esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A study on tomato processing revealed that while some compounds like rutin and chlorogenic acids exhibit notable stability in tomato paste, others like naringenin chalcone show significant degradation [16]. On the contrary, [PERSON] et al. found that the total phenolic content increased during the production of both tomato juice and tomato sauce, highlighting the different impact of processing methods and environments on antioxidant components [17]. Dehydration stands as the most common processing technique for tomatoes, and a study conducted by [PERSON] and co-authors highlighted that the hot break step results in substantial losses of flavonoids such as quercetin (54%) and kaempfer (61%), as well as reductions in ascorbic acid (63%) and \(\beta\)-carotene (30%) [18]. Spinach (_Spinacia oleraca_ L.) is renowned for its powerful antioxidants. With respect to carotenoids, spinach is rich in lutein and zeaxanthin (5930-7900 ug/100 g fresh weight), which offer protective benefits against coronary heart disease and stroke [19]. Moreover, spinach is particularly rich in flavonoids, primarily patuletin and spinacetin derivatives, which contribute to its nutritional profile [20; 21]. Factors such as genetics, maturation, the growing season, and processing methods influence the content of spinach flavonoids,which typically ranges from 1000 to 1200 mg/kg in leaves [21; 22; 23]. In a comprehensive study by [PERSON] and [PERSON] [24], the impact of various cooking methods on the flavonoid content in spinach leaves was thoroughly investigated. Their findings reveal significant reductions in flavonoid levels across different techniques, boiling, microwave heating, and sonication, which led to a considerable 50% decrease, while frying resulted in a 25% reduction. Interestingly, thawing showed a comparatively lower impact, with a 15% decrease in flavonoid content. This study aimed to enrich the phenolic content of traditional semolina pasta, by incorporating tomato and spinach powder in the traditional recipe. A comprehensive analysis of free and bound phenolic compounds by HPLC coupled with mass spectrometry was performed. To the best of our knowledge, this research represents the first in-depth investigation into the effects of incorporating tomato and spinach to dry semolina pasta on its total phenolic content. This study specifically shows how these widely consumed vegetables, both rich in phenolic compounds, contribute to the phenolic profile of pasta, taking into account the effect of the pasta-making process, from raw materials to the final cooked product.
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 3 Proposed Methodology
### Query Matching  
We proposed an offline analysis to identify similar structure queries. The result of the previously issued queries are extracted from the log. By using query matching, we pre-fetch the results of the similar queries and place in a cache for future access. This approach lowers the burden on SPARQL endpoints. To find a similar query, we compute the _Levenshtein Distance_ between two queries, normalized by the size of the query strings. We define the Distance Score as:  
\[DistanceScore=\frac{Levenshtein\left(Q_{1},Q_{2}\right)}{max\left(\left|Q_{1},Q_ {2}\right|\right)}. \tag{1}\]  
In Equation (1), we define the distance score to match the triple patterns. The overall distance of the triple pattern is calculated by aggregating the individual score of the _subject_, _predicate_ and an _object_. The structure base similarity is not sufficient to return a search result, as it only relies on the ordering of symbols. It is possible that two queries represent the same structure of ordering but share different content. We utilize the distance function score to determine the query matching. The query distance between the two graphs is the minimum number of edit operations, such as addition, deletion and insertion, to transform one graph to another. However, the cost is determined by the distance between triple patterns \(\Delta\left(T_{1},T_{2}\right)\). Complete matching is only possible in the case of bipartite graphs, where _Basic Graph Patterns (BGP)_ occur with the same number as for the triple patterns. Maximum matching is determine in polynomial time. We utilize the Hungarian method [34] to obtain the individual triple patterns with the assignment of minimum cost. Therefore, the higher cost of triple matching is set to \(\Delta\left(T_{1},T_{2}\right)>1\) which is considered the infinite cost, where there is no matching between the BGP elements. The score derived from the complete matching is defined as:  
\[\Delta\left(BGP_{1},BGP_{2}\right)=\frac{\sum\left(Triple_{i},Triple_{j}\in M _{T}\right)\Delta\left(Triple_{i},Triple_{j}\right)}{M_{T}}. \tag{2}\]  
As shown in Equation (2), \(M_{T}\) assigns the cost to triples. In the case that \(M_{T}\) is finite, there is a valid triple pattern matching scenario that exists, and if \(M_{T}\) is infinite, no such scenario exists. However, real-world queries are more complex than the basic graph patterns, therefore, we use a bottom-up approach to derive the minimum cost between BGP. We continue to check the alignment of query patterns, in the case of no alignment, then the matching has an infinite cost. In the case of maximal matching, two BGPs can be matched if they are aligned and matching with finite cost can be established between all the patterns.
