esa-sceva/satcom-chunk-collection huggingface dataset:
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 2 Materials and Methods
### Synthesis of Gold Nanords (AuNR)  
Aqueous AuNR dispersions were obtained by seed-mediated surfactant-capped synthesis [77]. Cetyl trimethylammonium bromide-capped seeds (Seed@CTAB) were prepared as follow: 25 \(\upmu\)L of 50 mM HAuCl\({}_{4}\) were added to 4.7 mL of 0.1 CTAB solution, in water-bath kept to 30.5 \({}^{\circ}\)C, and gently stirred for 5 min. Subsequently, 300 \(\upmu\)L of freshly prepared 10 mM sodium borohydride were added and vigorously stirred for 5 min, until color turned to brown-yellow. The growth solution was prepared by mixing 25 \(\upmu\)L of 50 mM HAuCl\({}_{4}\) to 2.5 mL of 0.1 M CTAB solution under stirring for 10 min, in 27.5 \({}^{\circ}\)C water-bath. Afterwards, 19 \(\upmu\)L of 100 mM ascorbic acid (solution turned colorless) and 20 \(\upmu\)L of 5 mM AgNO\({}_{3}\) were added. Lastly, 30 \(\upmu\)L of Seed@CTAB were added and the solution left undisturbed under vigorous stirring at 27.5 \({}^{\circ}\)C for 15 min. Turn in color was observed at the end of this time. The obtained colloidal dispersion was washed by reactants' excess through two centrifugation steps (9766\(\times\)_g_ RCF, 15 min), with water washings, by keeping the temperature just above 27 \({}^{\circ}\)C. The actual concentration of AuNR dispersion was calculated using molar extinction coefficient \(\varepsilon\) (nM\({}^{-1}\) cm\({}^{-1}\)) by Equation (4):  
\[\varepsilon=1.2\uplambda_{\max}-4.8, \tag{4}\]  
where \(\uplambda_{\max}\) is the maximum absorption of longitudinal plasmon resonance SPR peak [78], which position is affected, with a linear dependence, by the aspect ratio value [79], as shown in Equation (5):  
\[\uplambda_{\max}=95\text{R}+420. \tag{5}\]
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design
### Function Design
#### 4.2.2 Interface Design  
Figure 5 is the interface of the remote upgrade configuration. Message Queuing Telemetry Transport (MQTT) is a lightweight communication protocol for embedded devices [26]. The remote upgrade configuration can communicate with the web server and DPU via this protocol [27]. It identifies senders and recipients by including a \"Subject\" tag in the application's messages. Using its unique device number, every device subscribes to at least one subject. MQTT defines three quality-of-service (QoS) levels: QoS 0, QoS 1, and QoS 2 [28]. In QoS0 (at most once), the publisher sends a message once to the broker,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Components** & **Address Interval** & **Size/KB** \\ \hline bootloader & \begin{tabular}{c} bootloader Program \\ Area \\ \end{tabular} & \begin{tabular}{c} 0X08000000- \\ 0X080 OFFSET \\ \end{tabular} & 64 \\ \hline user application & \
\begin{tabular}{c} Information Reserved \\ Area \\ APP Program \\ Running Area \\ APP Program Backup \\ Area \\ \end{tabular} &
\begin{tabular}{c} 0X08010000- \\ 0X08011 FFF \\ 0X08012000- \\ 0X0807 FFFF \\ \end{tabular} & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flash Partitions.  
Figure 4: Network architecture of the remote upgrade system.  
which sends a message once in turn to the subscriber. The receiver does not send an acknowledgment, and the sender does not resend the message. QoS 1 (at least once) is the standard level used in MQTT, which guarantees that PUBLISH messages are delivered at least once. Finally, when messages are lost and duplicates are not acceptable, QoS 2 (exactly once) is used. QoS 2 performs a two-step acknowledgment (PUBLISH-PUBREC) and (PUBREL-PUBCOMP) process to ensure that messages are delivered once. The system employs the highest level of quality of service (\"QoS 2\"). This reduces the likelihood of upgrading issues caused by improper upgrade instruction.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Comparative Analysis and Evaluation Metrics  
The core of the research involved conducting a comparative analysis of these machine learning algorithms using a range of evaluation metrics. The aim was to assess their performance in predicting employee capability and identifying the most effective model.  
