esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Work
### User Similarity-Based Candidate Generation Model (USCG)  
To generate high-quality initial recommendation candidate sets, this section employs a candidate generation method based on user similarity analysis. While user similarity computation represents a classical approach in traditional collaborative filtering, the innovation of our research lies in its deep integration with graph convolutional networks. Unlike conventional methods that solely rely on direct user-item interaction data, our approach leverages high-order embedding representations learned from the user-item graph structure through graph convolutional networks. These representations not only encapsulate users' historical preference information but also capture implicit connection patterns in the network topology through message-passing mechanisms. Through this sophisticated collaborative mechanism, user embedding vectors more comprehensively express users' latent interests and seasonal and regional preferences, thereby enabling the  
Figure 2: Feature transformation and graph construction process in APGCN-CF.  
identification of more precisely similar user groups when quantifying inter-user similarity relationships and subsequently generating more personalized and context-aware candidate item sets for target users.  
It is important to highlight that the user similarity pattern generation process in our framework is entirely automated, requiring no manual intervention. The cosine similarity measure between users is formally calculated as follows:  
\[sim(u,v)=\frac{\mathbf{h}_{u}\cdot\mathbf{h}_{v}}{||\mathbf{h}_{u}||\cdot|| \mathbf{h}_{v}||}, \tag{4}\]  
where \(\mathbf{h}_{u}\) and \(\mathbf{h}_{v}\) represent the embedding vectors of users \(u\) and \(v\), respectively, derived from the graph convolutional network module. These embedding vectors are particularly valuable as they encapsulate both explicit user preferences and implicit high-order connectivity patterns learned through the message-passing mechanism of GCNs.  
The automation of similarity computation is implemented through vector operations that efficiently calculate pairwise similarities between the target user and all other users in the system. For each target user, the algorithm automatically identifies the \(k\) most similar users based on the computed similarity scores and aggregates their historical interactions to form the candidate set. This automated mechanism dynamically adapts to evolving user preferences as new interaction data become available in the system.  
By utilizing GCN-derived embedding vectors for similarity computation, our approach captures nuanced preference correlations that conventional collaborative filtering methods might overlook. The resulting candidate set serves as a preliminary filtered pool that effectively narrows the search space for the subsequent preference probability-based precise recommendation module described in Section 4.5, thereby establishing a seamless pipeline from user embedding generation to the final personalized recommendation.
## 3 Capacity Optimization Model
### Evaluation Mechanism
#### 3.2.2 Energy-Saving Evaluation  
\(T\)C defined in Equation (13) represents the total energy consumption of the entire CCHP hybrid system [26]. The smaller the energy consumed, the better the energy conservation thatthe system achieves.  
\[TEC=F_{mt}+F_{gb}+\lambda\sum_{t=1}^{T}\left[\begin{array}{c}E_{vacancy}(t)+ E_{vaste}(t)+\\ H_{vacancy}(t)+H_{vaste}(t)\end{array}\right] \tag{13}\]  
where \(F_{mt}\) is the fuel consumptionof the micro-gas turbine. \(F_{gb}\) is the fuel consumption of the gas boiler. \(E_{vacancy}\) and \(E_{vaste}\) represent the power shortage and power waste; \(H_{vacancy}\) and \(H_{vaste}\) represent the heat shortage and heat waste [13]; \(\lambda\) is the punish coefficient.
## 3 Use Cases of Graphs
### The Naive Algorithm
For all three algorithms, we varied the percentage of randomly-selected files among all files (denoted as _file percentage_) and compared their average end-to-end elapsed time of 10 runs to answer _on-demand size_ queries. For each file percentage, the same set of files are selected to run _on-demand size_ queries for 10 times for each algorithm, and we computed the average elapsed time of these 10 runs for each algorithm. For example, for _WL1_ and the percentage of 1%, we randomly selected 2893 files out of the total 289,295 files, and use these 2893 files as input to answer _on-demand size_ queries for all three algorithms for 10 times. Figure 14(a) and Figure 14(b) show the results of all three algorithms. We can draw two conclusions from the results. First, graph-based algorithms are at least one order of magnitude better than the _Naive_ algorithm with respect to elapsed time. In particular, _GraphOpt_ can achieve the elapsed time less than 1% of what can be achieved from the _Naive_ algorithm. For example, for _WL1_, when the file percentage is 50%, the elapsed time of _Naive_ is 744 s, the elapsed time of _Graph_ is 21 s, and that of _GraphOpt_ is 2.6 s. Second, _GraphOpt_ is more scalable than _Graph_. For example, when the file percentage is 10%, both algorithms have an elapsed time less than 5 s. But when the percentage of files is 50%, the _Graph_ algorithm has an elapsed time of 21 s, while the _GraphOpt_ has an elapsed time of 2.6 s.  
