esa-sceva/satcom-chunk-collection huggingface dataset:
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 3 Materials and Methods
### Evaluation Measures  
We used three measures: Intersection of Union (IoU), Distance-IoU (DIoU), and confusion matrix, for the bounding box and classifier evaluation.  
IoU is a classical and popular measure to evaluate the similarity in object detection [45]. It calculates the ratio of intersection to union with the predicted box and ground truth box. The formula of IoU is shown in Equation (1).  
\[\text{IoU}=\frac{\text{Ground Truth Bounding Box}\ \cap\ \text{Predicted Bounding Box}}{\text{Ground Truth Bounding Box}\ \cup\ \text{Predicted Bounding Box}} \tag{1}\]  
Figure 3 shows the intersection and union of two overlapping boxes. We assumed that Box A is for the predicted box and Box B is for the ground truth box. The red area is the intersection, and the green area is the union. The IoU of Boxes A and B was calculated by dividing the red area by the green area.  
In addition, we measured the Distance-IoU (DIoU) [46] of the predicted box and ground truth box. The computation of DIoU is shown by Equation (2), where \(d\) is the Euclidean distance between the central points of the two overlapping boxes, and \(c\) is the diagonal length of the smallest rectangle covering the two boxes, as shown in Figure 4.  
\[\text{DIoU}=\text{IoU}-\frac{d^{2}}{c^{2}} \tag{2}\]  
Figure 3: Intersection and Union of two overlapping boxes A and B.  
A confusion matrix is a common measurement for the performance of a classifier in machine learning and statistics. The precision, recall, and F1 score are calculated by Equations (3)-(5).  
\[\text{Precision}=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{3}\]  
\[\text{Recall}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{4}\]  
\[\text{F1 score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{5}\]  
For a binary classification, TP, FN, FP, and TN refer to the following:  
True Positive (TP): both the actual and predicted classes are positive.  
False Negative (FN): a positive sample is predicted as a negative.  
False Positive (FP): a negative sample is predicted as a positive.  
True Negative (TN): both the actual and predicted classes are negative.  
In this research, when a predicted bounding box and a ground truth bounding box overlay, their IoU and DIoU are higher than the specified thresholds, and they have the same category (RBC, WBC, platelets, and background); they are considered to be TP; FN, FP, and TN can be similarly defined.
## 1 Introduction
[24] described the discharge mechanism of deep rock-mass damage from the crack tip and systematically analyzed the response characteristics of the self-potential from field detection data with this discharge mechanism. [PERSON] [25] studied the characteristics of the self-potential response during the whole stress-strain process of loaded coal, sandstone and limestone and found that the fluctuations in the self-potential were greatest when the rocks were subjected to a maximum load. In addition, the surface potential signal generated by the coal rock mass under a load failure exhibited a good agreement with the stress. [PERSON] et al. [26] found that the surface potential generated by coal under loaded breaking had a good positive correlation with the load. [PERSON] [27] preliminarily studied the one-to-one correspondence between the self-potential and the stress of coal under uniaxial compression. The stress and acoustic emission also exhibited a good correlation under uniaxial compression [28]. The electrical properties of rock samples under uniaxial compression are affected by the moisture content, loading mode, loading rate and anisotropy [25; 29; 30; 31]. Anisotropy is important for rock physical properties; however, the effect of anisotropy on the self-potential response characteristics of coal under compression has been little studied. Therefore, by conducting uniaxial compression tests with an MTS816 rock mechanics test machine, we explored the self-potential response characteristics of anisotropic bituminous coal and their relationship with the stress and acoustic emission. The aim of this study was to answer the following two questions. First, what are the synchronous response characteristics of the self-potential, stress and acoustic emission of anisotropic bituminous coal? Second, what is the relationship between the self-potential and stress of anisotropic bituminous coal?coal? In this study, we attempted to understand the response of the self-potential of anisotropic bituminous coal during uniaxial compression; the results helped to elucidate the mechanism of the self-potential induced by rock fractures.
## 3 Experiments and Functioning Tests
### Quadruped Robot Dog LiDAR Floor Mapping and Acoustic Visual Confined Space Monitoring Experiment  
In order to provide the inspection capability of devices in confined spaces, the QRD is utilized in the proposed framework. To test this feature, a two-part experiment is conducted. In the first part of this experiment, the goal is to scout the whole area of the lab using the QRD LiDAR and compare the accuracy of the given floor mapping with the original floor plan to ensure the inspection of the whole area. In this experiment, the QRD navigates around the room using the controller and transfers the data to the PC using ROS.  
