esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 2 Results and Discussion
### In-Field Volatile Sampling
For example, 3-methyl-1-butanol and phenylethyl alcohol were reported as key volatiles for noble rot and grey mould discrimination [30]; and 3-octanol, phenylethyl alcohol, and 1,5-dimethylnaphthalene were regarded as key biomarkers for the detection and prediction of _B. cinerea_ infection in grapes [5]. The detection of hexenal and 3-octanone was significantly different (Supplementary Table S2) between healthy (LFD signal intensity 12-14) and infected bunches (LFD signal intensity, 37-97), with \(p\)-values of 0.080 and 0.034, respectively. Figure 3: In-field sampling results of volatiles detected from _B. cinerea_ infected _Chardonay_ and _Riesling_, using ZIF-8 and Tenax®-TA as sorbent materials with thermal desorption GC-MS. The box represents lower and upper quartile; median represents the mid-point of the data; the red dot represents the mean; the upper and lower whiskers represent scores outside the middle 50%; the open circles represent outliers of 1.5 \(\times\) interquartile range (IQR) and the closed circles represent extreme outliers greater than 3 \(\times\) IQR. Compared to the sampling in controlled conditions (Figure 2, left), relatively weak correlations were observed between the LFD scores and the detection of 3-methyl-1-butanol, 1,5-dimethyltetralin, phenylethyl alcohol, and 1,5-dimethylhaphthalene from in-field sampling (Supplementary Table S4). The apparent difference between sampling strategies is likely to be related to the uncontrolled environmental conditions encountered during the in-field sampling of grape bunches. Whilst every effort was made to position extraction tubes closely and evenly to grape bunches, changing conditions during the day will alter the relative volatility of the target molecules. Variances in sample-extraction efficacy with controlled environments are corrected through the use of the IS [38]; however, their incorporation into a suite of target compounds is not possible in the field. Key volatile metabolites were able to be extracted from in-field sampling using ZIF-8 as absorbent materials, thus demonstrating a proof of concept for in-field sampling for the detection of _Botrytis cinerea_ infection. The use of a hermetically sealed controlled environment such as a glass container to eliminate air movement and maintain standard sampling conditions enabled a higher level of sensitivity for some volatiles to be achieved for ZIF8 compared to Tenax(r)-TA.
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 2 Model for Ultrafast Transmission Measurements
### Computational Details  
We describe here the way of solving the set of coupled equations numerically. For the discretization of Equation (6), we employed the forward differentiation, as the equations can be solved numerically following the dynamics of the pulse, using discretized grids with \(\Delta t=0.4\) as and \(\Delta z=1.2\) nm, which satisfy the _Courant condition_,  
\[c\leq\frac{\Delta z}{\Delta t}, \tag{7}\]  
so that the _Upward differencing_' method [17] can be applied safely to Equation (6):  
\[\frac{I(z_{j},t_{n+1})-I(z_{j},t_{n})}{c\Delta t}=-\frac{I(z_{j},t_{n})-I(z_{j -1},t_{n})}{\Delta z}+g(z_{j},t_{n})I(z_{j},t_{n}). \tag{8}\]  
In Equation (8), \(j\) runs along the incident direction of the FEL pulse inside the sample and \(n\) represents the discrete time grid.  
In the rate Equation (5), we just used the _Euler_ method; thus, for example, we obtain the following expression for \(N_{2}\):\[\frac{N_{2}(z_{j},t_{n+1})-N_{2}(z_{j},t_{n})}{\Delta t}=-\frac{g(z_{j},t_{n})I(z_ {j},t_{n})}{h\
u}-\left[\frac{1}{\tau_{21}}+\frac{1}{\tau_{23}}\right]N_{2}(z_{j },t_{n}). \tag{9}\]  
We remark that for actual calculations concerning intense ultrafast pulses and nano-sized samples we need extremely small \(\Delta t=0.4\) and \(\Delta z=1.2\) nm, so that efficient algorithms like those mentioned are needed for best accuracy and lower computing times.  
