esa-sceva/satcom-chunk-collection huggingface dataset:
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 3 Few-Shot Learning with Many Novel Classes
### Problem Formulation
#### 3.1.1 Few-Shot Learning  
We let \(\mathsf{C}_{\text{base}}\) and \(\mathsf{C}_{\text{novel}}\) denote the set of base and novel classes, respectively, which are disjoint, i.e., \(\mathsf{C}_{\text{base}}\cap\mathsf{C}_{\text{novel}}=\emptyset\). Given the datasets \(D_{\text{base}}\) and \(D_{\text{novel}}\) containing labeled samples from \(\mathsf{C}_{\text{base}}\) and \(\mathsf{C}_{\text{novel}}\), respectively, the objective of FSL is to train model \(f_{\phi}\) using \(D_{\text{base}}\) to perform effectively on few-shot tasks sampled from \(D_{\text{novel}}\). Each task \(\mathcal{T}_{i}\) comprises a support set \(\mathcal{S}^{i}=\big{\{}\big{(}\mathbf{x}_{j},\mathbf{y}_{j}\big{)}\big{\}}_{j =1}^{N\times k}\) and a query set \(\mathcal{Q}^{i}=\big{\{}\big{(}\mathbf{\hat{x}}_{j},\mathbf{\hat{y}}_{j}\big{)} \big{\}}_{j=1}^{N\times q}\), where \(\mathcal{S}^{i}\) contains \(N\) classes with \(k\) labeled examples per class, and \(\mathcal{Q}^{i}\) features \(q\) unlabeled query examples per class. The terms \(N\) and \(k\) are commonly referred to as \"way\" and \"shot\", respectively.
## 3 Materials and Methods  
The design steps deployed in this study are depicted in Figure 2, providing an overview of the research process. The initial stage, denoted by the green block, encompasses developing and fabricating the sensing platform for measuring environmental variables such as air temperature, relative humidity, atmospheric pressure, CO\({}_{2}\) concentration, and particulate matter concentration (PM 1, PM 2.5, PM 10). During this phase, the information the WMO provided was used. This international agency ensures high-quality data delivery to end users.  
Subsequently, once data for all variables were acquired, a comparison was conducted against commercial products to assess the proximity of the proposed station's measurements to those of specialized devices. For indoor comparisons, a Schneider thermostat was chosen as the reference tool, while for outdoor comparisons, a Purple Air sensor was employed. It is crucial to emphasize that the reference sensors, although not entirely immune to measurement errors, were selected based on their use in industry and market availability and their certifications guaranteeing minimal errors. Consequently, the comparison focused on evaluating the monotonic relationship between the OSWS and these devices and determining errors (RMSE and MAPE) between them.  
Moreover, to facilitate data storage, local storage (SD Card) and remote storage (ThingSpeak server ([[https://thingspeak.com/](https://thingspeak.com/)]([https://thingspeak.com/](https://thingspeak.com/)), accessed on 3 October 2023)) were implemented to capture and retain all collected sensor data. An ARIMA forecast model was also applied to predict values within a short-term timeframe using historical data. Notably, the model employed was univariate, considering only one variable per model. Thus, the time series containing values of a single variable (e.g., temperature) exclusively predicted values of that same variable. Finally, the forecast model underwent evaluation and comparison against known data, resulting in calculating parameters to assess its performance.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Proposed Algorithm Description
#### 2.2.3 IFM and Channel Arbitration
\[a=\left\{\max(\vec{f}_{k-1}^{avg})>\min(\vec{f}_{k}^{avg})\right\}||\left\{ \min(\vec{f}_{k}^{avg})-\max(\vec{f}_{k-1}^{avg})<\Delta B\right\} \tag{22}\]  
The signals in channel \((k-1)\) and channel \(k\) are the same signal if  
\[a=1 \tag{23}\]  
A different result for  
\[a=0 \tag{24}\]  
After channel arbitration, the pulse parameters (frequency, amplitude, pulse width, and time-of-arrival) are fed into the pulse descriptor word (PDW) formatter and output.
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 5 Results and Discussion
The proposed algorithm simply models received SINR as whether the average SINR is greater than the target SINR to take account of the interference. To evaluate and discuss the performance of the proposed algorithm, we compared it with the BA and the two compared algorithms. The proposed algorithm exhibited the best performance in terms of system energy efficiency, achievable data rate, and SINR in cases of increasing D2D distance thresholds. The proposed algorithm learned to maximize the system energy efficiency while ensuring achievable data rates. The system energy efficiency and achievable data rate have a trade-off relationship, so the proposed algorithm used Q-learning in two ways. To maximize the system energy efficiency, the proposed algorithm sets the system energy efficiency as a reward function of centralized Q-learning. In addition, to ensure an achievable data rate, the proposed algorithm sets the received SINR as a reward function of distributed Q-learning. This implies that the proposed algorithm can achieve the highest energy efficiency when compared to other algorithms. It can also guarantee QoS while increasing the efficiency of the resource and system energy.
