esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of Guided-Wave Circumferential Scanning Technique for a Pipe  
The guided-wave circumferential scanning technique can obtain two-dimensional (2D) information such as the circumferential size and distribution of a reflector over the conventional A-scan, meaning that the location and extent of the reflector can be then presented on a map. Compared with the imaging technique using the guided-wave multi-channel phased array technique [15; 16], the circumferential scanning technique with fewer circumferential transducers is more flexible and easier to operate [17]. As illustrated in Figure 2a,b, a pipe with a reflector is unwrapped by moving the transducer with a given step \(\eta\) along the circumferential direction while the scanned 2D circumferential scanning image of the reflector is reconstructed.  
In this work, firstly, we can discriminate the geometric features like a flange, pipe end or girth weld from the defect features by utilizing the circumferential scanning technique illustrated in Figure 3a-f, as these reflecting features are continuous from bottom to top in the 2D circumferential scanned image shown in Figure 3e,f due to their axisymmetric geometry. Compared with Figure 3c,d, Figure 3e,f contain the circumferential distribution of the reflectors. However, it is difficult to judge whether a reflector like a support is a defect feature such as a corrosion, since both the support and the corrosion are both discontinuous from the bottom to the top in the circumferential scanned image as a consequence of them being usually located within a small circumferential range (angle), as illustrated in Figure 2b. This phenomenon introduces the serious possibility of false positives or negatives, such as a support being incorrectly identified as a defect.  
Figure 3: _Cont._  
Figure 2: Schematic showing the pipe and its unrolled image by the guided-wave circumferential scanning technique: (**a**) numerical model; (**b**) circumferential scanning image.  
For the above problem, in order to further discriminate whether a reflector is a defect feature decreasing the cross-sectional area or a geometric feature increasing it, a novel method based on the phase characteristics is proposed. A further detailed discussion about its algorithm is covered in Section 4.
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 3 Results
### Sensitivity of the Model to the Availability of Renewable Resources  
When the installed capacity of each renewable technology increases, measured as a percentage of the demand, the portion of the total demand satisfied by renewable generation sources increases.  
Figure 3 shows that the demand coverage trend from renewable sources as they increase their availability is linear, marking more extensive use in terms of the solar source, as it uses approximately 100% of its installed capacity within the time horizon, which is not the case for the wind source. However, despite the inferior performance of the wind source compared to the solar source, its use in microgrids is justified due to its generation availability over the entire time horizon.  
In conjunction with the previous results, Figure 4 presents the operating result of the microgrid when the renewable capacity is established at 45% of the maximum demand. This result shows that, in order to better meet the demand from renewable sources, it will  
Figure 3: Trend of use for renewable sources.  
Figure 2: Sensitivity of the model to changes in demand. (**a**) Percentage of renewable energy to meet the demand for each experimental demand level. (**b**) Cost savings comparison for each level of demand using the same installed capacity.  
be necessary to install a greater wind generation capacity, since this generation source is available over the entire operating horizon of the microgrid. However, a similar effect can be achieved with a smaller additional solar capacity and a larger battery capacity to be able to supply the night demand from stored solar energy.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Hazard Analysis and Risk Evaluation
#### 5.1.2 PEF Ignition Modeling  
The probability of PEF ignition is assessed by considering both internal and external ignition sources. A dual-cause ignition model is commonly used to capture the probabilistic nature of fire ignition in cascading earthquake-fire events [2]:  
* External ignition sources typically arise due to ground-shaking-induced failures, such as ruptured gas pipelines, damaged electrical substations, or compromised industrial storage facilities;
* Internal ignition sources are linked to structural response mechanisms, such as electrical short circuits, sparking due to mechanical deformations, or ignition from heated surfaces in damaged infrastructure.  
A Fault Tree Analysis (FTA) approach is often employed to quantify the likelihood of ignition, considering both direct damage mechanisms and secondary effects (e.g., gas accumulation leading to delayed fire outbreaks) [2].
## 4 Experiments
### The Performance of Similar User Recommendation
#### 4.2.3 Effects of Multimodal Feature Fusion  
To validate the effectiveness of multimodal feature fusion, we further compared the performance of combining text and image features with CNN models using early fusion and late fusion. In early fusion, we concatenate text and image features as inputs to a single CNN model, while, in late fusion, we separately input text features and image features into TextCNN and ImageCNN models, with the resulting feature map and categories are combined as the user feature for recommendation. The performance comparison is shown in Figure 9.  
