esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Strengths and Limitations  
Unlike previous reviews which tended to focus on one geographic region, a strength of the current work lies in the fact that it provides a global overview of fat intake among children. A second merit is that this review provides information on individual PUFA (LA, ALA, EPA, DHA) and SFA intakes, and not just total fat or total PUFA intakes. Additionally, this review employed a detailed age stratification (1-2, 2-3, 3-5 and 5-7 years), allowing to highlight the low intake of total fat in the younger children (1-3 years), the low LA intake in the younger group (1-2 years) and more specifically, the low intake of EPA + DHA in the younger (1-2 years) and the older (5-7 years) sub-groups. This stratification may help for a more targeted approach to assessing intake and developing interventions for particular age groups. A limitation of the current review is that it did not employ systematic search methods. Also, there is substantial heterogeneity in the methodologies employed by the included studies to assess dietary intake, food databases were not all the same quality and the age grouping differed between countries. Furthermore, about 30% of the included studies were local studies, and it is possible that a high proportion of the volunteer participants were concerned about their diet, which may not be entirely representative of the total population. The interpretations made in this paper are dependent on the available guidelines, which themselves have certain limitations. For example, the FAO/WHO guidelines for total fat, total SFA, LA, ALA, MUFA and total PUFA intake in children cover a wide age range (2-18 years), while the EFSA guideline for SFA is \"as low as possible\". These vague recommendations, when considered in isolation, make it challenging to compare observed vs. recommended intakes. However, an advantage of the current study is that it takes into consideration both FAO/WHO and EFSA guidelines, as well as recent literature in the field, to generate cut-offs relevant for each fatty acid within each age group, thus providing more precise values against which intakes were compared.
## 4 Experiment and Analysis  
### Experimental Data and Platform  
In order to verify the effectiveness of the proposed algorithm, this experiment uses the domestic Gaofen-3(GF-3) satellite SAR images for related verification work. The GF-3 SAR image used in the experiment is in the Ultra Fine Strip (UFS) mode, with a total of 21 images. The imaging time is from 2 July 2019 to 20 October 2021, with a resolution of 3 m, the imaging angle is from 19.99\({}^{\circ}\) to 36.31\({}^{\circ}\), and the actual ground sampling interval is 1.12 m \(\times\) 1.54 m\(\sim\)1.12 m \(\times\) 1.75 m. The coverage area of the data used in the experiment is the southeast coastal area of China, as shown in Figure 4. The terrain fluctuates from 0 m to 2358 m. The specific experimental data information is shown in Table 1.  
Figure 4: Distribution map of original experimental data.  
This experiment uses a high-performance computing server. The specific hardware parameters of a single node are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S/N** & **Image Identification** & \begin{tabular}{c} **Angle of** \\ **Incidence (\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **Average Elevation (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Ground Sampling** \\ **Interval (m)** \\ \end{tabular} &
\begin{tabular}{c} **Imaging Time** \\ **Time** \\ \end{tabular} \\ \hline  
\end{tabular}
\end{table}
Table 2: Table of hardware parameters of the experimental platform.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 4 Glider Performance Analysis  
This section presents the performance analysis of the underwater gliders in order to obtain the characteristic equations for the estimation of the basic flight parameters and the required sizing of its VBS propulsion system.  
Considering the underwater glider as an asymmetrical aerodynamic profile at small angles of attack, the hydrodynamic forces are modeled as follows:  
\[D=\Big{(}K_{D0}+K_{D1}\alpha+K_{D2}\alpha^{2}\Big{)}U^{2}=\frac{1}{2}\rho_{f} A_{C}C_{D}(\alpha,Re_{L})U^{2}, \tag{8}\]  
\[L=(K_{L0}+K_{L1}\alpha)U^{2}=\frac{1}{2}\rho_{f}A_{C}C_{D}(\alpha,Re_{L})U^{2 }\, \tag{9}\]  
where \(K_{Di}\) and \(K_{Li}\) are the polynomial coefficients used to estimate the hydrodynamic forces of drag and lift respectively, both dependent on the turbulence regime (\(Re_{L}\)), the fluid density \(\rho_{f}\) and the characteristic area \(A_{C}\). Considering that the coefficients \(K_{Di}\) and \(K_{Li}\) are stable in the vehicle's operating range, they can be considered as a constant for practical purposes of glider performance analysis. As shown in the Figure 7, the drag force is in line with the glider slope line and perpendicular to the lift force. It is observed that the resultant \(R\) balances the buoyancy force \(B\).