# Hypernetwork Representation Learning with the Set Constraint
## 6 Experiments
### Parameter Sensitivity  
HRSC has a harmonic factor \(\beta\) to balance the model based on the topology-derived objective function and the model based on the set constraint objective function. We fix the training ratio to 50%, and test node classification accuracies of HRSC with different \(\beta\). We let \(\beta\) range from 0.1 to 0.9 on the MovieLens and wordnet datasets. Figure 3 shows the comparisons of node classification accuracies of HRSC with different \(\beta\).  
Figure 3: Parameter sensitivity on the two datasets. (**a**) Sensitivity on MovieLens; (**b**) sensitivity on wordnet.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Training Ratios**} \\ \cline{2-7}  & **60\%** & **65\%** & **70\%** & **75\%** & **80\%** & **85\%** & **90\%** \\ \hline Deepwalk & 0.7780 & 0.8181 & 0.8305 & 0.8341 & 0.8708 & 0.8765 & 0.8880 \\ node2 vec & 0.7807 & 0.8242 & 0.8309 & 0.8285 & 0.8519 & 0.8503 & 0.8595 \\ LINE & 0.8063 & 0.8184 & 0.8056 & 0.8091 & 0.8000 & 0.7938 & 0.7926 \\ GraRep & 0.7685 & 0.7742 & 0.7888 & 0.7806 & 0.7958 & 0.7972 & 0.7756 \\ HOPE & 0.6902 & 0.7314 & 0.7417 & 0.7403 & 0.7649 & 0.7763 & 0.7700 \\ SDNE & 0.3712 & 0.5348 & 0.4784 & 0.4824 & 0.4254 & 0.6159 & 0.4850 \\ HRSC & 0.8953 & 0.9079 & 0.9086 & 0.9036 & 0.9093 & 0.9045 & 0.9034 \\ \hline \hline \end{tabular}
\end{table}
Table 8: AUC values on wordnet.  
From Figure 3, the variation trend of node classification accuracies is different with the increase of the parameter \(\beta\) on the two datasets, but all of the variation ranges are within 2%, which indicates that the overall performance of HRSC is not sensitive to the parameter \(\beta\), demonstrating the robustness of HRSC.  
For the MovieLens and wordnet datasets, the best evaluated results in terms of node classifications are achieved at \(\beta=0.5\). Although the hyperedges are fully incorporated into the process of hypernetwork representation learning to learn node representation vectors, partial hypernetwork topology structure information is still lost, which indicates that the quality of node representation vectors is optimal.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 1 Introduction
Internal decoupling techniques are related to polarization diversity, where orthogonal polarization and multi-polarization are commonly used. The rest of this paper is as follows. The second part emphasizes the causes of mutual coupling and its negative effects on MIMO systems. The third part briefly describes how to evaluate the degree of mutual coupling by performance parameters. Part four discusses some decoupling schemes for MIMO wireless systems applicable in 5G devices. The fifth part is a detailed analysis of the advantages and disadvantages of each decoupling technique. Finally, it is conclusion and envisages the future development direction of decoupling techniques.
## 5 Conclusions  
The possibility of using a cylindrical concentric meniscus as a waveguide combiner for augmented reality systems is theoretically substantiated and experimentally confirmed in the present research. It has been experimentally demonstrated that a test image is transmitted through a curved waveguide without doubling or breaks in the FOV.  
Further work on the subject of a curved waveguide requires an improvement in the optical scheme described in the paper in order to add multiplication of exit pupils of the system also in the vertical plane. In addition, several important next steps are as follows: expanding the color range of the combiner--developing an optical system that transmits a full-color (RGB) image; improving image quality--reducing scattering, suppressing doubling and blurring of the image; increasing image uniformity--aligning the image in brightness both in the angular field and over the aperture of an EMB, as well as a decreasing the curvature radius of the waveguides. In addition to everything described above, the present work and the approach described in it could become a starting point for the development of an optical combiner based on a waveguide with spherical surfaces.  
