esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System and Channel Model Development  
### System Model  
The system model used in this paper for a cluster based WSN architecture for \(N_{r}\) receive antennas is shown in Figure 1. We consider a cluster based WSN architecture with N number of identical sensors deployed over a wide area. The goal is to collect the observations gathered by all the sensors to the cluster head to be transmitted to the receiver. We assume that all the sensors collect the same data and are capable of developing an _ad-hoc_ network to disseminate the information among them via efficient flooding. The sensors pass on the information to the cluster head, where this information is filtered and modulated using BPSK and sent to the receiver. Another assumption is that the whole architecture is synchronous and the communication channel between the cluster head and the receiver is subjected to fading, multipath, and noise.  
When the signal is transmitted, reflections from large objects, diffraction of the waves around objects, and signal scattering dominate the received signal resulting in the presence of multipath components, or multipath signals, at the receiver. Figure 2 depicts a general example of this multipath environment. Each signal component propagates through a different path, determining the amplitude \(\alpha_{l}\), time delay \(\tau_{l}\), angle of arrival \(\theta_{l}\), the power for the multipath components, and Doppler shift \(f_{d}\) of the \(l^{th}\) multipath signal component. Accordingly, each of these signal parameters will be time-varying [13].  
In the GBSBEM, scatterers are uniformly distributed within an ellipse, as shown in Figure 2. An essential attribute of this model is the physical interpretation that only the multipath signals which arrive with an absolute delay \(\leq\tau_{max}\) are accounted. The sensors are placed in such a way that they are surrounded by scatterers and each signal transmitted by each sensor experiences a different multipath environment that determines the amplitude, the time delay, Direction-of-Arrival (DOA), and the power for each multipath component for each sensor.  
Figure 1: High-Level System Model.  
Considering the distance between the sensor nodes and the receiver to be D, all the scatterers giving rise to single bounce components arriving between time \(\tau\) and \(\tau+\Delta\tau\) lie in the region bounded by the ellipse with semi-major axis, \(a_{m}\) and its semi-minor axis, \(b_{m}\) and are related to the maximum specified delay \(\tau_{max}\) as:  
\[a_{m}=\frac{c\tau_{max}}{2} \tag{1}\]  
\[b_{m}=\frac{1}{2}\sqrt{c^{2}\tau_{max}^{2}-D^{2}} \tag{2}\]  
where c is the speed of propagation. The choice of these parameters is determined by the maximum delay, \(\tau_{max}\) of the multipath. Larger values of \(\tau_{max}\) imply greater path loss for the multipath and, consequently, lower relative power compared to those with shorter delays.
## 3 Materials and Methods
### Traditional Machine-Learning Models  
The traditional machine-learning models used in this study were designed to make a categorical prediction of the participant's mental workload based on the assumption that the outcome of each time point was independent and identically distributed. Within the traditional machine-learning methods, we utilized logistic regression as a baseline model using scikit-learn's logistic regression function on the training data set, which used the one-vs-rest (OvR) scheme to model multiclass output. Random forest is an ensemble-learning technique in which many decision trees are used to provide solutions, and was shown to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mental Effort Level**} & \multicolumn{3}{c}{**EDA (mS)**} & \multicolumn{3}{c}{**TEMP (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**HR (bpm)**} & \multicolumn{3}{c}{**ACC X**} & \multicolumn{3}{c}{**ACC Y**} & \multicolumn{3}{c}{**ACC Z**} \\  & **Time (s)** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
1 & 21,593 & 0.16 & 0.12 & 32.1 & 1.9 & 83.2 & 14.2 & \(-32.8\) & 28.0 & \(-4.0\) & 33.7 & 17.0 & 32.2 \\
2 & 29,858 & 0.15 & 0.12 & 30.3 & 1.7 & 84.3 & 14.6 & \(-36.3\) & 23.6 & \(-4.6\) & 33.8 & 18.2 & 30.2 \\
3 & 28,424 & 0.14 & 0.13 & 31.9 & 1.8 & 85.0 & 13.7 & \(-36.0\) & 22.9 & \(-3.1\) & 32.2 & 20.2 & 32.0 \\
4 & 33,296 & 0.15 & 0.12 & 30.9 & 1.7 & 83.4 & 12.3 & \(-30.4\) & 25.1 & \(-2.5\) & 34.0 & 24.3 & 30.6 \\
5 & 10,271 & 0.13 & 0.11 & 33.2 & 1.0 & 87.0 & 17.9 & \(-31.7\) & 22.4 & \(-5.2\) & 36.7 & 19.6 & 31.2 \\
6 & 50,703 & 0.17 & 0.13 & 31.4 & 1.4 & 81.7 & 13.2 & \(-32.6\) & 24.8 & \(-9.9\) & 28.8 & 29.1 & 27.5 \\
7 & 71,633 & 0.13 & 0.10 & 31.4 & 1.4 & 83.5 & 14.2 & \(-30.2\) & 24.8 & \(-12.2\) & 30.5 & 25.8 & 30.5 \\
8 & 64,138 & 0.15 & 0.12 & 30.9 & 1.6 & 82.6 & 14.2 & \(-29.4\) & 22.3 & \(-14.2\) & 29.8 & 31.4 & 27.3 \\
9 & 22,330 & 0.19 & 0.20 & 29.9 & 5 & 82.7 & 12.0 & \(-27.1\) & 23.8 & \(-15.9\) & 37.1 & 21.8 & 27.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive analysis of the physiological features extracted from the Empatica E4.  
