esa-sceva/satcom-chunk-collection huggingface dataset:
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 3 General Framework for ERO-ATSC
### ERO and Alternating ERO
#### 3.1.1 ERO Framework  
ERO is a novel framework that uses a neural network to predict replay transitions. It is used to enhance the experience selection mechanism in the DDPG algorithm [9]. As illustrated in Figures 1 and 2, a replay policy is combined with the standard ER framework to form the ERO framework. The transitions generated by the agent's choices of actions are stored in the replay buffer and subsequently sampled into the replay policy after a specified number of transitions are buffered. The replay policy prioritises the transitions through a Boolean (\(0,1\)) vectorisation process based on the TDEs. Transitions with Boolean number \(1\) are uniformly sampled to train the agent. When the replay buffer is full, the naive FIFO retention strategy is used to retain experiences in the standard ER and ERO frameworks. However, in this study, an enhanced sequential retention strategy is used for the experience retention mechanism of the ERO framework.  
The replay policy of ERO relies on Equations (1)-(4), and the notations used are clearly explained in Table 2.  
\[\lambda=\left\{\phi\big{(}f_{B_{i}}|\phi^{\phi}\big{)}\big{|}\ B_{i}\in B \right\}\ \in\mathbb{R}^{N} \tag{1}\]  
\[B^{s}=\{B_{i}|B_{i}\in B\ \Lambda\ l_{i}=1\} \tag{2}\]  
\[r^{\prime}=r_{\pi}^{c}-r_{\pi}^{c}. \tag{3}\]  
\[P(I|\phi)=\sum_{j:B_{i}\in B^{\text{batch}}}r^{\prime}\
abla\phi^{\theta}[l_{i }\log\phi+(1-l_{i})\log(1-\phi)] \tag{4}\]  
where \(\phi\) denotes the function approximator and \(B_{i}\) is a transition in the replay buffer \(B_{i}\). \(\phi^{\phi}\) denotes the parameters of \(\phi\), \(f_{B_{i}}\) is a feature vector, and N is the number of transitions in a mini-batch. The priority score function is expressed as \(\phi\big{(}f_{B_{i}}|\phi^{\phi}\big{)}\in(0,1)\), with the priority score represented by Lambda (\(\lambda\)). \(I_{i}\ \in\{0,1\}\) is a Bernoulli distribution of sample \(B^{s}\). The  
Figure 1: An illustration of the standard experience replay (ER) framework. A specified batch size of transitions is uniformly sampled at random from the replay buffer to train the agent. When the buffer is full, the First-In-First-Out (FIFO) retention strategy is applied to ensure that older transitions are replaced with newer ones.  
replay reward, cumulative reward of current policy, and cumulative reward of previous policy are denoted as \(r^{\prime}\), \(r^{c}_{\pi}\), and \(r^{c}_{\pi^{\prime}}\) respectively.
## 2 Methods
### Background
This approach depends on accurate and precise assumptions regarding the workings of RS structures and their particular elements. Modeling errors cause significant inaccuracies in the output results. Thus, such methods require good theoretical background regarding the mechanisms of deformations in RS structures in order to be useful. Although the number of studies has increased, so far there is no single accepted method which could be used in the design process to predict deformations in RS structures. The calculation methods that are available can be classified into a few groups (Figure 2), all of which have advantages and disadvantages [51]. Figure 1: Deformations of RS structure under serviceable loading. The second approach utilizes numerical methods to investigate the scope and magnitude of the deformations. Such calculations allow the assignment of material parameters that would be difficult to recognize in experimental studies. It should also be noted that the development of typical numerical procedures led to some important idealizations of the problem with regard to the geometry of the RS structure, the type of loading, the properties of the materials, and the boundary conditions. Therefore, the correctness of the proposed models and assumptions should be verified against extensive experimental data. The methods used to solve particular cases (engineering or scientific problems) should be carefully selected. A short overview of the main information regarding analytical and numerical methods is presented below.
# Radiometric Identification of Signals by Matched Whitening Transform
## 4 Results
### Signal Phase and Offset Frequency Correction  
Data are simulated for a QPSK signal subject to local oscillator frequency offset. Table 1 shows the simulation parameters.  
The phase curve is built from the estimates of the instantaneous phases computed from signal blocks short enough to insure stationary phase. Each block of data generates one phase estimate. Multiple blocks define a segment where symbols rotate a maximum of \(5.62^{\circ}\).  
