esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Final Remarks  
Currently, data-intensive workflows are widely used in several application domains, such as bioinformatics, astronomy, and engineering. This paper introduced and evaluated a system, named the Intelligent In-memory Workflow Manager (IIWM), aimed at optimizing the in-memory execution of data-intensive workflows on high-performance computing systems. Experimental results suggested that by jointly using a machine learning model for performance estimation and a suitable scheduling strategy, the execution of data-intensive workflows can be significantly improved with respect to state-of-the-art blind strategies. In particular, the main benefits of the IIWM resulted when it was applied to workflows having a high level of parallelism. In this case, a significant reduction of memory saturation was obtained. Therefore, it can be used effectively when multiple tasks have to be executed on the same computing node, for example when they need to be run on multiple immovable datasets located on a single node or due to other hardware constraints. In these cases, an uninformed scheduling strategy will likely exceed the available memory, causing disk writes and, therefore, a drop in performance. The proposed approach was also shown to be a very suitable solution in scenarios characterized by a limited amount of memory reserved for execution, thus finding possible applications in data-intensive IoT workflows, where data processing is performed on constrained devices located at the network edge.  
The IIWM was evaluated against different scenarios concerning both synthetic and real data mining workflows, using Apache Spark as a testbed. Specifically, by accurately predicting the resources used at runtime, our approach achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45\(\times\) and 1.66\(\times\) on the synthetic workflows and the real case study, respectively.  
In future work, additional aspects of the performance estimation will be investigated. For example, IIWM can be extended also to consider: (i) other common stages in workflows besides data analysis, such as data acquisition, integration, and reduction; (ii) more complex algorithms like Artificial Neural Networks or Random Forests; (iii) other information about tasks, input data, and hardware platform features.  
Methodology, R.C., F.M., A.O., D.T. and P.T.; software, R.C and A.O.; validation, R.C., F.M., A.O., D.T. and P.T.; formal analysis, R.C., F.M., A.O., D.T. and P.T.; investigation, R.C., F.M., A.O. and P.T.; writing--original draft preparation, R.C., F.M., A.O. and P.T.; writing--review and editing, R.C., F.M., A.O., D.T. and P.T.; visualization, R.C., F.M. and A.O.; supervision, D.T. and P.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the ASPIDE Project funded by the European Union's Horizon 2020 Research and Innovation Programme grant number 801091.  
Figure 11: Average peak disk usage and execution time, varying the size of available memory.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 2 Materials and Methods
### Experimental Measurements
#### 2.4.3 Machines Measured  
Two machines were measured: one of 18 slots and 8 pole pairs (18p8) and another of 48 slots and 8 pole pairs (48p8). The application of these machines is the elevator sector. Their characteristics are set out in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **18p8** & **48p8** \\ \hline Pole pairs (\(p\)) & 8 & 8 \\ Slots (\(Q_{s}\)) & 18 & 48 \\ Air gap & 1 mm & 1 mm \\ Length & 100 mm & 100 mm \\ Rotor outer diameter & 220 mm & 220 mm \\ Rated speed & 300 rpm & 300 rpm \\ Maximum speed & 400 rpm & 400 rpm \\ Rated torque & 180 Nm & 160 Nm \\ Rated power & 5.7 kW & 5 kW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the electric motors 18p8 and 48p8.  
Figure 3: Experimental setup of the measurement of the vibration response.  
Figure 4 shows the diagram of the winding of the 18p8 machine. Each coil has 40 turns and the winding topology is a double-layer concentrated winding.  
Figure 5 shows the drawing of the rotor of the 18p8 machine. It is a surface-mounted permanent magnet rotor, and the shape of the magnets is rectangular.
## 1 Introduction  
A coronary stent is a wire mesh or cut tubular structure inserted into a stenosed or blocked coronary artery to improve blood flow to the myocardium subtended by that artery. In the past years, bioresorbable coronary devices have been developed, seeking to overcome the limitations of the state-of-the-art drug-eluting permanent stents, including the risks of target lesion revascularization, neo-atherosclerosis, hindrance of late lumen enlargement, and the lack of reactive vasomotion in the stented vessel [1]. These devices, which are referred to as bioresorbable scaffolds, fully dissolve in the vessel wall in two or three years after recovering the lumen passageway [1].  
