esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Road Boundary Optimization Results  
During the boundary extraction process, the key aspect of the Alpha Shape algorithm is determining the value of the radius. In this study, the value of \(\alpha\) was set to 6, based on the characteristics of the highway road point cloud. An allowable error of 0.004 was considered. The extracted boundary results are shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & \begin{tabular}{c} **Number of Point** \\ **Clouds** \\ \end{tabular} & \begin{tabular}{c} **Supervoxelization/s** \\ \end{tabular} & \
\begin{tabular}{c} **Partition/s** \\ \end{tabular} &
\begin{tabular}{c} **Total Time/s** \\ \end{tabular} \\ \hline Euclidean clustering-based segmentation & 4,815,042 & / & 315 & 315 \\ RANSAC-based segmentation & 4,815,042 & / & 344 & 344 \\ Traditional region growing-based segmentation & 4,815,042 & / & 399 & 399 \\ Improved region growing algorithm & 4,815,042 & 23 & 141 & 164 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of four segmentation algorithms for MLS point cloud data in sloped road scene.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experimental** & **Road** & **Detected Road** & **TP** & **FN** & **FP** & **Recall/\%** & **Precision/\%** & **F1/\%** \\
**Scenarios** & **Surface Points** & **Surface Points** & **TP** & **FN** & **FP** & **Recall/\%** & **Precision/\%** & **F1/\%** \\ \hline Flat road & 3,483,979 & 3,576,981 & 3,470,824 & 13,155 & 106,157 & 99.6 & 97.0 & 98.3 \\ Sloped road & 4,304,106 & 4,468,155 & 4,242,854 & 61,252 & 225,301 & 98.5 & 94.9 & 96.7 \\ Average & & & & & 99.1 & 96.0 & 97.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of classification results on two experimental datasets.  
It can be observed from Figure 16 that the extracted road boundary has a complete and clear structure, providing a reliable foundation for the subsequent output of standardized road surface results.
## 3 Experiments and Discussion
### The Experiments of Defective Image Generation Models  
In this section, several methods for generating several images based on different principles are tested qualitatively and quantitatively. These methods include BAGAN for generating imbalanced class samples, BAGAN-GP with a penalty term, RSSA for aligning the spatial structure of pre-trained models on large-scale datasets to generate new images, and WaveGAN for generating images using rich frequency information.  
Qualitative evaluation is generally based on the subjective judgment of the quality of the generated images and their similarity to the original images. The three types of defective welds generated by different networks are shown in Figure 11, where the images generated by BAGAN and BAGAN-GP have insufficient clarity, and the images generated by RSSA differ significantly from the original images. The images generated by WaveGAN and the FastGAN proposed in this paper have a better intuitive feel.  
Figure 11: Some examples of images generated by GANs.  
Figure 10: MobileViT network structure.  
The quantitative evaluation used Frechet Inception Distance (FID) [41] as an evaluation metric, which measures the feature-level distance between the real and generated data distributions. FID is defined as follows:  
\[FID=\left\|\mu_{r}-\mu_{g}\right\|^{2}+T_{r}(\Sigma_{r}+\Sigma_{g}-2(\Sigma_{r} \Sigma_{g})^{\frac{1}{2}}) \tag{5}\]  
where \((\mu_{r},\Sigma_{r})\) and \((\mu_{g},\Sigma_{g})\) are the mean and covariance of the real and the generated features.  
The FID score was calculated between the real samples of the test set and the samples generated by GANs. A lower FID indicates a higher quality of generated images. We used the FID of real samples in the training set as a baseline, and the experimental results are shown in Table 1, which indicates that FastGAN performed better in augmenting the welding defect dataset.  
To further analyze the generated results, t-SNE was used to visualize the original and augmented datasets. It can embed high-dimensional data into a two-dimensional space for the easy observation of the data distribution [42]. From Figure 12, it can be seen that the generated image manifolds are uniformly distributed around the real image manifolds. This indicates that, for each class, FastGAN learned the real data distribution comprehensively and there was no problem with model collapse. Therefore, the generated samples can be considered as valid enhanced images.
