esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Line-Scan Hyperspectral Measurement Techniques
### Spectroscopy Techniques
#### 2.1.3 Raman Spectroscopy  
High-energy monochromatic light (e.g., a laser) will be absorbed, reflected, and scattered when its photons interact with molecules of a sample. The scattered light consists of both elastic and inelastic scattering. The vast majority of scattering is elastically scattered light (Rayleigh scattering), which has the same frequency as the incident radiation. A very small portion of the photons will lose energy to the sample molecules when exciting them from the ground state to the excited state (Stokes scattering) or gain energy in the opposite process (anti-Stokes scattering). The Stokes and the anti-Stokes scattering are collectively called Raman scattering [7]. Typical Raman measurements only acquire the longer-wavelength Stokes scattering signals owing to the much higher intensity of Stokes scattering compared to that of anti-Stokes scattering. Since the probability of a Raman photon appearing is on the order of one out of \(10^{6}\)-\(10^{8}\) scattered photons, the Raman scattering intensity is very weak, and it is proportional to the laser intensity and the reciprocal of the fourth power of the excitation wavelength [7]. A typical Raman spectrum features a series of narrow and sharp peaks. The Raman peak positions are correlated to particular molecular vibrations at different frequencies, and they can be used to determine the sample compositions. The intensity of the Raman peak is linearly proportional to the concentration of the molecule, which can be used for quantitative analysis of the analyte.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 3 Methodology
### Proposed Model Architecture  
Deep learning techniques have been demonstrated to be effective in classification tasks that are highly related to fake news detection, such as sentiment analysis, spam detection, etc. [24]. An essential advantage of deep learning methods is that they are capable of effectively extracting the deep features of the input data during the training process. The powerful representation learning ability of the deep learning algorithms leads to reduces feature dimensions, and hence the computational costs [24]. CNN and RNN (LSTM and BiLSTM) are the most commonly used deep learning algorithms to identify news veracity in other languages, specifically the English language. They have attracted much interest because they have the most effective architecture for a more accurate and efficient performance [36]. To find the best classification performance, two deep learning models are tested in this study to handle the detection of fake news, namely CNN and BiLSTM.
# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 5 Experiments
### Baselines  
To verify the effectiveness and generality of the AKGP framework proposed in this paper, models such as BPRMF (Bayesian Personalized Ranking Matrix Factorization) [47], NEUMF (Neural Matrix Factorization) [11], CKE [37], RippleNet [33], KGCN [34] and KGAT [35] were selected for use in the experiments.  
BPRMF [47] is a recommendation model based on matrix decomposition. It uses Bayesian theory to maximize the posterior probability based on prior knowledge, and also uses two-by-two matrix decomposition to optimize implicit feedback.  
NEUMF [11] is a recommendation model based on neural collaborative filtering. It uses the flexibility and nonlinearity of neural networks instead of the dot product of matrix decomposition to improve the expressiveness of the model with implicit feedback.  
CKE [37] is an embedding-based model. It combines collaborative modules with structured, textual knowledge embeddings in a unified Bayesian framework. User and item representations are obtained via collaborative filtering. The initial item representations are then enriched with knowledge information related to the items.  
RippleNet [33] is a propagation-based model. It is continuously extended to enrich user representation via preference propagation of the knowledge graph.  
KGCN [34] is a propagation-based model. It captures the structural and semantic information of the knowledge graph by selectively and biasedly aggregating neighborhood  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Element** & **Last.FM** & **Book-Crossing** & **MovieLens-20M** \\ \hline \multirow{3}{*}{User–Item Interaction} & \#Users & 1872 & 19,676 & 138,159 \\  & \#Items & 3846 & 20,003 & 16,954 \\  & \#Interactions & 42,346 & 172,576 & 13,501,622 \\ \hline \multirow{3}{*}{Knowledge Graph} & \#Entities & 9366 & 25,787 & 102,569 \\  & \#Relationships & 60 & 18 & 32 \\  & \#Triples & 15,518 & 60,787 & 499,474 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed statistics of the three datasets (# means ‘the number of’).  
information. The neighbors of each entity are used as perceptual domains, which are extended to multi-hop neighbors to model higher-order neighborhood information, thus mining information about the personalization and potential interest of users.  
