esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### True Positives Rate Studies of Smokeless Powders
#### 3.2.2 SPME Coupled with GC-MS  
Since SPME is limited to static extractions, the same extraction time of 10 min was repeated for the SPME-GC-MS TPR studies. The TPR curves based on the equivalent mass detection (from the integrated area) of the target analytes are shown in Figure 2.  
In comparison to the PSPME-IMS studies, SPME-GC-MS led to poorer sensitivity with a TPR of 0.88 and a minimum detection of 17 ng for NG and a TPR of 1.0 with equivalent mass detection of 6 ng for 2,4-DNT. Detection of DPA was slightly better with a TPR of 0.58 with detection equivalent to 2 ng of  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline  & & \multicolumn{2}{c|}{**Benchtop IMS**} & \multicolumn{2}{c|}{**Portable IMS**} \\ \hline
**Container Volume (L)** & **Analyte** & **Static** & **Dynamic** & **Static** & **Dynamic** \\ \hline \multirow{4}{*}{0.94} & NG & 1.0 & 1.0 & 1.0 & 1.0 \\  & DPA & 0.70 & 0.37 & 0.58 & 0.15 \\  & 2,4-DNT & 1.0 & 1.0 & 1.0 & 0.87 \\ \hline \multirow{4}{*}{3.8} & NG & 1.0 & 1.0 & 1.0 & 1.0 \\  & DPA & 0.82 & 0.53 & 0.25 & 0.08 \\  & 2,4-DNT & 1.0 & 1.0 & 0.98 & 0.98 \\ \hline \multirow{4}{*}{45 *} & NG & 1.0 & 1.0 & 1.0 & 1.0 \\  & DPA & 0.0 & 0.23 & 0.07 & 0.47 \\ \cline{1-1}  & 2,4-DNT & 1.0 & 1.0 & 0.80 & 0.98 \\ \hline \end{tabular}  
* denotes \(n=30\).  
\end{table}
Table 4: True positive rates for smokeless powders in different containers (1–45 L) for bench top and portable IMS systems with 60 replicates.  
Figure 2: SPME-GC-MS true positive rates with varying equivalent mass threshold for (**a**) NG, (**b**) DPA and (**c**) 2,4-DNT.  
DPA. These results show that the SPME-GC-MS system is sensitive for 2,4-DNT and DPA; however, detection of NG is much more sensitive using PSPME coupled with commercial IMS.
## 3 Methodology
### Data Sources  
The COSMO-SkyMed dataset comprises 375 ascending mode StripMap HIMAGE scenes of Wuhan collected and provided by ASI over the period from 29 May 2011 to 21 November 2020. The whole dataset of 375 was exploited for the full stack processing. On the other hand, as there were only 11 images available in 2011, we did not use these earliest images in the sub-stack processing, which therefore focused on the images acquired between June 2012 and November 2020. These images feature horizontal transmit and receive (HH) polarization, with an incidence angle of 26.6\({}^{\circ}\) at the scene's center. The data have a spatial resolution of 3 \(\times\) 3 m (range \(\times\) azimuth) and a swath width of approximately 40 km. The temporal resolution varies throughout the dataset; while the average revisit time was approximately 8 days, there were several gaps in acquisition timing (see Figure 3a) as detailed below. COSMO-SkyMed images were collected through the COSMO-SkyMed background mission [37]. The background is a low-priority acquisition plan by which the constellation collects images using the available capacity when no other orders are tasked. The images are taken over predefined targets, e.g., large cities, and the idea is to build time series as regularly as possible. However, given the low priority and the fact that the collection was made only if there was capacity onboard the satellites, there was no requirement to ensure that the revisit time was always kept. Therefore, the time series displayed in Figure 3a is the best that the constellation could provide over Wuhan. These data were processed using SARPROZ software (version 2021.0) for PSInSAR analysis [38; 39].  
Of the 375 images used (2012-2020), 119 images were acquired between 2012 and 2015. After this period, as already observed in [40], there was a notable data gap of 120 days. For effective PSinSAR analysis, it is essential to exploit a sufficient number of images in each processing unit to ensure consistent phase unwrapping and scatterer identification.  
