esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Structure and Properties of BCCC  
The BCCC structure connects servers by using recursively defined squares. In \(BCCC(n,k)\), \(n\) denotes the n-port server and \(k\) denotes the dimension. First, we let \(n\) servers connect to an n-port switch as an element, denoted by BCCC(n,0). BCCC(n,k) is composed of \(n\) BCCC(n,k -1)s connecting \(n^{k}\) elements. Two types of switch are used in BCCC, which are the type A switch and type B switch. A type A switch has n ports for forming an element and a type B switch has \((k+1)\) ports for connecting different elements. Any server \(x\) in BCCC(n,k) can be represented by \(x_{k}x_{k-1}\ldots x_{i}\ldots x_{0},x_{i}\in\langle n-1\rangle\) and \(i\in\langle k\rangle\). Any switch s can be represented by \(s_{k}s_{k-1}\ldots s_{i}\ldots s_{0}\) and \(s_{i}\in\langle n-1\rangle\), \(1\leq i\leq k+1,s_{0}\in\langle n+k\rangle\). Overall, to build BCCC(n,k), we need \((k+1)n^{k+1}\) dual-port servers, \((k+1)n^{k}\) type \(A\) switches, and \(n^{k+1}\) type \(B\) switches. An example of \(BCCC(3,2)\) is shown in Figure 1.  
We can see that a pair of servers \(a_{k+1}a_{k}a_{k-1}\ldots a_{0}\) and \(a_{k+1}^{{}^{\prime}}a_{k}^{{}^{\prime}}a_{k-1}\ldots a_{0}^{{}^{\prime}}\) are neighbors, meaning that they are connected by the same switch, if and only if \(\exists i,i=a_{0}+1\) or \(i=0\), such that \(a_{i}\
eq a_{i}^{{}^{\prime}}\), and \(\forall j,0\leq j\leq k+1,i\
eq j\), such that \(a_{j}=a_{j}^{{}^{\prime}}\).  
According to the numbering rules of the \(BCCC\) server, \(x_{0}\) connects different layers. \(x_{i}\in[0,n-1]\), \(1\leq i\leq k+1\). Therefore, BCCC can be divided into \(n\) subgraphs. We divide BCCC into \(n\) subgraphs according to the second digit number of the server from right to  
Figure 1: Structure of \(BCCC(3,2)\).  
left. Servers with the same second digit number are located in the same subgraph. These subgraphs are named \(BCC^{1}_{n,k}\), \(BCC^{2}_{n,k}\),..., \(BCC^{n}_{n,k}\). The second digit number is 0 and the subgraph is \(BCC^{1}_{n,k}\). We named the server connected to other subgraphs \(u_{1}\), and named those not connected to other subgraphs \(u_{2}\). An example of \(BCCC^{1}_{3,2}\) is shown in Figure 2.  
**Lemma 1**.: _BCCC (n,k) has the following properties [4]:_  
1. \(BCCC(n,k)\) _is (n+_\(k-1\)_)-regular graph._ \(N=(k+1)n^{k+1}\)_._
2. _The connectivity of_ \(BCCC(n,k)\) _is_ \(\kappa(G)=n+k-1\)_._
3. _There are_ \(n^{k}\) _vertex disjoint paths connecting different_ \(BCCC(n,k-1)\) _in_ \(BCCC(n,k)\)_._
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 3 Systematic Errors
### Viewing Geometry (Space)
#### 3.1.3 Orbital Phase  
Orbital phase arises from the differences between orbit estimation and the true orbit position, often referred to as orbit indetermination or orbit error [73]. Similar to altitude error and baseline error, the error in orbit estimation emerges due to the imperfect InSAR geometric parameters. The impact of orbit error becomes evident when these parameters are employed to correct the flat-Earth phase through a process known as phase flattening. In cases where orbit parameters lack precision, the correction becomes imperfect, resulting in residual flat-Earth phase error [46; 74]. Consequently, the orbital phase represents the remaining part of the flat-Earth phase after applying phase flattening [46]. These forms of phase error exhibit gradual changes over large spatial scales, earning them the label of \"long wavelength phase contribution\". This term is aptly descriptive as the phase error displays smooth variation across large spatial extents.  