perform well in previous work [11]. The scikit-learn random forest implementation was used for this purpose, with the number of decision trees set to 1000. K-nearest neighbors (KNN) classifies the outcome on new data based on the k-nearest training examples. We used the scikit-learn k-nearest neighbors function to model KNN on our data set, with 3 as the number of neighbors using the Euclidean distance metric.
## 3 Design of a Memory-Efficient Lightweight Object-Detection Model
### Multi-Scale Feature-Fusion Neck Module Based on PConv  
The extracted multi-scale feature information by the FasterNet-16 backbone, specifically the feature maps from the second, third, and fourth groups, generally necessitates further processing prior to being fed into the detection head for object classification and bounding box regression. A prevalent approach for feature fusion is the feature pyramid network (FPN). FPN effectively amalgamates multi-scale feature information from both high-resolution shallow and semantically rich deep layers, thus enhancing the object-detection model's efficacy by feeding these integrated feature maps into variously scaled detection heads. However, in the context of ultra-lightweight object-detection models, the FPN module's additional convolutional operations for feature fusion can become a computational burden. When integrated with the backbone network, these additional operations significantly impact the total computational cost, especially if the backbone itself is already resource-intensive. To address this, the model adopts a fusion strategy inspired by the YOLOF design, where the output feature maps from FasterNet-16 undergo spatial resolution adjustments and are then concatenated along the channel dimension to achieve efficient multi-scale feature stacking. The three distinct sets of feature maps produced by FasterNet-16 are harmonized to the same spatial resolution before concatenation. This approach not only ensures computational efficiency but also integrates features across various levels, encapsulating both granular details and deep semantic insights. The total dimension of the stacked multi-scale features equals the combined dimensions of the three backbone output feature maps. This novel approach is illustrated in Figure 6 and is designated as the P-Inception structure, which allows for the capturing of diverse scale features within a single feature map, thus enriching feature representation and enhancing detection accuracy. In the depicted P-Inception module (Figure 6), the central component is the newly designed multi-scale feature-fusion neck module. Following the stacking of the three backbone output feature maps, a \(1\times 1\) convolution performs dimension transformation before entering a pseudo-inception structure. This structure consists of three groups of PConv convolutions in varying quantities (1, 2, and 3), enabling diverse scale information fusion within a singular layer and maximizing the utilization of the extracted backbone features. Subsequent to the convolutional processing, the outputs of the three PConv groups are merged via another \(1\times 1\) convolution for further feature fusion and dimension alignment, ensuring that the output dimensions are consistent with those of the input feature map. Ultimately, the output feature map from the P-Inception module is reintegrated
## 3 The Use of Supercontinuum Lasers in Biomedical Diffuse Optics
### Novel Instrument Development  
The first paper reporting the use of a supercontinuum laser in biomedical diffuse optics was reported in the early nineties [29; 30; 31] by a group based at the Lund Laser Centre (LLC, Lund, Sweden). The femtosecond white light supercontinuum was generated by a Terawatt laser running at 10 Hz. The spectral decomposition was performed on the detection side, by coupling an imaging spectrometer and a streak camera, enabling to acquire parallelly the DTOF at all wavelengths. To improve the signal-to-noise ratio, the measurement had to be repeated for several laser shots, but the overall acquisition time was of just a few minutes, which is acceptable when no dynamic contrast is targeted. Using such a system, the very first in vivo absorption and scattering spectra of the female breast were acquired from 650 to 850 nm. This system was then updated in the early 2000s with a supercontinuum generation system based on a crystal fibre, which allowed to increase the optical power of the SCL generation [32].
