esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
###### Abstract  
The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.  
Keywords: radar anti-jamming; UAV radar networking; low-bit beam synthesis; radar information representation; double-greedy algorithm +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Strengths and Limitations  
The strength of this study is its use of real-world clinical data from the EHR of multiple community hospitals and an academic medical center. The study did not rely on administrative data sets for coding of in-hospital AKI since these are fraught with error [19]. The data from this study provide insight into routine SCr monitoring that can be used to optimize timing components of AKI criteria for the development of real-time alerts or analysis of large EHR-based datasets. The approach in the present study uses rolling windows of observation rather than reliance on a pre-hospital baseline SCr, which was not available in the present study and is infrequently routinely available. Another limitation of this study was the unavailability of urine volume data. The study relies solely on SCr and is limited by the lack of more accurate verified biomarkers of AKI.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 2 System Model
### Jamming Model  
The single-tone jamming signal refers to a sinusoid signal as \(s_{j}(t)=\sqrt{P_{j}}\exp\{j2\pi f_{j}t+\varphi_{0}\}\), in which \(P_{j}\), \(f_{j}\), and \(\varphi_{0}\) are the power, carrier frequency, and initial phase of the jamming signal, respectively. The single-tone jamming signal after sampling can be given by  
\[s_{f}(n)=\sqrt{P_{j}}e^{i\left(\frac{2\pi}{N}(m+\alpha)n+\varphi_{0}\right)}, \tag{6}\]  
where \(m\) denotes the index of the nearest subcarrier to the jamming signal's center frequency, and \(\alpha\in(-0.5,0.5]\) represents the relative frequency offset of the jamming signal from the nearest subcarrier. We can also obtain the frequency-domain representation of the single-tone interference through \(N\)-point FFT as follows:  
Figure 1: Diagram of the OTFS system.  
\[S_{f}(k)=FFT\big{\{}s_{f}(n)\big{\}}=\sqrt{\frac{P_{f}}{N}}e^{j\varphi}\frac{1-e^{ j\pi\alpha}}{1-e^{\frac{j\pi\alpha}{N}(m-k+\alpha)}}. \tag{7}\]  
We can conclude that when the normalized frequency aligns perfectly with an integer multiple of the subcarrier spacing (i.e., \(\alpha=0\)), the single-tone jamming primarily affects the corresponding subcarrier, leaving others unaffected. However, when \(\alpha\
eq 0\), spectral leakage occurs, causing the jamming to spread across multiple subcarriers and significantly degrading system performance.  
Another very important type of jamming is the partial-band jamming. This type of jamming is particularly effective in scenarios where attackers aim to disrupt communication across a broader portion of the frequency spectrum while conserving power. Partial-band jamming refers to jamming signals whose power is concentrated within a specific portion of the useful signal bandwidth. The jamming factor is defined as \(\xi=B_{f}/B\), where \(B_{f}\) and \(B\) represent the bandwidth of the jamming signal and transmitted signal, respectively. Specifically, \(\xi=0\) indicates the absence of jamming, \(\xi=1\) represents full-band jamming, and \(0<\xi<1\) denotes partial-band jamming.  
For simplicity, the partial-band jamming signal is modeled as white Gaussian noise filtered by a band-limiting filter with a center frequency \(f_{j}\). The frequency response function of the filter can be expressed as  
\[H(j2\pi f)=\begin{cases}1,&\big{|}f-f_{j}\big{|}\leq B_{f}/2\\ 0,&\big{|}f-f_{j}\big{|}>B_{f}/2\end{cases}. \tag{8}\]  
The common characteristic of single-tone and partial-band jamming is that the energy of the jamming signal is concentrated at one or more specific frequency points. As shown in Figure 2, the jamming signal spreads in the DD domain when transformed using SFFT. Therefore, it is more effective for us to detect and suppress the corresponding jamming signals in the time-frequency domain.
