esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Infrared Thermography for the Estimation of Melting Enthalpy
## 2 Materials and Methods
### Validation  
The melting enthalpies calculated using the IRT-based method were compared with measurements made using a standard DSC method. A DSC 131 provided by Setaram (Caluire, France) was used for the measurements. Before the measurements, the DSC had been calibrated with Gallium (Purity: 99.9999%), Indium (Purity: 99.9995%), Tin (Purity: 99.999%) and Lead (Purity: 99.999%) in order to validate the measurement accuracy in the 29-330 \({}^{\circ}\)C range. For each sample, two heating/cooling cycles were performed: one at 1 \({}^{\circ}\)C/min to replicate the conditions of the IRT experiment and one at 0.3 \({}^{\circ}\)C/min to make sure that results are independent of the heating rate. Indeed, the interpretation of the DSC curves were made according to the guidelines in [41] stating that the onset temperature must be considered as the melting temperature if possible and that peak temperatures should be considered otherwise. If only peak temperatures can be reliably retrieved, one has to make sure to revise this value to account for the influence of the heating rate or to show that its influence is negligible.
# Imaging Characteristics of Disturbance Flow Field Surrounding a Hypersonic Target
###### Abstract  
The disturbance flow field arises naturally with hypersonic target flying in near space. In situations where traditional infrared and radar systems lose effectiveness, space-based optical detection of this surrounding flow can serve as an alternative method for detecting high-speed targets. This paper presents a remote sensing imaging analysis of the disturbance flow field surrounding a hypersonic target at different flight altitudes and Mach numbers. Utilizing Fourier Optics and Background-Oriented Schlieren, in conjunction with the fourth-order Runge-Kutta ray tracing algorithm, the imaging blurring and imaging deviation of three typical backgrounds under the influence of the disturbance flow field are obtained. Additionally, the study analyzes the influence of flight conditions and parameters of the imaging system on the imaging characteristics, and provides optical design recommendations. The results indicate that the presence of disturbance flow fields leads to varying degrees of visually apparent blurring effects and indiscernible deviation effects on the background images. Furthermore, the profiles of the disturbance flow field are extracted, in agreement with current experimental research. This study verifies the feasibility of space-based optical detection of hypersonic targets through disturbance flow field remote sensing imaging and contributes to the advancement of imaging research in this field.  
hypersonic target; disturbance flow field; imaging characteristic; space-based remote sensing +
Footnote †: journal: Medical Imaging
# PET/CT Radiomics in Lung Cancer: An Overview
## 5 Conclusions and Perspectives  
Quantitative image analysis of PET/CT scans has attracted increasing research interest in the last few years. Clearly, the field is still at an early stage and further work needs to be done to confirm the benefits and the potential advantages in the clinical practice. As of now, the evidence of a superiority of radiomics beyond standard imaging analysis tools including SUV, kinetic data, etc. is yet to be confirmed for the tasks discussed in this work.  
In [17], the authors identified three major obstacles to overcome before radiomics can effectively translate into clinical practice: _standardization_, _automatization_, and _data availability_. The first involves the definition of guidelines and/or standards for all the steps described in Section 2. The Image Biomarker Standardization Initiative (IBSI) is an interesting attempt toward this end [80]. The second requires the automatization of those steps of the procedure that still rely too heavily on human intervention--for instance, segmentation. Finally, the availability of large, possibly multi-center image datasets is crucial for the development and validation of radiomics methods.  
**Author Contributions:** Conceptualization, F.B., B.P., and M.L.F.; methodology, F.B., B.P., I.P., and M.L.F.; validation, B.P., A.S., I.P., and S.N.; resources, F.B., B.P., and M.L.F.; writing--original draft preparation, F.B. and B.P.; writing--review and editing, all authors; supervision, A.S., I.P., and S.N.; project administration, F.B., B.P., and A.S.; funding acquisition, F.B. All authors have read and agreed to the submitted version of the manuscript.  
