esa-sceva/satcom-chunk-collection huggingface dataset:
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 5 Simulation Results and Performance Evaluation
### Recovery Times  
The link recovery (restoration) time at the MS and BS (\(T_{rec}^{\text{MS}}\) and \(T_{rec}^{\text{BS}}\)) is a key evaluation metric for beamforming-based mmWave networks. This parameter is measured for the proposed scheme, as depicted Figure 8. It accounts for the scan time period needed to specify a new (best) spatial direction that yields the highest signal level and its affiliated best beam at both the MS and BS. Figure 8 shows that the scheme features near-instantaneous recovery times. This is attributed to the prior prediction of the subsequent directions and their associated beam indices using the deep learning algorithm in the case of blockage. Thus, it eliminates the requirements to reset beam scanning, unlike conventional schemes. For example, it only requires transmitting the primary synchronization signals (PSS) duration over the beamforming (or combining) vectors. It is important to note that this value is fixed regardless of the blockage intensity or the density of the objects. Along these lines, the proposed recovery scheme features fast recovery times (approx. 200 \(\upmu\)s) versus 500 \(\upmu\)s for the link recovery technique in [9], 9600 \(\upmu\)s for the codebook designs in [2,3], and 7100 \(\upmu\)s for the metaheuristics-based recovery scheme.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 2 Related Work
### Self-Supervised Learning in Structured Data  
Self-supervised learning (SSL) is a subclass of unsupervised learning that has emerged as a powerful paradigm. SSL enables model training without relying on manual annotations by leveraging intrinsic data structure to construct pretext tasks [37]. SSL has shown outstanding success in domains such as computer vision and natural language processing, with models like BERT demonstrating the ability to learn rich semantic representations through masked prediction objectives [38; 39]. In the context of structured data, SSL has received growing attention in recent years. Existing approaches typically design pretext tasks such as masked feature prediction, pseudo-label generation, and contrastive learning [40; 41]. For instance, masked feature prediction involves randomly hiding parts of the input vector and training the model to reconstruct the missing features, thereby promoting the learning of intra-sample dependencies. Contrastive learning, on the other hand, encourages the model to learn discriminative representations by contrasting similar and dissimilar data pairs under different augmentations or views [42]. These techniques have achieved promising results across various tabular benchmarks, improving downstream performance under limited supervision. However, applying SSL to structured data presents unique challenges compared to unstructured domains. Tabular data often exhibit complex feature dependencies, heterogeneous subspaces, and varying sparsity patterns, which may not be fully exploited by conventional SSL strategies [43]. For example, naive masking may overlook semantic roles or correlations among attributes, while contrastive objectives may suffer from insufficient augmentation diversity in tabular formats.  
As a result, existing SSL methods often struggle to generalize under severe missingness or in highly sparse regimes. These limitations underscore the need for SSL frameworks that are tailored to the characteristics of structured data, particularly those capable of modeling inter-feature relationships and exploiting partial supervision signals. Addressing these gaps remains a critical direction for improving model robustness and generalization in real-world applications.
