esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
An AO-SLO system with an acquisition rate of 30 Hz was used for imaging the posterior part of the eye of human subjects. The field of view and frame size of the human eye are 1.5\({}^{\circ}\) and 512 \(\times\) 449 pixels, respectively. Thus, a transverse region of 445 \(\times\) 445 \(\upmu\)m was scanned using an effective focal length of 17 mm for the eye. More details of the AO-SLO system can be found in [41]. Before imaging, the pupil was dilated with 1% tropicamide and 2.5% phenylephrine hydrochloride to increase its diameter to 6-8 mm. Throughout the procedure, light exposure was maintained in accordance with the maximum permissible exposure limits specified by the American National Standards Institute [42].  
The automated processing of a 100 \(\times\) 100-pixel image required 8.72 s for denoising based on TV-L1 optical flow registration, 0.01 s for bias field correction, 0.52 s for identification based on \(K\)-means clustering, 0.01 s for duplicate identification removal, 1.56 s for identification based on threshold segmentation, and 1.65 s for merging of closed identification results. These computational times were obtained from a system equipped with an Intel Core i5-9400 CPU at 2.90 GHz, an NVIDIA GeForce GTX 1660 Ti graphics card, and 16.0 GB memory. Python (64 bits) was used for implementation of the algorithms: the numerical calculations were mainly implemented by using NumPy and SciPy libraries; the computer vision algorithms, especially \(K\)-means, were mainly implemented by OpenCV library.  
To verify the effectiveness of the proposed method for cone cell identification, we acquired five retinal images around the foveola of healthy subjects (\(n=5\)). The proposed method successfully identified the cone cells from the five subjects. The identification on three representative images is shown in Figure 9. By regarding manual cone cell identification as the ground truth, the overall precision, recall, and F1 score of the identification are listed in Table 1. The proposed method provides high identification accuracy near the foveola of the healthy subjects.  
We further evaluated the performance of the proposed method on different types of AO-SLO images. Figure 10 shows three examples of AO-SLO images [43; 44] and their corresponding cone cell identification results. The first example (Figure 10(a1,b1)) shows an AO-SLO image at different retinal locations in the same healthy eye with cone cell density below that of the examples shown in Figure 9. The examples in Figure 10(a2,b2) and Figure 10(a3,b3) show AO-SLO images of an eye with diabetic retinopathy [43] and an eye with acute zonal occult outer retinopathy [44], respectively, along with the corresponding cone cell identification results. The proposed method accurately identifies AO-SLO images of eyes with low cone cell density, diabetic retinopathy, and acute zonal occult outer retinopathy.  
Figure 8: Merging of closed identified cone cells. (**a**) Denoised AO-SLO image patch. Identification results (**b**) before and (**c**) after merging (green dots) on the denoised AO-SLO image patch.
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals
### Future Expansion of the Study
The research to be conducted in Guinea-Bissau will thus serve as a natural and innovative extension of this work, with the potential to modernize hospital infrastructure in low-income settings, assess public policy and regulation enhancements, and help reduce global disparities in access to digital healthcare. The relevance of this expansion is also demonstrated by recent studies addressing healthcare challenges across Africa. [PERSON] [11] demonstrated that the development of information and communication technologies (ICTs) significantly enhances the impact of public health investment, improving indicators such as life expectancy and infant mortality. However, the study also underscores that these benefits are contingent on the availability of reliable communication infrastructure, the focus of the interventions proposed here. Likewise, [PERSON] and [PERSON] [12] emphasize that fragile digital health infrastructure and poor data governance are critical barriers to implementing big data analytics for infectious disease surveillance in sub-Saharan Africa. Hence, improving cellular coverage for ICT availability enhancement in hospitals directly aligns with the recommendations of [11; 12]. The Guinea-Bissau case study will benefit from incorporating these relevant lines of research. Mobile network coverage maps for the area surrounding Cumura Hospital, based on publicly available data from the nPerf platform, are prospectively addressed in Figures 19 and 20, even before performing the indoor coverage assessment. They illustrate the estimated signal coverage provided by the local MTN and Orange operators, respectively. Although these maps are not based on in situ measurements, they offer an overview of the existing coverage conditions in the region, and justify the relevance of this study. Figure 19: Mobile network coverage in the Cumura region (MTN operator), Guinea-Bissau. Colours represent different mobile network technologies: blue—2G, green—3G, orange—4G, red—4G+, and purple—5G. Source: nPerf platform, July 2025. Nevertheless, it is important to note that nPerf data does not distinguish indoor measurements. The apparent display of results within the building results from its geolocation accuracy, which is limited to approximately 50 m.  