For the initial pulse intensity \(I_{0}\), we assumed a Gaussian time distribution:  
\[I_{0}(z,t)=I_{0}^{\max}\,e^{\frac{(z-(t+y_{0}))^{2}}{2(\sigma_{t})^{2}}}, \tag{10}\]  
where \(\mu_{0}\) and \(\sigma_{t}\) are the mean and standard deviation of the photon pulse time distribution. \(I_{0}^{\max}\) corresponds to the maximum intensity of initial beam. For a given pulse shape, \(\sigma_{t}\) is estimated looking from the full-width at half maximum FWHM= \(2\sqrt{2\ln 2}\,\sigma_{t}\). The window of the pulse in time is set at just double of FWHM (namely 30 fs), multiplied by the speed of light. The origin of \(z\) is set at the surface of a sample. The three-channel model is implemented in a Fortran 90 code using simple instructions and running on any current operating systems and with any Fortran compiler.
## 6 IoT Encapsulation Network Layer Standards
### IPv6 over Bluetooth Low Energy (BLE)  
IPv6 provides larger pools of addresses. Local areas nowadays use WLAN that in the majority comprise Wi-Fi-based connectivity. Though the technology is cheap, it consumes much battery power and abruptly drains power. On the other hand, Bluetooth technology gives almost the same range with sufficiently less power drainage [36]. This allows the local NAT fields to be occupied with Bluetooth connectivity rather than using Wi-Fi. Body area networks (BANs) majorly consist of sensing nodes that require less bandwidth and throughput. IPv6 over BLE is a smart choice for such low data-rate transmissions. Bluetooth watches, gears, computer accessories, multimedia, and maybe industry-grade equipment use IPv6-powered BLE. IPv6 enables each node to directly talk to the internet.  
The following are the issues that need to be addressed before bringing this technology to the consumer level.  
* Bluetooth is available in almost every handheld smartphone. This makes the Bluetooth-based network relatively less secure and more vulnerable.
* Only enterprise routers are enabled with low-power Bluetooth connectivity and bringing IoT into use would need more plugging of Bluetooth hubs into our normal 802.11 routers.
* IoT mainly integrates using multi-hop topologies, and thus, every device would be served as a repeater to transfer its data to the main BLE hub, which is a software-based problem. This problem must be finalized before implementing BLE for dense networks to avoid battery drainage and expand the physical access range.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 3 Brief Description of ITU-T G.9904 (PRIME) Physical Layer  
The PLC communication system described in ITU-T G.9904 [15] (also known as PRIME) is based on an Orthogonal Frequency Division Multiplexing (OFDM) physical layer.  
The block diagrams of the physical layer transmitter and receiver form a classical OFDM chain, as depicted in Figures 1 and 2.  
The most important parameters of the OFDM modulation are summarized in Table 1. For more detailed information about the ITU-T G.9904 standard, refer to the specifications in [15; 16; 17].  
Figure 1: PRIME PHY layer transmission block diagram.  
Figure 2: PRIME PHY layer reception block diagram.
## 1 Introduction
In recent years, biotechnological exploits such as expression vectors have been thoroughly implemented and used across different fields in both industry and research applications, ranging from enabling protein expression in target organisms, to the creation of transgenic organisms [34; 35]. Modified bacteria like some _Escherichia coli_ strains possess plasmids containing fluorescent reporters like red fluorescent protein (mRFP) and are of great use as they can serve as easily distinguishable colorimetric bioindicators for environmental contaminants, which is the case of leftover antibiotics from both pharmaceutical and agricultural applications [36]. Despite the success expression vectors have had in other areas, they have not yet been employed in the control of arboviral diseases and, more specifically, in the differentiation of public health-relevant mosquito species, as is the case of _Ae. albopictus_ and _Ae. aegypti_. _Lysinibacillus sphaericus_ is a Gram-positive, mesophilic, rod-shaped bacterium commonly found on soil [37]. It can form resistant endospores that are tolerant to high temperatures, chemicals, ultraviolet light, and can remain viable for long periods of time [37; 38]. It has shown extensive potential as an insect pathogen and a biocontroller, mostly because of the Binary (_BinA/BinB_) toxins [37], and previous studies have shown the specificity and little collateral damage caused by the implementation of _L. sphaericus_ as a mosquito biocontrol on ecosystems and their dynamics [39]. Extensive research has been conducted on the entomogenic properties of the binary toxins present in the parasporal crystal [40; 41; 42], concluding that they are generally refractory to _Aedes_ mosquitoes [43; 44]. However, _L. sphaericus_ has other mechanisms of toxicity against mosquitoes, namely the S-Layer protein [45; 46; 47] and the Mosquitotoidal (_Mtx 1/2/3_) toxins present in the vegetative cell [48]. In the past, assessments with _Mtx_ toxins, S-Layer preparations and vegetative _L. sphaericus_ cells have been tested against _Ae. aegypti_ larvae [40; 42; 47], but only one report exists on the S-Layer's entomopathogenic action against the novel invader species _Ae. albopictus_[45]. In this study, vegetative cells of _L. sphaericus_ III(3)7, and 2362 (WHO reference) were assessed against Rockefeller reference _Ae. aegypti_ and Colombian reference _Ae. albopictus_ strains in order to assess the effectiveness of the bacterial biocontrol on the novel arboviral vector _Ae. albopictus_. In addition, the study aimed to determine the potential of two _L. sphaericus_ strains in the control of the aforementioned _Aedes_ species present in the country in both single and mixed species nurseries, employing an mRFP-expressing _Escherichia coli_ strain as a differentiator between species.