## 3 Dynamic Multi-Hop Routing (DMR) Technique
### Phases of the DMR Protocol
#### 3.2.1 The Initialization Phase
Consequently, the CHs and INs are distributed across these levels based on their respective distances from the BS. Furthermore, each cluster is internally divided into two levels, each having a length of \(\mathrm{d_{o}}/2\). Accordingly, the Ns within each cluster are assigned to levels according to their distances from the corresponding CH.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 1 Introduction
### AI Within Metaverse for Education  
The fusion of the metaverse and AI technologies promises to revolutionize how we teach and learn. The study in [15] provides a thorough classification and projection of algorithms informing AI techniques, and indicates that the learning process could be dramatically improved by integrating education-focused AI within the metaverse. Such integration would enable the development of immersive, personalized, and interactive learning environments to meet the varying needs of students. In particular, it would be possible to adapt course material according to each student's performance. AI in education has shown significant promise as an instrument for personalizing learning experiences and promoting active participation. Supervised learning algorithms such as decision trees and support vector machines (SVM) could help develop adaptive learning systems in which course material is adjusted according to individual students' progress, e.g., AI-based intelligent tutoring systems [16]. These systems can provide real-time feedback, adapt content delivery, and enable learning tailored to each student's pace. AI can also assist with analysing large datasets, identifying patterns in students' behavior, and improving learning outcomes. Unsupervised learning techniques, such as K-means clustering, can uncover correlations between students' learning styles and performance. This then facilitates targeted interventions for those struggling, or excelling beyond the standard curriculum [17]. Reinforcement learning models (like deep Q-networks) can dynamically adjust learning content based on students' interactions, ensuring an engaging and responsive learning experience. AI systems can further monitor students' emotional responses or engagement levels, using these data to provide additional support or increase the challenge as required [17]. In addition, AI-powered NLP tools, such as transformers, recurrent neural networks (RNN), and convolutional neural networks (CNN), can further enhance the metaverse by improving communication between students and educational resources. These tools can enable voice commands, translations, or context-aware learning activities within virtual spaces [17]. Thus, it is vital to understand the various AI approaches available for use in the metaverse for educational purposes.
# Hydrogel Tissue Expanders for Stomatology. Part II. Poly(styrene-maleic anhydride) Hydrogels
## 3 Results and Discussion  
In the present work, crosslinked copolymers of styrene and MA were synthesized and examined in respect to their potential application as soft hydrogel TE. The strictly alternating character of the styrene-MA copolymers as usually obtained under the conditions of their radical copolymerization [25] results in polymer chains of high structural regularity [26] and an interesting amphipathic nature: The polySMA can be dissolved in polar such as alcohols, even aqueous as well as in nonpolar or weakly polar organic solvents such as tetrahydrofuran or ethyl acetate. The behavior of radically polymerizing MA monomer and its reactivity towards the styrene comonomer is driven by the electron resonance stability on its double bond. The MA is known not to homopolymerize but it copolymerizes with the other monomers of the opposite electron polarization, e.g., vinyl monomers such as styrene.  
In aqueous medium at presence of ions, MA units incorporated in the polySMA chains undergo hydrolysis leading to the formation of carboxylate groups (Figure 1b). As a consequence, the hydrophilic character of the polySMA-based macromolecular network increases with the degree of MA hydrolysis, which results in a further absorption of water inevitably accompanied with an increase of the hydrogel volume in later stage of swelling process.  
In this study, the equimolar ratio of these two monomers was used in all prepared hydrogels. The DVB was selected as a crosslinking agent as it is a structural analogue of styrene and it copolymerizes readily with the both monomers. In order to find optimal concentration of the crosslinker, we made preliminary polymerization and swelling experiments. The hydrogels prepared with less than 0.5 mol % of the DVB showed very fast growth of volume upon swelling in PBS, whereas the hydrogels containing more than 1 mol % of DVB were in their swollen state weak and mechanically failing even in the initial stage of swelling. Therefore, in this study, the DVB crosslinker content was set in the range between 0.5 and 1.0 mol %.
## 3 Process Optimisation
### Trace Element Supplementation  
Like all living organisms, the microbial consortium of anaerobic digesesters require a suite of essential elements to maintain good health and normal function. In the absence of these elements the microbial consortium will begin to underperform over time until a reactor fails. Trace elements, like temperature regulation, are critical to the underlying biological functioning of the anaerobic digester. The effects stemming from TE supplementation are diverse but include improved FOG degradation; process stability and; biogas quality and quantity.  
In what was a first in the treatment of Australian RMP waste, [PERSON] et al. [28] investigated the effect of TE addition on the ability for continuous stirred-tank reactors to treat high-fat RMP waste. These reactors were continually mixed and operated at 38 \({}^{\circ}\)C while the organic loading rate was increased incrementally (Figure 4a). Reactors supplemented with TE exhibited improved organic degradation over an extended period of time compared to controls (Figure 4b), at day 140 with an OLR of 1.82 g COD L\({}^{-1}\)d\({}^{-1}\) and HRT of three days the difference was 28%. Process stability was improved in the TE reactors, maintaining a VFA/alkalinity ratio below 0.3 for 40 days beyond the controls and allowing for a stable OLR increase from 0.91 g COD L\({}^{-1}\)d\({}^{-1}\) to 1.82 g COD L\({}^{-1}\)d\({}^{-1}\) (Figure 3c). As a result of increased OLR capacity and degradation efficiency, biogas production was enhanced by up to 84% prior to failure (Figure 4d). Consequently, TE supplementation can be useful to manage accumulation of VFA, improve FOG degradation, overcome process instability from overloading, low temperatures, high FOG concentrations and improve start-up after shutdown periods [28].  
