esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In-line inspection (ILI) data are subject to uncertainties due to the limitations of inspection tools and the complex behavior of corrosion. These uncertainties challenge feature matching and negatively influence its accuracy. This study proposed an extensible feature matching model based on consecutive ILIs and data clustering to effectively capture spatial variability and interactions between adjacent defects. Matching strategies, such as point matching and distance-based filtering, are then applied following a classification approach to solve the matching problem independently within each cluster. This framework successfully demonstrated its ability to address key challenges in feature matching. For instance, isolated corresponding features and merging defects were efficiently identified. Localized transformations within clusters minimized the influence of distortions and outliers, enabling more precise feature alignment. Additionally, a new clustering technique, DENV, was developed to capture directional variability and identify outliers in ILI data by utilizing spatial graph structures and directional proximity thresholds, further enhancing the model's performance.  
The proposed model, including its DBSCAN-based alternative, was evaluated on six internally corroded pipeline segments with varying ILI data complexities. The model achieved a recall of 91.5%, outperforming the DBSCAN-based alternative, which achieved 89.6%, reflecting DENV's ability to align with the directional variability in ILI data. Nevertheless, both alternative models demonstrated noticeable performance and stability improvements over exclusively point matching models such as the Voronoi model by [PERSON] et al. [10], which achieved a recall of 84.4%.  
Future research could explore adaptive parameter tuning strategies to optimize the model for varying conditions along the pipeline. Parameters such as \(\varepsilon_{x}\), \(\varepsilon_{y}\), \(\alpha\), and \(\lambda\) could be dynamically adjusted based on feature density, position variability, alignment patterns, or interaction complexities across different pipeline sections. Furthermore, future research could address the limitations of spatial clustering techniques such as DENC and DBSCAN, which cannot guarantee the accurate representation of all defect merging configurations, as these methods rely on spatial proximity, and merged defects may be misclassified if they are close in proximity to unrelated defects.  
Conceptualization, M.S.; methodology, M.S.; software, M.S.; validation, M.S. and P.F.; formal analysis, M.S.; investigation, M.S.; resources, M.S. and P.F.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S. and P.F.; visualization, M.S.; supervision, P.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Parametric Studies
#### 2.3.3 Mass Ratio  
The effect of the mass ratio (\(s_{\rm m}\)) on the frequency ratios (\(\omega_{0,i},\ i=1,\ 2\)) in the 1 st basic mode for different boundary conditions is depicted in Figure 7. Both \(\omega_{0,1}\) and \(\omega_{0,2}\) decrease with \(s_{\rm m}\) and the decreasing rate of \(\omega_{0,2}\) is very large when \(s_{\rm m}\) is small. Figure 8 shows the effect of \(s_{\rm m}\) on the mode amplitude ratios (\(\phi_{0,i},\ i=1,\ 2\)) in the 1 st basic mode for different boundary conditions. Both \(\phi_{0,1}\) and \(\phi_{0,2}\) increase with \(s_{\rm m}\) and the increasing rates are large when \(s_{\rm m}\) is small. \(\phi_{0,1}\) is smaller than 1 when \(s_{\rm m}\) is smaller than 1 when \(s_{\rm m}\) is smaller than 1 and \(\phi_{0,1}\) is larger than 1 when \(s_{\rm m}\) is larger than 1. \(\phi_{0,2}\) is smaller than \(-1\) when \(s_{\rm m}\) is smaller than 1. \(\phi_{0,1}\) and \(\phi_{0,2}\) are closer to 1 for looser boundary conditions, which means that the modes of two beams are closer to each other for looser boundary conditions.  
Figure 8: Mode amplitude ratios in two sub-modes corresponding to the 1 st basic mode versus contact stiffness ratio for different boundary conditions when \(s_{\rm e}=100\) and \(s_{\rm k}=1\).  
Figure 6: Mode amplitude ratios in two sub-modes corresponding to the 1 st basic mode versus contact stiffness ratio (\(s_{\rm e}\)) for different boundary conditions when \(s_{\rm m}=1\) and \(s_{\rm k}=2\).  
Figure 7: Frequency ratios in two sub-modes corresponding to the 1 st basic mode versus contact stiffness ratio for different boundary conditions when \(s_{\rm e}=100\) and \(s_{\rm k}=1\).
