esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scintillation Characteristics
Figure 8 displays the scintillation decay curves of LuYAGG single crystals doped with Tb\({}^{3+}\) at concentrations of 0.5, 1, 5, and 10 mol\(\%\). The corresponding decay parameters are summarized in Table 3. The X-ray scintillation decay profiles exhibit a bi-exponential behavior, comprising a fast radiative recombination component and a slower delayed emission, both of which are significantly influenced by Tb\({}^{3+}\) concentration. At 0.5 mol\(\%\), the decay profile was well-balanced, showing moderate speed and minimal non-radiative losses. Increasing the doping to 1 mol\(\%\) could enhance the prompt decay component, indicating efficient energy transfer and strong luminescent output. Interestingly, the 5 mol\(\%\) Tb\({}^{3+}\) sample demonstrated the fastest overall decay response, dominated by a rapid component with minimal tailing, suggesting a highly efficient radiative recombination at this concentration. The 10 mol\(\%\) sample retained relatively characteristic \({}^{5}\)D\({}_{4}\rightarrow{}^{7}\)F\({}_{\text{j}}\) transitions of Tb\({}^{3+}\), with a slight broadening in the tail, signaling the onset of concentration quenching effects, likely due to cross-relaxation or energy migration among Tb\({}^{3+}\) ions [15; 24; 30].
# Non-Cannabinoid Metabolites of _Cannabis sativa_ L. with Therapeutic Potential
## 2 Non-Cannabinoid Compounds
### Alkaloids in Cannabis  
In 1876, [PERSON] claimed to find nicotine in cannabis resin (hashish) from Uzbekistan in 1876 [52]. This was later rejected on the basis that cannabis users from that part of the world tend to mix the resin with tobacco before smoking. This made it likely that the presence of nicotine was due to the tobacco. Later in 1881, at the British Pharmaceutical Conference, [PERSON] and [PERSON] reported on the isolation of the alkaloid cannabinine [53]. Two years later, in 1883, [PERSON] isolated tetanocannabin, another biologically active alkaloid. It was so called because it produced strychnine-like convulsions in frogs [54]. In 1986, even Merck (of Darmstadt) began marketing and advertising a \"cannabine alkaloid\" product [55].  
Alkaloids are a class of heterocyclical organic compounds that contain one or more nitrogen atoms. They may also contain an oxygen, sulfur, chlorine, bromine, or phosphorus atom attached to the molecule. They are most associated with plants, but are also produced by microorganisms and animals [56]. In plants, alkaloids are a form of chemical defense against herbivores. Many alkaloids are pharmacologically active. In fact, alkaloids make up about 60% of plant-derived drugs [57]. On the same tangent, dndogenous indole alkaloids have been confirmed in hemp [58].  
Alkaloids have a variety of therapeutic applications as analgesics, antibacterial, anticancer, antiarrhyth, antiathma agents, antimalarial, anticholinergics, bronchodilatory, laxative, mitotic, oxytocic, vasodilatory, psychotropic, and stimulating agents. This class of compounds include morphine, cocaine, nicotine, caffeine, quinine, ephedrine, among others.  
Cannabinaceous alkaloids are alkaloids produced by _Cannabis sativa_. These include, but are not limited to, cannabisativine and anhydrocannabisativine [59].  
A 1971 study by [PERSON] and colleagues investigated the constituents of alkaloids mixtures extracted from cannabis plants and reported the isolated of four alkaloids, namely cannabimines A-D [60]. It was also noted that both cannabimine A and anhydrocannabisativine (isolated in 1976) share the same molecular formular (C21 H37 N3 O2) [59].  
Cannabisativine was the first cannabinaceous alkalioid to be fully identified. It was isolated in 1975 in Mississippi from the roots of a Mexican variant of _Cannabis sativa_[61]. See Figure 8 below. These alkaloids demonstrated antiparasitic, antipyretic, antiemetic, antitumor, diuretic and analgesic properties [62]. In a 2004 study, [PERSON] and [PERSON] were able to achieve total asymmetric synthesis of cannabisativine with a high degree of stereo control [63].
