esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Qualitative Analysis  
Qualitative analysis has been based on comparing the mass spectrum of analytes peaks with the National Institute of Standards and Technology (NIST) library (NIST 14).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Harvest Area** & **Province** & **Altitude (m)** & **Harvest Time** & **Solar Radiation (M/m\({}^{2}\))** \\ \hline AL & Albarracin & Teruel & 1337 & September & 492 \\ CH & Chelva & Valencia & 984 & September & 484 \\ HU & Huétor & Granada & 1354 & November & 509 \\ LO\_S & Loarre & Huesca & 1401 & September & 491 \\ LO\_N & Loarre & Huesca & 1401 & November & 236 \\ LI & Lierta & Huesca & 590 & September & 491 \\ PI & Pina de & Castellón & 1278 & September & 483 \\  & Montalgrao & & 999 & September & 483 \\ TO & El Toro & Castellón & 999 & September & 483 \\ VE & Los Velez & Almería & 1369 & September & 536 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Origin of analysed samples.  
Moreover, Kovats Retention Index (KI) was calculated for each compound by using a 10 \(\upmu\)g/g standard mixture of \(n\)-alkanes (C\({}_{7}\)-C\({}_{40}\)) in hexane. External standards have not been used for qualitative analytes confirmation therefore the component identifications should be considered tentative.
## 3 Non-Thermal Technologies in Food Processing
### UV Radiation
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **Type** & **Process Parameters** & **Use** & **Effects** & **Reference** \\  & & **(Type/Power/Frequency/Intensity/Time)** & & & & \\ \hline Chicken broilers & US bath/25-130 kHz/28 W/L/5 to 30 min & \begin{tabular}{c} Tenderization \\ and decontamination \\ \end{tabular} & Reduction of about 40\% prechiller & [96] \\ \hline Beef & Probe/300 W/20 kHz/20-30 min & Curing & \begin{tabular}{c} Water-holding capacity and \\ tenderness are enhanced. \\ \end{tabular} & [97] \\ \hline Beef m. Longissimus & US Bath/40 kHz/11 Wcm\({}^{-2}\)/60 min & \begin{tabular}{c} Reduction in red color and \\ increased pH, luminosity, size of \\ inside, and greater \\ interfibrillar space. \\ \end{tabular} & [98] \\ \hline Beef semitendinous muscle & Water bath/1500 W/40 kHz/10-60 min & \begin{tabular}{c} Post-US \\ process modifications \\ \end{tabular} & \begin{tabular}{c} US treatment deteriorates the \\ stability of collagen and meat \\ textural properties. \\ \end{tabular} & [99] \\ \hline Squid & Water bath/186.9 W/25.6 kHz/30.8 min & \begin{tabular}{c} Tenderization \\ degradation create a softer meat. \\ \end{tabular} & \begin{tabular}{c} Increased water solubility and \\ in vitro starch digestibilityillity, \\ decreased gelatination \\ temperatures, enthalpy changes in \\ viscosity, splitting capacity, \\ antioxidant activity, and total \\ phenolic content. \\ \end{tabular} & [100] \\ \hline \begin{tabular}{c} Cerenal Products \\ \hline Dehulled rice flour \\ \end{tabular} & Water bath 2000 W/25 kHz/16 W/L/ 5 min & \begin{tabular}{c} Post-US \\ process modifications \\ \end{tabular} & \begin{tabular}{c} Transformered surface \\ microstructure to facilitate \\ moisture transfer during \\ steam-cooking, enhanced starch \\ hydrolysis, and glucose content. \\ \end{tabular} & [103] \\ \hline Backwheat grains & Water bath/100 W/45 kHz/15 min & \
\begin{tabular}{c} Post-US \\ process modifications \\ \end{tabular} &
\begin{tabular}{c} Rise in the water absorption index, \\ the swelling power of the flour, \\ the water solubility index, and \\ insoluble polyphenols \\ content. Redness and \\ yellowish enhancement. \\ \end{tabular} & [104] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Type** & **Process Parameters** & **Use** & **Effects** & **Reference** \\ \hline \multirow{9}{*}{Nocdles} & & & & & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & \ \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  \\ \cline{3-6}  & & & & & &  
Microbial inactivation, but also protein damage, is caused by UV light on the DNA genes, in particular by UV-C waves whose maximum germicidal peak is between 260 and 265 nm, the same as the maximum DNA absorption. These types of waves cause the formation of DNA photoproducts, for example pyrimidine 6-4 pyrimidine photoproducts and cyclobutane pyrimidine dimers, which imply DNA mutagenesis and cell death [113; 114].
