esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 1 Introduction
### Contribution of This Paper  
Based on the analysis provided, this paper proposes an improved Q-learning algorithm that specifically addresses scenarios where multiple trains experience delays simultaneously. The algorithm focuses on several key aspects to enhance its practical applicability in metro train delay issues. The contributions of this paper are outlined as follows:  
1. The proposed algorithm narrows down the scope of state variables, providing a clear and concise description of delay situations, which facilitates efficient real-time processing to improve its effectiveness in practical applications.
2. A simulated annealing dynamic factor is introduced to improve convergence stability and computation speed.
3. This algorithm is trained on scenarios involving delayed interaction between multiple trains, improving its universality and portability.  
The structure of this paper is as follows: Section 2 explores the delay process of trains and the measures taken during train delays. Section 3 models the problem of train delays, constructs the objective function, and defines the corresponding constraints. Section 4 proposes the use of the improved Q-learning algorithm to address the TTR problem. Section 5 evaluates the algorithm's effectiveness through case studies. The final section summarizes the advantages and significance of this paper.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Analysis of Noise-Invariant Speech Enhancement  
As the network is trained with different types of noise, it is easily anticipated that the performance may vary depending on the noise types even when given the same SNR value. This could be problematic, especially under various real-world noise environments, because lower performance improvements for certain noise types could certainly result in lower performance in overall for the entire system. Figure 6 describes the variances of the PESQ scores obtained from different noise types. We separately measured the PESQ scores for each noise type and computed the variances of 14 different noise types used in the matched and mismatched noise conditions. The results show that the proposed algorithm yielded the smallest performance gap among the noise types in all of the SNR environments. It is noted that the \(snDT\) model produced much smaller variances at the low SNR level compared to the baseline models. This demonstrates that the proposed model was less sensitive to different noise types during the enhancement process because it disentangled the speech attributes well from the noisy speech in the latent feature space. Experimental results, therefore, suggest that the proposed model is a speech enhancement system with an improved noise-invariant property.  
Figure 5: Results of subjective preference test (\(\%\)) comparing the speech quality for the \(snT\) and \(snDT\) models with various SNR values.
Nanoparticles can be synthesized mainly by two methods, which are (i) the bottom-up method which refers to the formation of nanoparticles from atoms and molecular clusters in liquid or vapor phase, and (ii) the top-down method, which involves the process of breaking up bulk materials into nanoscale particles [17]. The quick precipitation method is an example of a bottom-up method that has received great attention due to its simplicity, cost-effectiveness, and ease of mass production [18; 19]. Several researchers have adopted quick precipitation in the synthetization of different nanomaterials such as (i) nano copper oxide [19; 20; 21], (ii) nano iron oxide [22; 23], (iii) nano ferric hydroxide and nano aluminum hydroxide [24], and (iv) nano calcium hydroxide and nano magnesium hydroxide [18] in various applications. However, to the best of the authors' knowledge, limited to no studies have utilized nanoparticles prepared using the quick precipitation method in soil stabilization. Addressing the above-mentioned gap, this study aimed at exploring the feasibility of using magnesium and calcium hydroxide nanoparticles prepared using a quick precipitation method for improving the geotechnical properties of a tropical residual soil. To this end, a series of geotechnical tests, including Afterberg limits, compaction, hydraulic conductivity, and UCS were conducted to study the effects of magnesium and calcium hydroxide nanoparticles on the engineering properties of a residual soil. Moreover, the changes occurring in the soil engineering properties were examined by performing microstructural tests including: X-ray diffraction (XRD), energy-dispersive x-rayspectroscopy (EDX), and variable-pressure scanning electron microscope (VP-SEM). Finally, descriptive statistics were employed to better assess the effect of magnesium and calcium hydroxide nanoparticles treatments on the UCS development.