# Automated Penetration Testing Framework for Smart-Home-Based IoT Devices
## 5 Discussion
### Scope  
Given below is a list of points that cover the scope of this research work:  
* The framework can run on other Unix platforms (apart from Kali Linux) provided that Python 3.6, the tools mentioned in the Design chapter, and the python-pip packages like'scapy', and 'pyshark' are installed.
* This research only deals with the top five list of vulnerabilities mentioned in the Design subsection of the Proposed Framework section. It does not involve detecting cloud, mobile application, hardware, and physical device vulnerabilities.
* Although the Common Vulnerability Scoring System (CVSS) score depends on several metrics, this research mainly focuses on the base metrics and impact metrics, i.e., the base score for determining the severity of the vulnerability.
* Currently, the framework can only test the devices which are accessible over the network and, preferably, on the same network.
* Currently, this research does not involve providing a fix for the vulnerabilities found, it only involves discovering them.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.4 AUV-Aided Localization Technique  
AUV-Aided localization technique is proposed in [53], where an UWSN consists of many sensor nodes and one AUV. The sensor nodes are dropped into ocean and move with water currents. The AUV traverses the UWSN periodically following a predefined trajectory (a lattice-like and an Archimedean spiral trajectory). Moreover, all nodes can communicate (omni-directionally) with the AUV. The AUV can surface to obtain its coordinates by GPS, then dives to a predefined depth (provided by pressure sensors) and starts exchanging three types of messages with unknown nodes: wakeup, request and response. \"Wakeup\" messages are sent by the AUV as it enters the network to declare its presence to unknown nodes in its communication range. Unknown nodes that receive \"wakeup\" respond with a \"request\" message to commence range measurement. The \"request/response\" messages are exchanged between the AUV and unknown nodes to estimate their positions according to the round trip time. Then, localization is investigated using two methods, bounding-box [54] and triangulation.  
Bounding box method draws a rectangular region with the intersection of the distance estimates (see Figure 10). The positions of unknown nodes are obtained by applying the distance measurements asconstraints on the X and Y coordinates of unknown nodes. In Figure 10, \(A\) and \(B\) represent the positions of the AUV at two different time slots and \(C\) is the position of the unknown node. If the distance between the unknown node and the position of AUV \(A\) is \(d_{AC}\), then the \(x\) coordinates of node \(C\) are bounded by \(d_{AC}\) to the left and to the right of the \(x\) coordinate of \(A\), \(x_{A}-d_{AC}\) and \(x_{A}+d_{AC}\). The \(y\) coordinates of node \(C\) are bounded by \(y_{A}-d_{AC}\) and \(y_{A}+d_{AC}\). Similarly, the bounds for \(C\)'s coordinates with respect to B are obtained. The intersection of the diagonals gives the coordinates of the unknown node. The performance of bounding box is highly dependent on the positions of the AUV. An unknown node is better localized if the beacons are sent from opposite sides of the box. Compared with triangulation, bounding-box achieves a higher localization ratio but with a higher error.  
AUV scheme does not assume any fixed infrastructure or time synchronization. Simulation results show that 100% localization can be achieved with only 3% localization error. However, localization time and accuracy are greatly influenced by the velocity and the location accuracy of the AUV. Since the velocity of the AUV is relatively slow, AUV-Aided localization technique has high localization delay (about up to 2 hours).
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Linewidth Calculation Method Based on Frequency Noise
#### 2.5.1 Frequency Discrimination Method  
Frequency discrimination is a method of directly measuring frequency noise based on a frequency discriminator. The frequency discriminator converts frequency fluctuations into intensity fluctuations and obtains frequency changes by detecting intensity changes [71; 72]. Its principle and structure are shown in Figure 5b. Frequency discriminators can be used to measure frequency jitter and linewidth, but they cannot directly generate linewidths, making data interpretation more difficult. In addition, the measurement of the frequency discriminator is assumed to be carried out under the condition of continuous operation of the laser, and its intensity will be constant and only change in its optical frequency or phase. The experimental setup and calibration of frequency discriminators are more complex. There are currently multiple interference configurations available for constructing frequency discriminators, such as the MI interferometer, F-P interferometer, and MZI  
Figure 5: (**a**) Schematic diagram of PSD with frequency noise [65], (**b**) basic structure of the frequency discrimination method, (**c**) self-homodyne optical coherent reception method with FRM, and (**d**) optical coherent reception method based on a 3 \(\times\) 3 coupler.  
interferometer. Electronic frequency discriminators in the frequency domain have also been developed to improve stability and resolution.
## 2 Platform Architecture
### Particle Waveform Data FPGA Forward Inference Module
Each computation requires one clock, and the final extrapolation time required to compute a convolutional layer is shown in Equation (6):  
\[ClkNum=x\times y\times z\times a+b \tag{6}\]  
where \(ClkNum\) is the number of the running clk to be consumed; \(x\) represents the degree of serialization within the convolutional kernel; \(y\) represents the degree of the serialization of the input channel element computation; \(z\) represents the degree of the serialization of the sliding window; \(a\) represents the degree of the serialization of the output channel; and \(b\) is the number of extra clocks due to synchronization or some bias computation, as determined by the actual program. The kernel architecture of the pooling layer is shown in Figure 6.  
