esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 3 Architecture of QKD Network Enabled by SDN
### QKD Layer  
As an additional layer, QKD layer can be implemented as a separate key distribution subsystem. This layer is at the bottom of the architecture concerning with the performances of QKD devices. QKD resources in this layer include WDM links and QKD nodes, which can be used to complete point-to-point QKD and end-to-end QKD respectively. For the QKD nodes, major devices include quantum transmitter (QT), quantum receiver (QR), quantum key pool (QKP) and trust repeater (TR). Among them, QT is used for the preparation of quantum signals according to different QKD protocols such as BB84 protocol. QR is responsible for the detection of quantum signals at receiver ends, and performs quantum state decoding and single-photon detection according to QKD protocol. QKP is  
Figure 3: The architecture of QKD network enabled by SDN.  
used for the storage of secret keys between any two adjacent nodes to satisfy the security demands, and each node has QKP. For the repeaters, there are two types, i.e., quantum repeater [25] and TR [26]. Both of them can be used for secure communication in long distance, since end-to-end secret keys can be relayed by multi-hops point-to-point QKD through quantum repeaters. The former has a better relay ability for quantum signals but it is still in development level, and it is depending on the underlying technologies. The latter has been adopted in actual networks and it can be gradually updated to the former [27]. Thus, TR is generally considered in networking studies. Moreover, process of QKD allows two QKD nodes to exchange secure keys via three types of channels, which are quantum channels (i.e., QC, placed at approximately 1510 nm [28]), measurement-basis channels (i.e., MC, placed at approximately 1530 nm [29]) and data channels (i.e., DC, placed at approximately 1530 nm [29]). These channels can be multiplexed in a fiber by WDM technology [30; 31] (e.g., a quantum encryption system can achieve Mb/s key rates with a bandwidth of 200 Gb/s over a 100-km fiber [14]), which imposes a condition that the quantum channel needs to have a guard bandwidth with the other channels of at least 100 GHz. In addition, the interaction of classical information is also required during the above processes according to QKD protocols such as BB84 protocol [32] and two-dimensional distributed phase-reference protocol [33]. Taking BB84 protocol as an example, the processes required by classical information interaction include the comparison of basic measurement and error correction, etc.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 2 Materials and Methods
### Biochemical Analysis  
#### 2.4.1 ELISA  
Sample Preparation  
In order to carry out the IF analyses by ELISA, the samples were first subjected to the treatment described below. Thereby, 1 g of mortar-crushed soy-based material was dispersed into 50 mL of water by two-step stirring at room temperature for 20 min, and then at 100 \({}^{\circ}\)C for 10 min. After cooling down to room temperature, 500 \(\upmu\)L samples were collected in triplicate under stirring to insure homogeneity. Washing- and drenching-water samples were also treated as 500 \(\upmu\)L triplicates. Two millilitres of acetate buffer (sodium acetate 0.1 M; EDTA 0.14 M; 100 UI/mL penicillin G (Sigma, P-3032); and 0.1 mg/mL streptomycin (Sigma, S-6501)) at pH 5 were added to each sample vial, together with 10 \(\upmu\)L of \(\upbeta\)-glucuronidase aryl-sulfatase from _Helix pomatia_ (Roche\({}^{\circledR}\), 10127698001, Mannheim, Germany) to allow the digestion of glycosylated IFs. The samples were then incubated overnight at 37 \({}^{\circ}\)C under shaking [36]. Afterwards, the extraction of aglycone compounds was performed: 3 mL of acidified ethyl-acetate (500 \(\upmu\)L HCl 38% per L) was added; the vial vortexed for 30 s, centrifuged at 500 g (JouanTM CR3, Fisher Scientific, Illkirch, France) for 10 min at 4 \({}^{\circ}\)C and then stored at \(-\)22 \({}^{\circ}\)C to allow phase separation. The organic phase containing the IFs in aglycone forms was evaporated to dryness using a Speed-Vac (Thermo-ElectronTM Corporation, Fisher Scientific, Bordeaux, France). Afterwards, each sample was diluted in 0.5 mL of assay buffer, i.e., phosphate-buffered saline (PBS) 0.01 M, 0.9% NaCl, 0.2% Tween, 1% DMSO, pH 7.3, and sonicated when required. Samples were stored at \(-\)22 \({}^{\circ}\)C until they were processed for IF analysis.  
