means and standard deviations for these two-time series, the value of the Student criterion \(t=8.33\) was deduced. This corresponds to a high statistical confidence (\(p<0.001\)) of the difference between the background intensity and the effect that appeared in the gamma-ray flux before the earthquake.  
Earlier, similar cove-like depressions of gamma ray intensity in the borehole have been repeatedly observed prior to \(M4.2\)-\(M7.1\) earthquakes within the limits of the Dobrovolsky radius [33]. Presumably, this effect may be related to the emanation dynamics of radon and its gamma-radioactive daughter products, which depend on the change in the load on the rock massif and on the modification of the stress-strain state of the environment before a seismic event.
# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
###### Abstract  
Communication in industrial wireless networks necessitates reliability and precision. Besides, the existence of interference or traffic in the network must not affect the estimated network properties. Therefore, data packets have to be sent within a certain time frame and over a reliable connection. However, the working scenarios and the characteristics of the network itself make it vulnerable to node or link faults, which impact the transmission reliability and overall performance. This article aims to introduce a developed multipath routing model, which leads to cost-effective planning, low latency and high reliability of industrial wireless mesh networks, such as the WirelessHART networks. The multipath routing model has three primary paths, and each path has a backup node. The backup node stores the data transmitted by the parent node to grant communication continuity when primary nodes fail. The multipath routing model is developed based on optimal network planning and deployment algorithm. Simulations were conducted on a WirelessHART simulator using Network Simulator (NS2). The performance of the developed model is compared with the state-of-the-art. The obtained results reveal a significant reduction in the average network latency, low power consumption, better improvement in expected network lifetime, and enhanced packet delivery ratio which improve network reliability.  
Keywords:wireless networked control systems (WNCS); WirelessHART; wireless mesh networks; multipath routing; fault tolerance; network deployment strategies; QoS in WSN; IWSN +
Footnote †: journal: Applied
## 2 Theoretical Background and Mathematical Models
### Electric Field Change in the Initially Uncharged Channel of the LEMP Sprout  
The electric field change was investigated when the LEMP sprout was uncharged and just before it fell to the ground. The unloaded state of the LEMP sprout is depicted in Figure 10. The equations used to analyze the problem are expressed by Equations (28)-(30). In these expressions, 'ct' denotes the distance traveled, 'd' is the line length, 'x' is the position of the LEMP channel concerning the degree of falling to the earth, \(\delta\) is the time constant, and '\(\Gamma\)' is the value of the Lorentz factor. \(\mathrm{e_{x}}^{1}\) shows the electric field formed in the uncharged channel [30; 31; 32].  
\[\mathrm{ct}>\sqrt{\mathrm{x}^{2}+\mathrm{d}^{2}+\mathrm{h}^{2}} \tag{28}\]  
\[\delta=\sqrt{\frac{\mathrm{d}^{2}+\mathrm{x}^{2}}{\Gamma^{2}}} \tag{29}\]  
\[\mathrm{e_{x}^{1}}=\frac{Z_{0}I_{0}}{4\pi\beta}\cdot\frac{\mathrm{x}}{ \mathrm{d}^{2}+\mathrm{x}^{2}}\left(\begin{array}{c}\frac{\beta ct-\mathrm{ h}}{\sqrt{(\beta ct-\mathrm{h})^{2}+\delta^{2}}}-\frac{\beta ct+\mathrm{h}}{ \sqrt{(\beta ct+\mathrm{h})^{2}+\delta^{2}}}\\ -\frac{2\mathrm{h}}{\sqrt{\mathrm{x}^{2}+\mathrm{d}^{2}+\mathrm{h}^{2}}} \end{array}\right) \tag{30}\]
## 2 Literature Review
### Impact of Weather and Incidents  
Weather conditions, such as rainfall, temperature, and humidity, also significantly affect traffic flow on arterial roads. These conditions can alter driver behaviour and impact road safety, complicating the prediction of traffic speeds and volumes. Studies have shown that incorporating weather data into traffic prediction models can improve their accuracy, particularly in non-parametric models like NNs and LSTMs, which can better capture the complex, non-linear effects of weather [60]. Furthermore, incidents that are non-recurrent events further complicate traffic prediction. Manual incident reports, while informative, are often slow and costly. Automated systems, such as those using neural networks, have been developed to detect incidents faster and more efficiently [21].  
In summary, while parametric models like ARIMA and Kalman Filters have been useful for short-term traffic prediction, they are limited in their ability to capture complex, non-linear relationships in real-world traffic data, such as those for arterial roads. LSTM and BiLSTM models, on the other hand, offer a robust alternative for predicting traffic speed, flow, and volume, particularly on arterial roads where irregular traffic behaviours are common. By incorporating external factors such as weather and incidents, these models provide more accurate and adaptive predictions, making them essential tools for modern ITS. BiLSTM models, in particular, have shown superior performance in capturing bidirectional dependencies in traffic data, making them highly effective for real-time traffic management and congestion mitigation. Hence, the focus of this research will be on their application in the context of a case study for Melbourne, Australia.  