# Software for Monitoring the In-Service Efficiency of Hydraulic Pumps
## 2 Algorithm for Assessing Pump Efficiency
The operation of the pump in this range will be marked with \(\langle\)GREEN\(\rangle\);
* Operation at the limit (LO), from an efficiency point of view: needs scheduled maintenance (\(\langle\)YELLOW\(\rangle\)): \[H=\frac{(p_{d}-p_{s})}{g\rho}+\frac{8Q^{2}}{g\pi^{2}}[\frac{1}{D_{d}^{4}}- \frac{1}{D_{s}^{4}}]+(z_{d}-z_{s}) \tag{2}\]5 \[H=\frac{(p_{d}-p_{s})}{g\rho}+\frac{8Q^{2}}{g\pi^{2}}[\frac{1}{D_{d}^{4}}- \frac{1}{D_{s}^{4}}]+(z_{d}-z_{s}) \tag{2}\]6 A pump operating at the limit (LO) suggests the pump is operating with an acceptable efficiency but requires more power than is ideal. A pump operating under these conditions requires scheduled maintenance to identify and address any issues that may have occurred. The operation of the pump under these conditions will be signaled using the color \(\langle\)YELLOW\(\rangle\);
* Abnormal operation (AO) from an efficiency point of view: needs urgent maintenance (RED): \[H=\frac{(p_{d}-p_{s})}{g\rho}+\frac{8Q^{2}}{g\pi^{2}}[\frac{1}{D_{d}^{4}}- \frac{1}{D_{s}^{4}}]+(z_{d}-z_{s}) \tag{2}\]7 A pump operating under abnormal operation (AO) is pumping with low efficiency and requires additional power to pump. A pump operating under these conditions requires urgent maintenance to identify and address any issues that may have occurred. The operation of the pump under these conditions will be signaled using the color \(\langle\)RED\(\rangle\). The green stripe corresponds to Preferred Operating Region (POR) in our selection, while the extreme limits of the yellow area define the Allowable Operating Region (AOR) [4]. To be more explicit, the allowable operating region (AOR) is the operating zone provided by the manufacturer and it includes the yellow zones together with the green zone in our selection. The reference values predefined by \(\eta_{P_{BEP}}\) to circumscribe the fields of operation can be selected by each user based on the pump's operating conditions and on the recommendations issued by its manufacturer. The algorithm allows us to define these reference values for each pump by introducing them into their set of input values. Thus the predefined reference levels can be customized from one pump to another, depending on the required operating conditions and the specific in situ conditions [27] ch. 3. The boundaries of the operating regions (e.g., POR, AOR) in our algorithm are selected based on the pump efficiency boundaries. The limits of the operating regions defined in other references are related to the volumetric flow rate limits (e.g., range from \(-30\%\)\(Q_{BEP}\) to \(+15\%\)\(Q_{BEP}\)[28; 29] range from \(-10\%\)\(Q_{BEP}\) to \(+10\%\)\(Q_{BEP}\)[4] and range from \(-20\%\)\(Q_{BEP}\) to \(+20\%\)\(Q_{BEP}\) reference [30]. We consider that this concept of defining operating region boundaries using pump efficiency is better suited when the pump operation strategy is targeted, rather than defining operating region boundaries by selecting volumetric flow rate.
###### Abstract
In conventional radar charts, index values solely impact the shape without influencing evaluation results [18]. Nevertheless, in practical evaluations, index values often become a focal point of attention. Therefore, this paper proposes an improved radar chart that incorporates both index weights and values into its design to ensure more accurate and comprehensive evaluation results. This paper proposed an improved fuzzy analytic hierarchy process (IFAHP) based on an improved radar chart to evaluate and rank the performance of earthquake search and rescue robots, ultimately facilitating the selection of the search and rescue robot with superior overall performance. Firstly, a comprehensive set of evaluation indexes for search and rescue robots is constructed to address the limitations associated with using a single evaluation index. Subsequently, three types of alternatives for earthquake search and rescue robots are evaluated using performance tests to obtain a comprehensive evaluation set for the corresponding criteria. Additionally, the criterion layer and the weight of each evaluation index are determined using hierarchical analysis and a trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed, aiming to mitigate the influence of subjective factors on expert decision-making results. Subsequently, the performance indexes of earthquake search and rescue robots are analyzed, followed by a comparative assessment of the overall performance among different types of such robots using an improved radar chart. Finally, the accuracy and effectiveness of the proposed method on three types of earthquake search and rescue robots are verified in practical examples. The remaining sections of this paper are organized as follows: the related evaluation criteria and hierarchical structure model are shown in Section 2. The content and process of the performance test are described in Section 3. Then, the introduction to the improved fuzzy analytic hierarchy process is given in Section 4. A comprehensive and visual evaluation method for ranking and selection for the performance of earthquake search and rescue robots is presented in Section 5. Finally, the proposed method is illustrated with a case study of the three types of earthquake search and rescue robots in Section 6. A sensitivity analysis of the results is presented in Section 7. The main conclusions, limitations, and future works are summarized in Sections 8 and 9.
