esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 5 Conclusions  
In conclusion, this research successfully developed a novel antenna specifically engineered for seamless integration with conventional eyeglasses, operating efficiently within the 3.5 GHz and 5.8 GHz frequency bands. These bands are critically important for current and emerging 5G and Wi-Fi networks, respectively. A primary design objective was to ensure effortless attachment to existing eyeglass frames without necessitating any structural modifications, thereby opening up compelling new avenues for applications, particularly in assistive technologies for the visually impaired. Both comprehensive simulation and empirical measurement data consistently confirm the antenna's exceptional impedance matching and demonstrate a commendable maximum gain of 4 dBi across both operational bands. These favorable characteristics position this antenna as a highly viable solution for indoor wireless environments, where effective path loss mitigation is paramount for reliable communication. Furthermore, the antenna's optimized geometry is inherently compatible with the use of lightweight and flexible materials, such as the one-sided DuPont Pyralux laminate, which is crucial for comfort and wearability in practical applications.  
Looking ahead, future research endeavors will concentrate on significantly broadening the antenna's operating frequency range. This expansion will enable the antenna to accommodate an even wider spectrum of diverse wireless communication systems, enhancing its versatility and applicability across various technological platforms. Additionally, subsequent investigations will explore the integration of this antenna design as a fundamental element within a two-antenna system. This dual-antenna configuration is envisioned as a strategic approach to compensate for the inherent directional radiation patterns typically observed when a single antenna is placed on the human head, thereby improving overall signal coverage and reliability. To achieve this, future work will specifically consider implementing a dual-transmitter architecture in conjunction with frequency diversity techniques. The demonstrably good performance parameters of the currently designed antenna make it an exceptionally promising candidate for these advanced research directions, laying a solid foundation for more robust and adaptable wearable communication systems.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Research Question and Eligibility Criteria  
Considering that there is no broad comparison between distinct cold cornea storage media effects on corneal preservation, the outline of the main research question was motivated by four different questions to address the gap of the literature, as follows: population (P): which types of corneas were investigated?; exposure (E): which methods of corneal preservation should be considered?; comparators (C): Is there a \"gold-standard\" corneal cold preservation medium?; outcome (O): which were the cornea preservation quality parameters? As a result, following the PECO framework (Table 1), the main research question was formulated as \"What are the effects of different cold preservation media versus Optisol-GS on corneal quality preservation?\"  
Similarly, the eligibility criteria that guided the selection process of the studies included were based on the PECO structure. The inclusion criteria comprises in vitro and ex vivo studies conducted on human or animal corneas, which compared the use of distinct cold storage corneal medium to the use of Optisol-GS regarding corneal quality preservation parameters. The exclusion criteria comprised studies that reported data after cornea transplantation, case reports, reviews, observational studies, letters to the editors, editorials, commentaries, conference abstracts, and book chapters that did not present primary data.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt}} \hline \hline \multicolumn{2}{c}{**PECO Framework**} \\ \hline
**P Population** & Human or animal corneas \\
**E Exposure** & Distinct cold storage corneal medium \\ \hline
**C Comparator** & Optisol-GS \\ \hline
**O Outcomes** & Evaluation of qualitative parameters: corneal transparency (CT) and endothelial cells (EC) morphology; quantitative parameters: endothelial cell density (ECD), central corneal thickness (CCT), and EC mortality \\ \hline \hline \end{tabular}
\end{table}
Table 1: PECO framework.  
In addition, studies that were not conducted on cornea and those that did not include Optisol-GS as a comparative group for the evaluation of corneal quality preservation were excluded. There was no year/time constraint for the selected studies.
