esa-sceva/satcom-chunk-collection huggingface dataset:
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Model Size (M)**} & \multicolumn{2}{c}{**FLOPs (G)**} & \multicolumn{2}{c}{**Time (s)**} \\ \hline \multicolumn{6}{c}{End-to-end methods based on deep learning} \\ \hline IFCNN-MAX [24] & 0.08 & 8.54 & 0.09 \\ U2 Fusion [25] & 0.66 & 86.40 & 0.16 \\ SDNet [35] & 0.07 & 8.81 & 0.10 \\ MFF-GAN [36] & 0.05 & 3.08 & 0.06 \\ SwinFusion [27] & 0.93 & 63.73 & 1.79 \\ MUFusion [37] & 2.16 & 24.07 & 0.72 \\ FusionDiff [29] & 26.90 & 58.13 & 81.47 \\ SwinMFF [26] & 41.25 & 22.38 & 0.46 \\ DDBFusion [28] & 10.92 & 184.93 & 1.69 \\ \hline \hline \multicolumn{6}{c}{Decision map-based methods using deep learning} \\ \hline CNN [22] & 8.76 & 142.23 & 0.06 \\ ECNN [23] & 1.59 & 14.93 & 125.53 \\ DRPL [19] & 1.07 & 140.49 & 0.22 \\ SESF [30] & 0.07 & 4.90 & 0.26 \\ MFIF-GAN [31] & 3.82 & 693.03 & 0.32 \\ MSFIN [10] & 4.59 & 26.76 & 1.10 \\ GACN [67] & 0.07 & 10.89 & 0.16 \\ ZMFF [11] & 6.33 & 464.53 & 165.38 \\ LightMFF & **0.02** & **0.06** & **0.02** \\ Reduction (\%) & 60.00\% & 98.05\% & 66.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The proposed LightMFF achieves remarkable efficiency improvements across all metrics. With only 0.02 M parameters, LightMFF is approximately 2.5\(\times\) smaller than its closest end-to-end competitor (MFF-GAN [36] with 0.05M parameters) and 3.5\(\times\) smaller than the most compact decision map-based method (SESF [30]/GACN [67] with 0.07 M parameters). In terms of computational complexity, LightMFF requires merely 0.06 G FLOPs, representing a 98.05% reduction in computational cost compared to the most compact prior method (MFF-GAN [36] with 3.08 G FLOPs). The practical benefits of our efficient design are evident in the inference time measurements, where LightMFF executes in 0.02 s on GPU, achieving a 3\(\times\) speedup over MFF-GAN (0.06 s) [36] and an 8\(\times\) improvement over GACN (0.16 s) [67].
## 4 Experimental Verification  
### Experimental Data Processing and Experimental Process Design  
#### 4.1.1 Data Enhancement  
The data enhancement method proposed in this section uses the overlapping sampling method, i.e., for the training samples, each segment of the signal is acquired from the original signal with an overlap between its subsequent segments, as shown in Figure 9. For the test samples, there is no overlap in the acquisition, and the offset is set to 28 in this paper.
## 5 Methodology
### Data Model  
As shown in Section 1, the raw data provided by the radar scans during one day (24 h) on a certain geographic region was exported in the form of a sequence of matrices of \(m\times n\) dimensional matrices, one matrix corresponding to a certain time moment \(t\) and a certain meteorological product \(p\) (i.e., each element from the matrix represents the value for the product \(p\) at a certain location from the map). As a set _Prod_ of multiple meteorological products are provided by the radar, the radar data collected at a time \(t\) may be visualized as a 3D data grid in which the OZ axis corresponds to the radar products.  
For instance, Figure 1 depicts a sample 3D grid with \(m=2\) rows, \(n=2\) columns and three products (\(Prod=\{R01,R02,R03\}\)) recorded at a certain time stamp \(t\). In the figure, the values for \(R01\) are in the front matrix, \(R02\) values are in the middle one and \(R03\) in the matrix behind.  
During one day, a sequence of 3D data grids (as shown in Figure 1) corresponding to various time stamps is provided by the radar. Assuming that the radar records data every 6 min, 240 3D data grids are provided. For a certain location (\(i\),\(j\)) on the map, a time moment \(t\) and a set _Prod_ of radar products, we are denoting by \(V_{t}(i,j,l,Prod)\) the vector representing the linearized 3D data subgrid containing the radar products' values for a neighboring area of a certain length \(l\) surrounding the point (\(i\),\(j\)), at time moment \(t\).  