**Funding:** This work was partially supported by the Department of Engineering, Universita degli Studi di Perugia, Italy, under the project '_Shape, colour and texture features for the analysis of two- and three-dimensional images: methods and applications_ (Fundamental Research Grants 2019), and by the Universita degli Studi di Sassari, Italy, within the framework '_Fondi d'Ateneo per la Ricerca_' (University Research Funds) 2019.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
This scoping review aimed to provide an overview of available research on the evaluation of chronic kidney disease from SPECT and planar scintigraphy utilizing machine learning approaches. Regarding the first research question (RQ1), preliminary screening identified limited research focused specifically on this application. For the second research question (RQ2), a broader screening of ML applications across these two imaging modalities revealed significant variability among the methods applied. Classical approaches typically involve extracting handcrafted features (e.g., uptake ratios, curve slopes) from scintigraphy images and feeding them into algorithms such as artificial neural networks or support vector machines. In contrast, more recent studies apply deep learning methods directly to the image data. When datasets are limited, data augmentation and transfer learning are commonly used. Overall, the reviewed studies indicate that ML models can produce accurate results or effectively support human decision-making processes. Regarding the last research question (RQ3), several recurring challenges were identified: the scarcity of large, annotated datasets, limited clinical validation of ML models, insufficient reporting of technological details, and the need for explainable AI methods. Given the limited number of studies specifically targeting CKD diagnosis from renal SPECT and planar scintigraphy using ML, and in light of recent ML advancements in addressing other organs and diseases, there is a clear opportunity to further explore emerging deep learning methods. Along with advanced data augmentation techniques and transfer learning approaches, such methods could produce a promising toolkit for CKD diagnosis and functional evaluation of kidney health.  
Conceptualization, D.V. and K.M.; methodology, D.V.; validation, B.V. and K.M.; investigation, D.V.; data curation, D.V. and K.M.; writing--original draft preparation, D.V.; writing--review and editing, B.V. and K.M.; visualization, B.V. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the Ministry of Science, Technological Development and Innovation (Contract No. 451-03-137/2025-03/200156) and the Faculty of Technical Sciences, University of Novi Sad through project \"Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad 2025\" (No. 01-50/295).  
The charted data used in this scoping review were extracted from publicly available published studies, which are cited throughout the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ABAdaptive Boosting AEAutoencoders AIArtificial intelligence BCBagging Classifier BTBoosted Tree CADCoronary artery disease CAMClass activation mapping CKDChronic kidney disease CNNConvolutional Neural Network CTComputed tomography DMDiffusion Maps DTDecision tree EHRElectronic health record
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.4 Simulation Results for Different Packet Loss Ratios of Uplink  
In this scenario, all the parameters are set as default except for the packet loss ratio of uplink \(\theta_{1}=0.01\), 0.02, 0.03, respectively. Figure 5 shows the performance of different packet loss ratios of uplink, i.e., \(\theta_{1}\). Figure 5c reveals that \(\theta_{1}\) has little effect on the queue packet loss ratio. However, window size increases as \(\theta_{1}\) increases, as shown in Figure 5b. This is reasonable because when the queue length is fixed, more packets need to be sent from the source to make up for the packet lost in the uplink. In addition, bigger \(\theta_{1}\) leads to more serious overshoot of the queue, as shown in Figure 5a.
## 5 Field Application and Monitoring Analysis
### On-Site Mechanized Installation Process  
(1) The preparation before construction  
The staff observes whether the road surface of the tunnel meets the safe passage of the multi-functional operation trolley. If the traffic conditions are not met, the road surface needs to be repaired; one must check the overbreak and underbreak of the tunnel face, if the tunnel shows signs of underbreak, it needs to be dealt with in advance. Field processing is shown in Figure 24.  
(2) Mechanized installation  
The erection sequence of the spatial reticulated grid is the arch top A component, the spandrel B component, the arch waist C component, the arch foot D component, and the arch bottom E component. When installing the arches on the upper bench, each of the left, middle, and right arms grab an A component for ground assembly, and at the same time, the reinforcement mesh is bound. After that, the left and right mechanical arms complete the action of releasing the arches, and the middle arm lifts the three A components to the design position. The height and position of the arch installation are based on the previous cycle arch, and there should be no misalignment. Afterwards, the left and right arms respectively grab a B component and assemble it with the A components, tighten the connecting bolts in a reasonable and orderly manner, firmly pad the arch feet, and set up locking foot anchor pipes for fixing. The on-site implementation is shown in Figure 25.  
