esa-sceva/satcom-chunk-collection huggingface dataset:
Such a method provides an efficient reconstruction of the clusters, although the classical formulation of the cellular automaton requires several iterative processes along the readout cell values that are programmed as loops in the algorithm's code. As this causes a strong dependency of the algorithm's complexity on the number of clusters in the data, other approaches have attempted to avoid this dependency by exploiting the architecture similarities between cellular automata and neural networks [9; 10]. However, these approaches focus on a proof of concept rather than providing a specific reconstruction solution for the LHCb calorimeter. Within recent years, the evolution of deep learning models has encouraged the use of image processing techniques for this challenge. An early stage project has shown promising results when approaching the LHCb calorimeter reconstruction with a deep neural network structure [11] based on the YOLOv3 network [12]. This particular case uses the order of 100,000 simulation samples to train a network of the order of 65 million parameters. In this article, we propose a different formulation of the LHCb calorimeter reconstruction problem to improve the generalized learning of small deep learning architectures [13]. Those networks have been trained using artificially generated sets of data and preprocessed pieces of simulated LHCb data. With this methodology, we aim to have an efficient cluster reconstruction algorithm that performs at a nearly constant speed with independence of the event complexity.
## 5 Experimental Scenarios and Results
### ADR-Det Stage 2 Evaluation Using Selected Datasets
#### 5.3.2 ADR-Det Stage 2 Detection Evaluation Using a Dataset of 250 Non ADR Images  
In this experimental case, 250 photos were selected, all of which depicted a large vehicle without any ADR license plates. False-positive results showed ADR 20 out of 250 vehicles (8.00%). Correct MH-ADR detection results showed MH-ADR 230 out of 250 vehicles (92.00%). The average percentage of certainty was \(88.53\%\pm 16.48\%\), with a minimum percentage of certainty of 51.82% and a maximum of 100.00% (3 out of 20 false positives). The following Figure 7 includes a false-positive ADR result.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Toll Station**} & **Number Vehicles** & **Number of Photos** & **Number of Trucks** \\  & **from Stage 1** & **Detected at Stage 2 as** & **with ADR Signs** \\  & **Output** & **Trucks (\% Percentage)** & **Detected at Stage 2** \\  & & & **(\% Percentage)** \\ \hline MAL-1H & 3302 & 400 (12.1%) & 111 (3.36\%) \\ MAL-1K & 2986 & 193 ( 6.5\%) & 29 (0.97\%) \\ \hline Total & 6288 & 593 ( 9.4\%) & 140 (2.23\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the ADR-Det stage 1 and stage 2 parts testing, during March 2021, using two toll posts input data.
## 4 Research Opportunities in O-RAN  
Applying ML in O-RAN networks has the potential to significantly improve network performance, automate complex network operations, and enable new use cases and business models. ML algorithms can analyze large volumes of network data, enabling intelligent decision-making in real-time and optimizing network resources for enhanced user experiences. In the previous section, we provided overview of the state-of-the-art research works that demonstrate the use of ML to improve O-RAN and the underlying network elements. However, challenges remain to apply and integrate ML into O-RAN for network automation. These challenges include the need for high-quality and diverse training datasets, ensuring robustness and fairness of ML models, addressing privacy concerns, and developing efficient computational frameworks capable of handling the scale and complexity of O-RAN deployments. In this section, we shall describe the potential of ML applications in O-RAN and their challenges. Table 13 shows a summary of identified research opportunities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2021 & [55] & Investigated how O-RAN can be used for 5G/XG to mitigate interference between terrestrial and space communication systems. \\ \hline \hline \end{tabular}
\end{table}
Table 12: Existing study of ML applications for Satellite NTN relevant to O-RAN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2022 & [54] & Proposed online adversarial bandit learning for energy-aware scheduling policy optimization to maximize the performance of virtualized Base Stations (vBS) in O-RAN. \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary of ML studies for energy efficiency in O-RAN.
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation  
Sand and dust storms create hazards for human society in numerous ways [1; 3] and these hazards are classified in Table 1 according to the three processes of the wind erosion system: on entrainment of fine particles, during their transport, and on their deposition. SDS hazards range from local sand-blasting of agricultural crops to human health issues associated with dust haze. Indeed, SDS are a unique form of natural hazard in that the source and impact regions can be separated by great distances.  
