esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background of the Uncertainty Theory
### Applications and Benefits of Uncertain Models
#### 2.4.3 Robust Optimization (RO)  
Robust optimization (RO) has been used to develop resilient supply chains in the face of natural disasters or market fluctuations. The RO process helps devise strategies to maintain operations under various uncertain conditions, thereby increasing supply chain efficiency and reliability. Recent research has focused on improving the computational performance of RO models and integrating them with real-time data analysis [18; 19]. Robust optimization is particularly useful in scenarios involving potential manufacturers facing worst-case conditions, such as network design, inventory creation, and production scheduling. This concept helps address the uncertainties and peculiarities facing the supply chain [18]. The formulation of the model under robust optimization is as follows:  
\[\text{Minimize }f(x)\]subject to:  
\[g_{i}(x,\xi)\leq 0,\quad\forall\xi\in\Xi,\quad\forall i\]  
where \(g_{i}(x,\xi)\) are the constraints affected by uncertainty \(\xi\), and \(\Xi\) is the uncertainty set.  
The use of fuzzy decision-making processes in supply chain management (SCM) has been well-documented, allowing for better management of uncertainty and improved communication complexity within supply chains. Recent developments in metaheuristic algorithms, such as the genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and hunger games search algorithm (HGSA), have shown great promise in enhancing SCM performance and robustness. These algorithms are designed to find near-optimal solutions to complex problems by efficiently searching large solution spaces.  
GA, inspired by natural selection, has been successfully used to optimize multi-level conservation systems, resulting in significant cost reductions and service level improvements [22]. PSO, based on the social behavior of birds and fish, has been effectively applied to dynamic vehicle routing and warehouse location planning, achieving better delivery times and reduced operational costs [23]. ACO, by simulating bee feeding behavior, has been used to solve combinatorial optimization problems, improving distribution efficiency and reducing costs in distribution networks [24]. Additionally, hybrid methods that combine different algorithms, such as GA-PSO, have gained traction, resulting in improved solution quality and computational efficiency [25]. Recent advancements focus on increasing convergence speed, solution quality, and robustness through parallel and distributed computing techniques, making these algorithms suitable for large-scale SCM problems. Furthermore, the integration of machine learning techniques with metaheuristics has opened new possibilities for adaptive and intelligent optimization, greatly enhancing supply chain operations and efficiency [26; 27]. The effectiveness of HGSA in optimizing supply chain performance under uncertainty has been demonstrated, highlighting the potential of advanced metaheuristic techniques in designing robust supply chain strategies [28].
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Molecular Targets of Curcumin
#### 2.2.5 Curcumin and Cell Cycle Regulation  
Programmed cell death, or apoptosis, is a mechanism of vital importance in maintaining normal cell growth. Apoptosis is initiated by regulation of protein 53 (p53) and by proteins of the B-cell lymphoma 2 family (Bcl-2). Activated p53 induces activation of two pro-apoptotic proteins, Bcl-2 homologous antagonist killer (Bak), and Bcl-2 associated x protein (Bax), which in turn release cytochrome c into the cytoplasm to activate the caspase cascade. Curcumin is able to induce apoptosis in prostate cancer PC-3, DU-145, and LNCaP cells via p53-dependent mitochondrial pathways [145]. Activation of p53 by curcumin leads to over-expression of Bak, Bax, and several caspase proteins [136; 137; 138; 139; 140]. In addition, curcumin inhibits the activity of a few anti-apoptotic proteins, such as Bcl-2 and B-cell lymphoma extra-large protein (Bcl-XL) [136; 137; 138; 139; 141].  
Cyclin-dependent kinases (CDKs) are also involved in the life cycle of cells, being in charge of its progression through the different stages [149]. Malignant cells have thus frequent alterations in CDK expression, with overexpression of cyclins and suppression of CDK inhibitors. Curcumin induces cell cycle arrest in colon cancer cells (HTC116 line) by CDK2-dependent effects [135]. The mechanism underlying cell cycle arrest by curcumin may involve overexpression of CDK inhibitors and blockage of the expression of cyclin E and cyclin D1 [142; 143].