The key advantage of the waveguide combiner considered in the present paper is the impossibility of observing a virtual image from outside the combiner due to the fundamental design of the optical system. The virtual image in the presented system will be the superposition of a number of images that come out of the display and will be seen by an external observer as defocused. In everyday use of an AR device with the proposed combiner, including public places, the impossibility of observing a virtual image for an outside observer could be a significant factor for the consumer. In turn, a flat waveguide, which is used as a basis in most of the known solutions, does not have the described advantage.  
In addition, the curved combiner proposed in the paper, when introduced into an AR device, can improve the weight and size characteristics of the latter and make its design similar to sunglasses or prescription glasses familiar to the user. Such a device, due to its compactness and ease of wearing, can be used continuously throughout the day, which will allow the users to be in an information environment formed personally for them by using such means as social networks, the media, messaging programs, information retrieval tools that work out user requests, including elements from the real environment surrounding the users.  
Conceptualization, A.N.P., N.V.M. and G.N.V.; methodology, G.N.V. and N.V.M.; investigation, G.N.V., A.E.A., R.A.O., N.V.M.; software, G.N.V., R.A.O. and A.S.P.; resources, N.V.M.; writing--original draft preparation, G.N.V., A.E.A. and R.A.O.; writing--review and editing, A.E.A., R.A.O. and J.R.; supervision, N.V.M., J.R. and A.N.P.; project administration, N.V.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 6 Application to a Drone Cloud with Ring Topology  
In the context of a drone cloud, a group of drones intercommunicates using a network configuration known as a ring topology, where each drone communicates directly with its two neighbors. This setup is particularly advantageous for coordinating movements, sharing sensor data, and maintaining a cohesive network structure. The application of true random frequency hopping in this environment can significantly enhance the security and reliability of communication, especially in scenarios where the drone cloud operates in hostile or adversarial conditions.  
The communication infrastructure of the drone cloud is represented in Figure 3, showing how drones are interconnected in a ring topology and utilize FHSS with true random frequency hopping. The ring topology has been shown to improve communication efficiency and fault tolerance in networked systems [30], which is particularly beneficial for our drone cloud configuration.
## 4 Post-Retrofit vs. Pre-Retrofit Performance
Thus, the actual monthly average air temperatures recorded in Odense for the pre-retrofit and post-retrofit periods are shown in Figure 7. It could be noted that there is not that much difference between the two profiles, where the ambient air temperature is comparable or even lower in the majority of the months. The two major exceptions are the months of February and March. Specifically, February and March (2019) in the post-retrofit year are warmer than February and March (2018) months in the pre-retrofit year considered. This also supports the fact that a very large heating energy consumption reduction is exhibited after retrofitting in those two months (23-40%). It means that part of the energy savings in these months is due to the warmer weather and not only due to the energy retrofit improvements. On the other hand, the maximum difference in terms of the overall yearly temperature is depicted in May, where the ambient air temperature in May 2019 after retrofit is, on average, lower than the temperatures that were recorded in May 2018 before the retrofit process with around 5 \({}^{\circ}\)C. This is also reflected by the number that is shown in Figure 6, where the schools consume almost double the amount of heating in May after retrofit when compared to the consumption before retrofitting. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **School A** & **School B** & **School C** \\ \hline Pre-retrofit annual heating consumption (MWh) & 1383 & 1088 & 983 \\ Post-retrofit annual heating consumption (MWh) & 1167 & 920 & 892 \\ Annual Heating Savings (\%) & 15.7 & 15.4 & 9.3 \\ Pre-retrofit annual electricity consumption (MWh) & 240 & 136 & 165 \\ Post-retrofit annual electricity consumption (MWh) & 235 & 115 & 143 \\ Annual Electricity Savings (\%) & 2.2 & 15.0 & 13.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the retrofit projects impact in the three schools considered.