Figure 5 shows the process by which instantaneous phase values are gathered and used in the model fitting step. This step can also be explained as the sampling of the phase curve. The symbol phases per block are histogrammed followed by fitting a polynomial. The peak of the polynomial is \(\hat{\theta}_{k}\) for the \(k\)th block. This step is repeated over multiple blocks and shown in Figure 5a-f. The estimated phases \(\{\hat{\theta}_{k},k=1,2,\dots,M\}\) define the linear phase trajectory the slope of which determines \(f_{d}\). Figure 6 is the least squares fit of the phase model to the data. Figure 6a,b correspond to SNR = 20 dB and 10 dB, respectively. Figure 6c illustrates that a nonlinear phase trajectory can be modeled and tracked as well. The estimated \(\hat{f}_{d}=0.0505\) Hz and \(\hat{f}_{d}=0.0455\) Hz at SNR = 20 dB and 10 dB, respectively. The true offset frequency is 0.05 Hz.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Modulation & QPSK \\ Symbol rate & 1000/s \\ Block length & 0.312 s \\ Symbols/block & 3120 \\ No. of blocks & 8 \\ Segment length & 2.5 s \\ Total no. of symbols & 2504 \\ Max. rotation per block & 5.62\({}^{\circ}\) \\ Total rotation per segment & 45\({}^{\circ}\) \\ Offset frequency (\(f_{d}\)) & 0.05 Hz \\ SNR & 20 dB \\ \hline \end{tabular}
\end{table}
Table 1: Simulated data used for carrier recovery.  
Figure 5: Phase trajectory build by estimation of phase rotation per block. Block lengths are chosen short enough to keep the phase trajectory linear. (**a**) Phase offset snapshot 1, (**b**) Phase offset histrogram used for phase estimation, (**c**) Phase offset snapshot 2, (**d**) Phase offset histrogram used for phase estimation, (**e**) Phase offset snapshot 3, (**f**) Phase offset histrogram used for phase estimation.  
Symbols rotate by \(2\pi f_{d}T\) radians over the length of a block. This rotation must be kept to a small fraction of the quadrant that the symbols belong to. For example, in QPSK, each quadrant is \(\pi/2\) radians. The proper block length is guided by the modality of phase histograms. A unimodal phase histogram with a distinct peak indicates that phase variations remain close to the nominal value, Figure 7a. For large \(2\pi f_{d}T\), either due to large \(f_{d}\) or long block length \(T\), the histogram becomes multimodal with no distinct peaks, Figure 7b. Another disadvantage of large \(f_{d}T\) is the \(2\pi\) phase ambiguity where symbols move around the circle multiple periods.
## 3 Results
### RQ1: What Are the Existing Recognition-Based Methods?
#### 3.2.2 Publication Sources  
Out of the selected 28 primary studies, 15 (54%) were published in scholarly journals, while 13 (46%) were presented at conferences. Figure 4 indicates a comparable level of active publications in RBGP authentication schemes across journals and conferences.  
Figure 3: The distribution of the chosen publications is categorized based on the year of publication.
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Formulation of the Channel Selection as a Multi-Armed-Bandit Problem  
The MAB problem [12] is a gambler model that plays multiple slot machines. This problem aims to obtain a strategy to decide which slot machines to be played to earn maximum rewards. Initially, the gambler had no prior information regarding the reward probability of any of the machines. The player gathers information about each slot machine when playing a slot machine. In the MAB problem, resolving the tradeoff between \"exploration\" to search and identify good slot machines and \"exploitation\" to play the optimal slot machine and obtain rewards is essential. The MAB problem has various applications, such as in-network advertising, medical fields, network routing, and channel selection. Various algorithms have been proposed that include (among others) \(\varepsilon\)-greedy [25], Soft-max [26], upper confidence bounds (UCB) [27] and UCB1-tuned [28], which is a champion algorithm improved UCB. Besides those well-known MAB methods, a MAB method based on Tug-of-War (TOW) dynamics has been proposed, and the high performance with a small computational cost is shown in [29; 30; 31; 32; 33]. Ref. [29] proposes a decision-making mechanism inspired by the behavior of unicellular organisms. Ref. [30] proposes the application of decision-making method based TOW to channel access selection in cognitive radio. Ref. [31] analyzes how to give optimal reward and punishment values in the learning process of TOW. Ref. [32] analyzes individual rewards and social rewards in a competitive environment. Ref. [33] proposes a model in which TOW works on atomic switch.  