The comparative analysis involved running each of the selected machine learning algorithms on the prepared dataset and evaluating their performance using a range of metrics, including Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Median Absolute Error, Mean Squared Logarithmic Error, Root Mean Squared Logarithmic Error, Mean Bias Deviation, Huber Loss, and MAPE.  
**Mean Squared Error (MSE)**  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2} \tag{12}\]  
MSE assesses the accuracy of a predictive model. It measures the average squared difference between actual \(y_{i}\) and predicted \(\hat{y_{i}}\) values. Lower MSE values indicate better accuracy.  
**Root Mean Squared Error (RMSE)**  
\[RMSE=\sqrt{MSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}} \tag{13}\]  
RMSE is a variant of MSE that assesses accuracy. It represents the square root of the MSE, providing a measure of the average absolute error between actual and predicted values.  
**Mean Absolute Error (MAE)**  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y_{i}}| \tag{14}\]  
MAE assesses a model's accuracy by measuring the average absolute difference between actual and predicted values. It is less sensitive to outliers than MSE.
# Deep CNN for IIF Images Classification in Autoimmune Diagnostics
###### Abstract  
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a \(k\)-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database.  
Keywords:autoimmune diseases; IIF images; AlexNet; convolutional neural networks (CNNs); support vector machine (SVM); k-nearest neighbors (KNN); accuracy +
Footnote †: journal: Applied Sciences
## 2 Governing Equations and Numerical Simulations
### Mesh Design  
The mesh design plays a critical role in the performance of a flow simulation. As discussed above, Nek5000 is based on the spectral-element method developed by [PERSON] [37] and the considered mesh comprises a set of elements along with an eight-point GLL quadrature inserted in each of the elements. The elements are distributed uniformly in the domain with exception of the near-obstacle regions (where the element distribution is refined to provide additional resolution). Refinement is applied progressively in the wall-normal direction of the obstacles and depends upon an expansion rate that determines the level of refinement as well as the extent of the refined region. Figure 2 shows slices of the spectral-element mesh used for the flow simulation. The refinement ensures that the resolution is sufficient in the most relevant areas, i.e., the spaces between the obstacles. Note that this representation only shows the limits of the elements. Recall that within the elements, we have a GLL quadrature and thus the actual number of points is greater than what can be observed by inspecting Figure 2. We employed 70,000 elements to discretize the domain and a polynomial order \(N=7\), which yields a total number of grid points of around 36 million. The criteria for mesh design approximately followed the guidelines by [PERSON] et al. [43], who thoroughly validated them in the context of Nek5000 simulations of turbulent wings. In particular, these guidelines dictate that the inner-scaled mesh spacing in the turbulent boundary layer should satisfy \(\Delta x^{+}<20\), \(\Delta z^{+}<9\) and \(\Delta y_{w}^{+}<1\). In the wakes of the obstacles, the guideline for mesh design is \(h/\eta<9\), where \(h\) is the local cubic root of cell volume and \(\eta\) is the Kolmogorov scale.  
The meshing process relies on a meshing tool developed in the context of this project, which is based on a Swift parallel-computing scripting language as well as the Parsl parallel scripting library [47]. This tool allows the meshing process to be significantly more efficient, since the meshes can be created by defining a set of geometrical and resolution parameters. Furthermore, these tools make use of parallel-computing resources such that the mesh-building process is carried out efficiently.
# Motion Estimation of Non-Cooperative Space Objects Based on Monocular Sequence Images
## 4 Discussion  
Through simulation, it can be proven in entirety that the proposed method is feasible; that is, the attitude motion of the target can be obtained by a sequence of images of the object using only the monocular camera, and the required conditions for estimating the translation of the object are analyzed. In terms of hardware resources and the motion estimation process, the consumption is low. However, the simulation only verifies the validity of the motion estimation, and the image processing algorithm is not the focus of this paper; the image processing algorithm is very important and is the premise of this method. Therefore, motion estimation can only be achieved if accurate matches of more than eight feature points can be obtained. Overall, the rotation speed and observation distance of the object, as well as the image processing algorithm, will affect the extraction accuracy of the feature points, which is another important issue and requires further research.  