As an example, let us consider a \(5\times 5\) dimensional data grid, the set \(Prod=\{R01,R02\}\) of radar products, a time stamp \(t\), the location (3, 3) (\(i=3,j=3\)) and a length \(l\) of 3 for the neighboring subgrid. Figure 2 depicts the 3D data grid containing values for \(R01\) (front) and \(R02\) (behind) for each cell from the data grid surrounding the point (3, 3), at time stamp  
Figure 1: A sample 3D data grid.  
\(t\). The point of interest as well as the 3D data subgrid of length 3 surrounding the point (3, 3) are highlighted.  
The linearized 3D data subgrid (highlighted in Figure 2) is the 18-dimensional vector \(V_{t}(3,3,3,Prod)=(15,0,0,10,10,15,20,10,40,5,25,30,25,20,40,15,20)\).
## 3 Results and Discussion
### Selection of the Polymerisation Solvent  
The amount of polymerization solvent used determines the morphology of the final polymer. With a low solvent content of approximately 50%, bulk polymerization occurs, whereas with a high solvent content (above 91%, usually approximately 98%), precipitation polymerization takes place.  
In this study, the volume percentage of solvent was 86% of the volume of the polymer mixture, which is between the percentages that give rise to bulk polymerization and precipitation polymerization. In this case, the type of polymerization that is predominant depends on the polymerization time. Because polymerization for periods longer than 6 h results in a bulky and rather hard polymer and unsuccessful chip coating, we set the polymerization time to 6 h.  
The type of polymerization solvent used determines the properties of the resulting polymer, such as surface polarity, porosity, and swelling. Acetonitrile and chloroform, the most commonly used solvents in the preparation of MIP, were selected as the polymerization solvents for investigationbecause the low-polarity organic solvents can improve the selectivity of MIP and because IBA exhibited good solubility in both solvents. The polymers to be investigated were prepared using MAA as a functional monomer and EGDMA as a cross-linker. The adsorption performance of the polymer coatings was investigated by incubating each chip in an IBA chloroform solution. After adsorption, the chip was rinsed with the solvent, and then the fluorescence intensity was directly measured by placing the chip in the measurement cuvette. The characteristic fluorescence spectra of IBA were observed on both the MIP and NIP chips. When the polymerization solvent used was chloroform and acetonitrile, the relative fluorescence intensity measured from the MIP chip was 86.10 and 32.94, respectively; this result indicates that the polymer formed in chloroform had a greater binding capacity. In our study, EGDMA-MAA polymers were observed to exhibit relatively stronger interaction via hydrogen bonding compared to the hydrophobic interaction and electrostatic interaction with IBA. This behavior was observed because chloroform is a better solvent for polymer and a better template to form stable hydrogen bonds than acetonitrile is.
## 5 Discussion
### Lessons Learned
#### 5.1.4 Smart Badge Accreditation  
The smart badge accreditation functionality of the QualiChain platform was the most well received by the pilot's stakeholders. Smart badges offer verifiable and immutable validation for skills gained and tasks completed and as such, they were positively evaluated by both students and professors. However, the integration of the smart badge functionality in the everyday life of the university could potentially lead to some resistance and complaints by some professors and groups of students that do not agree with this type of student evaluation. Some students thought that such a process could lead to the creation of unfair leaders in the cases of exceptional or popular students. Of course, such an issue would be easy to solve by setting an upper limit to how many instances of a smart badge can be awarded to a student by other students. An additional functionality that would solve this issue is a filtering mechanism that would allow a user to only see smart badges in a profile that have been validated by professors, as they can be trusted to be more impartial. In addition, based on the received feedback, it seems that despite the explanations provided during the workshops, students and professors still had some misunderstandings concerning the role of blockchain in the platform. Some thought that the right to data deletion could not be respected even though no personal data are stored in the platform's blockchain. In fact, the blockchain was used for the verification of transactions in the system, while students' personal data were stored off-chain to respect the right to data deletion that is imposed by the GDPR. This solution is widely used in the research community. Others failed to see the merit of having blockchain-verified smart badges in their profiles, given that their understanding of the technology is lacking. It is strongly believed that knowledge around blockchain should be increased via the inclusion of blockchain in the curriculum, special lectures, and platform exhibitions, as well as cooperation of HEIs with national and European blockchain initiatives. On another note, the smart badge functionality provided the opportunity to verify soft skills as well. Skills like teamwork, ability to communicate, leadership, etc., can manifest during presentations and collaborative assignments. While these skills are also requested by the labor market, job seekers are having difficulties in proving them to prospective employers and thus, a smart badge ecosystem that includes students, universities, job seekers, and job offerors would have a positive effect on such processes.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 5 Comparative Analysis
### Computation Analysis  
This part evaluates the cost of computation for the various schemes. The computation times for the ECC multiplication, symmetric encryption/decryption, fuzzy extractor, bilinear pairing, and hash are 13.405, 1.657, 13.405, 32.713, and 0.056 ms, respectively, where \(T_{fe}\approx T_{ccm}\) as explained in [49]. Table 8 also demonstrates the estimated time frames required for specific cryptographic operations and associated notations.  