To maintain consistency in height extraction while managing computational requirements and accounting for the fact that the COSMO-SkyMed time series is long but made of subsets of more regular acquisitions, we established that each processing unit should contain at least 20 images. Upon reviewing the temporal distribution of our data, we determined through testing that using 28 images per stack provided an optimal balance. This number adequately balanced (a) processing efficiency, (b) the statistical reliability needed for robust PSinSAR analysis (comfortably exceeding the 20-image minimum), and critically, (c) the need to avoid the most significant data acquisition breaks. This division created 13 separate stacks of 28 images each (Figure 3a). While the calendar duration covered by these stacks varied (Figure 3b), the use of a consistent number of images allowed for standardized processing.
## 8 Qualitative and Quantitative Evaluation
### Qualitative Evaluation  
Score-CAM (Score-Weighted Class Activation Mapping) [29] is a developed version of the original CAM approaches, such as GradCAM [30]. It overcomes the constraints of gradient-based CAM approaches by removing the necessity of gradients and instead using the model's confidence ratings to weight the value of various activation maps. This method frequently produces more accurate and visually interpretable heatmaps, especially in models where gradient information may be noisy or inaccurate.  
The steps in Score-CAM are as follows: first, a forward pass through the model is used to collect activation maps from the target convolutional layer. These activation maps are then retrieved and up-sampled to the input image's size. Each up-sampled activation map is used to mask the input picture, which is then processed through the network to calculate the confidence score for the target class. The activation maps are then weighted by their respective confidence ratings and added together to create the final class-specific heatmap, which is normalized to be between 0 and 1. In conclusion, the normalized heatmap is superimposed on the original picture to show the regions critical to the model's prediction.  
As shown in Figure 5, the proposed Hybrid 3D convolutional and Transformer correctly recognize the frame sequences class. The class activation mapping frame sequences show the participation of each region of the input frame sequence in the prediction class \"SMM\". Red areas are the most contributing regions. As presented in frame sequence 1, the red region corresponds to the Stereotyped Motor Movement produced by the child when he knocks on the ball in a stereotyped and repetitive manner, and in frame sequence 2, the red region presents the most representative region when the girl flaps her hands stereotypically.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** &
\begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} & **Precision** & **Recall** & **F1-Score** \\ \hline
**Full Model (3D-ResNet + Swin-3D)** & **92.0\%** & **0.922** & **0.902** & **0.9116** \\ Without Swin-3D (3D-ResNet only) & 87.5\% & 0.890 & 0.870 & 0.880 \\ Without 3D-ResNet (Swin-3D only) & 85.0\% & 0.870 & 0.850 & 0.860 \\ Without Data Augmentation & 83.0\% & 0.850 & 0.830 & 0.840 \\ Without Cross-Validation & 81.5\% & 0.845 & 0.820 & 0.832 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the proposed model under different configurations in the ablation study.  
Figure 5: Region contributions of Hybrid 3D Convolutional–Transformer predictions using score-CAM.
## 2 Aircraft Engine and Its Prediction Model
### A Prediction Model of an Aircraft Engine
#### 2.2.1 Elastic BP Neural Network (EBPNN)  
Because a BP neural network usually uses a sigmoid function as an activation function, the network output may enter a flat area when the net input of each node is too large. The flat area causes a small partial derivative of the error with respect to the weight value, which results in a correcting process which stagnates and slows down the convergence rate. In order to reduce the influence of the small change in gradient amplitude on the weight and threshold correction, the elastic BP algorithm is used to adjust the weight and threshold by judging the change in the positive and negative directions of the gradient [29].  
For convenience of description, we take the training of the weights and thresholds of a node in the network as an example to briefly summarize the main correction formulas of the conventional BP neural network and the elastic BP neural network.  
Figure 1: Generalized schematic of the twin-shaft aircraft engine [19]: I: fan (LP compressor); II: HP compressor; III: bypass duct; IV: main combustion chamber; V: HP and LP turbines; VI: mixing chamber; VII: nozzle.  
The conventional BP neural network weight and threshold correction formula obtained by the gradient descent method is as follows:  
\[w_{ij}(t+1)=w_{ij}(t)+\Delta w_{ij}(t)(1) \tag{2}\]  
\[\Delta w_{ij}(t)=-\eta_{1}\frac{\partial E}{\partial w_{ij}} \tag{3}\]  
\[b_{j}(t+1)=b_{j}(t)+\Delta b_{j}(t) \tag{4}\]  
\[\Delta b_{j}(t)=-\eta_{2}\frac{\partial E}{\partial b_{j}} \tag{5}\]  
where \(w_{ij}\) is the weight of the network node, \(b_{j}\) is the threshold value of the network node, \(\Delta w_{ij}\) and \(\Delta b_{j}\) are the correction values of the node weight and the threshold value, respectively, \(t\) is the number of iterations, \(\eta_{1}\) and \(\eta_{2}\) are the learning rates of the weight and the threshold value, respectively, and \(E_{nn}\) is the error function.  