## 4 Simulation of Use Cases
### Design of the Generic Use Case
#### 4.1.3 Creation of a Generic Simulation Model  
After the problem has been defined, modelers must start generating assumptions as well as defining the standard values that define the models. These provide the basis for the model behavior and how the research question arose.  
First, assumptions are defined to simplify the model with a focus on the simulation goal:  
* Distribution of finished products as given;
* Procurement of raw material as given;
* Each order has a production unit;  
Figure 7: Structure of the simulation study (own elaboration).  
* Bill of materials are not considered. The following assumptions were made related to the points to be fulfilled in order to conduct a comparison between the simulation models for a defined simulation demand scenario:
* The same demand using replication;
* Same number of employees with same initial distribution and same capacity to perform warehouse activities;
* Same production logic for all simulation models;
* The warehouses have no stock capacity limitations;
* There is no transport limitation between the different production stages;
* There are two products, one existing product is in a mature stage with stable demand and provides 10,000 euros/unit of margin. The new model is in the process of being launched and provides 20,000 euros/unit. These values were used to calculate profits. If there is loss in volume, it is assumed that the new model will have the loss in volume due to unknown future demand;
* The simulation model considers sales losses starting from a customer order lead time greater than 60 days;
* A product is a finished product after it leaves the production facility;
* Time restrictions: first, the modeler must define a time horizon and the units of time. It is easy to fulfill that step by asking to what extent the simulation should be considered. In the case of the study, it has been decided to simulate four working years to evaluate influences in the medium and long term. The system dynamics model consists of five sub-models: the supply chain flow sub-model, the production planning sub-model, the maintenance sub-model, the decision-making sub-model and the evaluation of improvements initiatives sub-model.
## 4 Experiment Verification  
### Performance Degradation Test Data Collection  
#### 4.1.1 Performance Degradation Test Data Collection  
As the source of compressed air for brake systems of trains, the quality of the compressed air produced by the compressed air supply unit (CASU) directly affects the reliability and service life of other components of the brake system due to the continuity of the compressed air. As the CASU draws air directly from the external natural environment, the air is mixed with impurities such as water vapor, sand, and dust, and direct use can cause blockage, stagnation, wear, or corrosion of valves, pipelines, and other components, which can affect the normal operation of the brake system. The intake filter of the CASU is used to remove impurities such as sand and dust from the air to improve the cleanliness of the intake air, and its filtration performance directly affects the quality of compressed air. To verify the effectiveness of the proposed performance degradation prognosis method, the air  
Figure 3: Prognosis flowchart.  
intake filter was selected, and the performance degradation test of the CASU of the brake system was carried out. By adding sensors to the CASU, the performance degradation test data of the intake filter were collected. The performance degradation test rig of the compressed air supply unit is shown in Figure 4.  
The performance degradation test rig of the compressed air supply unit was mainly composed of a compressed air supply unit (CASU); a total air cylinder; collection equipment; air pressure, temperature, flow rate, and other sensor sets; a temperature box, etc. In order to realistically simulate the stress of the working environment, the inlet air temperature of the CASU was set to the actual ambient temperature, the inlet air pressure was the atmospheric pressure in the actual environment, the load was the same volume of the total air cylinder as what is used on the vehicle, and the total air pressure was controlled at around 900 kPa. By supplying three-phase AC 380 V voltage to the CASU, the CASU was driven to work continuously with load. During the test, the input and output signals of the intake filter as well as the ambient temperature were collected in real time by the sensor sets, as shown in Figure 5. The sampling interval for the performance degradation test of the CASU was set as 600 s, and the sampling frequency was 1 Hz.  
Figure 4: Performance degradation test rig of compressed air supply unit.  