Hence, in situ measurements carried out in this work were essential to rigorously assessing signal quality. These maps will also serve as reference material for planning the next phase of fieldwork in Guinea-Bissau.
## 2 Materials and Methods
### Stability Labeling
#### 2.2.2 Evaluation Index (\(EI\))  
Based on the vibration signals, we need to design an evaluation index to measure the overall running stability of the robotic arm, where the Jerk and the average power of different vibration components are considered.  
1. Jerk: As shown in (3), Jerk is defined as the mean absolute value of the derivative of acceleration \(a\) in the X, Y, and Z axis directions with the unit of g/s. Jerk indicates how fast the acceleration changes during the movement. Severe acceleration changes harm the stability of the manipulator. The rapidly changing acceleration corresponds to the rapidly changing torque applied to the manipulator, which causes the violent vibration of the arms. Therefore, Jerk and stability should be negatively correlated, and the larger the Jerk, the worse the stability. \(T\) is the time of each sample in (3). \[Jerk=\frac{1}{T}\sum_{x,y,z}\sum_{t=0}^{T}\biggl{|}\frac{da(t)}{dt}\biggr{|} \Delta_{t}\] (3)
2. Average Power (AP): AP is defined as the energy of acceleration (\(a\)) divided by time and has the dimension of \(g^{2}\). When the energy of the vibration is greater and the time is shorter, it indicates that the vibration is more intensive and severe. Since the signal of \(a\) is separated into components of \(a_{main}\) and \(a_{vib}\), \(AP_{main}\) and \(AP_{vib}\) stand for the average power of the corresponding components. \[AP_{main}=\frac{1}{T}\sum_{x,y,z}\sum_{t=0}^{T}\biggl{|}a_{main}(t)\biggr{|}^{ 2}\Delta_{t}\] (4) \[AP_{vib}=\frac{1}{T}\sum_{x,y,z}\sum_{t=0}^{T}\biggl{|}a_{vib}(t)\biggr{|}^{ 2}\Delta_{t}\] (5)  
The design of the stability evaluation index (\(EI\)) comprehensively considers the Jerk and AP as follows:  
\[EI=\alpha\times\frac{AP_{main}-\overline{AP_{main}}}{\delta_{AP_{main}}}+ \beta\times\frac{AP_{vib}-\overline{AP_{vib}}}{\delta_{AP_{vib}}}+\gamma\times \frac{Jerk-\overline{Jerk}}{\delta_{Jerk}} \tag{6}\]  
where \(\overline{AP_{main}}\), \(\overline{AP_{vib}}\), and \(\overline{Jerk}\) represent the average value and \(\delta_{AP_{main}}\), \(\delta_{AP_{vib}}\), and \(\delta_{Jerk}\) are the standard deviations of the corresponding statistics of the samples. \(\alpha\), \(\beta\), \(\gamma\) are weight coefficients, ranging from 0 to 1. Here, they are set default as \(\frac{1}{3}\). To sum up, the larger the \(EI\), the worse the motion stability of the manipulator. On the contrary, the smaller the \(EI\), the better the stability.
# Mo(VI) Complexes Immobilized on SBA-15 as an Efficient Catalyst for 1-Octene Epoxidation
## 2 Results and Discussion
### Catalytic Performance in 1-Octene Epoxidation  
#### 2.2.1 Influence of the Catalyst Loading  
The influence of the catalyst loading on the epoxidation extension of 1-octene was evaluated by testing three different molybdenum/olefin molar ratios: 1:500, 1:1000 and 1:2000, quite low, as compared to some analogue previously reported investigations. These experiments were carried out using TBHP as oxidant and isopropanol as solvent. Figure 4 shows the epoxide yield progress as a function of the reaction time for the three molybdenum/olefin ratios. Likewise, results of a blank reaction (performed without catalyst) are also displayed for comparison purposes. This result suggests the reaction requires a catalyst, as a negligible production of epoxide was obtained under these conditions. 1,2-epoxy octane was the sole product detected in the reaction media, and 1,2-epoxioctane or octanal, expected because of using silica-based catalysts, were absent, probably because of the low amount of catalyst loading. The similarity of the results obtained with 1:500 and 1:1000 ratios is remarkable; on the contrary, adding the molybdenum-based catalyst in a 1:2000 Mo to substrate molar ratio provided a substantial lower epoxide formation. Thus, we selected 1:1000 as the optimal molybdenum/olefin molar ratio for the rest of the experiments.  