## 3 Results and Discussion
### Sequence, Physicochemical Nature and Bioactivity of Identified Peptides
All 9 samples from the three sampling sites, Mulroy bay, Killary harbour and Ardgoom, collected from November 2018 to October 2019, were used for gas chromatography mass spectrometry (CGMS)-based fatty acid analysis. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  
1. DPP (IV) inhibition: The DPP-IV inhibitory activity at 30 min for the generated mussel by-product hydrolyses ranged from 0% to 90.51% \(\pm\) 0.18%, whereas it was 81.14% \(\pm\) 0.47% for sitagliptin when samples were assayed at a concentration of 1 mg/mL compared to the positive control, sitagliptin, which was assayed at a concentration of 18 nM (IC\({}_{50}\) value) (Figure 7). 2. AChE inhibition: A trend similar to DPPIV inhibition was seen. AJuly19, KHS19 and KHO19 displayed AChE inhibition (Figure 8), though the % inhibition (KHO19: 29.59% \(\pm\) 1.88%) of the samples (1 mg/mL) was low as compared to positive control acotiamide dihydrochloride (94.201% \(\pm\) 0.89%) assayed at 100 uM concentration (IC\({}_{50}\) 3 uM). Researchers have found AChE, ACE-I and DPPH inhibitory activity in subcritical extracts of blue mussels [47]. The obtained results warrant further studies to explore the possible potential of some of these hydrolyses in promoting mental health. 3. ACE-I inhibition: All the samples showed appreciable ACE-I inhibition as compared to un-hydrolysed mussel broth, as seen in Figure 9. The percentage of ACE-I inhibition was seen to vary between 22.23% \(\pm\) 1.79% and 86.08% \(\pm\) 1.59% for hydrolyses as compared to 97.31% \(\pm\) 0.255% by positive control captorpil. The KHN18 sample inhibited ACE-I by 86.08% \(\pm\) 1.59% when assayed at a concentration of 1 mg/mL, compared to captorpil which was assayed at a concentration of 0.5 mg/mL. Further, the IC\({}_{50}\) value for KHN18 was 0.2944 mg/mL, which is lower than reported values of other marine hydrolyses (1.50-2.54 mg/mL) [48], highlighting the superior ACE-I inhibitor potential of the mussel hydrolyses. An interesting study conducted on _Mytilus edulis_-based fermented sauce also established the formation of ACE-I inhibitory peptides from blue mussel sources [14]. The findings from the in vitro assay also confirm the results obtained from bioinformatics mining of the sequenced peptides, as 92% of the peptides identified in the 3-kDa permeate fraction and assessed using BIOPEP had ACE-I inhibitory activity. Figure 7: DPP (IV) inhibition (%) by mussel Protamex® hydrolyses. Nine samples from the three sampling sites, Mulroy bay, Killary harbour and Ardgroom, collected from November 2018 to October 2019, were used for analysis at a concentration of 1 mg/mL. Analysis was performed in triplicate (n = 3). No significant difference was found between positive control Sitagliptin and samples AJuly19, KHS19, KHO19 after Dunn Bonferroni (nonparametric) post hoc analysis.
# Parameterized Algorithms in Bioinformatics: An Overview
## 3 Genome Assembly and Sequence Analysis
### Identifying Common Patterns
#### Center and Median Strings  
**Problem Description.**: In the problems described so far in this section, the goal is to find a string minimizing the distance to all input strings, where the distance counts the number of substitutions (Closest String and Consensus String), deletions (Longest Common Subsesquence) or insertions (Shortest Common Supersequence). Each problem has its own applications, depending on the input data and allowed operations. A more generic variant allows for all three operations, i.e., uses the edit distance (also referred to as the Levenshtein distance, denoted \(\mathsf{Lev}\)) to measure the similarity to the solution string.  
Center String and Median String  
**Input:** Strings \(s_{1},\ldots,s_{k}\), and some \(d\in\mathbb{N}\)  
**Question:**\(\exists\) string \(s^{*}\) with the following property?  
(Center String) \(\forall_{i}\,\mathsf{Lev}(s^{*},s_{i})\leq d\)  
(Median String) \(\sum_{i=1}^{k},\mathsf{Lev}(s^{*},s_{i})\leq d\)  
**Results.**: Both problems are NP-hard and W[1]-hard for \(k\), even on binary strings [70]. On the other hand, Center String is FPT for parameter \(d+k\)[71].  