## 3 Materials and Methods
### Sensors and IoT Devices  
The main sensors used in the aquaponics system include sensors for water temperature (DFROBOT DS18B20 digital), water pH (DFROBOT Gravity Analog-SEN0161), water turbidity (DFROBOT Analog), dissolved oxygen (DFROBOT Analog DO), total dissolved solids (DFROBOT Analog TDS) and CO2 (DFROBOT Infrared). These sensors were connected to the fish water tank for data collection. The sensors were attached to the Arduino UNO Microcontroller and an ESP8266 Wi-Fi module, which is connected to a standard PC. All these sensors and IoT devices were operated on the USB power from the connected PC. Arduino UNO IDE 2.0 firmware was used for this project.  
Overall, the length of the plant, the lengths of the plant leaves, breadth size and leaf-stem size measurements as well as the fish weights and lengths were calculated on a 7-day interval so that the changes would be noticeable. Fish and plant attributes were measured using a measuring scale and a high-precision weighing scale. Fish were fed once daily and the same source of water was used for water replenishment. After the first phase of the project, the growbed media, trays, fish, plants, water, connecting pipes and filters were replaced with new stock to avoid any possibilities of contamination or influence in the new results. The room was sanitized before the second run, using soap water and antibacterial wipes. The second phase of the project was performed for the next 3 months with weekly water replenishment. Figure 2 shows the plants' attributes, whose data were collected, and that both testbeds were used along with the red tape earthworms added to the vermiponics growbed media.  
Figure 1: IoT communication in this project.  
The fish and plant data collected were segregated as 4 CSV files: two of them for the standard testbed's data before and after water replenishment and the other two for the vermiponics testbed's data before and after water replenishment. Python code was used to perform data analysis and comparison. Plant length, leaf length and breadth, and leaf-stem perimeter measurements were stored, and average values were calculated. Fish weight and length were measured for each fish and averaged for 6 fish within the system.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 3 Preliminaries  
In this subsection, a brief description of the important concepts is introduced and the problems are addressed, as well as a formal definition of them:  
Traffic sensors are sensors deployed at various intersections on the traffic network to record and collect information such as traffic flow and speed.  
The road traffic network is the traffic network formed by intersecting urban roads. Intersections are regarded as nodes in the traffic network, and urban roads connecting two nodes are regarded as edges in the traffic network.  
Definition 1: (Road Traffic Network): This model defines the road traffic network as a traffic network or a traffic graph, denoted as \(G=(V,E,A)\), where \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) is a finite set of nodes in the graph \(G\), with \(|V|=N\), where \(N\) is the total number of nodes in the graph \(G\), corresponding to \(N\) traffic sensors deployed at various locations in the traffic network. \(E\) represents the set of edges in the traffic graph \(G\). \(\mathbf{A}=\left\{a_{ij}\right\}_{N\times N}\in\{0,1\}\) is the adjacency matrix of the traffic graph \(G\). For any node \(v_{i},v_{j}\in V\) in the traffic graph \(G\), if there is an edge connecting node \(v_{i}\) and node \(v_{j}\), then \(a_{ij}=1\), otherwise \(a_{ij}=0\)..  
Traffic signals refer to the actual traffic flow measurements recorded by all traffic sensors in a traffic network at a given time step.  
Definition 2: (Traffic Signal): The traffic signal \(X_{t}=\left\{x_{1}^{t},x_{2}^{t},\ldots,x_{N}^{t}\right\}\in R^{N\times D}\), represents the measurement values of all the sensors in the traffic graph \(G\) at time \(t\). \(D\) denotes the feature dimension of the traffic information such as vehicle speed, traffic volume, etc. \(N\) represents the total number of nodes in the traffic graph \(G\).  
Traffic flow prediction refers to the process of predicting the traffic conditions in a road network for a future period based on the traffic data collected by sensors deployed in the network over a certain historical time interval.  