As shown in Figure 9, the best performance of early fusion could be obtained when the number of dimensions was 2100, with the average top-k accuracy of 0.473. For late fusion, the best performance could be obtained with the average top-k accuracy of 0.491, when we used the categories as features, with the number of dimensions of 4096. We could observe the better performance of both early fusion and late fusion than the baseline_pic model with a simple concatenation. This shows the effectiveness of feature fusion, especially for late fusion. In the case of late fusion, we further address the effect of k on the average top-k accuracy and number of hits in Figure 10.  
As shown in Figure 10, the average number of hits in our recommendation increased when k increased. Moreover, we could obtain the best average top-k accuracy of 0.491 when k = 50. This was also the best performance in all of our experiments. In the remaining experiments, we used the value of k as 50.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Linear Triangulation Method  
Denoting the \(n^{\text{th}}\) row of \(\mathbf{Q}_{c_{j}}\) as \(q_{c_{j}}^{n}\) the projection of \(\hat{x}_{w}\) to \(\hat{x}_{s_{1}}\) is described by the following three equations  
\[\alpha_{1}\hat{x}_{s_{1}}=q_{c_{1}}^{1\ T}\hat{x}_{w},\alpha_{1}\hat{y}_{s_{1}}= q_{c_{1}}^{2\ T}\hat{x}_{w}\text{and }\alpha_{1}=q_{c_{1}}^{3\ T}\hat{x}_{w}. \tag{46}\]Similarly, for \(\hat{\pi}_{s_{2}}\) this is given as  
\[\alpha_{2}\hat{x}_{s_{2}}={q_{c_{2}}^{1}}^{T}\hat{\pi}_{w},\ \alpha_{2}\hat{y}_{s_{2}}={q_{c_{2}}^{2}}^{T}\hat{\pi}_{w}\ \text{and}\ \alpha_{2}={q_{c_{2}}^{3}}^{T}\hat{\pi}_{w}. \tag{47}\]  
Using the third relation of Equations (46) and (47) to remove the scaling variables, \(\alpha_{1}\) and \(\alpha_{2}\), the following relation is obtained.  
\[\left[\begin{array}{c}\hat{x}_{s_{1}}{q_{c_{1}}^{3}}^{T}-{q_{c_{1}}^{1}}^{T} \\ \hat{y}_{s_{1}}{q_{c_{1}}^{3}}^{T}-{q_{c_{1}}^{2}}^{T}\\ \hat{x}_{s_{2}}{q_{c_{2}}^{3}}^{T}-{q_{c_{2}}^{2}}^{T}\\ \hat{y}_{s_{2}}{q_{c_{2}}^{3}}^{T}-{q_{c_{2}}^{2}}^{T}\\ \end{array}\right]\hat{x}_{w}=S\hat{x}_{w}=0 \tag{48}\]  
\(\hat{x}_{w}\) is computed, using singular value decomposition, as the unit eigenvector of \(S^{T}S\) associated with the smallest eigenvalue [65].
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 1 Introduction
The altitude, slope, direction, latitude, longitude and other factors impact the accuracy of TRMM precipitation [18]. Therefore, TRMM precipitation products should be corrected before being applied in some areas, especially high mountain areas. Based on the evaluation of TRMM 3B42 RT and 3B43 in the Qaidam Basin, northwest of China, this paper fused the gauged data and remote sensing data of precipitation by machine learning and assessed the rainfall resources in the Qaidam Basin. The methodology can be used in other sparse gauged areas.
