esa-sceva/satcom-chunk-collection huggingface dataset:
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 2 The Proposed Methodology
### Attention Fusion Block  
After obtaining the feature map \(M\) from the final dense block, both global and local attention mechanisms are employed to capture feature information across various aspects. By integrating these features, related features are obtained. The computation process for global attention is outlined below:  
\[Q_{j}=W_{q}^{j}M\quad K_{j}=W_{k}^{j}M\quad V_{j}=W_{v}^{j}M \tag{7}\] \[head_{j}=softmax(\frac{Q_{j}\cdot K_{j}}{\sqrt{d_{k}}})V_{j}\]  
where \(head_{j}\) represents the attention value of the j-th head, which is set to 8 in this paper. \(W_{q}^{j},W_{k}^{j}\), and \(W_{v}^{j}\) are the parameter matrices for the scientific system, and \(\sqrt{d_{k}}\) is the scaling factor. Subsequently, the attention values from multiple heads are concatenated together and a skip connection is applied. The computation process is as follows:  
\[G=(head_{1}\oplus head_{2}\oplus\cdots\oplus head_{8})W_{0}+M \tag{8}\]  
where \(W_{0}\) represents the weight matrix and G denotes the extracted global features. The computation process for local attention is outlined as follows:  
\[L=Sigmoid(Conv3(AvgPool(M)))\odot M+M \tag{9}\]where \(Sigmoid\) is represented as a non-linear activation function and \(\odot\) denotes element-wise multiplication. \(L\) represents the extracted local features. We integrate global and local attention, and the process is as follows:  
\[\begin{split} F&=G\oplus L\\ S&=Conv6(F)+Conv5(Conv4(F))\\ O&=S+FC(FC(S))\end{split} \tag{10}\]  
where \(FC\) represents the fully connected layer and \(O\) denotes the fused features.
## 3 Results
### Blockchain Publications per Capita  
The second regression model examines the factors influencing blockchain publications per capita across countries, revealing some notable differences compared to both total scientific publications. The most striking feature of the blockchain model is the even stronger positive effect of GNI per capita (beta = 0.736), the largest among all the studied dependent variables. This suggests that blockchain research is highly concentrated in economically advanced countries, possibly due to the field's novelty, its ties to financial technology, and the need for sophisticated technological infrastructure.  
Research spending as a percentage of GDP maintains a positive relationship (beta = 0.200) with blockchain publications, though slightly weaker than for the total publications. This might indicate that while general research investment benefits blockchain research, the field may also draw support from sources outside the traditional academic funding structures, such as private sector investments or cryptocurrency-related ventures.  
Interestingly, life expectancy shows a stronger positive association with blockchain publication rates (beta = 0.132) compared to the total publications. This could suggest that blockchain research is more prevalent in countries with higher overall development levels, possibly due to its interdisciplinary nature spanning technology, economics, and law.  
The most surprising finding is the negative relationship between average years of schooling and blockchain publications (beta = \(-\)0.165) when controlling for the other variables in the model. This contrasts with the negligible associations seen in the total publications. It might indicate that blockchain research is driven more by specialized expertise or entrepreneurial activity rather than broad educational attainment. Alternatively, it could suggest that countries with less established traditional education systems might be more open to emerging technologies like blockchain.  
The liberal democracy index shows a very weak positive relationship (beta = 0.031) with blockchain publications, much smaller than for the total publications. This could imply that blockchain research is less sensitive to political systems, possibly reflecting its origins in decentralized and alternative economic thinking.  
The specificities of blockchain publications compared to the total scientific output suggest that blockchain research is even more strongly tied to economic factors and less influenced by the traditional indicators of societal and educational development. This could reflect the field's close ties to financial innovation, its appeal in countries looking to leapfrog the traditional financial systems, and its potential attraction for countries seeking technological niches for economic development.
