esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Acquisition  
The Python programming language [54] and the Numpy [55], OpenCV [56], and Pandas [57] libraries were used to process and manipulate the images and the data obtained manually. The procedure followed is described next. First, an image acquisition program was developed to acquire data from a Kinect(r) sensor (Version 1) and implemented on a computer for data collection. For data acquisition, it should be considered that light is the sensed variable. Thus, sudden brightness changes should be avoided to obtain an adequate contrast between the animal and the background. Although the processing has a tolerance for changes, the measurements were made under a roof to prevent excess light. On the other hand, the sensor was ideally placed at 1.5 m. The camera calibration worked as long as the animal stayed between 1.2 and 1.8 m.  
The Kinect(r) sensor was placed on the side of the animal, with a separation distance of approximately 1.5 m, allowing the entire body length's registration. A reference line was added; a label with the identification number was placed on each animal, which helped identify them in the subsequent analysis (Figure 2).  
With the Kinect(r) sensor, two images were obtained, a color image with a resolution of 640 \(\times\) 480 pixels (Figure 3) and a depth image of 320 \(\times\) 240 pixels (Figure 4). Each image was taken and recorded every second. Next, the color image was saved in a Portable Network Graphics (PNG) file and the depth file in the Comma-Separated Values (CSV) format. Finally, this last file was processed through the OpenCV and Numpy libraries of the Python programming language.  
Figure 2: Configuration of the area for data collection.  
It was necessary to calibrate the depth images to obtain the actual measurements in the three spatial coordinates, which followed the procedure suggested in [58]. The calibration was tested on objects with regular shapes and some biometric measurements of sheep to ensure that the measurements were correct (see Figure 5).  
Figure 4: Depth image from the Kinect® sensor.  
Figure 3: RGB image from the Kinect® sensor.  
Figure 8 shows the points and distances of interest to make the measurements. These points were selected from Figure 7, which was calibrated in centimeters. The points of interest were extracted with OpenCV tools showing the image, and the user only has to place a small cross on the points of interest measurements, which were stored automatically (Figure 9).  
Figure 8: Schematic of the body measurements.  
Figure 7: Two-dimensional image with the information on the X-axis and the Y-axis.  
Then, these measurements were taken, and the calculations were executed considering that the scale for both the X- and Y-axes was in centimeters, that is 1 pixel is equal to 1 cm; the Euclidean distances for each pair of points were previously defined; an example can be seen in Figure 10.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 2 SDT Concept Development and Methods Used  
DT has many definitions, all revolving around the same idea of modelling, data acquisition, data processing, and simulation, as described in [35]. An integrated multiphysics, multiscale, probabilistic simulation of an as-built system that uses the best available models, sensor information, and input data to mirror and predict activities or performances over the life of its corresponding physical twin, as stated in [36], can be offered as the most appropriate one.  
Considering the definition and taking into account related work, the procedure of creating an SDT could be divided into four basic steps that include: defining the purpose of an SDT, collecting and processing the necessary data, and modelling and validating the model. Once the purpose of an SDT has been defined, the next three steps can repeat in a cycle depending on the data availability and connectivity level between the ship and its twin, as presented in Figure 1.  
Figure 1: SDT concept as an iterative process.
# Uncertainty-Aware Time Series Anomaly Detection
## 3 Proposed Approach
### Reconstruction Error  
The goal of an autoencoder is to reconstruct the input data sequence as well as possible. After training on normal data, it is able to reconstruct normal data patterns properly. In contrast, it will fail to properly reconstruct abnormal data patterns. In this work, we use the error between input data and reconstructed data as anomaly score. For a given observation, we define this in Definition 1.  
**Definition 1** (Anomaly Scores).: _We define any observation during prediction time by a 2-tuple:_  
\[s:(o,r)\]  
_where_  
* \(o\) _is the observation,_
* \(r\) _is the reconstruction error._  
Each observation of the time series data is assigned an anomaly score, resulting in a point-wise scoring of the data points. We summarize this in Definition 2.  