## 1 Introduction  
\begin{tabular}{l l}
1. & Introduction \\
1. & 1.1 & Malaria \\
1. & 1.2 & _Artemisia annua_ (Qinghao) \\
1. & 1.3 & Artemisinin (Qinghaosu) \\
2. & The Phytochemistry of _Artemisia annua_ L. (Qinghao) \\  & 2.1 & Aliphatic hydrocarbons, alcohols, aldehydes and acids \\  & 2.2 & Aromatic alcohols, ketones and acids \\  & 2.3 & Phenylpropanoids \\  & 2.4 & Flavonoids \\  & 2.5 & Monoterpenoids \\  & & 2.5.1 & Regular acyclic monoterpenes \\  & & 2.5.2 & Irregular acyclic monoterpenes \\  & & 2.5.3 & Monocyclic monoterpenes \\  & & 2.5.4 & Bicyclic monoterpenes \\
2.6 & Sesquiterpenoids \\  & & 2.6.1 Farnesane sesquiterpenes \\  & & 2.6.2 & Monocyclic sesquiterpenes \\  & & 2.6.3 & Bicyclic sesquiterpenes \\  & & 2.6.4 & Tricyclic sesquiterpenes \\
2.7 & Higher terpen \\  & & 2.7.1 & Diterpenes \\  & & 2.7.2 & Triterpenes and sterols \\  & & 2.8 & Nitrogen-containing natural products \\ \end{tabular}  
3 The Biosynthesis of artemisinin (Qinghaosu) 3.1 Phase 1 (isopentenyl pyrophosphate to amorpha-4,11-diene) 3.2 Phase 2 (amorpha-4,11-diene to dihydroartemisinin acid) 3.3 Phase 3 (dihydroartemisinin acid to artemisinin) 4. Strategies for the production of artemisinin from _A. annua_ and derived systems 4.1 Plant breeding programmes 4.2 Plant tissue culture 4.3 Endophytic fungi 4.4 Genetic engineering
5 Acknowledgements  
## 1 Introduction  
### Malaria  
Malaria is an infectious disease which has affected human beings since the dawn of recorded history. By the middle of the last century, however, many felt that malaria was on the retreat and that one day it might even be vanquished. Two factors were primarily responsible for this perceived reduction in the severity of the malarial threat. Firstly, the _Anopheles_ mosquito, which transmits the disease to humans, could at last be controlled by widespread application of the insecticide DDT. Secondly, the _Plasmodium_ parasite, which causes malaria (four members of the genus infect humans: _P. falciparum, P. vivax, P. malariae_ and _P. ovale_), could also be effectively be controlled by the use of synthetic analogues of quinine (itself also a natural product, obtained from the bark of the cinchona tree), such as chloroquine, which had been developed before World War II.  
By the 1960's, however, malaria was back with a vengeance. The mosquitoes were developing resistance to DDT, which was soon to be banned in any case because of environmental concerns. The _Plasmodium falciparum_ parasite, which is responsible for cerebral malaria, an often fatal complication, was also developing resistance to chloroquine. Thailand and South America were the first regions to be affected, but resistance to chloroquine soon spread to many other parts of the World. Nowadays, it is particularly serious in South East Asia. It was against this background of increasing resistance, and of the on-going wars in neighboring Cambodia and Vietnam, that the Chinese government began a major initiative to discover new antimalarials from plants used in Traditional Chinese Medicine (TCM).
## 5 Scenario Descriptions
### Scenario \(\beta\): Local Network Communication  
This scenario focuses on communication within the local network, where several _Smart Home Devices_ communicate with the _Owner Control Device_. _Server X_ and _Server Y_ represent the limited sets of connections a _Smart Home Device_ uses during normal (i.e., not compromised) operation. The _Owner Control Device_, belonging to the general-purpose computing device category, is in contrast to the _Smart Home Devices_ simply connected to the Internet as a whole. Notice that _Server X_ and _Server Y_ are also part of the Internet and might therefore be accessed by the _Owner Control Device_.  
Additionally, we assume that the _Owner Control Device_ is trusted for two reasons. First, it seems irrational that a homeowner would intentionally attack their own devices. Second, if the _Owner Control Device_ is compromised, and considering its unlimited access to all devices in the network, this would make any filtering or other mitigation method obsolete.  
This scenario is depicted in Figure 8, where the Internet is shown in grey because it is not part of the further threat modelling, as it covers a too broad space to be modelled meaningfully. Also, the branch of _Smart Home Device Y_ and _Server Y_ is not part of the modelling and is therefore greyed out because it is assumed that there cannot be any new threats for those devices and corresponding data flows compared to the equally arranged branch of _Smart Home Device X_ and _Server X_.
## 4 Experiments and Analysis  
Based on the data collection, processing strategy, and experimental design mentioned above, in this section, we describe the experiments and the series of analyses on the residual of the observations, orbital accuracy, and the constellation transform and rotation, in sequence.