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 7 Experimental Results  
In this section, we examine the performance of our GPU algorithms for matrix multiplication where the matrices are stored using the HM and CDM schemes. We conducted all of our experiments on a desktop computer that has an Intel i7-7700K processor (4.20 GHz) with four cores and 16 GB of RAM. Its L1, L2, and L3 cache sizes are 256 KB, 1.0 MB, and 8.0 MB, respectively. The GPU card that we used for our CUDA code execution is NVIDIA TITAN XP with 12 GB of memory of type GDDR 5X.  
The computational study is divided into two parts. The first part is concerned with experimentation with banded matrices, and in the second part, we experiment with structured sparse matrices. The sparsity patterns (i.e., nonzero diagonals) are randomly generated. The calculations are performed in double precision.
## 5 Discussion  
### Effects of an Object-Based Methodology on Estimating Impervious Surface Area  
This research used object-based SVM classification and therefore including textural information to train the SVM classifier. The combination of spectral information, textural characteristics, and neighborhood relationships allowed for a better classification of pervious/ impervious surfaces as compared to per-pixel analysis. In particular, the object-based textural information was found to be more efficient for ISA feature extraction than pixel-wise textural information, which was constrained by the window width in the classification procedure.  
Some previous studies [6; 18] suggested that the pixel-wise LSMA model tends to overestimate ISA and underestimate roads and rural areas in high-density impervious surface areas. While the object-based SVM model in this research dealt with the mixed-pixel problems that exist in traditional pixel-wise LSMA methods to some extent, especially to distinguish roads, building, and parking lots from the vegetation areas and small green spaces from high density residential areas. Therefore, the proposed object-based SVM classification resulted in higher amounts of pure high albedo and low albedo impervious surface areas compared to pixel-wise LSMA methods. At the same time, it is worth noting that the object-based SVM-LSMA model tends to slightly underestimate the ISA fraction in the central business district and to overestimate ISA fraction in less developed areas. This phenomenon may be caused by the constrained linear mixture model that requires that end member fractions are positive and sum to 1 [10].
# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 2 Brief Literature Review
Instead of averages, the K-Medoid method selects representative values which are the central values in the cluster. The elements/values in the database are associated with those clusters whose medoids are most similar. [PERSON] et al. [23] defined the medoid as \"the object of a cluster, whose average dissimilarity to all the objects in the cluster is minimal i.e., it is a most centrally located point in the given data set\" (p. 43). The working method is the following: from the multitude of data, a certain number of medoids are chosen at random, around which the groups are formed with the elements that are most similar to the representative values. Once these clusters are formed, new medoids are chosen that better represent the formed group. It continues until no medoid changes its position [30]. This method overcomes perhaps the biggest disadvantage of the K-Mean method, i.e., the sensitivity to extreme values. It is therefore based on the representative/central values of the groups called medoids [24]. The two most popular clustering methods, K-Medoid and K-Mean, were analyzed by [PERSON] et al. [31] and, as a result, they affirmed that the runtime is better for the K-Medoid method than for K-Mean, and also that K-Medoid is non-sensitive to outliers. Contrary to these results, [PERSON] [32] also compared K-Medoid and K-Mean clustering algorithms and the experimental results showed that the K-Mean algorithm yields the best resultscompared with the K-Medoid algorithm. Moreover, [PERSON] et al. [33] highlighted the positive and negative aspects of these two methods, concluding that K-Medoid is better in all aspects such as execution time, being non-sensitive to outliers and reduction of noise, but with the limitation that the complexity is greater as compared to K-Means. Summarizing the opinions in the literature, the advantages and disadvantages of the K-Mean and K-Medoid methods are illustrated in Table 1.  