Figure 4: 1,2-epoxioctane yields obtained with different Mo/olefin molar ratios in 2-propanol (Reaction conditions: Temperature = 75 \({}^{\circ}\)C; tert-Butyl Hydroperoxide as oxidant; 1-octene/oxidant molar ratio of 1).  
Figure 3: UV-Vis spectra of samples S2, S3 and S4.  
Reactions performed in 2-propanol as solvent depicted low epoxide yields even at high reaction times (Figure 4). This is probably related to the type of solvent used in these tests because of highly coordinating ligands and solvents (such as isopropanol) can compete with TBHP for binding to molybdenum sites, decreasing the overall catalytic activity [20; 25] of molybdenum-based catalysts. This has prompted us to deeply analyze the influence of the epoxidation solvent on catalytic activity and products distribution, which are discussed in the following section.
## 2 Related Work
### Network Slicing Background
Based on the storage and computational resources from both edge and cloud servers, a software-defined edge-cloud computing framework for the resource slice formulation is proposed in ref. [31]. To mitigate the impacts of the slice-initiated attacks, the Benders decomposition and quadratic transformation methods are applied for the interslice and intraslice resource orchestrations, respectively, with the optimization objectives of minimizing the delay and resource utilization simultaneously. Unmanned aerial vehicle (UAV)-enabled mobile edge computing is further incorporated to provide the cross-domain network slices in the ground and aerial segments and a software-defined scheme is proposed for customizing UAV-enabled services in ref. [32]. A Lyapunov optimization-based offloading algorithm is developed for the efficient selection of resource slices [33].
# Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
## 4 Third Phase: Review Results
### What Have Been the Attributes or Metrics That Have Been Measured in the Proposed Works? (RQ4)  
The selected primary studies in this SLR [P1-P22] have focused on evaluating the following attributes:  
* Response time: The evaluation focused on measuring the AMLSs' ability to support data transmission through various communication technologies.
* Usability: The capability of the prototype or application to be understood, learned, used, and engaged with by the user in specific scenarios. In the selected primary studies, the following usability subcategories were measured:
* Operability: Capacity of the developed prototype that allows the user to use it easily.
* Recommendable: This subcategory refers to how likely users are to recommend the software.
* Recognition of suitability: This subcategory measures whether the software meets users' needs.
* Adaptation: Overall, the studies assess the content, format, and path adaptation. This evaluation and its aspects depend on their approach to achieving the adaptation.
## 3 Results and Discussion
### TQ Content Estimation  
In our experiments, BCSO was subjected to an accelerated aging process at 60 \({}^{\circ}\)C in an oxygen atmosphere and with limited oxygen access. During this test some of the potentially important bioactive components could be degraded, thus reducing the quality of the oil. One of the most desired compounds of BCSO is thymoquinone (TQ), the main aroma compound of BCSO. In our work, the TQ content in the BCSO samples during the storage test was determined both qualitatively (FTIR spectroscopy) and quantitatively (UV spectrophotometry and HPLC assay).
## 3 Color Acquisition Based on a Digital Camera and the CDS
### Color Evaluation with CDS Notations  
As shown in Figure 8, for each XYZ value of the painting which was output by the color conversion model of the camera, a corresponding CDS notation can be searched for from the CDS lookup table. Note that, in the search algorithm, the CIE1976L*a*b*c color error was used to evaluate the difference between the XYZ value output by the color conversion model and the XYZ value related to a CDS sample. As a result, for a certain XYZ value of the painting, a CDS notation can be obtained, and the coordinate (w, b) on the CDS color triangle can be determined. In such a way, the color of the painting can be displayed on the CDS triangle as shown in Figure 8.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 3 Results
### Straight Fork Length (SFL) Evolution
Mean distance (Z) from the stereocamera to the fish in the measurements obtained over time is depicted as a dash-dotted line in the secondary axis. This finding challenges the conclusions drawn in one of our previous studies [13], where an analysis based on the evolution of means led to the incorrect conclusion that fish length growth was minimal from July to October in fattening cages. To substantiate the likelihood that the cohorts identified in July 2020 corresponded, at least partially, to annual cohorts, we compared our modal lengths with the expected body lengths as a function of age according to the well-known Von Bertalanffy growth equation applied to BFT [25; 26; 28; 29; 30; 31]. As depicted in Figure 6, the modal lengths aligned with the growth equations, suggesting that they likely represented annual cohorts between 5 and 16 years old.