The computing cost of the presented work is a little higher than [50], as seen in Table 9. However, the fact that these studies lack some security features in comparison to the security offered by the new scheme, as indicated in Table 6, justifies this difference. The computing cost of the newly implemented protocol is also shown in Figure 7.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Protocols** & **\# of Messages** & **\# of Bytes** \\ \hline [PERSON] et al. [49] & 4 & \((68+40+56+72)=236\) \\ [PERSON] et al. [50] & 4 & \((100+52+52+84)=288\) \\ [PERSON] et al. [51] & 4 & \((134+64+40+68)=304\) \\ [PERSON] et al. [52] & 4 & \((60+46+44+40)=208\) \\ [PERSON] & 4 & \((60+80+84+68)=292\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cost comparison for communication.  
Figure 6: Communication cost comparison of [49, 50, 51], and [52].
## 6 Physical Layer Aspects
As \(C_{dB}=P_{r}\) and for the QPSK modulation, \(\overline{\gamma}_{b}=\frac{\overline{\gamma}_{s}}{2}\), Equation (6) can be rewritten as:\[P_{r}=\frac{\left(Q^{-1}\left(P_{b}\right)\right)^{2}}{-4\ln(1-P_{out})}+\left( \frac{f_{b}}{B}\right)_{dB}+N_{dB}\,. \tag{7}\]  
By using Equation (7), the required received power can be calculated for a given bit error rate and outage probability, which can give us the range of communication from Equation (1) and (2). In wireless communication (PHY), typically a bit error rate of \(10^{-4}\) and an outage probability of 0.02 are considered for the receiver design. According to the IEEE 802.15.4 standard, these result in a short communication range of 20-50 m, with acceptable outage probability. However, if interference from the devices operating in the same frequency band is considered, an even lower communication range will result in guaranteeing the same outage. Experiments show that IEEE 802.15.4 systems can operate with almost no problems in the presence of IEEE 802.11 systems in range. However, if another IEEE 802.15.4 system exists nearby, the result can be catastrophic. Such a discussion about coexistence issues has been held in [4].
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 1 Introduction
Compared to traditional AR exhibition applications, the innovations of this research include the following:  
* Development of a Participatory Exhibition-Viewing System: By developing an AR app that integrates 'exhibition-viewing mode' and'message mode', this study introduces a novel interactive mode for exhibition viewing. The system enhances the exhibition experience by providing interactive features that engage viewers more deeply with the content. * In-Depth Analysis of Visitor Behavior: This research, through the collection and analysis of behavior data from viewers using the AR app, reveals the distinct impacts of the exhibition-viewing mode and message mode on viewer behavior. These findings not only deepen our understanding of the application of AR in exhibitions but also provide valuable data and insights for future research. This work advances AR applications in cultural exhibitions, presenting a significant shift from individualized experiences to fostering communal engagement.
## 1 Introduction
This is done by training a ResNet50 [17] deep learning model to classify real-time thermal images showing the material discharge positions in the grate cooler and accordingly identify the DCS data records that are measured and collected under abnormal operating conditions of the rotary kiln. Such classification not only can clean up the dataset and improve the accuracy of f-CaO content prediction, but also can help detect potential problems such as operating anomalies of cement production devices, thus improving the stability and safety of the production process. Secondly, based on the preprocessed DCS data, our proposed method predicts the f-CaO content in cement clinker under normal operating conditions of rotary kiln using the XGBoost algorithm [18], which has the advantage of high performance and high accuracy in capturing the correlation between various features (i.e., process status measurements in DCS) and the f-CaO content. The experimental results show that by correlating the operating conditions of a rotary kiln with the free calcium oxide content in cement clinker, monitoring and control of the clinker production process can be realized and the cement clinker quality and production efficiency can be improved. The adopted method based on image recognition/classification and the XGBoost algorithm can effectively predict the f-CaO content in cement clinker, and provide valuable guidance and optimization strategies for cement clinker production. Our work in this paper concurs with the recent trend of fusing artificial intelligence and high computational power into industrial manufacturing to save production costs while simultaneously producing higher-quality products. The rest of this paper is organized as follows. We conduct a survey of related work in Section 2. Section 3 reviews the background of the manufacturing process of cement clinker and presents the problem of cement quality control from a data-driven perspective. Section 4 details the design of the proposed two-step learning-based f-CaO content prediction method. Section 5 conducts experiments to evaluate the proposed approach and showcase how Industry 4.0 is transforming the future of the industry of construction materials. Section 6 concludes our work and discusses future work.