In both methods, the definition of the cluster number gets a lot of weight. There are several methods and procedures for this; the most frequently applied methods are the cluster elbow method and the silhouette method [29; 34; 35]. Another technique that can solve the problem of detecting the number of clusters is using VAT (Visual Assessment of Tendency) for clustering [36] and hierarchical cluster analysis. [PERSON] et al. [37] described the different cluster number determination methods applied to the K-Mean clustering algorithm in detail. Of the abovementioned methods, the Elbow method is considered to be the oldest and most commonly used. This is a visual method, the essence of which is to increase the number of groups by 1 to 1, starting with two. For each step we can see the variance, and when the graph shows a fracture (elbow) we can stop, as we have the optimal cluster number; the figure may show the break point (Elbow criterion: that is the elbow of this curve) where a \"jump\" occurs in the degree of heterogeneity [38].
## 2 Related Work
### Vision Transformers in Anomaly Detection  
Although originally developed for the application of natural language processing (NLP) [6], the vision transformer (ViT), a type of deep learning architecture, has recently gained attention for its promising applications in image anomaly detection. The architecture is a deep learning model for image classification that replaces traditional convolutional layers with self-attention mechanisms. It divides an image into non-overlapping patches, linearly projects them into flat vectors, and uses a transformer encoder to capture global contextual information. The resulting feature vectors are then passed through multiple layers of feed-forward neural networks to predict class labels. The vision transformer architecture has achieved state-of-the-art performance on various image classification benchmarks, demonstrating the power of self-attention in capturing long-range dependencies in images without relying on convolutional operations.  
Rapid advancements in the field can be attributed to a series of highly influential papers describing the evolution of modified NLP transformers and the adaptation of self-attention mechanisms [6] for implementation in image-oriented applications [2; 33; 34; 35]. Several vision transformer variations have been created as a result of these publications and the numerous machine-vision-based fields of research. One of the earliest transformer models specifically adapted to anomaly detection is the vision-transformer-based image anomaly detection and localisation network (VT-ADL) [36]. The ViT is a network designed to work on image patches, trying to preserve their positional information. Using these fundamental characteristics, a modified transformer network known as the VT-ADL was developed that can learn the unique and diverse features of the normal data in a semi-supervised way (normal data only) to localise anomalous regions using Gaussian approximation.  
The ViTAE model [37] is a tailored vision transformer model that uses spatial pyramid reduction modules to embed input images into tokens with multi-scale context to learn robust feature representations for objects at different scales. It also includes a convolution block in parallel with the self-attention module in each transformer layer, enabling it to learn local features and global dependencies together. This gives the model both scale invariance and locality-inductive biases, making it a powerful model for image analysis.  
Further variants of the vision transformer model worth mentioning include the Swin-ViT [38] and MiM Vanilla-ViT [39].
## 4 Results and Discussion
### DQ2: Most Active Organizations  
We have identified 115 unique organizations based on the number of mentions in the published primary papers. Most of the identified organizations are academic institutions based in Europe. Table 14 depicts a list of the 12 most active organizations.  
It is noteworthy that there is an imbalance between the amount of research from academia and industry (82% academic, 11.3% industrial, 6.08% research institutes and 0.86% government agencies), which highlights the need for further research in industrial contexts, or in academia-industry collaborations.
## 4 Results and Discussion
### Generation Events
#### 4.1.4 What Is the Frequency of Sentence Repetition in Small Time Slots?  
Data represent the characteristic behavior of certain users, tending to repeat sentences consecutively with a short time margin between them. This behavior might be a signal of urgency or necessity, or it might be a call for attention.  
To study this phenomenon, we defined two different time margins in order to consider when one sentence is reiterated under 3 s and under 5 s. We calculated the time differences between the **timestamp** fields of consecutive iterations. Table 15 shows the percentage of sentences that were considered repetitions for each category.  
We observe a greater incidence for categories associated with personal well-being and care and health, accounting for 34% and 23% of repetitions in less than 5 s, respectively.