KGAT [35] integrates the knowledge graph and a user-project interaction graph into the same graph space, known as collaborative knowledge graph. The modeling of higher-order information can be achieved using a collaborative knowledge graph with a three-layered structure of information dissemination, knowledge-based attention and information aggregation.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 2 Data Preparation  
### Data Collection and Pre-Processing  
Figure 1 shows the investigation and optimization process of EOR screening based on the machine learning algorithm. This study is organized into four stages: (1) collecting a global successful EOR dataset; (2) preprocessing and analyzing the dataset; (3) modeling and optimizing the EOR model; and (4) evaluating the model performance.  
A comprehensive and reliable EOR dataset is indispensable when applying machine learning algorithms for analysis, as the model's quality is directly contingent upon the dataset's integrity [29]. Simultaneously, it is crucial to factor in the outcomes of past EOR technique projects, where the data from successful EOR projects notably contribute to the robustness of classification models.  
The dataset is composed of 956 successful EOR projects that were collected around the worldwide oilfields [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55]. Each successful project includes porosity (\(\Phi\)), permeability (K), depth (D), gravity (\(\gamma\)), temperature (T), viscosity (\(\mu\)), net thickness (H), and initial oil saturation (S\({}_{0}\)). After the data collection, the duplicate project information was removed from the dataset and the missing value was filled by the mean value to ensure the high quality of the dataset. To balance the importance of reservoir/fluid parameters, permeability, depth, viscosity, and temperature were transformed to the logarithm scale because these parameters have a large statistical range. Our team has put in the effort to improve the quality of the dataset. This involves analyzing parameter ranges using boxplots to evaluate the maximum and minimum values. These analyzes ensure that parameters remain within reasonable limits and accurately represent most reservoir and fluid characteristics [4; 96]. The above data quality assessment work can ensure the feasibility and authenticity of the research.
# Semantic Communication on Digital Wireless Communication Systems
## 3 IER--A Novel Physical Layer Semantic Metric
### Relation Between IER, BER, and Semantic Metric
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Case** & Natural binary coding & Manhattan distance \\  & scheme & binary coding scheme \\ \hline
**Transmission Framework** & bit-conversion JSCC & bit-conversion JSCC \\ \hline
**Source File** & image & image \\ \hline
**Semantic Codec** & LSCI & LSCI \\ \hline
**Quantization Output Range** & [0–7] & [0–7] \\ \hline
**Integer-to-Bit Coding** & natural binary coding & Manhattan distance binary coding \\ \hline
**Channel Coding** & NO & NO \\ \hline
**Bit constellation mapping** & 3 GPP 5G & 3 GPP 5G \\ \hline
**Modulation** & 64 QAM & 64 QAM \\ \hline
**Simulation SNR Range** & [\(-\)5–30] & [\(-\)5–30] \\ \hline
**Channel Model** & AWGN & AWGN \\ \hline
**Channel Equalization** & LMMSE & LMMSE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation configuration for BER/IER comparison. Figure 10: Binary codecs for comparison. (**a**) Natural binary coding scheme; (**b**) Manhattan distance binary coding scheme. While measured with Manhattan distance and the IER, the case utilizing Manhattan distance binary coding obtained a better IER when the SNR was lower than 20. In addition, comparing natural binary coding and Manhattan distance binary coding from the perspective of semantic metrics, Manhattan distance binary coding can achieve a better PSNR and SSIM at the same BER condition. According to the BER, IER, PSNR, and SSIM curves shown in Figure 12, it can be observed that the IER is more relative to the semantics metric than the BER. This validates that the IER is a better evaluation metric than the BER for E2E semantic communication, especially in image semantic transmission scenarios. Figure 11: Simulation result—physical layer metrics. (**a**) Hamming distance vs. SNR; (**b**) BER vs. SNR; (**c**) Manhattan distance vs. SNR; (**d**) IER vs. SNR. Figure 12: Physical layer metrics vs. semantic metrics. (**a**) BER vs. SNR; (**b**) SSIM vs. SNR; (**c**) IER vs. SNR; (**d**) PSNR vs. SNR.