## 2 Preliminaries
For convenience, for a choice function, \(F\), let \(\overline{F}(A)=A\setminus F(A)\) denote the set of unselected elements. We list some important properties of set functions. **Definition 3**. : A set function, \(F:2^{E}\to 2^{E}\), is _monotone_ if \(F(A)\subseteq F(B)\) whenever \(A\subseteq B\subseteq E\) holds. A set function, \(F:2^{E}\to 2^{E}\), is _antitone_ if \(F(A)\supseteq F(B)\) whenever \(A\subseteq B\subseteq E\) holds. A choice function, \(F:2^{E}\to 2^{E}\), is _substitutable_ (sometimes called _comonotone_) if \(\overline{F}(A)\subseteq\overline{F}(B)\) for any \(A\subseteq B\) (that is, if \(\overline{F}\) is a monotone function). The substitutable property was originally defined by [2] with prices, differently from our definition. It was showed in, e.g., [6], that substitutability is equivalent with the property that if an agent chooses from an extended set of contracts, the set of rejected contracts expands. **Definition 4**. : A choice function, \(F:2^{E}\to 2^{E}\), is _path independent_, if \(F(A)\subseteq B\subseteq A\) implies \(F(A)-F(B)\) for any subsets, \(A\) and \(B\), of \(E\). Path independence is called \"irrelevance of rejected contracts (IRC)\" in the paper of [PERSON] and [PERSON] [4]:  
**Definition 5**. : [4] Contracts satisfy the irrelevance of rejected contracts (IRC) for choice function \(F\) if \(\forall Y\subset X,\forall z\in X\setminus Y\quad z\
otin F(Y\cup\{z\}) \Rightarrow F(Y)=F(Y\cup\{z\})\)  
Clearly, if the set of contracts is finite, this is equivalent to our path independence definition. There is an alternative way to define path independence (see e.g., [8]):  
**Theorem 3**. : _For a substitutable choice function, \(F\), the path independence of \(F\) is equivalent, so that \(F(A\cup B)=F(F(A)\cup F(B))\) hold for any sets, \(A\) and \(B\), of choices._Sometimes, the choice function is defined by a strict preference order over all subsets of \(E\), such that \(F(A)\) is that subset of \(A\) that is the first in the order. In that case, the choice function will be automatically path independent, since if the best set in \(A\) is \(S\) and \(S\subseteq B\subseteq A\), then the best set in \(B\) is \(S\), as well. We will see, however, that typical scoring choice functions are not path independent. Therefore, we shall study path functions that are not necessarily independent more generally. We can define the direct sum of two choice functions: if \(X\cap Y=\emptyset\) and \(F_{1}:2^{X}\to 2^{X}\), \(F_{2}:2^{Y}\to 2^{Y}\), \(F:2^{X\cup Y}\to 2^{X\cup Y}\); then, \(F=F_{1}+F_{2}\) means that for every \(A\subseteq X\cup Y\), \(F(A)=F_{1}(A\cap X)\cup F_{2}(A\cap Y)\). For example, on the graph of possible marriages, \(w_{1}\) chooses from the contracts, \(w_{1}m_{i}\), and \(w_{2}\) chooses from the contracts, \(w_{2}m_{i}\) (so these two sets are disjoint). The sum of all women's choice function will choose from all of the contracts.
## 4 Branch-and-Price-and-Cut Algorithm
### Branching and Cutting
#### 4.7.2 Arc Flow Branching  
Column generation is applied to solve the linear programming relaxation of the master problem, which might output fractional solutions. To achieve integer solutions in such a case, branching becomes necessary. While it might seem intuitive to consider branching on the master problem variables \(\theta^{r}\), it is worth highlighting that this approach may not be the most efficient choice. The reason for this lies in the fact that fixing these variables at 0 would necessitate preventing the subproblem from re-generating the associated routes again. This restriction significantly complicates the resolution of the pricing subproblems and can lead to inefficiencies in finding the optimal integer solution. Consequently, as proposed by several authors, a more effective strategy is to perform branching on the binary arc flow variables \(x_{ij}\), which are the variables in the original MILP formulation. The arc flow variables \(x_{ij}\) can be deduced from the master problem variables using the formula: \(x_{ij}=\sum_{r\in\Omega}\sigma^{r}_{ij}\theta^{r}\), where \(\sigma^{r}_{ij}\) is a 0-1 parameter that equals 1 if and only if arc \((i,j)\) is included in route \(r\).  
