esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 4 Conclusions  
This paper proves the feasibility of a low-complexity LED-to-LED communication link based entirely on RGB LEDs as transmitters and receivers. The novelty of this research is threefold: First, it proposes a low-complexity design based only on off-the-shelf RGB LEDs to keep the overall cost and power consumption at a minimum to allow for easy integration in IoT devices. Second, it derives a formula for the LED photodetector response at varying distances and empirically proves that the noise of such detector is AWGN. Finally, knowing the noise power and its distribution, it proposes a matched filter technique to increase the system performance even above that using a regular PIN photodiode.  
Without matched filter, successful OOK modulated signal detection is possible at data rates up to 200 kbps over a distance up to 3 cm with BER \(<10^{-3}\) and the distance rises to 6 cm at a transmission speed of 10 kbps. Employing the proposed matched filter technique results in a maximum error-free transmission distance comparable to that of a regular PIN photodiode as detector. The results prove that using that technique, the BER improves by up to 90% at a transmission speed of 200 kbps over a 5 cm distance.  
Figure 9: Effect of the matched filter on improving the system performance regarding (**a**) the bit error rate (BER) at different distances and (**b**) the maximum error-free transmission distance.  
Although the achieved data rates are considered low in comparison to other VLC systems, it is sufficient for most small IoT devices that require sending only status flags and small chunks of information, such as toy-to-toy communication. Moreover, the paper proves that the system performance lags due to the much smaller active area of the RGB LED compared to a single-coloured red LED. On the cost of increasing the complexity, the system performance could be further optimized using lenses, concentrators, pre-processing, and further post-processing techniques along with more complex modulation schemes such as OFDM.  
Conceptualization, M.G., W.P.N. and R.B.; data curation, M.G.; formal analysis, M.G.; investigation, M.G.; methodology, M.G.; project administration, W.P.N.; resources, W.P.N.; software, M.G.; supervision, W.P.N., R.B. and A.A.E.A.; validation, M.G., W.P.N. and A.A.E.A.; visualization, M.G.; writing--original draft, M.G.; writing--review and editing, W.P.N. and A.A.E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Methodology
### Image feature extraction
#### Shape Features  
We use eight common image shape features: circularity, roughness, elongation, compactness, eccentricity, solidity, extent, and the standard deviation of the radial distance. _Circularity_ is measured by dividing the circumference of the equivalent area circle by the actual perimeter of the nodule. _Roughness_ can be measured by dividing the perimeter of the region by the convex perimeter. A smooth convex object, such as a perfect circle, will have a roughness of 1.0. The _eccentricity_ is obtained using the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 (a perfect circle) and 1 (a line). _Solidity_ is the proportion of the pixels in the convex hull of the region to the pixels in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shape Features** & **Size Features** & **Intensity Features** \\ \hline Circularity & Area & MinIntensity \\ Roughness & ConvexArea & MaxIntensity \\ Elongation & Perimeter & MeanIntensity \\ Compactness & ConvexPerimeter & SDIntensity \\ Eccentricity & EquivDiameter & MinIntensityBG \\ Solidity & MajorAxisLength & MaxIntensityBG \\ Extent & MinorAxisLength & MeanIntensityBG \\ RadialDistanceSD & & SDIntensityBG \\  & & IntensityDifference \\ \hline \multicolumn{3}{c}{**Texture Features**} \\ \hline
11 Haralick features calculated from co-occurrence matrices (Contrast, Correlation, Entropy, Energy, Homogeneity, 3 rd Order Moment, Inverse variance, Sum Average, Variance, Cluster Tendency, Maximum Probability) \\ \hline
24 Gabor features are mean and standard deviation of 12 different Gabor images (orientation = 0°, 45°, 90°, 135° and frequency = 0.3, 0.4, 0.5) \\ \hline
5 Markov Random Fields (MRF) features are means of 4 different response images (orientation = 0°, 45°, 90°, 135°), along with the variance response image \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image features extracted from each lung nodule’s region of interest; SD stands for standard deviation and BG for background.  
intersection of the convex hull and the region. _Extent_ is the proportion of the pixels in the bounding box (the smallest rectangle containing the region) that are also in the region. Finally, the _RadialDistanceSD_ is the standard deviation of the distances from every boundary pixel to the centroid of the region.