The operation of the pooling layer is relatively simple due to the maximum pooling operation used in this architecture; the pooling size is 2. Thus, the dimensions of the input channels after the pooling operation will be reduced by half, and the larger value of neighboring data is taken as the output value. The pooling operation uses parallel processing operations for single-channel input data and serial processing operations for different input channel data. Finally, all data will be flattened and outputted. The pooling operation purely comprises comparator processing and therefore does not consume the DSP module, for which its time delay is calculated in Equation (7):  
\[ClkNumpool=clk_{pool}\timesclk_{channel}+c \tag{7}\]  
where \(clkpool\) is the serial degree of single-channel input data pooling; \(clk_{channel}\) is the serial degree of multi-channel input pooling processing; and \(c\) is the number of extra clocks due to synchronization as determined by the actual program. The kernel architecture of the full connectivity layer is shown in Figure 7.  
Figure 6: Pooling layer data flow block diagram. The fully connected layer processes all spreading data obtained after convolution and pooling and obtains the mapping computation of the final classification outputs. Since it is a fully connected computation, all spreading input data will correspond to the weight; thus, the parallelism of the process will directly and significantly increase the number of DSP modules used. The number of DSP modules used in the fully connected layer is shown in Equation (8):  
Figure 4:0  
where \(DSPmuti\) is the input node's multiplication computation parallelism and the \(DSPoutputnode\) is the output category computation parallelism. The full connectivity layer's inference time calculation is shown in Equation (9):  
Figure 4:1  
where \(clkmuti\) is the multiplicative computation seriality, i.e., the number of times a single channel completes the computation, and +1 is needed since a biased addition is eventually required; \(clkoutputnode\) is the output category computation seriality; and \(k\) is the number of extra clocks due to synchronization as determined by the actual program.
### Synthesis of Water-Based Dispersions of Polymer/TiO\({}_{2}\) Hybrid Nanospheres  
**[PERSON], [PERSON] * and [PERSON] * Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland; E-Mail: [EMAIL_ADDRESS]**  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (M.M.); Tel.: +41-44-6320635 (H.W.); Fax: +41-44-6321082 (H.W.).  
Academic Editor: [PERSON]  
_Received: 3 August 2015 / Accepted: 24 August 2015 / Published: 28 August 2015_  
**Abstract:** We develop a strategy for preparing water-based dispersions of polymer/TiO\({}_{2}\) nanospheres that can be used to form composite materials applicable in various fields. The formed hybrid nanospheres are monodisperse and possess a hierarchical structure. It starts with the primary TiO\({}_{2}\) nanoparticles of about 5 nm, which first assemble to nanoclusters of about 30 nm and then are integrated into monomer droplets. After emulsion polymerization, one obtains the water-based dispersions of polymer/TiO\({}_{2}\) nanospheres. To achieve universal size, it is necessary to have treatments with intense turbulent shear generated in a microchannel device at different stages. In addition, a procedure combining synergistic actions of steric and anionic surfactants has been designed to warrant the colloidal stability of the process. Since the formed polymer/TiO\({}_{2}\) nanospheres are stable aqueous dispersions, they can be easily mixed with TiO\({}_{2}\)-free polymeric nanoparticle dispersions to form new dispersions, where TiO\({}_{2}\)-containing nanospheres are homogeneously distributed in the dispersions at the nanoscale, thus leading to various applications. As an example, the proposed strategy has been applied to generate polystyrene/TiO\({}_{2}\) nanospheres of about 100 nm in diameter.  
**Keywords:** hybrid; TiO\({}_{2}\); nanocomposite; mini-emulsion; microchannel; intense turbulent shear
## 5 Common Systems
### Timing and Synchronization
#### 5.1.8 Pulsed Optical Links for Reference Signal Distribution to Critical Clients  
Clients like the BAM, the LAM, gun and experiment lasers, and X-band RF station require ultimate long-term stability below 10 fs peak-to-peak drift per day. In addition, they may need direct optical input signals. Therefore these clients will be fed with reference signals from stabilized pulsed optical links. This technology is commercially available and it is planned to purchase this system together with an optical power amplifier and splitter system. The evaluation process is ongoing.