Figure 24: The preparation before construction. (**a**) Road surface treatment and (**b**) the overbreak and underbreak treatment.  
When installing the lower bench arches, the left and right arms grab a C component respectively and connect it with the B component in a circumferential direction. Afterwards, the left and right arms grab a D component, respectively, the middle arm grabs the E component, and implement a temporary connection in sequence according to the execution sequence. Then, the arm manipulator is manipulated to adjust the spacing and height of the arches to complete the positioning. The on-site construction is shown in Figure 26.  
When the mechanized installation of the arches is completed, the longitudinal connection was carried out in time, the remaining reinforcement mesh was laid, the system bolts and foot locking anchor tubes were set, and the shotcrete and other processes were also carried out to ensure the construction quality of the initial support.
## 3 Results
#### Genotoxicity Evaluation
For all three tested concentrations, the incidence of micronucleeli was very low, and correlated with the RICC index showed a low degree of cyto- and genotoxicity (Figure 9a-c, and Table 2). No statistically significant micronucleeli incidence compared to the negative control was observed for all tested concentrations of PDMS-BF-TiO\({}_{2}\) nanocomposite (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Micronuclei Incidence Count (\%o): Mean (SD)/\(p\) Value**} \\ \cline{2-4}
**Compound Type** & \multicolumn{3}{c}{**Compounds Concentration in Culture Medium**} \\  & **2 mg/L** & **4 mg/L** & **8 mg/L** \\ \hline PDMS & 7.5 (\(\pm\)0.82)/\(p\) = 0.02 * & 10 (\(\pm\)3.65)/\(p\) = 0.03 * & - \\ PDMS-BF & 6.25 (\(\pm\)1.89)/\(p\) = 0.03 * & 7.5 (\(\pm\)0.58)/\(p\) = 0.03 * & 22 (\(\pm\)2.16)/\(p\) = 0.03 * \\ PDMS-BF-TiO\({}_{2}\) & 1 (\(\pm\)0.82)/\(p\) = 0.11 & 2 (\(\pm\)0.80)/\(p\) = 0.49 & 2 (\(\pm\)0.81)/\(p\) = 0.49 \\ Negative control & & 2.5 (\(\pm\)1) & \\ \hline PDMS–polydimethylsiloxane; BF–benzophenone; PDMS-BF-TiO\({}_{2}\)—PDMS-benzophenone-titanium dioxide nanoparticles; SD–standard deviation; \(p\) value—statistical significance compared to negative control; * statistically significant. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Micronuclei incidence (%o) in lymphocyte cells culture. Figure 7: (**a**) Polydimethylsiloxane (PDMS) concentration 2 mg/L Micronucleus and atypical dividing nucleus are indicated by arrows; (**b**) PDMS concentration 4 mg/L. A dividing nucleus with abnormally bridge, suggesting a nuclear stickiness (arrow). Figure 8: **(a)** Polydimethylsiloxane—benzophenone (PDMS-BF) concentration 2 mg/L. Atypical dividing nucleus are indicated by arrow; **(b)** PDMS-BF 4 mg/L. Deformed and genetically unbalanced nuclei are indicated by arrows. ; **(c,d)** Different analyzed area of PDMS-BF 8 mg/L. Many atypical and deformed nuclei lead to the micronucleeli formation and aneogenicity (arrows). Figure 9: Polydimethylsiloxane—benzophenone—titanium dioxide nanoparticles (PDMS-BF-TiO\({}_{2}\)) **(a)** concentration 2 mg/L; **(b)** 4 mg/L; **(c)** 8 mg/L (arrows indicate micronucleeli).
## 2 Line-Scan Hyperspectral Measurement Techniques  
### Spectroscopy Techniques  
Based on the nature of the interaction between the light energy and the sample, there are three major types of spectroscopy techniques for which line-scan hyperspectral imaging methods are currently in use for food and agricultural applications: reflectance and transmittance spectroscopy, fluorescence spectroscopy, and Raman spectroscopy. These spectroscopy techniques are presented in the following sections.