Research into these hazards varies in quality and quantity. Hazards associated with soil loss in agricultural areas are relatively well-understood and a range of mitigating soil management techniques is available. Health hazards associated with desert dust are generally well-researched but there are aspects that we do not fully understand, not least many of the causal associations between human exposure to desert dust particles and adverse health effects. The effects on various forms of transport (road, rail, air, and marine) are surprisingly little-studied and geographically, for all types of hazard, some areas of SDS activity have been subject to much greater research effort than others. Assessments of SDS impacts are also variable, and there have been relatively few attempts to assess their impacts in economic terms, although those assessments that have been made indicate that the annual cost of desert dust events in some parts of the world runs into the tens of millions if not billions of US dollars [62; 63; 64; 65].  
Measures to mitigate these hazards are logically classified into those designed to prevent wind erosion occurring at source and those designed to manage the adverse impacts of small particles in transport and on deposition (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline
**Entrainment** & **Transport** & **Deposition** \\ \hline Soil loss & Sand-blasting of crops & Salt deposition and groundwater salimization \\ Nutrient, seed and fertilizer loss & Radio communication problems & Reduction of reservoir storage capacity \\ Crop root exposure & Microwave attenuation & Drinking-water contamination \\ Undermining structures & Transport disruption & Burial of structures \\  & Local climatic effects & Crop growth problems \\  & Air pollution & Machinery problems \\  & Respiratory problems and eye infections & Reduction of solar power potential \\  & Disease transmission (human) & Electrical insulator failure \\  & Disease transmission (plants and animals) & Disruption to power supplies \\ \hline \end{tabular}
\end{table}
Table 1: Some environmental hazards to human populations caused by wind erosion and dust storms [3].
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
###### Abstract  
Affected by various complex factors, dam deformation monitoring data usually reflect volatility and non-linear characteristics, and traditional prediction models are difficult to accurately capture the complex laws of dam deformation. A multi-scale deformation prediction model based on Variational Modal Decomposition (VMD) signal decomposition technology is proposed in this study. The method first decomposes the original deformation sequence into a series of sub-sequences with different frequencies, then the decomposed sub-sequences are modeled and predicted by Long Short-Term Memory neural network (LSTM) and Random Forest (RF) according to different frequencies. Finally, the prediction results of all sub-sequences are reconstructed to obtain the final deformation prediction results. In this process, it is proposed to use the instantaneous frequency mean method to determine the decomposition modulus of VMD. The innovation of this paper is to decompose the monitoring data with high volatility, and use LSTM and RF prediction, respectively, according to the frequency of the monitoring data, so as to realize the more accurate capture of volatility data during the prediction process. The case analysis results show that the proposed model can effectively solve the negative impact of the original data volatility on the prediction results, and is superior to the traditional prediction models in terms of stability and generalization ability, which has an important reference value for accurately predicting dam deformation and has far-reaching engineering significance.  
dam deformation; multi-scale prediction; VMD; LSTM; random forest +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
In this paper, we have demonstrated simultaneous illumination and indoor visible light communication using a multi-colored lamp. To avoid the quenching effect, we have designed a multi-color LED lamp with a side-by-side arrangement of LEDs, leading to minimum quenching. Experimental results show that the average CRI, CCT, and CIE values are 90, 5114 K, and (0.33, 0.33), respectively, making it a good quality indoor lighting lamp and 16% better CRI than conventional LED.  
For VLC, we obtained jitter, eye diagram, SNR, PSD, and autocorrelation. The DL shows a fast rise and fall times, more bandwidth with a good Q factor, SNR, and bit error ratio below the forward error correction limit of the order of \(10^{-12}\). In summary, DL shows a faster communication response than conventional WL due to a 31% decrease in rise time and a 39% decrease in total jitter. Also, the autocorrelation function demonstrated a fast temporal pulse for DL. This paper finds future applications in mining optical bandwidth with spectral modulation schemes like multi-channel frequency coding and color shift keying.  