Figure 4: (**a**) Organic loading rate and hydraulic retention times in control and TE (trace element)-supplemented reactors [28]; (**b**) vs. and FOG degradation efficiency in control and TE-supplemented reactors [28]; (**c**) VFA and VFA/Alkalinity ratio in control and TE-supplemented reactors [28] and; (**d**) biogas production from control and TE-supplemented reactors as both functions of vs. and COD. Error bars represent one standard deviation from the mean.
## 2 The Development of Typical Rotating Motion Mechanisms
### Cam Mechanism
#### 2.4.4 3D-Printed Cam Formed by Melt Deposition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Cam roller \\ [81,82] \\ \end{tabular} } & High efficiency and low & Under high load, the & Conveyor system, low \\  & energy consumption, low & lifespan may be shortened & energy consumption \\  & balance cost & due to friction & demand \\  & Smooth movement, & High cost, requiring & High speed weaving \\ Conjugate cam [83, 84, 85] & minimal vibration, high & precision machining and & machine, high speed \\  & speed operation, compact & weight design & sorting, precision \\  & structure & & instruments \\  & Small space occupation, & Easy to wear and tear, high & Equipment vibration \\  & long-distance movement, & cost, poor stability & suppression, long stroke, \\  & adjustable damping & Significant axial dimension & and compact requirements \\  & Low cost, lightweight, & error, rough surface, low & Low power transmission \\
3D-Printed Cam [88] & suitable for geometric & interlayer bonding & scenarios, customised \\  & innovative design & strength, and inability to & motion mechanisms that \\  & & withstand high loads & require rapid iteration \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different types of cam mechanisms. Figure 37: Printing a constant-width cam on the bed of the Ultimaker 2+ printer. noise reducing, but their fatigue resistance is affected by the resin ratio, and the cost is high, making them suitable for corrosive environments or light-load energy-saving scenarios. 3D-printed polymer chain links (such as PETG materials) have customizable insulation, are lightweight and corrosion-resistant, but have low mechanical strength and poor material compatibility. They are only suitable for non-load-bearing or short-term light-load scenarios, such as decoration and pet traction chains. Various chain drives complement traditional machinery and emerging material technologies, covering diverse applications from low load to high precision, from conventional environments to special needs.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 4 Proposed ML Method
### Quantization Method  
The trained ML performs a quantization of 32-bit floating-point weights \(\mathbf{w}\) to 8-bit integer-point weights for the interval \([-128,127]\), as shown in Figure 6. For quantization, values that are 32 bits are divided into a certain range to quantize them into 8 bits. In this figure, different proximity weight values, such as \(w_{k-1}\) and \(w_{k}\), are quantized to the same value if they belong to the same interval when quantized to 8 bits. Different weight values within the interval are quantized to the same value, so performing inverse quantization to use ML converts them to the same weight value. Therefore, when the weights go through quantization, they are set to a value different from the original value, resulting in performance loss. The maximum value \(w_{\mathrm{max}}\) and minimum value \(w_{\mathrm{min}}\) of  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & \
\begin{tabular}{l} **The Number of** \\ **Multiplications/Inversions** \\ \end{tabular} &
\begin{tabular}{l} **Computational** \\ **Complexity** \\ \end{tabular} \\ \hline LS & \(N^{2}+N\) & \(O(N^{2})\) \\ MMSE & \(N^{3}+3N^{2}\) & \(O(N^{3})\) \\ Existing ML & \(N^{2}+N+2N\times 4N+3\times(4N\times 4N)+4N\times 2N\) & \(O(N^{2})\) \\  & \(=65N^{2}+N\) & \\ Proposed ML & \(2N\times Q+Q\times 2L+N_{fft}\times L\) & \\  & \(=2Q(N+L)+N_{fft}\times L\) & \(O(QN)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of ML-based estimators with LS and MMSE.  
Figure 5: (**a**) The tanh function, (**b**) derivative of tanh function.  
\(\mathbf{w}\) are stored in memory for dequantization. Then, when using the proposed ML for channel estimation, quantized weights must be applied to ML after dequantization. The dequantization normalizes the interval \([-128,127]\) with stored \(w_{\text{max}}\) and \(w_{\text{min}}\) to derive the interval \([w_{\text{min}},w_{\text{max}}]\).  
In the case of weight quantization for memory saving, \(w_{\text{max}}\) and \(w_{\text{min}}\) must be stored because it is to be restored based on them. However, if the length of the CIR is \(L=1\) and \(L=6\), then each cumulative distribution function (CDF) of the proposed ML for the weights \(\mathbf{w}\) is shown in Figure 7. As shown in this figure, the probability that the value of \(|w|\) exists below 0.5 is \(F(w)=P(|w|\leq 0.5)>0.99\). Therefore, almost all the weights are assigned within the interval \([-0.5,0.5]\), and the dequantization step uses the interval \([-0.5,0.5]\) to dequantize the interval \([-128,127]\) instead of using \(w_{\text{max}}\) and \(w_{\text{min}}\) for memory saving.