# Interferometric Study of the Heat Transfer Phenomena Induced by Rapid Heating of Nickel Sheet
## 4 Conclusions  
The visualization of thermo-acoustic phenomena induced by the rapid heating of compressible fluid near a nickel sheet has been studied experimentally. The following conclusions were obtained:  
1. Different discharging rates and heating characteristics could occur with the same energy supplied according to the material properties and the size of the metal sheet. These differences cause variations in the maximum temperature and the required time to reach the peak of the nickel sheet.
2. At the beginning of heating (before 0.008 s), several interference fringes were observed near the nickel sheet. The number of isothermal lines and the strain rate increased with the heating energy. The critical time denoted as the onset of disturbance was shortened as the heating energy  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Conditions**} & \multicolumn{1}{c}{**A**} & \multicolumn{1}{c}{**B**} & \multicolumn{1}{c}{**C**} \\ \cline{2-5}  & 25 V & 1201.6 & \(-\)353.06 & 41.70 \\ \cline{2-5} \(\delta\) = 0.0125 mm & 29 V & 1288.2 & \(-\)337.38 & 33.30 \\ \cline{2-5}  & 33 V & 1349.5 & \(-\)327.83 & 29.46 \\ \hline \multirow{3}{*}{\(\delta\) = 0.0100 mm} & 25 V & 1533.6 & \(-\)349.9 & 28.95 \\ \cline{2-5}  & 28 V & 2100.9 & \(-\)400.4 & 27.32 \\ \cline{2-5}  & 31 V & 2114.3 & \(-\)392.2 & 23.57 \\ \hline \multirow{3}{*}{\(\delta\) = 0.0075 mm} & 14 V & 687.7 & \(-\)336.7 & 46.71 \\ \cline{2-5}  & 17 V & 672.7 & \(-\)263.1 & 34.28 \\ \cline{1-1} \cline{2-5}  & 19 V & 1164.1 & \(-\)320.1 & 30.61 \\ \cline{1-1} \cline{2-5}  & 21 V & 1189.9 & \(-\)300.6 & 25.49 \\ \hline \end{tabular}
\end{table}
Table 3: Coefficients of Nusselt number in Equation (4).  
increased and the heating time decreased. The critical time was measured to be about 0.06-0.16 s after heating.
3. The heat transfer decreases as a second-order function with time. As the heating energy increased, the local Nusselt number decreased. It is concluded that the heat transfer mainly occurred by conduction and radiation rather than by convection, because the expansive force and compressive force between fluid layers on the wall increased due to an increase in the heating energy in the beginning.  
[PERSON] supported the theory background, developed experiments, analyzed the data, and wrote the paper. [PERSON] and [PERSON] supported the theory background, analyzed the data, and reviewed the paper. All authors have read and agree to the published version of the manuscript.  
This research was supported by a grant (19 IFIP-B089072-06#) from Plant Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.  
The authors declare no conflict of interest.