## 5 Simulation Results  
### Simulation Environment  
The performance of the proposed scheme is evaluated and compared with the conventional schemes. The simulation was conducted using Matlab. The mean of repeated simulation results is used to evaluate performance according to the Monte Carlo method. Table 5 shows simulation parameters used in this paper. The simulation parameters are determined by the IEEE 802.11 ac. Only blocks that affect performance comparisons in the IEEE 802.11 ac process are simulated for simplicity. Since simulation parameters at the IEEE 802.11 ac are used, the proposed scheme can be applicable to the actual IEEE 802.11 ac environment. The Rayleigh fading channel which is frequently used in wireless communication system is used. Many recent studies [2; 6; 23] also used Rayleigh fading channel. Perfect channel estimation is assumed. Since orthogonal frequency division multiplexing (OFDM) system is adopted, each subcarrier goes through the flat fading channel. All elements of the channel matrices are independent complex Gaussian random variables with zero mean and unit variance. The system which has same number of transmit antennas and receive antennas is considered. The number of transmit and receive antennas is 8 or 16. The number of antennas of each user is fixed to 2 to consider the case which accommodates numerous devices. Therefore, when the number of transmit and receive antennas is 8 or 16, there are 4 users or 8 users, respectively. The modulation which is used at the RBD is quadrature phase shift keying (QPSK). The 16 quadrature amplitude modulation (QAM) and 64 QAM is used at the GBD, MGBD and the proposed scheme. When the proposed scheme is compared with RBD, the modulations of RBD and the proposed scheme are QPSK and 16 QAM to achieve same data rate, respectively. When the proposed scheme is compared with GBD and MGBD, 16 QAM and 64 QAM is used to present simulation results with various parameter  
Figure 3: Computational complexity comparison at transmitter between the conventional schemes and the proposed scheme in FLOPs.  
FFT size is 128. The convolution code is used for channel coding. When the QPSK and 16 QAM is used, the code rate is \(\frac{1}{2}\). When the 64 QAM is used, the code rate is \(\frac{2}{3}\). The total number of data streams for RBD is fixed at \(L_{\text{total}}=N_{R}\). When the proposed scheme is compared with RBD, the total number of data streams for the proposed scheme is fixed at \(L_{\text{total}}=\frac{N_{R}}{2}\). Thus, the proposed scheme has higher modulation level in order that RBD and the proposed scheme have the same data rates. Since the GBD and the MGBD are schemes which reduce the data streams for obtaining diversity gain, the proposed scheme can be compared with GBD and MGBD in Section 5.3. Therefore, GBD, MGBD and the proposed scheme are evaluated with various numbers of the data streams. The various numbers of transmit data streams is limited to the number which is more than \(\frac{2}{3}\) of the number of receive antennas since the case with few data streams is impractical in terms of data rate.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 1 Introduction
Development of the aforementioned tools is vital not only to improve our understanding of our local surroundings but possibly also to address some novel, near-future challenges of astrobiology and space medicine--a trend that is currently gaining in popularity worldwide [29; 30; 31; 32]. As indicated in [33; 34; 35], several biological objects exhibit uncommon behaviour in microgravity conditions, but the nature of this behaviour has still not been fully recognised. For instance, enhanced biofilm formation of selected bacteria [35] and formation of good quality protein crystals have recently been observed by NASA and _SpacePharma_, pointing to a potential new generation of vaccines and antibiotic drugs. Moreover, according to the article [34], most cells (e.g., human cancer cells) create 3D forms--MSCs (multicellular spheroids)--in microgravity, which corresponds strongly with the in vivo environment; thus, microgravity can be treated as a perfect model habitat for investigation. Similarly important is the research concerning plants [36; 37] and fungi growth, since the recognition of development dynamics of these species may play a crucial role in our future colonisation aspirations. Based on all of this, herein we present a fully equipped, ready-to-use microfluidic device, dedicated to culturing cosmopolitan and ubiquitous fungi species, on the example of _Fusarium culmorum_ (Wm.G. Sm.) Sacc. This work is in good agreement with the trend in astrobiological