To assess the digestion and extraction recovery, the hydrolysis by \(\upbeta\)-glucuronidase aryl-sulfatase was monitored using genistien (EXTRASYNTHESET\({}^{\text{TM}}\), 1325 S, Genay, France), a pure compound used as a control reagent, in an external standard run in parallel to each measured sample. The compound was dissolved at 1 mg/mL in DMSO as the stock solution. In these operating conditions, the hydrolysis performance was always between 87 and 103%. Recoveries greater than 100% can be explained by the high accuracy of ELISA measurements and inter- and intra-assay variations.
# The Impact of Effective Microorganisms on Flesh Color and Chemical Composition of Raw Potato Tubers
## 1 Introduction
Observations of EMs in natural ecosystems laid the foundations for the so-called \"EM biotechnology\". The use of EM farming biotechnology in food processing ensures higher standards while reducing the burden of work, which leads to a significant reduction in production costs [13]. Moreover, EM cultures have high antioxidant potential. They are used in food  
Figure 1: Colors in the CIEL Lab system. Source: Own study based on Marszalek [5]. processing for a variety of reasons, e.g., to produce pectinases used for the clarification of fruit juices or proteinases used in the processing of milk. EMs can also help process potatoes into an enriched product; hence, research has been undertaken on using EMs in the production of potatoes for food processing into refined products. This study sought to develop an innovative technology for potato cultivation useful for food processing, introducing EMs and concurrently limiting the usage of agrichemicals (mineral fertilizers and plant protection products). The demand for safe food has been growing in Europe in recent times, leading to the search for substances (organic preparations) that could replace or partially eliminate pesticides used in plant protection. This quest is necessary because there has been a significant decrease in the number of active substances authorized for use in agriculture every year in the EU. Methods of obtaining stable potato yields, free from pesticide residues, are also being sought; this type of research is important for the end-user, the consumer, and the processing industry. This study investigated the impact of the management of EM practices in potato cultivation on the nutritional value of tubers in terms of the tendency of raw tubers to darken. Moreover, the objective of the study was to assess potato tuber suitability for consumption and processing purposes. To this end, the influence of microbiological preparations on the genotypes and enzymatic darkening of potato tubers was determined. In addition, the study aimed to find the cultivar most suitable for food processing for refined products (French fries, chips, crisps, dumplings, etc.). The research hypothesis assumed that as a result of the action of microbiological preparations (EM) used in potato cultivation, the chemical composition of tubers would improve, which would affect the accumulation of dry matter, starch, total sugars, reducing sugars, saccharose, of biologically active compounds, and thus the antioxidant properties of potato tubers, brightening the flesh of raw tubers, which would allow them to be used in the processing technology of refined products, contrary to the null hypothesis that EMs do not significantly affect the chemical composition of potato tubers or modify the color of raw tuber flesh.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 2 Display to Camera Communication System
### Encoding  
Before encoding, the input binary data \(\mathbf{b}\in\left\{0,1\right\}^{m}\) are first channel-coded and then mapped to the embedding symbols \(\mathbf{S}\) of length \(n\). Subsequently, the data bits are mapped to two symbol levels, which are represented as:  
\[\mathbf{S}=\!\left\{\begin{array}{c}-1,\;\forall\;\mathbf{b}\;:\;\mathbf{b}= 0\\ 1,\;\forall\;\mathbf{b}\;:\;\mathbf{b}=1\end{array}\right.. \tag{2}\]  
Utilizing both \(\mathbf{X_{F}}\) and \(\mathbf{S}\) from Equations (1) and (2), we applied three different spectral data-embedding mechanisms: M1, additive; M2, multiplicative; and M3, exponential. They are represented by the equations below:  
\[\mathbf{X_{E}}=\mathbf{X_{F}}+\alpha\mathbf{S} \tag{3}\]  
\[\mathbf{X_{E}}=\mathbf{X_{F}}(1+\alpha\mathbf{S}) \tag{4}\]  
\[\mathbf{X_{E}}=\mathbf{X_{F}}\times\mathbf{e^{\alpha\mathbf{S}}} \tag{5}\]  
where \(\mathbf{X_{F}}\in\mathrm{R}^{1\times m}\) is the MF coefficient vector selected for embedding, as shown in Equation (1). \(\mathbf{X_{E}}\in\mathrm{R}^{1\times m}\) is the coefficient vector after data embedding, and \(\alpha\) is a predetermined embedding factor that affects the visual quality of the data embedded image. More generally, \(\alpha\) can be viewed as a relative measure of the amount of adjustment needed to alter the visual quality of the image. We denote the three different mechanisms as follows: M1, additive embedding; M2, multiplicative embedding; and M3, exponential embedding. M1 is the most general type of data-embedding technique and is widely used for digital image watermarking and steganography. In such cases, the coefficient vector is directly added to the product of \(\alpha\) and S. M2 is a special case of M1, where \(\alpha=\alpha\mathbf{X_{F}}\), and M3 can be expressed as \(\log(\mathbf{X_{E}})=\log(\mathbf{X_{F}})+\alpha\mathbf{S}\). This is equivalent to M1 but uses the logarithms of the original values of the coefficient vectors.  