Figure 5: Performance degradation test curves of intake filter.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 5 Conclusions and Future Work  
This paper presents a solution aimed at enhancing the management of a CVD through early diagnosis. Despite the advancements in clinical treatment, the accurate detection of cardiac disorders remains a challenging task. Deep learning has emerged as a promising  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Year** & **Method** & **Dataset** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline
[5] & 2021 & MobileNet v2 & ECG images for & 97.5 & - & - \\
[43] & 2022 & CNN & Cardiac & 98.23 & 98.22 & 98.31 \\ Proposed & - & Lightweight CNN & patients & 98.39 & 98 & 98.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with recent techniques.  
Figure 10: (**a**) Input image of MI. (**b**) Predicted image as MI.  
approach to improve diagnosis, especially in remote patient scenarios, achieving an impressive accuracy of 98.39%. However, a limitation of this research is the absence of the segmentation process, which could have increased computational complexity and further improved the results.  
Incorporating the segmentation approach before classification is the work's future goal because it is anticipated to improve the diagnostic process' accuracy and dependability. The suggested solution intends to further improve and streamline the CVD detection process by utilizing robust features and investigating alternative deep learning models on more datasets. These initiatives could change early detection and open the door to the better management of heart diseases.  
Conceptualization, T.S. and M.S.; methodology, T.S.; software, S.A.; validation, I.K., R.K. and I.A.; formal analysis, S.A.; investigation and resources, I.A. and R.K.; data curation, S.A.; writing--original draft preparation, T.S.; writing--review and editing, T.S.; visualization, I.A.; supervision, M.S.; project administration, M.S. and S.A.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Researchers Supporting Project Number (RSPD2023R1027), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The dataset used in this research is available online at [[https://data.mendeley.com/datasets/gwbz3](https://data.mendeley.com/datasets/gwbz3) fggp8/2]([https://data.mendeley.com/datasets/gwbz3](https://data.mendeley.com/datasets/gwbz3) fggp8/2) (accessed on 20 July 2023).  
The authors extend their appreciation to King Saud University for funding this research through Researchers Supporting Project Number (RSPD2023R1027), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 3 Service Composition
### Service Composition's Research Landscape
#### 3.1.4 Interoperability, Execution, and Monitoring of Composite Services  
Interoperability between services is also a key issue in service composition. Factually, a composite service is represented by a composition pattern that reflects the logic of this service. This logic includes the information flow between services and settings. Two schemes of interoperability are defined in the state of the art of choreography and orchestration [18] (described below), where services communicate with each other through standardized languages. The defined composite service has to be defined according to the interoperability schema. Based on this schema, monitoring tools are designed to gather information about the composite service running state and issues that could occur due to the unpredictability of external partner services or unexpected behavior of composite services [19].
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming
#### 3.4.1 Complexity of the Scheduling Problem  
MRO scheduling is inherently a combinatorial optimization problem, with tasks having dependencies, limited resources, and specific temporal constraints. As demonstrated in the literature, such problems are often NP-hard, meaning that finding an optimal solution requires significant computational effort, especially as the problem size grows. While heuristic and metaheuristic methods like genetic algorithms or simulated annealing can be used to provide near-optimal solutions for large, complex problems, they do not guarantee optimality and can require significant parameter fine-tuning.  
The worst-case complexity of CP is \(O(d^{n})\), where \(d\) is the domain size of task start times, and \(n\) is the number of tasks. However, CP is well-suited for smaller, structured problems with well-defined constraints. CP systematically explores the search space using constraint satisfaction techniques, ensuring that the optimal solution is found when the problem size remains within a practical range. This is particularly advantageous in the MRO context, where the task set is not as large as in typical machine learning or deep learning applications. The finite nature of MRO tasks (e.g., scheduled inspections and repairs) makes CP an efficient method for exploring the solution space exhaustively, rather than for relying on approximation techniques.