## 4 Performance Analysis  
### Outage Probability Analysis  
The OP is related to the probability that the end-to-end SNR \(\gamma_{e2e}\) is below a specified threshold value \(\gamma_{th}\)[15]. For the proposed system with AF relaying, an exact closed-form analytic expression for the OP can be given as  
\[P^{\text{out}}(\gamma_{th})=\Pr[\gamma_{e2e}<\gamma_{th}]=F_{ \gamma_{e2e}}(\gamma_{th}). \tag{15}\]
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 3 Results  
Figure 2a shows the large-scale spatial variability of GWS rates from GRACE over China during 2002-2014. Among the three main aquifers in China, the North China Aquifer (NCA) experienced the most significant GWS depletion in its northern part (Figure 2a). Both WGHM and PCR-GLOBWB also highlight remarkable GWS depletion in the northern part of NCA, consistent with GRACE results (Figure 2b,c). It should be noted that, in the NCA, the magnitudes of GWS depletion signals from WGHM and PCR-GLOBWB are larger than that from GRACE (Figure 2). It is evident in Figure 2a, which shows the filtered GRACE-based GWS rates in the spatial domain. GRACE also reveals apparent significant GWS depletion in the eastern Himalaya Mountains and the north of the Tarim Basin (Figure 2a). However, these apparent mass loss signals are caused by glacier mass change rather than groundwater depletion, concluded by some previous studies [78; 79; 80; 81]. Note that glacier mass budget is not simulated in LSMs, so glacier mass change signals in these mountain regions still exist in GRACE-derived GWS changes. Spatio-temporal GWS changes in the three main aquifers of China are further discussed as follows.
## 3 The Dynamic Harmonic Compensation Sliding Mode Control Method
### Stability Analysis  
To ensure that the motion state points of the sliding mode observer approach and reach the selected sliding mode surface within an acceptable time frame, the Lyapunov stability condition must be satisfied. The Lyapunov function \(v\) is defined as follows:  
\[V=\frac{1}{2}S^{\mathrm{T}}S=\frac{1}{2}\Big{(}S_{d}^{2}+S_{q}^{2}\Big{)} \tag{22}\]  
where \(S=\begin{bmatrix}S_{d}&S_{q}\end{bmatrix}^{T}\).  
From Equation (22), the derivative of the Lyapunov function \(v\) can be derived, which is expressed as follows:  
\[\frac{dV}{dt}=S_{d}\cdot\frac{dS_{d}}{dt}+S_{q}\cdot\frac{dS_{q}}{dt} \tag{23}\]  
Then, substituting Equations (14) and (16) into Equation (24) yields:  
\[\begin{array}{l}\frac{dV}{dt}=s_{d}\cdot(-\epsilon_{d}z_{d}(s_{d})-\eta_{td} s_{d})+s_{q}\cdot(-\epsilon_{q}z_{d}(s_{q})-\eta_{td}s_{q})\\ =-\epsilon_{d}s_{d}z_{d}(s_{d})-\eta_{td}s_{d}^{2}-\epsilon_{q}s_{q}z_{d}(s_{ q})-\eta_{td}s_{q}^{2}\end{array} \tag{24}\]  
Since \(s_{d}sign(s_{d})=|s_{d}|\) and \(s_{q}sign(s_{q})=|s_{q}|\), this expression can be further simplified:  
\[\frac{dV}{dt}=-\epsilon_{d}|s_{d}|-\eta_{td}s_{d}^{2}-\epsilon_{q}|s_{q}|-\eta _{td}s_{q}^{2} \tag{25}\]  
where \(-\eta_{d}s_{d}^{2}\) and \(-\eta_{q}s_{q}^{2}\) are clearly negative because \(\eta_{d}\) and \(\eta_{q}\) are positive control gains. Similarly, \(-\epsilon_{d}|s_{d}|\) and \(-\epsilon_{q}|s_{q}|\) are also negative or zero because \(\epsilon_{d}\) and \(\epsilon_{q}\) are positive control gains, and \(|s_{d}|\) and \(\big{|}s_{q}|\) are non-negative. Therefore, \(\frac{dV}{dt}<0\). According to the Lyapunov stability theorem, if \(\frac{dV}{dt}\) is negative definite, then the system state variables \(s_{d}\) and \(s_{q}\) are asymptotically stable.  
