esa-sceva/satcom-chunk-collection huggingface dataset:
# SimCDL: A Simple Framework for Contrastive Dictionary Learning
## 4 Algorithms
### Incoherent Dictionary Learning (IDL)  
The Incoherent Dictionary Learning (IDL) problem aims to learn class-specific dictionaries \(\mathbf{D}_{i}\) from a set of feature vectors \(\mathbf{Y}=[\mathbf{Y}_{1},\ldots,\mathbf{Y}_{C}]\), where \(\mathbf{Y}_{c}\in\mathbb{R}^{m\times N_{c}}\) represents the samples from class \(c\) and \(C\) is the total number of classes. Each dictionary \(\mathbf{D}_{c}\) is optimized to accurately represent its corresponding class while being poorly aligned with the representations of other classes. The optimization problem is formulated as follows:  
\[\begin{array}{ll}\min_{\mathbf{D}_{i}\mathbf{X}_{i}}&\sum_{i=1}^{C}\|\mathbf{Y}_{i}-\bm {D}_{i}\mathbf{X}_{i}\|_{F}^{2}+\gamma\sum_{i=1}^{C}\sum_{j\
eq i}\bigl{\|}\mathbf{D}_ {i}^{\top}\mathbf{D}_{j}\bigr{\|}_{F}^{2}\\ \text{s.t.}&\left\|\mathbf{x}_{i,\ell}\right\|_{0}\leq s,\ell=1:N\\ &\left\|\mathbf{d}_{i,j}\right\|=1,j=1:n\end{array} \tag{12}\]  
Figure 2: DL with cutout; the grayed-out cells indicate features that are ignored during the atoms’ update, while the atoms’ normalization is made using all the features.  
where \(\mathbf{x}_{i,\ell}\) is the row vector on which the sparsity constraint is imposed, \(\mathbf{d}_{i,j}\) denotes the \(j\)th column of matrix \(D_{i}\) and \(\gamma>0\) is a trade-off factor that balances reconstruction accuracy with inter-class incoherence. After learning the dictionaries, a test signal \(\mathbf{y}\in\mathbb{R}^{m}\) is classified by finding the dictionary with the smallest reconstruction error:  
\[c=\underset{i=1:C}{\text{argmin}}\ \|\mathbf{y}-\mathbf{D}_{i}\mathbf{x}_{i}\|,\text{ subject to }\|\mathbf{x}_{i}\|_{0}\leq s, \tag{13}\]  
where \(s\) is the sparsity constraint.  
This formulation, as introduced in [16], projects the dictionaries into quasi-orthogonal spaces, thereby enhancing class discrimination while preserving their representational capabilities.  
The problem in (12) can be solved using an approach similar to the AK-SVD method. During the sparse representation phase, the Orthogonal Matching Pursuit (OMP) is employed as the incoherence terms are independent of the coefficients \(\mathbf{X}\). In the dictionary update phase, each atom in \(\mathbf{D}_{i}\) is sequentially adjusted to improve the representation of its class while maintaining near orthogonality to atoms of other classes. The focus is on atoms within a certain proximity, ensuring relevant incoherence for classification.