The ideal bioresorbable scaffold re-opens the artery without any long-term adverse sequelae, such as restenosis or thrombosis. Vascular tissue re-modelling after stent implantation is influenced by the local blood flow pattern [2]. The endothelium actively responds to wall shear stress (WSS), which triggers vascular smooth muscle cell proliferation and migration to the endoluminal surface, causing vessel healing and strut coverage. One method to quantify strut coverage is by measuring the neo-intimal thickening (NIT), defined as the perpendicular distance between the midpoint of the adluminal strut surface and the inner luminal surface measured on optical coherence tomography (OCT) images [2]. Another method is to calculate the restenosis area, defined as the difference between the scaffold area and the sum of the strut and luminal area at a particular cross-section of the OCT on follow-up imaging. The high in-plane resolution of OCT (axial resolution between 12 and 15 \(\upmu\)m, and lateral resolution between 20 and 40 \(\upmu\)m [3]) allows a detailed visualization of the stent struts and evaluation of the neointimal layer at follow-up [4]. The evaluation of WSS relies on the application of computational fluid dynamics (CFD) to model patient's hemodynamics. The incidence of adverse neointimal hyperplastic response may be predicted using surrogate quantities, such as a low WSS averaged over one cardiac cycle (<0.4 Pa) [5] and disrupted flow stream at bifurcation.  
In the present study, we sought to investigate the relation between patient's hemodynamics at baseline and NIT after nine-month from the intervention. A previously validated optical coherence tomography (OCT) based methodology [6] was applied to reconstruct a patient-specific coronary artery at baseline, after the insertion of a bioresorbable polymeric scaffold. The achieved high fidelity geometrical model was used to simulate the blood flow under the effect of the scaffold. The lumen contour was traced in the OCT image datasets at baseline and follow-up to compute the relative distance. Results of the CFD simulation were linked to the neo-intimal coverage, using the NIT and restenosis area.
## 3 Results and Discussion
### Modifications in the Off-Flavor Concentration  
The three main compounds that cause off-flavors in orange juice are \(\alpha\)-terpineol, 4-vinyl guaiacol, and 2,5-dimethyl-4-hydroxy-3-(2H)furanone (DMHF). Only \(\alpha\)-terpineol was present in the pasteurized orange juice used in this study. The formation of \(\alpha\)-terpineol occurs naturally in some oranges, but its concentration increases during orange processing due to limonene oxidation and the isomerization of linalool.  
The glow discharge plasma treatment significantly decreased the concentration of \(\alpha\)-terpineol in pasteurized orange juice. The contents of \(\alpha\)-terpineol were reduced at fluences between 200 and 400. A decrease of 61% in the \(\alpha\)-terpineol concentration was observed in the juice treated at 10 mL/min for 20 min. This significant change is desired for the treatment of pasteurized orange juice to restitute a more fresh-like flavor.
# _remote sensing_
## 6 Conclusions  
HORUS is a 6U CubeSat designed for multispectral planetary observation under a continuous range of view angles in the along-track direction, ranging from 70.5 degrees forward to 70.5 degrees afterward nadir. Spectral bands include blue, green, red, and near infrared. The instrument spatial resolution is in the sub-km range, targeting the analysis of large-scale phenomena. The instrument is based on Commercial-Off-The Shelf components, including 16 CMOS cameras with a global shutter, pointing at different view angles. The preliminary instrument and satellite design shows the feasibility of the mission, analyzing both the instrument optical performance and spacecraft system budgets. The overall mission design can take advantage of the flexibility and maneuverability of the proposed platform, to deploy a system of several spacecraft to improve coverage and revisit time. In addition, the HORUS system could operate as a stand-alone mission or in a synergetic approach to complement data collected by already operational large satellite platforms.  