**Author Contributions:** Conceptualization, A.S. and D.S.M.; Methodology, A.S.; Software, A.S.; Validation, A.S. and R.M.; Formal analysis, A.S. and R.M.; Resources, L.P. and D.S.M.; Data curation, A.S.; Writing--original draft, A.S.; Writing--review & editing, S.K.D. and D.S.M.; Visualization, S.K.D. and D.S.M.; Supervision, S.K.D. and D.S.M.; Project administration, D.S.M.; Funding acquisition, D.S.M.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors thank DST-SERB for financial assistance for Project Number CRG/2022/003490.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is available in text only.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Citation Analysis
### Source-Based Citation Analysis  
This subsection examines the citation analysis of publishing sources on OT security research. The criteria for analyzing these sources included a minimum of three published articles and five citations per source. The VosViewer search identified 561 such sources in total. However, only 41 sources satisfied the specified criteria. The top ten most cited sources, which have the highest TLS values, are outlined in Table 9. This table also presents  
Figure 20: A visual representation of the citation network among documents within the domain of OT security.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Title of Article** & **Source** & **Authors** & **Year** & **Citation** & **Link** \\ \hline \multirow{4}{*}{8} & An Ensemble Deep Learning-Based & & [PERSON] & & & \\  & Cyber-Attack Detection in Industrial & _IEEE Access_ & & [PERSON], [PERSON] & 2020 & 126 & 18 \\  & Control System [32] & & [PERSON], & & & \\  & Industrial cyber-physical systems & & [PERSON] & & & \\
9 & protection: A methodological & _Computers \& Security_ & [PERSON], & 2023 & 10 & 17 \\  & review [5] & & [PERSON] & & & \\  & CNN-based method for the & & & & & \\
10 & development of cyber-attack & _Computers \& Security_ & [PERSON], & 2022 & 36 & 16 \\  & detection algorithms in industrial & & [PERSON] & & & & \\  & control systems [33] & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._details on the citations, the number of unique citing organizations (links), the number of publications (Np), and the total strength of all these links (TLS) for each source. IEEE Access' holds the top spot on this list, having published 47 articles specifically on OT security. These articles have accumulated 929 citations from other journals within the WoS database that also publish OT security research. 'Computers and Security' is in second place with almost twice the smaller number of publications, but the number of citations is less only by 1.4, which means that articles in this journal are cited more frequently compared to the leader by the number of publications. 'Electronics' and 'Sensors' keep the third and the fourth places with a relatively similar number of publications to 'Computer and Security' but a lower citation rate, closer to 'IEEE Access'. The number of publications cited in other journals is twice as few or more.  
Figure 21 depicts the citation networks related to OT security derived from sources published in WoS. The nodes and connections are represented by different colors according to their co-citation relationships. There are three dominating sub-nets: the journals published by the IEEE organization (_IEEE Access_, series of IEEE transactions, _IEEE IoT Journal_), journals by MDPI (_Sensors_, _Electronics_, and _Applied Sciences_), and sub-net in yellow for the _'Computers and Security'_ by Elsevier.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 2 Basic Theory  
### Finite Element Method  
This paper will cover one-dimensional (1D), two-dimensional (2D) and three-dimensional (3D) problems. For simplicity, this section considers the following 2D partial differential equation (PDE):  
\[\left\{\begin{array}{ll}Lu\equiv-\
abla^{2}u=f&\text{in}\quad\Omega\\ u=\overline{u}&\text{on}\quad\Gamma_{D}\\ \partial u/\partial n=\overline{u}_{n}&\text{on}\quad\Gamma_{N}\end{array}\right. \tag{1}\]where \(\Omega\) is a bounded 2D domain, \(\Gamma_{D}\) its Dirichlet boundary and \(\Gamma_{N}\) its Neumann boundary with \(n\) being its outer normal direction. \(\overline{u}\) and \(\overline{u}_{n}\) are known functions on the corresponding boundary.  