## 2 Study Area  
The Corvara landslide is an active, complex earth slide-earthflow located above the tourist center of Corvara in Badia valley, Dolomites, Autonomous Province of Bolzano, Italy (Figure 1a,b). Elevations in the Corvara area range from 1400 m a.s.l. in the valley bottoms to more than 3000 m a.s.l. in the Sella group. The lithology of the Dolomites comprises primarily dolomite rocks consisting of marks or limestones alternating with clayshales outcropping in the slopes underlying the dolomite peaks [39]. The lower end of the Corvara landslide reaches the town (elevation of 1550 m a.s.l.) and the main scrap reaches maximum elevations of approximately 2070 m a.s.l. The landslide involves clayey silt or silty clay material, with mixed gravels and blocks made up of arenites, maryl limestone and dolostone [54].  
The landslide has been investigated extensively over the past years, including studies on landslide characterization and field-based monitoring [55], dating [56], and modeling [57]. The aforementioned studies concluded that the Corvara landslide has an estimated total volume of more than 30 million m\({}^{3}\), a depth of up to 100 m and has been active for more than 10,000 years. The presently active part of the  
Figure 1: Corvara landslide: (**a**) Landslide location; (**b**) geometric features of the X-Band Corner Reflectors (CR) and detail of the trihedral faces; (**c**) permanent Global Position System (GPS) monitoring site with X-band CR; (**d**) outlines of the landslide-affected area, monitoring system and mean annual displacements (September 2013–September 2015) measured by DGPS (background: 2.5 m DTM and 2011 orthophoto); and (**e**) field impressions from a highly active section north of point 51.  
landslide involves approximately 25 million m\({}^{3}\); with major active shear surfaces ranging at depths between 10 and 48 m. Ongoing displacements frequently cause damage to ski infrastructure, a golf course and the national road SS 244. Surface and subsurface measurement since 1997 have shown a spatially varying movement velocity which ranges from a few cm/year in the toe zone, and up to tens of meters per year in the most active track and source zones (Figure 1d). Phases of higher landslide velocities have been attributed to periods of prolonged rainfall and snow-melting, and recently also to the accumulation of large quantities of snow [58]. Monitoring of the Corvara landslide using satellite-based PS-MTI started 2010 [59] when a first set of CRs was installed in the field (Figure 1c). This was necessary as it was found that no prominent rocks or anthropic infrastructures such as houses acting as PS could be found with sufficient spatial density to monitor the entire landslide [60]. After some experience had been collected, a new CR design better adapted for the monitoring of ground deformation in alpine environments was prepared [61] and the current monitoring system (Figure 1b,c) was installed (see description in Section 3).
# Cavity Wavelength on Erbium-Doped Fiber Ring Laser Depending on Fabry-Perot Etalon Steering Angle
###### Abstract  
This study presents the liquid crystal Fabry-Perot etalon (LC-FP) as the preferred laser wavelength tuning solution within a erbium-doped fiber ring laser architecture. The laser cavity wavelength can be adjusted by applying varying voltages to the LC-FP. Furthermore, tuning the laser wavelength can be facilitated by modifying the incident light through changes in the steering angle of the LC-FP, which is attributed to the angular dispersion characteristics of the device. The operational range for the steering angle of the LC-FP is \(\pm\) 4 to 18 degrees. This architectural framework is adept at facilitating the generation of single-wavelength and dual-wavelength lasers within the C band. The tunable range for a single wavelength is approximately 13 nm, while the tunable range for dual wavelengths is around 14 nm, with a wavelength spacing of approximately 17.5 nm. These capabilities are primarily influenced by the operational wavelength of the erbium-doped fiber amplifier (EDFA), the operating wavelength of the collimator that directs the fiber optic beam into the LC-FP, and the fixed thickness of the LC-FP.  