research provided mainly by NASA and ESA [38; 39; 40; 41; 42; 43; 44] focussed on the investigation of the influence of microgravity on widely understood life (bacteria, microbes, cells, etc.) in both simulated and space-based conditions. The application of LOCs in this regard outlines their tremendous potential towards completely new insights into the behaviour of microscale objects in microgravity, compared to the conventional, bulky and relatively expensive approaches. The paper covers multiple steps that had to be undertaken to fulfil the assumptions of the work. At first, the selection of the biological object was done, with a focus put on its features and morphology. Next, the lab-on-chip structure, equipped with specific microchannels arrangement was proposed, designed and numerically modelled to evaluate the LOC performance. Afterwards, the technological works were employed to obtain the lab-on-chip structure and then, the culturing system was assembled. The article is finished with cell culturing experiments performed in standard laboratory conditions and in simulated microgravity, imitated utilizing Rotary Wall Vessel.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### High-Power Thrusters
#### 4.3.7 Nuclear Thermal Propulsion  
As shown by their absence in Figure 13, resistojets and arcjets are generally not considered for scaling to high power. However, a natural extension of these technologies is nuclear propulsion, where a sustained fission reaction is used to heat a propellant, producing thrust once expanded out of a nozzle. As will be discussed in Section 5.4, in-space reactor technology is still in its infancy, so naturally, no form of nuclear propulsion has been flown. Interestingly, ground tests of one example of this technology, the Nuclear Engine for Rocket Vehicle Application (NERVA), were performed in the 1960s [133]. The main obstacles for the development and flight of nuclear thermal thrusters--aligned with those for in-space reactors--include effective thermal management, launch safety, and efficient heat transfer.
# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis
### Longitudinal Comparative Experiments on Basic Algorithms
#### 4.2.2 Comparison of DLLO-DRF Detection Algorithms Used and Unused in the Cement Pole Damage Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Using DILO-DRF**} & \multicolumn{4}{c}{**Without DLLO-DRF**} \\ \cline{2-9}  & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline ATSS & 13.4 & **37.6** & **9.3** & 28.2 & **13.6** & 37.1 & 9.2 & **29** \\ DDOD & **15.6** & **47.2** & **12.2** & **21.2** & 15.4 & 47 & 12.1 & 21.1 \\ GFL & **21.9** & 44.8 & **18.7** & **30.7** & 21.8 & **44.9** & 18.1 & 30.3 \\ Cascade R-CNN & **16.9** & **35.4** & 15.2 & **21.8** & 16.8 & 34.2 & **15.6** & 21.4 \\ Faster R-CNN & **17** & **33.5** & 16.5 & 22.7 & 16.8 & 33.4 & **18.4** & **23** \\ Dynamic R-CNN & **13.4** & **26** & **10.5** & **20.1** & 13.2 & 25.6 & 9.4 & 19.9 \\ Grid R-CNN & **16** & **37.8** & 9.1 & **22.6** & 15.9 & 36.2 & **10.4** & 22.5 \\ Libra R-CNN & **18.8** & 45.6 & **13.7** & **24.1** & 18 & **45.7** & 13.2 & 23.6 \\ Sparse R-CNN & 18.3 & 47.7 & **5** & **26** & **18.4** & **47.8** & 4.9 & 25.6 \\ Average & **16.8** & **39.5** & 12.2 & **24.2** & 16.7 & 39.1 & **12.4** & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of DLLO-DRF algorithms used and unused in the cement pole damage dataset. The better results are indicated using bold and underline. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Using DILO-DRF**} & \multicolumn{4}{c}{**Without DLLO-DRF**} \\ \cline{2-9}  & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline ATSS & **28.2** & 64 & **19.4** & **38** & 27.7 & 64 & 19.3 & 37.8 \\ DDOD & **33.2** & **66.8** & **31.7** & **40.8** & 33 & 66.6 & 31.5 & 40.6 \\ GFL & **29.8** & **64.2** & 20.1 & **41.1** & 29.4 & 63.8 & 20.1 & 40.3 \\ Cascade R-CNN & 39.6 & **62** & **48.3** & 44.4 & **39.9** & 61.9 & 48.2 & **44.6** \\ Faster R-CNN & 40.9 & **67.3** & **46.5** & 46.7 & **41.4** & 66.9 & 46.2 & **47.3** \\ Dynamic R-CNN & **38** & **60.6** & **42.6** & **46** & 37.7 & 60.2 & 42.4 & 45.7 \\ Grid R-CNN & 40.2 & **64.4** & 43.6 & **47** & 40.2 & 63.7 & **44.4** & 46.9 \\ Libra R-CNN & **35.9** & 61.1 & **38.2** & **46.5** & 35.7 & **61.2** & 38 & 46.3 \\ Sparse R-CNN & 18.3 & **40.3** & 14.6 & **43.5** & **18.5** & 40 & **14.9** & 43 \\ Average & **33.8** & **61.2** & 33.9 & **43.8** & 33.7 & 60.9 & 33.9 & 43.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of DLLO-DRF algorithms used and unused in the shockproof hammer slip dataset. The better results are indicated using bold and underline.