The design was conceptualized, refined and revisited by all the authors. The mission was analyzed and the payload design was carried out by [PERSON], [PERSON] and [PERSON]; the bus design was performed by [PERSON], F.A. and P.M.; the manuscript was edited by [PERSON] and [PERSON]; the whole project was supervised by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The HORUS satellite mission design is an extension of the concept presented and awarded with the \"Student Prize\" during the University Space Engineering Consortium (UNISEC) Global 4 th Mission Idea Contest, held in October 2016 in Bulgaria. The authors wish to acknowledge the organization UNISEC Global for the chance offered during the 4 th Mission Idea Contest to present the HORUS mission concept [67].  
The authors declare no conflict of interest.
###### Abstract  
For the semi-physical simulation test process of the coordinates of aircraft landing points, the concept of a completeness test for landing point simulation data is proposed, and a completeness test criterion based on field test data and expert estimation information is established. An integrity detection method for semi-physical simulation data, including data dimensionality reduction, data expansion, and regularization, drawing the envelope of the confidence region and establishing the completeness test criterion steps, is proposed. For landing points that do not satisfy the requirements of completeness, a surrogate model of the semi-physical simulation system was constructed based on a backpropagation neural network method, and the test conditions for data of this point were supplemented. Finally, application verification was carried out. Through the field and semi-physical simulation test research of a 534A aircraft, it was verified that the method can well realize the inspection of the completeness of the semi-physical simulation test data and the design of supplementary test conditions. It lays a good foundation for the fusion of subsequent semi-physical simulations and field test data.  
a pacs: **Keywords:** aircraft landing point; semi-physical simulation; integrity detection; proxy model; BP neural network; supplementary experimental design +
Footnote †: journal: Applied Sciences
###### Abstract  
This study proposes a method for determining the optimal period for crop yield prediction using Sentinel-2 Vegetation Index (VI) measurements. The method operates at the single-field scale to minimize the influence of external factors, such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. By analyzing historical VI data, the method identifies the best time window for yield prediction for specific crops and fields. It allows adjustments for different space-time intervals, crop types, cloud probability thresholds, and variable time composites. As a practical example, this method is applied to a wheat field in the Po River Valley, Italy, using NDVI data to illustrate how the approach can be implemented. Although applied in this specific context, the method is exportable and can be adapted to various agricultural settings. A key feature of the approach is its ability to classify variable-length periods, leveraging historical Sentinel-2 VI compositions to identify the optimal window for yield prediction. If applied in regions with frequent cloud cover, the method can also identify the most effective cloud probability threshold for improving prediction accuracy. This approach provides a tool for enhancing yield forecasting over fragmented agricultural landscapes.  
remote sensing agriculture; crop monitoring techniques; field-level forecasting; phenological analysis; high-resolution vegetation data; S2 imagery applications; ideal timing acquisition; NDVI; clear pixel procedure; agricultural productivity +
Footnote †: journal: _Land_ **2024**, _13_, 1818. [[https://doi.org/10.3390/land13111818](https://doi.org/10.3390/land13111818)]([https://doi.org/10.3390/land13111818](https://doi.org/10.3390/land13111818))
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 3 Optimization of NAV
### Trying to Communicate with the Destination  
Figure 6 shows the case where a neighbor tries to communicate with the destination, after the current handshaking. The new RTS departing from the neighbor (bold arrow) has to arrive right after the ACK transmission, at the destination. Thus, similar to the previous case, the final \(NAV\) is determined by  
\[NAV^{DATA}=max\left[0,D^{DATA}-\tau(S,N)-\tau(S,D)-\tau(D,N)\right] \tag{9}\]  
In addition, the tentative NAVs are given by  
\[NAV^{RTS}=max\left[0,D^{RTS}-\tau(S,N)-\tau(S,D)-\tau(D,N)\right] \tag{10}\]  
\[NAV^{CTS}=max\left[0,D^{CTS}-2\tau(D,N)\right] \tag{11}\]  
However, in Equation (10), since the neighbor does not know \(\tau(S,D)\) and \(\tau(D,N)\) at the moment of overhearing the RTS, \(NAV^{RTS}\) is tentatively determined by  
\[NAV^{RTS}=max\left[0,D^{RTS}-\tau(S,N)\right] \tag{12}\]  
Figure 5: NAV determination when trying to communicate with source.  