Problem Definition (Traffic Flow Prediction): Given a traffic graph \(G\) and traffic signals \(X^{T}=\left\{X_{(t-T+1)},\cdots,X_{t}\right\}\) within \(T\) consecutive historical time steps, \(t\)'s objective is to learn a mapping function \(\mathcal{F}(\cdot)\) that accurately predicts the traffic flow conditions of graph \(G\) for the next \(T^{\prime}\) consecutive time steps:  
\[\left[X_{(t-T+1)},\cdots,X_{t};G\right]\overset{\mathcal{F}(\cdot)}{\to}\left[ X_{(t+1)},\cdots,X_{(t+T^{\prime})}\right] \tag{1}\]
## Nomenclature  
Acronyms  
4 GDH 4 th Generation District Heating  
DHC 4 th Heating and Cooling  
GA 4 Genetic Algorithm  
NOCT 4.1 Nominal Operating Cell Temperature  
RES 4.1 Renewable Energy Source  
STC 4.1 Standard Test Conditions  
Parameters  
\(a\) 4.1 Coefficient of the equipment cost curve, \(\xi\)/kW  
\(b\) 4.1 Exponent of the equipment cost curve, dimensionless  
\(c\) 4.1 Cost per unit of energy, \(\xi\)/kWh  
\(CAP\) 4.1 Capacity, kW  
\(CF\) 4.1 Correction factor, dimensionless  
\(COP\) 4.1 Coefficient of performance, dimensionless  
\(CRF\) 4.1 Critical recovery factor, dimensionless  
\(EAC\) 4.1 Equivalent annual cost, \(\xi\)  
\(Invest\) 4.1 Investment cost, \(\xi\)  
\(k\) 4.1 Weibull shape parameter, dimensionless  
\(Op\) 4.1 Annual operating cost, \(\xi\)  
\(r\) 4.1 Interest rate, dimensionless  
\(SCOP\) 4.1 Seasonal coefficient of performance, dimensionless  
\(t\) 4.1 Timestep, hour  
\(z\) 4.1 Height, m  
\(\gamma\) 4.1 PV power temperature coefficient, \({}^{\circ}\)C\({}^{-1}\)  
\(\varepsilon\) 4.1 Electric energy consumption per unit of rejected heat, dimensionless  
\(\eta\) 4.1 Efficiency, dimensionless  
\(\kappa\) 4.1 Heat pump capacity temperature coefficient, kW/\({}^{\circ}\)C  
Continuous variables  
\(C\) 4.1 Cooling power, kW  
\(E\) 4.1 Electric power, kW  
\(G\) 4.1 Solar irradiance, kW/m\({}^{2}\)  
\(L\) 4.1 Load factor (defined as the ratio between the power output of the unit and its capacity), dimensionless  
\(v\) 4.1 Wind speed, m/s  
\(Q\) 4.1 Heating power, kW  
\(T\) 4.1 Temperature, \({}^{\circ}\)C  
\(\Theta\) 4.1 Thermal energy, kWh  
Binary variables  
\(\delta\) 4.1 On-off state  
Subscripts  
\(AC\) 4.1 Absorption chiller  
\(B\) 4.1 Boiler  
\(C\) 4.1 Cooling mode  
\(CT\) 4.1 Cooling Tower  
\(D\) 4.1 Demand  
\(E\) 4.1 Electric energy  
\(EC\) 4.1 Electric chiller  
\(ex\) 4.1 Exergy  
\(F\) 4.1 Fuel  
\(gen\) 4.1 Generator  
\(H\) 4.1 Heating mode
## 2 Patients and Methods
### Echocardiographic Evaluation  
Echocardiography is the preferred imaging tool to assess valvular disease. Although, traditionally, MAC may be suspected or diagnosed on chest radiographs or more clearly on cine angiograms, which also serve to define the anatomical relationships with the coronary circulation, echocardiography represents the first-line step to depict the diseased MV with MAC. More in particular, transesophageal imaging is mandatory to define leaflet involvement by the calcification and secondary restricted motion, along with the corresponding variable degree of stenosis or regurgitation, or more often both. Extension of the MAC deep into the posterior basal or, at times, mid portion of the left ventricular free must be mandatorily assessed to estimate the risk of disruption. Also the extension of the MAC to the subaortic curtain and eventual involvement of the aortic valve must be excluded when planning the feasibility of a minimally invasive MV operation. Some degree of mild or mild-to-moderate aortic insufficiency is common in this clinical setting and can safely be tolerated, whereas more severe aortic incompetence would jeopardize the effective delivery of cardioplegia in the aortic root. Three-dimensional transesophageal echocardiography is also useful to simulate the MV anatomy as viewed by the surgeon from the left atrial side. Whereas echocardiography well depicts the dynamics of the MV and MAC during the cardiac cycle, we now routinely indicate computed tomography (CT) scan to better define the density of the MAC.