There are various models available, and the current study differs from others. At present, some methods of motion estimation for non-cooperative space objects need to be implemented through binocular cameras. However, on the satellite, the baseline length of the binocular camera is greatly limited. Other methods need to use microwaves or lidar, which will encounter difficulties in the equipment installation layout and consume satellite resources. Moreover, many methods use complex fusion filtering algorithms. These all reduce the application effect of onboard autonomous navigation. In contrast to other methods, which require binocular cameras or radar to measure data, this method only uses a monocular camera, which has low requirements for the configuration of the measurement sensors. Therefore, the computational complexity and processing speed are the advantages of this method. Similarly, this method has some disadvantages, such as being unable to estimate the real size of the translation and requiring high image processing accuracy. However, it is still a novel method for the motion estimation of non-cooperative space targets.
# The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
## 1 Introduction
The multi-objective is to minimize the makespan and total production cost. The authors proved that joint optimization is superior compared with independent decision-making. [PERSON] and [PERSON] [12] present the integrating preventive maintenance and tactical production planning method for a parallel production system. The authors assume two possible causes for system failure: the independent failure of single components, and the simultaneous common cause failure of all components. The objective is to minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs. [PERSON] et al. [13] propose the Ant Colony Optimization algorithm to solve the joint production and maintenance scheduling problem. The trade-off solutions between objectives of production and maintenance is searched. Reliability models are used to take into account the maintenance aspect. This paper faces the problem of generating a predictive schedule with given constrains in the conditions of disturbances for job shop/flow shop systems. The objective of the article is to develop an effective method of task scheduling, reflecting the operation of the  
Figure 1: Classification of production and maintenance scheduling approaches. production system and the nature of the disturbances. The method of estimation unknown system parameters such as Mean Time to Failure, Mean Time of Repair is based on the theory of probability. The original value of the paper is the development of the method of a basic schedule generation with the application of the Ants Colony Optimisation (ACO). A predictive schedule is built by planning the technical inspection of the machine at time of the predicted failure-free time. Flexible operations are allocated to the machine during an increased risk of a failure. Three algorithms: genetic (GA) [14], immune (MOIA) [15] and clonal selection (CSA) [16] have been developed and compared for the presented problem of predictive schedules generation. In this paper the concept of the ACO is presented and numerical examples are given for predictive scheduling. The ant colony optimization algorithm is applied to the problem of makespan minimization and schedule stability maksimisation. Comparative analyses of parameter variants of the ant colony optimization algorithm are performed. The paper is organized as follows: The job shop scheduling problem for experimental study is presented in Section 2. The general concept of ACO is presented in Section 3. The application of ACO for the problem of production and maintenance task scheduling is described in Section 4. Section 5 contains numerical simulations and experimental test results related to the research. The paper concludes with a brief summary of the results (Section 6).
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 1 Introduction
Secondly, if the algorithms such as the RDA, CSA and RMA were used for focusing, the parallel-to-orbit BIS would be the best data acquisition mode with the least data amount and lightest signal coupling. As has been demonstrated by the simulation experiments, comparing to the traditional SSS-SAR, the SSS-RSSAR contributes to a better performance as it requires less total data amount and lighter computational load in imaging a moderate-swath tilted-with-orbit ROI in a high resolution level. The paper is arranged as follows. Section 2 discusses the CVPI technique for the non-uniform azimuth sampling. Section 3 discusses the MPFA for the SSS-RSSAR imaging. Section 4 discusses the parameter design methods for the SSS-RSSAR. Section 5 compares the total data mount and the total computational load between the SSS-RSSAR and the traditional SSS-SAR. The presented approach is evaluated in Section 6 by using the point target simulations. The study is summarized in Section 7 with a brief plan for the next-step research.
###### Abstract  
Laser Time Transfer (LTT) has proven to be able to improve remote time transfer accuracy compared to microwave technology. The impact of satellite clock errors and atmospheric delays during LTT will be further reduced in the common-view mode. The challenge is presented as an optimization problem that is limited by satellite trajectories. This paper introduces an improved simulated annealing algorithm designed to maximize the common-view possibility among various station pairs within regional Satellite Laser Ranging (SLR) networks by optimizing satellite orbit trajectories. The study proposes a system model that integrates LTT principles with satellite visibility considerations. The simulated annealing algorithm is improved with new annealing strategies that incorporate control strategies, and modify the cooling function. Comparative simulation analyses demonstrate the effectiveness of the algorithm, resulting in a significant reduction in computation time by over 10 times. The optimized orbits exhibit common-view windows between 3.337 and 8.955 times longer than existing orbits. Further simulations are conducted to optimize the orbits, and common-view models are established for 45 pairs among 10 stations. The optimizations result in common-view times ranging from 6.183 to 60.633 min in the Asia-Pacific region and from 5.583 to 61.75 min in the Europe-to-Asia region. This can provide valuable references for designing satellite constellations.  