## 3 Discussion  
To explain the obtained experimental results of the TFISHG and retrieve \(\chi^{(3)}(-2\omega;\omega,\omega,0)\), we represent the total SH signal \(S_{entr}^{samp,gas}(\tau)\) detected from the entrance facet of the sample as a superposition of the contributions from the sample and the ambient gas in the following form:  
\[S_{entr}^{samp,gas}(\tau)=C_{entr}\Big{|}A_{ref}^{samp}(\tau)+r_{2\omega}^{samp }A_{ins}^{gas}(\tau)\Big{|}^{2}. \tag{6}\]  
Here, \(C_{entr}\) is a proportionality coefficient, \(A_{ref}^{samp}(\tau)\) is the complex amplitude of the SH field generated in the reflection direction from the corresponding sample, \(A_{ins}^{gas}(\tau)\) is the complex amplitude of the SH field generated in gas (air or helium) before the sample, and \(r_{2\omega}^{samp}\) is the gas sample Fresnel reflection coefficient of the SH field. In general, Equation (6) should also contain \(A_{ref}^{gas}(\tau)\), which is the complex amplitude of the SH field generated in the gas after the sample, but due to the decrease in the fundamental optical and terahertz fields after the reflection, \(A_{ref}^{gas}(\tau)\) is typically more than 10 times less than \(r_{2\omega}^{samp}A_{ins}^{gas}(\tau)\). So, in most cases, we will ignore \(A_{ref}^{gas}(\tau)\). The SH radiation emitted into SiO\({}_{2}\) and then reflected from the exit facet (with subsequent transmission through the entrance facet) gives the SH signal:  
\[S_{exit}^{SiO_{2},gas}(\tau)=C_{exit}^{SiO_{2}}R^{SiO_{2}}T^{SiO_{2}}\Big{|} A_{tr}^{SiO_{2}}(\tau)+t_{2\omega}^{SiO_{2}}A_{ins}^{gas}(\tau)\Big{|}^{2}, \tag{7}\]  
where \(C_{exit}^{SiO_{2}}=C_{entr}n_{2\omega}^{SiO_{2}}\cos(\beta_{2\omega}^{SiO_{ 2}})/\cos(\alpha)\approx 1.8C_{entr}\), \(n_{2\omega}^{samp}\), and \(\beta_{2\omega}^{samp}\) are the sample (SiO\({}_{2}\)) refractive index and the transmission angle of SH in the sample (SiO\({}_{2}\)), respectively; \(A_{tr}^{SiO_{2}}(\tau)\) is the complex amplitude of the SH fields emitted into SiO\({}_{2}\) (in the transmission direction); \(t_{2\omega}^{SiO_{2}}=0.71\) is the gas-SiO\({}_{2}\) Fresnel transmission coefficient of the SH field (generated in the gas before SiO\({}_{2}\)); and \(R^{SiO_{2}}=0.085\) and \(T^{SiO_{2}}=0.915\) are the power reflection and transmission coefficients of SH radiation from the SiO\({}_{2}\) boundary, respectively. The time-delay profile of the detected SH signal, as follows from Equations (6) and (7), significantly depends on the amplitude, relative phase, and time-delay profiles of the corresponding terms. Below we will assume that the electron nonlinearity is dominant, i.e., we will consider the nonlinear response to be instantaneous both in the samples and gases (see the discussion below). The amplitude of the SH field generated from the entrance facet of the material in the reflection direction \(A_{ref}^{samp}(\tau)\) does not strongly depend on the dynamics of the terahertz field in the media [37]. The main factor is the ratio of the THz penetration depth (\(d_{THz}\)) and the SH wavelength in the material (\(\lambda_{2\omega}/n_{2\omega}^{samp}\)), which affects the phase of \(A_{ref}^{samp}(\tau)\)[34; 49].
## 4 Conclusions  
A low astaxanthin extraction efficiency of 9.6-14.2% was achieved from 90-day-old _H. lacustris_ cysts (with a rigid and complex cell wall structure), when four different [Emim]-based ILs with SCN, DEP, HSO\({}_{4}\), and Cl anions were applied at room temperature (~28 \({}^{\circ}\)C). Under 5-min sonication, \(\alpha\)-Quartz NPLs disrupted the cyst cell wall, significantly improving the astaxanthin extraction efficiency of the room-temperature [Emim]-based ILs, regardless of the IL type (a 4.4-8.4-fold improvement compared to the yield in the control group). Astaxanthin-containing lipids were recovered by simple phase separation after subsequent  
Figure 4: (a) Microscopic images of _Haematococcus lacustris_ cysts treated with [Emim] HSO\({}_{4}\) and with or without \(\alpha\)-quartz NPLs obtained using optical and fluorescence microscopy. Intracellular blue fluorescence under the DAPI filter represents [Emim] HSO\({}_{4}\) uptake by the algal cells. Scale bars represent 20 \(\upmu\)m. (b) Average blue fluorescence intensities were obtained as a function of cell length from several fluorescence images (a).  
ethyl acetate addition and centrifugation. The findings of our study suggest that \(\alpha\)-quartz NPLs can be used as effective cell-wall-disrupting agents for the room-temperature IL-mediated extraction of bioactive compounds from various robust microalgae without significant thermal and chemical degradation of the target product.  