### Multidimensional Signals Processing  
_Software._ The online experimental paradigm was built from the ground up on Python open-source language, synthesizing and using the best-tested parts of specific BCI and EEG modules. For more detail on NeuroPrime, check [68]. The offline data analysis pipelineswere run first in Brain Vision Analyzer (Brain Products GmbH) for visual inspection of noisy channels, noisy epochs, and the processing pipeline's automation planning. Then, NeuroPrime (with Python modules like MNE) was used for advanced signal processing/classification and automation of the pipeline [68].  
**NFT.** During the EEG NFT online loop, the data were updated at an average rate of 200 ms in each iteration. It was concatenated in an epoch buffer of 1 s, meaning that the epoch is made from 800 ms of historical data and 200 ms of new data. There is no real-time loop in the offline analysis, the entire length of each task is analysed instead, and during pre-processing, these data were segmented into epochs of 1 s. Continuous EEG measurements were band-pass filtered with a low cut-off of 1 Hz and a high cut-off value of 40 Hz using a finite impulse response filter (FIR). The original sampling of 1 kHz was not subsampled to maintain a higher resolution on the fast Fourier transform (FFT). Although a common-reference was used for online data analysis, the data are re-referenced to an average-reference previously to offline data processing. Four EEG channels were selected for further processing: Fp1, Fp2, Fz, and Pz. In both online and offline processing pipelines, we excluded epochs with abnormally large amplitudes with a maximum peak-to-peak of over \(\pm\)100 \(\upmu\)V for online and \(\pm\)150 \(\upmu\)V for offline and also based on the flatness of the signal with a minimum peak-to-peak acceptance of \(\pm\)0.5 \(\upmu\)V. Additionally, in offline analysis, epochs contaminated by spurious gross-movement and other non-stereotyped artefacts were also identified by visual inspection and additionally rejected. Afterwards, during processing, the band power values were extracted from the power spectrum of the Pz channel (for theta 4-8 Hz, alpha 8-12 Hz, SMR 12-15 Hz, beta 15-35 Hz). For a list of descriptions of all the EEG features, please go to Appendix A.1.  
_GSR and HRV._ They were continuously monitored for all the participants during the session. Each signal was acquired at a 1 s interval. GSR tonic (skin conductance level--SCL) and phasic components (skin conductance responses--SCR) were extracted offline from each 1 s interval, with an excosomatic direct current sensor [87]. HRV time domain, frequency domain, and non-linear domain features were extracted from the 1 s PPG RR-intervals [88]. For a list of descriptions of all the features, please go to Appendix A.3.
