esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
###### Abstract  
Wireless and power line communication hybrid relay technology can realize complementary advantages and comprehensively improve the communication coverage and performance of power Internet of Things. In order to study the mechanism of the physical layer and Media Access Control (MAC) layer algorithm that affects the performance of hybrid relay systems, the cross-layer performance modeling, optimization, and simulation analysis are carried out for the non-orthogonal multiple access (NOMA) technology. Firstly, a two-hop NOMA media access control protocol is designed based on the CSMA algorithm. Considering the effects of non-ideal channel transmission at the physical layer and competitive access at the MAC layer on the system performance, a cross-layer performance analysis model of hybrid wireless and power line communication relay system under NOMA is established. Finally, a cross-layer optimization model based on multi-objective programming is established for the hybrid relay system. By analyzing the relationship between transmitting power and performance index, the joint optimization of transmitting power and power distribution factor between users is realized. Simulation results verify the validity and reliability of the proposed cross-layer model. The results show that the hybrid relay algorithm combined with NOMA and CSMA can effectively improve the performance of the system throughput, packet loss probability, and delay.  
NOMA; hybrid communication; relay; cross-layer analysis; power distribution +
Footnote †: journal: Applied
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 4 Complexity Analysis  
Given \(K\) as the maximum number of steps in the bisection procedure, Table 1 summarizes the computational complexities of the considered algorithms in this work. It can be seen from Table 1 that all estimators have linear computational complexity in \(N\). Nevertheless, it is worth mentioning that JAH requires computing the principal branch of the Lambert W-function [20] and that SR-WLS is executed iteratively [21]; hence the execution time of these estimators is somewhat higher than the remaining ones.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline HWLS in [18] & \(\mathcal{O}\left(N\right)\) \\ \hline NR in [23] & \(\mathcal{O}\left(KN\right)\) \\ \hline JAH in [20] & \(\mathcal{O}\left(KN\right)\) \\ \hline SR-WLS in [21] & \(T_{\text{max}}\times\mathcal{O}\left(KN\right)\) \\ \hline R-GTRS in [22] & \(\mathcal{O}\left(KN\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the Considered Algorithms.
## 2 Theoretical Background  
### Signal Model  
The purpose of VSME is to extract pipeline defect signal mode (PDSM) from magnetic anomaly detection signal.  
**Definition 1**.: _The PDSM is essentially an Amplitude Modulated and Frequency Modulated (AM-FM) function, which can be expressed as_  
\[g_{k}(t)=A_{k}(t)\cos\!\left(2\pi\int_{0}^{t}f(s)ds+\phi\right) \tag{1}\]  
_where \(A_{k}(t)>0\) and \(f(t)>0\) are the instantaneous amplitude (IA) and instantaneous frequency (IF) of signal, respectively. \(\phi\) is the initial phase._  
In this paper, the analytic signal \(z_{k}(t)\) can be expressed by Hilbert transform as follows:  
\[z_{k}(t)=g_{k}(t)+i\mathrm{H}(g_{k}(t)) \tag{2}\]  
where H represent Hilbert transform.  
The analytical signal \(z_{k}(t)\) expressed by amplitude and frequency can be defined as:  
\[z_{k}(t)=A_{k}(t)e^{i[2\pi\int_{0}^{t}f(s)ds+\varphi(t))} \tag{3}\]  
where \(\varphi(t)\) denotes the modulation source. Original signal can be represented by \(g_{k}(t)=\Re\{z_{k}(t)\}\), which means the component signal \(g_{k}(t)\) can be expressed as functions of amplitude \(A_{k}(t)\) and frequency \(f(t)\).