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Similarity Measure and Case Retrieval  
An important step in CBR is to retrieve similar cases. Case retrieval depends on an accurate assessment of its similarity to the target problem. This process involves the following two key factors: (1) Similarity measure, where the similarity between cases is an important basis for retrieving similar cases; (2) Retrieval method, where choosing the appropriate retrieval method can improve retrieval speed and accuracy.
## 2 Theoretical Background  
The implementation of Reduced-Order Models (ROM) in computational analysis follows a structured and systematic workflow. The process begins with the simulation of a full-order baseline model, which serves as a reference case. Key geometrical and physical parameters influencing system behavior are identified and subsequently parametrized to enable the generation of a _Design of Experiments_ (DoE), which spans a representative set of design points. The resulting dataset forms the basis for ROM construction.  
ROMs provide efficient yet accurate representations of system behavior across the parameter space, offering substantial advantages for design optimization and real-time analysis. In parallel, _Response Surface Methodology_ (RSM) is employed to construct statistical surrogate models relating input parameters to output responses, enabling rapid, single-point optimization. While RSM focuses on localized parameter tuning, ROMs capture the global system response over a broader range of operating conditions.  
A critical requirement for ROM generation is the preservation of mesh topology across all analyzed configurations. To satisfy this condition, mesh morphing techniques are applied to vary the geometry while maintaining the underlying connectivity of nodes and elements. In this work, Radial Basis Functions (RBFs) are used to interpolate nodal displacements, ensuring smooth shape updates without requiring complete mesh regeneration.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 4 PUF-Based Key Distribution for PQC
### PUF-Based Key Distribution for Code-Based Cryptography  
An example of a protocol to generate the key pairs with PUFs for code-based cryptography is shown in Figure 9. The overall protocol is similar to the one presented above for lattice cryptography. Much like NTRU, the only source of randomness is Seed K that is generated from the PUF to compute the two matrixes \(\mathbf{S_{(i)}}\) and \(\mathbf{P_{(i)}}\). The brief outline of the protocol for generating key pairs for code-based cryptography is the following:  
1. The CA uses random numbers to point at a set of addresses in the image of the **PUF-i**.
2. From these addresses, a stream of bits called Seed K' is generated by the CA.
3. The CA sent the handshake to the client (i) to find the same addresses.
4. Client (i) uses the PUF to generate Seed K.
5. Client (i) applies ECC on Seed K and generates the matrixes \(\mathbf{S_{(i)}}\) and \(\mathbf{P_{(i)}}\).
6. Computation of \(\mathbf{S_{(i)}}^{-1}\) and \(\mathbf{P_{(i)}}^{-1}\).
7. The public key \(\mathbf{Pk_{(i)}}=\hat{G}_{(i)}\) is computed with the generator matrix \(\mathbf{G}\): \(\hat{G}_{(i)}\leftarrow\mathbf{S_{(i)}}\cdot\mathbf{G}\cdot\mathbf{P_{(i)}}\).
8. The private key \(\mathbf{Sk_{(i)}}\) is \(\{\mathbf{G};\mathbf{S_{(i)}}^{-1},\mathbf{P_{(i)}}^{-1}\}\).
9. Client (i) communicates to the CA, through the network, the public key \(\hat{G}_{(i)}\).
10. The CA uses a search engine to verify that \(\hat{G}_{(i)}\) is correct.
11. If the validation is positive, the public key \(\hat{G}_{(i)}\) is posted online by the RA.  
Step six, the computing of inverses, does not work for all matrixes: request for new handshakes, or shared ways to find invertible matrixes and iterate.  
Figure 9: PUF-based key pair generation for code-based cryptography. The two matrixes \(\mathbf{S_{(i)}}\) and \(\mathbf{P_{(i)}}\) are generated from the Seed K that is extracted from the PUF. As was done in the previously presented PQC schemes, the search engine of the CA has access to an image of the PUF and can independently verify the validity of the public key.