## 3 Results and Discussion  
### Void Ratio, Alkalinity, and Mechanical Properties  
Table 4 provides a summary of the void ratio, alkalinity, and mechanical properties of the PC samples. The compressive strength continued to increase over the 28 days. The porosity of the larger aggregate concrete (PC-S2, 15-20 mm) was marginally higher than that of the smaller aggregate concrete (PC-S1, 5-10 mm), and the mean void ratios for each treatment were 30.5% and 31.9%. In the aggregate group, the size ranged from 5-10 mm, and the compressive strength of concrete specimens with smaller particle sizes was 17.5% higher than that of concrete specimens with larger particle sizes. The compressive strength increased as the aggregate particle size decreased, and this phenomenon is consistent with [PERSON]'s conclusion [34]. In the other group, with a particle size ranging from 15-20 mm, the compressive strength was 11.3 MPa on average. The correlation between the compressive strength, alkalinity, and void ratio was obtained and is listed in Table 5. The chart reveals that the correlation between the compressive strength, alkalinity, and void ratio was nonsignificant. The relationship between the alkalinity and void ratio was highly significant (\(p<0.01\)). Therefore, the compressive strength of PC was not correlated with the porosity and alkalinity, but the relationship between the porosity and alkalinity was highly significant.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Group Number** & **Particle Size (mm)** & **28-d Average Strength (MPa)** & **SD** & **Void Ratio (\%)** & **SD** & **pH Value** & **SD** \\ \hline PC-S1 & 5–10 & 13.7 & 1.01 & 30.5 & 1.33 & 8.8 & 0.18 \\ PC-S2 & 15–20 & 11.3 & 0.97 & 31.9 & 2.12 & 8.5 & 0.30 \\ \hline \end{tabular} SD- standard deviation.  
\end{table}
Table 4: Void ratio, alkalinity, and mechanical properties.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.3.2 Additional Repair Time  
Figure 16 shows the availability depending on the design stages with the additional repair time. Three additional repair times assumed in this study are 1, 3, and 5 h to investigate the impact of the delayed repair time. The availability decreased with the increment of the repair time. When additional 1, 3, and 5 h were considered at Stage III, the availabilities were 98.969%, 98.823%, and 98.701%, respectively. This result presented that one additional hour in the repair time decreased the availability by 0.065%.  
Figure 16: Availability with additional repair time.  
Figure 15: Availability with different failure rate.
# Large Scale Implementations for Twitter Sentiment Classification
## 6 Results and Evaluation
### Open Datasets for Evaluating Machine Learning Techniques in Spark Framework  
The results of our work are presented in the following Tables 9-13. F-Measure is used as the evaluation metric of the different algorithms. For the binary classification problem (Table 9), we observe that Naive Bayes performs better than Logistic Regression and Decision Trees. It is also obvious that the dataset size plays a rather significant role for Naive Bayes, as the F-Measure value rises from 0.572 for a dataset of 1000 tweets to 0.725 for the dataset of 25,000 tweets. On the contrary, the performance of Logistic Regression and Decision Trees is not heavily affected by the amount of the tweets in the dataset.  