## 3 Problem Formulation
### Constraints
#### 3.3.1 Equality Constraints  
The power flow equations are taken as the equality constraints for the RPP. Using the output power of wind farms and also considering the probabilistic nature of the problem, Equations (25) and (26) can be rewritten as follows:  
\[P_{i,s}=P_{Gi,s}+P_{Wi,s}-(1+\Gamma(s))P_{Di,s}-\text{Re}\{V_{i,s}\sum_{j=1}^ {N_{B}}\bigl{(}V_{j,s}Y_{i,j,s}\bigr{)}^{*}\},\qquad\forall s\in\Omega_{s} \tag{29}\]  
\[Q_{i,s}=Q_{Gi,s}+Q_{Wi,s}-(1+\Gamma(s))Q_{Di,s}-\text{Im}\{V_{i,s}\sum_{j=1}^ {N_{B}}\bigl{(}V_{j,s}Y_{i,j,s}\bigr{)}^{*}\},\qquad\forall s\in\Omega_{s} \tag{30}\]  
where \(P_{Wi,s}\) and \(Q_{Wi,s}\) show the output active and reactive power of the \(i^{th}\) wind farm for the \(s^{th}\) scenario, respectively. It should be noted that the output reactive power of the wind farms is neglected in this paper.
## 3 Results and Discussion  
### Accuracy  
The mass spectrum obtained by GC/MS, TIM was used to determine fragment ion (_m_/_z_) of the target chemicals (SM, SD\({}_{1,2}\), ST). The quantitative analysis was conducted by using the monitoring ion: Q (quantitative ion), _m_/_z_: 104, 154, 178, 196, 208, 312 and qualitative ion: _m_/_z_: 78, 105, 117, 152, 193 ions. Calibration curves were prepared by the internal standard method using the detected ion peak area ratio (I/Q). For quantitative analysis of the target chemicals by mass spectrometry, Selected Ion Monitoring (SIM) was used because target detection performance of SIM was 100 times higher than that in Total Ion Monitoring (TIM). It was thus possible to obtain very low concentrations (10\({}^{-9}\) level) of SOs by the SIM.  
A standard solution of each compound (SM, SD\({}_{1,2}\), ST) was prepared to obtain a target solution using a pipette and volumetric flask, and the calibration curve of each compound was prepared. Curve linearity ranged from 0.2 ug to 10 mg kg\({}^{-1}\) with a correlation coefficient, r = 0.9996 to 0.9999. The detection limit was found at S/N = 2 to be 10 \(\mu\)g kg\({}^{-1}\).
###### Abstract  
In this paper, the design, simulations, and experimental results related to new analog circuits for voltage controlled synthetic inductors (VCSI) are presented. The new circuits based on a generalized impedance converter (GIC) are proposed for adaptive resonant piezoelectric shunt damping. The VCSIs are implemented using (1) an analog multiplier and (2) an operational transconductance amplifier (OTA) as voltage-controlled resistor. The simulation and experimental results for the new proposed VCSIs are presented and a comparative analysis follows. The proposed VCSIs work in a stable manner in parallel with negative impedance converters (NIC) to enhance structural damping in resonant piezoelectric resistive-inductive shunt applications. The behavior of the synthetic inductor is identical to a real inductor only in a specific frequency range and this situation can explain the reported spreading performance in the literature for resonant piezoelectric shunt damping. The simulation results are validated by a group of experimental investigations that confirm the improved stability and linearity of the new circuits proposed as VCSIs. Experimental results show that the VCSI based on an analog multiplier have an enhanced linearity in comparison with the OTA version in a limited voltage control range.  
synthetic inductor; voltage control; resonant piezoelectric damping; analog multiplier; operational transconductance amplifier +
Footnote †: journal: Applied Sciences  
_Article_  
Voltage-Controlled Synthetic Inductors for Resonant Piezoelectric Shunt Damping: A Comparative Analysis
###### Abstract  
Development of satellite and radar applications has been continuously studied to reach the demand in the recent communication technology. In this study, a new oval-square-shaped split-ring resonator with left-handed metamaterial properties was developed for C-band and X-band applications. The proposed metamaterial was fabricated on 9 \(\times\) 9 \(\times\) 0.508 mm\({}^{3}\) size of Rogers RO4003C substrate. The proposed metamaterial structure was designed and simulated using Computer Simulation Technique (CST) Microwave Studio with the frequency ranging between 0 to 12 GHz. The simulated result of the proposed design indicated dual resonance frequency at 5.52 GHz (C-band) and 8.81 GHz (X-band). Meanwhile, the experimental result of the proposed design demonstrated dual resonance frequency at 5.53 GHz (C-band) and 8.31 GHz (X-band). Therefore, with a slight difference in the dual resonance frequency, the simulated result corresponded to the experimental result. Additionally, the proposed design exhibited the ideal properties of electromagnetic which is left-handed metamaterial (LHM) behavior. Hence, the metamaterial structure is highly recommended for satellite and radar applications.  