As the mathematical forms of data-embedding mechanisms differ, their values of \(\alpha\) also differ. For M1, a large \(\alpha\) is preferred for encoding, whereas M2 and M3 use comparatively smaller values. This is because \(\alpha\) variation for M1 minimally impacts the embedding results but significantly affects the results of M2 and M3.  
Figure 4 presents the data-embedded images obtained using the three different mechanisms. Figure 4a is the original image, and Figure 4b is the data-embedded image using M1 with a relatively large value of \(\alpha\). Figure 4c,d are data-embedded images using M2 and M3, which are respectively encoded with small values of \(\alpha\). Although their PSNR values differ, encoded images do not contain any obvious artifact when viewed by the normal human eyes. Thus, the proposed data-embedding method of a D2C system can transmit data while maintaining the original purpose of the display.
## 2 Materials and Methods
### High-Speed Flame Tomography  
Using the same HS-PIV setup, we have been able to optimize such that the burned gas region can also be identified and distinguished from the unburned region in the PIV images, which allows for flame front detection. Under combustion condition, the burned gas density and temperature will be drastically different to those of the unburned gas. The high temperature of the stoichiometric flame front can even lead to changes in the morphology of these solid seeds despite their resiliency to harsh environment. Moreover, the interaction between the PIV laser and combustion products and luminosity can also affect the laser signal intensity. As a result, these changes will be reflected in the seeding densities and scattering PIV laser intensities. Following these logics, the burned region should appear different to the unburned region. Hence, it is the gradient in intensity created that can be used for detection and identification of the burned region and hence, the flame front. To maximize the intensity gradient differences and avoid unwanted interferences from flame luminosity, a 527 nm bandpass-filter was placed in front of the camera. This pseudo flame tomography imaging was carried out at the same time as HS-PIV diagnostics under the same capturing procedures and operating condition.  
Therefore, such HS-PIV setup allows for the simultaneous measurement of both flow-field and flame-front. The characteristics of both elements can be derived from the same captured HS-PIV image, maximizing the temporal accuracy when discussing the simultaneous relationship between flow-field and flame.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 2 Analysis of Interference Mechanisms  
### Analysis of 5G Signals  
In order to improve spectrum utilization and enhance signal resistance to multipath fading, 5G signals employ orthogonal frequency-division multiplexing (OFDM) techniques. OFDM signals achieve frequency-division multiplexing by partitioning the available spectrum into multiple mutually orthogonal subcarriers. The orthogonal nature of these subcarriers ensures that they do not interfere with one another. In fact, 5G signals are specific configurations of OFDM signals. They are created by modulating information onto multiple orthogonal subcarriers. Assume that there are N sub-channels in an OFDM signal and that the subcarriers used in each sub-channel are defined as [17]  
\[x_{n}(t)=a_{n}(t)cos[2\pi f_{n}t+\theta_{n}],n=1,2\cdot\cdots\cdot,N. \tag{1}\]  
Here, \(a_{n}(t)\) is the amplitude of the nth subcarrier, modulated by the baseband codeword element; \(f_{n}\) is the frequency of the nth subcarrier; and \(\theta_{n}\) is the initial phase of the nth subcarrier. The OFDM signal is  
\[x_{OFDM}(t)=\sum_{n=1}^{N}x_{n}(t)=\sum_{n=1}^{N}a_{n}(t)cos[2\pi f_{n}t+\theta _{n}] \tag{2}\]  
In the code element duration \(T_{s}\), the minimum subcarrier spacing satisfies \(\Delta f_{min}=1/T_{s}\), so any two subcarriers of the OFDM signal remain orthogonal. In Equation (2), the OFDM signal is represented as a superposition of \(N\) orthogonal subcarrier signals in the time domain. Consequently, when investigating the blocking interference effects of 5G signalson RA receivers, the 5G interference signal can be effectively modeled as a composite OFDM signal composed of multiple orthogonal subcarriers.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 3 Single-Cell Manipulation Control  