Since the topographic phase persists in the interferograms for DEM generation, the orbital phase is difficult to see and measure due to its relatively small scale [74]. However, if the InSAR approach is used for deformation mapping, the topographic phase is subtracted from the interferograms, resulting in two outcomes. First, the topographic phase residuals will be present as the removal of the topographic phase also incorporates orbital parameters.  
Second, the flat-Earth phase residuals will be more prominent in the interferograms because the topographic phase is removed [74].  
[PERSON] and [PERSON] [75] conducted an experiment to investigate the influence of orbital errors and compare them with theoretical influences. The result has shown that the impact of orbit errors is significantly smaller than expected [75]. The authors outlined the worst-case range and azimuth uncertainties of the velocity gradients resulting from orbital errors for several satellite operations (Table 2). Modern satellites, such as TerraSAR-X and Sentinel-1, have much more precise orbit determination than older platforms, which constrains the influence of orbit errors to an extremely small extent (e.g., below 1 mm/year over 100 km). Because the impact is so minor, orbital phase is usually ignored.
# Approximate Networking for Universal Internet Access
## 2 Approximate Networking Technologies  
There exist some error-tolerant networking applications that are constrained by the needs for energy efficiency and real-time packet delivery. By using approximate computing, these applications can be deconstrained by the relaxation of the integrity requirements for the approximate data, thereby allowing these applications to communicate more efficiently (i.e., these applications can transmit faster, over a longer range, and using less power; see [19] and references therein).
## 5 Results and Discussion
### Life-Cycle Cost Analysis of Small Particle-Size Asphalt Pavement
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mixture** & **Age** & **Project** & **Cost/5** & **NPV/S** & **Total of** \\ \hline \multirow{6}{*}{HMA} & \multirow{2}{*}{0} & New pavement (thickness of top, middle, & \multirow{2}{*}{107,939} & \multirow{2}{*}{107,939} & \multirow{2}{*}{107,939} \\  & & and bottom layer is 4, 6, and 8 cm) & & & \\ \cline{1-1} \cline{2-6}  & 5 & 3 cm AC-13 thin overlay & 17,990 & 15,518 & \\ \cline{1-1} \cline{2-6}  & 10 & 3 cm AC-13 thin overlay & 17,990 & 13,386 & \\ \cline{1-1} \cline{2-6}  & 15 & 3 cm AC-13 thin overlay & 17,990 & 11,547 & \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Other} & Total of other maintenance costs & \multirow{2}{*}{189,968} & \multirow{2}{*}{188,514} & \\  & & (Annual cost $11,873) & & & \\ \hline \multirow{6}{*}{SMA} & \multirow{2}{*}{0} & New pavement (thickness of top, middle, & \multirow{2}{*}{109,652} & \multirow{2}{*}{109,652} & \multirow{2}{*}{291,604} \\  & & and bottom layer is 3, 6, and 8 cm) & & & \\ \cline{1-1} \cline{2-6}  & 7 & 2 cm SPS asphalt overlay & 17,133 & 13,930 & \\ \cline{1-1} \cline{2-6}  & 14 & 2 cm SPS asphalt overlay & 17,133 & 11,327 & \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Other} & Total of other maintenance costs & \multirow{2}{*}{167,773} & \multirow{2}{*}{156,695} & \\  & & (Annual cost $9869) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Life-cycle cost analysis of HMA and SMA pavements with SPS asphalt overlay. service level of an SPS asphalt mixture pavement, the total NPV of an SPS asphalt mixture pavement would be lower, which demonstrates the better life-cycle economic benefit of SPS asphalt mixture thin overlay over traditional AC-13 thin overlay.
## 2 Beam-Column Joints of Composite Frames Under Seismic Loads
### Beam-Column Joints of Composite Frames Under Seismic Loads: Key Findings  
The key findings regarding beam-column joints in steel-concrete composite frames subjected to seismic loads are as follows:  
* Composite action enhances the strength and stiffness of joints when compared to bare steel joints.
* Proper reinforcement detailing in the joint area is essential for achieving ductile behavior.
* The presence of a concrete slab significantly affects the moment capacity and failure mode of the joint.
* Panel zone deformation has a noteworthy role in the overall rotation of the joint.