erbium-doped fiber ring laser; Fabry-Perot etalon; liquid crystal 2020
## 3 Proposed Methodology
### Estimation of Link Delay
#### 3.3.3 Computing Normalized Network Contention  
Next, we numerically find the maximum of the average contention delay of wireless links by substituting \(\rho_{WL}=1\) into Equation (23) to compute (\(\overline{t_{cont,WL,max}}\)), as in Equation (25):  
\[\overline{t_{cont,WL,max}}=DIFS+CW_{min}(2)^{E-3}T_{slot} \tag{25}\]  
Similarly, we can numerically find the maximum of the average contention delay of wired links by substituting \(\rho_{WI}=1\) into Equation (24) to compute (\(\overline{t_{cont,WI,max}}\)), as in Equation (26):  
\[\overline{t_{cont,WI,max}}=E\times(IFG+T_{c})+CW_{min}(2)^{E-2}T_{slot} \tag{26}\]  
Now we can define normalized wireless contention (\(\mathcal{C}_{WL}\)), as given in Equation (27):  
\[\mathcal{C}_{WL}=\frac{\overline{t_{cont,WL}}}{\overline{t_{cont,WL,max}}} \tag{27}\]  
Similarly, we can define normalized wired contention (\(\mathcal{C}_{WI}\)), as given in Equation (28):  
\[\mathcal{C}_{WI}=\frac{\overline{t_{cont,WI}}}{\overline{t_{cont,WI,max}}} \tag{28}\]  
Finally, we can formulate the normalized network contention (\(\mathcal{C}\)) as a weighted summation of the normalized channel contention of each of the wired and wireless communication channels, as given in Equation (29):  
\[\mathcal{C}=\frac{N\times\mathcal{C}_{WL}+N_{r}\times\mathcal{C}_{WI}}{N+N_{r}} \tag{29}\]  
In Equation (29), \(N_{r}\) is the total number of RSUs, and \(N\) is the total number of nodes. The composed knowledge of normalized network contention is used in deciding the modeof the hybrid link stability-based routing framework. Specifically, we distinguish high contention when \(\mathcal{C}>0.50\), and low contention otherwise.
## 4 Conclusions  
We deposited Ag NPs and carbon materials in the channels of closed- and open-ended TiO\({}_{2}\) NTAs using UV irradiation and CVD, respectively. These modifications improved the energy conversion efficiency of the corresponding DSSCs; the electron generation was enhanced by plasmonics from the Ag NPs, while the resistance of TiO\({}_{2}\) NTAs was suppressed via the \(\pi\)-\(\pi\) conjugation from the carbon materials. DSSCs made of freestanding TiO\({}_{2}\) NTAs coated with both Ag NPs and carbon materials  
Figure 8: Incident photon-to-current efficiency (IPCE) of DSSCs based on: (a) unmodified open-ended TiO\({}_{2}\) NTAs, (b) embedded with Ag NPs, (c) applied with carbon materials, and (d) with both Ag NPs and carbon materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DSSCs Based on Open-Ended TiO\({}_{2}\)** & **R\({}_{8}\) (\(\Omega\))** & **R\({}_{1}\) (\(\Omega\))** & **CPE\({}_{1}\) (F)** & **R\({}_{2}\) (\(\Omega\))** & **CPE\({}_{2}\) (F)** \\ \hline without Ag NPs and carbon materials & 15.50 & 5.58 & 6.91 \(\times\)\(10^{-6}\) & 61.12 & 1.99 \(\times\)\(10^{-3}\) \\ with Ag NPs & 15.52 & 5.54 & 8.65 \(\times\)\(10^{-6}\) & 36.90 & 2.10 \(\times\)\(10^{-3}\) \\ with carbon materials & 15.56 & 5.07 & 1.62 \(\times\)\(10^{-5}\) & 36.40 & 2.03 \(\times\)\(10^{-3}\) \\ with Ag NPs and carbon materials & 14.99 & 4.88 & 1.16 \(\times\)\(10^{-6}\) & 24.55 & 2.99 \(\times\)\(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: EIS fitting results for DSSCs with open-ended TiO\({}_{2}\) NTAs.  
had the best energy conversion efficiency, due to the combination of these two factors. Comparing the open-ended and closed-ended TiO\({}_{2}\) NTAs (both with Ag NPs and carbon materials), the energy conversion efficiency of the DSSCs was higher for the former.  
Supplementary Materials are available online at [[http://www.mdpi.com/2076-3417/7/6/576/s1](http://www.mdpi.com/2076-3417/7/6/576/s1)]([http://www.mdpi.com/2076-3417/7/6/576/s1](http://www.mdpi.com/2076-3417/7/6/576/s1)).  