## 2 Materials and Methods
### Criteria of Accuracy Assessment of Models  
Five parameters were used in this research for the evaluation of the accuracy of regression models: coefficient of correlation (R), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe coefficient (NSC). These metrics were calculated according to the following equations:  
\[R=\frac{\sum(Y_{meas}-\overline{Y}_{meas})\left(Y_{pred}-\overline{Y}_{pred} \right)}{\sqrt{\sum\left(Y_{meas}-\overline{Y}_{meas}\right)^{2}\sum\left(Y_{ pred}-\overline{Y}_{pred}\right)^{2}}} \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{pred}-Y_{meas}\right)^{2}} \tag{9}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|Y_{pred}-Y_{meas}\right| \tag{10}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{meas}-Y_{pred}}{Y_{meas}}\right| \tag{11}\]  
\[NSC=1-\left[\frac{\sum_{i=1}^{n}\left(Y_{meas}-Y_{pred}\right)^{2}}{\sum_{i=1 }^{n}\left(Y_{meas}-\overline{Y}_{pred}\right)^{2}}\right] \tag{12}\]  
where \(Y_{pred}\) is the absolute predicted value, \(\overline{Y}_{pred}\) is the mean predicted value, \(Y_{meas}\) is the absolute measured value, \(\overline{Y}_{meas}\) is the mean measured value, and \(n\) is the amount of data in a dataset.  
The high values of R and NSC and low values of RMSE, MAE, and MAPE are not reliable enough for ML model assessment. Therefore, the multi-criteria approach [63; 64] was used, and the generalization ability (GA) was the additional metric calculated for the ANN and SVM models. GA, defined by [PERSON] et al. [65], is calculated as follows:  
\[GA=\frac{RMSE_{validation}}{RMSE_{train}} \tag{13}\]  
[PERSON] et al. [66] suggested the following classification of prediction models based on GA metrics:  
* The model is perfect if \(GA=1\);
* The model is excellent if \(0.75\leq GA<1\) or \(1<GA\leq 1.35\);
* The model is good if \(1.35<GA\leq 2\) or \(0.5\leq GA<0.75\);
* The model is poor and unsuitable for prediction if \(GA>2\) or \(GA<0.5\).  
The objective function (OBJ) used for the multi-criteria assessment of the precision of machine learning models was calculated according to the following equation:  
\[OBJ=\left(\frac{N_{T}-N_{V}}{N_{T}+N_{V}}\right)\frac{RMSE_{T}+MAE_{T}}{R_{T} ^{2}+1}+\left(\frac{2N_{V}}{N_{T}+N_{V}}\right)\frac{RMSE_{V}+MAE_{V}}{R_{V}^ {2}+1}\]  
where \(N_{T}\) is the amount of data in the training dataset and \(N_{V}\) is the amount of data in the validation (testing) dataset. The lower value of OBJ indicates a more accurate model.
## 4 Discussion
### Limitations  
It should be noted that there are limitations in the methods and data in this study. We used the conventional water balance equation with fewer data requirements to assess the effects of ET variations on each water balance component. It should be known that the physical hydrological models reflecting the complex hydrological processes of the catchment could have more precise results. Meanwhile, we estimated the irrigation water amount according to the ET without consideration of the influence of different agricultural management practices, especially with the assumption of the unchanged water mass storage under irrigation practices. Nevertheless, as the focus of this study was to compare the effects of different ET products on runoff, water storage, and irrigation water amount, this simplified method was good enough to address the research questions in this study.
# Centralized Mission Planning for Multiple Robots Minimizing Total Mission Completion Time
###### Abstract
In addition, the ant colony algorithm is used for solving TSP, so the calculation cost of the algorithm will be fatal when the number of hovering points becomes larger. [PERSON] et al. also proposed path planning algorithms for multiple fixed-wing UAVs for minimizing mission completion time [34]. The algorithm focused on the path planning of fixed-wing UAVs for UAV-to-UAV (U2U) communication with the aim of minimum information transmission time in the presence of ground transmitters. The constraints contain the communication throughput requirement, interference from ground transmitters, the speed and acceleration range of each UAV, and the minimum communication distance of UAVs. However, they only consider only one mission: communicating two UAVs with each other. Hence, the algorithm calculates the local path planning for each UAV to minimize information transmission time, not the sequence of missions for each UAV. In this paper, we propose a centralized mission planning algorithm for multi-robot-multi-mission problems, minimizing total mission completion time. The proposed algorithm is designed based on CBBA to overcome some limitations such as computation time and necessity to set the same home for all robots in the above algorithms. First, we introduced the algorithm