From Equations (3) and (5), it can be seen that the weight and threshold values of the conventional BP algorithm mainly depend on the change in gradient amplitude. When entering the flat region, the gradient amplitude is basically zero, the weight and threshold value no longer change, and the algorithm easily falls into a local optimum.  
Therefore, the elastic BP neural network modifies the conventional BP algorithm. The correction formula is as follows:  
\[x_{TD}(t)=\frac{\partial E(t)}{\partial w_{ij}}\frac{\partial E(t-1)}{\partial w _{ij}} \tag{6}\]  
\[\Delta w_{ij}(t)=\alpha^{sign(x_{TD})}\Delta w_{ij}(t-1) \tag{7}\]  
\[\Delta b_{j}(t)=\alpha^{sign(x_{TD})}\Delta b_{j}(t-1) \tag{8}\]  
where \(x_{TD}\) is the gradient product of two adjacent iterations.  
From Equations (6)-(8), it can be seen that the correction of weights and thresholds only considers the direction change of the gradient and not the change of the gradient amplitude, thus avoiding entering the flat area, preventing reaching the local convergence area, and improving the convergence speed and the training ability of the network.
## 1 Introduction  
Based on the characteristics of broad tunability, narrow linewidth and compactness, the tunable external cavity diode laser (ECDL) has a broad application in many fields, including atomic physics, laser radar, optical communication, optical measurement, and optical sensing [1; 2; 3; 4; 5; 6]. Particularly, ECDLs with high side mode suppression ratio (SMSR) emissions at around 1.5 \(\upmu\)m can be applied in many fields, such as high-dynamic-range testing and measurement [7; 8]. Therefore, it is important to build an ECDL system with high SMSR.  
The ECDL consists of a gain chip and an optical element. Grating is the typical dispersive element and Littrow configuration is a typical grating-coupled configuration for tunable ECDL [9; 10]. ECDLs with a Littrow configuration based on quantum-well or quantum-dot (QD) laser sources are now in widespread use and broadly investigated [11; 12]. An external cavity laser with a tuning range of 242 nm (1320-1562 nm) based on an InGaAsP/InP multiple quantum well (MQW) laser has been demonstrated, where the SMSR is between 17 and 42 dB [13]. A tunable external cavity laser with an InGaAs-InP strained MWQ has been demonstrated with a spectral range of 1494-1667 nm. The maximum SMSR is about 50 dB and the bandwidth of optical spectrum is around 0.4 nm [14]. An InAs/InP quantum-dot external cavity laser with a tunable range of 104 nm (1457-1561 nm) has been reported, and the injection current is 1 A, which is extremely high [15]. A tunable external-cavitylaser based on InAs/InP QD laser has been demonstrated, the tuning range of which is 140.4 nm (1436.6-1577 nm) [16]. A widely tunable InAs/InP QD external-cavity laser has been demonstrated, the tunable range of which reaches 190 nm under pulsed injection current, but the SMSR is only around 20 dB [17]. Although there have been some reports on the widely tunable grating-coupled external-cavity laser at 1.5 \(\upmu\)m, the SMSR of the ECDL is still relatively low.  
In this letter, we demonstrate a Littrow external cavity laser with a high SMSR and a wide tunable range based on a commercial InP gain chip. Three Littrow configurations with 600 grooves/mm and 1200 grooves/mm gratings were constructed to investigate the influence of grating features on the performance of laser, including the SMSR and tunable range. When the 1200 grooves/mm grating with 91% first-order diffraction efficiency was utilized, the maximum side mode suppression ratio exceeded 60 dB and more than 50 dB was achieved in most of the tuning spectrum range of the ECDL. Meanwhile, a tunable range from 1425.4 nm to 1635.3 nm was achieved, encompassing the telecommunication widow S (1460-1530 nm), C (1530-1565 nm) and L (1565-1625 nm), as defined by the International Telecommunication Union. And the output power was around 48.9 mW at the emitting wavelength of 1570 nm.