* Under cyclic loading, concrete crushing and bond deterioration of reinforcement can occur.
* The shear strength of joints is affected by the depth of the beam and the axial load on the column.
* Incorporating through-beam details can enhance joint performance in comparison to external beam-column connections.
* Composite joints generally demonstrate good energy dissipation capacity when subjected to seismic loading.
# Glycaemic Index of Bakery Products and Possibilities of Its Optimization
### Fermentation  
Fermentation processes play an essential role in the most common types of secondary processing of cereals. In the production of bread, and of common and most types of fine bakery products, they serve to leaven the dough and, more generally, create the conditions for the final structure, consistency and texture of the product crumb, which is their primary and original role. Fermentation processes have accompanied bread-making since its origins in antiquity. For many centuries, they have been skilfully exploited and modified on the basis of purely empirical knowledge. If we look at the basic types of fermentation (detailed in the following chapter), the most massive production of CO\({}_{2}\) occurs during ethanol fermentation, to a lesser extent this gas is produced during heterofermattative lactic acid fermentation, and practically no CO\({}_{2}\) is produced during homofermattative lactic acid fermentation [23; 79].  
However, lactic fermentation is, together with ethanol fermentation, the principal fermentation process used in fermentations, including traditional Central European rye sourdoughs. In the modern industrial production of rye and rye-wheat sourdough bread, strongly acidic sourdoughs are often used with only lactic fermentation initiated by starter cultures, and the ethanol fermentation necessary for sufficient CO\({}_{2}\) production is only achieved by the addition of yeast to the bread dough [23].  
Lactic acid sourdoughs are increasingly used in bakery technology for many reasons. In the production of traditional sourdough bread, the sensory effects are significant, resulting in the typical taste and aroma of these breads. However, this is by no means the only reason why the use of sourdough is expanding. The products of lactic fermentation have favourable nutritional properties, not only the acids themselves (especially lactic acid), but also for example the exopolysaccharides produced by certain strains of lactic acid bacteria (LAB). Similar to the above-mentioned hydrothermal processes used in baking, lactic acid fermentation results in an increase in the bioavailability of fibre components and co-formulants in sourdoughs and doughs, especially with longer maturation times. However, its effect on starch and its structure and properties, and thus on GI, is similarly ambiguous. On the one hand, the production of exopolysaccharides as well as the release of some fibre components from native fibre complexes may cause a partial effective substitution of digestible starch. However, the effect on pre-existing forms of MS is problematic [69; 74].
# Development of Simultaneous Dual-Resolution Digital Holography System
## 4 Discussion
### Separate the Spectrums  
After adjusting the two images with different fields of view into clear images on the image plane, it is also very important to adjust the reference beam of the two holographic setups to appropriate incident angles and planes. This is performed by introducing carrier fringes into the holograms. The two spectra transferred from the intensity images of the holograms should be separated to obtain good-quality phase maps. The orientation of the spectrum is determined by the direction of the carrier frequency. The advantage of the holographic system is the ease in changing the directions of carrier frequency; changing the direction is performed by adjusting the plane of the incident angle of the reference beam. The directions of the two carrier fringes should be checked before the Fourier transform is conducted. They should be orientated in different directions with a separation of at least 30 degrees. Figure 9 shows an ideal adjustment, in which the separation of the directions of the two carrier fringes is almost 90 degrees, resulting in two perfect mutually perpendicular spectrums. The phase distributions of the two holograms can then be easily separated by the inverse Fourier transform.  
The size of the aperture of the camera is also important. A big aperture size leads to more light intensity going through the lens, allowing a relatively large object to be tested. This is desired in practical applications. However, a big aperture size results in a small speckle size. According to the sampling theorem, the speckle size should cover at least two pixels theoretically [31] and two to three times more practically, or at least four to six pixels [32]. Therefore, the aperture of the camera cannot be too big. Two adjustments can be performed to ensure a good measuring result with the small aperture: (1) enhancing the laser power to increase the light intensity to allow a relatively small aperture size to be used, and (2) using a CCD camera with a high resolution that has a relatively small pixel size to allow speckling, which results in covering more pixels.  