# Automatic Correction of Indonesian Grammatical Errors Based on Transformer
## 7 Conclusions  
We introduced a Transformer-based global GEC framework equipped with a copy mechanism for low-resource languages, which in this case was Indonesian. The proposed model is the first end-to-end neural-based Indonesian GEC system which can also be used to correct the text of other low-resource languages. We presented the copying mechanism used in this model to deal with unfamiliar and special words that occurred frequently in the source sentences. In addition, in order to overcome the problem of data sparsity in Indonesian GEC, we proposed a semi-supervised confusion method to produce parallel training data from a monolingual out-of-domain corpus. The performance of the proposed model was significantly improved, and it had the ability to correct all types of Indonesian grammatical errors. Finally, the experimental results of this model achieved significant and satisfactory results that outperformed the previous GEC models and reported an average F1 score of 0.7194 and a BLEU score of 78.13, which are relatively effective in grammatical error correction tasks. Although the proposed confusion method was able to generate different types of training patterns, it still was not able to include the most difficult grammatical errors, such as semantic and synthetic errors.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
## 4 Transmission Analyses
### Eccentricity with \(\theta=\pi/4\)  
We now consider a two-dimensional transmission problem on a domain, shown in Figure 8a, composed of \(7\times 21\) cells having eccentricity inclined at \(\theta=\pi/4\). We impose a unit displacement in the \(\mathbf{e}_{\text{I}}\) direction only on the central portion of the left edge. The response is evaluated in points \(P_{1}\) and \(P_{2}\), on the right edge of the body, in terms of the displacement components in the principal direction of effective mass.  
Figure 8b (resp. Figure 8c) shows the transmitted displacement \(u_{\text{I}}\) (resp. \(u_{\text{II}}\)) versus frequency, evaluated in \(P_{1}\) (continuous lines) and \(P_{2}\) (dashed lines). Absorption of the elastic waves propagating in the media can be observed in the blue polarization band for \(u_{\text{I}}\) (Figure 8b) and in the red ones for \(u_{\text{II}}\) (Figure 8c). Nevertheless, the presence of a few peaks which are not completely damped out can be observed. These peaks, occurring in correspondence of eigenfrequencies of the holed matrix, arise due to the finite size of the domain under analysis and are dependent on the prescribed boundary conditions.  
For example, Figure 9a shows a detail of the peak occurring at \(f^{*}=11.34\) m/s in the transmission curves of \(u_{\text{I}}\) and \(u_{\text{II}}\) in \(P_{1}\). It can be noticed that, even if the displacement \(u_{\text{I}}\) is not completely damped out in the blue polarization band, its magnitude is far lower than the one of \(u_{\text{II}}\). This fact can also be qualitatively observed if one looks at the contours of \(u_{\text{I}}\) and \(u_{\text{II}}\) for \(f^{*}=11.34\) m/s shown in Figure 9b.  
Figure 7: (**a**) Model for the \(\mathbf{e}_{2}-\)transmission analysis with \(\theta=0\); (**b**) \(u_{\text{II}}-\)transmission as a function of frequency.  
Figure 8: **(a)** Model for the transmission analysis with \(\vartheta=\pi/4\); **(b)** transmission of \(u_{\rm I}\) at point \(P_{1}\) (continuous line) and \(P_{2}\) (dashed line); **(c)** transmission of \(u_{\rm II}\) at point \(P_{1}\) (continuous line) and \(P_{2}\) (dashed line).