### Wavelet Transformation
#### 1.1.3 Discrete Wavelet Transform (DWT)  
Discrete wavelet transform (DWT) is the discretized scaled and translated version of the mother wavelet \(\Psi(t)\). DWT is given by the following equation:  
\[X(a,b,t)=\sum\
olimits_{m=0}^{p-1}x(t_{m})\cdot\frac{1}{\sqrt{|a|}}\Psi\bigg{(} \frac{t_{m}-b}{a}\bigg{)} \tag{3}\]  
In CWT, the integral is replaced with a summation as discrete values are considered. In DWT, the discrete values are represented as \(a=k2^{j}\) and \(b=2^{j}\), where the dyadic power improves the efficiency of the analysis. DWT is used in multi-resolution analysis for tasks like signal denoising and signal compression [27; 28].
## 4 Experiments and Results
### Experimental Setting
#### 4.1.2 Experimental Environments  
Throughout our research, we conducted experiments using various compression methods for ship status data under the following configurations: The primary remote machine2 boasted robust specifications with an Intel Core i9-10900 CPU3, Nvidia GeForce RTX 3090 GPU4, and 128 GB of RAM; Operating on Ubuntu 20.04, we utilized Python 3.9, TensorFlow 2.5.0, tmux for prolonged program execution, and a Jupyter server hosted at a research institute under the Chinese Academy of Sciences. We accessed the remoted machine via SSH. After SSH connection, the Jupyter server on the remote machine linked to the local machine's 8888 port. We executed and debugged code using Microsoft Visual Studio Code5's Jupyter Notebook and Python. The high specifications of the remote machine expedited training and model exploration, typically requiring mere minutes for neural network model training. For practical implementation, less resource-intensive servers or cloud services could be employed to curtail costs during model training.
# Construction of Nonblocking Wavelength/Space Switches with AWGs and WSSes
## 4 Space-Wavelength Tradeoffs  
### Switch Size Constraint  
We first compare the constraints of these methods on the switch sizes. For given \(n\) and \(N\), the explicit formula for allowed \(r\) is shown below.  
1. AWG-based approach presented in this paper: The value of \(r\) can be any of \(k_{1}\times k_{2}\times\ldots\times k_{s}\) for \(k_{i}\in\{1,2,\ldots,N\}\), \(1\leq i\leq s\).
2. WSS-based approach: The value of \(r\) can be any of \(nk\) and \(n^{1/k}\) (provided \(r\) is a power of \(n\)) for any positive integer \(k\). Two examples are given below.  
**Case 1.**_We choose \(n\) = 90 and \(N\) = 32. As mentioned previously, the current largest commercially available AWG is 32 \(\times\) 32 [27]. Thus, we select \(N\) = 32._  
1. _AWG-based approach presented in this paper:_ \(r\) _can be any of {1, 2,..., 8,..., 32, 33, 34, 35,..., 63, 64, 65,... }._
2. _WSS-based approach:_ \(r\) = 90 \(k\) _for any positive integer_ \(k\).  
Figure 13: A WSS-based implementation with \(r\) = 3, \(n\) = 9. **(a)** Construct a variant of a three-stage C (3, 3, 9) network in the expanded space topology. **(b)** Decompose each 9 \(\times\) 9 (= \(n\times n\)) middle switch further into three stages. **(c)** The final architecture can be derived from **(b)**.  