# Wheeler Method for Evaluation of Antennas Submerged in Lossy Media
## 4 Conclusions  
We propose the Wheeler method to measure the radiation efficiency of an antenna submerged in lossy media as an alternative to gain comparison methods which are carried out in the far-field. The concept of modified radiation efficiency in lossy media has been studied but is still limited to theory and simulation [16; 17; 26; 27]. This work empirically studies the application method of the Wheeler method to achieve a breakthrough in the limitations of previous studies. Also, the radiation efficiency measured at the theoretical radian length is highly correlated with that proposed in a previous study [16], indicating that this method can be further applied to different frequencies and scenarios in the future.  
The proposed method achieves very fast and inexpensive efficiency measurements without the need for other antennas or an expensive chamber. Additionally, the modified radiation efficiency can be used as a new figure of merit for evaluating submerged antennas for applications such as underwater, underground, and in-body communication systems. Furthermore, since the antenna radiation efficiency and propagation loss can be separated using the method presented in [26], it is expected to be useful for calculating the link budget of submerged channel modeling scenarios.  
Research conceptualization and methodology, D.C. and W.H.; Design and simulation, Y.O. and D.C.; Fabrication and measurement, Y.O. and D.C.; Writing-original draft, Y.O. and D.C.; Writing-review and editing, Y.O., J.-Y.L., and W.H. For other cases, all authors have participated. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) under Grant 2018R1A4A1025679, in part by the Basic Science Research Program through the NRF of Korea funded by the Ministry of Education under Grant 2018R1A6A3A01013261.  
Not applicable.  
The authors would like to thank ANSYS Korea for their generous support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Required Measurement** & **Acquisition Method for** & **Error Between** \\  & **Facilities or Essential** & **Radiation Efficiency** & **Simulation and** \\  & **Accessories** & & **Measurement** \\ \hline \multirow{3}{*}{[32]} & 2.4 \(\times\) 2.4 \(\times\) 2.4 m (Shield room), reference antenna & Average static power & \multirow{3}{*}{\(<\)4.5\%} \\  & & transfer function at & \\ \cline{1-1}  & & different 3 position & \\ \hline \multirow{3}{*}{[26]} & - & Power integral normal & \multirow{3}{*}{Only simulation} \\  & & to the sphere & \\ \hline \multirow{2}{*}{This work} & \multirow{2}{*}{Lossy medium, Wheeler cap} & Variation of the input & \multirow{2}{*}{\(<\)3\%} \\  & & resistance & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed method with other works.
## 5 Discussion
However, we did not exclude plants that only failed the filter for one year because they retired midway through that year. 3. Satellite overpass time. The satellites' local overpass times are during the daytime and in the morning, averaging around mid-morning or \(\sim\)11 a.m. local time for all three sensors: Landsat 8, Sentinel-2, and PlanetScope. Therefore, all soundings, andthus all predictions, are based on images from this time window. If a power plant generates more/less electricity at times when the satellites do not capture the plant, our generation estimates are biased low/high, respectively. Furthermore, since our generation model is currently restricted to training on the US, Australia, and Europe (ENTSO-E), it may simply learn how mid-morning power plant snapshots predict rolling 30-day average generation in those regions (especially the US and Europe, as 93% of training plants are in these regions). Our method can, therefore, over- or under-predict in regions of the world where dispatch patterns differ significantly from those in the training regions. We are working to remedy this source of bias by both expanding our training set to include more regions and by investigating additional proxy signals to augment our current NDT and FGD signals with a more complete view of each power plant. 4. Weather influencing signal visibility. The primary proxy signal we currently use to estimate capacity factor is the water vapor plume, whose size is sensitive to temperature, humidity, and wind speed: cold and wet conditions favor large plumes, dry and hot conditions result in smaller or fainter plumes, and higher wind speeds elongate plumes. Wind is not currently taken into account in our models because we did not find it to be a significant factor in differentiating the model performance, perhaps in part due to the lack of precise wind data for specific power plant locations. To address the temperature and humidity, however, we currently employ a temperature and humidity filter for FGD models, since FGD plumes are especially at risk of disappearing under hot and dry conditions compared to their larger NDT counterparts. Still, this means that regions that are too hot and dry to pass the filter will lack observations for models to ingest, such that we are forced to make predictions based off less data. However, even if observations pass the filters, regions that are hotter and drier on average are at risk of underprediction. Adding additional proxy signals that are not as sensitive to local weather will reduce this bias, and this is an area we are actively working on. 5. More satellite images in recent years. The majority of PlanetScope satellites were launched in 2017 or later, with more satellites added through 2022. Furthermore, Sentinel-2B was launched in 2017. This makes satellite-derived estimates in the years 2015-2017 less accurate due to the limited satellite coverage and observations. Because there is less confidence on ML predictions prior to 2019, we restricted asset-level reporting on the Climate TRACE website to 2019-2022, while ML predictions on 2015-2018 are only used for aggregating into country totals.