The aim of the second part of the experiment is to maneuver the QRD into the narrow space of the pump and inspect it in low-light conditions visually and acoustically to look for any changes indicating damage over time. This visual and audio data acquisition is performed using an infrared LED light camera and microphone.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 1 Introduction
In this article, we aim to characterize the SCA dynamics in the main snow-dominated mountain ranges of the Iberian Peninsula. Firstly, we contrast the differences in SCA (Probability of Snow Cover Presence Index, hereinafter PSCPI, and snow season length) across these montane regions based on specific elevation ranges and latitude. Secondly, we appraise the correlation between geographical location (latitude) and spatial meteorological variables (temperature and precipitation) with SCA characteristics. In this study, as a novel aspect, we also aim to evaluate the potential contribution of radar remote sensing data from SAR in characterizing the SCA dynamics of the main snow-dominated mountain ranges in the Iberian Peninsula. The SAR product for spatial gap-filling optical remote sensing for snow cover monitoring is currently available only for the Pyrenees in the Iberian Peninsula. In this article, we aim to demonstrate the utility of this product for the other snow-dominated mountain ranges in the Iberian Peninsula, including Europe's southernmost mountain. We considered MODIS as the base for the computation of the PSCPI, which illustrates the differences in SCA dynamics among the mountain ranges. Furthermore, we utilized the AEMET 5 km climate dataset to relate SCA dynamics with precipitation and temperature patterns. Finally, we used the HRS&I SWS product to evaluate the additional SCA that radar remote sensing can provide for filling spatial gapsin optical satellite sensors (MODIS) under cloud cover conditions. This analysis involved comparing and combining both products (HRS&I SWS and MODIS) on the same dates in the Pyrenees (the only Iberian montane region available in the HRS&I SWS product).
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 6 Lessons Learned and Concluding Remarks  
The Locata technology provides a solution to the demand for high-accuracy indoor and outdoor positioning, where GNSS cannot on its own provide the requisite capability. In this paper, some technical aspects of the technology were presented, including the critical technological components, the mathematical models for precise positioning, and the signal error sources. The literature describing a variety of applications of a Locata standalone system, a Locata/GNSS, and a Locata/GNSS/INS integration systems were reviewed, and significant challenges that may restrict its future application for custom-grade navigation in urban environments were discussed.  
In summary, the major advantages of Locata technology are: (1) a flexible system and signal design that can prevent jamming and reduce the near-far problem under different signal environments; (2) the use of the TimeLoc technique ensures time synchronisation within a LocataNet as well as with respect to an external time scale (such as GNSS); (3) the installed LocataNet is easily expandable to increase the signal coverage; and, (4) the advanced VRay antenna design is very effective in mitigating multipath, and it provides angle measurements for more accurate position and attitude solutions. However, there are also some issues that may limit Locata's future deployment, such as the relative  
Figure 13: Positioning errors using code pseudorange measurements with robust estimation: (**a**) whole experiment, (**b**) the first 17 minutes.  
high cost, incompatible user hardware, complex configuration of LocataNet, environmental constraints, and RFI.  
As far as academic research is concerned, future investigations should focus on classifying, testing, and modelling the various Locata signal errors and biases, as well as the integer ambiguity fixing algorithms, so as to improve the positioning accuracy and reliability of a Locata standalone system. Further research is also required for data fusion algorithms that more robustly integrate Locata measurements with those from other navigation sensors and systems, especially GNSS and INS.  
[PERSON] contributes to the conception of the work and substantively revised it; [PERSON] mainly contributes to the data analysis and mainly drafts the work. Both of the authors have approved the submitted version; and agree to be personally accountable for the author's own contributions and for ensuring that questions related to the accuracy or integrity of any part of the work.  
This research was funded by National Natural Science Foundation of China, grant number 41504022 and the State Key Laboratory of Satellite Navigation System and Equipment Technology, grant number CEPNT-2017 KF-10.  
The authors declare no conflict of interest.