Figure 8: Sketch of two optical channels for two fields of view. (**a**) Optical channel for large field of view, and (**b**) Optical channel for small field of view.
# Ising-Based Kernel Clustering +
## 2 Ising-Based Combinatorial Clustering
### Combinatorial Clustering  
Combinatorial clustering is one of the machine learning methods and minimizes the sum of intra-cluster distances among data points. Well-known clustering methods such as K-means, K-means++, and spectral clustering perform quasi-optimization to reduce the computational complexity. Hence, quasi-optimized clustering methods cannot obtain the globally optimal clustering solution even with a sufficient computation time. On the other hand, combinatorial clustering can achieve high-quality clustering results. Finding the combination of data that minimizes the sum of intra-cluster distances guarantees that a globally optimal solution is found [15].  
The objective function to minimize the sum of intra-cluster distances of combinatorial clustering is represented as Equation (3).  
\[H=\frac{1}{2}\sum_{a=0}^{K-1}\sum_{C(i)=C_{a}}\sum_{C(j)=C_{a}}d(x_{i},x_{j}). \tag{3}\]  
To perform combinatorial clustering, the number of clusters \(K\) is set in advance. \(x_{i}\) indicates the coordinates of data point \(i\). \(d(x_{i},x_{j})\) indicates the degree of similarities between two different data points \(i\) and \(j\). Cluster assignments of data points are represented as \(C(i)=C_{a}\) when data point \(i\) belongs to cluster \(a\).  
Ising-based combinatorial clustering has been proposed to obtain the globally optimal solution [16].  
Ising-based combinatorial clustering uses one-hot encoding to represent decision variables by using binary variables. Figure 1 is an overview of the one-hot encoding. The clustering results expressed in one-hot encoding represent a cluster to which a single datum belongs by using bits, whose number is the same as that of clusters.  
Equation (4) is a decision variable using one-hot encoding.  
\[q_{a}^{i}=\begin{cases}1&(C(i)=C_{a})\\ 0&(C(i)\
eq C_{a}).\end{cases} \tag{4}\]  
For \(q_{a}^{i}\in\{0,1\}^{N\times K}\), \(N\) is the number of data points, and \(K\) is the number of clusters. The one-hot encoding indicates that \(q_{a}^{i}\) is 1 if data \(x_{i}\) belongs to cluster \(C_{a}\), and 0 if it does not.  
Equation (5) is the QUBO model of combinatorial clustering.  
\[H=\frac{1}{2}\sum_{i,j=0}^{N-1}d(x_{i},x_{j})\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}+ \sum_{i=0}^{N-1}\lambda_{i}(\sum_{a=0}^{K-1}q_{a}^{i}-1)^{2}. \tag{5}\]  
Here, the number of data points is represented as \(N\). The Lagrange multiplier \(\lambda_{i}\) is introduced to combine the first and the second terms of Equation (5). The first term of Equation (5) is the objective function transformed from Equation (3) by using binary decision variables. The second term of Equation (5) is the constraint function that obliges each data point to belong to only one cluster. It is generally known that a sufficiently large Lagrange multiplier avoids violation of the constraint. In the case of combinatorial clustering, it is already revealed that the appropriate value of the Lagrange multiplier is \(N-K\) when all the values of \(d(x_{i},x_{j})\) are normalized [16].
## 5 Proposed Hardware Architecture
### Arithmetic Logic Unit
#### 5.5.2 Montgomery Radix-2 Architecture  
The proposed hardware architecture for the radix-2 Montgomery multiplier is illustrated in Figure 2. The architecture comprises two multiplexers: MUX_A and MUX_B. MUX_A and MUX_B both have a size of \(2\times 1\). MUX_A is responsible for selecting the partial product \(PP_{i}\), which is either Yp or 0, depending on the value of \(X_{pi}\). Once the appropriate \(PP_{i}\) is selected, it is added to the output \(Z_{p}\). The least significant bit (LSB) of Adder_A is input into MUX_B, where it selects either 0 or the prime number. The remaining bits of Adder_A are then added to the output of MUX_B and shifted right by 1. The final output is computed by performing \(n\) iterations of this process.  
Figure 2: Montgomery radix-2 multiplier.