# Wind Pressure Coefficient on a Multi-Storey Building with External Shading Louvers
## 4 Results and Discussion
### Impact of Shading Louvers on Average Wind Pressure Coefficient
For most floors, \(\overline{C_{\rm P}}\) decreases with a higher \(\theta\), except for F5. On the lateral R3, the greatest difference occurs between the non-shaded condition and the shaded condition for any \(\theta\), but \(\overline{C_{\rm P}}\) does not have much fluctuation when \(\theta\) changes. The \(\overline{C_{\rm P}}\) results of R4, R5 and R6 show that the smallest difference of \(\overline{C_{\rm P}}\) for different floors occurs near the edge between the lateral and leeward surfaces. The results of R4, R5, R6 and R7 show that the impact of shading louuvers on \(\overline{C_{\rm P}}\) can be neglected in some non-shaded facades. In Figure 16, the impact of shading louvers on \(\overline{C_{\rm P}}\), which is described as \(\Delta\overline{C_{\rm P}}\), becomes higher for the higher floors in general. Considering the different rows, R2 and R3 have the largest and the second largest \(\overline{\Delta C_{\rm P-R}}\) for most floors with the presence of shading louvers. And the impact of shading louvers on \(\overline{C_{\rm P}}\) is obviously smaller for R4, R6 and R7. With shading louvers, \(\overline{C_{\rm P}}\) on R3 are larger than those without shading louvers for all floors. And the situation are almost the same on R4 except for F5. But on the most parts of other rows, \(\overline{C_{\rm P}}\) decreases when shading louvers are installed. For F1, the largest and the second largest \(\overline{\Delta C_{\rm P-R}}\) occur on R1 and R2. The largest absolute value of \(\Delta\overline{C_{\rm P}}\) occurs on R4 for just \(\theta\) = \(0^{\circ}\) and on R2 for the other \(\theta\). For F2 to F5, the largest and the second largest \(\overline{\Delta C_{\rm P-R}}\) occur on R2 and R3. The presence of shading louvers has the largest influence on \(\Delta\overline{C_{\rm P}}\) of R3 for \(\theta\) = \(0^{\circ}\)-\(45^{\circ}\) but on \(\Delta\overline{C_{\rm P}}\) of R2 for higher \(\theta\). Figure 15: _Cont._  
Figure 16: _Cont._  
Figure 15: Average wind pressure coefficient \(\overline{C_{\rm p}}\) on outside surfaces of rooms under different shaded conditions for (**a**) R1; (**b**) R2; (**c**) R3; (**d**) R4; (**e**) R5; (**f**) R6; (**g**) R7.
# The Rise of Quantum Information and Communication Technologies
## 5 Lessons Learnt  
This paper addressed some of the main challenges and opportunities of emerging quantum information and communication Technologies. Specifically, in order to provide a concrete example of innovation activities, the paper focused on quantum-secure communications based on QKD as is being carried out in the European project EQUO.  
Some of the main lessons learnt in Euro-QCI and, in general, in other international activities [18; 19; 20] include the following:* Common terminology and language for quantum technologies and services are essential in all steps from innovation to equipment and platform developments and exploitations.
* Technological breakthroughs are required, such as a quantum repeater, the control of errors (error corrections) in quantum computing, and the development of quantum systems supporting entanglement [4].
* There is a need to develop and consolidate a standard architectural solution enabling multi-vendor interworking and interoperability, not only at the physical level but also at the management and control ones. This is mandatory for the mature industrial exploitation of QICTs solutions.
* A quantum hardware abstraction layer may accelerate the development and exploitation of quantum platforms, services, and applications. In fact, today, the industry choices have not yet consolidated one type of quantum hardware.
* QKD and PQC have different applicability scenarios in future quantum infrastructures, but their potential integration in end-to-end cybersecurity services is very promising.  
The next steps will address these key challenges in the direction of developing and exploiting the quantum internet [5].  
All authors have equally contributed to the paper. All authors have read and agreed to the published version of the manuscript.  