Regarding ternary classification, Naive Bayes outperforms the other two algorithms as well, as it can be seen in Table 10, with Linear Regression following in the results. Interestingly, unigrams seem to be the feature that boosts the classification performance more than all of the other features that we examine, while the highest performance is observed for the vectors excluding trigrams. Moreover, the binary field representing the existence of a hashtag in the tweet affects the results, as, in all the experiments, the performance records smaller values without it. It can also be observed that all three algorithms perform better for positive and negative tweets than they do for neutral messages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Positive** & **Negative** & **Neutral** & **Total** \\ \hline Decision Trees & 0.646 & 0.727 & 0.557 & 0.643 \\ Logistic Regression & 0.628 & 0.592 & 0.542 & 0.591 \\ Naive Bayes & 0.717 & 0.75 & 0.617 & 0.696 \\ \hline \hline \end{tabular}
\end{table}
Table 11: F-Measure for Ternary Classification for Decision Trees for 12,500 tweets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Positive** & **Negative** & **Neutral** & **Total** \\ \hline
**Complete Feature Vector** & 0.646 & 0.727 & 0.557 & 0.643 \\
**w/o Unigrams** & 0.57 & 0.681 & 0.549 & 0.597 \\
**w/o Bigrams** & 0.647 & 0.729 & 0.557 & 0.644 \\
**w/o Trigrams** & 0.646 & 0.728 & 0.557 & 0.644 \\
**w/o User** & 0.646 & 0.727 & 0.557 & 0.643 \\
**w/o Hashtag** & 0.639 & 0.601 & 0.529 & 0.594 \\
**w/o URL** & 0.64 & 0.615 & 0.554 & 0.606 \\
**w/o POS Tags** & 0.659 & 0.729 & 0.56 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 11: F-Measure for Ternary Classification for Decision Trees for 12,500 tweets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Size** & **Decision Trees** & **Logistic Regression** & **Naive Bayes** \\ \hline
1.000 & 0.597 & 0.662 & 0.572 \\
5.000 & 0.556 & 0.665 & 0.684 \\
10.000 & 0.568 & 0.649 & 0.7 \\
15.000 & 0.575 & 0.665 & 0.71 \\
20.000 & 0.59 & 0.651 & 0.728 \\
25.000 & 0.56 & 0.655 & 0.725 \\ \hline \hline \end{tabular}
\end{table}
Table 9: F-Measure for Binary Classification for different dataset sizes.
## 2 Results
### Ab-FAR-1\({}^{\mathrm{Asp}}\) Localized in Plant Cytoplasm and Nucleus  
The localisation of Ab-FAR-1\({}^{\mathrm{Asp}}\) in plant cells was predicted to be in the nucleus with the highest probability of 82.6% using PSORT II software [41]. To demonstrate it, eGFP-tagged Ab-FAR-1\({}^{\mathrm{Asp}}\) was expressed in _A. thaliana_ protoplasts and tobacco leaf cells, and proteins extracted from the transformed cells and tissues were analysed by Western blotting using an anti-GFP antibody. Clear green, fluorescent signals were observed in both the cytoplasm and nucleus of _A. thaliana_ protoplasts and tobacco lower epidermal cells transformed with the 35S:Ab-FAR-1\({}^{\mathrm{Asp}}\):eGFP and 35S:eGFP vectors (Figure S2 and Figure 3a,b). Additionally, the hybridising band sizes of their proteins were consistent with the predicted sizes (Figure 3c). Therefore, the fusion proteins were intact and not further modified by the plant cells. The subcellular localisation results were consistent with the  
Figure 2: Localisation of Ab-FAR-1 protein in nematode and in plants. (**a–c**) No fluorescence signal was found when Ab-FAR-1 antibody was replaced by antiserum before immunization; Ab-FAR-1 protein secreted was located in the body wall of nematodes (**d–f**) and host cells (**g-i**) (white arrows). Micrographs (**a,d,h,l**) were observations of Alexa Fluor 594-conjugated secondary antibody. Micrographs (**g,k**) shows 4, 6-diamidino-2-phenylindole (DAPI)-stained nuclei. Micrographs (**b,e,i,m**) are images of differential interference contrast. Micrographs (**c,f,j,n**) were superpositions of images. The white arrows and asterisks indicate the location of the fluorescence signal and nematodes. Bar = 25 μm.  
predictions of the software, which indicated that Ab-FAR-1, without the signal peptide, was localised in the nucleus and cytoplasm. This might indicate that Ab-FAR-1 without a signal peptide was secreted by nematodes and acted on the cytoplasm and nucleus of the host during RWTN infection.  
Figure 3: Subcellular localisation of Ab-FAR-1\({}^{\text{Asp}}\) in protoplast cells of _Arabidopsis thaliana_ and leaves of _Nicotiana benthumaniana_. (**a**,**b**) The fusion proteins were expressed in protoplast cells of _A. thaliana_ and leaves of _N. benthumaniana_. Fusion protein of Ab-FAR-1\({}^{\text{Asp}}\):eGFP and free eGFP signal were localised to the cytoplasm and nucleus. (**c**) The band size of each fusion protein was consistent with the size predicted by Western blot. EV, empty vector. Bar = 50 \(\upmu\)m.