## The Influence of Different Non-Conventional Yeasts on the Odour-Active Compounds of Produced Beers  
**[PERSON] \({}^{\star}\)\({}^{\copyright}\)** and **[PERSON] \({}^{\copyright}\)**  
Department of Fermentation Technology and Microbiology, Faculty of Food Technology,  
University of Agriculture, Balicka Street 122, 30-149 Krakow, Poland  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The interest in new beer products, which has been growing for several years, forces technologists and brewers to look for innovative raw materials, such as hops, new sources of carbohydrates or yeast. The aim of the presented study was to evaluate the effect of selected _Saccharomyces_ (_Saccharomyces_ (CBS 7302), _S. kudriuzvevii_ (CBS 3774), _S. cerevisiae_ (Safbrew T-58)) and non-_Saccharomyces_ yeast (_W. anomulus_ (CBS 5759), _Ha. uvarum_ (CBS 2768), _D. bruxellensis_ (CBS 3429), _Z. bailii_ (CBS 749), and _T. dellbrueckii_ (D10)) on the fermentation process, basic parameters and odour-active compounds of the produced beers. The chemical composition and key aroma components of the obtained beers were determined using various chromatographic methods (HPLC, GC-FID, GC-MS, and GC-O). We showed large differences between the key aroma components depending on the culture of microorganisms used. Forty different compounds that have an active impact on the creation of the aroma of beers were detected, among which the most important are: \(\beta\)-phenylethanol, ethyl hexanoate, ethyl 4-methylpentanoate, ethyl dihydrocinamate and \(\beta\)-damascenone. We also found the presence of components specific to the yeast strain used, such as 2-methoxy-4-vinylphenol, \(\gamma\)-decalactone, metholand, metholand and others. Among the analyzed yeasts, _S. kudriuzvevii_ and _W. anomulus_ should be distinguished, which produced beers with intense fruity and floral aromas and were also characterized by favorable features for brewing. The _Z. bailii_ strain also turned out to be interesting as a potential starter culture for the production of low-alcohol beers, significantly differing in sensory characteristics from the standard ones.  
non-conventional yeasts; beer; aroma compounds; olfactory
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 4 Data Pre-Processing
### Registration  
The registration function aligns the input image/volume according to the reference image/volume. This function was also developed using the DIPY library. The library function performs the translation, scaling, rotation, and affine translation operations to register 2D images or 3D volumes. There are a few parameters we need to set to register image or volume data using the DIPY library function:  
* **Reference_data**: the image or volume data, according to which the input data will be registered;
* **Input_data**: the image or volume to be registered;
* **Nbins**: a value indicating the number of bins to compute the intensity histogram. The default value of 32 was used in this case;
* **sampling_proportion**: a value between zero and one, indicating the sampling ratio. A \"None\" value was used here to specify full sampling;  
Figure 10: Illustration of the 3D background subtraction.  
* **level_iters**: a sequence of numbers indicating the number of iteration per resolution. Three resolutions with \([1000,100,100]\) iterations for 2D images and \([10,000,1000,100]\) iterations for 3D volumes were specified in this case;
* **Sigma**: a sequence of floats indicating the custom smoothing parameters. The default value of \([3,1,0]\) was used;
* **Factors**: sequence of floats indicating the custom scale factors. The default value of \([4,2,1]\) was used.  
However, this library function alone cannot perform registration correctly. Therefore, an additional feature was developed to improve the DIPY library and fit our input CT data. In the proposed Toolbox, a new improvement denoted DIPY+ was implemented to improve the registration function so that it fit our real CT dataset based on the following features:  
1. Propose a new metric to access the registration performance to estimate the rotational angle between the scaled-down input and reference images/volumes;
2. Improve the quality reduction and execution time by performing a single transformation using the estimated rotational angle and transformation factors, applied once to the original-sized images/volume instead of multiple small rotation steps using the default DIPY library.  
Figure 11 shows an example of the 2D image registration of the input image according to the reference image. Similarly, Figure 12 shows an example of a tilted volume registered according to the reference volume.
## 3 Antenna Characteristics  
The circular Minkowski arc-slotted antenna analysis considers various parameters influencing its performance. The simulations are conducted using the CST microwave studio. The investigation of the proposed antenna reveals that several factors, namely the length of the ground plane, the dimensions of the groove, and the size of the slot on the radiating patch, significantly impact its overall performance. The subsequent subsections provide detailed explanations of the effects of these parameters.