## 3 Results and Discussion  
### Droplet Size of Water Mist in Twin-Fluid Atomizer  
In Figure 3, the SMD of water mist sprayed from the twin-fluid atomizers is shown under varying water and air flow rate conditions. Measurements of droplet size were conducted three times under each condition. It is noted that in all the figures showing the experimental data, the average values of repeated tests are presented, and the error bars indicate their standard deviations. With respect to both twin-fluid atomizers, a decrease in water flow rate and increase in air flow rate led to a decrease in the droplet size of the water mist. Under the present experimental conditions, the SMD of large and small atomizers were in the range of approximately 12-60 and 12-49 \(\upmu\)m, respectively. Under the same air flow rate conditions (i.e., 20 and 30 L/min), the SMD of large atomizer was larger than that of small atomizer because the discharge velocities of atomizing gas in large atomizer were reduced and the energy required to atomize the liquid decreased owing to its large annular gap area.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Mathematical Foundations
#### 3.1.3 Montgomery Curves  
A Montgomery curve is a special form of an elliptic curve. Let \(\mathbb{F}_{q}\) be a finite field, where \(q=p^{n}\), \(p\) a prime number with \(p\geq 3\), and let \(A,B\in\mathbb{F}_{q}\) be field elements satisfying \(B\big{(}A^{2}-4\big{)}\
eq 0\) in \(\mathbb{F}_{q}\). A Montgomery curve \(E_{A,B}\) defined over \(\mathbb{F}_{q}\), denoted by \(E_{A,B}/\mathbb{F}_{q}\), is defined to be the set of points \(P=(x_{P},y_{P})\) of solutions in \(\mathbb{F}_{q}\) to the equation  
\[By^{2}=x^{3}+Ax^{2}+x.\]  
The set of points of \(E_{A,B}/\mathbb{F}_{q}\) together with the point at infinity, denoted by \(O\), form a finite abelian group under a point addition operation, denoted by \(E_{A,B}\big{(}\mathbb{F}_{q}\big{)}\). That is, given the points \(P\) and \(Q\) in \(E_{A,B}\big{(}\mathbb{F}_{q}\big{)}\), we can compute \(R=P+Q=Q+P\), where \(R=(x_{R},y_{R})\) is a point in \(E_{A,B}\big{(}\mathbb{F}_{q}\big{)}\). Consequently, we also can compute \([2]P=P+P\), \([3]P=P+P+P\) or in general \([k]P=P+P+P+\cdots+P\) (\(k\) times, with \(k\geq 0\)). These operations are called point doubling, point tripling or point multiplication, respectively. To see more details on how to do these computations, we refer the reader to [1].  
The order of an elliptic curve \(E\) over a finite field \(\mathbb{F}_{q}\), denoted by \(\#E\big{(}\mathbb{F}_{q}\big{)}\), is the number of points in \(E\) including \(O\). Additionally, the order \(ord(P)\) of a point \(P\) is the smallest positive integer \(n\) such that \([n]P=O\).  
Given an abelian group \(G\), we say a set of elements \(\{P_{1},P_{2},\cdots,P_{t}\}\subseteq H\) forms a basis of the subgroup \(H\leq G\) if every element \(P\) of \(H\) can be represented by a unique expression of the form \(P=[k_{1}]P_{1}+[k_{2}]P_{2}+\ldots+[k_{t}]P_{t}\), where \(0\leq k_{i}\leq ord(P_{i})\) for \(1\leq i\leq t\).  
Consider an elliptic curve \(E\big{(}\mathbb{F}_{q}\big{)}\) and a positive integer \(m\), we define the group of the \(m\)-torsion elements of \(E\big{(}\mathbb{F}_{q}\big{)}\) as \(E[m]=\Big{\{}P\in E\Big{(}\mathbb{F}_{q}\big{)}\;:\;[m]P=O\Big{\}}\), where \(\overline{\mathbb{F}_{q}}\) is the algebraic closure of \(\mathbb{F}_{q}\). An elliptic curve \(E\big{(}\mathbb{F}_{q}\big{)}\) over a field of characteristic \(p\) is called supersingular if \(p\mid\big{(}q+1-\#E\big{(}\mathbb{F}_{q}\big{)}\big{)}\), and ordinary otherwise. Additionally, the \(j\)-invariant of the elliptic curve \(E_{A,B}\) can be calculated as \(j(E_{A,B})=\frac{256\big{(}A^{2}-3\big{)}^{3}}{A^{2}-4}\). The \(j\)-invariant of an elliptic curve over a field \(\mathbb{F}_{q}\) is unique up to the isomorphism of the elliptic curve. The SIKE protocol defines a shared secret as a \(j\)-invariant of an elliptic curve.