The weak form of problem (1) is to find \(u\) in \(H_{E}^{1}(\Omega)\) such that  
\[a(u,v)=(f,v)\ \ \ \ \ \forall v\in H_{E}^{1} \tag{2}\]  
where \(H_{E}^{1}(\Omega)\) is the Hilbert space on \(\Omega\) with its functions satisfying the essential boundary condition (BC) on \(\Gamma_{D}\) and  
\[a(u,v)=\iint_{\Omega}\left(u_{x}v_{x}+u_{y}v_{y}\right)\,\mathrm{d}A,\ \ \ \ (f,v)=\iint_{\Omega}f\,v\,\mathrm{d}A \tag{3}\]  
Here, and below, \(\left(\ \right)_{x}=\partial\left(\ \right)/\partial x\), \(\left(\ \right)_{y}=\partial\left(\ \right)/\partial y\). Let \(u^{h}\) denote the FE approximation to the exact solution \(u\). Quadrilateral linear elements are adopted in this paper, and the trial function \(u^{h}\) on each element is modeled by  
\[u^{h}=\sum_{i=1}^{2}\sum_{j=1}^{2}N_{i}(\xi)N_{j}(\eta)d_{ij} \tag{4}\]  
where \(N_{i}\) and \(N_{j}\) are usual linear shape functions in local coordinates \(\xi,\ \eta\in[-1,\ 1]\), and \(d_{ij}\) is the associated element nodal displacements. Let \(S^{h}\) denote the usual FE trial space (also the test space) of linear functions. Then, the FE solution \(u^{h}\in S^{h}\subset H_{E}^{1}\) is obtained by  
\[a(u^{h},v^{h})=(f,v^{h}),\ \ \ \forall v^{h}\in S^{h} \tag{5}\]  
From the arbitrariness of \(v^{h}\), the matrix stiffness equation of the following form can be derived as follows:  
\[\mathbf{K}\,\mathbf{D}=\mathbf{P} \tag{6}\]  
where \(\mathbf{K}\) is the global stiffness matrix, \(\mathbf{P}\) the nodal load vector and \(\mathbf{D}\) the nodal displacement vector. Let \(e_{i,\max}^{h}\) denote the maximum error of nodal displacements between the exact nodal displacement \(u_{i}\) and its FE solution \(u_{i}^{h}\), i.e., \(e_{i,\max}^{h}=\max\left|u_{i}-u_{i}^{h}\right|\). Let \(e_{\max}^{h}\) denote the maximum error of the FE solution \(u^{h}\) on the whole domain, i.e., \(e_{\max}^{h}=\max\limits_{\Omega}\left|u-u^{h}\right|\). The mathematical analysis has shown that nodal displacements \(u_{i}^{h}\) achieve convergence \(O(h^{2})\) in maximum norm, which is the same convergence order as interior displacement \(u^{h}\)[25].
## 4 Conclusions  
This study elucidated the curing mechanism of epoxy resins on sand by employing molecular dynamics simulations, encompassing DGEBA, DGEDDS, AEOR. The following conclusions were derived from the findings:  
1. DGEDDS exhibited a significantly higher number of hydrogen bonds after adsorption, forming eight compared to zero for DGEBA and one for AEOR. This is because the high hydrogen bond density per unit molecular weight of DGEDDS after adsorption was 9.188 \(\times\) 10\({}^{-3}\), which was larger than those obtained from DGEBA and AEOR. The results indicated that DGEDDS established stronger intermolecular interactions with sand compared to DGEBA and AEOR.
2. The mechanical properties of three epoxy resins (DGEBA, DGEDDS, and AEOR) after sand adsorption showed minimal significant differences. While DGEBA exhibited slightly higher shear and Young's moduli, approximately 18.1 \(\pm\) 0.2 GPa and 29.5 \(\pm\) 0.2 GPa, respectively, DGEDDS showed greater directional variability in these properties, potentially due to its increased polarity and enhanced intermolecular forces. AEOR, on the other hand, displayed the lowest shear and bulk moduli, indicating a weaker resistance to deformation.