## 4 Parameter Sensitivity Analysis
### The Effect of the Specific Heat Capacity of the Soil Layer on Frost Heave Displacement  
By varying the specific heat capacity of the silty clay surrounding the pipeline, the impact on frost heave displacement was analyzed, as shown in Figure 17. As the specific heat capacity of the silty clay increases, the frost heave displacement gradually decreases. However, the displacements after thawing remain essentially the same across all conditions. This occurs because a higher specific heat capacity allows the soil to absorb more heat, acting as a thermal buffer during the cooling process. The increased specific heat capacity slows the cooling of the soil, reducing the temperature gradient during the freezing stage and slowing moisture migration towards the cold end. This suppression of moisture migration inhibits the formation and expansion of ice crystals within the soil, thereby reducing frost heave displacement. During the thawing phase, the ice crystals in the soil completely thaw into water, leading to a relatively uniform deformation across the soil layers.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 3 H-Transformer
### Testing Step  
The testing step of the system, shown in Figure 2, uses all three modules:  
1. **The trained linear module \(M_{L}\)** forecasts the next hour target \(Z_{test+1}\) using the n-lagged data \(Z_{test}\). It outputs the linear component \(\hat{L}_{test+1}\);
2. **The trained nonlinear module \(M_{N}\)** uses the n-lagged exogenous data \(exo_{test}\) and \(Z_{test}\) to predict the nonlinear component of the target \(\hat{N}_{test+1}\);
3. **The combination module** sums \(\hat{L}_{test+1}\) and \(\hat{N}_{test+1}\) to produce \(\hat{Z}_{test+1}\) (Equation (5)). The resulting forecast can then be compared with the real value \(Z_{test+1}\) using loss functions such as \(RMSE\) and \(MAE\). \[\hat{Z}_{test+1}=\hat{L}_{test+1}+\hat{N}_{test+1}.\] (5)
# Research on Blockchain-Based E-Bidding System
## 1 Introduction  
Blockchain is a decentralized public ledger based on P2P networks, which has attracted wide attention in distributed application systems in recent years. In this technology, a tamper-resistant digital platform for data storage and sharing is realized by applying the chain-block structure and establishing a trusted consensus mechanism to synchronize data changes. At the same time, the decentralization, traceability and immutability of on-chain information storage makes blockchain a trusted machine with high reliability and security. Based on these characteristics, researchers began to analyze the application of blockchain in various fields, such as the Internet of Things, supply chain management, voting system [1, 2] and bidding system. Blockchain in application can improve the availability of data and reduce costs, while maintaining the openness and transparency of the application [3, 4].  
In recent years, electronic bidding (E-bidding) has become an efficient and convenient service, which aims to provide an open and safe bidding environment for suppliers to protect the public interest. Tendering and bidding is a kind of commodity trading behavior; in simple words, it is an organized selection of excellent transaction ways by the tenderee. Compared with traditional offline bidding, it has an obvious difference in efficiency, information collection and other aspects, and is better in the identity authentication of the bidding object, confidentiality of the bidding content, fairness of the bidding process and other aspects.  
Emerging blockchain technology combined with smart contracts could revolutionize traditional E-bidding systems in a decentralized and autonomous manner. It paves the way for a secure, immutable and auditable E-bidding process, while maintaining strong accuracy and completeness.  
At present, there are also blockchain-based E-bidding designs, but they are limited to using the characteristics of the blockchain to achieve the preservation of informationin all aspects of the whole bidding process. In the process of preserving information, the blockchain may need to receive sensitive data to execute a smart contract. Therefore, it is crucial to ensure the privacy and authenticity of the data sent to the blockchain, so that everyone can verify the data without compromising sensitive information.  
This paper presents a system framework that uses blockchain technology and smart contract to solve the privacy and security problems of E-bidding systems. Firstly, the overall architecture of E-bidding systems based on blockchain technology is described. Afterwards, the implementation process of tendering, bidding and bid evaluation is studied. Finally, the paper analyzes the possible problems of the E-bidding system, and provides a new solution for the reform of the E-bidding system based on blockchain technology.
## 2 Methods  
### Dataset and Language Selection  
#### 2.1.1 Dataset Selection Rationale  
We utilize the CrossSum dataset [22] as our primary data source for cross-lingual summarization experiments. CrossSum was selected based on several methodological criteria. First, it provides comprehensive language coverage with parallel article-summary pairs for over 1500 language pairs, enabling systematic evaluation across diverse linguistic contexts. Second, the dataset employs rigorous quality control measures, including human verification of cross-lingual alignments and content preservation validation. Third, CrossSum provides established train/validation/test splits and evaluation protocols, ensuring reproducibility and comparability with existing research. Finally, the dataset specifically addresses low-resource language scenarios, directly aligning with our research objectives.
# A Novel Tempogram Generating Algorithm Based on Matching Pursuit
## 4 Discussion
### Flexibility
#### 4.4.2 Flexibility of Sparsity  
Like tempo resolution, we may want to change sparsity to adapt to a specific application rather than always to keep highest sparsity. As we explained in the motivation Section 3.1, sparsity is mainly determined by the matching degree between the real tempo curve and the compared curve. In fact, sparsity lies in the quantity and quality of the compared curves. The quality cannot be changed for FT and ACF because their compared curves cannot be changed. For FT, though the quantity can be changed just as altering the tempo resolution, sparsity cannot be changed in fact, as we explained above.  