## 5 Pseudo-Dynamic Test Results and Analysis
### Crack and Failure Pattern  
(1) PD-FR unreinforced specimen  
For the PD-FR specimen, initial flexural cracks occurred in the lower part of the column in approximately 2.03 s (6.4 mm) under the seismic wave of 200 cm/s\({}^{2}\). The flexural cracks expanded and shear cracks occurred in the upper and lower parts of the column from 2.4 s (17.7 mm). After 2.89 s (38.9 mm), the width of the shear cracks in the lower part of the column increased. At 3.3 s (50.0 mm), severe peeling of concrete began, and the width of shear cracks seriously increased. The maximum displacement occurred at 3.43 s (70.5 mm), and the final collapse was observed as shear failure occurred in the lower part of the framework on the first floor, as shown in Figure 10.  
This is similar to the result of a previous study [12], which suggests that R/C school buildings with non-seismic features are highly likely to exhibit serious seismic damage under the ground motion of 200 cm/s\({}^{2}\). This is judged to be important information for securing the seismic safety of R/C buildings without seismic design, i.e., seismic reinforcement.  
(2) PD-CCSS (CCSS-reinforced specimen)  
Figure 11 shows the final step (10 s) of the PD-CCSS, which is the specimen reinforced with the proposed CCSS, for the seismic wave of 200 cm/s\({}^{2}\). At approximately 2.25 s (displacement: 4.11 mm), slight initial flexural cracks were observed in the lower part of the column. After 3.07 s (displacement: 6.8 mm), the number of flexural cracks increased, but the degree of the cracks was minor. After 4.14 s, when the maximum seismic response was observed (displacement: 10.5 mm), the number of flexural cracks gradually increased, but the crack width was small. Finally, light damage occurred.  
Figure 10: Test result of PD-FR specimen (200 cm/s\({}^{2}\), final).  
Meanwhile, under the seismic wave of 300 cm/s\({}^{2}\), a small number of flexural cracks were additionally observed at 6.22 s (displacement: 20.0 mm) when the maximum seismic response was observed, as shown in Figure 12. Afterwards, only the size of the cracks slightly increased, but the degree was insignificant. Finally, small cracks occurred unlike the unreinforced PD-FR specimen (shear failure) shown in Figure 10.  
Figure 11: Test result of the PD-CCSS specimen (200 cm/s\({}^{2}\), final).  
Figure 12: Test results of the PD-CCSS specimen (300 cm/s\({}^{2}\), final).  
Figure 13 shows the test results of the final step (10 s) for the input ground motion of 400 cm/s\({}^{2}\) assumed to examine the seismic strengthening effect for the CCSS-reinforced specimen in the event of a large earthquake. At 3.6 s (displacement: 35.2 mm), when the maximum displacement response was observed, the degree of flexural and shear cracks was higher than the situation of 300 cm/s\({}^{2}\) described above, and the crack width also increased. The size of cracks, however, was suppressed below 1.0 mm, indicating that medium-sized cracks occurred in the PD-CCSS specimen under the ground motion of 400 cm/s\({}^{2}\).