In Figure 14(a) and Figure 14(b), for both _WL1_ and _WL2_, the elapsed time does not increase monotonically with the file percentage for the _Naive_ algorithm because we have no direct control over how the database software scheduled its foreground queries and background activities, which can significantly change the elapsed time. For example, when the file percentage is 30%, the elapsed time is 870 s, but when the file percentage is 40%, the elapsed time is 576 s. But for the other two algorithms, the elapsed time generally increase with the file percentage because larger file percentage reflects larger number of edges, which is a dominant factor of the elapsed time.
## 2 Materials and Methods
### Cell Wall Sugar Composition Analysis
#### 2.5.2 GC-MS Analysis and Data Analysis  
The soluble monosaccharide compositions of cell wall samples were analyzed using an Agilent 7200 series-accurate mass Q-TOF GC-MS together with a 7890A GC system (Agilent Technologies, Santa Clara, CA, USA), equipped with an EI (electron ionization) ion source; 0.6 \(\upmu\)L of each sample was injected into a non-deactivated, baffled glass liner with a 12:1 split ratio (14.448 mL min\({}^{-1}\) split flow) and the inlet temperature was maintained at 250 \({}^{\circ}\)C. A Zebra semi-volatile (Phenomenex, Torrance, CA, USA) column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m), with a 10 m guard column, was maintained at a constant helium flow of 1.2 mL min\({}^{-1}\). The temperature gradient of the GC was ramped up at a rate of 15 \({}^{\circ}\)C min\({}^{-1}\), from 70 \({}^{\circ}\)C to 310 \({}^{\circ}\)C, over 16 min, and then held at 310 \({}^{\circ}\)C for a further 6 min. The total run time of 22 min was followed by a 7 min backflush at 310 \({}^{\circ}\)C to clean the column at the end of every run. The MS emission current and emission voltage were held at 35 \(\upmu\)A and 70 eV, respectively, and the MS was automatically calibrated after every run. The mass range was set from 50 to 600 amu, with an acquisition rate of 5 spectra s\({}^{-1}\), and a solvent delay of 3.5 min.  
Data were analyzed using Agilent technologies MassHunter qualitative version B.07.00 and quantitative software version B.08.00 (Agilent Technologies, Santa Clara, CA, USA). Samples were normalized to dry weight and the internal standard, Myo-inositol. Concentrations were calculated from the standard mix calibration curve.
## 2 Routing Protocols Overview
### EIGRP Features and Characteristics  
EIGRP (Enhanced Interior Gateway Routing Protocol) significantly enhances the efficiency and flexibility of network operations. Developed by Cisco, EIGRP is a highly effective and proprietary interior gateway protocol that combines elements of both link-state and distance vector protocols. One of its key features is the Diffusing Update Algorithm (DUAL), which enables rapid convergence and efficient use of network resources. EIGRP excels in load-sharing scenarios due to its ability to dynamically distribute traffic across multiple paths, optimizing network efficiency and ensuring a more balanced workload distribution. Additionally, EIGRP's adaptability in quickly responding to changes in network topology makes it well-suited for environments where link failure is a critical concern. In the context of load-sharing and link failover, EIGRP plays a pivotal role by continuously monitoring the network for changes and selecting the best routes based on real-time metrics. When evaluated alongside OSPF and BGP in an IPv6 environment, EIGRP's performance analysis provides a comprehensive understanding of its contribution to enhancing network resilience and efficiency.
# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 5 Results and Analysis
### Apply LSTM on X Dataset  
In addition to the primary testing conducted on the two Instagram datasets, this study incorporated the X database as an additional evaluation step to further validate the performance of the proposed model. While the Instagram database served as the main testbed due to its relevance to the research objectives, the inclusion of the X database provided an opportunity to assess the generalizability and robustness of the model across different social media platforms. This multi-platform evaluation approach enhances the reliability and comprehensiveness of the findings by contributing to knowledge advancement in social media analytics.  
The framework effectively detects X bot accounts by utilizing LSTM cells. The optimal selection of the optimizer significantly enhances the results. In this experiment, the RMSProp optimizer demonstrated the best performance. Table 14 shows the investigation in dataset 3. The study analyzed the impact of increasing iterations on key performance metrics, including accuracy, recall, loss rate, precision, and F-measure.  
