esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In the last decades, great advances have been achieved in the understanding of the electrospinning process, in terms of instrumental development and perspective applications. As a result, it is now possible to easily produce uniform nanofibers from most synthetic polymers and many natural polymers in the lab scale and also for industrial needs. Moreover, numerous biocides have been successfully incorporated into nanofibers to obtain electrospun mats potentially used as medical implants, scaffolds, wound dressings, textiles, or as filter media. Among others, different inorganic nanomaterials composed of transition metals were considered, thanks to their wide range of biocidal activity and because their ions are well tolerated by human beings at concentration levels that are effective towards several microorganisms.  
Despite the significant advances, several issues still need to be further addressed in antibacterial nanofibers, mainly related to the controlled release of the antibacterial agents from the nanofibers.  
With this in mind, we believe that the combination of metal nanoparticles with innovative polymer synthesis and/or processing technologies, such as the electrospinning technique, affording a high level of morphological and composition control over the resulting material, will represent a proper solution for a further step in the diffusion and exploitation of these agents.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antibacterial Agent** & **Electrospun Polymer Composite** & **References** \\ \hline CuNPs, CuO NPs & Copper salt + PVP & [92] \\  & Commercial CuO + PHBV & [109] \\  & Supported copper salt + CA & [110] \\  & Cu/CuOx + PAN & [103, 104, 111] \\  & Copper salt + PVA & [100, 101] \\  & Copper salt + CMC & [107] \\  & Copper salt + PVDF & [112] \\  & Copper salt + PU & [113, 114] \\ \hline AgNPs & Silver salt + PVP & [92, 93] \\  & Supported silver salt + CA & [105, 110] \\  & Silver salt + PAN & [108] \\  & Silver salt + PVA & [94, 95, 96, 97, 98, 99, 100, 99, 111, 112, 113, 114] \\ \hline Other Me/Me oxides NPs & Copper salt + zinc salt + PVA & [102] \\  & Zinc salt + CMC & [107] \\  & Iron salt + CMC & [107] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some representative studies where electrospun antibacterial nanofibers incorporate metal nanoparticles.  
In the last year, several engineering fields have benefited from the introduction of additive manufacturing technologies and, most recently, a growing tendency to the convergence of different processing techniques into a single, computer-aided manufacturing technology has been reported. It is an opinion of the Authors that this scenario could also affect the field of antimicrobial materials, enabling the large scale fabrication of complex-shaped structures with tailored properties.  
Each of the authors has equally contributed to the writing of the paper.  
This research received no external funding  
**Conflicts of Interest:** \"The authors declare no conflict of interest.\"
## 3 Methods
### Land Use and Land Cover Classification and Associated Changes
#### 3.2.3 Change Detection  
To assess historical land cover changes, a spectral change vector analysis can be performed if the sensor and band properties are consistent over time (i.e., the Landsat Legacy). Using this method, spectral properties of the reference year 2019 can be compared to its historic spectral properties for each epoch and for each pixel, respectively. If a spectral difference between two compared epochs for a certain pixel lies above a predefined threshold, a potential change is identified and flagged.  
A high change vector length value is, for example, expected during urbanization activities (i.e., the conversion of vegetation into sealed surface). Low change vector lengths may occur if there is no change at all and the spectral properties only differ due to meteorological effects, i.e., because of a delayed or shortened rain season or also during vegetation regrowth due to afforestation activities.  
The applied change vector analysis is not based on a single comparison of the spectral properties of two spectral bands but considers the comparison of the entire spectrum and can therefore be called a multidimensional change vector analysis.  
As already highlighted above, the algorithm considers each epoch of the reference period (1991-2019) and compares it to the reference year (2019). Ultimately, change areas are detected by dynamically thresholding each comparison individually via a high percentile value (i.e., 0.99).  
Figure 7 depicts this approach in a two-dimensional space where small change vector lengths are considered stable and high differences are flagged as change. By summing up the occurrences of abnormally high differences over time per pixel, a probability of change or even an exact timing of change can be derived if of interest.  
Figure 7: (**a**) left: Example of a two-dimensional change vector analysis between two timesteps using the red and near-infrared spectral bands and (**b**) right: Example of a time-series-based two-dimensional change vector analysis ©GeoVille.  