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 2 Related Work
### HIN Embedding  
In reality, various types of network data, different sources, and complex relationships constitute HIN. To capture multiple semantic relationships in HIN, a significant amount of research work is devoted to HIN embedding, including research on the node and meta-path aggregation methods, and research on network attribute completion [39]. Metapath2 Vec [40] is similar to Deepwalk [6], but used in heterogeneous graphs. It works with a meta-path based random walk strategy to construct heterogeneous neighbourhoods of vertices, and then completes the vertex embedding with the Skip-Gram model. [PERSON] et al. [41] believe that different types of edges in HIN have different weights, and different neighbor nodes of the same type of edges have different weights, so they employ node-level and semantic-level attention to learn the importance of different nodes and meta-paths, respectively. HERec [26] utilises a random walk strategy to generate node sequences based on the meta-path, and then fuses the node representations of the multiple meta-path. HetGNN [42] introduces a random walk strategy with restart, samples strongly correlated heterogeneous neighbors for nodes, and classifies them according to node types. They design two encoding modules to learn the attribute information and types of nodes, respectively, and aggregate the information of nodes. Heco [43] introduces synergistic contrastive learning and view mask mechanisms to synergistically learn node representations from the network architecture and meta-path perspectives. In the aspect of attribute completion, [PERSON] et al. [44] propose HGNN-AC to complete the attribute information of heterogeneous graphs. However, the above methods either ignore the features within the node or only use a single meta-path, and ignore the correlation between views, so there is still room for improvement in semantic retention.
## 4 Discussion
### ES Trade-Offs/Synergies  
In the study of tradeoffs/synergies among ESs, many quantitative methods have been used to quantify and analyze ESs trade-offs/synergies, including correlation analysis [37; 38], cluster analysis [3], redundancy analysis (RDA) [39], and multivariate regression trees [40]. Studies on trade-offs/synergies in ESTD consider the ecosystem services at two time points. Current research in this approach has made some progress [41]. Although many studies have been carried out to investigate the trade-offs/synergies relationships and their spatiotemporal variations, the factors underlying these complex interactions are still poorly understood [25]. It is essential to reveal the trade-offs/synergies and the spatial differences in the services. Understanding the trade-offs/synergies among ESs is insufficient without carefully exploring the drivers and mechanisms behind the relationships [42].The formation of tradeoffs and synergies among ecosystem services can be attributed to one or more of the following three factors: land use conflict or consistency, common drivers, and interactions among ecosystem services [43]. The spatial variation of land use/cover caused by human activities is an important factor affecting the degree of ES tradeoffs and its scale effect [44].
## 2 Materials and Methods
### Preparation of SDexF on a Semi-Industrial Scale  
During project implementation, more than 400 kg of SDexF were produced using a prototype machine type UHP-01.00 (Mysak, Poznan, Poland) designed for obtaining indigestible dextrins on a semi-industrial scale. The most important systems of this device included (Figure 1) feeding and mixing of solid raw material (heating chamber with a mixer), liquid dispensing, air and oil heating, dedusting, temperature control, mixing and regulation of the frequency of regeneration and dust ejection.  
SDexF was prepared according to patent PL220965 [24]. First, 10 kg of food-grade potato starch was weighed and placed in the mixing chamber of the machine. Then mixing was started and the starch was sprayed with 0.5% (\(v/v\)) aqueous food grade hydrochloric acid solution and the same amount of 0.5% (\(v/v\)) aqueous food grade citric acid solution to a final concentration of each acid amounting to 0.1% \(w/w\) related to dry starch basis (d.s.b). The next step involved pre-drying the acidified starch to a water content below 5% using two different heating methods: hot air at 110 \({}^{\circ}\)C and the oil heating jacket at 105 \({}^{\circ}\)C. The water content was checked for a sample taken from the heating chamber using a moisture analyser (Radwag, Radom, Poland). After confirming the water content was below 5%, the next step was dextrinization, which was carried out by heating only with the oil heating jacket at 140 \({}^{\circ}\)C for 4 h. The product was continuously mechanically mixed throughout the entire process. The obtained SDexF was cooled to room temperature in the desiccator, then ground up and sieved through laboratory stainless steel sieves (pore diameter of 250 \(\upmu\)m) to remove the burnt formed during heating. The sieved product was washed with several portions of food-grade ethyl alcohol in order to remove low-molecular starch thermolysis products and citric acid residues. Residual ethyl alcohol was removed from the final product by evaporation under the following conditions: air drying for 2 h and then drying in a dryer at 60 \({}^{\circ}\)C for 48 h (until the smell of ethanol was no longer detected). Initially, in order to evaluate the process of obtaining SDexF on a semi-industrial scale, samples were taken from 4 batches, where each batch consisted of a product obtained from the dextrinization process carried out twice (1 batch = 2 \(\times\) 7 kg) marked as SDexF1, SDexF2, SDexF3, SDexF4. For these samples, total dietary fibre content (TDF), solubility in water at 20 \({}^{\circ}\)C, dextrose equivalent (DE), residual citric acid and residual ethanol were determined. The remaining experiments were evaluated by testing an average of the sample obtained from the mixed SDexF1-SDexF4.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 2 Materials and Methods
### Research Methodology
#### 2.2.6 Scatter Plot  
A scatter plot visually illustrates the similarity and correlation between two datasets, revealing functional relationships between algorithms [45]. By plotting one interpolation error on the \(X\)-axis and another on the \(Y\)-axis, an \(XY\) scatter plot can be generated. If the interpolation errors are consistent, the scatter plot will appear as a straight line with a slope of 1, indicating a functional relationship \(Y=X\). If the errors are correlated but not identical, the plot will exhibit a functional relationship of the form \(Y=aX+b\).
