esa-sceva/satcom-chunk-collection huggingface dataset:
# Computing RF Tree Distance over Succinct Representations
## 3 Methodology
### Balanced Parentheses Representation  
A balanced parentheses representation is a method frequently used to represent hierarchical relationships between nodes in a tree, closely related to the newick format described before. Figure 4 depicts an example of a rooted labelled phylogenetic tree and its balanced parentheses representation. In this kind of representation, there are some key rules to understand how a tree can be represented by a sequence of parentheses. The first rule is that each node is mapped to a unique index given by the tree pre-order transversal. For instance, in Figure 4, a node with label \(A\) is mapped to 4. The second rule is that each node is represented by a pair of opening and closing parentheses. For example, in Figure 4, a node with index 4 is represented by the parentheses in 4 th and 5 th positions. In terms of memory representation, the bit vector keeps this information, where the opening parentheses are represented as a 1 and the closing ones as a 0. This is the reason why the size of the bit vector is 2 times the number of nodes. We notice that it is not necessary to explicitly store the index of each node.  
Figure 3: Two rooted labelled and weighted phylogenetic trees (**a**) \(T_{2}\) and (**b**) \(T_{3}\). Both have a unique set of labels \(\{A,B,C,D,E,F,G,H,I\}\). Each cluster of each tree is defined by an edge and it has a corresponding weight.  
The third rule is that for all nodes present in the tree, all their descendants appear after the opening parentheses and before the closing parentheses that represent them. For example, in Figure 4, the opening and closing parentheses that represent the third node are in Positions 3 and 8, respectively. Then, we can conclude that the parentheses that represent its descendants (Nodes 4 and 5) are in Positions 4, 5, 6, and 7, which are between 3 and 8. Throughout this document, we refer to an index of a node as \(i\) and to a position in a bit vector as \(p\).
# Photoacoustic Imaging of Tattoo Inks: Phantom and Clinical Evaluation
###### Abstract  
Photoacoustic imaging (PAI) is a novel hybrid imaging modality that provides excellent optical contrast with the spatial resolution of ultrasound in vivo. The method is widely being investigated in the clinical setting for diagnostic applications in dermatology. In this report, we illustrate the utility of PAI as a non-invasive tool for imaging tattoos. Ten different samples of commercially available tattoo inks were examined for their optoacoustic properties in vitro. In vivo PAI of an intradermal tattoo on the wrist was performed in a healthy human volunteer. Black/gray, green, violet, and blue colored pigments provided higher levels of PA signal compared to white, orange, red, and yellow pigments in vitro. PAI provided excellent contrast and enabled accurate delineation of the extent of the tattoo in the dermis. Our results reveal the photoacoustic properties of tattoo inks and demonstrate the potential clinical utility of PAI for intradermal imaging of tattoos. PAI may be useful as a clinical adjunct for objective preoperative evaluation of tattoos and potentially to guide/monitor laser-based tattoo removal procedures.  
photoacoustic imaging; tattoo; dermatology; ultrasound
## 4 Results and Discussions
### The Discussion on the Main Findings of the Research in Practical Reference  
Based on the results and evaluation of the four cases regarding placing WTs on a grid with different settings, as implemented in Sections 4.1-4.4, the paper is focused on the following terms:  
1. For the planning problems, the first priority of these problems is to determine the optimal sizes and positions of the wind turbines (WTs) placed in the grid so that the power loss value will be reduced, and other operational characteristics of the original grid will still be reserved. The presence of WTs must improve both economic and engineering benefits. Hence, the research has implemented different cases of placing WTs on the grid, as presented in the first three cases. Particularly, if WTs can only supply active power to the grid, the WTs should be placed at the positions and sizes found by the WOA, as shown in Table 1, to reach the best power loss value. However, this option results in a relatively high WT capacity, leading to a high capital cost for construction, operation, and maintenance. In addition, the lack of reactive power supply from WTs is considered to be a disadvantage of this option and leads to the limited enhancement of the voltage profile at some periods in the operation process. To partly remove the downsides of the first option, which was implemented in Case 1, Case 2 and Case 3 have been conducted. As a result, the power loss values in Case 2 and Case 3 are dramatically reduced, as shown in Figures 12 and 18. Especially in Case 3, the total capacity of WTs resulted in this case is the lowest value compared  
Figure 28: Total power supplied by the two WTs within a year in two scenarios.  
Figure 27: Total power supplied by two WTs at each hour of each month in the fourth quarter.  
to previous cases. Hence, Case 3 will be an affordable option to implement while considering both economic and engineering aspects.