###### Abstract  
In a laser-induced breakdown spectroscopy (LIBS) system that performs elemental analysis of a target by acquiring the emission of plasma generated on the material surface by a focused laser, the plasma signal may be affected by the flow of the surrounding gas. A flow of gas may be present when LIBS measurements are performed in a special environment where an inert gas is normally applied. In such an environment, the flow of gas may affect the intensity of emission generated in plasma. The LIBS spectral intensity in the gas flow field changes according to the direction of the gas flow and the signal detector of the LIBS. In this case, the correlation between the flow rate and intensity of the spectral line can be confirmed both theoretically and experimentally. In this study, changes in the signal according to the flow rate were theoretically evaluated using the view factor and wave equation. In addition, LIBS signals were examined based on the flow of Ar, N\({}_{2}\), and He gases in the experiment. The experimental results confirmed the range of effective gas flow rates over which the correlation between the flow rate and intensity of the LIBS spectral line could be inferred. These results could be used for calibration to achieve accurate measurement of LIBS signals in gas flow fields. In addition, this analysis has the potential to shed light on the properties of flowing gases that affect plasma by reversibly tracking changes in the signal of LIBS in a confined environment.  
laser-induced breakdown spectroscopy; gas flow; view factor +
Footnote †: journal: Physics Letters B
# A Numerical Model for Simulating Ground Motions for the Korean Peninsula
## 1 Introduction  
The Korean Peninsula is located in stable continental regions about 400 km from the boundaries of four plates: the Philippine Sea, Pacific, North American, and Eurasian Plates [1,2]. The seismic activity in this region is lower than that in regions located near plate boundaries such as Japan, Indonesia, or the Western United States. However, Korean historical documents reported that big earthquakes with a magnitude of 6 or larger occurred in the peninsula from 2 A.D. to 1904 A.D. [3]. The Pohang earthquake, with a magnitude of 5.4 and focal depth of 9 km, occurred near the city of Pohang, located in the Korean South-East province. This earthquake caused the largest casualties and economic loss among the earthquakes that have occurred in Korea.  
To protect structures from earthquakes, it is necessary to conduct the seismic performance evaluation of structures with reliable input ground motions, and to retrofit the structures based on the results of the seismic performance evaluation [4, 5, 6]. However, it is difficult to collect available ground motions recorded from mid- to large-size earthquakes that have occurred in sites located in low-to-moderate seismic regions. In this case, artificial ground motions can be simulated and used in seismic performance evaluation. Simulated ground motions should retain the characteristics of the local source mechanism, travel path, and geology of a site.  
Previous studies [7, 8, 9, 10, 11, 12] have developed and improved the ground motion simulation model to generate artificial ground motions by considering key components of seismological characteristics. Ground motions can be simulated using the stochastic point-source model in the frequency domain and the shaping window model in the time domain.  
In this study, a ground motion simulation model is proposed. The ground motion parameters are determined based on the ground motions recorded from the mainshock of the 2017 Pohang earthquake.  
To verify the accuracy of the proposed model, the key components of measured and simulated ground motions are compared, such as peak ground acceleration (PGA) and 5% damped pseudo spectral acceleration (PSA).  
In order to develop the proper ground motion simulation model for the Korean Peninsula, this article is organized into three sections. In Section 2, the collected ground motions in three orthogonal directions (East-West, North-South, and Vertical) are presented, which were recorded from 111 stations in the inland Korean Peninsula during past earthquakes. In Section 3, a stochastic point-source model and shape window model are proposed to simulate the ground motions recorded during past earthquakes that occurred in the Korean Peninsula. In Section 4, the proposed model is verified by comparing the generated artificial ground motions and ground motions recorded during the 2017 Pohang earthquake event.