The identified \"change candidate areas\" are then flagged and reclassified into target change classes via a supervised classification. The algorithm itself is a supervised stratified random forest classifier as described above.
## 2 Related Work  
### Datasets  
The datasets used in this study comprise two parts. The first part consists of remote sensing images of lake areas from the scene classification datasets [32] proposed by Northwestern Polytechnical University in 2017. The original image has a size of 256 \(\times\) 256 pixels and consists of RGB three-channel spectral data. The spatial resolution ranges from 30 m to 0.2 m per pixel. All images were extracted from Google Earth (Google Inc., Menlo Park, CA, USA). This part contains 700 images obtained by collecting and cropping the satellite remote sensing images from lakes worldwide. However, these datasets were originally intended for scene classification without semantic segmentation annotations. As neural network training requires substantial data support, the original sets' limited size is insufficient for model training, leading to poor performance due to inadequate data [7]. Therefore, we employed a generative adversarial network (GAN) for the generation of remote sensing lake image data to address the lack of datasets [33]. The fundamental concept behind GAN network involves designing a generative network and an adversarial network. The generator is continuously updated to effectively deceive the discriminator, while the discriminator consistently enhances its discriminatory capabilities. The application of the GAN network for generating image data has been validated in several relevant research articles [34; 35; 36; 37; 38]. In this study, 300 lakes are generated by the GAN network, and the remote sensing images that meet the requirements of lake segmentation are selected as the data source. The final data source contained 983 remote sensing images of lakes with a size of 256 \(\times\) 256. The obtained data were labeled with Labelme software (5.2.1) to mark the water body part, and the JSON format after annotation was converted into.png format. Refer to Figure 1 for the datasets and annotation of scene classification, and consult Figure 2 for the generated data and its corresponding annotation.  
Figure 1: Remote sensing image and label of lake with scene classification.  
Figure 2: GAN network generated data lake remote sensing image and annotation.  
Additionally, we opted to validate the model's performance across various datasets by utilizing the publicly available lake datasets from the Qinghai-Tibet Plateau lake datasets, as shown in Figure 3. The Qinghai-Tibet Plateau lake datasets [24] is an open semantic segmentation datasets of lakes. The whole datasets contained 1600 lakes, and a total of 6774 remote sensing images with RGB three-channel composition were acquired, featuring a spatial resolution of 17 m and cropped to a size of \(256\times 256\) pixels per image. The datasets were labeled with a water body and background using Labelme software, which is similar to the datasets we prepared above. Therefore, these data are selected for the generalization performance test of the model.
# Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario
## 3 Modelling QoS Metrics for the CLARC Scenario  
Non-functional properties relate to how a system performs rather than to what it does. Examples of non-functional properties include timing, dependability, safety or resource consumption, among others. In this paper, we will consider that a QoS metric expresses the degree of fulfilment of a non-functional property as a real number in the range [0,1]. In other words, a QoS metric will be a score of how well the system performs according to, e.g., safety. If the metric is 1, the system is optimal in terms of this property, while 0 indicates the opposite. Besides, this value can change as the behavior of the robot evolves during its operation.  
In parallel to the work on CLARC, our team carried out the RoQME project ([[https://robmosys.eu/roqme](https://robmosys.eu/roqme)]([https://robmosys.eu/roqme](https://robmosys.eu/roqme))) aimed at providing robotics software engineers with a model-driven framework to: (1) model system-level non-functional properties in terms of contextual information; and (2) generate runtime software artifacts to evaluate the specified properties as QoS metrics [12; 13; 14]. In RoQME, the modelling of QoS metrics involves three major concepts:  
* The _non-functional properties_ relevant to the scenario. At run-time, the QoS metrics provider, whose code is generated from the models, will update the estimates (the value of the metrics) for those properties.
* The _context_ information available to the robot. The fulfilment of a property evolves over time according to the evidences collected by the robot from its context.
* The _observations_, which are specific context patterns whose occurrence reinforces or undermines the fulfilment of non-functional properties.  