**Open Problems.**: Are these problems FPT when parameterized by \(d\) only, or by the string length?
###### Abstract
It is, therefore, of utmost importance to use updated coefficients. Nowadays, the focus of satellite product analysis is more and more shifted towards time series analysis. As such, LST products should be consistent and fit to the concept of climate data records (CDR) [3]. This work therefore includes the generation of new coefficient sets for each of the AVHRR sensor, to enable analyses that focus on time series applications. The generation of the coefficients follows the development of e.g., [16], by generating different coefficient sets for different ranges of columnar water vapour, temperature, mean band emissivity and--if applicable--the difference of the two band emissivities. The focus of our analysis not only emphasises on the performance, as expressed by accuracy and precision measures, of the split-window algorithms themselves, but also on the sensitivity of the split-window algorithms to their input data (e.g., columnar water vapour). Performance assessments provide information on the magnitude of variation that can be expected from using one or the other split-window approach. The sensitivity analysis highlights the influence of the input variables into the split-window algorithms. This is important mainly in cases where the quality of an input variable is of varying quality. In such cases, reduced quality of input data caused by systematic and/or random effects is propagated to the LST generation--depending on its sensitivity. Both, the performance and the sensitivity measures can be further used for uncertainty and quality estimation of a final LST AVHRR time series product which relate product quality to state of the art user requirements (accuracy better than 1 K as given for example by GCOS ([23]). Current data providers of LST, which aim for long time series with high accuracy, are for example the LSA SAF, which will soon provide consistent Meteosat LST in a joint effort with CM SAF [24]. In addition, the Copernicus Land Programme provides LST from MeteoSat data ([4; 5]). Furthermore, the GlobTemperature Project using (Advanced) Along-Track Scanning Radiometer (A)ATSR data ([25]), and NASA's MODIS LST product [26] provide LST time series. The latter product was used in the study for a comparison with one of the proposed algorithms to confirm the soundness and validity of the approach.
## 4 Experiment
### Experimental Setup  
To validate the effectiveness of the proposed CAT-IADEF, we have set up 5 experiments as follows:  
1. **Model Performance Comparison.** Observe the performance of our method compared to the baseline method. The experimental results can be found in the \"Anomaly detection\" part in Section 4.4.
2. **Anomaly Explanation Results.** Anomaly Explanation Results of CAT-IADEF. The experimental results can be found in the \"Anomaly explanation\" part in Section 4.4.
3. **Validation of Anomaly Explanation Framework.** Validate the framework on different models. The experimental results can be found in the \"Anomaly explanation\" part in Section 4.4.
4. **Ablation Analysis.** Observe the effect of model components on model performance. The experimental results are shown in Section 4.5.
5. **Sensitivity Analysis of Parameters.** Observe the effect of different parameters on model performance. The experimental results are shown in Section 4.6. Experimental Environments: (1) CPU: Intel (R) Core (TM) i7-7500U [EMAIL_ADDRESS] GHz; (2) RAM: 8 GB; (3) Python version: 3.7.11; and (4) Pytorch version: 1.6.0.  
We selected the following methods for performance comparison experiments: PCA [6] based on dimensionality reduction, LOF [9] based on density, COPOD [8] based on statistics and machine learning, and OCSVM [11] based on classification; these four are relatively classic methods. MAD_GAN [22], OmniAnomaly [25], USAD [4], and TranAD [28] are the latest deep learning methods with excellent performances.
## 3 Methods
### Feature Selection and Fusion  
To identify the most representative and significant features, the following procedures were carried out:  
(1) Radiomics Feature Selection: First, a homogeneity of variance test ([PERSON]'s test) was performed to assess whether features with homogeneous variances could impact the accuracy of subsequent statistical analyses. Next, T-tests (Student's \(t\)-test) and [PERSON]'s \(t\)-test were applied to eliminate features with non-significant mean differences. Features were retained based on the following criteria: \(p\)-value (PL) > 0.05 and \(p\)-value (PT) < 0.05, or \(p\)-value (PL) \(\leq\) 0.05 and \(p\)-value (PW) < 0.05. These selected features provided a solid foundation for subsequent tasks by ensuring clear distinctions. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) method was applied with 5-fold cross-validation to optimize the alpha parameter, resulting in the most representative and generalized radiomics features after \"double filtering\".  