## 5 Formulation of the LMI Stability Criterion
### Development of the Stability Condition
Due to the presence of the nonlinear terms \(\mathcal{P}\mathcal{B}\mathcal{K}\), \(\mathcal{P}\mathcal{M}_{1}\mathcal{P}\), \(m_{2}\mathcal{P}\mathcal{D}_{2}\mathcal{D}_{2}^{\mathrm{T}}\mathcal{P}\), \(\mathcal{P}\mathcal{M}_{3}\mathcal{P}\), \(\bar{\delta}^{2}\mathcal{B}^{\mathrm{T}}\mathcal{M}_{3}^{-1}\mathcal{B}\), and \(\xi_{1}\epsilon^{2}\), the numerical resolution of the nonlinear matrix inequality (53) is not traceable. Then, to solve this computation problem, the goal is to convert the nonlinear matrix inequality (53) into an LMI.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 2 Dynamic Measuring Principle and Device of Atom Gravimeter
### Dynamic Measurement Device of Atom Gravimeter  
Although the combination algorithms of different research teams vary, the dynamic measurement device of the atom gravimeter is roughly the same. The dynamic measurement device mainly includes three parts: an atom gravimeter, an accelerometer, and a dual-axis stabilized platform. The atom gravimeter mainly includes a vacuum system, an optical system, and a control system. The vacuum system is the site for atomic trapping, falling, and detection, and the vacuum environment is helpful to reduce the influence of air resistance. The optical system provides lasers with different polarizations, powers, and frequencies needed in the working process of the atom gravimeter. The control system realizes the functions of sequential control, signal acquisition, and algorithm operation. Theaccelerometer is fixed on the mirror to measure the vibrational acceleration or gravitational acceleration near the vacuum sensor. The vacuum sensor and the accelerometer are placed on the dual-axis stabilized platform so that the direction of the Raman light wave vector coincides with the gravity direction during the motion. This reduces the influence of the inclination angle [50] and ensures that the atom gravimeter and accelerometer measure the acceleration in the vertical direction. The inertial measurement unit in the stabilized platform can not only provide the attitude reference for the platform but also output navigation information such as the velocity and position of the carrier, which is used for subsequent Eotvos correction and accuracy assessment for the gravity output from the test system. In order to improve the accuracy of navigation and attitude, a navigation solution scheme combining inertial navigation and the global navigation satellite system is usually adopted. Figure 5 shows the schematic diagram of the dynamic measurement device of the atom gravimeter of the author's team [40].
## 3 Results and Discussion
### Wound Scratch  
Migration is a characteristic feature of cancer cells promoting their aggressiveness and the formation of metastases. Counteracting metastases, which are responsible for 90% of all deaths, is one of the main goals of cancer treatment. To evaluate the effect of _C. sativa_ leaf methanolic extract on cell migration, a wound healing assay was performed. The Caco-2 cell line was selected for testing, as it was characterized by the highest sensitivity to the tested extract. The test was carried out until the wound was completely closed in the control sample. Cells were treated with the extract at a concentration below the IC\({}_{50}\) value. As shown in Figure 1D,E, the _C. sativa_ leaf methanolic extract significantly inhibited Caco-2 cell migration compared to the control. Administration of the extract at a concentration of 10 \(\upmu\)g/mL after 12 h of culture resulted in inhibition of cell migration by 63.00%. After extending the culture time to 24 and 48 h, the wound was closed in 76.74 and 99.95%, respectively. Complete wound closure occurred after 48 h of control cell culture. The strongest anti-migration effect was observed after treating cells with 100 \(\upmu\)g/mL _C. sativa_. After 12, 24 and 48 h of incubation, 26.76, 23.84 and 50.26% of the scratch surface were covered by migrating cells, respectively. Previously unpublished results indicated that _C. sativa_ leaves are a strong inhibitor of Caco-2 cell migration.
## 2 Materials and Methods
### Materials  
Silicon (Si) wafers (single-side polished, (<111>), N-type phosphorus doped, resistance 1-5 \(\Omega\)cm, MicroChemicals, Germany) were cut into substrates of 10 \(\times\) 10 mm\({}^{2}\), which were used as a model surface for the characterization of the coating.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Plasma Input** & **Ar Gas Flow** & **Distance** & **Treatment** & **HEMA Volume** \\
**ID** & **Power (W)** & **(sLM)** & **(mm)** & **Time (min)** & **(\(\mu\)L)** \\ \hline S1 & 10 & 1 & 4 & 5 & 8 \\ S2 & 7 & 0.7 & 4 & 5 & 8 \\ S3 & 10 & 1 & 3 & 5 & 10 \\ S4 & 10 & 1 & 3 & 2.5 + 2.5 & 5 + 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample identifier (ID) employed in this work and corresponding process parameters for the generation of HEMA-based coatings.  