Figure 1: (**Left**) CLARC interacting with a patient for performing a Barthel test. (**Right**) The external remote control.  
The following three Sections describe the RoQME specification of the above elements to include QoS metrics in the CLARC scenario. Finally, Section 3.4 presents the probabilistic framework behind this specification.
# Influence of the Radial Gap on the External Gear Pump Performance
## 1 Introduction and Motivation
The verified CFD model accuracy motivated us to continue our research with a study on the influence of one of the main design features determining the efficiency--the radial gap between the tip of the gears and the pump housing. The value of the minimum radial gap is determined mainly by the possible value of the clearance in the bearings and their misalignment, as well as by the value of the eccentricity in the position of the gears in the housing seats [45]. Taking into account the possibility of an unfavorable coincidence with manufacturing tolerances, the radial gap is usually chosen as up to 0.03-0.05 mm per side [46; 47]. However, in long-term operations of the pump with a heavy load or unsuitable working modes, this gap increases as a result of housing wear, which leads to an increase in pump volumetric losses. This leads to a sharp decrease in volumetric efficiency, which is a dominant part of the overall efficiency of every hydraulic rotary displacement machine. The main goal of the article is to present the results of the study of the radial gap influence on the external gear pump performance. For this purpose, the authors developed a methodology for determining the overall pump efficiency based on an existing advanced 2D CFD model. The model was verified in a wide operating range through a laboratory experiment and a technical pump specification. In contrast to the existing studies, in this article, results are presented not only of the influence of the radial gap dimensions on the overall efficiency and its components (volumetric and hydromechanical efficiency) but on the flow rate at different rotation frequencies, which is another important performance characteristic. The main contribution of the present work is the original methodology and verification approach which can be used on other types of hydraulic machines. From an applied point of view, the represented deductive relationships between the investigated performance characteristics can be used to evaluate the current state of hydraulic machines. This article is organized as follows. Section 2 includes a short description of the design features and CFD model development, Section 3 presents the main relations of the pump performance at different values of the radial gap (theoretical background and methodology), Section 4 shows the CFD results and verification, Section 5 presents the radial gap size influence on the volumetric flow rate, Section 6 shows the pump efficiency estimation at different radial gap sizes, and in Section 7, some conclusions are given.
###### Abstract  
The out-of-band (OOB) response is one of the key specifications for satellite optical sensors, which has important influences on quantitative remote sensing retrieval. In this paper, the effect of OOB response on the radiometric measurements made just above the sea surface is evaluated for the three broad visible bands (i.e., blue, green, and red) of the Coastal Zone Imager (CZI) onboard China's ocean satellite HY-1C to be launched in September 2018. For the turbid coastal (Case 2) waters whose optical properties are mainly dominated by suspended sediment and colored dissolved organic material, the OOB effect can be neglected (<2%) for all three CZI visible bands. For the phytoplankton-dominated (Case 1) waters which are mainly distributed in the clear open ocean, a significant (>2%) OOB effect was found in the green band over oligotrophic waters (chlorophyll a concentration \(\leq\)-0.1 mg/m\({}^{3}\)), and accordingly a model based on the CZI blue-green band ratio is proposed to correct this effect. The OOB influence on the CZI ocean color retrieval is discussed. This research highlights the importance of the comprehensive pre-launch radiometric characterization and the OOB effect correction for the broad band space-borne sensor, in order to achieve a high-quality quantitative ocean product.  
out-of-band response; Coastal Zone Imager (CZI); HY-1 26 June 2018; 18.3067; doi:10.3390/s18093067
# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Antenna Position, Direction, and Radiation Estimation
ML has been shown to be more robust in complex environments compared to most other signal processing techniques and offered an increased signal-to-noise ratio and efficient beamforming architectures. Different types of fractal antennas, star antennas, and pattern reconfigurable antennas are now utilizing ML algorithms to provide automatic reconfigurability features to serve modern applications [104; 105].