###### Abstract  
The operational safety of oil and gas storage units is crucial, as any potential imperfections or cracks in a container wall could result in fracture. The key parameter in ensuring the integrity of oil and gas storage units is their fracture toughness. For its determination, compact tension (CT) and single-edge notched bend (SENB) specimens are commonly used. However, these specimens may lead to unnecessary conservatism in safety assessments. Single-edge notched tension (SENT) specimens, on the other hand, could be a more acceptable substitute, as they are shown to lead to less conservatism. The aim of this study was to determine the J-R curve for SENT specimens from pipe steel X65 using an experimentally determined J-R curve for CT specimens. This study was motivated by the fact that the use of the Gurson--Tvergaard--Needleman (GTN) model for R-curve determination can significantly reduce the number of physical tests. Calculations were performed using the GTN constitutive model, calibrated on a CT specimen of width w = 50 mm, thickness B = 8.7 mm, and initial crack length a = 25.15 mm, used for the determination of the R-curve. The width and thickness of the SENT specimens considered in the calculations were similar to those for the CT specimens. Ideal clamping of the SENT specimens in the jaws was considered. A comparison of the SENT R-curve with the experimental CT R-curve showed that these curves were practically equidistant, with the former R-curve being superior to the latter one. The SENT R-curve has not been verified experimentally, because the physical specimens that match the models used for calculation do not meet the requirements of the related standards on specimen thickness. However, a comparison of this SENT R-curve with another SENT R-curve, published in the literature and obtained on the basis of the results of a two-parameter fracture mechanics approach, showed a very good agreement between the two R-curves.  
GTN model; finite element modelling; SENT specimens; R-curve; fracture toughness +
Footnote †: journal: Applied Sciences
# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 1 Introduction
For these reasons, it is essential to study methodologies that allow starting from the most recent data and studying the impact of adding mobile windows with historical data to build forecasts, allowing the generalization capacity of the model to be observed [14]. This research, therefore, aims to challenge the traditional view of data quantity and emphasize the potential of optimized training and strategic data usage. By doing so, this study contributes a novel methodology for data selection in deep learning for time series forecasting, demonstrating that, in some cases, intermediate dataset sizes yield optimal predictive accuracy. The key contributions of this study are as follows:  
This work critically examines the impact of dataset size on time series forecasting using deep learning techniques. Through an experimental approach, it is demonstrated that optimal predictive performance is not always achieved with the complete dataset, but rather in intermediate iterations, challenging the assumption that larger datasets always yield better metrics. The key contributions of this study are as follows:  
* Identification of a nonlinear relationship between dataset size and predictive performance, with significant variations in metrics such as RMSE (up to 66%) and MAPE (up to 44%) across different models and time series. * Proposal of a methodology that optimizes data usage, illustrating how strategic selection of observations can enhance computational efficiency without compromising model accuracy. * Demonstration that, in certain cases, smaller datasets can outperform larger ones in terms of overall model performance, depending on the time series structure and the model employed. This article is organized as follows: Section 2 provides a detailed description of the methodology. Section 3 gives information on the databases used and details of the parameters of the models employed. Section 4 focuses on the main results. Section 5 presents a discussion based on the obtained evidence. Finally, Section 6 describes the conclusions and future lines of work.