The paper has been developed under the project EQUO (European Quantum ecOsystems) funded by the European Commission in the Digital Europe Programme under the grant agreement No. 101091561.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
## 1 Introduction  
Coalbed methane (CBM) is one kind of natural gas found in a coal reservoir [1]. A report by [PERSON] et al. shows that, in 2014, the global CBM production reached over 240 billion cubic meters [2]. Despite its large production and considerable economic value, CBM is fundamentally different from the conventional gas, and its production put forward numerous scientific and technical challenges [3; 4]. It is generally presumed that gas holding capacity and permeability are two important factors which characterize a CBM reservoir. The former factor determines the gas capacity of the reservoir, while the latter one suggests the easiness of gas extraction [1]. Moreover, CBM is often found with other components, including water and carbon dioxide, in the coal reservoir. Water can also be introduced into the coalbed via hydraulic fracturing during well development processes. Therefore, a study on the adsorption and diffusion behaviors of methane on coal, especially in the presence of water, can generate a better understanding of CBM reservoirs and provide insights on the development of the exploration technologies.  
Methane adsorption in the coal matrix has been widely studied at the molecular level by using both first-principles methods [5; 6; 7; 8; 9; 10] and molecular simulation techniques [11; 12; 13; 14; 15; 16]. These works provide valuable insights into the mechanisms of methane adsorption and diffusion in the coal matrix. However, there are still several topics which are not fully discussed, as follows: 1. The molecular models for coal used in previous first-principles studies are often simplified as graphene or a single carbon sheet, while the effect of other functional groups in coal is neglected. 2. The effect of water, including its competitive adsorption with methane and its effect on methane diffusion, is not fully covered. However, in engineering practice, it has been found that the water blocking effect (WBE) is one of the major reasons that limit the production rate of CBM wells [17; 18; 19; 20]. 3. The effect of microstructures, including micropores, are usually not considered in previous molecular models, although it is generally believed that methane is held within micropores in the CBM reservoirs.  
In this study, we aim at bridging these gaps above by performing a combined density-functional theory (DFT) and molecular dynamics (MD) study, with emphasis on the effect of functional groups in coal, the competitive adsorption between water and methane, and the desorption and diffusion of methane with micropores in the presence and absence of water. In Section 3.2.1, insights generated from DFT calculations on the adsorption of methane and water on different sites of coal are presented, and in Section 3.2.2, the diffusion of the CH4/H2O system within micropores of coal is analyzed.
## 3 Methodology
### Multi-Scale Features Fusion with Self-Attention  
Feature maps are two-dimensional data that need to be converted into one-dimensional vectors for fusion and sequence modeling. Conventional techniques like global average pooling (GAP) or global max pooling (GMP) compress feature maps directly but often result in information loss [47]. Moreover, studies have indicated that the averaging operation in GAP excessively smoothes feature maps, thereby submerging information from crucial regions, whereas GMP is sensitive to outliers due to its maximization operation [48].  
To address these issues, we propose a self-attention-based approach that dynamically integrates global information from feature maps. As depicted in Figure 3, the two-dimensional feature map is initially unfolded into a one-dimensional sequence, to which a randomly initialized special token \(<cls>\) is appended at the beginning, serving as the label for the feature map. Subsequently, a two-layer Transformer encoder is employed to process the sequence, with the \(<cls>\) token representing the feature map's output as it encapsulates global information. Finally, the outputs of multi-scale feature maps are concatenated to achieve fusion. This method dynamically integrates global information from feature maps, offering a more flexible approach. The success of the Vision Transformer (ViT) model, which shares similarities with our method, provides a research foundation for our feature fusion strategy.  