3. DGEBA, with its more compact and rigid structure due to the bisphenol-A group, exhibited a smaller average Rg of 0.8-0.9 nm, while DGEDDS, containing sulfur atoms that increase flexibility, had a slightly larger average Rg of 0.8-1 nm. AEOR, characterized by its linear structure with long alkyl chains, displayed the largest Rg value, averaging 1.1-1.2 nm, indicating a more extended and flexible conformation. While DGEBA and DGEDDS displayed flexible conformations, AEOR maintained a notably rigid structure. These structural differences likely contributed to the varying mechanical properties observed for these epoxy resins after adsorption onto sand.  
D.S.: conceptualization, funding acquisition, supervision, writing--review and editing and validation; X.P.: writing--original draft, methodology, software, and validation; L.C.: project administration, funding acquisition, resources, and validation; X.W.: writing--review and editing, investigation, formal analysis, and validation; C.W.: visualization and data curation; R.L.: visualization and validation. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support of the Fundamental Research Funds for Central Universities of China (Grant No. B230201060) and JSTI Group (Grant No. 823088816).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
[PERSON] and [PERSON] were employed by the company Nanjing Ningtong Intelligent Transportation Technology Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas
### Mechanically Frequency-Reconfigurable Antennas
#### Microfluidic Antennas
For antennas in which reconfigurability is achieved by changing the permittivity, the limited range of liquid dielectric, does not allow the researcher to have a wide range of reconfigurable antennas and frequency bands. Moreover, their ability for stretching up is only in one direction for complex geometries, or in some cases the accurate mechanically control of antenna is difficult [69].
## 3 Results and Discussion  
### Influence of Pulse Duty Cycle  
Figure 4 shows the photographs of holes machined with different values of the duty cycle. With the increase of the duty cycle, stray corrosion is clearly more severe. The hole diameter decreases gradually when the duty cycle is reduced from 50% to 20%. However, when the duty cycle is reduced to 10%, short circuits occur during processing and the hole fabrication fails. Too small duty cycle results in less material being removed, which eventually leads to short circuits between the helical electrode and the tube. [PERSON] et al. (2020) reported that too small duty cycle will bring about unstable processing. This result is consistent with the report. Therefore, to obtain better processing quality while avoiding a short circuit, we select 20% as the duty cycle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Applied voltage (V) & 7.5 \\ Frequency (kHz) & 10, 20, 50, 100 \\ Pulse duty cycle & 20\% \\ Feed rate (\(\upmu\)m/s) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machining parameters with different pulse frequency.  
Figure 4: _Cont._
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 1 Introduction
### Exosomes in Dermatology  
Exosomes are lipid bilayer-enclosed nanovesicles released by almost all types of cells carrying proteins, DNA, long non-coding RNAs (lncRNA), micro-RNAs (miRNA), and other bioactive molecules. These nano-sized vesicles typically range in diameter from 30 to 200 nanometers [21] and are formed inside cells in endosomal compartments known as multivesicular bodies (MVBs). When the MVBs fuse with the plasma membrane, they release exosomes into the extracellular space. These exosomes play a key role in intercellular communication and can significantly impact numerous physiological and pathological processes [22]. Exosomes exert local paracrine or distant effects and can be found in various body fluid including plasma, urine, amniotic fluid, and saliva [23]. The cargo of exosomes is dependent on the cell of origin, reflecting its physiological state, type, and environment. It can influence the behavior of recipient cells in several ways, including modulation of gene expression and alteration of cellular processes. The interest in exosomes spans across various fields of biomedical research and clinical application, especially due to their role in signaling mechanism, their potential as biomarkers for disease diagnosis, and their possible role as therapeutic agents and as vehicles for drug delivery. In the field of dermatology and aesthetic medicine, exosomes are emerging as a promising therapeutic option in various conditions, such as hair loss, scar treatment, wound healing, skin aging, and pigmentation disorders [24; 25]. In the skin, endogenous exosomes facilitate a complex network of interactions involving keratinocytes, fibroblast, melanocytes, macrophages, adipocytes, and immune cells. They are necessary to maintain cellular functions and tissue homeostasis [26]. Exosomes can influence skin cell behavior, promoting wound healing and collagen synthesis, and modulating melanogenesis. Exosomes carry Wnt proteins to induce Wnt signaling activity in target cells [27]. Wnt signaling is essential for skin development and maintenance, as well as regulation of skin stem cells.