## 2 System Model and Problem Statement  
### System Model  
We consider a single-input single-output single-eavesdropper model that operates in time-division duplex (TDD) mode, as shown in Figure 1. All the users are equipped with a single antenna. Two legitimate users, [PERSON] and [PERSON], wish to establish shared keys to ensure secure communication in the presence of the passive eavesdropper, [PERSON]. The wireless channel is a slowly varying Rayleigh fading channel and remains constant during the coherence time, where \(h_{AB},h_{BA}\)\(\sim\)\(\mathcal{CN}(0,1)\). We define \(h_{AB}\), \(h_{BA}\), and \(h_{AE}\) as denoting the channels from [PERSON] to [PERSON], [PERSON] to [PERSON], and [PERSON] to [PERSON], respectively. According to the reciprocal of the wireless channel, \(h_{AB}=h_{BA}\). [PERSON] is located more than half a wavelength away from legitimate users. In addition, [PERSON] and the legitimate users have no line of sight (LoS). Therefore, [PERSON] experiences a wireless channel independent of that of the legitimate users, because they are spatially decorrelated between different geographic locations [19; 20]. We assume that [PERSON] and [PERSON] have the same transmitting power, i.e., \(P_{A}=P_{B}=P\), where the power allocation parameters of the pilot and data signals are \(\alpha\) and \(1-\alpha\), respectively. In addition, [PERSON] knows the complete communication process and the information transmitted over public channels. [PERSON] and [PERSON] probe the channel to generate keys, which are employed to encrypt data information to realize secure communication.
## 3 Feature selection for HMMs using Heuristic and Genetic Algorithms
### Searching the feature space  
Given a set of N features, the number of possible feature subsets to evaluate is \(2^{N}\). As N grows, the feature selection problem becomes intractable and exhaustive search methods become unpractical. To avoid this _course of dimensionality_, suboptimal search methods have to be employed. In this paper two different methods are proposed: Best First Search and Genetic Search.  
Best First Search (BFS) [18] is a tree search method that explores the search tree expanding the best node at each level. In this case, the best node is the one who has the best accuracy on its level. Algorithm pseudocode is shown on 1. This search method does not guarantee to find the optimal solution, only a local optima, because when selecting only the best child, the path to the optimal solution could be left unexplored.  
```
\(Open\gets root\) while\(Open\
eq\phi\)do  Pick the best node of \(Open\) \(Open\leftarrow\phi\)  Generate its children and introduce into \(Open\) endwhile
```  
**Algorithm 1** Best First Search  
To search the feature subset space using Best First Search, the root node of the tree will be the empty feature set. The successors of a node will be that nodes that contain the same features that its parent plus a new one that its parent does not have. The search will finish were the accuracy of children is equal or smaller than the accuracy of their parent.  
Genetic Algorithms (GA) [19] are powerful search techniques inspired in the Darwinian Evolution Theory. Populations of individuals, where each one represents a different solution to the problem, are evolved using crossover and mutation operators. The pseudocode of a simple genetic algorithm can be seen on 2. Again, this search method does not guarantee to find the optimal solution, and even if it is found, there is no way to ensure that is the optimum.  
```
\(Population\gets init\) whilestop condition is not satisfied do \(Evaluate\left(Population\right)\) \(NextPopulation\gets selection\left(Population\right)\) \(NextPopulation\gets reproduction\left(Population\right)\) \(Population\gets replacement\left(Population,NextPopulation\right)\) endwhile \(Solution\gets best\left(Population\right)\)
```  
**Algorithm 2** Genetic Algorithm  
The individual that is going to be used to explore the feature subset space is a bit string of length N, being N the total number of given features. If the ith bit is set to 1, it means that the ith feature is selected, whereas if it is set to 0, it means that the ith feature is not selected.
## 3 System Model
### AO-PF Fusion Algorithm
#### 3.6.2 AO-PF Utilized in WiFi/PDR Fusion
(6) The Effectiveness of AO  
By introducing AO particles, we enhance the particle set diversity, enabling a more comprehensive exploration of the state space. The resampling process with AO particles improves coverage of the true posterior distribution, mitigating the issue of traditional particle filters converging on local optima. Inspired by biological processes, the AO-PF framework employs controlled randomness and strategic mutation to maintain a dynamic and adaptive particle set. This enhances the algorithm's ability to adapt to changes in the localized system, thereby improving the accuracy of state estimation for indoor localization. Figure 8 shows how AO particles improve coverage and distribution uniformity in the AO-PF framework, ensuring that particles better represent the actual distribution. This approach preserves particle diversity and mitigates particle impoverishment, thereby enhancing positioning accuracy.
# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System  
In this section, we delve into the conventional approach of soil sensing through reflectance spectroscopy. Diffuse reflectance spectroscopy [17, 23] emerges as a swift, economical, and environmentally friendly method, showcasing consistent prediction outcomes when compared to previous chemical analysis. In recent times, the endeavors to harness vis-NIR-MIR reflectance spectroscopy for predicting soil attributes [17, 19, 20, 24] are surging. Attributes such as total carbon (TC), total organic carbon (TOC), soil moisture, and essential chemical constituents like total soil nitrogen (N), extractable phosphorus (P), and potassium (K), among other foundational soil components, have garnered significant attention as researchers aim to pinpoint their absorption properties within the vis-NIR-MIR range.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 6 Conclusions  
In this paper, we proposed a security scheme for groups of SMs in a Neighbourhood Area Network that enable entire groups to authenticate themselves, rather than on at a time. In particular, the scenario of a multi-hop network is considered where the nodes require multiple hops to communicate with the NOC, which is the entity that issues the keys. Two topology scenarios, star-star and mesh, are considered and separate authentication processes are defined for their operation. We propose a hierarchical control scheme for authentication; all nodes initially authenticate with the NOC and, subsequently, the group gateway autonomously issues an authentication token to the authenticated members in its group. We mention how the proposed approach is two-way secure as both involved parties, the group controller and the smart meters, are able to successfully verify each other.  
Figure 11: RTT from L-SM to NOC in a star topology (one hop).  
Figure 12: RTT from L-SM to NOC in Mesh topology (three hops).  
The authentication scheme was implemented in real-world environment using TelosB motes. We found out that the average encryption and decryption times on end nodes are 6 ms and 6.2 ms, on intermediate nodes (authenticated forwarding), including the GW node, 3.9 ms and 4.2 ms, and on the NOC, 8.2 ms and 6.5 ms. The whole authentication protocol took 331 ms.  
In our future work we are going to evaluate the performance of the authentication scheme in a large network using a simulation tool [30]. We intend to examine the performance of the scheme in a large network, simulating failures of gateway nodes and measuring the impact of single and multiple gateway node failures in terms of the number of nodes that are rendered unreachable. The topology of the networks will be star, partial mesh, and full mesh between the gateway nodes. In addition, the impact of node failures resulting from attacks on the overall network will be evaluated. In addition several of security scenario case studies will be analysed [21].  
This paper has been written by the first author ([PERSON]) and is based on the research conducted by him under the guidance of the second, the third authors and the fourth authors.  
The authors declare no conflicts of interest.
## 2 Operation Principles and Analysis Methods
### Dual Modes U-Shaped Resonator with One Grounded Metallic Via
According to Equation (2), the calculated resonance frequency for the TE polarization is approximately 19 GHz, resulting in a significantly smaller current amplitude at 10.2 GHz compared to the TM polarization. Equations (1) and (2) illustrate that when the arm width \(w\) is close to the stub width \(l_{c}\), the resonance frequency ratio between TE and TM polariz  
Figure 3: Equivalent transmission line model of the U-shaped resonator without grounded via. Figure 2: Simulation results of the U-shaped resonator without ground via under TE and TM polarizations. (**a**) The reflection magnitude and phase versus operating frequencies under different polarizations. (**b**) The TM mode current distribution of the U-shaped metallic surface at 10.2 GHz. (**c**) The TE mode current distribution of the U-shaped metallic surface at 10.2 GHz. meet the smaller frequency ratio requirements of FDD systems and to tune the resonance frequencies, a metallic grounded via with a radium of \(r_{v}\) is positioned in the stub center. The via-hole grounds in the microstrip line can be represented by the inductance of a cylindrical conductor derived from the Maxwell equations [43; 44; 45]. Figure 4 illustrates the new structure simulation results. Under TM polarization, the influence of the introduced inductance on the resonant frequency is minimal because the symmetry plane functions as a magnetic equivalent boundary. Nonetheless, the resonant frequency for TE polarization decreases to 9.31 GHz when a significant odd-mode current is activated. In this case, the resonant frequency ratio is adjusted to approximately \(1.11\), which is close to the norm for duplex systems.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 3 Methodology
### Comprehensive Trust Value  
The comprehensive trust value of any node is calculated by adding direct trust, indirect trust and energy trust values of the node as given in Equation (12).  