C-band; left-handed metamaterial; oval-square; satellite; X-band Article
# News Recommendation Method Based on Candidate-Aware Long- and Short-Term Preference Modeling
## 4 Experiments
### Overall Performance  
To ensure fairness, we followed the hyperparameters given by the authors in implementing the baseline model and did not tune them. From the performance comparison of our method NRCLS with the baseline methods, we can lead to the following conclusions from Table 4:  
1. NRCLS achieves better recommendation performance than traditional methods like LibFM and Wide Deep, which model users through traditional methods. NRCLS has a more complex model structure than these methods, which can exploit the hidden features of the data and have better adaptive capabilities.
2. NRCLS achieves better than NRMS and LSTUR, which model users through traditional deep learning methods (e.g., GRU and LSTM). This indicates that modeling users through GATs can additionally take into account high-order structural information in the graph, rather than just the news users have clicked on, as is the case with methods that do not apply GATs. A more accurate representation of the user can be obtained by using GATs.
3. NRCLS considers the relationship between candidate news and user preferences, and the performance comparison results of the two datasets significantly outperform methods that model users based on candidate agnosticism, such as NPA, and NAML. The results demonstrate the effectiveness of considering the effect of candidate news on user preferences to improve model performance.
4. Modeling fine-grained short-term user preferences helps to achieve high-quality news recommendations. Baseline methods, such as GNewsRec, model users' short-term preferences in a coarse-grained manner, ignoring the fact that users' short-term preference changes are generally drastic and frequent. In this paper, we find that fine-grained modeling of short-term user preferences can improve model performance. In addition, GNewsRec only considers the relationship between short-term preferences and news candidates, while our method considers the relationship between short- and long-term preferences and news candidates, which is the reason why NRCLS achieves better recommendation performance.
## 4 Results
### Profile \"Southern Branch\"  
The last along flow profile addressed as \"Southern Branch\" and situated furthest south (see Figure 1) is dedicated to cross \"Lake iCUPE\". This supraglacial lake is used to study the effect of in-situ deposition of impurities in detail as described in the following section. Figure 4 is showing a similar figure than for profiles \"Old Ice\" and \"Northern Branch\". Also there the transition into an impermeable surface layer (pore-close off density is \(\sim\)830 kg/m\({}^{3}\)) is at an elevation of about 900 to 1000 m a.s.l. The backscatter shows more variation in the upstream area in this profile. Again the crevassed area is coincident with higher backscatter, both in summer and winter conditions. Similar to profile \"Northern Branch\", the downstream part shows significant lower reflectance values, indicating a surface with high impurity content (Figure 3).  
The average for the areas with elevation above 1000 m a.s.l. (distance from the ice margin > 20 km) showed on April about 0.99, 1.00 and 0.066 for bands 3, 8 and 11, respectively. The values in areas closer to the ice margin (<1000 m a.s.l.) were 0.95, 0.96 and 0.10 for the above mentioned bands and same season. The indexes were also in this case close to each other highlighting the occurrence of similar surface types (Index1: \(-\)0.017 and \(-\)0.004: Index2: 0.88 and 0.81). The same analysis evidences on July reflectance values significantly different: 0.94, 0.78 and 0.034 far from the ice margin; 0.78, 0.60 and 0.031 close to the ice margin. The Vis-NIR index (Index 1) highlighted limited differences (0.10 against 0.13) as well as the NIR-SWIR index (Index 2) (0.92 against 0.91).  
In addition to these profiles we were using AWI's snow radar (UWB-M) to detect reflections in the upper layers. Normally, summer layers are forming a stratigraphy that is also often used to determine accumulation rates over past decades to centuries ([PERSON], 2018). Figure S4 displays radargrams along some short transects. Our analysis shows a decline in penetration depth with the transition into the impurity covered area. In the accumulation zone the stratigraphy can be resolved down to 13 m, with a decline to 1.5 m at the onset of the ablation zone. Further downstream no reflections of the last summer were found at all.