**Case 2**.: _We choose \(n=64\) and \(N=32\)._  
_Possible switch sizes are listed below._  
1. _AWG-based approach presented in the paper:_  
_r can be any of [1, 2,..., 8,..., 32, 33, 34, 35,..., 63, 64, 65,... ]._
2. _WSS-based approach:_  
_The value of \(r\) can be any of 2, 4, 8, 64 k for any positive integer k._  
Comparing the AWG-based and the WSS-based approaches, we can see that the former is more flexible in this regard.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 2 Swarm Intelligence
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Convergence Speed** & **Scalability** & **Complexity** & **Suitability for Dynamic Environments** \\ \hline PSO & Fast in early stages; may stagnate at local optima & Moderate; performance decreases with increased dimensionality & Low computational complexity; easy to implement & Moderate; adaptable but may require modifications for real-time applications \\ \hline ACO & Moderate; effective in finding global optima & High; can handle large-scale problems but at the cost of increased computational time & High due to the need for maintaining pheromone trails & High; well-suited for dynamic and changing environments due to its distributed nature \\ \hline ABC & Moderate; depends on the neighborhood search strategy & High; can be scaled but with increased complexity & Moderate complexity; balancing exploration and exploitation can be challenging & Moderate to High; adaptable to dynamic environments but may require fine-tuning \\ \hline FA & Fast; good at avoiding local optima & High; can scale well with parallel implementation & Moderate; requires careful parameter tuning & Low to Moderate; sensitive to dynamic changes, requires adaptation strategies \\ \hline CS & Fast; effective in both global and local search & High; performs well on large-scale optimization problems & Moderate to high; requires proper tuning of parameters like step size & Low to Moderate; not inherently designed for dynamic environments but can be adapted \\ \hline BA & Fast; quick convergence due to echolocation and frequency adjustment & Moderate; performance can degrade with problem size & Moderate; involves complex mechanisms like frequency and loudness adjustments & Moderate; can adapt to dynamic environments with parameter tuning \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Swarm Intelligence algorithms. * Suitability for Dynamic Environments: Indicates how well the algorithm can adapt to changes in environmental or problem parameters. Algorithms well-suited for dynamic environments are typically more flexible and can adjust to changes without significant performance degradation.
# 2D Ultrasonic Antenna System for Imaging in Liquid Sodium
## 2 Imaging Principle of the 2-Antenna System  
### Description of the 2-Antenna System  
The 2-antenna system is made up of two identical linear-phased arrays disposed in a \"T\" arrangement (Figure 1a) which allows three-dimensional imaging. One antenna operates in emission (E; the red one) and the other in reception (R; the blue one). The surface of each element of the arrays is planar in the focusing plane and convex with a radius of curvature R in the orthogonal plane of the antenna (Figure 1a,b). The values of the antenna parameters are provided later.
# Numerical and Experimental Study of a Large-Scale Natural Circulation Helium Loop
## 3 Mathematical Model
### Pipe Model  
The one-dimensional fluid flow can be described by three governing equations--those of the conservation of mass (1), momentum (2), and energy (3). The equation system is closed by the real gas equation of state (4), where the coefficient of compressibility \(z\) is calculated according to the Soave-Redlich-Kwong real gas equation of state.  
Figure 1: Test facility.  
Figure 2: The base scheme of the helium loop STU.  
\[\frac{\partial(p/z)}{\partial t}+\frac{r\,T}{A}\,\frac{\partial Q_{m} }{\partial x} =0 \tag{1}\] \[\frac{\partial Q_{m}}{\partial t}+\frac{A\,g\,p}{z\,r\,T}\frac{ \mathrm{d}h}{\mathrm{d}x}+\frac{r\,T}{A}\frac{\partial(z\,Q_{m}^{2}/p)}{ \partial x}+A\frac{\partial p}{\partial x}+\lambda\frac{z\,r\,T}{2\,d\,A}\frac{ |Q_{m}|Q_{m}}{p} =0\] (2) \[\frac{\mathrm{d}q}{\mathrm{d}x}-\frac{k_{A}\,\pi\,D_{e}\,(T-T_{ ext})}{Q_{m}} =0\] (3) \[p =\rho\,z\,r\,T \tag{4}\]  
The signs used are the following: \(p\)--pressure, \(z\)--coefficient of compressibility, \(Q_{m}\)--mass flow rate, \(T\)--temperature, \(\rho\)--density \(A\)--area of the cross-section of a pipe, \(\lambda\)--Darcy-Weisbach head loss coefficient, \(r\)--specific gas constant, \(g\)--gravity acceleration, \(t\)--time, \(x\)--space coordinate, \(h\)--height, \(d\)--internal diameter of a pipe, \(c_{p}\)--specific heat capacity, \(i\)--specific enthalpy, and \(u\)--specific internal energy.  
The estimation of the Darcy-Weisbach head loss coefficient is based on an assumption of hydraulically smooth pipes. Then, the coefficients are evaluated according to the formulas of [PERSON] and [PERSON] (depending on the flow regime).  
The conservation of mass (1) and momentum (2) is defined according to a control volume approach, and the conservation of energy (3) is defined by using a particle-tracking approach. This concept has its roots in the assumption that temperature is transferred only via mass transfer. The mass transfer is calculated from the continuity (1) and momentum (2) equations, and the temperature information is carried by a fluid particle moving along the flow direction.