The increase in iterations has a marginal impact on accuracy recall, loss rate, precision, and F-measure. The results showed a significant relationship between the loss rate and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Approach** & **Accuracy** & **Dataset Source** \\ \hline
[45] & Neural Network & A = 91\% & 576 Instagram accounts: 288 were fake \\  & RF, ANN, LR, NB, & & and 288 were genuine. \\
[34] & and J48 Decision Tree & & 32,869 fake users and 32,460 authentic \\
[6] & NB, LR, ANN, and SVM & A = 91.76\% & users, totaling 65,329 users on \\  & J48 Decision Tree & & 1 nstagram. \\
[6] & NB, LR, ANN, and SVM & A = 86\% & 1203 Instagram accounts, 201 were fake, and 1002 were genuine. \\  & SVM & & & Two datasets \\ Proposed & LSTM & A = 97.42\% & Dataset 1: 1203 Instagram accounts, 201 were fake and 1002 were genuine. \\  & & & & Dataset 2: 696 Instagram users, 348 were \\  & & & & real users and 348 were fake users. \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of approaches for fake account detection on Instagram.  
number of iterations, as seen in Figure 8, showing that the framework is learned with each iteration. Furthermore, there is a positive relationship between time and the number of iterations; as the number of iterations increases, so does the duration.
## 3 Results and Discussion
### Phase 5: Attribute Selection Processes  
Histograms show that the distribution of some Size Information Descriptors is the same as others, sometimes being inverted independent of NLT, meaning that there is no new information revealed. As a result, only one of these variables is selected, for instance: (1) AtoP and PtoA have equal but inverted distributions; (2) PtoAnor, Complex, Compact, and Shape, also have equal distribution but Compact is inverted from the three other. Of these variables, we only keep PtoA and Compact, as Area and Perimeter appear in opposition in their formula: PtoA has area in the denominator, as opposed to Compact, which has area in the numerator; the contrary holds true for the perimeter (see Phase 3: Section 2.3.2).
## 1 Introduction  
The Easternmost Mediterranean (EMM) is a region belonging to the transition zone of the most prominent tectonic structures of the Earth--Eurasia and Gondwana [1, 2, 3, 4]. In the Cenozoic, four lithospheric plates were formed here: Nubian, Arabian, Aegean-Anatolian, and Sinai [5]. The area is characterized by unique geodynamics, which simultaneously expressed the elements of the geodynamic collision associated with the Tethys Ocean evolution [6, 7, 8], and the Red Sea rift system's initial spreading [9].  
This tectonic-geodynamic circumstance determines the severe complexity and diversification of the developed tectonic elements and the various rock complexes in the Earth's crust. They form a very bizarre knot that combines heterogeneous blocks of the continental and oceanic crust, terrane complexes, ophiolite belts, traps, folded island arc, and the intricate zones of the tectonic strike-slip, thrusts, and underthrust faults [10; 11; 12; 13; 14].  
The extensive development of the magmatic associations of different ages and origins triggered the wide employment of the magnetic method in the studied region, e.g., [15; 16; 17; 18; 19; 20]. However, paleomagnetic methods are necessary considering the presence of practically all possible positions of the magnetization vectors. Nevertheless, the significance of paleomagnetic methods goes beyond just clarifying the magnetic anomalies. Paleomagnetic mapping has methodologically successfully and widely recommended itself for decoding the structure and origin of intricate objects from the subsurface up to the Curie discontinuity in the continents and marine areas [21].  
Some scientists initially attempted to apply the single paleomagnetic method in the transition zones from ocean to continent but without tangible success. The accomplished experience of our studies, e.g., [14; 16; 17; 20; 22; 23], indicates that the combined paleomagnetic investigation is highly effective for analyzing the complex structures of the EMM (sometimes more than all other geological-geophysical methods).  
The main objectives of this paper are the following:  
1. The application of effective methodologies for magnetic anomaly quantitative analysis under complex EMM's physical geological environments,
2. Demonstrating the possibilities of 3D combined gravity-magnetic modeling,
3. Description of the methodologies of paleomagnetic-radiometric mapping combined with tectonic-structural analysis and data of the deep geophysics and geodynamics.
# Optimization of Water Pressure of a Distribution Network within the Water-Energy Nexus
## 4 Results and Discussion
### Hourly-Based Pressure Optimization  
Because of the hourly water consumption fluctuations, an hourly time step analysis should be conducted to obtain more precise results. The pressure of the WDN changes by the alterations in the nodes' consumption, and, therefore, investigating the optimum value of pressure in each hour based on the method developed in this study is necessary.  
To apply pressure management on an hourly basis, the electronic control method, particularly the remote node-based modulation mode, is used. To use the remote node-based modulation, a node is required to be selected as the control node. In the present research, the critical node of the network with the minimum pressure was considered to be the control node. The control node sends the adjustment settings required for supplying the necessary minimum pressure in this node to the PRV. The reason for utilizing the remote node-based modulation was the topography of the case study. Because of the structure of the studied WDN, by supplying minimum pressure for the critical node, the minimum required pressure for all other nodes will be satisfied.  