for single-robot-multi-mission problems, minimizing total mission completion time based on the method using the bundle construction step in CBBA. It focused on how to decide the sequence of missions that the robot has to work on. Then, the algorithm was expanded for multi-robot-multi-mission problems. To overcome limitations of CBBA, CCBBA, and TACTAR, we proposed bundle adjustment, which adjusts the mission plan only if the total mission completion time could be lower by the adjustment. In order to apply the status of each robot in real time, the proposed algorithm should be run iteratively. However, if the positions of robots are changed, the mission plan could fall into another local optimal solution, so the mission plan could be changed frequently. To prevent this phenomenon, the initial bundle construction, which is based on the arrival times to missions for robots, is performed only at the first run, and the previous mission plan is used from the second iteration instead of the initial bundle construction. Then, some simulation results using the proposed algorithm will be provided.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
###### Abstract  
The important disease Ramularia leaf spot of barley is caused by the fungus _Ramularia colllo-cysni_. The disease causes yield and quality losses as a result of a decrease in photosynthesis efficiency due to the appearance of necrotic spots on the leaf surface. The development of these typical Ramularia leaf spot symptoms is thought to be linked with the release of phytotoxic secondary metabolites called rubellins in the host. However, to date, neither the biosynthetic pathways leading to the production of these metabolites nor their exact role in disease development are known. Using a combined in silico genetic and biochemistry approach, we interrogated the genome of _R. collo-cysni_ to identify a putative rubellin biosynthetic gene cluster. Here we report the identification of a gene cluster containing homologues of genes involved in the biosynthesis of related anthraquinone metabolites in closely related fungi. A putative pathway to rubellin biosynthesis involving the genes located on the candidate cluster is also proposed.  
Keywords:anthraquinone biosynthesis; Baeyer-Villiger monooxygenase; polyketide synthase; _Ramularia collo-cysni_; rubellin; chrysophanol; secondary metabolite +
Footnote †: journal: _Molecular Sciences_
# Dosimetric Evaluation in Micro-CT Studies Used in Preclinical Molecular Imaging
## 4 Discussion
The total dose corresponds to the injection of 37 MBq (1 mCi) of \({}^{99\mathrm{m}}\mathrm{T}\mathrm{c}\mathrm{M}\mathrm{D}\) (resulting in 1.7 \(\mathrm{G}\mathrm{y}\) dose), a CT scan operating at 45 kV, 400 \(\mathrm{\SIUnitSymbolMicro}\)A, and 600 views in 7 min (10 \(\mathrm{c}\mathrm{G}\mathrm{y}\) dose), and 3.7 MBq of \(\mathrm{Na}^{18}\mathrm{F}\) (2 \(\mathrm{c}\mathrm{G}\mathrm{y}\) dose). However, the present study's results showed an IDS of 22.3 \(\mathrm{c}\mathrm{G}\mathrm{y}\) for the same acquisition modality (Best, i.e., 600 views), two times higher. The highest dose comes from the CT scan, which makes it necessary to characterize the performance of this equipment in terms of the radiation dose resulting from the CT scan acquisitions (_Standard_, _Good_, _Best_, and _High Res_) to evaluate the possible biological effects of radiation associated with the CT scan while maintaining the best resolution achievable. The imparted dose at surface delivered to mice during a tomographic study using the Albira's microCT, at different image-quality settings and at the highest potential (i.e., 45 kV) of the X-ray tube, is in the 30 to 300 mGy range. This dose is below the lethal range, which is between 5.0 and 7.0 Gy [5]. However, the upper limit, which corresponds to _High Res_ quality, is just above the threshold for deterministic effects in rodents (250 mGy), which can affect both DNA repair and the cell cycle [5, 6, 7, 34]. Therefore, particular caution should be exercised when considering the use of this acquisition modality, since the imparted dose may interfere with the experimental outcome. The results of this study also show that for any combination of current and voltage, in _Standard_, _Good_, and _Best_ modalities, the dose imparted to the surface level in rodents is below the threshold of deterministic effects. Besides the strain on the mice, other factors, such as age at exposure and health must be considered, since they also define the extent of the biological effects of exposure to X-ray radiation [5, 6, 7, 34, 35]. Therefore, cautious criteria should be used to optimize microtomography studies on the use of the Albira system to deliver sufficiently low doses of radiation to rodents without compromising image quality.