Figure 5: Bode plot of the system.
# Polarimetry for Photonic Integrated Circuits
## 3 Results
### PIC Characterization
#### 3.2.2 Electro-Absorption Modulator  
The characterization of EAMs enables insight into their birefringence and PDL as a function of bias voltage. Results obtained from an EAM with an active length of 200 \(\upmu\)m are shown in Figure 8. The birefringence is deduced from the retardance measurement together with the device length.  
By assuming that the refractive index and loss change induced by the bias voltage in an EAM is negligible along TM as compared to TE, one can show that the chirp \(\
u\) may be written as  
\[\
u=2\frac{\partial\varphi}{\partial\alpha}\;\approx\;\frac{4\pi}{\lambda} \frac{\partial\Delta n}{\partial PDL} \tag{10}\]  
Together with the measured depolarization of the EAM, the deduced chirp parameter is given in Figure 9.  
Figure 8: Electro-absorption modulators (EAM) measurements. The active length of the EAM is 200 \(\upmu\)m, the substrate temperature is 20 \({}^{\circ}\)C and the input power is 3 dBm. **(Left)** Birefringence (orange) and loss (purple) at a wavelength of 1.57 \(\upmu\)m. **(Right)** summary of the birefringence and polarization dependent loss (PDL) for all measured wavelengths. The band edge of the device is at 1.54 \(\upmu\)m.  
Figure 7: Polarization rotator measurements. The DUT temperature is 20 \({}^{\circ}\)C and the input power is 3 dBm. **(Left)** results in terms of polarization extinction ratio (PER) and excess loss. The excess loss is dominated by a crystal defect in the input waveguide of the device. **(Right)** results in terms of fast axis and retardance. The ideal values for both parameters are depicted as dashed lines. The fast axis is measured with respect to the TE state on the Poincaré sphere, so a 45\({}^{\circ}\) fast axis in real space corresponds to a 90\({}^{\circ}\) fast axis in Stokes space.
# Preparation of Cementitious Material with Wet Fly Ash by Hydrothermal Reaction and Calcination
## 3 Results and Discussion
### Properties of Wet Fly Aslt Cementitious Material
#### 3.3.1 Compressive and Flexural Strength  
Figure 7 shows the compressive and flexural strength of the mortar after hydration of the calcined test block powder. The compressive strengths of the self-made cement at 3 days, 7 days, 28 days, 90 days, and 180 days were 8 MPa, 11.8 MPa, 19 MPa, 22.2 MPa, and 34.4 MPa, respectively. The early compressive strength of self-made cement mortar is relatively low, and the strength is only 19 MPa at the age of 28 days. For the 90-day and 180-day ages, the compressive strength still increases, and the 180-day strength increases by 81.1% compared with the 28-day compressive strength. The flexural strength of self-made cement at 3 days, 7 days, 28 days, 90 days, and 180 days are 3.1 MPa, 3.7 MPa, 5.7 MPa, 6.3 MPa, and 6.4 MPa, respectively. The flexural strength increase in the 90-day and 180-day curing age is insignificant compared with the 28-day curing age, which is 10.1% and 12.3%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Calcination** & **f-CaO** & **Calcination** & **f-CaO** & **Calcination** & **f-CaO** \\
**Conditions** & **/wt\%** & **Conditions** & **/wt\%** & **Conditions** & **/wt\%** \\ \hline
800 \({}^{\circ}\)C–0.5 h & 7.02 & 850 \({}^{\circ}\)C–0.5 h & 5.82 & 900 \({}^{\circ}\)C–0.5 h & 4.35 \\
800 \({}^{\circ}\)C–1.0 h & 7.32 & 850 \({}^{\circ}\)C–1.0 h & 6.60 & 900 \({}^{\circ}\)C–1.0 h & 4.08 \\
800 \({}^{\circ}\)C–1.5 h & 7.55 & 850 \({}^{\circ}\)C–1.5 h & 5.76 & 900 \({}^{\circ}\)C–1.5 h & 5.27 \\
800 \({}^{\circ}\)C–2.0 h & 6.65 & 850 \({}^{\circ}\)C–2.0 h & 6.07 & 900 \({}^{\circ}\)C–2.0 h & 5.95 \\ \hline \end{tabular}
\end{table}
Table 6: Content of calcined sample f-CaO.