**Definition 2** (Time Series Scoring).: _The result of a time series anomaly detection algorithm is a time series scoring_  
\[S=\{s_{1},s_{2},...,s_{n}\}\]  
_with \(s_{i}\in\mathbb{R}\) being assigned to its corresponding input \(d_{i}\in D\) and \(D\) corresponds to data._  
A widely used metric for the reconstruction error is the MSE [10]. For this algorithm, we again use the loss function described in Equation (2) As described, it is based on MSE which is popular for autoencoders. MSE is a mathematically simple metric that provides clear and easy interpretability. In comparison to \(L_{1}\) loss, it takes the square over the difference between input and reconstruction. This makes it more sensitive to larger  
Figure 2: Visualization of uncertainty terms in the loss function.  
errors and such supporting the task of anomaly detection. In addition, the extension by integration of aleatoric uncertainty allows the direct influence of data uncertainty on the anomaly probability.
We analyze the performance of the proposed scheme in term of estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of RMSE, variability analysis of RMSE and CDF of RMSE over Monte Carlo runs. Organization and Notation of PaperThe rest of the paper is organized as follows. A mathematical model of the proposed scheme is derived in Section 2, Section 3 elaborates on the frame work of the FPA and PSO, results and discussion are explained in Section 4 and finally, the conclusion of the presented model is concluded in Section 5.
## 3 Results
### Antibacterialativity of GPPS Blended with ADIP-PS  
The antibacterial activity of GPPS blended with AD-23K is shown in Figure 5. In antibacterial activity evaluation compliant with ISO standards, a reduction of two orders of magnitude in log CFU compared to the control is evaluated as demonstrating significant antibacterial activity. Both 25%_AD-23K and 10%_AD-23K exhibited antibacterial properties similar to those of 100%_AD-23K. In these samples, the log CFU of the _S. aureus_ decreased by more than four orders of magnitude compared to the control. Furthermore, in the 5%_AD-23K blend, the log CFU of _S. aureus_ decreased by more than three orders of magnitude compared to the control, which is sufficient antibacterial activity. Pure PS showed no significant antibacterial effects. These results suggest that incorporating 5-10% ADIP-PS into GPPS effectively enhances the antibacterial properties without sacrificing transparency or strength (Figures 3 and 4). Figure S4 shows _S. aureus_ that was in contact with ADIP-PS kneaded into GPPS for 3 h and stained with PI, a non-membrane permeable fluorescent dye. The staining of _S. aureus_ on the ADIP-PS kneaded sheet suggests that the cationic groups on the surface damage the bacterial cell membrane, thereby exhibiting antibacterial properties. By contrast, slight staining by PI was observed on pure PS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Tensile** & **Elongation** & **Young’s** \\  & **Strength (MPa)** & **at Break (\%)** & **Modulus (MPa)** \\ \hline Neat PS & 37.4 & 2.6 & 2108 \\ PS & 37.8 & 2.5 & 2100 \\
5\%\_AD-23K & 32.8 & 2.5 & 1979 \\
10\%\_AD-23K & 33.7 & 2.2 & 1886 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical properties of PS and ADIP-PS (AD-23K) resins.  
Figure 5: Antibacterial activity of ADIP-PS (AD-23K) sheets with different ratios against _S. aureus_. Error bars represent the standard deviation (SD) with \(n=3\).
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 4 Extending the Framework: Tailored Strategies for Enhanced Decision Making  
KR methodologies are essential for creating structured frameworks to organize and represent medical data. They enable healthcare professionals to effectively store and leverage critical information, such as risk factors, treatments, symptoms, and other patient-specific details [29].  
Our generic framework addresses the iterative nature of patient-expert interactions across various medical domains. To enhance its precision, we introduce the concept of a strategy--a targeted extension designed to address specific medical decision-making needs and enable more accurate decisions in specialized medical contexts.  
Strategies tailor the iterative framework to specific healthcare scenarios while preserving its core structure. Each strategy is defined by its goal and a set of characteristics, formulated as KG attributes and algorithm-based rules.  
The modularity of the KG model simplifies the integration of new strategies. The KG, composed of nodes and edges, allows attributes to be easily appended or adjusted. This flexibility enables the KG to incorporate domain-specific attributes such as patient-specific factors, risk thresholds, or clinical priorities. An adapted set of algorithms further supports each strategy to ensure effective implementation.  
In addition to the strategies proposed in the Introduction, other potential strategies include the following:  
Figure 1: An example of integrating a hierarchical tree of symptoms into the KG. Disease nodes are represented in yellow, KG symptom nodes in gray, and ontology nodes in red.  
* Chronic disease management: focused on conditions like diabetes or hypertension, incorporating attributes for long-term monitoring and personalized treatment plans.