_Article_  
**Lock-In Thermography and Ultrasonic Testing of**  
**Impacted Basalt Fibers Reinforced Thermoplastic**  
**Matrix Composites**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1(), [PERSON] 2, [PERSON] 1,*(), [PERSON] 1, [PERSON] 3() and [PERSON] 3**  
1 Department of Industrial Engineering--Aerospace Division, University of Naples Federico II,  
Via Claudio, 21, 80125 Napoli, Italy  
2 Department of Science and Technology, University of Naples Parthenope, Centro Direzionale, Isola C4,  
80143 Napoli, Italy  
3 Institute for Polymers, Composites and Biomaterials, National Council of Research,  
80078 (NA) Pozzuoli, Italy  
* Correspondence: [EMAIL_ADDRESS]  
Received: 27 June 2019; Accepted: 25 July 2019; Published: 26 July 2019  
**Featured Application: The term \"new material\" may raise enthusiasm as well as skepticism at the same time. The acceptance of new material is generally linked to its characteristics with respect to the commonly used materials such as better performance, the solution to an open question, non- or less-polluting, cheaper, easy to handle, etc. Of course, these characteristics must be certified; therefore, the production should be accompanied by effective testing techniques and procedures. The scope of this work is to contribute to assessing the effectiveness of non-destructive testing through the examination of both lock-in thermography and ultrasonic testing techniques applied to basalt-based composites.**  
**Abstract:** Basalt fibers are receiving increasing consideration because they seem to be adequate as reinforcement of composites and to comply with the environmental safeguard rules. However, many factors affect the performance of composite material, demanding specific testing; one may be performance assessment under impact tests. The attention of the present work is focused on the detection of impact damage in basalt-based composites with two non-destructive testing techniques: lock-in thermography (LT) and ultrasonic testing (UT). Two different types of materials are considered which both include basalt fibers as reinforcement but two different matrices: Polyamide and polypropylene. Polypropylene is used either pure or modified with the addition of a coupling agent; the latter improves the fiber/matrix interface strength, giving in practice, a material of different characteristics. Specimens are first subjected to low-velocity impact tests and then non-destructively examined with the two above mentioned techniques. The obtained results are analyzed and compared to highlight the advantages and limitations of the two techniques to detect impact damage in basalt-based composites. Both techniques seem effective for the inspection of polyamide/basalt composite; in particular, there is a general agreement between results. Conversely, UT seems not suitable for the inspection of polypropylene/basalt composites because of their superficial porosity, while lock-in thermography is effective also for this type of composite material.  
**Keywords:** basalt fibers; polyamide; polypropylene; composites; impact damage; lock-in thermography; ultrasonic testing
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 4 Experiments
### Computational Cost Analysis  
This section conducts the computational cost analysis on the whole work, in comparison with other methods. We divide the training phase of our method into two parts: training for cross-domain adaptation, to extract domain-invariant features, and training for the few-shot meta-learning model, to estimate the density map from the feature map. Thus, the whole training time consumed mainly concerns domain-invariant feature-extraction training and density-map-estimation training. In the adaptive domain-invariant feature-extracting module, we first pre-trained the feature extraction module on the synthetic dataset for 80 epochs and then generated pseudo labels for real-world images. We utilized synthetic and real images to train the domain-invariant feature-extracting layer for 80 epochs. In the following training, this layer can be integrated into the network. In the density-map-estimator module, we divided the synthetic data into multiple tasks and used the synthetic data to train a meta-learning model for 1000 epochs. As shown in Figure 9, we compare our proposed algorithm with other algorithms in terms of computational cost. In the domain adaptation phase, the number of epochs to convergence of our method was the same as that of SFCN and better than cycleGAN; and, in the phase of meta-learning training, the Dynamic-\(\beta\) MAML we have proposed can improve this convergence, such that the number of epochs to convergence of our method is better than SFCC and Reptile.  
If the whole work is compared with other crowd-counting algorithms, the time spent is much greater than others because of additional domain adaptation and meta-learning. However, we can train the domain-adaptation module as a pre-training model. The remaining training time of our algorithm is almost the same as that of other algorithms. In addition, in real-time crowd-counting estimation scenarios, as shown in Table 4, our algorithm can reach 1 one\(\sim\)two frames per second, which can satisfy the real-time density estimation.