# Unfolded Algorithms for Deep Phase Retrieval
## 3 UPR: The Proposed Framework
### Architecture of the Encoder Module  
In this part, we formally define the class of encoder functions for the phase retrieval problem. Recall that the governing dynamics of the data-acquisition system in a phase retrieval problem are given by (2). We now use those dynamics as a blueprint to design our encoder module parameterized on the sensing matrix. To this end, we define the hypothesis class (\(\mathcal{H}_{e}\)) of the possible encoder functions (module) as follows:  
\[\mathcal{H}_{e}=\{f_{A}:\mathbf{x}\mapsto|\mathbf{Ax}|\,:\mathbf{A}\in\mathbb{C}^{m\times n }\}, \tag{7}\]  
where \(f_{A}(\mathbf{x})\triangleq|\mathbf{Ax}|\). The above hypothesis class encompasses the possible encoder modules whose computational dynamics mirror the behavior of the data-acquisition model in a phase retrieval context. Alternatively, this hypothesis class can be viewed as a one-layer neural network with \(n\) input neurons and \(m\) output neurons, where the activation function is given by \(|\cdot|\) and the weight matrix (\(\mathbf{A}\)) is trainable. Specifically, the input to this neural network is \(\mathbf{x}\in\mathbb{C}^{n}\), while the output is \(|\mathbf{Ax}|\in\mathbb{R}^{m}\).  
The problem of learning a proper encoder module in the context of phase retrieval problems can be re-expressed as a deep learning problem by considering the training of \(\mathcal{E}_{\mathbf{A}}\) according to a loss function that is the same as the objective function in (4), i.e.,  
\[\min_{\mathbf{A}\in\mathbb{C}^{m\times n}}\ \ \mathds{E}\big{\{}\mathrm{D}(\mathbf{x}, \mathcal{D}_{\phi}\circ\mathcal{E}_{\mathbf{A}}(\mathbf{x}))\big{\}}. \tag{8}\]  
Hereafter, we seek to formulate the joint design of the sensing matrix and the reconstruction algorithm (see (6)) from a deep learning and statistical learning point of view and eventually formulate (6) as a purely deep learning problem and tackle it by utilizing well-established deep learning techniques. The following subsection, in particular, is devoted to mathematically defining two parameterized classes of decoder functions: one specialized for sparse phase retrieval and the other specialized for the conventional class of phase retrieval problems. Moreover, we formally introduce the UPR framework and its training procedure.
## 2 Methods  
The Lower Calorific Value is considered as an important quality parameter for the estimation of coal reserves. However, the LCV of a coal deposit often is not measured for all available borehole samples and should thus be estimated from other measured parameters.  
The aim of the geostatistical analysis performed in this research is to determine the best estimation conditions of the Lower Calorific Value, taking into account all the available information concerning the quality parameters of coal, provided in the data set.  
The main issues faced in our analysis are the following:  
* The examination of the LCV correlation to the other available coal properties in order to determine the auxiliary variables which would be used for the estimation.
* The utilization of proper methodologies to ease the inclusion of these auxiliary variables in the estimation procedure. Among such methodologies, kriging and cokriging are geostatistical tools that exploit the spatial structure in order to map the objective variable on a given domain [18; 23]; kriging by taking into account measurements of the objective variable in the neighborhood of the estimation points, while cokriging by considering also auxiliary variables [23; 24]. However, as long as cokriging with multiple variables may not in fact be feasible, a third method was examined, where quality parameters were replaced with their principal factors derived from Principal Component Analysis [25]. Only 8% of the total variability were missing as a result of this substitution (see following section), thanks to the overabundance of information.
* The adoption of the most convenient method to employ, according to its performance on the present case study. This was achieved by cross-validating the results from all the examined methods [26]; first by estimating the points where LCV is already known and then by comparing the results to the measured values according to appropriate statistics.  
Before proceeding further, the above methodologies are briefly outlined in the following paragraphs:
## 4 Experimental Evaluation
### Evaluation Criteria  
In this study, key evaluation metrics include mAP at IoU 0.5 (mAP50), mAP50-95, model parameter count, and GFLOPS. The mAP provides a comprehensive assessment of model performance across different precision and recall scenarios that are commonly utilized in object detection evaluations. Parameters (Params) and floating-point operations (FLOP) assess algorithm or model complexity. Equations (18)-(20) define precision (P), recall (R), and mAP.  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{18}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{19}\]  
\[mAP=\frac{\sum_{i=1}^{K}\int_{0}^{1}P(R)dR\times 100\%}{K} \tag{20}\]  
Among them, TP represents the number of correctly predicted positive samples, TN represents the number of correctly predicted negative samples, FP represents the number  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Initial Learning Rate & 0.02 \\ Epochs & 100 \\ Batch Size & 20 \\ Imgsz & 640 \\ Optimizer & SGD \\ Weight Decay & 0.0005 \\ Momentum & 0.937 \\ \hline \end{tabular}
\end{table}
Table 1: Model training parameters.  