### Dynamic Model Analysis  
Since there is a certain distance between the center of the optical trap and the cell center, the cell may overflow during the operation of holographic optical tweezers, so the distance between the center of the optical trap and the cell center should be limited to a certain range \(r_{0}\). In this paper, the position of the center of the optical trap and the center of the cell is described by the two-dimensional plane coordinate system, and the X and Y direction parameters are limited. Therefore, \(||\mathbf{C}-\mathbf{X}||\leq r_{0}\) can be disassembled and expressed as:  
\[\begin{cases}||\mathbf{C_{x}}-\mathbf{X_{x}}||\leq\frac{\sqrt{2}}{2}r_{0}\\ ||\mathbf{C_{y}}-\mathbf{X_{y}}||\leq\frac{\sqrt{2}}{2}r_{0}\end{cases} \tag{6}\]  
where \(\mathbf{X}=\left[\mathbf{X_{x}},\mathbf{X_{y}}\right]^{T}\in R^{2\times 1}\), \(\mathbf{X_{x}}\) represents the position of the cell center in the X direction; \(\mathbf{X_{y}}\) represents the position of the cell center in the Y direction. \(\mathbf{C}=\left[\mathbf{C_{x}},\mathbf{C_{y}}\right]^{T}\in R^{2\times 1}\), \(\mathbf{C_{x}}\) represents the position of the optical trap center in the X direction; \(\mathbf{C_{y}}\) represents the position of the optical trap center in the Y direction;  
Substituting (6) into (5), the following can be obtained:  
\[\frac{\beta}{k}\dot{\mathbf{X}}=(\mathbf{C}-\mathbf{X}),\begin{cases}||\mathbf{C_{x}}-\mathbf{X_{ x}}||\leq\frac{\sqrt{2}}{2}r_{0}\\ ||\mathbf{C_{y}}-\mathbf{X_{y}}||\leq\frac{\sqrt{2}}{2}r_{0}\end{cases} \tag{7}\]  
Figure 2: The relation between the optical trap force and \(\frac{r_{0}}{r_{1}}\)[43].  
where \(\beta/k\) is the environmental coefficient of the cell solution. The value \(\beta/k\) of holographic optical tweezers operating yeast cells at different speeds is shown in Table 1:
## 4 Discussion
### Study Assumptions and Potential Limitations
The last assumption is that our sample was representative of the conditions of the entire urban and exurban areas. We had a very large sample, so this is reasonable to assume. However, the areas of no data predictions could be biased due to some uniform trait and therefor underrepresented. For example, the proportion of the sample remaining toward the center of the urban and exurban areas was similar for the informal and 95% selection criteria. However, for the 90% selection criteria the central areas were disproportionally under represented (Table 6). Previous research [21] has shown a relationship between the UHI and distance to urban core area and should be considered. In addition, selecting Landsat images with less than 10% cloud cover as an initial screening process could potentially omit additional dates of clear Landsat images for the study area, thus reducing the number of base pairs used. Based on previous research [59], the risk of omitting these scenes potentially leads to a less than optimal quality of fused image since there could have been a potential base date nearer to some of our predicted dates than what we used for prediction. Adding to this, using a visual inspection to remove Landsat images could introduce human error. \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Parameter** & **Total Sample Size** & \
\begin{tabular}{c} **Percent Remaining at** \\ **500 m Inward** \\ \end{tabular} &
\begin{tabular}{c} **Percent Remaining at** \\ **1500 m Inward** \\ \end{tabular} \\ \hline \multirow{2}{*}{**Informal Selection**} & SOS & 1,241,726 & 56 & 12 \\  & EOS & 1,681,156 & 56 & 13 \\ \hline \multirow{2}{*}{**\textgreater{}95\% Selection**} & SOS & 1,263,499 & 56 & 13 \\  & EOS & 1,632,057 & 57 & 13 \\ \hline \multirow{2}{*}{**\textgreater{}90\% Selection**} & SOS & 511,615 & 45 & 5 \\  & EOS & 634,573 & 44 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total original sample size for all developed areas based on base pair selection and the percent of the original sample remaining 500 m and 1500 m inward away from the urban and exurban edges.