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 2 Materials and Methods
### Fibres' Physical, Chemical, Thermal, and Mechanical Characterization
#### 2.4.3 Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
The surface chemistry and chemical composition of different fibres were analysed using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR). The equipment used was an IRAffinity-1S (Shimadzu, Kyoto, Japan) coupled with a HATR 10 accessory with a diamond crystal. The spectra were obtained over a wavenumber range of 400-4000 cm\({}^{-1}\) at a scanning velocity of 200 scans with a resolution of 2 cm\({}^{-1}\).
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 3 Few-Shot Learning with Many Novel Classes
### Problem Formulation
#### 3.1.3 Class Hierarchical Structure  
In addressing FSL-MNC, we utilize a class hierarchy to enhance model performance. The formal definition of this class hierarchy is represented by a Directed Acyclic Graph (DAG), denoted as \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}=\mathcal{C}\cup\mathcal{R}\). Here, \(\mathcal{C}\) comprises specific classes, and \(\mathcal{R}\) consists of abstract parent classes. This graph forms a single tree, with leaf nodes corresponding to specific classes from \(\mathcal{C}\) and non-leaf nodes from \(\mathcal{R}\) representing abstract parent classes encompassing subsets of specific classes. Relationships between nodes are defined by \(\mathcal{E}\subseteq\left\{(x,y)\mid(x,y)\in\mathcal{V}^{2}\right\}\), indicating parent-child connections.  
The tree's height, denoted as \(H\), reflects the length of the path from the root node to its leaf nodes. For any specific class \(c_{i}\in\mathcal{C}\), a set of parent classes \(\mathcal{A}^{c_{i}}=\left\{\mathcal{P}_{i}^{c_{i}}\,\mathcal{P}_{2}^{c_{i}}, \cdots,\mathcal{P}_{H}^{c_{i}}\right\}\) is derived by tracing the shortest path from \(c_{i}\) to the root node. Each \(\mathcal{P}_{h}^{c_{i}}\) represents an ancestor node of \(c_{i}\) at the \(h\)th level of \(\mathcal{G}\).  
Figure 2: Comparison of \(|C_{\text{novel}}|\) and novelness ratio of Omniglot, minilImageNet, tieredImageNet, Meta-Dataset, and ImageNet-MNC.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Comparisions with State-of-the-Arts
#### 4.5.2 Impact of Radius in Local Adjustment  
We also investigate the effect of the radius parameter in the local adjustment module. To demonstrate the impact across different races and individuals, the experiment is conducted on Morph II, MegaAge-Asian and CACD datasets. As shown in Table 5, the radius is varied from 5 to 20. For the Morph II and CACD dataset, the best age estimation performance is achieved when the radius is set to 10. It reaches 2.18 MAE and 79.93% CS(3) on Morph II and 4.47 MAE and 52.91% CS(3) on CACD. For the MegaAge-Asian dataset, however, the optimal radius is 15, which yields an MAE of 3.18 and a CS(3) of 64.55%. These results suggest that the population in the MegaAge-Asian dataset appears to age more slowly compared to that in Morph II and CACD. When comparing the characteristics of the three datasets, MegaAge-Asian contains more diverse image variations, such as differences in lighting, cluttered backgrounds, glasses, and makeup. These factors likely influence the optimal radius setting, aligning with the common perception that Asians seem to age more slowly, partly due to makeup. In summary, we conclude that the radius parameter in the local adjustment module significantly affects the age estimation results, and the optimal radius should be carefully tuned according to the specific characteristics of each dataset.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Discussions  
Despite its state-of-the-art performance in identifying ambiguous nodes (Section 4.2.2), FONDUE-NDA's node splitting functionality falls short compared to that of MCL (Section 4.2.4). Nonetheless, we argue that FONDUE-NDA's main feature is to facilitate the identification of ambiguous nodes, which is one if the highlight contributions of this paper, as its results are consistent across different datasets and contraction ratio, rendering it a versatile tool for network ambiguity detection in the challenging situation when besides the network topology itself no additional information (such as node attributes, descriptions, or labels) is available or may be used.  
For node deduplication, FONDUE-NDA performed well in settings where the duplicate nodes have a higher than average degree compared to the network, which is arguably the case for this NDD, as duplicate nodes tend to have higher degree.  
The main limitation of FONDUE is its reliance on the scalability of the embedding method. The current backend NE method being CNE, the scalability is limited to medium-sized networks with sub-100,000 nodes.  
Implementing additional NE methods for FONDUE-NDA and FONDUE-NDD could be one future areas for exploring and improving the state-of-the-art of NDA and NDD.