A channel access model aided by cognitive radio has been proposed in [10; 11]. In that model, the authors assume that there are \(K\) available channels, and each frame is separated into time slots in the network. The cognitive user selects one channel from \(K=\{1,\ldots,k\}\) channels to send data. In this model, the availability probability \(p_{k}\), which denotes the degree of channel congestion, is defined. Figure 4 illustrates the correspondence between the channel selection problem and the MAB problem when the ACK frame from the gateway is used as a reward. First, the IoT device has no information about the IoT networks, including the state of the available channels. As a node accesses some channels to send data, it gradually learns the communication conditions depending on whether or not ACK frames can be received. This channel selection model is consistent with the MAB problem and can be considered a problem that maximizes the number of successful data transmissions to the gateway.
# Wideband Circularly Polarized Filtering Hybrid Antenna
###### Abstract  
In this paper, a wideband circularly polarized (CP) filtering hybrid antenna is presented. The liquid dielectric resonator (DR) is excited by a microstrip-coupled cross-slot to generate CP radiation. Four inverted L-shaped strips are loaded on the ground plane to introduce an additional CP resonant point, then two adjacent axial ratio (AR) minima are combined to improve the bandwidth of the single-feed CP antenna. By etching a U-shaped slot and loading a C-shaped microstrip stub on the microstrip line, two tunable radiation nulls near the edges of the working band can be obtained without any extra filtering circuits. To verify the design, a filtering CP antenna is designed, fabricated, and tested. Measured results show that the antenna provides wider 3-dB AR and an impedance bandwidth of 38.8% from 1.36 to 2.02 GHz and 39.4% from 1.37 to 2.04 GHz, respectively. The realized gain of the proposed antenna is stable at about 7.8 dBi within the whole working band. A reasonable agreement between measured and simulated results is observed.  
circular polarization; filtering antenna; hybrid antenna; liquid antenna; wideband +
Footnote †: journal: 2022, 12, 11018. [[https://doi.org/10.3390/app122111018](https://doi.org/10.3390/app122111018)]([https://doi.org/10.3390/app122111018](https://doi.org/10.3390/app122111018))
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 6 Experiments
### Evaluation of the \(F_{1}\) Scores over a Dataset of Partial Sensor Maps  
To test the limits of the algorithm, we also applied URSIM to a scenario other than sketch-to-metric map matching: partially overlapping sensor maps built by one or more robots. We evaluated the accuracy of the correspondences on a dataset of 14 indoor sensor maps ([[https://github.com/saeedghsh/Halmstad-Robot-Maps](https://github.com/saeedghsh/Halmstad-Robot-Maps)]([https://github.com/saeedghsh/Halmstad-Robot-Maps](https://github.com/saeedghsh/Halmstad-Robot-Maps))) provided by [PERSON] et al. [17]. The dataset of indoor maps is composed of multiple partial views of an indoor environment scanned by a robot using a Google Tango tablet. Since the maps were built at different times and over different trajectories, they have different noise levels and cover different sets of rooms. As such, while the maps are largely metrically correct, the missing information between the maps make this dataset a challenge for matching algorithms. As for the sketch map dataset, the correct correspondences were manually annotated by both an expert in mobile robotics and an architect. However, while an actual metric map of the full environment was available for the sketch map dataset, this was not the case for the robot map dataset since the whole environment was never covered in one mapping run and every partial sensor map is, by definition, metric. Hence, since no partial sensor map is inherently a better metric map than the others for URSIM, each user created ground truth correspondences using a given partial sensor map as URSIM's _metric map_. Both sensor maps used as URSIM's _metric maps_ are visible in Figure 10. We then ran the same tests with the same conditions as in Section 6.2.  