\[Comprehensive\ Trust\ value_{xy}^{n}=\omega 1\times T.Direct_{xy}^{n}+\omega 2\times T. Indirect_{xy}^{n}+\omega 3\times E_{y} \tag{12}\]Here the \(\omega 1\), \(\omega 2\) and \(\omega 3\) are the weighting coefficients for direct trust, indirect trust and energy trust values, respectively, and \(0<\omega 1\), \(\omega 2\), \(\omega 3<1\), \(\omega 1+\omega 2+\omega 3=1\).
## 2 Theory and Methodology  
### Illumination Analysis and Artifact Formation in Conventional RTM  
RTM imaging is generally considered to consist of three main steps: First, the selected seismic wavelet is used as the source for forward extrapolation to generate the complete source wavefield. Due to the presence of noise, which significantly impacts the imaging quality and consequently the accuracy of seismic wavelet extraction from the seismic data, RTM imaging typically employs artificial sources such as the Ricker wavelet [27]. Next, seismic data recorded at the receivers are used as the boundary and final conditions for back-propagation along the time direction (t = 0) to obtain the receiver wavefield. Finally, imaging results are obtained by applying specific imaging conditions to the wavefields.  
Figure 1 illustrates RTM imaging in a horizontal single-layer model using a VSP observation system. The vertical observation well is represented by a green rectangle, with three geophones indicated by triangles. The solid red line represents the seismic wavelet that reaches the receiver after a single reflection at the reflection point, illustrating the wave propagation path. In contrast, the solid blue line depicts the propagation path of multiple waves, which reach the receiver after undergoing multiple reflections within the subsurface medium. Figure 1 shows that the primary reflections in a VSP observation system provide a narrow illumination range limited to the vicinity of the observation well. In contrast, multiples with longer propagation paths can carry information on a wider range of subsurface structures. Therefore, effectively utilizing multiples for imaging can partially compensate for the narrow illumination range inherent to VSP imaging.  
However, due to the complexity of multiple-wave propagation paths, directly applying conventional RTM methods to process multiples often results in a large number of artifacts, significantly affecting the quality of the final imaging result. Thus, multiples are typically treated as noise and removed in RTM. Figure 2 illustrates the mechanism of artifact formation during the migration of multiples in conventional RTM. The model consists of two horizontal reflectors, with the source location marked by a red dot and the observation well represented by a green rectangle containing one receiver indicated by a triangle. The seismic wavelet is excited at the source location and propagates along the paths shown by solid lines in the figure, where blue and orange represent down and up-going waves, respectively. The wavefield at M1 is back-propagated and cross-correlated with the source wavefield at R1, forming a correct image. Simultaneously, the wavefield at M1 reflects off the horizontal layer and can be considered a secondary source. Its wavefield cross-correlates with the back-propagated wavefield at M2 to form a correct image at R2.  
However, if the source wavefield continues to propagate downward after reaching R1, it may cross-correlate with the back-propagated higher-order multiples at M2, forming artifacts F1 and F2, as indicated by the dashed lines in Figure 2.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 5 Performance Analysis
### Required Battery Capacity  
Only if there is enough energy in the battery of sensor \(n\) carried by the patient, the BAN system can have a stable operation; otherwise, it will not collect data or transmit data. Thus, we can get the required battery capacity in such a way that if the available energy is less than the maximum energy consumption, i.e., \(J_{n}(t)\leq a^{max}\), the sensor will not collect data or transmit data. Theorem 2 has shown the required battery capacity.  