Specifically, given a video \(V=\{p^{1},p^{2},...,p^{T}\}\in\mathbb{R}^{T\times H\times W\times C}\) and \(p^{i}\in\mathbb{R}^{H\times W\times C}\), where \(T\) denotes the number of frames in \(V\), where \(T\) is the number of frames, and \(H\), \(W\), and \(C\) represent the height, width, and channels of each frame, respectively. Thus, the process of handling and fusing multi-scale features is illustrated as shown in Equations (1)-(4):  
\[m_{1}^{i},m_{2}^{i}=SE(p^{i}) \tag{1}\] \[v_{1}^{i}=f_{trans\_enc}([m^{cls},Flatten(m_{1}^{i})])\] (2) \[v_{2}^{i}=f_{trans\_enc}([m^{cls},Flatten(m_{2}^{i}))\] (3) \[c^{i}=Concat(v_{1}^{i},v_{2}^{i}) \tag{4}\]  
where \(SE\) is spatial encoder and \(f_{trans\_enc}\) is Transformer encoder layer. In addition, \(p^{i}\in\mathbb{R}^{224\times 24\times 3}\) denotes the \(i\)th frame image and \(m_{1}^{i}\in\mathbb{R}^{7\times 7\times 256}\) and \(m_{2}^{i}\in\mathbb{R}^{3\times 3\times 256}\) are the feature maps. The outputs \(v_{1}^{i}\), \(v_{1}^{i}\in\mathbb{R}^{1\times 256}\) represent the outcomes derived from the application of the Transformer encoder to process the sequences, which are both flattened and supplemented with the \(<cls>\) token. Finally, by concatenating \(v_{1}^{i}\) and \(v_{2}^{i}\), we derive \(c^{i}\in\mathbb{R}^{1\times 512}\), which serves as the image features for subsequent temporal modeling, providing a comprehensive representation that integrates both fine-grained and coarse-grained information.
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Neutralization Line (NL)  
In this DT, NL is attached to the two AEs to enhance isolation. This produces the extra current path between the dual AEs of the MIMO antenna. In reference [60], a semi-circular patch was combined with a modified rectangular patch to design the AE, as shown in Figure 5a. A pair of antennas were combined with the NL to improve the isolation between the two ports. In [61], two AEs had a circular patch with a microstrip feed line. An NL was inserted between two AEs. The NL consisted of two conducting strips and a circular disc, as shown in Figure 5b. The circular disc allowed several possible decoupling current paths with different lengths to cancel the coupling current on the ground plane. Hence, wideband isolation was achieved.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Data-driven Intelligent Diagnosis Algorithm
#### 3.3.1 Neural Network  
Neural networks (NN), or artificial neural networks (ANN), have been proposed with the development of artificial intelligence, which have been widely used in the fields of pattern recognition, automatic control etc., as well as fault diagnosis [83]. NN can simulate the human brain and automatically identify the fault type. Traditional neural network models generally consist of an input layer, a hidden layer and an output layer; each layer containing many nodes [84]. The output is obtained from the activation function after calculation of fully connected nodes from multiple layers.  
The goal of NN is to minimize the classification errors on the training set and to react appropriately to new inputs, which requires adjusting the initial values of the parameters and training the network multiple times. Therefore, different training algorithms and network models are used in fault diagnosis studies, such as back-propagation (BP) network [85], dynamic recurrent NN [86] and so on. [PERSON] trained feed-forward perceptron ANNs for the static eccentricity fault of PMSM [87]. [PERSON] et al. used the third harmonic in the PMSM current as the input to the ANN to achieve a good diagnosis result for the inter-turn short circuit [88]. However, it also has the disadvantage of relying on a large amount of input data, easy over-fitting, \"black box\" and so on.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 2 TDOA/FDOA Measure Model  
The model of the geodetic coordinate system is established as shown in Figure 1. The \(X\)-axis is pointing to the \(0^{\circ}\) longitude direction, the \(Z\)-axis is pointing to the center of the earth, and the \(Y\)-axis can be obtained according to the right-hand rule. It can be found that the main lobe of the interference transmitter is pointed towards the main satellite. At the same time, the two adjacent satellites can receive the sidelobe signal from the interference source.  