laser time transfer; trajectory optimization model; simulated annealing algorithm; common-view +
Footnote †: journal: remote sensing
# RNA Modification Related Diseases and Sensing Methods
## 5 Detection Methods for RNA Modifications
### Single Molecule Detection Using Raman Spectroscopy  
It was reported that the method for detecting queuosine modifications of transfer RNA in single living cells using a plasmonic affinity sandwich assay [125]. In this method, specific antibodies against queuosine modifications are immobilized on affinity-controlled nanoplates and selectively bind to transcriptional RNA in living cells. By using spectroscopic measurements based on plasmon resonance, the queuosine modification of RNA can be detected [125]. This approach has demonstrated high sensitivity for detection at the single-cell level for analyzing RNA modifications within cells. Furthermore, it is possible to simultaneously detect multiple RNA modifications, indicating its potential as a valuable tool for RNA modification analysis in the future.  
However, there are still issues that need to be addressed with this method. For example, the current method can only detect a single modification and is not suitable for the simultaneous detection of multiple modifications. The specificity of the antibodies against RNA modifications has not been fully validated either. Once these issues are addressed, this method will become a more widely applicable tool for RNA modification analysis.
###### Abstract  
In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on the one hand, allowing us to obtain a longer historical series of reflectance data, while on the other hand making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time-series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most-used harmonization coefficients proposed in the literature, revealing significant differences. The root-mean-square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values leads to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area.  
harmonization; surface reflectance; Landsat-7; Landsat-8; Sentinel-2; Mediterranean basin 2023  
[PERSON], [PERSON], [PERSON] Landsat-7; Landsat-8; Sentinel-2; Mediterranean basin 2023
## 2 Materials and Methods
### Study of Tumor Regression Pattern in Breast Tumor-Induced Mouse Model (In Vivo)  
For in vivo tumour regression study, MCF-7 xenograft mouse model was developed using standard procedure and subsequently treated with NPs carrying siRNA against PRKCA and/or PTEN plasmid, and the tumour growth was monitored in a longitudinal way. The Monash University Animal Ethics Committee approved all the procedures involved in this experiment (MUM/2018/12). Female Balb/c mice (6-8 weeks old) of 15-20 gm of body weights were purchased from School of Medicine and Health Science animal facility, Monash University and maintained in 12:12 lightdark condition by providing them ad libitum chow and water. Using standard methodology, approximately 1 \(\times\) 10\({}^{6}\) MCF-7 cells (in 100 \(\mu\)L PBS) were injected subcutaneously on the mammary pad of mice unilaterally, and the mice were checked between three to five days for the development of tumour by touching the area of injection with index finger. As the tumour volume reached around 25-30 mm\({}^{3}\), the mice were randomly distributed into four different groups (5 mice per group) for treatments. On day 8, mice were treated intravenously through tail-vein injection with 100 \(\mu\)L of NP (8 M Ca\({}^{2+}\)), PRKCA siRNA+NP (100 nM siRNA), PTEN+NP (40 \(\mu\)g of plasmid) and PRKCA siRNA+PTEN+NP (100 nM siRNA and 40 \(\mu\)g of plasmid). FBS was skipped in case of NPs for intravenous delivery of siRNA and plasmid; rather, the formulation was kept on ice to prevent aggregation of nanoparticles. The second dose was administered at day 10, i.e., after 2 days from the 1 st dose. During the treatmentperiod, the size of the tumour was measured at regular intervals (4 times/week). The gross body weights of mice were monitored and the lengths and widths of the tumours were measured using the vernier caliper in mm scale for 22 days. Moreover, the gross body weights of mice and their activities were monitored for study purpose. The volume of the tumour was calculated using the following formula:  
\[\text{Tumour volume}\left(\text{mm}^{2}\right)=\frac{1}{2}\left(\text{Length}\; \times\;\text{Width}^{2}\right)\]  
The data are presented here as the mean \(\pm\) SD of tumour volumes from each group. At the end of the study, at day 22, mice were humanly sacrificed by cervical dislocation following 100% CO\({}_{2}\) exposure for few seconds and tumours were excised for further study.