# A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands
## 1 Introduction
### Particulars of the Reference Application Case  
As we previously mentioned, the methodology elaborated in this work is general and it can be applied to other cases. However, practical relevance of these results relies upon the magnitude and diversity of renewable energy sources in the particular application. Uruguay is one the countries of the world with highest penetration of renewable energies. Nowadays, over 98% of the annual energy consumed by the country or exported to neighbors (i.e., Argentina and Brazil) comes from renewable sources. Table 1 presents the installed power plant and annual generation by type of energy source (Information regarding capacity and energy is available at: [[https://portal.ute.com.uy/composicion-energetica-y-potencias](https://portal.ute.com.uy/composicion-energetica-y-potencias)]([https://portal.ute.com.uy/composicion-energetica-y-potencias](https://portal.ute.com.uy/composicion-energetica-y-potencias)) while geographic distribution is in: [[https://www.ute.com.uy/institucional/infraestructura/fuentes-de-generacion](https://www.ute.com.uy/institucional/infraestructura/fuentes-de-generacion)]([https://www.ute.com.uy/institucional/infraestructura/fuentes-de-generacion](https://www.ute.com.uy/institucional/infraestructura/fuentes-de-generacion)) Other historical or real-time data is available at: [[https://www.adme.com.uy](https://www.adme.com.uy)]([https://www.adme.com.uy](https://www.adme.com.uy)) and [[https://adme.com.uy/mmee/infannual.php](https://adme.com.uy/mmee/infannual.php)]([https://adme.com.uy/mmee/infannual.php](https://adme.com.uy/mmee/infannual.php)). A few years ago, when data of this work was acquired, that fraction was 96%, slightly lower but still remarkable high.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Energy by** & **Installed Power** & **Relative** & **Produced Energy** & **Relative** \\
**Type of Source** & **Plant (MW)** & **Subtotal** & **Total 2019 (GWh)** & **Subtotal** \\ \hline
**Hydroelectric** & 1534 & 31.5\% & 6134 & 55.6\% \\ \hline
**Wind-power** & 1506 & 30.9\% & 3690 & 33.5\% \\ \hline
**Solar-Photovoltaic** & 254 & 5.2\% & 314 & 2.8\% \\ \hline
**Biomass (thermal)** & 413 & 8.5\% & 660 & 6.0\% \\ \hline
**Fossile (thermal)** & 1170 & 24.0\% & 225 & 2.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the installed power plant and energy by type of source [ADME: 2019, UTE: 2019].  
The information previously presented is public and accessible through the provided URLs. Detailed historic information about actual wind-power and related forecasts was also public by the time this work was realized, but unfortunately is no longer so.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 5 Energy-Balancing Techniques  
This section presents the energy-balancing techniques aimed at prolonging the lifespan of WSNs and evaluates their effectiveness in LWSNs.  
To achieve a longer network lifespan, both efficient and balanced power consumption are highly significant [65]. Energy-balancing techniques seek to ensure that the energy consumption is evenly distributed in the WSN so that the nodes have a fairly equal amount of energy. This reduces the likelihood of a black hole (energy hole) developing in the WSN and prolongs the WSN's lifetime [37]. Thus, the objective of the energy-balancing technique is to balance the communication burdens of the sensor nodes in the WSN by ensuring that they spend their energy at approximately the same rate. The main rationale behind energy-balancing techniques is to maintain the same residual battery energy for all sensor nodes in the network. This can be achieved via energy-efficient routing schemes, load balancing, mobility-based schemes, topology control, and wireless energy transfer/charging. Figure 7 presents a taxonomy we developed for classifying the energy-balancing techniques for extending the WSN's lifetime. Table 5 presents the different energy-balancing techniques and the strategies they employ. Table 6 presents an evaluation of the energy-balancing technique discussed in this subsection. The pros and cons of each technique and an analysis of the impact of these techniques when applied to LWSNs are presented.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### System Description
#### 3.2.5 Power Grid  
The power grid is typically an interconnected structure that delivers power to the load. In this study, the suggested hybrid solar PV/BG system supplies surplus power to the grid.
## 2 Materials and Methods
### Evaluation Metrics  
\(PSNR\) is commonly used as a quality measure in the reconstruction of code with losses. Generally, the higher it is, the better its quality. For a double precision image, which has pixel values between zero and one, the \(PSNR\) is calculated according to Equation (13) [20].  
\[PSNR(x,y)=20\cdot log_{10}(\frac{1}{\sqrt{MSE}}) \tag{13}\]  
The \(RMSE\) is a reference metric with clear physical meaning and easy implementation, obtained according to Equation (14) [7]:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}[u-v(x_{j})]^{2}} \tag{14}\]  
\(SSIM\) measures the similarity between two images and assesses visual quality based on the degradation of structural information. It is calculated between two images, \(x\) and \(y\), and given by Equation (15) [20].  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+\epsilon_{1})(2\sigma_{xy}+\epsilon_{2})}{( \mu_{x}+\mu_{x}+\epsilon_{1})(\sigma_{x}+\sigma_{y}+\epsilon_{2})} \tag{15}\]