* Rehabilitation management: supporting post-surgical or injury recovery by tracking progress and suggesting therapy adjustments. Integrating tailored strategies transforms the framework into a robust adaptable tool for medical decision making across diverse healthcare scenarios. This modularity enables
* Targeted interventions: addressing specific healthcare challenges to enhance diagnostic precision and improve patient outcomes.
* Personalized care: incorporating patient-specific attributes to generate customized contextually relevant hypotheses.
* Scalability: facilitating seamless integration of new strategies without disrupting the fundamental framework.  
Section 5 illustrates the strategy concept through a detailed case study of the emergency strategy.
## 2 Materials and Methods  
NiTi alloy samples from Ingpuls GmbH were processed by laser nitriding in nitrogen gas. The NiTi alloy sample was cut into 20 mm \(\times\) 10 mm with a thickness of 1 mm. The NiTi alloy samples were cleaned in ethanol before and after laser processing. The laser nitriding process was done in the chamber as shown in Figure 1, in which the sample was placed on the holder made from copper. The laser beam can pass through the cover with a small window glued with thin glass. The nitrogen gas can enter the chamber through the upper vent and exit through the below vent with stable pressure of 2 Mbar. The fiber laser (FLR-50-SC-OEM) with a power of 50 W was used in the pulse mode in the present research. The samples were in the gas chamber and processed at room temperature (about 20 \({}^{\circ}\)C) and low temperature (liquid nitrogen temperature, about \(-\)190 \({}^{\circ}\)C) with different laser parameters as shown in the Table 1. The diameter of the laser spot was about 170 \(\upmu\)m and the overlapping of scanning lines was 76% during the laser process. The highest backside temperature of the NiTi sample during the laser nitriding was measured by a temperature meter (GTH 1150). After laser treatment and wear testing, the surface images of NiTi were observed with Scanning Electron Microscope (SEM-Zeiss EVO MA 15) and optical microscopy in this research. The microhardness along the cross-section of NiTi alloy after laser nitriding was measured with the KB 30 BVZ hardness device (load force of 100 g and load time of 10 s). The surface roughness of NiTi before and after laser nitriding was tested with Ra (arithmetical mean deviation of the profile) and Rz (the point height of irregularities) with mobile roughness measurement devices of Perthometer M1 (Mahr) which is an important factor for wear and abrasion property of the NiTi alloy. The wear and abrasion testing were performed with a reciprocating ball-on-plate machine (UMT with load force of 2 N and testing time of 1000 s). An Al\({}_{2}\)O\({}_{3}\) ball was used as abrasive ceramic specimens during the testing at room temperature.
# Dual aptamer-Functionalized 3D Plasmonic Metamolecule for Thrombin Sensing
## 2 Materials and Methods
### Preparing and Measuring 3D Plasmonic Metamolecule  
Conjugated AuNRs were mixed with purified DNA nanostructures at a molar ratio of 10:1 in 1x tris-acetate-EDTA (TAE) buffer (40 mM Tris, 20 mM Acetate, and 1 mM EDTA at pH 8.0) supplemented with 11 mM MgCl\({}_{2}\). As the structure has two binding sites for AuNRs, this resulted in a 5:1 ratio for AuNRs over their binding sites. After overnight incubation, AuNR conjugated plasmonic metamolecules were purified from excessive AuNRs by two hours of gel electrophoresis in 0.7% agarose gel at 90 V. After purification, the concentration of the metamolecules was determined by measuring the absorbance at the longitudinal surface plasmon wavelength (700 nm) of AuNRs and comparing the result with that of the unconjugated AuNRs. (Nanodrop, Thermo Fisher Scientific, Waltham, MA, USA). Purified metamolecules were diluted to 100 pM concentration in 1x TAE buffer supplemented with 11 mM MgCl\({}_{2}\) for the circular dichroism (CD) measurement.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Staple Strand** & **Sequence** \\ \hline \multirow{2}{*}{Arm a TBA} & CAA ATT AAC TGA ACA CAA CAA CAA CAA TTG AGT TAA GCC \\  & TTA CAG AAT ttG G \\ \hline \multirow{2}{*}{Arm b2 HH22} & CAT CAC TAA GGG AAG AAA GCG CCC CGC TGC GGG \\  & AGt tAG TCC GTG GTA GGG CAG GTT GGG GTG ACT \\ \hline \multirow{2}{*}{Arm b blank} & AAG CCT TTA TTT CAT CCC AAA ATAA AAA GGG \\  & AGT TGA TTA AAG A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequence of modified staple strands.