## 4 Simulation Results
### Noise Tolerance Analysis
#### 4.3.1 Noise Tolerance--LEO  
To begin with, the effect of background noise in LEO-to-ground QKD links is discussed. Various night-time radiance conditions are being considered according to [33], and they correspond to different radiance values, which are mainly dependent on the moon's cycle. Once again, an emission rate of 250 MHz and a transmitted block size of \(10^{10}\) bits were considered. Figure 8 presents the distilled key bits over time for a single satellite pass, and this was achieved by considering different sky radiance values for (a) \(D_{r}=1\) m and SMF-coupled SNSPD detection, (b) \(D_{r}=1\) m and SMF-coupled SPAD detection, and (c) \(D_{r}=1\) m and free-space SPAD detection. It is evident that fiber coupling renders the system much more robust to background noise as it enables QKD communication even under full moon, night-time conditions. For the case of SNSPD detection, since the SNR is improved due to the higher quantum efficiency granted by the detectors, the system can also (barely) perform under daytime conditions (\(H_{rad}=1.5\) W/m\({}^{2}\)sr \(\mu\)m), yet only with extremely limited communication time windows.  
Additionally, despite the lower overall link budget, the SPADs also seemed to be robust in terms of sky radiance when fiber coupling was considered, and this was without being severely disturbed by the moon's presence. On the other hand, free-space-coupled SPAD systems are much more vulnerable to background noise, yet they perform with higher key rates due to the lack of the lossy SMF coupling during different night-time conditions.
## 4 Results and Discussion
### Diaphragm Movement and Stress Characteristics of the DSDC
#### 4.2.3 Diaphragm Stress Distribution Under the Specific Case #3 Condition  
Figure 17 shows the simulation results of surface stress under the condition of case #3. Figure 17a,b shows the stress distributions of the first-stage and second-stage diaphragms at the TDC position, respectively. The results revealed that the first-stage gas-side diaphragm surface exhibited a maximum radial stress of 176.48 MPa at the edge region, while the oil-side surface showed a peak circumferential stress of 180.16 MPa at the center. For the second-stage diaphragm, maximum radial stress of 185.56 MPa occurred at the diaphragm center, while the oil-side surface showed peak radial stress of 139.44 MPa at the edge region. Moreover, it was observed that the gas-side center regions exhibited higher stress magnitudes at the TDC position, whereas the oil-side edge regions exhibited higher stress magnitudes. Combined with the diaphragm movement behavior under the case #3 condition, the derived stress distributions at the BDC position are illustrated in Figure 17c,d. The first-stage oil-side diaphragm center reached a maximum stress of 47.19 MPa, while the second-stage oil-side center recorded 40.90 MPa at the BDC position. Furthermore, these results indicated that the surface stress of the diaphragm at the TDC position was significantly higher than that at the BDC position. This strategy was designed and developed  
Figure 15: Diaphragm deformation under different pressure conditions. (**a**) Swept volume variation in the deformed diaphragm (\(V_{m0}\)). (**b**) Radial deflection variation in the first-stage diaphragm. (**c**) Radial deflection variation in the second-stage diaphragm.  
to prevent diaphragm rupture caused by adhesion to the oil-side chamber surface under oil-deficient operating conditions. Moreover, the variation in maximum surface stress with the Young's modulus is shown in Supplementary Material S7.