## 3 System Model
### Space Segment  
The space segment consists of a satellite in geosynchronous (GEO) orbit. This satellite is a general purpose satellite in another communication system, e.g., a commercial service and civil service satellite system. It is temporarily rented for emergency communications rather than being a specified satellite, which is researched, developed and launched for emergency communications [29]. Therefore, the emergency system will be low cost. The functions of the space segment mainly include four aspects:  
* Data back to the core network, _i.e._, the Ka/Ku band or laser backhaul link [30];
* Emergency links for the rescuers' and victims' terminals, _i.e._, the L/Sband links [20];
* Relay links from the HAPs, _i.e._, the Ka/Ku band or laser relay links [8];
* Broadcasting the LSA for the HAPs and terminals. The detail of the LSA is given in Section 4.1.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 5 Improvement of Consensus Mechanisms and Quality Testing of Recipes
### Recipe Evaluation Criteria  
For the current production environment, the data transmitted by the nodes are digital recipes, and the improved consensus mechanism is to classify the nodes by evaluating the quality of the recipes, so we need to adopt an open, transparent, and reliable way to evaluate the quality of the recipes.  
This thesis uses Logic (\(L\)), detail (\(D\)), and completeness (\(C\)) to rate the quality of recipes.  
Completeness (\(C\)): The user must submit a recipe containing three types of data, i.e., recipe name, ingredients, and steps. These three parts are needed to build a complete recipe.  
Logic (\(L\)) is determined according to whether each item in the list of ingredients appears in the corresponding step: \(1=\) (number of ingredients that appear in the cooking step/total number of ingredients) \(*\) 100.  
Degree of detail (\(D\)) measures the number of modifiers and verbs in the ingredients and steps of the recipe. Specifically, a recipe is considered more detailed if it contains a large  
Figure 10: Improved DPOS.  
number of modifiers for ingredients (such as \"fresh\" and \"tender\") and specific quantities required for the ingredients (such as \"grams\", \"stalks\", and \"spoons\"), as well as a large number of verbs in the steps (such as \"cut\", \"saute\", and \"stir-fry\"). The specific calculation formula is: ((number of ingredient modifiers + number of step verbs)/(number of types of ingredients + number of types of step verbs)) \(*\) 100.  
The total score is \(S=(\alpha*L+\beta*D+\gamma*C)\), where \(\alpha\), \(\beta\), and \(\gamma\) represent the different weights of each score. After actual comparison, it is found that beginners usually pay more attention to the logic and the degree of detail, so to address this phenomenon, the values of \(\beta\) and \(\gamma\) are increased, as well as the weight of logic and the degree of detail. The specific allocation is \(\alpha=0.2\), \(\beta=0.4\), \(\gamma=0.4\), as described in Algorithm 4.  
```
0: Dictionary recipe, float \(\alpha\), \(\beta\), \(\gamma\)
0: float \(S\)
1: Assign 'ingredients' and'steps' from recipe to variables
2: Calculate \(L\) as the percentage of ingredients mentioned in steps
3: Calculate \(D\) as the percentage of detail words in ingredients and steps
4: Calculate \(C\) as the percentage of keys ('name', 'ingredients','steps') present in recipe
5: Calculate \(S\) as the weighted sum of \(L\), \(D\), and \(C\) (\(S=\alpha*L+\beta*D+\gamma*C\))
6:return\(S\)
```  
**Algorithm 4** Recipe Scoring Algorithm
## 1 Introduction
Fourth, real-time data visualization is needed to efficiently manage entire manufacturing sites by monitoring and managing work status, facility management, quality inspection data, and abnormalities in real time. In this paper, we propose an NGIM HCPS 2.0-based smart factory system comprising an alarm level-based double verification framework that is applicable to heterogeneous equipment, a deep learning-based algorithm, a web-based integrated MES, and data visualization for real-time field management. This paper makes the following specific contributions:  
1. The proposal of a cloud-fog-edge distributed network that can connect heterogeneous devices in industrial sites using fog- and edge-based (rather than cloud-based) central control in order to address the bottleneck;
2. The proposal of a knowledge distillation-based algorithm that can efficiently apply deep learning-based algorithms that require a large amount of computing resources;
3. The proposal of an NGIM HCPS 2.0-based MES function that is optimized for PCB manufacturers to help them grow into world-class small- and medium-sized businesses by increasing global corporate competitiveness in the twenty-first century. This paper is organized as follows. The technical framework and core technology are reviewed in Section 2. The core technology of the smart factory system that was designed for PCB manufacturers is detailed in Section 3. Section 4 describes the smart factory system that was implemented and Section 5 presents our conclusion and future challenges.