However, for MP, we could design the atoms other than the tempo atoms that are provided in this paper, in other words, change the quality of the compared curves, so as to change the sparsity. Furthermore, we could be easy to change the quantity of the atoms. First, we could do just as altering the tempo resolution. Second, we could do that by changing the hop size in the step of shifting the mother atom when creating the tempo dictionary. The bigger the hop size is, the smaller the quantity of the atoms is, and the lower the sparsity is.  
Figure 7 above shows three tempgrams of MP with different sparsity that is caused by different hop sizes, in which the iteration numbers of MP are set to 20 and the values of tempo resolutions are set to 1. The hop sizes of Figure 7a-c are 2, 5, and 20 respectively. As can be seen, the corresponding none-zero coefficients around the same tempi are getting more and more with the hope size getting bigger and bigger. For example, the stripe around 250 bmp in Figure 7c is apparently broader than the corresponding regions in Figure 7a,b. It shows that the number of none-zero coefficients of Figure 7c is bigger than Figure 7a,b. In other words, it indicates that the sparsity of the tempogram is getting lower and lower with the hop size getting bigger and bigger.  
Table 3 shows that the numbers and the percent of the none-zero coefficient of the tempgrams in Figure 7. The number and the percent with the hop size of 20 is the biggest, while the smallest number and the percent come from the hop size of 2. It validates the above conclusion of Figure 7 again. It is also worthwhile to note that the computational cost will rise because the quantity of the atoms will grow if we decrease the hop size.
# Microstructured Phononic Crystal Isolates from Ultrasonic Mechanical Vibrations
## 6 Conclusions  
The main findings of this work can be summarized as follows:  
* three prototypes made by a 2 \(\times\) 2 \(\times\) 3 periodic repetition of unit cells of 100, 150 and 200 um width were fabricated through the 2 PP technique, numerically simulated in COMSOL Multiphysics and experimentally tested through a Michelson interferometer;
* a good agreement between theoretical predictions based on dispersion analyses of the ideally periodic material, attenuation analyses of the designed metastructures and experimental results on the fabricated metastructures was achieved;  
Figure 7: (**a**) Close-up view of the unit cell beams for \(a=100\), 150, 200 μm. The layered structure caused by the 2 PP fabrication process is put in evidence: _one, two, at least three_ overlapped layers areas are shown in orange, green and grey, respectively. (**b**) Bandgap simulated through dispersion analyses under the hypothesis of uniform Young’s Modulus (grey bars) and layered structure (orange bars). The reference Young’s modulus of 2.3 GPa was adopted for the uniform structure and for the _at least three_ overlapped layers area, while reduced Young’s moduli are adopted for the _one_ and _two_ overlapped layered areas.  
* the influence of the layered structure obtained by 2 PP on the elastic properties of the polymerized material was highlighted and simulated.  
The possibility to grade the properties and the anisotropy of the resin can allow the design of new micro-structures. As an additional perspective, it is worth mentioning that the range of materials that can be 3D structured by 2 PP is rapidly extending, encompassing glass [48], metal [49], and other inorganic compounds [50]. The multiscale fabrication capabilities of 2 PP, from few micrometers to millimeters, combined with the use of materials with different stiffnesses, open unexplored possibilities in the development of application-tailored 3D phononic structures operating at different frequency intervals.  
V.Z., conceptualization, software, formal analysis, methodology, writing--original draft preparation; L.P., data curation, investigation, writing--review and editing; T.Z., data curation,writing--review and editing; R.O., project administration, supervision, writing--review and editing; C.C., conceptualization, project administration, supervision, writing--original draft preparation; A.C., conceptualization, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors thank [PERSON] from Politecnico di Milano for providing the SZ2080 photoresist.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 4 Conclusions  
This numerical study analyzed the fatigue failure of the spot-welded structure for its different influential parameters such as the amount of cyclic load, the load ratio, and size of the spot welding. The XFEM modelling approach was used to simulate the crack propagation in the welded-structure under cyclic tensile loading. The model was validated using the experimental results from the literature. The following conclusions were drawn:  
* The crack in the structure was nucleated around the spot-welding point and later it expanded along the width of the spot-welded structure when the cycling loading was continually applied.
* The applied cycling load influenced the fatigue life substantially. When the \(F_{\max}\) was increased by 25%, the \(N_{f}\) decreased more than 2.7 times.
* For smaller load ratios, the fatigue life of a structure decreased notably, where, in parallel, the crack propagation rate increased. For instance, this increase was more than 25 times when \(R\) equaled 0.0 compared to that of 0.75 for the simulated spot-welded austenitic steel plates.
* The fatigue life of the welded structure was significantly reduced as the crack propagated rapidly when a smaller nugget size was used.