In the hourly mode of the network analysis, the optimization model was used for acquiring the optimum point in the interval of each hour. Figure 10 provides the results of this investigation. According to this figure, the total cost is sensitive to the pressure of the network during the peak hours of water consumption. Moreover, during such peak periods, it is crucial to increase the pressure of the water network to reduce the energy consumed by WPBSs. Decreasing the pressure during non-peak hours reduces the total cost of the system. While this increases the system's energy cost, the reduction in costs related to leakage is more than the extra energy cost. Finally, the impact of change in energy price at 8, 16, and 23 o'clock on optimum water pressure can be observed in Figure 10. The peak and non-peak hours of water and energy consumption cause dramatic increases and decreases in the total cost during specific hours of the day.  
Changing the pressure of WDNs on an hourly basis would significantly reduce the total daily cost. The results of the present study show that choosing the optimized pressure for each hour would result in a total daily cost of USD 598, which corresponds to a cost reduction of approximately 41%.
## 2 Materials and Methods
### Isolation of HFMSCs  
The protocols for human cell investigations were reviewed and approved by the Taipei Medical University-Joint Institutional Review Board under a project identification code of TMU-JIRB N202012035. To acquire HFMSCs, donor hair follicles were uniformly obtained from adult occipital scalps by an ARTAS(r) robotic system (San Jose, CA, USA). Isolation techniques were designed according to previously established protocol with some modifications. Firstly, the biopsy sample of hair follicles was thoroughly rinsed with phosphate-buffered saline (PBS; Sigma, Taipei, Taiwan) containing penicillin/streptomycin(P/S), then the excess adipose tissues were removed. After that, the samples were treated with trypsin/EDTA (Sigma, Taipei, Taiwan) at 37 \({}^{\circ}\)C for 30 min and then digested with dispase at 4 \({}^{\circ}\)C for over 16 h. After the digestion procedure, the hair and scalp portions were separated from the hair follicles using fine forceps, then filtered through a 70 \(\upmu\)m cell strainer, and washed extensively with PBS. Prior to the proliferation assay, the cells were cultured in a stromal medium (Dulbecco's modified Eagle's medium/F12 (DMEM; Gibco, Taipei, Taiwan) and 10% fetal bovine serum (FBS; Gibco, Taipei, Taiwan) for 7 days. After 7 days, cells from individual wells were harvested by 0.25% trypsin in 0.01% EDTA following centrifugation at 800 rpm for 5 min. The centrifuged liquid was filtered through a 100 \(\upmu\)m cell strainer. Then, the hair and the scalp were separately transferred into a sterilized 6-well plate and maintained in MSCs culture medium (100 \(\upmu\)L DMEM, 10% FBS, 1% P/S, 2.45 g/L sodium bicarbonate, and 10 ng/mL human epidermal growth factor). The cells were maintained in a culture dish for 2 weeks and the medium was replaced every 3 days.
## 3 Experimental Investigation
### Experimental Verification of the Design Scheme  
The mode shapes and dominant modal frequencies for the selected structural scheme (see Figure 1) depend only on the rod and rotating masses inertia forces, rod flexural rigidity, and natural damping. Free vibrations should be harmonic, and their spectrum has  
Figure 10: Dependence of the displacement X on the load F at the end of the rod for the rigid–rigid connection and rod length of 610 mm (\(L/R=2\)).  
one dominant frequency. Additionally, the values of dominant vibration frequencies for the selected end connection schemes should correspond to the calculated values (Equation (1)).  
To verify the above-mentioned requirements, free vibration tests were carried out for rigid-rigid connection schemes, and further spectral analysis of the measured data was performed. The \(L/R\) ratio was 2, and \(N\) = 0. The ratio \(\delta\) between the equivalent mass of the wheel and the rod was 20.5. It allowed decreasing the error due to approximation in calculating the coefficient \(\alpha\) (Equation (2)).  
Figure 11 presents the vibration time histories of the structure and the corresponding spectra (obtained using FFT for 100 vibration periods). The calculated value (according to Equation (1)) was 0.62 Hz. Differences between experimental and calculated values were up to 3%. The measured damping ratio was 3%.  
Thus, the experiments confirmed the selected design scheme and the validity of the assumptions that were used for the calculation of modal frequencies.
# A Review of Image Inpainting Methods Based on Deep Learning
## 1 Introduction
Additionally, the incorporation of convolution operations [24] and attention mechanisms [25] in these deep learning models enables them to harness more of the known image information, capturing both low-level textures and high-level semantics, enhancing the prediction for the damaged area. Currently, deep-learning-based image inpainting techniques are applied to industrial visual image processing [26; 27; 28; 29; 30; 31], object removal [32; 33; 34], cultural relic inpainting [26; 35; 36; 37; 38; 39; 40; 41; 42], face repair [6; 7; 8; 9], artistic creation, and special effects production for games and movies. They have a significant impact on the fields of visual image processing, image editing, virtual reality technology, and the maintenance of historical and cultural heritage.