We evaluate the values of \(x_{ij}\) for all arcs and select the one closest to 0.5. Following this, we branch on the selected arc. More specifically, the current node is divided into two child nodes: the left one with constraint \(x_{ij}=0\), and the right one with constraint \(x_{ij}=1\). The arc flow branching is illustrated in Figure 6. In the case where \(x_{ij}=0\), it means that arc (\(i\), \(j\)) cannot be included in any route. In order to enforce this, the deletion of arc (\(i\), \(j\)) from the graph is executed, followed by the elimination of all variables \(\theta^{\prime}\) associated with routes \(r\) containing the arc (\(i\), \(j\)) from the master problem. Conversely, in the case where \(x_{ij}=1\), it indicates that arc (\(i\), \(j\)) should be used exactly once. This can be guaranteed by removing all incoming arcs that terminate at node \(j\) and all outgoing arcs that originate from node \(i\). Subsequently, we should also eliminate all variables \(\theta^{\prime}\) for routes \(r\) that contain at least one of these deleted arcs from the master problem. Clearly, this branching strategy primarily involves restructuring the underlying graph and removing certain variables from the RLMP. As such, the label setting algorithm does not need any modifications to handle pricing subproblems at the child nodes. This merit makes it a particularly appealing branching choice for searching integer solutions in the branch-and-price-and-cut framework.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## Appendix A A Recurrence for \(S^{0}_{nl}(r)\)  
We start with an integral representation of the 3D Zernike radial polynomials [57]:  
\[R_{nl}(r)=\frac{2}{\pi}(-1)^{\frac{n-l}{2}}\int_{0}^{+\infty}j_{n+1}(q)j_{l}( rq)q\mathrm{d}q, \tag{10}\]  
where \(j_{l}\) are spherical Bessel functions. The derivative of \(R_{nl}(r)\) with respect to \(r\) is then  
\[\frac{dR_{nl}}{dr}(r)=\frac{2}{\pi}(-1)^{\frac{n-l}{2}}\int_{0}^{+\infty}j_{n +1}(q)\frac{\partial j_{l}(rq)}{\partial r}q\mathrm{d}q. \tag{11}\]  
Using the following relationship for spherical Bessel functions ([70], Equation (10.51.1))  
\[qj_{n+1}(q)=(2n+1)j_{n}(q)-qj_{n-1}(q),\]  
we get  
\[\begin{split}\frac{dR_{nl}}{dr}(r)&=\frac{2}{\pi} (-1)^{\frac{n-l}{2}}\int_{0}^{+\infty}qj_{n+1}(q)\frac{\partial j_{l}(rq)}{ \partial(rq)}q\mathrm{d}q\\ &=\frac{2}{\pi}(-1)^{\frac{n-l}{2}}(2n+1)\int_{0}^{+\infty}j_{n} (q)\frac{\partial j_{l}(rq)}{\partial(rq)}q\mathrm{d}q-\frac{2}{\pi}(-1)^{ \frac{n-l}{2}}\int_{0}^{+\infty}qj_{n-1}(q)\frac{\partial j_{l}(rq)}{\partial (rq)}q\mathrm{d}q\\ &=\frac{2}{\pi}(-1)^{\frac{n-l}{2}}(2n+1)\int_{0}^{+\infty}j_{n} (q)\frac{\partial j_{l}(rq)}{\partial(rq)}q\mathrm{d}q+\frac{dR_{n-2l}}{dr}( r).\end{split} \tag{12}\]  