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 2 Background
### Hartree-Fock Method
The two-dimensional matrix \(\mathbf{h}\) is calculated as a preprocessing step in the Hartree-Fock method. The ERIs \((\mu\
u|\lambda\sigma)\) describe the repulsive force between electron pairs. Evaluating \((\mu\
u|\lambda\sigma)\) and reducing them into \(\mathbf{J}\)- and \(\mathbf{K}\)-matrices constitutes the computational bottleneck in constructing the Fock matrix. In the restricted Hartree-Fock method, the density matrix \(\mathbf{D}\) is defined using the orbital coefficients, expressed as  
\[D_{\lambda\sigma}=\sum_{i=1}^{N_{e}/2}C_{\lambda i}C_{\sigma i}, \tag{10}\]  
where \(N_{e}\) denotes the number of electrons in a given system. Although the Roothaan-Hall equation appears straightforward to solve, it has a complex form where the Fock matrix depends on the desired eigenvectors \(\mathbf{C}\). Therefore, Equation (5) is solved using a Self-Consistent Field (SCF) procedure, which involves initializing the orbital coefficients \(C_{\mu i}\) with some values and performing iterative calculations. The high computational cost of the Hartree-Fock method stems primarily from the iterative calculation of the Fock matrix in the SCF procedure. The Roothaan-Hall equation is solved using the SCF procedure as follows:  
1. Initialize the orbital coefficients \(\mathbf{C}\),
2. Calculate the Fock matrix \(\mathbf{F}\) and update \(\mathbf{C}\) by solving Equation (5),
3. Iterate Step 2 until \(\mathbf{C}\) and the energy converge. While the density matrix \(\mathbf{D}\) (and the orbital coefficient matrix \(\mathbf{C}\)) changes with each SCF iteration, the values of the molecular integrals \(S_{\mu\
u}\), \(h_{\mu\
u}\), and \((\mu\
u|\lambda\sigma)\) remain constant during the procedure. Therefore, evaluating these integrals and storing them in advance can avoid redundant computations. However, storing all ERI values involves an \(O(M^{4})\) space complexity, which can exceed the available memory or storage capacity for large molecules. To address this issue, a method that allows redundant computations is widely used to reduce the required space complexity from \(O(M^{4})\) to \(O(M^{2})\) by evaluating the ERIs on-the-fly every SCF iteration. This approach is known as Direct-SCF [9], and it allows us to handle larger chemical systems compared to Stored-SCF, which requires storing all molecular integrals in memory. On the other hand, constructing the Fock matrix in the SCF procedure becomes computationally more demanding. In this paper, we focus on acceleration techniques for the Fock matrix computation in Direct-SCF.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 3 Seed Generator
### Random Bitstream Post-Processing
The SHA hashing family is one of the most popular techniques among whiteners and is included on NIST listed HMAC conditioners [18]. After post-processing, it has been observed that, at all sampling rates, the NIST randomness tests are passed. As an example, Figure 9 shows the NIST test results before and after post-processing for a sequence obtained at a sample rate of 2.5 kSps.