Recent models, such as JointSTNet, have introduced joint pre-training techniques that capture spatial-temporal dependencies critical for accurate traffic forecasting. JointSTNet influences spatial correlations across different locations and temporal patterns within traffic sequences, which enhances its robustness in complex urban networks [61]. By comparison, this study focuses on BiLSTM's ability to handle missing data and capture dependencies through bidirectional temporal modelling, which is especially suited for real-time applications on arterial roads. Integrating these approaches demonstrates the range of methodologies available to address spatial-temporal forecasting challenges effectively.
## 4 Performance Evaluation
### Simulation Parameters Setup  
The simulation settings (see Table 2) for the experiments performed were made to evaluate and compare the performance of our proposed routing scheme. We ran the Q-learning  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Description** \\ \hline \(\alpha\) & 0.1 & Learning rate \\ \(\delta\) & 0.99 & The discount factor \\ \(e\) & 0.3 & Exploration rate \\ \(\varphi\) & 0.01 & Minimum exploration rate \\ \(\beta\) & 0.995 & Exploration decay rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary of key parameters.  
algorithm (see Algorithm 2) to compute optimal paths for given source-destination pairs under varied network conditions, such as the number of traffic sources and the number of RNs in the network of the size of a 1500 by 1500 m area. To get an accurate estimate of the simulation results, we run each simulation scenario 10 times to obtain the averaged results. To simplify the experiments, we also make the following assumption for the RNs in the network: all the nodes have the same flow table size, same buffer size, start with the same initial energy, and have the same transmission/reception power and communication range.
More interestingly, we find that the difference between naive invalid action policy gradient and invalid action policy gradient is a state-dependent factor that directly impacts the probabilities associated with invalid actions. Based on this, we prove that the naive invalid action policy gradient (taking the masked policy as behavior policy and using the original policy to calculate the gradient) is actually a valid gradient that concurrently optimizes the masked policy and the original policy. From this conclusion, we design a Composite Objective Invalid Action Masking (CO-IAM) algorithm that concurrently optimizes the masked policy and reduces the probabilities of invalid actions. Furthermore, we present on-policy and off-policy algorithms for Invalid Action Masking, called On-PIAM and Off-PIAM, respectively. The On-PIAM algorithm aims to optimize the performance of the masked policy, which is the same as that of the invalid action policy gradient in [23]. The Off-PIAM adopts the Importance Sampling (IS) technique and optimizes the original policy. The main contributions of this paper are summarized as follows:  
* We introduce the concept of action-level masking, which is proven to be equivalent to logit-level masking. The theoretical analysis based on action-level masking demonstrates that the difference between the invalid action policy gradient and the naive invalid action policy gradient is a state-dependent gradient that impacts the probability distribution of invalid actions. This contributes to a better understanding of the impact of invalid action masking on gradients. * We prove that the naive policy gradient is indeed a valid gradient that optimizes both the masked policy and original policy concurrently. Although this is a counter-intuitive conclusion, it ensures that we can directly use the naive policy gradient for training without causing instability. In addition, we also propose an off-policy algorithm for invalid action masking that focuses on optimizing the original policy. * We conduct experiments based on the Gym-\(\mu\)RTS platform [24] to compare the performance of the proposed algorithms and invalid action penalty. The results show that Off-PIAM outperforms other algorithms even if the masking is removed, and CO-IAM scales well on complex tasks. Based on these findings, we conclude that the Off-PIAM algorithm is suitable for addressing most tasks, while the CO-IAM algorithm is well-suited for handling more complex tasks.
## 6 Simulation and Experiment of Weak Signal Detection Using Double-Coupled Duffing System
### Experimental Study on Weak Fault Signal Detection of Rolling Bearing
The extracted characteristics are analyzed as the excitation, and the variation diagram of the variance jump rate of the Duffing system with the built-in driving force frequency is shown in Figure 16.  
Figure 16a shows that the system does not have a variance jump rate greater than the threshold value on all built-in driving force frequencies. At this time, the signal to be tested at the same frequency is not detected, which means that no fault is detected at this time. By observing Figure 16b, it can be seen that the variance jump rate exceeds the threshold of the jump rate at 1015 rad/s and 1017 rad/s. According to the evaluation criteria of Equation (4), it can be determined that the signal with the corresponding frequency has been detected, and the frequency error is small compared with the theoretical calculation, indicating that the fault has been detected. The detection results are consistent with the actual experimental conditions, indicating that this method is effective and feasible. It Is verified that the proposed method can effectively identify the weak fault signals of bearings. The research content of this paper has certain guiding significance in the field of weak fault diagnosis.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases  
In this study, a specific subset of operations and military scenarios that can be conducted on the battlefield is chosen to illustrate the application of various emerging technologies in tactical communications. Table 3 presents a list with the use cases selected to be analyzed, as well as the correspondence between the possible technologies presented as candidates in the renewal of the tactical panorama and each of the use cases.