# Enhancing Smart Cities' Resilience Through Competency Assessment and Open Data Utilization
## 10 Evaluation
### Evaluation Cycle 2: Usability and Usefulness
Many reported significant improvements in knowledge and skills, particularly appreciating the practical assignments and detailed feedback from instructors. The assessment design, which integrated multiple-choice tests and project-based tasks, effectively supported learning objectives and contributed to a certification completion rate of 73.24%, significantly exceeding average MOOC completion rates reported in the academic literature. Among the 20 competencies assessed, participants demonstrated particularly strong proficiency in general crisis management (competency 09) and the establishment of smart city security and safety measures (competency 10). However, lower average ratings were observed in areas requiring advanced technological expertise, such as geoservices and digital twins (competency 16) and the development of financial programs for resilience building and disaster recovery (competency 19). These results indicate that while respondents are well-versed in established resilience protocols, there is a need for targeted training in advanced digital solutions and specialized financial management strategies. Further qualitative feedback reinforced this gap, with many participants expressing uncertainty about navigating digital twin platforms or designing structured financial programs to support disaster recovery. These findings highlight the critical need for ongoing, specialized training initiatives that address both the operational and strategic dimensions of smart city resilience. These insights have been instrumental in improving the tool's functionality and usability, ensuring it can effectively support resilience assessment and competency development across the smart city and municipal sectors. By refining these areas, the tool can better meet the needs of professionals dedicated to enhancing urban resilience. While the program achieved its goals, areas for improvement remain. Specifically, restructuring the course to distribute workloads more evenly and implementing strategies to maintain learner engagement in later phases could enhance outcomes. Nonetheless, the pilot demonstrated the efficacy of MOCCs in providing accessible, professional-level training and equipping participants with critical skills for advancing resilience in smart cities. These findings underscore the potential of online learning platforms to democratize education and foster skill development in emerging fields.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 2 Related Works
### Attention Mechanism  
The attention mechanism was proposed in deep learning-based natural language processing (NLP) [19]. Generally, attention mechanism generates importance masks for the input, so as to guide the network to allocate more processing resources towards the most informative part. In recent years, it has also shown the potential to become a dominant tool in computer vision. Non-local attention in deep Convolutional Neural Networks (CNNs) allows the network to concentrate more on noticeable areas. [PERSON] et al. [20] took the lead in introducing self-attention to computer vision and presented a novel non-local network with great success in video understanding and object detection. In image compression, [PERSON] et al. [21] used discretized Gaussian mixture likelihoods along with attention models to reduce the spatial redundancy after quantization operations. [PERSON] and [PERSON] [22] proposed a post-processing-based neural network containing spatial and channel attention modules connected in parallel. However, the application of the attention mechanism in point cloud attribute compression has not been explored. Our proposed model first adopts the attention mechanism in neural network for voxelized point cloud compression. The attention mechanism modules are placed at the shallow layer and bottleneck layer to implicitly and adaptively allocate more bits to more representative latent features for compression.
## 1 Introduction
Outlier detection can be applied to identify and remove outliers; thus, improving the performance of classification models [50; 51]. One of the techniques used for outlier detection is Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [52]. DBSCAN has been implemented in different fields and has been demonstrated to be effective at detecting true outliers [53; 54]. The integration of DBSCAN-based outlier detection and Random Forest is necessary for more accurate detection of abnormal events during the manufacturing process. The results of aforementioned studies have shown significant advantages of IoT-based sensor, big data technology, and machine learning models on improving decision-making for management. Nevertheless, there is no study on integration of the IoT-based sensor, big data technology, and machine learning models into a complete monitoring system specifically for automotive manufacturing. Thus, we propose a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model for the automotive industry. The proposed IoT-based sensor collects temperature, humidity, accelerometer, and gyroscope data from the assembly line process while the big data processing platform handles and stores the large amounts of generated sensor data. Finally, the proposed hybrid prediction model, which consists of DBSCAN-based outlier detection and Random Forest classification, is used to remove outlier data and provide fault detection during the manufacturing process, respectively. The remainder of this paper is organized as follows. Section 2 presents a literature review. In Section 3, the methodology is explained, while in Section 4, the results and discussions are presented. In Section 5, concluding remarks are presented and several limitations and remaining challenges are discussed.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
###### Abstract  
In recent years, with the widespread use of non-linear loads power electronic devices associated with the penetration of various renewable energy sources, the distribution system is highly affected by harmonic distortion caused by these sources. Moreover, the inverter-based distributed generation units (DGs) (e.g., photovoltaic (PV) and wind turbine) that are integrated into the distribution systems, are considered as significant harmonic sources of severe harmful effects on the system power quality. To solve these issues, this paper proposes a harmonic mitigation method for improving the power quality problems in distribution systems. Specifically, the proposed optimal planning of the single tuned harmonic filters (STFs) in the presence of inverter-based DGs is developed by the recent Water Cycle Algorithm (WCA). The objectives of this planning problem aim to minimize the total harmonic distortion (THD), power loss, filter investment cost, and improvement of voltage profile considering different constraints to meet the IEEE S19 standard. Further, the impact of the inverter-based DGs on the system harmonics is studied. Two cases are considered to find the effect of the DGs harmonic spectrum on the system distortion and filter planning. The proposed method is tested on the IEEE 69-bus distribution system. The effectiveness of the proposed planning model is demonstrated where significant reductions in the harmonic distortion are accomplished.  