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 2 Materials and Methods
### GA-SA Hybrid Optimization Algorithm
In a variation operation, a number \(i\)\(\in\)[1, n] within the range of the population size is randomly generated; that is, a binary string of individual \(Ai\) is selected for variation operation. Then, randomly generate an integer \(m\) in the binary string length range. The \(m\)th point in the binary coding string is selected as the variation point. Finally, generate a random number \(r^{\prime\prime}\)\(\in\)[0, 1], and compare it with the variation probability \(pm\). If \(r^{\prime\prime}\) is less than the \(pm\), the variation point is inversely calculated. On the contrary, there is no variation. For example, when \(m=7\), the change process of the binary coded string \(Ci\) of individual \(Ai\) is shown in Equation (8). \[\left\{ \begin{array}{l}C_{i}^{k}=0110010001\\ C_{i}^{k}=0110010001\end{array} \right.\rightarrow\left\{ \begin{array}{l}C_{ i}^{k+1}=0110011001(r^{\prime\prime}<pm)\\ C_{i}^{k+1}=0110010001(r^{\prime\prime}\geq pm)\end{array} \right. \tag{9}\]  
\(C_{i}^{k}\) and \(C_{j}^{k}\) represent the binary strings of the \(k\)th generation and \(k+1\)th generation of individual \(Ai\). 5. Simulated annealing searches are performed on each individual of the population. Take the first individual \(A1\) in the population as an example:  
1. Generate individual new states: \(A_{1j}=A_{1i}+\Delta A,\Delta A=y(UB-LB)\) where \(y\) is a zero random number with a bilateral symmetric distribution, and \(UB\) and \(LB\) are the upper and lower bounds of the solution range. For the current difference equation coefficients, take \(-10\) and \(10\), respectively. 2. If \(\min\left\{1,.\exp\left[-\left(f_{opt}(A_{1j})-f_{opt}(A_{1 best})\right)/T_{k }\right]\right\}\geq random[0,1]\), accept the new state of the individual, namely, \(A_{1 best}=A_{1j}\). Otherwise, deny updating the new state. 3. Determine whether the Metropolis criterion is met. If 'Yes', go to Step 6. Otherwise, repeat Step 5. 6. Annealing: \(T_{k+1}=\alpha T_{k}\), set \(k=k+1\)
7. Repeat Step 2 and Step 3 until the algorithm convergence rules are met and the current result is outputted.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 2 Related Studies
Inspired by BPSQM, this paper proposes an NR-IQA method based on a multitask image restoration network, in which the main task is to generate a pseudo-reference image and the auxiliary task is to generate the SSIM map. By leveraging the mutual promotion between the two aims, a high-quality pseudo-reference image can be generated. Furthermore, when predicting quality scores, not only the quality restoration features but also the difference features between the distorted and pseudo-reference images are utilized. This fully exploits the information from the pseudo-reference image. Lastly, a feature fusion module is devised to incorporate multi-scale information, enabling the model to effectively capture both global and local features.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Decision-Making Algorithm  
This algorithm was developed to make the appropriate decision about reducing the rock-fall risk. It fuses the outputs of the rock-fall prediction model (the deep learning model) with the outputs of the detection model in order to obtain the augmented prediction, as well as calculating the risk of a rock fall, categorizing it into three levels, and creating a warning strategy to handle a severe hazard situation. The subsequent steps demonstrate how the proposed Algorithm 1 utilizes and controls a rock-fall danger level.  
```
The first step: Gathering information with the IoT layer Read rainfall by rain sensors Read temperature by temperature sensors Read IoT camera video frames Read seismic waves by seismic sensor The second step: Detection of falling rocks in accordance with Equation (1) The third step: Determine the rock-fall occurrence probability (\(P\)) in accordance with deep dearning model The fourth step: Compute the total rock-fall risk probability \(P_{(j)}\) in accordance with Equation (17) The fifth step: Classifying the hazard in to three levels: When \(P_{(Risk)}\) is greater than or equal to (\(1\times 10^{-3}\)) then hazard is at an unacceptable level. When \(P_{(Risk)}\) is greater than (\(1\times 10^{-6}\)) and less than (\(1\times 10^{-3}\)) then hazard is at a tolerable level. When \(P_{(Risk)}\) is less than or equal to (\(1\times 10^{-6}\)) then hazard is at an acceptable level. The sixth step: Performing the risk reduction action Reducing the risk of rock falls by sounding and lighting warnings Turn on the red light + sound when the hazard is at an unacceptable level. Turn on the yellow light when the hazard is at a tolerable level. Turn on the green light when the hazard is at an acceptable level. The seventh step: Return to first step.
```  
**Algorithm 1**Figure out the rock-fall risk, identify the risk level, and carry out the rock-fall risk reduction process
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 4 Prototype System
### Yagi Antenna Sizing
The results obtained through the simulator, compared to the experimental values, were slightly different regarding the half power beam width, which translates into the antenna directivity. In this case, a more directive antenna than expected was obtained. Values may differ due to fabrication imperfections and due to measurement conditions, which were not perfect. Regarding antenna gain, the result was very close to the expected one. A low-cost amplifier, SPF5889Z was used, as we can see in Figure 10. It is important to note that, while not being the ideal power amplifier, it was sufficiently capable for the outlined objective. According to the specifications, the SPF5189Z possesses a gain of 17.9 dB of at 1000 MHz and 13.8 dB at 1700 MHz.