## 3 Results
### Statistical Analysis
However, this effect diminishes for bottom-10 pieces in support-per-transition, a fact that justifies the overall weak correlation of those two values for all pieces (0.15 in support-per-transition and melody matching over all generated pieces--see row 4, column 2 in Table 3). For instance, the piece with the smaller support-over-transitions value (\"Falling in Love with Love\") has indeed a lower-than-average melody matching mean value (0.19). Correlation analysis is conducted on the values of Table 2 for each generated piece, the number of constraints, support and smoothing applications divided by the total number of transitions to obtain results that are relative to the length of each piece. Table 3 shows the results of this correlation analysis, where melody matching is represented by the mean value of melody matches within each piece. The interesting result of this analysis, indicated in bold fonts, is the relatively strong relation between the appearance of new chords with the application of support, over constraints or independently. This fact indicates that, when support is required, the algorithm employs information on chord grouping to provide a solution that best matches the underlying melody with a chord based solely on the rPCP of the latter (since new chords do not include information about the melodic context they expect). It is also interesting that this correlation does not exist for chords that were selected through smoothing. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Label** & **Average** & \
\begin{tabular}{c} **Straight, No Chaser** \\ **(Max. Support)** \\ \end{tabular} &
\begin{tabular}{c} **Falling in Love with Love** \\ **(Min. Support)** \\ \end{tabular} \\ \hline Transitions & 38.16 & 12 & 44 \\ Constraints & 5.32 & 2 & 2 \\ Support & 16.12 & 10.77 & 5.46 \\ Supp. no Constr. & 11.81 & 8.79 & 4.19 \\ Smoothing & 0.17 & 1 & 0.00 \\ Smith. no Constr & 0.03 & 1 & 0 \\ Melody mean & 0.26 & 0.13 & 0.19 \\ Melody std & 0.19 & 0.11 & 0.12 \\ New chords & 2.22 & 0.01 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of applying support and chord grouping when a piece acts as Song A, i.e., when it provides the melody and the constraints.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 4 Proposed Tree Routing Protocol
### Parent Selection
#### 4.2.1 RL Model  
Here, we define the state, action, and reward function as follows.  
* State: The state space can be defined as a set of three decision criteria, denoted by \(s=\{P_{i},B_{i},E_{i}\}_{i\in N}\). That is, each node (i.e., agent) selects a parent node in consideration of the link quality, congestion level, and remaining energy of the neighboring node.
* Action: The action space is defined as a set of candidate parent nodes in the neighbor table. It should be noted that the set of candidate parent nodes only contains the neighboring nodes with the same or fewer hops to the sink node than itself.
* Reward: Our approach to define the reward function is that if the agent selects a node as a parent node and that action increases the frame retransmission, packet error rate, and energy consumption, then the agent obtains a lower reward. Otherwise, the agent gets a high reward.
## 3 Challenges
### Hardware  
CR wireless sensors have hardware constraints in terms of computational power, storage and energy. Unlike conventional wireless sensors, they have a responsibility to sense channels, analyze, decide, and act. CR wireless sensors should be capable of changing the parameter or transmitters based on an interaction with its environment.  
As shown in Figure 4, a CRWS consists of six basic units: (i) a sensing unit; (ii) a processing and storage unit; (iii) a CR unit; (iv) a transceiver unit; (v) a power unit; and (vi) a miscellaneous unit.  
Sensing units contain sensors and analog to digital converters (ADCs). The analog signal observed by the sensor is converted to a digital signal and sent to the processing unit. The CRWS should have cognition capability using a state-of-the art artificial intelligence technology. This capability is accommodated in the CR unit. The CR unit needs to adapt the communication parameters dynamically, such as carrier frequency, transmission power, and modulation. The unit needs to select the best available channel, share the spectrum with other users, and manage the spectrum mobility, i.e., vacate the currently using channel in the case the PU wants to use that channel. A transceiver unit is responsible for receiving and sending data.  
Because the energy harvesting techniques in wireless sensor nodes have developed rapidly, the energy harvesting or recharging units are optional and sensor specific. A miscellaneous unit is an application- specific additional unit, such as a location-finding unit, energy harvesting unit, and mobilizing unit, _etc._ [PERSON] _et al._[7] proposed a similar hardware structure of a CR wireless sensor.  
Designing intelligent hardware for CR-WSNs is a very challenging issue. Many artificial intelligence techniques have been proposed to fulfill the basic principle of CR, i.e., observation, reconfiguration and cognition. Some examples include artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). The factors that affect the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed in reference [32]. Nevertheless, it is unclear how much intelligent hardware for WS-CRNs is intelligent enough and no threshold has been defined for it.