###### Abstract  
In areas where solar energy is abundant, such as the Tibetan plateau, passive solar buildings are attracting more and more attention and becoming a popular form of rural building. However, it is often difficult to achieve the satisfactory indoor thermal environment in a local rural passive solar single-family house. In order to improve the indoor thermal environment of passive solar buildings through building design, a systematic study of rural single-family buildings in Tibet was conducted. The basic parameters were investigated on the outdoor thermal environment, space-arrangement, envelope structure, and the indoor thermal environment. The building model considering space-arrangement modes was developed based on the survey data in multi-space passive solar buildings. The general physical and mathematical analysis models of multi-space passive solar buildings were established based on the heat transfer theory. Furthermore, the effects of space-arrangement and exterior wall thermal resistance on indoor air temperature were analyzed by numerical simulation. Results show that the indoor air temperature of the passive solar building is influenced by space-arrangement and wall thermal resistance together. When the space-arrangement of the building model was changed from north-south through type\" (mode _a_) to \"through and separation combination\" (mode _b_) and \"north-south separation\" (mode _c_), the indoor air temperature of the living room increased from 8.8 \({}^{\circ}\)C to 10.6 \({}^{\circ}\)C and 11.6 \({}^{\circ}\)C, with increases of 20.5% and 31.8%, respectively. In addition, equally increasing the thermal resistance of exterior walls in different orientations has different effects on the indoor air temperature. In the space-arrangement mode \(c\), comparing with the temperature increment of the living room and bedroom caused by increasing thermal resistance of the south wall and north wall, the temperature increment of the living room caused by increasing thermal resistance of the east/west wall increased by 151.7% and 32.7%, and that of the bedroom increased by 609.1% and 239.1% respectively. This study can provide a reference for the optimal design of passive solar buildings in solar energy abundant areas.  
passive solar building; indoor space-arrangement; wall thermal resistance; coupling effect; Tibetan Plateau +
Footnote †: journal: Applied Soft. 2019, 9, 3594; doi:10.3390/appr9173594
# Neural Network Based Country Wise Risk Prediction of COVID-19
## 4 Results and Discussion
### Prediction Accuracy  
Here we discuss the prediction accuracy of the proposed method. The final fuzzy-rule based classification depended on death rate, case rate, and recovery rate. The suitable model chosen for each country was trained to predict these three values. We calculated root-mean-square error (RMSE) on the validation data to evaluate the methods. We compared using baseline algorithms such as linear regression, lasso linear regression, ridge regression. A single model was used to predict the values of all the countries. It is observed that such methods performed very poorly due to the small dataset. We also compared the method with some advanced neural networks such as a variation of LSTM combined with a fully convolutional network [55], a variation of residual RNN [56], and GRU [57]. It is also noted that very deep networks also failed to predict accurately using such a small dataset. Bayesian optimized shallow GRU performs closer to our method. The results are summarized in Table 3. Example results on USA trend prediction are shown in Figure 9 for numbers of active cases, recovered cases and deaths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMSE**} \\ \cline{2-4}  & **COVID-19 Cases** & **Recovered** & **Death** \\ \hline Liner Regression & 2705.6 & 856.6 & 427.2 \\ \hline Lasso Linear Regression & 1905.9 & 333.4 & 175.3 \\ \hline Ridge Regression & 2307.5 & 614.2 & 213.3 \\ \hline Elastic Net [58] & 2307.3 & 2105.9 & 300.2 \\ \hline LSTM-FCNS [55] & 2605.4 & 1305.3 & 269.5 \\ \hline Recidual RNN [56] & 2905.4 & 1109.5 & 242.3 \\ \hline GRU [57] & 2605.3 & 923.0 & 163.5 \\ \hline GRU+Baysian & 1275.0 & 422.9 & 100.2 \\ \hline Proposed & 1103.5 & 329.0 & 101.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average root-mean-square error (RMSE) of the last 25 days’ prediction of numbers of active, recovered and death cases.  
Figure 8: Case study of the network training for USA. (**a**) training loss during training, and (**b**) active case prediction on validation data.