# Machine Learning for Decision Support and Automation in Games: A Study on Vehicle Optimal Path
## 3 Environment
### Phase 1: Basic Navigation
#### 3.2.1 Reward System  
The reward structure in this phase is designed to encourage the car to reach the target efficiently. The car receives a significant reward when it reaches the target, which is registered as a collision with the target object, and receives an extra reward based on time efficiency. Additionally, incremental rewards are given as the car approaches the target, incentivizing it to move in the correct direction based on the angle to the target. Conversely, the car is penalized for actions that are counterproductive to reaching the target. These penalties include moving away from the target, significant backward movement, and taking too long to reach the target. The car also incurs a significant penalty when it reaches the environment's limits, indicated by a collision with the boundary.  
1. Target Reaching Reward: \[R_{target}=R_{base_{t}}+R_{time}\] (7) \[R_{base_{t}}\] : Base reward for reaching the target. \[R_{time}\] : Extra reward based on time efficiency.
2. Proximity-Based Incremental Rewards: \[R_{proximity}=R_{base_{d}}\times R_{angle}\] (8) \[R_{base_{d}}\] : Base reward for approaching the target. \[R_{angle}\] : Reward based on the correct angle towards the target.
3. Penalties: \[\bullet\] _Moving Aaway from the Target: \[P_{anway}=-P_{move\_away}\] (9) - \[P_{move\_away}\] : Penalty for moving away from the target. \[\bullet\] _Backward Movement: \[P_{backward}=-P_{backward\_move}\] (10) - \[P_{backward\_move}\] : Penalty for significant backward movement. \[\bullet\] _Time Penalty: \[P_{time}=-P_{time\_elapsed}\] (11)  
Figure 2: Target and agent side by side view.  
- \(P_{time\_elapsed}\): Penalty for taking too long.
* \(P_{boundary\_collision}\): Penalty for colliding with the environment's boundary.  
The reward structure is strategically designed to promote efficient and direct movement towards the target while discouraging deviations from this goal. The combination of significant target-reaching rewards, incremental proximity-based rewards, and penalties for counterproductive actions forms a comprehensive framework that guides the car towards optimal performance.
## 4 Discussion  
This paper introduces a computational efficient sinogram interpolation network for sparse-view CT reconstruction, which can work directly in series with an FBP module or be embedded into an existing dual-domain LDCT reconstruction framework. The proposed network has a very simple structure, consisting of only four convolutional layers and one residual connection, which makes it well suited for clinical real-time CT reconstruction tasks. The fully convolutional structure of the network allows it to adapt to sinograms of varying sizes and effectively handle sinograms with different levels of sparsity. We designed different loss functions for the network, the sinogram domain loss and image domain loss, and investigated the performance of the training of two different losses. The proposed network is validated using two publicly available datasets (LIDC-IDR dataset and AAPM dataset), and compared with the linear interpolation method and two baseline deep learning models. Despite the far simpler model structure than the baseline models, the proposed network performs comparably to the baselines on both datasets and even shows some advantages at higher sparsity levels. The experiments comparing the two loss functions show that the image domain loss can improve the final reconstructed CT images by sacrificing the prediction accuracy in the sinogram domain. As the sparsity level increases, the performance of both losses gradually increases. Although the performance gap between the two losses is large at low sparsity levels. As the sparsity level increases, the performance of both losses gradually approaches. Finally, the computational efficiency of the involved models is compared. The proposed network has significant advantages in terms of both the FLOPs and trainable parameters.
## 4 Experimental Results
### Performance Metrics
#### 4.2.2 Accuracy  
The reconstruction accuracy of the time series is assessed. The main purpose of this experiment is to compute the amount of evidence loss, which is indicated by the results for the dimensionality reduction and time series reconstruction processes. The reconstruction accuracy depends on the Euclidean interval and the dimensionality of the time series. We can calculate it as a root-mean-square deviation:  
\[a_{req}(T,T^{\prime})=\sqrt{\frac{\sum_{i=1}^{N}(t_{i}^{\prime}-t_{i})^{2}}{D_{ i}}}, \tag{48}\]  
where \(T\) is the original time series, \(T^{\prime}\) is the time series reconstruction, \(a_{rec}\) is the reconstruction accuracy between \(T\) and \(T^{\prime}\) and \(D_{i}\) is the dimensionality between time series \(T\) and \(T^{\prime}\).  