## 2 Scientific Objectives and Requirements
### Scientific Objectives
This highlights the importance of space-based missions that perform such measurements, including the UVSQ-SAT pathfinder mission associated with its possible small satellites constellation [1] named Terra-F. Further efforts to implement new space-based observing systems are more than necessary, as the EEI represents additional global warming that will occur without additional changes in radiative forcings. Direct measurements of changes in the EEI are of paramount importance in determining the rate of climate change on regional and global scales.
## 3 Principles of Infrared Spectroscopic Analysis Techniques
### Imaging and Mapping Spectroscopy
#### 3.3.2 Measurement Modes  
The measurement can be carried out either in the imaging or mapping mode according to the following strategies:  
1. Point mapping: different areas of the sample are analyzed consecutively
2. Line mapping: defines a series of spectra along one dimension
3. Area mapping: defines a series of spectra to be collected in two dimensions. Sampling of large areas requires multiple positioning of a sample  
Spectra can either be recorded in transmission according to Lambert-Beer or in reflection according to Kubelka-Munk.
## 4 Joint Parameters Optimization
### Optimization of Pico CRE Bias  
For the sake of getting the optimal CRE bias for network EE maximization, suppose that the ratio between PBS density and MBS density \(\rho_{p,m}\) is a known quantity. Further assume that \(\lambda_{u}\) is a constant value, which can be adjusted to represent different network loads. Then, the pico CRE bias optimization problem can be formulated as follows:  
\[\begin{split}& B_{p}^{*}=\operatorname*{arg\,max}_{B_{p}}EE\left(B_{p} \right)\,\Big{|}_{\lambda_{p},\rho_{p,m}},\\ & s.t.\quad 0<B_{p}\leq 25\,\mathrm{dB},\\ &\rho_{p,m}\text{ is an a arbitrary constant between 0 and 30}.\end{split} \tag{16}\]  
Suppose that the variable step length of \(B_{p}\) is 0.1. Thus, the optimal pico CRE bias for the network EE maximization can be obtained by means of a linear search algorithm, which is described in Algorithm 1.  
```
1. Initialization: (1) Initialize the network scenario and the values \(\lambda_{u}\), \(\lambda_{m}\) and \(\rho_{p,m}\), where \(\rho_{p,m}\in(0,30]\). (2) Set the initial value of \(B_{p}=0.1\). (3) Denote \(\kappa\) as the variable step length of \(B_{p}\). Denote \(EE^{*}\) as the optimal value of the network EE. Denote \(B_{p}^{*}\) as the optimized CRE bias. Let \(B_{p}^{*}=B_{p}\), \(EE^{*}=EE\left(B_{p}\right)\,\Big{|}_{\lambda_{p},\rho_{p,m}}\) and \(\kappa=0.1\). 2. Calculate the optimal pico CRE bias while\(B_{p}\leq 25\)do \(B_{p}=B_{p}+\kappa\) \(EE^{\prime}=EE\left(B_{p}\right)\,\Big{|}_{\lambda_{p},\rho_{p,m}}\) according to Equation (14) if\(EE^{\prime}>EE^{*}\)then \(B_{p}^{*}=B_{p}\), \(EE^{*}=EE^{\prime}\) endif endwhile
```  
**Algorithm 1**CRE Bias Optimization (CBO) Algorithm.