## 2 Materials and Methods
Laser ablation: The laser used was a femtosecond pulsed near-infrared laser, specifically the Spirit system by Spectra-Physics, with emission wavelength 1040 nm and pulse width < 400 fs. The intensity profile at the laser output was near-Gaussian (M2 < 1.2) and the beam diameter at the exit of the laser head was 1.5 mm with horizontal polarisation (>100:1). The pulse rate can be set from a single shot to 1 MHz, with maximum pulse energy of 40 \(\mu\)J at 100 kHz. The maximum mean power output is >4 W. A two-mirror galvanometric scanner (Raylase SuperscanIII-15) was used and scanned the laser beam in the X-Y directions. The beam was focused on the sample surface by means of a F-theta objective lens, 160 mm focal length, up to a diameter of roughly 30 \(\mu\)m.  
To collect the dust generated during the laser ablation process, a plastic sample holder was produced by 3D printing (Figure 4). This sample holder device was manufactured ad hoc for this purpose, and it was coupled to the aspiration system of the laser laboratory. The mask that acted as a filter was located at the entrance of the suction tube; this was the reason that the dust was deposited directly on the outside face of the filter and did not pass through it, proving that this is a potential plausible protective measure for personnel who carry out laser cleaning. When selecting the processing parameters, it was necessary to consider that our goal was to obtain, under laboratory conditions, sufficient powder to be analysed, and not to investigate the most suitable laser cleaning technique to remove unwanted crusts or layers, while preserving the integrity of the rock. Thus, the ablation process of the serpentinite samples was carried out in an area of 15 mm \(\times\) 40 mm, with a pattern of horizontal and vertical lines, a separation of 20 mm and at a scan speed of 100 mm/s at the maximum laser power (100 W). A total of 20 repetitions of the ablation pattern were performed on the sample; therefore, the duration of the ablation process was approximately 2 h (Figure 5). Figure 4: Ad hoc receptacle to collect sample during laser ablation. Figure 5: Rock sample after laser ablation.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 5 Results  
The results section is comprised of two parts: First we discuss and compare the results of ICI unaware iterative receiver and then we discuss the results of the ICI aware progressive iterative receiver for MIMO OFDM communication.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 1 Introduction
The proposed LSTM model with multiple attention modules is called multiple-attention LSTM (MA-LSTM). This paper uses MA-LSTM to conduct TEC prediction experiments in Harbin (45\({}^{\circ}\) N, 125\({}^{\circ}\) E), Beijing (40\({}^{\circ}\) N, 115\({}^{\circ}\) E), Guangzhou (22.5\({}^{\circ}\) N, 115\({}^{\circ}\) E), Tibet (30\({}^{\circ}\) N, 90\({}^{\circ}\) E), Yushu (35\({}^{\circ}\) N, 90\({}^{\circ}\) E), Ziyang (30\({}^{\circ}\) N, 105\({}^{\circ}\) E), Hangzhou (30\({}^{\circ}\) N, 120\({}^{\circ}\) E), and Heze (35\({}^{\circ}\) N, 115\({}^{\circ}\) E) in the next 2 h, compared with other time series prediction models, such as LSTM [23], GRU [24], and Att-BiGRU [25]. We also tested the performance of the MA-LSTM model for predicting TEC in a magnetic quiet period and a medium magnetic storm period, and for different months. The contributions of this paper are as follows:  
1. The LSTM model with multiple attention modules is applied to TEC prediction to make TEC modelling more adaptive and obtain higher prediction accuracy. 2. An L1 constraint is added to TEC prediction, which can avoid overfitting caused by excessive attention to a historical observation value in TEC modeling.
## 1 Introduction
Manual extraction allows pairwise pixels in different ground types to be selected, decreasing the matching error for paired pixels [7; 15]. Although spatial mismatch problems in RRN are well\(-\)addressed in [18; 33; 34], three issues remain: (1) the spectra of paired pixels may differ due to the pixel\(-\)level spatial mismatch, especially for pairwise images with different sensing angles that create spatial distortion; (2) a regression analysis in which one variable does not consider the influence of adjacent bands, and; (3) applications are not focused on crop\(-\)growing areas and extracted PIFs are untrained to different types of crop or ground materials. To address these issues, we propose new relative radiometric normalization methods by replacing invariant points or pixels with invariant polygons or surfaces to apply to a crop\(-\)growing area that has been surveyed on the ground. We show decreases in the influence of pixel\(-\)level spatial mismatch and demonstrate the effect of adjacent bands using multiple variable regression. The proposed algorithms show superiority in crop area radiometric normalization and contribute to the improvement in normalization accuracy, particularly in human\(-\)impacted areas of the Earth's surface.