## 4 Discussion
Another limitation of AHP methodology for landslide susceptibility mapping in Darjeeling is that it requires extensive data inputs, which can be challenging to obtain and verify in remote and mountainous regions. The AHP method assumes that the weights assigned to each criterion remain constant over time, which may not be the case in dynamic and rapidly changing environments such as Darjeeling. The AHP method does not consider the temporal and spatial variability of landslide triggering factors, which can vary significantly depending on the location and time of year. The AHP method does not account for the complex interactions and feedback mechanisms between factors, which can lead to underestimating or overestimating the risk of landslides in certain areas. Moreover, the trustworthiness ofthe LSM map is dependent on the findings obtained from the ROC-AUC and success rate curve. The GSI's released data were used to create a landslide inventory map. A total of 114 landslides were recorded, with 79 (70%) serving as training data and 35 (30%) serving as testing data. The AHP model generated maps with AUC values of 96.8 percent. The success rate curve was also plotted for determining the accuracy of the model, which was found to be 81.30 percent. Since time is an important factor in hazard research, this finding is useful in an emergency. One may conclude that the models' accuracy is comparable. We recommend using the AHP model in landslide investigations because it can generate excellent and reliable landslide hazard maps for risk reduction and management planning. Figure 8: Histogram analysis of the LSM model. Figure 9: The graph shows the area percentage of different landslide susceptibility classes.
## 6 Baseline Statement and AAMO Approach Results
### Baseline Statement
#### 6.3.2 Baseline Assessment
Contrarily, as the fleet size increase beyond five robots, the shape of the scenario and the peculiar wall distribution all together seem to make the crowding unavoidable for the _MinPos_ approach, causing a severe worsening on its _PL_ performance. Figure 14: Path length (PL) under ideal communication conditions. The trend of PL is upward in both cases. Figure 13: Total exploration time (TT) under ideal communication conditions. Both approaches show a decreasing trend of TT as the fleet size increase. Nevertheless, the fact that the performance improvements are decreasing suppose the existence of a limit on the benefit from robots adding. Finally, it is interesting to observe the over-sensing-cell phenomenon, because, by observing the amount of rework done by the fleet during exploration tasks, it also gives a good measure of the system efficiency. In this case, we start the analysis pointing that in an ideal world--with perfect communications, perfect sensing and instantaneous actions--there would be no place for over-sensing. Nevertheless, in the real world, communications and sensing systems are not perfect and, more important, all actions take time. Even the ones which do not involve motion such as sensing, computing and communicating actions need some window time to be executed. Therefore, many things can happen simultaneously, e.g., sensing actions conducted on the same objects. In such a case, two or more robots might report the discovery of the same cells. In conclusion, even under ideal communication conditions, it is possible to register some level of over-sensing, and this level is unavoidable because of the parallel nature of the system. However, it is equally interesting to analyse the over-sensing results: (i) When the fleets are obeying different policies; (ii) To have a baseline against which the results obtained under non-ideal communication conditions may be compared. Backing to the experiments, during the simulation runnings we verify that the most significant over-sensing record is mainly generated at starting steps when all robots are very close to each other (recall that all robots start from the same corner of the scenario, see Table 3) and, in consequence, its sensing scopes overlap each other, significantly. In Figure 15 the robot placement setup at the starting time is shown. Conversely, after this initial period, the robots overlap each other less frequently, and hence the _OSR_ remains almost unchangeable over time, in both approaches. Despite this, minor differences may be highlighted. Due to a better fleet distribution on the terrain--which decreases the probability of simultaneous sensing events--the fleet makes slightly less rework under _MinPos_ approach than under _Yamauchi_ approach (see Figure 16).