This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF), and funded by the Korean government (MSIP & MOHW) (2016-A423-0045).  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON], and [PERSON] analyzed the data. [PERSON], [PERSON], and [PERSON] wrote the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Internal Mechanisms
#### 3.4.1 Mechanisms for Role Selection and Activation  
Once the set of devices that will take part in the collaborative sensing activity is determined, all the participating units share their hardware capabilities. Then, they have to select and activate the local roles in each tier. In this sense, we distinguish between two basic operations: (i) Role Selection and Activation in the Control Tier and (ii) Role Selection and Activation in the Sensing Tier. MASU supports dynamic centralized or distributed approaches for conducting this operation. Table 1 shows a summary of the methods followed for the selection of roles in the different Tiers, according to the role chosen for the Control Tier. If the role activated in the Control Tier is Dynamic Centralized, the selection of roles in this Tier will be determined by the results of the election algorithm. Furthermore, due to the fact that in this architecture we only have one manager, the selection of roles in the Sensing Tier will be determined by such a manager. Nevertheless, if the role architecture of the Control Tier is distributed, all the units that are part of the collaborative sensing activity and that are active and have connectivity will play the roles of the Control Tier. In addition, the Selection and Activation of roles in the Sensing Tier will be determined by the results of the consensus algorithm.  
Figure 4: Interactions between MASU roles.
## 4 Results and Discussion
### Mixture Testing Results and Discussion
#### 4.2.4 Self-healing Capacity  
The fatigue-healing-fatigue test results are shown in Table 7. It can be seen from the number of cycles to failure (\(N_{\text{f}}\)) that the ranking of fatigue life under the 0.5 stress ratio is 4%EMAA/3 SBR \(>\) 3%  
Figure 12: Failure stress (TSRST) of different asphalt mixtures.  
Figure 13: Fracture strain tolerance (disk-shaped compact tension (DCT)) of different asphalt mixtures.  
SBR \(>\) virgin \(>\) 4% EMAA, which can reflect the fatigue performance ranking to some degree. Moreover, it can be seen that the fatigue lives (\(N_{1}\) + \(N_{2}\)) of asphalt mixtures increase after the healing. In which, \(N_{1}\) is the 30% fatigue life of each mixture and \(N_{2}\) is the full fatigue life of 30% fatigue damage's mixture after 24 h interval in room temperature. This is because the asphalt mixtures can heal themselves through a time interval or under high-temperature conditions. Therefore, the fatigue life of all asphalt mixtures has been extended. In addition, the 4%EMAA/3%SBR mixture has the highest healing rate, which is nearly three times as much as that of the virgin asphalt mixture. However, the mechanism of self-healing still needs investigations.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis
### Ablation Study
#### 5.3.2 Validation of Reblurring Process  
The proposed IDR\({}^{2}\)ENet employs a reblurring process to help the degradation estimation subnetwork to better estimate the implicit degradation representation \(E\). Thus, an ablation experiment is designed to verify it in this section. Specifically, the network framework with the reblurring process removed and only the degradation estimation process and deblurring process retained is shown in Figure 14. This framework is denoted as IDR\({}^{2}\)ENet-R, which is also retrained on exactly the same experimental settings. The deblurring results of IDR\({}^{2}\)ENet-R on the RealBlur-J dataset are also shown in Table 3. In Table 3, the result of IDR\({}^{2}\)ENet-R is 28.64 dB/0.870, which is a decrease of 0.17 dB/0.005 compared to the original IDR\({}^{2}\)ENet. This proves that the degradation estimation subnetwork can better estimate the implicit degradation representations in real blurred images through the reblurring process, which in turn better helps the reconstruction process.  
Figure 13: Network structure of IDR\({}^{2}\)ENet-Q.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Network Framework**} & \multicolumn{2}{c}{**RealBlur-J**} \\ \cline{2-3}  & **PSNR (dB)** & **SSIM** \\ \hline IDR\({}^{2}\)ENet-Q & 28.4 & 0.863 \\ \hline IDR\({}^{2}\)ENet-R & 28.64 & 0.87 \\ \hline IDR\({}^{2}\)ENet & 28.81 & 0.875 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the performance of different network structures.