Assume that the \(s_{0}(t)\), \(s_{1}(t)\) and \(s_{2}(t)\) are the signal received by the main satellite, the first adjacent satellite and the second adjacent satellite from the interference source in time \(t\). The received signals from the interference source in three satellites from can be expressed as:  
\[s_{0}(t)=s(t)+n_{0}(t) \tag{1}\]  
\[s_{1}(t)=A_{1}s(t-D_{1})e^{\beta 2\pi f_{d1}t}+n_{1}(t) \tag{2}\]  
\[s_{2}(t)=A_{2}s(t-D_{2})e^{\beta 2\pi f_{d2}t}+n_{2}(t) \tag{3}\]  
where \(s(t)\) is the complex envelope of the signal received by the interference source in the second adjacent satellite, \(A_{1}\) and \(A_{2}\) are the amplitude ratio coefficients between the signals, \(D_{1}\) and \(D_{2}\) are the delay time of the signal transmission for the first adjacent satellite and the second adjacent satellite, \(f_{d1}\) and \(f_{d2}\) are the frequency differences, \(n_{0}(t)\), \(n_{1}(t)\), and \(n_{2}(t)\) are observation noise indicated.  
Since the received interference signal relayed by the adjacent satellite is the signal from the side lobe of the interference transmitter that points to the main satellite, its signal-to-noise ratio is generally low, even submerged in the noise. The communication terminal downlink signal relayed by the main satellite has a high signal-to-noise ratio generally. Aiming at this problem, the cross-correlation technique is used to realize the detection of weak sidelobe signals.  
The second-order complex CAF of the received signal from the main and adjacent satellites is expressed as:  
\[A_{s0_{1}}^{(s)}(f,\tau)=Ae^{\beta 2\pi f_{d1}t}A_{s}^{(s)}(f-f_{d1},\tau-D_{1}) \tag{4}\]  
\[A_{s0_{2}}^{(s)}(f,\tau)=Ae^{\beta 2\pi f_{d2}t}A_{s}^{(s)}(f-f_{d2},\tau-D_{2}) \tag{5}\]  
\[A_{s}^{(s)}(f,\tau)=\frac{1}{\tau}D_{0}^{T}s(t)s^{*}(t+\tau)e^{\beta 2\pi ft}dt \tag{6}\]  
Figure 1: Configuration of the tri-satellite interference localization.  
According to the triangle inequality and the Schwartz inequality, we have:  
\[A_{s}^{(s)}(f,\tau)=A_{s}^{(s)}(0,0) \tag{7}\]  
The estimation of the time delay \(\hat{D}_{1}\) and \(\hat{D}_{2}\) and the frequency difference \(\hat{F}_{d1}\) and \(\hat{F}_{d2}\) can be obtained corresponding to the maximum value of CAF:  
\[\left(\hat{F}_{d1},\hat{D}_{1}\right) =\text{argmax}\Big{\{}\Big{|}A_{s\text{o}s_{1}}^{(s)}(f,\tau)\Big{|} \Big{\}} \tag{8}\] \[\left(\hat{F}_{d2},\hat{D}_{2}\right) =\text{argmax}\Big{\{}\Big{|}A_{s\text{o}s_{2}}^{(s)}(f,\tau)\Big{|} \Big{\}} \tag{9}\]
## 6 Results and Discussion
### Training Data Size vs. False-Positive Rate  
Figure 8 shows the FP rate of the proposed FT-CID and baseline CNN and MLP models under different training data sizes and 20 communication rounds in AMI IIoT environments. As the size of the training data increases, the FP rate of the FT-CID, CNN, and MLP decreases. When the training data size is 80%, the FP rate of the FT-CID is still below 16%, but the FP rates of the CNN and MLP are higher, at 24.98% and 41.76%, respectively. The principle behind the above phenomenon is that the proposed FT-CID learns the related attack patterns from a similar intrusion detection task using FL and TL methods. Moreover, the FT-CID optimises the local learning model with the assistance of SGD and relevant knowledge learned from the global model. Consequently, the FT-CID improves the intrusion detection performance with a reduced number of FPs in the target task and maintains higher intrusion detection accuracy even with a small training data size. The numerical points obtained from the experiments for the FP rate are shown in Table 5.