Figure 9: Dataset label distribution. **(a)** The position of the bounding box center point relative to the entire image; **(b)** The aspect ratio of the target in the image relative to the entire image.  
of negative samples classified as positive, FN represents the number of positive samples classified as negative, and K represents the number of categories.  
In the evaluation criteria, mAP50 refers to the average precision when the intersection over union (IoU) is above 50%. IoU is used to measure the degree of overlap between the area detected by the model and the actual target area. Its calculation formula is shown in Equation (21), where \(S_{A}\) and \(S_{B}\) refer to the bounding box and ground truth, respectively.  
\[IoU=\frac{|S_{A}\cap S_{B}|}{|S_{A}\cup S_{B}|} \tag{21}\]  
Similarly, mAP50-95 represents the average mAP at different IoU thresholds (ranging from 0.5 to 0.95, with a step size of 0.05).
## 4 Results
### Analysis of Enhancing Question Diversity
#### 4.3.4 Differences in Performance Across Cognitive Levels by Generation Method  
To visually demonstrate the performance differences across cognitive levels for different generation methods, we created an interaction effect, Figure 8.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Source of** & **Sum of** & **Degrees of** & **Mean Square** & **F-Value** & \(p\)**-Value** & **Partial \(\eta^{2}\)** \\
**Variation** & **Squares** & **Freedom** & **Mean Square** & **F-Value** & \(p\)**-Value** & **Partial \(\eta^{2}\)** \\ \hline Generation Method & 232.67 & 2 & 116.34 & 122.48 & \(<\) 0.001 & 0.09 \\ Cognitive level & 56.04 & 5 & 11.21 & 11.80 & \(<\) 0.001 & 0.02 \\ Interaction & 15.74 & 10 & 1.57 & 1.66 & 0.086 & 0.01 \\ Error & 1008.71 & 1062 & 0.95 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of two-way ANOVA for question quality scores.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Comparison** & **Mean Difference** & **Standard Error** & \(p\)**-Value** & **95\% CI** \\ \hline Multi-Examiner vs. GPT-4 & 0.81 & 0.09 & \(<\) 0.001 & [0.64, 0.99] \\ Multi-Examiner vs. human & 0.28 & 0.09 & 0.0015 & [0.11, 0.46] \\ GPT-4 vs. human & 1.09 & 0.09 & \(<\) 0.001 & [0.92, 1.27] \\ Evaluation vs. creation & \(-\)0.74 & 0.19 & \(<\) 0.001 & [\(-\)1.11, \(-\)0.36] \\ Analysis vs. evaluation & 0.83 & 0.23 & \(<\) 0.001 & [\(-\)1.29, \(-\)0.37] \\ Application vs. analysis & 0.69 & 0.20 & \(<\) 0.001 & [\(-\)1.09, \(-\)0.29] \\ \hline \hline \end{tabular}
\end{table}
Table 12: [PERSON]’s HSD post-hoc test results for question quality scores (after Bonferroni correction).  
From Figure 8, we can observe the following trends: (1) Multi-Examiner significantly outperforms GPT-4 in higher-order thinking skills (particularly in the evaluation and creation levels) and is close to the level of human-generated questions. (2) GPT-4 performs well in lower-order thinking skills (memory, understanding) but shows a marked decline in higher-order thinking skills. (3) Human-generated questions exhibit the most stable performance across all cognitive levels, especially maintaining a high level in higher-order thinking skills. (4) The performance gap between Multi-Examiner and human-generated questions is small in the evaluation and creation levels, indicating that Multi-Examiner has good potential in generating questions that require higher-order thinking.