Looking at the results in Table 2, one can see that again the planar matching algorithm scores higher than both the Hungarian matching and VF2. The planar algorithm outperforms Hungarian matching by \(0.21\) for the median and by \(0.19\) for the mean. Furthermore, VF2 did not manage to find any correct correspondence between the partial robot maps since the differences between the map's graphs were too large. Looking at the results of URSIM, the \(F_{1}\) scores are better than in our previous work [3]. However, URSIM was not designed for partial sensor maps: the median only increases significantly when using the anchors to build the descriptors while the mean stays the same with or without using the anchors. On the other hand, using the standardized size of regions decreased the matching accuracy. It should be noted that, even though using the anchors to create the region descriptors is the best strategy for partial sensor maps, worst-case matches got a lower score; only the median increased while the mean stayed stable. Hence, while using the LFS and anchors for matching sensor maps is better than the strategy used in our previous work [3], URSIM is not an ideal choice for finding correspondences between partial sensor maps.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 4 Methodology
### Task Decomposition Strategy Based on Graph Theory and Lagrangian Relaxation
#### 4.1.3 Task Dependency Constraint  
If subtask \(T_{ij}\) depends on \(T_{ik}\),  
\[t_{ij}\geq t_{ik}+d_{ik},\quad\text{if }T_{ij}\text{ depends on }T_{ik} \tag{24}\]
## 2 Results and Discussion
### Thermal Stability Analysis  
Figure 3a shows the results from TG analysis of TTO, P(St-BMA) microspheres, and TTO microspheres. As shown in the TGA curve of TTO, the mass fraction of TTO was 0 at 110 \({}^{\circ}\)C, and the TTO proved to be highly volatile and further decomposed at a temperature range of 40 \({}^{\circ}\)C to 110 \({}^{\circ}\)C. The latter finding indicates that TTO featured poor thermal stability. As shown in the curve of the P(St-BMA) microspheres, two weight loss peaks could be observed. The first peak corresponds to the weight loss peak of complete evaporation and decomposition of TTO. The second peak corresponds to the weight loss peak after decomposition of the P(St-BMA) microspheres. To further investigate the condition of weight loss, the DTG of TTO microspheres was studied. As shown in Figure 3b, the TTO microspheres volatized and decomposed completely at 270 \({}^{\circ}\)C. At this temperature, the remaining mass fraction of TTO was 30.01%, and it accounted for 69.99% of the total quantity. At 110 \({}^{\circ}\)C, the total mass fraction of TTO was 86.09%, and the weight loss rate was 13.91%. Thus, the overall weight loss rate of TTO was only 19.87%. Upon comparison, it becomes clear that the stability of TTO after adsorption to the P(St-BMA) microspheres was significantly improved. The latter finding is most likely due to the fact that the P(St-BMA) microspheres, which began to decompose at 360 \({}^{\circ}\)C, exhibited suitable thermal stability. The material was therefore able to provide good physical protection for TTO and further prevented TTO from volatizing and decomposing by direct heating. On the other hand, the underlying force of the P(St-BMA) microspheres to adsorb TTO mainly involved Van der Walls forces and solvation between the lipophilic group of the P(St-BMA) microspheres and the TTO [24]. Taken in concert, the results described above outline why the TTO microspheres were able to significantly improve the stability of TTO.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Wavelength (cm\({}^{-1}\))**} \\ \cline{2-4}
**Functional Group** & **TTO** & **P(St-BMA)** & **TTO** \\  & **Microspheres** & **Microspheres** \\ \hline O–H stretching vibration & 3478. & - & 3447 \\ C–H (benzene ring) stretching vibration & - & 2926, 1450 & 2926, 1452 \\ C–H (C–C) stretching vibration & 2965 & - & 2965 \\ carbonyl stretching vibration & - & 1729 & 1729 \\ C–C (benzene ring) stretching vibration & - & 1600 & 1600 \\ C–O (ester) stretching vibration & 1166 & 1174 & 1174 \\ C–O (tertiary alcohol in terpen) stretching vibration & 1127 & - & 1127 \\ C–C stretching vibration & 902 & - & 910 \\ C–H (benzene ring) plane bending vibration & - & 763, 688 & 763, 689 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FTIR spectra absorption peaks of TTO, P(St-BMA) microspheres, and TTO microspheres.  
Figure 2: Surface topography of P(St-BMA) microspheres (**a**) and TTO microspheres (**b**).
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 3 Three Basic Model Structures in Water Quality Prediction
### Emerging Methods  
CNN is a feed-forward neural network, primarily used in the image field. Input, convolution, pooling, full connection, and output layers are the basic elements of the traditional CNN. In recent years, CNN has been used as an emerging method in water quality prediction. The operation of convolution can be implemented more than one time to reveal the relationship between the parameters hidden in the input matrix [57]. However, since the purpose of the prediction model is to extract potential factors rather than simply raise the convolutional layer's results to a higher level, the pooling layer is removed (see Figure 5). In the meantime, the number of calculations can be reduced.  
Figure 4: The modeling process of three data-intensive approaches.  
Figure 3: Five categories of recurrent model architectures.  
Deep belief network (DBN) is a kind of neural network based on deep learning which is similar to feedforward structure and has been widely used in recent years. The blue virtual box in Figure 6 shows several visible and hidden layers, stacked in order to make up the DBN [74]. However, the researches about dynamically determining the structure are seldom investigated. To overcome the limitations above, a SODBN has been proposed. The structure of the SODBN is not determined by artificial experience but the automatic growing and pruning algorithm (AGP) [58]. Especially, the hidden layers and neurons are changed by the AGP at first. Then, the weights of the SODBN are continuously adjusted in the process of self-organization. Finally, some aspects of network performance, such as running time and prediction accuracy have been improved.