The percentage of reconstructed accuracies can be seen in Figure 5b. In addition, we increase the length of the time series to 135,000 to obtain exact plotting results.  
The results in Figure 5a show that all representations have good results, but the NMP algorithm gives better results than the other algorithms. We observed that by increasing the length of the data, the accuracy of the STAMP and STOMP algorithms started to decrease. When NMP shows stable accuracy, it is found to be 99.76% with a maximum time series data length of 135,000. The compared STAMP and STOMP reduce the accuracy with an increase in the length of the time series. STAMP and STOMP show 98.62% and 98.09% accuracy, respectively.  
In Figure 5c, we observe that for a time series of length 180,000, the accuracy of all representations starts at 100%. After increasing the data length to 180,000, the accuracy  
Figure 4: (a) Run-time execution of NMP, STAMP, and STOMP with a maximum query length of 18,000. (b) Run-time execution of NMP, STAMP, and STOMP with a maximum query length of 45,000. (c) Run-time execution of NMP, STAMP, and STOMP with a maximum query length of 90,000.  
of STAMP drops to 98.58%. Additionally, STOMP's reconstruction accuracy decreases to 97.61% when NMP shows an almost constant accuracy of 99.5%.
## 4 Experiments and Analysis
### Evaluation Metrics  
We used Recall, Precision, Overall Accuracy (OA), IoU, and the F1-Score to assess the performance of different network architectures. Their definitions are as follows:  
Figure 6: Experimental datasets.  
_Recall_ is defined as:  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
_Precision_ is defined as:  
\[Precision=\frac{TP}{TP+FP} \tag{16}\]  
_OA_ is defined as:  
\[OA=\frac{TP+TN}{TP+FN+TN+FP} \tag{17}\]  
_IoU_ is defined as:  
\[IoU=\frac{TP}{TP+FN+FP} \tag{18}\]  
The \(F1\)-_Score_ is defined as:  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{19}\]  
where \(TP\) denotes the number of pixels correctly classified as building areas, \(TN\) denotes the number of pixels correctly classified as non-building areas, \(FN\) denotes the number of pixels incorrectly classified as non-building areas, and \(FP\) denotes the number of pixels incorrectly classified as building areas.
## 2 Materials and Methods
### Stimuli
#### 2.2.2 Noise  
To assess the efficacy of VTT accuracy, three SNRs were tested: +5, 0, and \(-\)5 dB, which commonly represent everyday listening scenarios. For instance, in acoustically untreated classrooms, SNRs can range from \(-\)7 to +6 dB [30]. Two noise types were employed: speech-weighted steady-state and multi-talker babble noise [31], representing the most common energetic and informational masking in daily life. [PERSON] et al. [32] found that the use of babble noise generally resulted in higher speech reception thresholds than speech  
Figure 1: Equipment setup from the overhead view. AP = AirPods Pro; KEMAR = Knowles Electronics Manikin for Acoustic Research.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sentence Played** & **Scoring (Points)** \\ \hline A/The column has/had a/the funny face & 6 \\ The bath water is/was warm & 5 \\ She injured four of her fingers & 6 \\ He paid his bill in full & 6 \\ They stared at a/the picture & 5 \\ A/The driver started a/the car & 5 \\ A/The truck carries fresh fruit & 5 \\ A/The bottle is/was on a/the shelf & 6 \\ The small tomatoes are/were green & 5 \\ A/The dinner plate is/was hot & 5 \\ \hline Total & 54 \\ \hline \hline \end{tabular} NOTE: The symbol \"/” is used to indicate that either description is considered a correct recognition and is counted as one point towards the overall score.  