Figure 6: NAV determination when trying to communicate with destination.
## 5 Analysis of Template-Monomer Functional Interactions
### Raman Spectroscopy  
Raman spectroscopy can be used to detect and characterize the binding of template molecules to MIP by seeing the shifting of the Raman spectrum [84; 128]. Raman spectroscopy has been extensively used to study polymerization processes and characterize polymers [129]. Raman spectroscopy is one of the promising techniques for understanding hydrogen bonds [130]. With Raman spectroscopy, laser photons will be scattered by the sample molecules resulting in a loss of energy during the process. The amount of energy lost will be detected as a change in the energy (wavelength) of the irradiating photon and represent certain bonds in molecules [128].  
In the study of [PERSON] et al. [131], investigations of intra and intermolecular interactions using Raman spectroscopy were carried out on non-covalent MIPs with three minor analgesics (aspirin, caffeine, and acetaminophen) as templates using MAA as a monomer. The investigation was carried out through changes in vibrational mode frequency shifts induced at engineered binding cavities [131]. Vibrational frequencies are sensitive to small chemical environmental changes such as temperature [132], phase transitions [133], and pressure [134]. Collective Intermolecular interactions between analgesic and MIP were assumed to influence the vibrational band higher than non-specific interactions that happened in the surfaces of the polymer particles. [PERSON] et al. [131] measured Raman spectra of analgesics and polymer in solution, with non imprinted and MIP-bound, then examined the vibrational bands sensitive to intermolecular interactions, such as C\(-\)N\(-\)CH\({}_{3}\) deformation phenyl bending and CH\({}_{3}\) rocking. Higher shifting of the vibrational frequencies is related to stronger hydrogen bonding. When non-specific binding exists, vibrational frequencies will shift minorly or not shift at all [135; 131].
# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 6 Results
### Results of Five-Fold Cross-Validation
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Fold \#1**} & \multicolumn{2}{c}{**Fold \#2**} & \multicolumn{2}{c}{**Fold \#3**} & \multicolumn{2}{c}{**Fold \#4**} & \multicolumn{2}{c}{**Fold \#5**} \\ \cline{3-14}  & & **TPR** & **PPV** & **F\({}_{1}\)** & **TPR** & **PPV** & **F\({}_{1}\)** & **TPR** & **PPV** & **F\({}_{1}\)** & **TPR** & **PPV** & **F\({}_{1}\)** & **TPR** & **PPV** & **F\({}_{1}\)** \\ \hline \multirow{3}{*}{C-SVM} & 1 & 1.0 & 0.17 & 0.29 & 0.67 & 0.80 & 0.73 & 0.67 & 0.67 & 0.67 & 0.67 & 1.00 & 0.80 & 0.83 & 0.71 & 0.77 \\  & 2 & 0.41 & 0.86 & 0.56 & 0.92 & 0.77 & 0.84 & 0.86 & 0.79 & 0.82 & 0.92 & 0.79 & 0.85 & 0.95 & 0.79 & 0.86 \\  & 3 & 0.5 Table 6 lists the five-fold cross-validation results of the five benchmark methods when SMOTE is used to alleviate the class imbalance problem. The results in this table demonstrate that SMOTE improves the TPR, PPV and \(F_{1}\) score values of the five methods compared to the results in Table 5. By using (12) to (14), we computed the average metric values over five-fold cross-validation, as shown in Table 7. The results in this table demonstrate that it is better to use SMOTE instead of the undersampling technique to alleviate the class imbalance problem. For all benchmark methods, average performance improvements are achieved when SMOTE is applied. In particular, the \(k\)-NN model outperforms the other methods, achieving results of TPR = 0.9276, PPV = 0.8512, \(F_{1}\) = 0.8798 and accuracy = 0.9279. Based on these results, we decided to use \(k\)-NN models to construct the proposed ensemble model. Figure 6 presents the confusion matrices for the proposed ensemble model for each cross-validation fold. The diagonal elements in each confusion matrix indicate the numbers of correctly classified samples. The other elements indicate the numbers of incorrectly classified samples. One can see that the proposed ensemble model classifies class 1 as the minority class among the three classes without misclassification. Additionally, for class 3 (the second minority class), the ensemble model also exhibits a low misclassification rate.