2. For operational problems, load demand and power supplied by WTs are not constant values. In fact, load demand and power supplied by WTs consistently change within a day based on the rate of use and the wind speed at a specific period. Therefore, Case 4 is conducted to evaluate the power loss values and the operational characteristics of the grid with load demand variation and dynamic power supplied from WTs in 24 h using the optimal position as determined by the previous cases.  
In summary, the main findings of the entire research are that different cases and scenarios for solving the planning problems and operational problems while integrating WTs in the grid to optimize the benefits are proposed. Besides that, the research is only executed and reveals the conclusions in the radial distribution power grid, specifically IEEE-85. Research must be completed with other RDPG configurations, load demand, and wind data before coming to any conclusions.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model
### Data Preprocessing
#### 3.2.2 Feature Screening  
Since not all sensor monitoring params can offer valuable info in model prediction, and excessive redundant data in the training set will increase training time and cause overfitting (as the model may learn too much noise, affecting prediction accuracy), it is crucial to filter out high-quality and valid data before RUL prediction to enhance model prediction performance.  
Taking the FD001 dataset as an example, through visualizing the sensor data, certain representative data during a total of 20,631 cycle counts for 100 engines within the dataset are presented in Figures 6-8.  
Figure 8 illustrates the third type of sensor data. Overall, these data decreased monotonically with the increase in the number of engine cycles for each engine. This type of sensor encompasses a total of four sensors, namely No. 7, No. 12, and No. 20 and 21. Moreover, these sensors also exhibited a certain periodicity in their monitoring data.  
With regard to the three types of sensor monitoring data mentioned above, the monitoring data of the first category of sensors remained constant as the number of cycles changed. From this, it can be inferred that this category of sensors did not incorporate information regarding the degradation of engine performance. Consequently, the seven sensors belonging to the first category were excluded. In contrast, the other two types of sensor data exhibited monotonic variations in tandem with the number of engine cycles. Hence, it can be assumed that they contain certain information related to performance degradation. Ultimately, the remaining 14 sensors were selected for the subsequent research work.  
Figure 6: Sensors of the first type.  
Figure 7: Sensors of the second type. (**a**) The variation in the values of three representative sensors with respect to the engine running time. (**b**) The specific values of sensor #2 as the running time of engines #1-#4 increases.
## 3 Methodology
### Evaluation Metrics  
In order to evaluate the performance of the proposed model, we employ several commonly used evaluation metrics, including accuracy, recall, precision, and F1 score. Accuracy is a reliable metric when dealing with balanced datasets. However, in this study, imbalanced datasets were used. Imbalanced datasets consist of a significantly larger number of samples in the majority class, often resulting in model bias towards the majority class while neglecting the minority class. Therefore, accuracy should be regarded as a supplementary metric to evaluate model performance rather than the sole metric. When handling imbalanced datasets, the F1 score is widely employed as an evaluation metric as it incorporates both precision and recall, offering a more comprehensive assessment of model performance [54]. The evaluation metrics were computed using the values in the confusion matrix, which is presented in Table 6.  
Accuracy is the proportion of samples correctly identified out of all the samples. The calculation equation is as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{17}\]  
Recall, also called detection rate, refers to the ratio of the number of correctly identified samples belonging to a specific class to the total number of samples in that class. Recall is used to evaluate the ability of an IDS to identify attacks of a specific class. The calculation equation is as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
Precision is the proportion of samples identified as a specific class that are correctly identified, relative to the total number of samples identified as that class. The calculation equation is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{19}\]  
The F1 score represents the harmonic mean of precision and recall. It effectively assesses the balance between precision and recall in the classifier. The calculation equation is as follows:  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{20}\]
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.4 Landcover Data  
A 40-year (1982-2015) landcover dataset of GLASS-GLC product was developed by Professor [PERSON] at Tsinghua University using the global GLASS CDR dataset via the Google Earth Engine (GEE) platform. This dataset has a high spatial resolution of 5 km and a yearly temporal interval. The land cover is classified into the following seven types: cropland, forestland, grassland, shrub, tundra, barren land, and ice and snow. The dataset was openly obtained from the official website of the multi-discipline research database ([[https://doi.pangaea.de/10.1594/PANGAEA.913496](https://doi.pangaea.de/10.1594/PANGAEA.913496)]([https://doi.pangaea.de/10.1594/PANGAEA.913496](https://doi.pangaea.de/10.1594/PANGAEA.913496)), (accessed on 23 April 2019)).