## 3 Results and Discussion
### X-ray Diffraction  
Figure 7 depicts the X-ray diffraction (XRD) patterns of the PLA-based electrospun nanofiber textiles, which were categorized based on their hydrophilicity and the method of AgNP incorporation (M1, M2, M3, and M4). The XRD curves for these multi-functional electrospun textiles exhibit prominent diffraction peaks between 2\(\theta\) = 13 and 19\({}^{\circ}\), confirming the amorphous structure of PLA and PEG-PPG-PEG in the membranes, corresponding to the (203) crystal plane [34]. The integration of PEG-PPG-PEG and AgNPs resulted in decreased diffraction peak intensity, indicating reduced crystallization in the PLA/PEG-PPG-PEG/AgNP samples (M3 and M4). Moreover, the peak broadening observed in M3 and M4 suggests an increase in the amorphous regions due to the PEG-PPG-PEG content in the nanofiber fabrics. Compared to the pristine sample, the XRD peaks for the PLA blends were nearly identical to those of the pure hydrophobic PLA and hydrophilic PLA/PEG-PPG-PEG membranes, as shown in Figure S2. However, the diffractive intensities of the blends increased due to the presence of AgNPs. Minor diffraction peaks were detected in all samples, which could be attributed to the microcrystallinity of the PLA, PEG-PPG-PEG, and AgNPs. Notably, a distinct and broad peak at around 38-44\({}^{\circ}\) was observed in the composites containing AgNPs, which can be ascribed to the 111 and 200 crystallographic planes of the face-centered cubic (FCC) silver crystals, respectively [34]. The increased crystallinity in samples M3 and M4 was evident from the decreased amorphous region area around 2\(\theta\) = 13 and 19\({}^{\circ}\). This enhancement resulted in significant reductions in the amorphous region area, with M3 showing a 62% decrease and M4 demonstrating a 49% decrease when compared to samples M1 and M2, respectively.  
Figure 7: XRD results of the prepared nanofibrous electrospun cloth: (M1) PLA-Ag-E, (M2) PLA-Ag-S, (M3) PLA-PEG-Ag-E, and (M4) PLA-PEG-Ag-S.
# Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
## 2 Experiments
### Google Earth Engine  
As Google mentions is its own site \"Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's Surface\" [26].  
It is a powerful platform to run geospatial algorithms. Besides that, the API is available in JavaScript which is really convenient, since you can try it from any modern browser.  
Google Earth Engine's geometry constructors build geodetic geometries by default using the WGS84 ellipsoid [26]. To make planar geometries (Cartesian coordinates) an additional argument must be specified at the time the geometry is built.  
One can use Google Earth Engine API for free after registering. A very easy way to test the API is to use the \"Earth Engine Code Editor\" which is an online javascript editor ready to use the API.  
Notice in Scheme 3 the ErrorMargin function, which specifies a maximum error for geometric operations only in the case of using map projections [27]. If this value is changed the results remain still. For the first case the source code is.  
\(\mathrm{var}\) AB1 = ee.Geometry.LineString ( [[5; 52], [6; 51.4]] );  
\(\mathrm{var}\) CD1 = ee.Geometry.LineString ( [[4.5, 51.5], [5.5, 52]] );  
\(\mathrm{var}\) px;  
\(\mathrm{px1}\) = AB1.intersection(CD1, ee.ErrorMargin(0.001));  
\(\mathrm{print}\) (px1.coordinates());  
**Scheme 3.** Intersection of two geodesics with Google Earth Engine.  
Table 1 shows that the coordinates obtained from Google Earth and Oracle Spatial are exactly the same. Actually, there is a negligible difference of about 1 \(\times\) 10\({}^{-12}\) degrees, which represent around 0.3 \(\mathrm{\SIUnitSymbolMicro m}\), attributable to machine precision.  
It proves that both Google Earth Engine and Oracle Spatial use the same methodology, which consists in using a sphere instead of an ellipsoid.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Semisupervised Learning  
In semisupervised learning, the model is trained upon a collection of labeled and unlabeled data. Typically, the dataset will have a minimal amount of labeled data and a huge amount of unlabeled data. This type of learning is helpful for medical videos, because labeling a massive amount of videos for supervised learning is time-consuming and expensive.