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
###### Abstract  
The micro-displacement measurement system with 2D/3D has become increasingly important in the field of scientific research and technology application. In order to explore the application of an optical surface in micro-displacement measurement, a novel and simple 2D micro-displacement measurement method based on the elliptical paraboloid was designed and subjected to experiment. The measurement system takes advantage of the elliptical paraboloid instead of a plane mirror in the optical structure of an autocollimator which has been ameliorated to adapt to curved surface measurement. Through the displacement of the light spot on the CCD (Charge Coupled Device) detector, the displacement of the target could be measured with a linear correlation coefficient of 0.9999. The accuracy of the system is about \(\pm\) 0.3 \(\upmu\)m in a wide range in two dimensions. The results were in good agreement with the theoretical analysis and indicated the potential applicability of the proposed system in the detection of geometric errors of CNC (Computerized Numerical Control) machine tools.  
Keywords:elliptical paraboloid; two-dimensional; micro-displacement; median filtering algorithm; manufacturing error +
Footnote †: journal: Applied Physics Letters
## 4 Proposed System and Architecture
### RTPM User, Device, and Key Management  
The keys are managed as shown in Figure 6. The client IoMT devices are capable of generating keys. To build and retain confidence and trust among the users who adopt such monitoring technology, the end user's devices generate their public and private key pair, the public key is shared with the server, and the server does likewise. This does not mean the server cannot generate and distribute keys to the client. It means the user's trust level will be higher when it generates their key rather than a third party providing it. In this proposed system, every user must register on the web server to receive a unique ID for the IoMT identification and authentication process. Every successful validation of the user registration leads to the creation of three different unique hashed HEX digits using SHA 256, namely verified ID (\(\Upsilon\)) = SHA256 (user registration information), unique client ID (\(\vartheta\)) = SHA256 (MAC address), and random number (\(\mu\)) = SHA256 (32 digits), and these data are used for the IoMT identification and authentication during the device connection to the server, as shown in Figure 6. The unique ID used during the device identification = SHA256 (\(\Upsilon+\vartheta+\mu\)), and since its raw data are within the server, it can validate the hash value and authenticate it. Thus, in the first step, user registration along with verification is conducted as shown in Figures 7 and 8, which elaborate on how the authorisation process is conducted to verify the authenticity of the user. Figure 9 shows the connection request made by the IoMT client to the IoMT server and how the signature validation is executed when exchanging the public keys. The IoMT server and the web server are hosted on the same machine. The public keys of the IoMT server and the IoMT devices are exchanged once the client's connection is established successfully, as shown in steps 2, 3 and 4 of Figure 6. The IoMT server is responsible for creating the session keys for secure data transmission from the IoMT clients. The session key is an AES key and is delivered to the IoMT clients  
Figure 5: RTPM controller architecture of the client.  
by the IoMT server by securely signing to ensure that the originator's identity and data integrity (using SHA 256) are maintained, as shown in step 5. The data transmission from the IoMT clients for the session is conducted by using AES 256, which is provided by the IoMT server, and signing the data using SHA 256, as explained in step 6 of Figure 6 where the IoMT client's private keys = {RSA PrivK-1, RSA PrivK-2,..., RSA PrivK-N} and public keys = {RSA PubK-1, RSA PubK-2,..., RSA PubK-N}. The session key is represented by the AESSession Key, the IoMT server's private key is RSA PriK-S, and the public key is RSA PubK-S.  
Figure 6: Model network diagram of key management.  
Figure 7: User registration for monitoring.