We use now the following relationship for spherical Bessel functions ([70], Equation (10.51.1))  
\[\frac{dj_{l}(x)}{dx}=\frac{l}{2l+1}j_{l-1}(x)-\frac{l+1}{2l+1}j_{l+1}(x), \tag{13}\]  
to get  
\[\frac{dR_{nl}}{dr}(r)=\frac{2}{\pi}(-1)^{\frac{n-l}{2}}(2n+1)\int _{0}^{+\infty}j_{n}(q)\bigg{(}\frac{l}{2l+1}j_{l-1}(rq)-\frac{l+1}{2l+1}j_{l+ 1}(rq)\bigg{)}q\mathrm{d}q\\ +\frac{dR_{n-2l}}{dr}(r). \tag{14}\]  
This equation leads to  
\[\frac{dR_{nl}}{dr}(r)=\frac{(2n+1)l}{2l+1}R_{n-1J-1}(r)+\frac{(2n+1)(l+1)}{2l+ 1}R_{n-1J+1}(r)+\frac{dR_{n-2l}}{dr}(r).\]After integration over \([0,r_{0}]\)  
\[R_{nl}(r_{0})=\frac{(2n+1)l}{2l+1}S^{0}_{n-1,l-1}(r_{0})+\frac{(2n+1)(l+1)}{2l+1} S^{0}_{n-1,l+1}(r_{0})+R_{n-2,l}(r_{0}). \tag{100}\]  
Shifting \(n\to n+1\) and \(l\to l+1\), we get  
\[R_{n+1,l+1}(r_{0})=\frac{(2n+3)(l+1)}{2l+3}S^{0}_{nl}(r_{0})+\frac{(2n+3)(l+2)} {2l+3}S^{0}_{n,l+2}(r_{0})+R_{n-1,l+2}(r_{0}). \tag{101}\]  
This leads to  
\[S^{0}_{nl}(r_{0})=\frac{2l+3}{(2n+3)(l+1)}(R_{n+1,l+1}(r_{0})-R_{n-1,l+2}(r_{0} ))-\frac{l+2}{l+1}S^{0}_{n,l+2}(r_{0}), \tag{102}\]  
which concludes the proof of Equation (25), the recurrence over the \(S_{nl}(0,r)\).  
The initialization follows from  
\[S^{0}_{nn}(r_{0}) = \int_{0}^{r_{0}}R_{nn}(r)dr=\int_{0}^{r_{0}}r^{n}dr=\frac{r_{0}^{n +1}}{n+1}\]  
where we have used Equation (22) for \(R_{nn}(r)\).
## 5 Advanced MPPT Techniques
### Smart MPPT Techniques
#### 5.1.3 Fibonacci Series Algorithm  
The Fibonacci Series (FS) MPPT algorithm uses principles from the Fibonacci sequence, a mathematical series where each number is the sum of the two preceding ones, to optimize the tracking of the MPP in PV systems. This algorithm systematically narrows down the search space for the MPP by using Fibonacci-based steps to adjust the operating point in a manner similar to interval reduction techniques [63; 64]. Advantages of this approach include its simplicity and ease of implementation, as well as its efficiency in converging to the MPP by reducing the search space with each iteration. Additionally, it generally avoids oscillations around the MPP, leading to stable performance. However, disadvantages include its potential for slower convergence compared to more advanced algorithms and its sensitivity to changing conditions. The FS MPPT algorithm may require careful tuning of parameters to ensure optimal performance in varying environmental conditions [65]. Equation (5) provides the iterative sequence, with \(R\) representing the points on the PV curve used to track the MPP. Table 3 presents a comparison of the smart MPPT algorithms.  