Conceptualization, Y.-K.O. and S.C.; Methodology, N.L., A.L.N., G.M., Y.-E.K. and M.P.; Investigation, N.L. and A.L.N.; Resources, G.M. and Y.-E.K.; Data Curation, B.K. and R.M.; Writing--Original Draft Preparation, N.L., A.L.N. and Y.-K.O.; Writing--Review & Editing, Y.-K.O. and S.C.; Visualization, B.K. and R.M.; Supervision, Y.-K.O. and S.C.; Project Administration, Y.-K.O.; Funding Acquisition, Y.-K.O. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Research Foundation of Korea [grant number NRF-2019R1A2C100346313] funded by the Ministry of Science and ICT, the Basic Science Research Program through the National Research Foundation of Korea [NRF-2019R1F1A1060060] funded by the Ministry of Education of Korea, the Research/Development Program of the Korea Institute of Energy Research [KIERCO-2424], and the 2020 BK21 FOUR Program of Pusan National University.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Localized Recovery of Query Losses
### The Impact of Detector Number on Successful Recovery
At first we can plot the impact of the detector number on PHY layer performance in recovery according to Equation (10), where  
\[P_{\textit{succ}}=1-P_{\textit{PHY}}^{n}=1-0.5^{n} \tag{12}\]  
The result in Figure 8 shows that the larger the number of detectors is, the higher the probability of successful recovery in the PHY layer performance with error-free MAC becomes. This is because the more detectors there are, the more diversity in recovery unicast exists, leading to a larger probability of overcoming the PHY layer packet losses due to the fading, multipath propagation, _etc._ The second curve in Figure 8 shows the impact of detector number on MAC layer performance in recovery according to Equation (11) with error-free PHY. The larger the number of detectors is, the lower is the probability of successful recovery in the MAC layer performance. This is because more detectors cause more contentions in recovery, leading to a higher probability of recovery packet collisions. The third curve shows the integrated recovery result of both PHY and MAC layers according to Equation (9) with errors on both MAC and PHY layers. There is an optimal detector number that achieves the highest probability of successful recovery. Too few detectors result in poor performance on PHY diversity, while too many detectors result in poor performance on MAC layer.
# LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
## 4 Experiments
### Analysis of LCF design Models  
Both LCF-GloVe and LCF-BERT are equipped with CDM and CDW local context focus layers. The experimental results indicate that LCF designs are very effective. In addition, CDM and CDW are feature-level operations (see Figures 4 and 5), instead of directly operating the input tokens. Owing to the MHSA encoder, CDM and CDW can involve less-semantic-relative tokens in the learning process, then retain their semantic features towards semantic-relative tokens. However, CDM and CDW mitigate the features of less-semantic-relative tokens on corresponding output positions, which enable LCF design models to focus on the local context. Another significant thing is that LCF design also learns the features of the global context. Owing to the interactive learning of local context and global context that LCF design achieves such remarkable results (see Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{3-8}  & & **Accuracy (\%)** & **F1 (\%)** & **Accuracy (\%)** & **F1 (\%)** & **Accuracy (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Baselines} & TD-LSTM & 68.13 & - & 75.63 & - & 70.8 & 69 \\  & ATAE-LSTM & 68.7 & - & 77.2 & - & - & - \\  & IAN & 72.1 & - & 78.6 & - & - & - \\  & RAM & 74.49 & 71.35 & 80.23 & 70.8 & 69.36 & 67.30 \\  & MGAN & 75.39 & 72.47 & 81.25 & 71.94 & 72.54 & 70.81 \\ \hline \multirow{2}{*}{BERT Models} & BERT-PT & 78.07 & 75.08 & 84.95 & 76.96 & - & - \\  & BERT-SPC & 80.25 & 77.41 & 85.98 & 78.79 & 75.29 & 73.63 \\ \hline \multirow{2}{*}{LCF-GloVe} & Glo-CDM & 76.02 & 70.58 & 82.5 & 73.92 & 72.25 & 70.92 \\  & Glo-CDW & 75.24 & 71.46 & 81.61 & 72.26 & 71.82 & 69.83 \\ \hline \multirow{2}{*}{LCF-BERT} & Glo-CDM & **82.29** & **79.28** & **86.52** & **80.4** & **76.45** & **75.52** \\  & Glo-CDW & **82.45** & **79.59** & **87.14** & **81.74** & **77.31** & **75.78** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of performance. “Glo-CDM” and “Glo-CDW” indicate that both global context and local context participate in the learning process. We set superior SRD thresholds for LCF designs (see Section 4.5.5). We use “ - \" to represent unreported experimental results. The top two scores are in bold.