s +
Footnote †: journal: Applied Sciences
## 2 Research Methods  
This study primarily employs the experimental technique of argon ion polishing combined with field emission scanning electron microscopy (FESEM) to observe and identify organic matter. Prior to SEM observation, irregular shale samples are prepared into a regular shape (1 cm \(\times\) 1 cm \(\times\) 1 cm) suitable for analysis. The observation surface, perpendicular to the shale layers, is polished using argon ion beams. Apreo LoVac 30 kV double-probe low-vacuum field emission scanning electron microscope (FESEM) was used for imaging, with an A+B composition mode in backscattered electron (BSE) mode. High-resolution FESEM is characterized by its high resolution, large depth of field, stable signal, simple sample preparation, wide field of view, and clear imaging [22; 48]. In organic-rich shales, mineral particles are typically small, and nanometer-scale pores are widely developed [49]. FESEM allows for the clear and direct observation of morphological features and pore development in organic matter. However, the rough surface of fresh natural fractures in shale samples can obscure the observation of primary pores. Therefore, argon ion polishing is commonly applied to shale samples to smooth and flatten the surface.  
\begin{table}
\begin{tabular}{c c c} \hline
**Criteria** & **Classification of Pores** & **Characteristics** \\ \hline \multirow{3}{*}{Pore size [35]} & Macropores & Pore diameter \(>\) 50 nm \\  & Mesropores & Pore diameter from 2 to 50 nm \\  & Micropores & Pore diameter \(<\) 2 nm \\ \hline \multirow{3}{*}{Developmental morphology [42]} & \multirow{3}{*}{Zonal pores} & The morphology is variable, constrained by the shape of organic matter particles, and is formed during hydrocarbons generation from the degradation of \\  & & organic matter. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{3}{*}{Deepe of opening and Connectivity [7]} & Open pores & Connected to the external environment \\ \cline{1-1}  & & \\ \cline{1-1}  & & Isolated from external connections \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._High-speed ion beams bombard the sample surface, reducing roughness and improving the quality of the SEM images [50,51]. Compared to mechanical polishing, argon ion polishing causes less sample damage and enhances imaging, making it an indispensable tool for characterizing shale reservoirs [52, 53, 54, 55]. In addition, focused ion beam scanning electron microscopy (FIB-SEM) was employed to analyze pore distribution in Tuscaloosa Marine shale and reconstruct pore connectivity in three dimensions. This method is crucial for studying pore networks and fluid flow within reservoirs.
## 2 Materials and Data
### Data Preprocessing  
Based on the spatial resolution of winter wheat planting area in Hengshui City being 250 m, in order to maintain the consistent spatiotemporal resolution of all data, we re-sampled satellite vegetation index, GPP, and meteorological data to 250 m and one-month intervals. Satellite and meteorological data were masked using the distribution of winter wheat cultivation from 2005 to 2019, and all variables of the county-wide monthly average  
Figure 1: Location of the study area.  
were summarized. All data are pre-processed on the Google Earth Engine (GEE) platform, a free geoprocessing service launched by Google, which provides a large number of geoprocessing algorithms and massive image datasets based on the Google Cloud Platform, and the summary of data is performed on Python and Excel.
## 4 Results and Discussion
### Results
#### 4.2.3 Wage-Inducing Effects
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sectors** & \multicolumn{3}{c}{**South Korea**} & \multicolumn{1}{c}{**Japan**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Value**} & **Rank** & **Value** & **Rank** \\ \hline
1. & Agricultural, forest, and fishery goods & 0.00021 & 22 & 0.00017 & 27 \\
2. & Mined and quarried goods & **0.00798** & 2 & 0.00071 & 8 \\
3. & Food, beverages, and tobacco products & 0.00023 & 20 & 0.00066 & 30 \\
4. & Tetifile and leather products & 0.00009 & 29 & 0.00084 & 18 \\
5. & Wood, paper products, printing, and reproduction of recorded media & 0.00020 & 23 & 0.00412 & 13 \\
6. & Petroleum and coal products & 0.00051 & 16 & 0.00126 & 7 \\
7. & Chemical products & 0.00068 & 12 & 0.00293 & 14 \\
8. & Non-metallic mineral products & 0.00010 & 28 & 0.00083 & 19 \\
9. & Basic metal products & 0.00032 & 18 & 0.00124 & 17 \\
10. & Fabricated metal products, except machinery and furniture & 0.00075 & 11 & 0.00211 & 15 \\
11. & Computing machinery, electronic equipment, and optical instruments & 0.00058 & 15 & 0.00023 & 24 \\