\[R_{(n+2)}=R_{(n+1)}+R_{n},\ (n=1,2,3,\ldots and\ R_{1}=R_{2}=1) \tag{5}\]
# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
## 4 Superpixel Algorithm Incorporating Image Statistics
### Superpixel Algorithm Outline
#### 4.3.1 Cluster Update  
Two distance functions are used during the pixel cluster update operations which score which cluster, \(C_{j}\), an individual pixel, \(i\), should belong to. The calculation of this score is based on two different distance metrics, \(D_{\mu}(i,j)\) and \(D_{L2}(i,j)\). The metric \(D_{\mu}(i,j)\) is the absolute difference between the pixel's intensity (in the input image \(\mathcal{I}(i)\)) with respect to the average pixel intensity, \(\mu_{j}\), of a cluster. The other metric \(D_{L2}(i,j)\) is defined as the Euclidean distance from the pixel's coordinates, \((x_{i},y_{i})\), to the centroid, \((x_{j},y_{j})\), of the cluster. A distance score, \(D_{Score}(i,j)\), for a pixel is calculated as  
\[\begin{split} D_{\mu}(i,j)=|\mu_{i}-\mathcal{I}(i)|\\ D_{L2}(i,j)=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}}\\ D_{Score}(i,j)=\left(\frac{D_{\mu}(i,j)}{\lambda_{\mu}}\right)+ \left(\frac{D_{L2}(i,j)}{\lambda_{L2}}\right)\end{split} \tag{36}\]  
with \(\lambda_{\mu}\) and \(\lambda_{L2}\) being two tunable parameters that weight the importance of the average intensity distance and Euclidean distance. The distance value of each pixel, with respect to the cluster to which it currently belongs, is stored in the distance image \(\mathcal{D}(i)\).  
The goal of the cluster update step is to minimize the total of the distance score for all (valid) pixels in the image. Here, the method for updating the clusters is similar in most respects to the SLIC superpixel algorithm [31]. Minimization of the total distance score is achieved by moving a pixel to a different cluster if the pixel's score, with respect to that new cluster, is less than its current score value \(\mathcal{D}(i)\). To determine if a pixel should be moved, the distance score for each pixel, \(i\), is calculated with respect to each cluster \(C_{j}\). This calculation is evaluated individually for all of the clusters within the cluster array \(\mathbf{C}\) in a loop. If, during the loop, the distance score, \(D_{Score}(i,m)\), for a cluster, \(C_{m}\), is less than the current value in \(\mathcal{D}(i)\), then the pixel is relabelled through the function  
\[\mathcal{L}(i)=m\text{ if }D_{Score}(i,m)<\mathcal{D}(i), \tag{37}\]  
so that the pixel is now a member of the cluster \(C_{m}\). To speed up processing, only the distance values for pixels within a square region (of size \(2r_{desarea}\times 2r_{desarea}\)) around each cluster's centroid are evaluated, based on the suggested method in [31]. After all of the clusters have been iterated through, with pixels relabelled to minimize the distance scores, the values in the cluster array, \(\mathbf{C}\), are updated using (24). Note that the distance image value for invalid pixels, those outside of the ROI, was set to \(-1\) when the distance image was created. As the distance score is positive semi-definite (\(D_{Score}(i,j)\geq 0\)), invalid pixels will never be incorporated into a cluster and do not need to be considered separately.
## 3 Case Study
### OSP Considering the Evolution of Structural States and Material Properties
#### 3.4.4 Evaluation of OSP Results  
The quantitative analysis of OSP results for 18 cases is conducted using MAC, fitness values, and S criteria. MAC matrix diagrams for 18 cases are presented in Figure 22. Based on the information from Figure 22, it is evident that the MAC-MAX values for all 18 cases are significantly below 0.25. This indicates that the sensor locations corresponding to the modal matrices exhibit minimal correlation. Consequently, the sensor placement results for the arch dam under various cases are deemed satisfactory. This also affirms the rationality and viability of the OSP framework for an arch dam with cracks, based on the IAHA.  
Figure 22: MAC matrix diagrams corresponding to OSP results under different cases.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design
### Power  
By all means, the node should not only communicate in a wireless fashion, it should also be powered by an independent power source. Being designed with multiple research applications in mind, the proposed system can be powered by any battery producing a voltage in between 3.3 V and 12 V. To further expand the amount of possible power sources, a micro-USB connector is provided as well, facilitating the use of commercial USB battery packs. The power drawn by the node roughly ranges from 4 mA when in standby mode to 40 mA when sending or receiving LoRa packets. To prevent the RTC from resetting when the battery is changed, a 0.22 F supercapacitor is added, giving the user more time to attach the new battery.
## 4 Results and Discussion
### Chloride Ion Penetration  
Figure 10 shows the results of the chloride ion penetration tests for RCLMFRRHCC samples according to the type of reinforcement fiber. All samples satisfied the goal of chloride ion penetration of 2000 Coulombs after 28 days of curing. However, the target value of 4000 Coulombs or less at 4 h of curing were not achieved by the plain mixture without added fibers.  