## 3 Results
### In Silico Prediction of Mechanism by Molecular Docking Analysis
The docking data for the second subset of protein targets, corelated to the antioxidant activity, showed a tendency for the majority of compounds to potentially inhibit xanthine oxidase (3 NRZ) and lipoxygenase (1N8Q), the results being very close. Xanthine oxidase (XO) is the enzyme responsible for the metabolism of hypoxanthine to xanthine and further to uric acid. The inhibition of XO was shown to reduce vascular oxidative stress and circulating levels uric acid [71]. Docking results show that four of the assessed compounds recorded a superior affinity compared to that of the native ligand, hypoxanthine (\(-6.7\) kcal/mol). These compounds include 4-(3,4-Methylenedioxyphenyl)-2-butanone (29), (2E)-2-Hexenyl benzoate (6), (S)-(+)-Carvone (16), and 2-Bornene, with compound 29 showing the highest calculated affinity for XO. Binding analysis reveals the formation of 3 HB (Val1011, Thr1010, Ala1079), two of which are also observed in the case of the native ligand hypoxanthine and several other hydrophobic interactions, which stabilize the molecule in a tight conformation (Figure 4). Previous reports showed that rich monoterpene EOs exerted a significant antioxidant effect, assessed by a HPLC-based assay that quantifies the activity of xanthine oxidase [72]. Figure 3: Structure of DDI (2180) in presence of 4-(3,4-Methylenedioxyphenyl)-2-butanone (29). (**A**) the native ligand, 3-chloro-2,2-dimethyl-N-[4-(trifluoromethylomethyl)phenyl]propenamide and (**B**) compound 29 and the native ligand’s structures superimposed; (**C**) HB interactions are depicted as green dotted lines; interacting amino acids are shown as violet sticks. In the case of lipoxygenase (LOX) (INSQ), three compounds scored higher than the native ligand. These structures include the two stereoisomers of 4-Carene (3,12) and p-Cymene (9). The most active compound, 4-Carene interacts with the active site of LOX through multiple hydrophobic interactions, as presented in Figure 5. LOX is, among others, a polyunsaturated fatty acid (PUFA) metabolizing enzyme. PUFA metabolites profoundly affect inflammatory diseases and cancer progression [32]. Therefore, antioxidant compounds that act as LOX inhibitors may reduce these problems. These findings are in line with a previous study that showed the inhibitory LOX activity of terpene-containing orange juice extracts. The study also showed that some extracts elicited LOX inhibitory activity comparable to the known inhibitor quercetin [73]. Figure 4: Structure of XO (3 NRZ) in complex with 4-(3,4-Methylenedioxypphenyl)-2-butanone (29). (**A**) the native ligand, hypoxanthine; (**B**) compound 29 and the native ligand’s structures superimposed; and (**C**) HB interactions are depicted as green dotted lines; interacting amino acids are shown as violet sticks.
## 3 Design and Calculation Results of Vertical Shaft Multi-Rope Friction
### Selection Parameters of Hoisting Wire Rope and Balancing Tail Rope  
The selection parameters of hoisting wire rope and balancing tail rope are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Parameter** & \multicolumn{3}{c}{**Selection Scheme**} \\ \cline{2-4} Structure & Round strand & Triangular strand & Compacted strand \\ \cline{2-4}  & 6 \(\times\) 37S + IWR & 6V \(\times\) 37 + FC & 35W \(\times\) K19S \\ \hline Number of hoisting rope, \(n_{1}\) & 6 & 6 & 6 \\ \hline Minimum safety coefficient, \(m_{a}\) & 6.7 & 6.7 & 6.7 \\ specified by Regulations & 6.7 & 6.7 & 6.7 \\ \hline Minimum nominal diameter, \(d_{\min}/(\text{mm})\) & 60 & 67.2 & 54 \\ \hline Actual selected nominal diameter, \(d_{1}/(\text{mm})\) & 62 & 58 & 60 \\ \hline Weightlift factor, \(K_{1}\) & 0.418 & 0.405 & 0.48 \\ \hline Minimum breaking force factor, \(K_{2}\) & 0.356 & 0.36 & 0.395 \\ \hline Conversion factor, \(K_{3}\) & 1.321 & 1.177 & 1.287 \\ \hline Nominal tensile strength, \(R_{0}/(\text{MPa})\) & 1770 & 1770 & 1960 \\ \hline Unit weight, \(p_{k}/(\text{kg}/\text{m})\) & 16.6 & 13.6 & 17.3 \\ \hline Cross-sectional area, \(A/(\text{mm}^{2})\) & 3019 & 2642 & 2827 \\ \hline Elastic modulus, \(E_{S}/(\text{GPa})\) & 210 & 206 & 120 \\ \hline Minimum breaking force, \(F_{0}/(\text{kN})\) & 2420 & 2140 & 2787 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selection design of hoisting rope and balancing tail rope.