## 3 Evaluation II: Factors Affecting the Evaluation of Signs
### Results of Evaluation II
#### 3.3.3 Analysis of Causal Relationships between Factors by Multiple Regression Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Factors**} \\ \hline
**Items** & **Harmonicity** & **Clarity** & **Visibility** & **Comprehensibility** \\ \hline Calming & 0.885 & 0.058 & \(-\)0.213 & \(-\)0.056 \\ Concordant & 0.660 & \(-\)0.053 & \(-\)0.152 & 0.013 \\ Safe & 0.649 & \(-\)0.074 & 0.067 & 0.216 \\ Pleasant & 0.602 & 0.269 & 0.073 & \(-\)0.072 \\ Well balanced & 0.506 & \(-\)0.009 & \(-\)0.081 & 0.339 \\ Liked & 0.428 & 0.102 & 0.177 & 0.216 \\ Neat & \(-\)0.134 & 10.001 & \(-\)0.061 & 0.066 \\ Concise & \(-\)0.061 & 0.684 & \(-\)0.097 & 0.343 \\ Open & 0.155 & 0.551 & 0.153 & \(-\)0.067 \\ Wide & 0.305 & 0.508 & 0.201 & \(-\)0.258 \\ Standing out & \(-\)0.203 & 0.033 & 0.883 & 0.083 \\ Flashy & \(-\)0.125 & 0.011 & 0.737 & \(-\)0.020 \\ Easy to find & \(-\)0.079 & \(-\)0.013 & 0.729 & 0.155 \\ Friendly & 0.247 & 0.215 & 0.426 & 0.058 \\ Dynamic & 0.390 & \(-\)0.171 & 0.394 & \(-\)0.139 \\ Easy to understand & \(-\)0.109 & 0.087 & 0.033 & 0.950 \\ Clear & \(-\)0.006 & 0.208 & 0.148 & 0.628 \\ Kind & 0.334 & \(-\)0.192 & 0.173 & 0.535 \\ Readable & 0.040 & 0.468 & \(-\)0.086 & 0.471 \\ Unity & 0.235 & 0.262 & \(-\)0.005 & 0.264 \\ \hline Contribution ratio & 47.185 & 5.473 & 4.268 & 4.079 \\ Cumulative contribution ratio & 47.185 & 52.658 & 56.927 & 61.006 \\ \hline \multicolumn{6}{c}{**Inter–Factor Correlations**} \\ \hline  & **Harmonicity** & **Clarity** & **Visibility** & **Comprehensibility** \\ \hline Harmonicity & \(1\) & 0.672 & 0.614 & 0.638 \\ Clarity & & \(1\) & 0.642 & 0.716 \\ Visibility & & \(1\) & 0.677 \\ Comprehensibility & & & \(1\) & 1 \\ \hline \end{tabular}
\end{table} 
Table 5: Factor loadings for each item and the inter-factor correlations. The results showed that the standard partial regression coefficient for \"Comprehensibility\" was high (0.603), while the standard partial regression coefficients for \"Harmonicity\", \"Clarity\", and \"Visibility\" were all low. These three factors were moderately correlated with \"Comprehensibility\", suggesting that they have an influence on \"Comprehensibility\". Therefore, the multiple regression model was modified and analyzed again in order to examine the relationship between \"Harmonicity\", \"Clarity\", \"Visibility\", and \"Comprehensibility\". The modified model consisted of a single regression analysis (Dependent variable: \"Good-Bad\", Independent variable: \"Comprehensibility\") and a multiple regression analysis (Dependent variable: \"Comprehensibility\", Independent variables: \"Harmonicity\", \"Clarity\", \"Visibility\": Variable selection method: forced input method). The results of the modified multiple regression analysis are shown in Table 7, and the modified path diagram is shown in Figure 12.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Amino-Acids neurotransmitters
#### 1.1.4 Glycine  
Glycine is the major inhibitory neurotransmitter in the spinal cord, with a higher concentration at the ventral horn [15; 40; 41; 42]. Glycine also acts as a neurotransmitter in the brainstem and medulla, and is also a required co-agonist with glutamate for N-methyl-d-aspartate (NMDA) receptors [43]. It is involved in voluntary motor control and sensory processing, and also in auditory, cardiovascular and respiratory functions [41; 42]. There are glycine receptors in the bipolar, ganglion and amacrine cells of the retina, with different subunit expressions [44]. As with GABA, Glycine acts as an excitatory neurotransmitter in early development, and may thus play a role in neuronal differentiation, proliferation, and connectivity [41; 44].  
The mutations in glycine receptors seem to be involved in motor control disorders like hypertonia and hyperekplexia [40; 42]. Glycine transmission may also be involved in alcohol intoxication [42].  
Studies have shown that GABA and glycine are co-released in certain neurons in the cerebellum, spinal cord and superior olive, but the interaction between the two neurotransmitters is not well understood [41]. Both glutamate and glycine sites must be occupied to lead to channel opening, with glutamate being released by the presynaptic neurons and glycine by glial cells [42].