## 3 Design Methodology
### TR Effects  
This section shows that the effects of the proposed TR introduction on the antenna performance are characterized numerically. It is conducted to couple the electromagnetic energy inductively at the resonance frequency [5]. It is found that the proposed TR realizes a bandwidth enhancement due to the effects of the inductive coupling that stores the accumulated charges on the edges of the conductors [11]. Therefore, the antenna bandwidth is enhanced with an insignificant reduction in the antenna gain as shown in Figure 4a. The antenna gain at this stage is found to be increased up to 6.8 dBi after introducing the proposed TR structure. Such an increase in the antenna gain is attributed to the charge accumulation cancelation by the inductive part of the proposed TR structure [7].  
Figure 3: Antenna performance with different cell numbers: (**a**) \(S_{11}\) spectra and (**b**) gain.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 5 Conclusions  
Comprehensive numerical investigations and comparisons have been made between 40 Gb/s lane rate electrical Duobinary, optical Duobinary, and PAM-4 systems, using practical linear and nonlinear equalizations and low complex MLSE for NG-PONs. The results are useful for choosing the optimum modulation scheme and the DSP configuration for upgrading the capacity in optical access networks. The trade-off between link power budget performance and transceiver complexity determines the choice of modulation format and DSP configuration. DSP-free optical Duobinary is always preferred for short-reach SMF links of up to 10 km. For 20 km SMF, DSP-free PAM-4 is the only option with the least complexity, otherwise electrical Duobinary is preferred if a high-link power budget is critical. Electrical Duobinary outperforms PAM-4 for cases of SMF beyond 20 km.  
[PERSON] conceived, designed and performed the simulations; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper.  
This research was funded by the European Union under CEEOALAN project with grant number No. 623,515.  
The authors thank [PERSON], [PERSON], and [PERSON] from ADVA Optical Networking SE, Germany for useful discussions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Protocol
In this regard, a study evaluated the image quality and diagnostic effectiveness of LIE-CT with knowledge-based iterative model reconstruction (IMR) for MI detection, comparing it with LGE-CMR. It confirmed that the use of IMR significantly improves the image quality and diagnostic accuracy of LIE-CT. However, despite this, they also emphasized that LIE-CT cannot completely replace LGE-CMR, as it lacked precision in accurately assessing the extent of MI compared to the latter [14]. Nonetheless, further technological advancements could overcome these limitations in the future and achieve additional incremental improvements in LIE-CT.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
###### Abstract  
INSPIRE-SAT 7 is a French 2-Unit CubeSat (11.5 \(\times\) 11.5 \(\times\) 22.7 cm) primarily designed for Earth and Sun observation. INSPIRE-SAT 7 is one of the missions of the International Satellite Program in Research and Education (INSPIRE). Twice the size of a 4 \(\times\) 4 Rubik's Cube and weighing about 3 kg, INSPIRE-SAT 7 will be deployed in Low Earth Orbit (LEO) in 2023 to join its sister satellite, UVSQ-SAT. INSPIRE-SAT 7 represents one of the in-orbit demonstrators needed to test how two Earth observation CubeSats in orbit can be utilized to set up a satellite constellation. This new scientific and technological pathfinder CubeSat mission (INSPIRE-SAT 7) uses a multitude of miniaturized sensors on all sides of the CubeSat to measure the Earth's energy budget components at the top-of-the-atmosphere for climate change studies. INSPIRE-SAT 7 contains also a High-Frequency (HF) payload that will receive HF signals from a ground-based HF transmitter to probe the ionosphere for space weather studies. Finally, this CubeSat is equipped with several technological demonstrators (total solar irradiance sensors, UV sensors to measure solar spectral irradiance, a new Light Fidelity (Li-Fi) wireless communication system, a new versatile telecommunication system suitable for CubeSat). After introducing the objectives of the INSPIRE-SAT 7 mission, we present the satellite definition and the mission concept of operations. We also briefly show the observations made by the UVSQ-SATCubeSat, and assess how two CubeSats in orbit could improve the information content of their Earth's energy budget measurements. We conclude by reporting on the potential of future missions enabled by CubeSat constellations.  
climate observation and monitoring: Earth's energy budget; ionosphere; nanosatellite; CubeSat constellations