Chloride ion penetration amount affects indirectly the concrete's water penetration properties [5; 14; 30]. The mixes with added reinforcement fibers showed decreased chloride ion penetration [10; 11; 12]. Generally, in cases of latex-modified concrete, the addition of latex fills air pores inside the concrete and forms a thick latex film layer, having the effect of reducing water penetration [1; 2; 3; 5; 6]. In this study, with latex addition (5% of cement weight) and under pressure vibration compaction, lower chloride ion penetration amounts were achieved. When reinforcement fibers were added, there was a further reduction in the penetration amount [5; 14].  
Particularly in the case of rapid-hardening cement concretes, cracks occur inside the cement due to high hydration heat in the early stages [1; 2; 3]. The addition of reinforcement fibers appeared to offset hydration heat-induced crack formation [5; 13]. The mixes/test samples showed similar results in this regard. After curing for 4 h, water penetration was fairly high in the mix with no fibers. However, all cases with added reinforcement fibers showed moderate water penetration properties. Since the reinforcing fibers inhibit the internal cracks due to the generation of hydration heat before sufficient strength is developed, the chlorine ion penetration amount decreases earlier than the mixture without the fibers. Therefore, it can be concluded that the addition of reinforcing fibers is effective in reducing the chloride ion penetration at the initial curing period in the repair concrete before the initial sufficient strength is developed. However, after curing for 28 days, all samples showed low water penetration.
## 2 Materials and Methods
### Bee Inoculation and Experimental Design  
_A. mellifera_ adults were acquired from the experimental apiary located in El Nogal Experimental Station (36\({}^{\circ}\)35\({}^{\prime}\)8.25\({}^{\prime\prime}\)\(S-\)72\({}^{\circ}\)04\({}^{\prime}\)51.77\({}^{\prime\prime}\) W), University of Concepcion, Chillan, Chile. Previous to the assay, the health status of the apiary was determined by identifying the viral level in each colony (\(n=54\) beehives). The pathogen level was established by molecular techniques according to [PERSON], et al. (1999). Brood combs with capped worker bees were removed from colonies with low DWV load (1.0 \(\times\) 10\({}^{2}\) copy number per bee). In addition, no colonies were detected to be DWV-free. The brood combs were maintained under controlled conditions in a rearing room (30 \({}^{\circ}\)C \(\pm\) 1; 60% \(\pm\) 3 RH). Afterwards, newly emerged worker bees (24 h old) were carefully collected from the brood combs and randomly confined in plastic cages (base = 8 cm diameter, mouth = 10 cm diameter, and height = 15 cm). Bees were inoculated orally with 5 \(\upmu\)L of a viral suspension (1.0 \(\times\) 10\({}^{9}\) copy number per bee) in a 50% sucrose solution according to the methodology described by [PERSON] et al. (2016). The bees that did not consume the total amount of viral inoculum were discarded from the assay. Next, four treatments consisting of different doses of GPP (0.5%, 1%, 2.5% and 5%) dissolved in a sugar syrup at 50% weight/volume ratio were used ad libitum. Two control treatments without GPP were also included, a non-inoculated (N-DWV) control and an inoculated (I-DWV) control (see Klapan-Meier survival curves in Supplementary Material Figure S1). For all of the GPP treatments and controls, four replicates were carried out with 80 worker bees each. A dietary regimen was established for the treatments with the GPP. The regimen consisted of 24 h for the bioactive supplement and 48 h for the sugar syrup, both _ad libitum_, starting on day 0 and alternating until day 14 of the experiment, where the maximum viral load occurred as a result of viral replication [33]. In addition, all cages contained 3 g of pollen substitute. Five bees were collected per cage, starting on the day of virus inoculation according to the dietary regimen described. Finally, 11 collections were obtained during the whole assay, 17 days post-inoculation (20-day-old bees).