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
## 4 Discussion  
In this work, we employed the proposed MaRNN to predict chlorophyll concentration in Tongan Bay and the Western Water area adjacent to Xiamen City, Fujian Province, China. We also visually analyzed the impact of various marine environmental factors on changes in chlorophyll concentration. Our conclusions are as follows:  
1. In chlorophyll concentration prediction, the influence of individual exogenous factors on the predictive task exhibits temporal variability, with each factor's impact evolving over time. Furthermore, a correlation is discernible between the effects of exogenous factors on fluctuations in chlorophyll concentration and the spatial configuration of the marine environment.
2. To enhance the multi-step predictive performance of chlorophyll concentrations, the MaRNN model exhibits a greater expenditure of computational resources relative to other benchmark methodologies. Specifically, the Encoder, incorporating a two-stage spatial attention mechanism, alleviates the adverse effects of extraneous variables. Concurrently, the Decoder, leveraging temporal attention, dynamically identifies and forecasts the most pertinent historical data. These constituents not only elevate the precision of long-range forecasts but also entail substantial computational demands. Additionally, existing research underscores the potential of incorporating prior temporal information to enrich predictive models with deeper insights, however, this dimension was not explored in our study. Consequently, future endeavors will concentrate on investigating the cost-effectiveness of the model and the integration of such prior temporal information.
## 4 Simulation Analysis  
The simulation and evaluation of the SP-Rife algorithm, along with the comparison to other algorithms (Rife [6], ASIQ-Rife [7], and PAI-Rife [20]), are performed using the MATLAB platform. The evaluation metrics include computational complexity, and mean and standard deviation. To evaluate the accuracy of the estimation, additive Gaussian white noise with a zero mean is introduced into the signal, with the Cramer-Rao lower bound (CRLB) used as the benchmark. The simulation parameters are outlined in Table 1.
## 4 Experimental Validation
### Multiple Impacts at Fixed Intervals  
Now we aim to validate the proposed algorithm implementation using an experimental approach. Again, we consider a simply supported beam of the lab. In this work the beam length, width and thickness are \(a=0.695\) m, \(b=0.04\) m and \(h=0.008\) m, respectively. The model beam has a rectangular cross section \(w=b\times h=0.00032\) m\({}^{2}\). The material properties are: density \(\rho=7900\) kg/m\({}^{3}\), modulus of elasticity \(E=2.06\times 10^{11}\) Pa and [PERSON]'s ratio \(\
u=0.30\). The time interval is \(\delta t=0.001\) s. The sampling rate is 1024 Hz. It should note that the length and the width of the considered beam has no impact on the algorithm performance i.e., we expect the same conclusions when repeating the experiment with beams of other dimensions. The first seven mode shapes and their properties are evaluated experimentally and listed in Table 3.  
To verify the implementation, we impose an impact load and try to identify it in real time using the data collected by the accelerator sensor. The load identification parameters in the software are shown in Figure 13. The actual load is then compared to the identified load in real time. The load identification results of the first and second impacts are plotted in Figure 13. The plots zoom on the results within 0.1 s of the impact. The algorithm recovers with good accuracy the peak value of the first impact. In general the identified load matches the actual load up to the peak value of the impact.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mode Shape Number** & **Frequency (Hz)** & **Damping Ratio** \\ \hline
1 & 38.82 & 2.90\% \\
2 & 150.95 & 0.54\% \\
3 & 341.46 & 0.92\% \\
4 & 612.10 & 0.48\% \\
5 & 936.24 & 1.90\% \\
6 & 1328.78 & 2.52\% \\
7 & 1809.64 & 2.75\% \\ \hline \end{tabular}
\end{table}
Table 3: The modal test results of the beam.  
Figure 12: Experimental photo for the online identification.  
Thereafter the identification results start to fluctuate due to the observation noise and model error before it is stable again within 0.02 s. Similar observation can also be made for the second impact also plotted in the figure. Again, the peak value is recovered with good accuracy and some fluctuations are observed beyond the peak value. The errors with the PREM and the ACM are, respectively, 4.15% and 0.5839. The relatively large global error can be attributed to the fluctuations in the recovered load. Furthermore, the error in the measured damping compared to the actual damping can also contribute to these oscillations.