12. & Electrical equipment & 0.00033 & 17 & 0.00018 & 26 \\
13. & Machinery and equipment & 0.00061 & 14 & 0.00054 & 23 \\
14. & Transport equipment, manufacturing services, and repair services of industrial equipment & 0.00113 & 8 & 0.00075 & 20 \\
15. & Other manufactured products & 0.00005 & 30 & 0.00021 & 25 \\
16. & Electricity and steam supply & 0.00064 & 13 & 0.00737 & 10 \\
17. & Water supply, sewage, waste treatment, and disposal services & 0.00016 & 25 & 0.0030 & 12 \\
18. & Construction & 0.00013 & 27 & 0.01236 & 4 \\
19. & Wholesale, retail trade, and commodity brokerage services & 0.00123 & 7 & **0.02358** & 3 \\
20. & Transportation & 0.00159 & 6 & **0.03932** & 2 \\
21. & Food services and accommodation & 0.00080 & 10 & 0.00000 & 31 \\
22. & Communications and broadcasting & 0.00079 & 9 & 0.01137 & 5 \\
23. & Finance and insurance & **0.00825** & 1 & 0.00086 & 9 \\
24. & Real estate services & 0.00190 & 5 & 0.01114 & 6 \\
25. & Professional, scientific, and technical services & **0.00207** & 4 & 0.00000 & 32 \\
26. & Business support services & **0.00429** & **3** & **0.0183** & 1 \\
27. & Public administration, defense, and social security services & 0.00023 & 21 & 0.00061 & 21 \\
28. & Education services & 0.00004 & 31 & 0.00057 & 22 \\
29. & Health and social care services & 0.00014 & 26 & 0.00008 & 29 \\
30. & Art, sports, and leisure services & 0.00018 & 24 & 0.00015 & 28 \\
31. & Other services & 0.00031 & 19 & 0.00734 & 11 \\
32. & Others & 0.00000 & 32 & 0.00144 & 16 \\ Sum (A) & 0.03668 & 0.0368 & 0.03648 & 9 \\  & 0.16578 & 0.31443 & & \\ Total (A+B) & 0.020246 & 0.51990 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value-added creation effects of natural gas supply sector in South Korea and Japan.
## 4 Evaluation Metrics  
### Peak Signal-to-Noise Ratio  
Peak signal-to-noise ratio (_PSNR_) assesses image character ground on the errors of matching pixels.  
\[\text{MSE}= \frac{1}{\text{H}\times W}\sum_{i=1}^{\text{H}}\sum_{j=1}^{W}\left(X(i, j)-Y(i,j)\right)^{2} \tag{18}\]  
_MSE_ denotes the mean square error of the images \(X\) and \(Y\), where \(X\) and \(Y\) represent the noisy and denoised images, respectively, while \(H\) and \(W\) are the image dimensions. The _PSNR_ ratio is denoted by:  
\[PSNR=20\log_{10}(\frac{255}{\sqrt{MSE(X,Y)}}) \tag{19}\]
###### Abstract  
Neck and lower back pain, often caused by spinal disorders such as scoliosis and degenerative disc disease, affects over 80% of the global population, with an estimated from 250,000 to 500,000 spinal cord injuries occurring annually according to the WHO. As the demand for spinal procedures continues to rise, advancements in implant materials have become essential. Orthopedic implants play a vital role in restoring mobility and improving the quality of life of patients with musculoskeletal disorders. Metallic implants, such as stainless steel, titanium, and its alloys, are commonly used to make fixation devices for spinal fusion surgery due to their excellent mechanical properties. However, complications such as stress shielding have been recorded. Polymeric materials offer new prospects as an alternative to metal-based materials such as those based on Polyaryletherketone (PEAK). Among the advanced materials used in these implants, PAEK has emerged as the preferred choice due to its exceptional mechanical strength, thermal stability, and chemical resistance. Polyetheretherketone (PEEK) and Polyetherketoneketone (PEKK) offer notable advantages, such as radiolucency and mechanical properties resembling those of natural bone, reducing stress shielding and facilitating postoperative imaging. Although PEEK and PEKK are considered as bioinert, it has been demonstrated that adding bioactive agents such as hydroxyapatite (HA) into the matrix to make composites solves this problem and can help with aiding direct bone opposition. Furthermore, PAEK's compatibility with 3 DP enables the creation of patient-specific implants with intricate geometries, enhancing the surgical outcomes. In addition, the lattice structures of orthopedic implants can alleviate stress shielding, provide an enhanced surface area for the release of bioactive agents (or antimicrobial materials), and eliminate more imaging artifacts compared to that of simple, solid metal implants. PAEK/HA composite implants represent a transformative solution, addressing the psychological, social, and economic burdens of spinal disorders, while enhancing the surgical outcomes. With continuous technological evolution, PAEK/HA composites are poised to play a pivotal role in modern spinal care.  