Figure 1: Schematics of the atmospheric pressure plasma jet used in this study, employing Ar as a working gas to polymerize the liquid 2-hydroxyethyl methacrylate (HEMA) monomer on titanium samples or silicon wafers.  
Commercially pure titanium (Ti) grade 2 was purchased as rods 10 mm in diameter (VDM metals, Germany) and cut into 2 mm disks. Ti samples were ground in a sequence of silicon carbide grinding papers with decreasing particle sizes (P400, P600, P800, P1200, P2500, Neurtek, Spain) and polished with a colloidal silica suspension (Eposil M11, particle size 0.06 \(\upmu\)m, Neurtek). The samples were cleaned in a sequence of solvents--distilled water, ethanol and acetone (Panreac, Germany)--in an ultrasonic bath.  
The 2-hydroxyethyl methacrylate at 98% purity (HEMA) was purchased from Sigma Aldrich (Germany) and used without further purification.  
Dulbecco's modified Eagle medium (DMEM, Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS, Invitrogen, Carlsbad, CA, USA), 2 mM L-glutamine (Invitrogen), 50 U/mL streptomycin/penicillin (Invitrogen), phosphate buffered saline (Gibco, UK), a lactate dehydrogenase (LDH) cytotoxicity detection kit (Roche, USA) and a mammalian protein extraction reagent (mPER, Thermo Scientific, USA) were used for biocompatibility assays.  
Brain heart infusion (BHI, Scharlab, Spain) and agar bacteriological (Scharlab, Spain) were used for bacterial adhesion assays.
# High-Risk AI Systems--Lie Detection Application
## 3 Current AI Technologies Used for Lie Detection  
Traditionally, lie detection has mostly depended on polygraph examinations, which assess many physiological responses, including heart rate, blood pressure, respiration rate, and skin conductivity [11]. Physiological indicators are thought to shift during deception since lying may induce stress and anxiety, leading to quantifiable changes in physical functioning. Polygraph tests use advanced machinery to record physiological measures with precision and require professional people to conduct the tests, as well as for the interpretation of the results. Yet, even with their usage, polygraph tests face many limitations. Firstly, requiring specialized equipment and skilled operators makes them resource-intensive and logistically challenging to deploy, especially in high-volume settings like border control, where many individuals must be screened quickly and efficiently [12]. Secondly, the accuracy and reliability of polygraph tests are the subject of ongoing debate within the scientific community. Thus, the validity and reliability of polygraph tests  
Figure 2: Risks from the EU AI Act and their interconnection.  
remains disputable even today within the scientific community. Emotional states such as nervousness, fear, or other non-deceit emotions bring about physiological reactions similar to those recorded by polygraphs, and yield false positives or negatives. Moreover, this ambiguity in the data further lowers their value in critical, high-stakes situations. Because of these empirical and theoretical constraints, there is a burgeoning interest in developing more complex and scalable methods of lie detection--especially those that rely on artificial intelligence and machine learning [13].  
For the most recent techniques, many attempts are being made to analyze more behavioral and physiological cues in a much more holistic and non-intrusive way, with potentially higher levels of precision and applicability for tasks like border control [14]. Recent developments in machine learning and computer vision have led to AI systems being capable of analyzing micro-expressions, vocal patterns, and behavioral cues. These systems aim to detect deception and assist border control officers [15].
# Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
## 5 LCNC Used in Existing Research
### Applying AI/ML Algorithms
#### 5.1.3 CNN-Based Deep Learning Algorithm  
The anomaly detector enhances line charts by automatically detecting anomalies within time series data. It also provides explanations for the anomalies to facilitate root cause analysis. Researchers in [15; 18; 20; 25; 27] used CNN-based deep learning using the anomaly detection feature provided by Microsoft Power BI's line chart visualization. Within [18], CNN algorithm was implemented with the LCNC platform to detect anomalies from live social media feeds on disaster events. For implementing complex CNN algorithms in [18], social media analysts and researchers did not have to manually write algorithms using any programming languages. Similarly, media analysts and political scientists can find out abnormalities in global events using a CNN algorithm by using interactive visualization or tools, as demonstrated in [20]. Geologists, who are unable to program complex CNN algorithms, can now implement CNN-based anomaly detection to automatically identify and analyze abnormalities in landslides [25] and tornadoes [27]. Moreover, the LCNC-based solution in [15] used CNN for evidence-based strategic decision-making on the COVID-19 crisis. The implementation of CNN within [15; 18; 20; 25; 27] used a saliency reduction (SR) model [53; 54] and NLP-based explanations [55].
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 2 Materials and Methods
### Underwater Measurements  
To simulate an underwater setting, setup (A) in Figure 2 was used with the paint sample fully submerged in water illustrated in Figure 2B. The silicone contact patch and TOPAS polymer were integrated into a 3D-printed holder that made it possible to apply pressure to the silicone and thereby remove water in the path of the THz beam. It should be noted that this experiment only tested the ability to remove water in the path of the THz beam and not the water resistance of a fully integrated device.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Taxonomy
#### 2.1.1 Local vs. Global  
Local explanation refers to providing explanations for individual samples. This approach is useful when explanations for individual patients are of interest. An example of this is SHAP [80; 81] which provides explanations for single classifications. On the other hand, global explanation refers to providing explanation for a group of samples or the entire model. This shows the overall feature importance for a group of patients. An example of global explanations are Global Model Explanation via Recursive Partitioning [82] and Garson's Algorithm [83] for depicting feature importance.
## 4 Discussion and Application
### Application of the Proposed Network in Different Projects  
To verify its generality, the PDNetwork is applied to actual projects under different geological conditions (Micang Mountain and Zijin tunnels). The same microseismic monitoring system with the same data recording format is employed, and the waveform visualization operation is used to ensure the availability of the test data. For the test, we used 800 microseismic waveforms, including 400 micro-fractures and 400 noise (blasting and other noise) waveforms. The test results show that the hit rate and average deviation of the micro-fracture waveforms are 96.9% and 0.84 ms, respectively. The proposed method could accurately detect 393 micro-fracture waveforms and 394 noise waveforms with Precision values of 0.985 and 0.983, respectively. Evidently, the blast waveforms are not missed.  
The performance of the PDNetwork at different moment magnitudes \(M_{w}\) is also analyzed and then used to evaluate the intensity of one microseism/seism. The detection performance of the network for micro-fracture waveforms with different \(M_{w}\) is discussed (see Figure 11). The \(M_{w}\) ranging from \(-0.5\) to \(1.5\) of the micro-fracture waveforms are analyzed. The results show that an increase in \(M_{w}\) brings about an improvement in the detection accuracy. When the \(M_{w}\) exceeds 0.9, the detection accuracy increases and tends to converge. When the \(M_{w}\) exceeds 0.3, the micro-fracture waveforms can be effectively detected. However, detecting the micro-fracture waveform with a low \(M_{w}\) is challenging and can be a topic for future research. Although the PDNetwork is trained on semisynthetic data, the proposed approach can be extended to real microseismic wave  
Figure 10: Detection results of the micro-fracture waveforms with different SNRs.  
Figure 9: Relationship between the SNR and onset time error of micro-fracture waveforms: (**a**) the influence of different SNR on average onset time error; (**b**) the influence of different onset time error on average SNR..  
forms and directly applied to actual engineering for the onset time picking and detection of microseismic waveforms.  
In summary, the PDNetwork performs effectively for the onset time picking and detection of micro-fracture waveforms in different practical projects, even when the recorded data are polluted by noises of different types and intensities. Notably, all the recorded data used in this study are in the time domain. If the shape of a noise waveform is highly similar to that a of micro-fracture waveform, the proposed method may miss the onset time picking and detection. This also provides new insights and direction for further research.