## 7 Discussion and Conclusions
The reported assessment, together with resorting to the methodology used for representing the real network via a realistic model (i.e., the synthetic network), can be a valid option for estimating such extent by players different from the DSOs (i.e., TSO and aggregators). * The main issues caused by flexibility are voltage regulation problems, as overvoltage conditions caused by upward bids (i.e., extra production or load shedding) or excessive voltage drops in case of uncontrolled downward bids (i.e., curtailment of production or increasing of consumption by the active customers). In some cases, such violations of the technical constraints can be solved, with extra costs, by resorting to the reactive power support from DG. In other cases, the potential bids might be definitely reduced. In any case, resorting to flexibility cannot disregard the distribution grid limitations. * The operational actions that enable flexibility at the distribution level rely mainly on the interactions between system operators. In the case of uncontrolled participation of DERs in the whole ancillary service market, possible blocks to the bids by the DSO may occur. The assessment in advance of the possible violations of the network constraints may prevent such situations and simplify the use of the flexibility for global services achieving a reduction in the system operation costs. * The expected costs to enable flexibility depends on the regulatory framework. In the proposed Case Study, a possible rational behaviour in the market of the DERs owner has been proposed. The final costs are quantified according to the hypothesis of remunerating the reactive power support. The final remark of this study is that a sort of prequalification of the DER bids to the TSO should be assessed in advance for preventing distribution network operation problems. Such prequalification could facilitate the TSO to use flexibility coming from the distribution system and also reduce costs because the requested quantities do not receive any block by the DSO. Conceptualisation, G.P. and G.G.S. ; methodology, G.P. and G.G.S. ; software, N.N. and G.G.S. ; validation, G.P. and G.G.S. ; data curation, N.N., G.P. and G.G.S. ; writing--original draft preparation, G.P. ; writing--review and editing, G.P., G.G.S. and F.P. ; visualisation, G.G.S. and G.P. ; supervision, G.P. ; funding acquisition, F.P. All authors have read and agreed to the published version of the manuscript. This work has been partly funded by the European Commission project \"OSMOSE-Optimal System-Mix of Flexibility Solutions for European Electricity\", within the Horizon 2020 framework (Programme: H2020-EU.3.3.4.--A single, smart European electricity grid; Topic: LCE-04-2017--Demonstration of system integration with smart transmission grid and storage technologies with increasing share of renewables; Grant agreement ID: 773406). Not applicable. The authors warmly thank ENEL for funding the initial step of this research activity. The authors declare no conflict of interest.
## 3 NTNs for IIoT Applications
### Overview on Spaceborne Platforms for IIoT
Actility, an industry leader in LPWAN, has recently become part of Inmarsat's Certified Applications Provider Programme (CAPP). Aided by this partnership, a global L-band satellite connectivity platform can be effectively combined with Actility's ThingPark IoT management platform. Hence, an integrated solution for IIoT can be provided by third-party companies which are in compliance with the specifications of Inmarsat's satellite network. Although the satellite networks have a huge initial cost, they still constitute the exclusive means of wireless connectivity in traditional maritime and aeronautical markets. The spaceborne networks can support navigation, Earth observation, emergency rescue, and communication/relaying as well as strongly enhancing the terrestrial backhaul networks. HTS can provide effective backhauling of aggregated IIoT traffic and transfer data from LPWANs nodes to cloud platforms. GEO satellites are widely available and can enable multicast/broadcast and trucking of video and other data across a large area or a central site while providing data distribution to local cell sites. LEO satellite constellations require less on-the-ground power and smaller antennas due to the significantly lower orbit altitude (normally lower than 2000 km) and smaller propagation losses. Since most IoT terminals are small-sized and energy-constrained, the aforementioned benefits of LEO satellites are substantial. The CubeSats are also expected to contribute to the cost-effective and extended radio coverage for the IIoT operating at relatively low altitudes, i.e., \(<\)1000 km, without suffering from the traditional satellite drawbacks, e.g., long development cycles, high costs, increasing congestion, lack of sequential redundancy, and high-risk exposure [37, 39]. In addition, the form factor of CubeSats, typically \(10\times 10\times 10\) cm\({}^{3}\), allows for the integration of accurate attitude determination and control systems (ADCS) and effective solar panels, which in turn increases the range of applications. However, constellations with multiple CubeSats in several orbital planes are indispensable for extended coverage due to the small footprint of a single CubeSat. Satellites have also a major role in assisting IIoT systems to meet low latency requirements. Although GEO satellites' latency of 250 ms (500 ms round-trip time--RTT) is acceptable for many IIoT applications and is comparable with the RTT of a long terrestrial link (100-200 ms), MEO and LEO satellite constellations allow for lower latency in the special case of voice and video transmission (less than 100 ms RTT). The competence of satellite networks in terms of the latency, even in the case of harsh propagation conditions, is partially due to the adoption of adaptive coding and modulation (ACM), as foreseen in the DVB--Satellite--Second Generation (DVB-S2) standard.