\end{table}
Table 3: Hearing in Noise Test list 20.  
shaped noise in humans and automatic speech recognition tasks. This suggests the need to evaluate the potential benefits of RM systems in both types of noise to determine if differences are related to the greater difficulty of informational masking over energetic masking during speech recognition. The noise was continuously presented at 60, 65, and 70 dB SPL (SNRs of +5, 0, and \(-\)5 dB, respectively) through a speaker located at 180\({}^{\circ}\) azimuth from the KEMAR dummy head at eight feet. The babble noise was generated on the Lenovo ThinkPad P70 laptop and transmitted to channel B of the GSI-61 audiometer, while the speech noise was directly produced by the GSI-61 audiometer. The order of noise was fixed for the SNR conditions but counterbalanced across the technology conditions.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### System Environment Modeling
#### 3.1.3 Construction and Implementation of Control the Algorithm  
The Control Algorithm involves constructing and implementing rules or policies that determine the Control Actions. It focuses on generating an environment that can derive loss scenarios by addressing the question of \"Why does UCA occur?\". Additionally, the Control Algorithm sets up controlled states and conditions for each action to ensure precise operation.  
The control algorithm can be represented as a policy function denoted by \(\pi\), which maps states to actions. In the mathematical notation of \(\pi(s)\to a\), \(\pi(s)\) represents the action chosen by the policy function \(\pi\) in state \(s\).
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 2 Description of the Method  
The main goal of the hybrid algorithm is to invert a slowness field according to the channel wave's travel time picked up from seismic surveys. Furthermore, the distribution of coal thickness could be obtained according to the relation between slowness and disclosed coal thickness.
# Stationary Phases for Green Liquid Chromatography
## 3 Stationary Phases Used in Pure Water Conditions  
The first reported separation using subcritical water as a mobile phase was carried out by [PERSON] and [PERSON] in 1996 [55]. The separations of several phenols, parabens, and barbiturates on a polystyrene-divinylbenzene (PS-DVB) stationary phase were carried out at 210 \({}^{\circ}\)C. The authors demonstrated that retention factors of separated substances decrease with increasing temperature. Compared to conventional reversed-phase conditions, superheated water separation at 180 \({}^{\circ}\)C provides a similar result to a 20:80 acetonitrile-water mobile phase at ambient temperature. No degradation or oxidation of any analytes or the stationary phase structure was observed in the experiments performed. Thus, the study concluded that SHWC, compared to the conventional RP-LC technique, gives better separation and allows shorter analysis times [55].  
As discussed above, the thermal stability of analytes can be an obstacle to using water at elevated temperatures. The thermal stability of analytes was investigated by [PERSON] and [PERSON] [7; 56] in the case of drugs and alkaloids; they concluded that in order to not affect the analytes' stability, it is necessary to reduce the analysis time. Doing so will allow even compounds that degrade at high temperatures to be separated, as the short residence time in the elevated-temperature column will not affect their stability [6]. The observed rule also has some exceptions; several other cases have been reported in which degradation has occurred. Thiamine can only be analyzed below 50 \({}^{\circ}\)C, while a temperature of 160 \({}^{\circ}\)C results in many degradation products [57]. Nitrobenzene was degraded above 220 \({}^{\circ}\)C using a PS-DVB column; however, a reduced temperature and a less retentive phase allowed the decomposition to be prevented [7]. Even polycyclic aromatic hydrocarbons can degrade; analyses in the range of 100-350 \({}^{\circ}\)C and an extensive range of heating times from 10 to 240 min in pressurized hot water showed that even at the shortest time interval, they degraded above 300 \({}^{\circ}\)C; at longer times, damage to the structure of the compounds occurred even at 100 \({}^{\circ}\)C [58].  
Despite the examples of degradation described above, SHWC can work for substances with a broad polarity range, mainly due to decreased water viscosity and polarity with increasing temperature. Several commercial chromatographic columns may be used to separate low-molecular-weight compounds using supercritical water. By selecting a suitable stationary phase or suitable analysis conditions, working with pure water at room temperature is also possible. These materials are described in the literature and listed in Tables 1-4.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Block Model Creation and Validation for Resource Estimation
Kriging calculates each grid point by taking a weighted average of surrounding samples, which can hinder its ability to detect local differences in the data. To overcome these limitations, the study employed machine learning algorithms and hybrid methods to construct a gold deposit model. These algorithms can identify non-linear associations and patterns within the data, improving the accuracy and reliability of models that predict gold content.