polyaryletherketone; PEEK; PEKK; 3D printing; spinal implants; lattice structures; advanced manufacturing; bone
## 3 Results and Discussion
### Master Curve  
Figure 13 shows master curves for the control, PG 70-28, 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS binders at a reference temperature of 25 \({}^{\circ}\)C. The data for both axes were plotted on logarithmic scales of dynamic modulus versus reduced frequency. As shown in Figure 13, the master curves of the 5%PCM, 5%GPCM, and 7%GPCM binders had a similar trend to that of the control binder. At intermediate frequencies, a slight increase in the stiffness of the 5%PCM binder was observed compared to the control binder, yet there was a slight decrease in the stiffness of the 7%GPCM binder. On the contrary, the dynamic modulus of the PG70-28 binder increased at intermediate frequencies due to the presence of SBS in the asphalt binder matrix. The SBS polymers are usually added to the asphalt binder to mitigate high temperatures and traffic-related issues, since the SBS polymers contribute to increasing the stiffness.  
As shown in Figure 13, the dynamic modulus of the 5% and 7%GPCM-SBS binders decreased as GPCM contents increased. The reduction in the dynamic modulus was more important at intermediate frequencies due to the softening effect of the GPCM. As a result, the intermediate stiffness (stiffness at intermediate temperature) was reduced compared to that of the PG70-28 binder. Moreover, the reduction in the intermediate stiffness can negatively affect the fatigue cracking resistance of binders since the initiation of the fatigue cracking is usually associated with the intermediate stiffness. Moreover, the 5% and 7%GPCM-SBS binders showed a similar evolutionary trend to the control binder throughout the test.  
Figure 13: Master curves for all binders.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 5 Broadcasting in Path-Connected Cliques
### Broadcasting in Path-Connected Cliques from Path Vertices
Originator \(u_{i}\) informs vertex \(u_{i+1}\) at time 1. 2. Vertices \(u_{i}\) and \(u_{i+1}\) broadcast separately within \(G_{k}\setminus H_{r}\) and \(H_{r}\), respectively. Since \(b(u_{i-1},H_{l})\geq b(u_{i+1},H_{r})\), we have \(b(u_{i},G_{k}\setminus H_{r})\geq b(u_{i+1},H_{r})\). Thus, \(b(u_{i},G_{k})\leq 1+b(u_{i},G_{k}\setminus H_{r})\)  
Consider the following broadcast scheme from originator \(u_{k}\), which begins by informing path vertices \(u_{k-1},u_{k-2},\ldots,u_{i}\) before informing any clique vertices. Once vertex \(u_{i}\) is informed it begins informing the subgraph \(G_{k}\setminus H_{r}\). Then, \(b(u_{k},G_{k})\geq k-i+b(u_{i},G_{k}\setminus H_{r})\) since \(b(u_{k},G_{k})\geq b(u_{k},G_{k}\setminus H_{r})\geq\text{ minimum time required to inform }u_{i}\) from \(u_{k}+b(u_{i},G_{k}\setminus H_{r})\). By assumption, \(k-i\geq 1\). Hence,  
\[b(u_{k},G_{k}) \geq k-i+b(u_{i},G_{k}\setminus H_{r})\] \[\geq 1+b(u_{i},G_{k}\setminus H_{r})\] \[\geq b(u_{i},G_{k})\]  
The same proof applies when \(b(u_{i-1},H_{l})<b(u_{i+1},H_{r})\). According to Lemma 11, path-end vertices exhibit a larger broadcast time compared to other path vertices in any path-connected cliques graph. This behavior is illustrated in Figure 3, where the broadcast times of vertices \(u_{1}\) and \(u_{4}\) exceed those of the intermediate path vertices \(u_{2}\) and \(u_{3}\).