(2) Deep Feature Selection: To eliminate dimensionality effects, the deep features were first standardized. Principal Component Analysis (PCA) was then applied for dimensionality reduction, reducing the deep feature samples to the target dimensions while retaining the principal components.  
Before feature fusion, it was necessary to address the scale differences between the various feature types: (1) For clinical annotated features, two treatments were applied: binarization (as shown in Table 1) and One-Hot encoding. (2) For radiomics features, normalization was performed. (3) For deep features, standardization had already been carried out. Two fusion methods were designed for subsequent modeling and comparison:deep features + binarized clinical annotated features + radiomics features and deep features + One-Hot encoded clinical annotated features + radiomics features.
## 2 Preliminaries
### Modeling
\begin{bmatrix}\cos\theta&0&-\cos\phi\sin\theta\\ 0&1&\sin\phi\\ \sin\theta&0&\cos\phi\cos\theta\end{bmatrix}\begin{bmatrix}\dot{\phi}\\ \dot{\phi}\\ \dot{\psi}\end{bmatrix} \). Characterizing \(l\) as the distance of rotors from the center of mass of the body, replacing \(F_{B}\) with \(U_{1}\) and \( \begin{bmatrix}\tau_{B\phi}&\tau_{B\theta}&\tau_{B\psi}\end{bmatrix} ^{T}\) with \( \begin{bmatrix}IU_{2}&IU_{3}&U_{4}\end{bmatrix} ^{T}\) and assuming transformation from Euler angular rates to body angular rates as identity, we can extract the following mathematical model of the quadrotor UAV from Equations (1)-(8):  
\[\ddot{x}=(\cos\psi\sin\theta+\cos\theta\sin\phi\sin\psi)\frac{1}{m}U_{1} \tag{9a}\] \[\ddot{y}=(\sin\psi\sin\theta-\cos\psi\cos\theta\sin\phi)\frac{1}{m}U_{1}\] (9b) \[\ddot{z}=-g+(\cos\phi\cos\theta)\frac{1}{m}U_{1}\] (9c) \[\ddot{\phi}=\dot{\theta}\dot{\psi}\bigg{(}\frac{I_{y}-I_{z}}{I_{x}} \bigg{)}-\frac{I_{r}}{I_{x}}\dot{\theta}\Omega_{r}+\frac{l}{I_{x}}U_{2}\] (10a) \[\ddot{\theta}=\dot{\phi}\dot{\psi}\bigg{(}\frac{I_{z}-I_{x}}{I_{y}} \bigg{)}+\frac{I_{r}}{I_{y}}\dot{\phi}\Omega_{r}+\frac{l}{I_{y}}U_{3}\] (10b) \[\ddot{\psi}=\dot{\phi}\dot{\phi}\bigg{(}\frac{I_{x}-I_{y}}{I_{z}} \bigg{)}+\frac{1}{I_{z}}U_{4} \tag{10c}\]  
where \(I_{x},I_{y}\) and \(I_{z}\) are the terms of inertia tensor, \(I_{r}\) symbolizes rotor inertia and \(\Omega_{r}\) is the relative speed of cross coupled rotor pairs and is equivalent to \(\Omega_{2}+\Omega_{4}-\Omega_{1}-\Omega_{3}\).
## 3 Materials and Methods
### Data Preprocessing  
Typical real-world datasets have useful information, but it is not in a form appropriate for the required data mining procedure. Thus, data pre-processing and feature selection are applied to a given set of raw data. First, this component considers removing all missing values and outliers. One of the relevant problems in data quality is a missing value, which occurs when data values are not stored in the feature. The absence of data values can significantly affect the representativeness of the sample and statistical power. Therefore, the estimation of those values is important to build prediction models accurately. Outlier refers to the observation that has abnormal distance from other values on tails of the distribution. Therefore, the lost data and outliers can cause bias in the estimation of parameters and complicate the analysis of the study.  
Second, the lasso method [32] and multicollinearity-analysis-based feature selection approach [33] excludes redundant and irrelevant features in an effort to reduce the complexity of the training model. Lasso eliminates the number the features in terms of their significance of the dependent variable. Furthermore, a rational decision is taken to execute the multicollinearity analysis in order to demonstrate the correlation between independent features. The value of the variance inflation factor is used to verify multicollinearity in regression analysis. In essence, this component takes the complete set of features and loops through all of them applying the appropriate test. After that, if deemed significant, the features are kept for the next component. Otherwise, such features are eliminated.