## 3 Results
### Effect of Nanoemulsions on the Quality Characteristics of Liquid-Smoked Salmon Fillets  
#### 3.5.1 Microbiological Parameters  
The TVC, TPC, PBC, and HBC among all of the treatments were determined during refrigerated storage at 4 \({}^{\circ}\)C. Different NEs reduced the number of microorganisms in the fish by varying degrees. As shown in Figure 4a, the TVCs of all treatments increased with the increasing storage time. The TVCs were initially 2.26 log\({}_{10}\) CFU/g for all of the treatments. After 4 \({}^{\circ}\)C storage for 49 days, the TVC reached 10.86, 9.15, 9.09, and 6.28 log\({}_{10}\) CFU/g for the control, ENT-NE, CEO-NE, and CO-NE groups, respectively. Compared with the control, the TVCs of the NE treatments (ENT-NE, CEO-NE, and CO-NE) significantly decreased by 1.71, 1.77, and 4.58 log\({}_{10}\) CFU/g, respectively. The TVCs of the control (6.66 \(\pm\) 0.17 log\({}_{10}\) CFU/g) exceeded the limit on the 28 th day of storage, and the ENT-NE-treated samples (6.26 \(\pm\) 0.25 log\({}_{10}\) CFU/g) and the CEO-NE-treated samples (6.31 \(\pm\) 0.21 log\({}_{10}\) CFU/g) exceeded the limit (6 log\({}_{10}\) CFU/g) on the 35 th day of storage. However, the TVC values of the CO-NE-treated fillets were 4.59 \(\pm\) 0.15 log\({}_{10}\) CFU/g on the 28 th day, 4.97 \(\pm\) 0.15 log\({}_{10}\) CFU/g on the 35 th day, and remained over the limit until the 49 th day. Therefore, the smoked salmon control, ENT-NE, CEO-NE, and CO-NE remained within the microbiological limits for up to 21, 28, 28, and 42 days of storage at 4 \({}^{\circ}\)C, respectively. Compared with single ENT-NE and CEO-NE, CO-NE could more effectively reduce the TVC of the salmon fillet.  
Similar growth trends were observed for TPC, PBC, and HBC during storage (Figure 4b-d). The TPC, PBC, and HBC in the control reached more than 6 log\({}_{10}\) CFU/g on the 28 th, 35 th, and 35 th day, respectively. The TPC, PBC, and HBC of both the ENT-NE-treated and CEO-NE-treated fillets exceeded 6 log\({}_{10}\) CFU/g from the 35 th, 42 nd, and 42 nd day, respectively. However, the TPC of the CO-NE-treated fillets increased to above the minimum acceptable level until the 49 th day, and the PBC and HBC in CO-NE did not reach the acceptable limit until the 49 th day. Moreover, the TPC, PBC, and HBC of the ENT-NE-, CEO-NE-, and CO-NE-treated fillets decreased by 1.71-4.46, 1.40-4.43, and 1.34-4.48 log\({}_{10}\) CFU/g on the 49 th day, respectively, as compared to the control. CO-NE showed the lowest TPC, PBC, and HBC on the 49 th day, demonstrating the best antimicrobial effects among the three NEs, and the antimicrobial effect of ENT-NE and CEO-NE was not significantly different (\(p>0.05\)).  
Figure 3: The DPPH radical scavenging activity of nanoemulsions containing entercin Gr17 alone (ENT-NE), cinnamaldehyde alone (CEO-NE), entercin Gr17 and cinnamaldehyde (CO-NE) and no entercin Gr17/cinnamaldehyde (Control). Bars with no letter in common for each treatment are significantly different (\(p<0.05\)).
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 5 Bistatic Radar Receiver Synchronization and Ambiguity Function
### Synchronizing the Bistatic Radar Receiver
is plotted in the blue color plot. Similarly, Figure 10b shows the auto-correlation in a solid line (black color) and cross-correlation (red color) plot for the Burke-Shaw CBFM waveform. In both instances, the cross-correlation closely replicates the auto-correlation of \(s(t)\) with adjacent sidelobe levels (SLL) at \(-13.33\) dB or below. These correlation properties show that the proposed synchronization method is effective for the bistatic radar system.