## 1 Introduction
The third method employs a novel categorization method based on input-output correlation, wherein patients are categorized according to the correlation of inputs (effect-site concentration of Propofol CeProp (\(\mathrm{\SIUnitSymbolMicro m}\)/mL) and effect-site concentration of Remifentanil CeRemi (\(\mathrm{\SIUnitSymbolMicro m}\)/mL)) with the output MAP (\(\mathrm{\SIUnitSymbolMicro m}\)/Hz), and a specific architecture is identified for each category. Central to our study is to investigate the possible balance between computational efficiency and estimation accuracy across these diverse methodologies. This study introduces a novel method that compares 288 different architectures to determine the optimal model for each patient and category. All 70 patients were subject to routine monitoring during TIVA, and they were sedated using Propofol. Additionally, Remifentanil was administered through target-controlled infusion (TCI) to manage supplementary analgesia. We calculated CeProp (the effect site concentration of Propofol) and CeRemi (the effect site concentration of Remifentanil), using the Schnider and Minto pharmacokinetic models, respectively [15; 16]. Through this multifaceted approach, we simultaneously enhance accuracy, computational efficiency, and the applicability of LSTM models in the context of physiological parameter prediction in anesthesia. This paper is organized as follows: Section 2 presents the description of the clinical trial protocol. Section 2 also provides an in-depth exploration of the methodology, focusing on the regression employing LSTM networks and the critical parameters influencing the performance of the model. Section 3 presents the results obtained from three distinct methods, population-specific, patient-specific, and category-specific, comparing their performance based on key metrics. The discussion in Section 4 critically examines the implications of our findings, the novelty of the paper, and its limitations. Finally, Section 5 offers concluding remarks and highlights the future aspects of research in this field.
## 2 Materials and Methods
### Additive Secret Sharing Theory  
The additive secret sharing theory provides a theoretical foundation for us to design the secure functions to process encrypted data shares from a vehicle in a vePOD object-detection network.  
The concept of secret sharing was originally proposed to protect key security [17]. Informally, secret sharing works as follows. A trusted party divides a secret into \(N\) shares, which are kept by \(N\) participants. No less than \(k\) (\(k\leq N\)) participants can reconstruct the secret. The major operations on secret shares are _divide_ and _assemble_, as expressed in Equation (1).  
\[\begin{array}{l}D(\overrightarrow{s})=(\overrightarrow{s}_{1}, \overrightarrow{s}_{2}\ldots\overrightarrow{s}_{n}),\ \overrightarrow{s}\in V\\ A(\overrightarrow{s}_{1},\overrightarrow{s}_{2}\ldots\overrightarrow{s}_{n} )=\overrightarrow{s}\end{array} \tag{1}\]  
The additive secret sharing theory [18] employs additional constrains, that is, (1) secret shares maintain an additive relation; i.e., \(s=\sum_{n=1}^{k}s_{n}\); and (2) \(k=N\); i.e., a secret \(s\) can be rebuilt only when all the secret shares are collected. Additive secure sharing enables secure operations on cipher text. For example, secrets \(t\) and \(s\) are divided into \(\left(t_{1},t_{2},\ldots,t_{n}\right)\) and \(\left(s_{1},s_{2},\ldots,s_{n}\right)\) among participants; then, the sum of secrets can be calculated without revealing their values; i.e., addition can be performed on secret shares separately, which can then be added up.  
Based on the additive secret sharing theory, we develop three secure protocols for linear operations that are used in vePOD, i.e., secure addition (SecAdd), secure subtraction (SecSub), and secure scalar multiplication (SecSMul). An input array \((u,v)\) to SecAdd is split into \((u_{1},v_{1})\) and \((u_{2},v_{2})\), and sent to edge servers \(S_{1}\) and \(S_{2}\), respectively. Independently, \(S_{1}\) calculates \(f_{1}=u_{1}+v_{1}\) and \(S_{2}\) calculates \(f_{2}=u_{2}+v_{2}\). The addition of \(f_{1}\) and \(f_{2}\) produces the same result as the original addition; i.e., \(f_{1}+f_{2}=u+v\). Similarly, an input array \((p,q)\) to SecSMul is divided into \((p_{1},q)\) and \((p_{2},q)\); edge servers \(S_{1}\) and \(S_{2}\) compute \(f_{1}=q*p_{1}\) and \(f_{2}=q*p_{2}\), respectively. We have \(f_{1}+f_{2}=p*q\).