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Implementation Details  
To facilitate comparison with the baseline, we utilize the same encoder and generator described by SV-Seg [19]. The encoders for each dataset are different, i.e., FKAConv [43] for ScanNet, ConvPoint [44] for S3 DIS, and KPConv [28] for SemanticKITTI. The generator uniformly employs the Generative Moment Matching Network (GMMN) [20]. All encoders are pre-trained on seen class data using parameters recommended in their related papers. When pre-training is complete, we train the generator using the frozen encoder to obtain features from the seen classes. We use \(GloVe+Word2 Vec\) to compute a \(600D\) semantic embedding vector for each category label. The model is trained for a total of 20 epochs with the Adam optimizer. The learning rates are 0.0002 for the generator, 0.007 for the encoder, and 0.07 for the segmentor. As for the visual-semantic fusion branch, we adopt the SGD optimizer with a setting of 0.0002 as the initial learning rate. We use the same hyperparameters for all three datasets. Specifically, the parameter \(\rho\) in Equation (9) is set to 1, the parameter \(\beta\) in Equation (10) is set to 1.2, and the parameter \(\lambda\) in Equation (13) is set to 0.7.
## 2 Mixer Design
### Improved Active Circuit Model  
After studying the active circuit model based on the SDM and the GDM, an improved active circuit model was proposed for the design of 0.34 THz terahertz mixer. As Figure 4 shows, an external auxiliary circuit was creatively added to the ADS to simulate together with mixing circuit and was used to manipulate RF insert loss. The mixing circuit includes a DC grounding, suspended microstrip transmission line, RF probe transition, suspended microstrip step structure, hammer-head filter, LO probe transition, CSMRs (compact suspended microstrip resonators) filter, RF/LO waveguide transition, and the shorted circuit end of RF/LO reduced waveguide. The external auxiliary circuit was taken from the RF part of the mixing circuit and had the same parameters as it. The impedance of port impedance termination for S-parameters was extracted from the suspended microstrip transmission line at one side of the diode. Four optimization goals were applied to this model, which include conversion loss, RF/LO return loss, and RF insert loss. In addition, a two-stage reduction process was applied for the waveguide to broaden working bandwidth.  
Figure 3: The schematic diagram of an anti-parallel Schottky diode. (**a**) The 3D electromagnetic (EM) model of the diode and its photograph; (**b**) A SPICE model of the anti-parallel Schottky diode.  
The improved active circuit model has many advantages. Compared with the active circuit model based on the SDM, there are more optimization variables and larger optimization space. It is much easier to obtain optimal results and the working bandwidth can be broader. Compared with the active circuit model based on the GDM, it can be more effective to regulate and control conversion loss and RF return loss due to the existence of the external auxiliary circuit. The simulations of the mixer's RF transition model and LO-IF diplexer model are necessary for this model as the optimization of initial parameters can save considerable time spent on the optimization process. By utilizing this model, the performance of terahertz mixer can be optimal.
## 5 Implementation and Evaluation Tests
### Flatting Module  
The schema-oriented ontology view focuses on only two main aspects of the ontology model: the property-object relationship and the specialization-generalization relationship. In this modeling approach, the other ontology elements do not play a direct key role. To extract the relevant elements, we perform a preprocessing step that generates a simplified representation of the input ontology. This flattening process converts the complex structures into a set of simple structures. The resulting description contains:  
* Spezialization relations (SubClassOf, ClassAssertion)
* Property-based relations (ObjectSomeValuesFrom, DataSomeValuesFrom, ObjectAllValuesFrom, DataAllValuesFrom, DataPropertyAssertion). The flattening process introduces temporal classes to substitute for complex class expressions. The transformation process generates new concepts for these constructional expressions. Then, all occurrences of the complex expression are replaced with a reference to the corresponding new concept. This substitution step can be used to reduce the depth of the global ontology. The transformation rules implemented in the flattening process are summarized in Tables 3 and 4. The conversion program manipulates the ontology description on the base XML level. The transformation module was developed in the XQuery language using the eXist-db _exist-db.org/exist/apps/homepage/index.html_ development framework. The XQuery language provides a number of important features for efficient management of XML documents, including XPath-based data selection, control flow of functional programming, and data updating. The code snippet in List (Section 5.1) presents the conversion code fragment used for processing the element ObjectInverseOf. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Element** & **Conversion** \\ \hline ObjectPropertyDomain, DataPropertyDomain & The axiom PropertyDomain(p,c) is equivalent with SubClassOf( ObjectSomeValuesFrom (p, *), c). Based on this rule, having PropertyDomain(p,c), we extend the ontology with SubClassof (c\({}^{\prime}\),c) for every c\({}^{\prime}\) with SubClassof(c\({}^{\prime}\),ObjectSomeValuesFrom(p, *)) \\ \hline  ObjectPropertyRange, DataPropertyRange & As the data range is irrelevant, this axiom is ignored. \\ \hline FunctionalObjectProperty, InverseFunctionalObjectProperty, ReflexiveObjectProperty, SymmetricObjectProperty, TransitiveObjectProperty, FenctionalDataProperty, HasKey, SameIndividual, DifferentIndividuals, ObjectHasSelf & As the model neglects the semantic relationship among the properties, this axiom is ignored. \\ \hline Annotation* & All annotation elements are ignored. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transformation rules used in prepocessing.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Parameters