**Theorem 2**.: _The required battery capacity \(\Omega\) supporting to the operation of BAN system is given by :_  
\[\Omega=\frac{V\
u}{\alpha}+a^{max},\forall n\in N. \tag{16}\]  
_Sensors will not collect and transmit data, i.e., \(c_{n}(t)=0\) and \(r_{n}(t)=0\) if and only if \(J_{n}(t)\leq a^{max}\), i.e., length of energy queue is less than the maximum of energy consumption._Proof.: See proof of theorem 2. To enable the BAN system to operate under the proposed resource allocation algorithm, the sensor \(n\) must be equipped with a battery of capacity \(\Omega\).
## 2 Materials and Methods
### Experimental Campaign
#### 2.2.3 Oxygen Consumption during the Process  
The stability of the biomass was measured during the process itself thanks to an oxygen sensor, partially reproducing the methodology applied for the adiabatic solid respirometer in non-limited oxygen conditions (AIR-nl), realized by [PERSON] [15; 16] at the University of Trento and adopted by the Autonomous Province of Trento (Italy) as the standard procedure for respirometric tests [27]. Each sample was kept in the closed cell (the bioreactor itself) with a forced bottom-up airflow, and the exhaust air was measured in oxygen content; differing from [PERSON]'s work, the air here was not dried, but kept wet, and the temperature was settable and not fixed at 30 \({}^{\circ}\)C.  
To calculate the quantity of oxygen inside each bioreactor starting from the oxygen content measurement \(\chi_{\text{O}_{2}\%}\) divided by 100 to obtain the molar fraction \(\chi_{\text{O}_{2}}\), the ideal gas law (Equation (1)) was used to find the number of moles of oxygen \(n_{\text{O}_{2}}\):  
\[n_{\text{O}_{2}}=\frac{p_{\text{O}_{2}}\cdot V_{air}}{R\cdot T_{air}} \tag{1}\]  
with:  
* \(p_{\text{O}_{2}}\), partial pressure of oxygen inside the reactor, found as the product of \(\chi_{\text{O}_{2}}\) and the atmospheric pressure in lab (average considered constant p\({}_{\text{atm}}\) = 98,300 Pa, 190 m a.s.l.);
* \(V_{air}\), volume of voids occupied by air inside the bioreactor;
* \(R\) = 8.314463 J mol\({}^{-1}\) K\({}^{-1}\), ideal gas constant;
* \(T_{air}\), temperature of air inside the reactor.  
Thus, through Equation (2), the mass of oxygen inside each bioreactor can be calculated (molecular mass of oxygen \(MM_{\text{O}_{2}}\) = 15.999 g/mol).  
\[m_{\text{O}_{2}}=MM_{\text{O}_{2}}.\frac{\big{(}\chi_{\text{O}_{2}}\cdot p_{ atm}\big{)}\cdot V_{air}}{R\cdot T_{air}} \tag{2}\]  
After the \(m_{\text{O}_{2}}\) in the air inside the bioreactor has been calculated for each measurement, it is possible to calculate the rate of oxygen consumption from the decreasing slope of the oxygen content measured at every consuming cycle (under a certain threshold the oxygen content is restored with fresh air and then a new consuming cycle starts). This calculation was performed with a linear regression using the function _fitlm_ in Matlab(r). A data quality analysis was performed, neglecting all the cycles where R\({}^{2}\) < 0.95.  
The oxygen consumption rate was then divided by the initial quantity of volatile solids (in kg) inside the bioreactor; the Dynamic Respirometric Index (DRI) was expressed in mg O\({}_{2}\) kg\({}_{\text{VS}}\)\({}^{-1}\) h\({}^{-1}\) consumed. The steps are visualized in Appendix A.  
In order to characterize the compost stabilization according to the Italian legislation (Legislative Decree 75/2010 [28]), a sample of each mix before and at the end of the composting process was tested with the standardized solid respirometric test AIR-nl [27], in an adiabatic bioreactor with semi-continuous aeration at the controlled temperature of 30 \({}^{\circ}\)C.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Types
#### 3.3.1 Point Anomaly  
Point anomaly occurs when anomalies differ significantly from the expected patterns. The detection of this type of anomaly involves the observation of any point that can be detected as different from other data flows. It is also referred to as an outlier [19]. Figure 3 demonstrates a point anomaly.