* XFEM analysis provide valuable insights into the fatigue life estimation and failure mechanisms of spot-welded structures, aiding in the design and optimization of such components for enhanced durability and reliability.
* The correlated XFEM methodology introduced in this study is new to literature and can be applied for any kind of material at the design phase of spot-welded structures.
* The resistance spot-welding parameters affected the heat-affected zone and, thus, induced residual stresses, which may impact fatigue life estimations in the nugget region. The assessment of fatigue life including the simulations of heat-affected zone and resistance spot-weld parameters should be considered in a future work.  
Conceptualization, M.D. and E.T.D.; Methodology, M.D.; Formal analysis, M.D.; Investigation, M.D. and E.T.D.; Writing--original draft, M.D. and E.T.D.; Writing--review & editing, M.D. and E.T.D.; Funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the American University of the Middle East, Kuwait.  
**Institutional Review Board Statement:** Not applicable.  
Figure 6: Numerically obtained number of cycles to failure (\(N_{f}\)) for the spot-welded structures with different diameters of the spot-welding.
## 3 Methodology  
In this section, we propose the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). A diagram outlining REACT is shown in Figure 3, and its detailed structure is shown in Figure 4. REACT consists of four main parts: the Encoding layer, the Head Entity Identification layer, the Entity Attention network layer (feature fusion layer), and the Tail Entity and Relation Identification layer. Our main contribution is in the Entity Attention network layer. In the following sections, each of these four parts is introduced.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 4 Effect on the Acquisition Process for the BeiDou Receiver
The simulation results shown above are under the assumption that there exist no fading during the signal propagation, which means the input signal power is equal to the received signal power. The curves indicate that the average correlation peak decreases as the scintillation intensity increases. Under the same SNR scope, the acquisition probability decreases as the scintillation intensity increases.
## 4 Experimental Verification
### Field Test in Underground Coal Mine  
In order to further verify the practicability of the proposed identification model of a pressure relief hole, the industrial camera installed on the drilling robot was used to capture scene images in the pressure relief area of the underground roadway in a coal mine. The scene is shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Multi-Level Feature Fusion** &
\begin{tabular}{c} **Multi-Level Feature Fusion +** \\ **Image Multi-Scale Input** \\ \end{tabular} \\ \hline Number of identified samples & 1086 & 1109 \\ Number of missed samples & 42 & 19 \\ Number of false samples & 113 & 122 \\ Accuracy rate/\% & 90.58 & 90.09 \\ Recall rate/\% & 96.28 & 98.32 \\ Average training time/s & 238 & 251 \\ Average recognition time/s & 0.19 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relief hole target recognition results based on improved SqueezeNet + BN with different measures.  
Figure 15: Pressure relief hole image acquisition site in an underground coal mine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Improved SqueezeNet** & **VGG-16** & **ZF-Net** \\ \hline Number of identified samples & 1109 & 1113 & 1101 \\ Number of missed samples & 19 & 15 & 39 \\ Number of false samples & 122 & 137 & 122 \\ Accuracy rate/\% & 90.09 & 89.04 & 90.02 \\ Recall rate/\% & 98.32 & 98.67 & 96.58 \\ Average training time/s & 251 & 310 & 332 \\ Average recognition time/s & 0.19 & 0.24 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relief hole target recognition results based on different basic network models.
## 3 Results
### Antenna Prototype Fabrication and Measurement
It means that a phased array can be extended to a larger array with better scanning performance in a larger aperture for direct-to-cellular communications. The simulated and measured AR at the different scanning angles of the S-/C-band phased array antennas are shown in Figures 15a and 15b, respectively. The AR values below 3 dB for both the S- and C-band are obtained with measured results a little higher than that of the simulation, which is probably caused by the phase errors imported by the phase-shifter channels of the test equipment. The good performance also demonstrates the enhancement of the circular polarization radiation performance of the array by the sequential rotation technique. Figure 14: Array radiation patterns of the C-band array at 3.55 GHz in \(\varphi=0^{\circ}\) plane when scanning to different angles. (**a**) 64–element array; (**b**) 128–element array. Figure 13: Simulated and measured radiation pattern of the S-band array at 1.97 GHz in \(\varphi=0^{\circ}\) plane when scanning to different angles. The measured parameters of the proposed dual-band, dual-CP phased array are summarized in Figure 12.0 It can be seen that the proposed array with a single-layer structure has achieved RHCP and LHCP operations, respectively, within the proposed operating frequency band. In addition, it can be observed that the proposed array has the ability to scan up to 50\({}^{\circ}\) in the elevation direction within two frequency bands, with sidelobe level suppressions of \(-12.5\) dB and \(-13.1\) dB, respectively. All the measured results effectively verified the validity of our design, which indicates that the large-scale, dual-band, dual-circular-polarization, shared-aperture antenna array can be realized on a single-layer structure.