#### 2.3.1 Water Quality Parameters
Our approach was based on a Support Vector Regression model, using data from CMEMS, Sentinel-3 and Sentinel-2, and _in-situ_ observations. The values of chl-a and SST were stored in an array created for every set of coordinates, also including the values of one, two, and three days prior to the sampling. The technical aspects regarding the SVR model's usage, training, optimization, and testing are extensively discussed in our earlier publication [19]. Biogenic Oil Film (BOF): Surface oils can be detected in both SAR and optical images based on their effect on the sea surface. They affect the sea surface roughness, causing elimination of short gravity capillary waves, which appears as a dark formation in SAR imagery [14; 15; 16; 17]. Additionally, they absorb most of the visible light, resulting in lower reflectance values, affecting the color of the sea surface, and appearing darker than the surrounding area in optical images. Thus, according to previous research [14; 24; 25; 26; 27; 28], multispectral imagery can also be used for the detection of sea surface oils. To benefit from the advantages of the different capabilities of each sensor, we used a combined approach to detect the surface oils near the cages, using both Sentinel-1 and Sentinel-2 data, in order to identify the dark formations and classify them as \"biogenic oil film\" or lookalike [14]. The detected objects were vectorized and classified and then compared with _in-situ_ photos to validate the methodology. Satellite data availability is greatly dependent on the atmospheric conditions at the time of acquisition. Poor atmospheric conditions and cloud coverage hinder the ability of the sensors to make accurate observations of the Earth's surface, often leading to faulty or missing measurements. However, successful aquaculture monitoring needs a continuous stream of data in order to be useful in detecting near real-time threats to the facilities and assisting in meaningful management decision making. The most widespread technique in remote sensing applications for filling missing pixels is spatial interpolation. In this application, a spatiotemporal kriging technique is applied to interpolate missing information. Kriging, as a family of interpolation methods, scopes to model the spatial variability of the parameter in question by its anisotropy [29]. This is accomplished through the construction of a semivariogram which depicts the parameter's spatial autocorrelation at various lagged distances. Spatiotemporal kriging is a more specialized form of ordinary kriging in which temporal variability of the parameters is taken into account [30]. In this case, a 3D semivariogram is constructed that calculates the temporal anisotropy along with the spatial. Spatiotemporal interpolation is conducted for both Sentinel-3-derived chl-a and SST through the gstat package in R. The interpolation results are projected into a common 300 m spatial grid which enables further co-processing of the results.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 2 Methods  
In the previous GAPM method [15; 16], the observing sphere was divided into latitudes and longitudes grids by \(1^{\circ}\times 1^{\circ}\), and the probability of a satellite appearing in one grid can be determined by its velocity. This probability is regarded as the satellite observing probability, which is the basis of the estimation of satellite visibility and DOP. For one specific station on the earth ellipsoid, the number of visible satellites is the sum of probability of the constellation in the observation area of the station. Furthermore, the satellite probability is regarded as weighting factors in the calculation of DOPs. Based on the DOPs, the performance and characterization of satellite visibility and geometry distribution for one GNSS constellation can be further evaluated. It is noted that the opportunities of a satellite's appearance in certain areas are zero where the latitude is higher than the satellite's orbit incline angle. Because the previous GAPM method is only applicable for the approximately circular orbit, we modify and extend the GAPM method in this paper to make it applicable for BDS-3 IGSO, QZO, and HEO satellites.
## 3 Maintenance and Lifespan Management
### Developing the Means that Enable Signals to be Digitally Acquired  
The sensors must be integrated into the digital world, and the information generated must be safe and robust. Potential subareas are:  
* The self-diagnosis in sensors will allow advanced maintenance, therefore, intelligent sensors are necessary.
* The capture of the data should be more dynamic, which will allow recognizing the problems more exhaustively.
* The anticipation of problem detection suggests the use of monitoring of partial discharges in the electrical lines.
* Development and implementation of the IEC 61850 standard will allow the SG to become digital.  
After the review of the works, the author has considered grouping them in the following subsections: Sensors and Parameters, Standardization and normalization and Partial Discharges.