esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Daily Ambient Air Pollutant Concentrations in Kanazawa and Fukuoka  
Figure 1 shows the daily mean ambient PAH and TSP concentrations at Kanazawa and Fukuoka, Japan, in the examined period. Patterns of the PAH and TSP concentrations course were different between the two sites during the study period. As shown in Table 3, the mean total PAH concentrations over the 2-month period were lower in Kanazawa compared to those in Fukuoka (0.26 \(\pm\) 0.15 vs. 0.52 \(\pm\) 0.34 ng/m\({}^{3}\)). Similar results were observed for the nine individual PAHs. Mean TSP concentrations were higher in Kanazawa than those in Fukuoka (23.0 \(\pm\) 12.4 vs. 15.9 \(\pm\) 7.5 \(\mu\)g/m\({}^{3}\)). Pearson's correlation matrix of air pollutants during the study period in Kanazawa and Fukuoka is shown in Tables S1 and S2, respectively. Daily total PAH concentrations were associated with TSP concentration both in Kanazawa (r = 0.564, \(p\) < 0.001) and Fukuoka (r = 0.366, \(p\) = 0.004), which were similar to the findings in the non-Asian dust period, as previously reported [16]. The monthly mean PM\({}_{2.5}\) concentration in Kanazawa was lower than that in Fukuoka (6.6 \(\pm\) 3.7 vs. 12.4 \(\pm\) 4.2 \(\mu\)g/m\({}^{3}\)), contrary to the TSP levels. Atmospheric PM and PAH concentrations in Kanazawa are known to show seasonal variations. The period covered by this study is spring, which is the season when PM concentrations are greatest, and PAH concentrations are second highest after winter [26]. Furthermore, [PERSON] and his colleagues have calculated the contributions of vehicles and coal combustion and the effects of long-range transport from China on atmospheric pollutants in Kanazawa using the 1-nitrophenyl-pyrene method. They have reported that the average annual contributions of coal heating facilities and vehicles to combustion-derived particulates in the PM\({}_{2.5}\) (Pc) were 69% and 31%, respectively, and that high concentrations of Pc and PAHs in the winter and during the Asian dust event in the spring were largely attributed to the long-range transport of emissions from coal heating facilities in China [26]. In addition, the differences in TSP and PAH concentrations between Kanazawa and Fukuoka in the study period may be due to differences in transboundary transport routes from the Asian continent to Japan in addition to the local TSP and PAH sources in each city, as reported in a previous study [16]. These results might be a part of the source responsible for respiratory diseases with chronic cough, as discussed previously [16].  
Additionally, the 2-month mean NO\({}_{2}\) and SO\({}_{2}\) concentrations in Kanazawa were lower than those in Fukuoka. For both regions, the mean air pollution exposures were lower than the daily limits of Japan for PM\({}_{2.5}\) (35 \(\mu\)g/m\({}^{3}\)), NO\({}_{2}\) (40 ppb), and SO\({}_{2}\) (40 ppb). However, regarding the annual air quality guideline values that were recommended in 2021 by the World Health Organization (WHO) (PM\({}_{2.5}\): 5 \(\mu\)g/m\({}^{3}\); NO\({}_{2}\): 10 \(\mu\)g/m\({}^{3}\)), only the mean exposure to NO\({}_{2}\) was below the limit.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 3 Multi-Objective Path Planning  
In the study of multi-objective route planning, this research focuses on five key objectives for in-depth analysis and optimization, aiming to enhance the driving experience across various application scenarios and to improve the efficiency and quality of route planning.
# Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images
## 4 Results
### Hyperparameter Tuning  
After justifying the choice of our model architecture and data format, we explore the effect of hyperparameter values: learning rate, batch size, and number of dimensions of latent space z. By default, we set learning rate at 0.0002, batch size at 64, and dimension of z at 100. Figure 10 shows the learning curves for the model trained with different learning rates: 0.001, 0.0005, 0.0002, and 0.0001. Opposite to common sense, when the learning rate is smaller, _Loss_D_ and _Loss_G_ converge faster. For models lr = 0.001 and lr = 0.0005, the average FID for a single class reaches the minimum at the 150 th epoch and then goes up at the 200 th epoch. For model lr = 0.0002, FID scores increase slightly at the 200 th epoch. For model lr = 0.0001, FID scores keep decreasing but converge slowly. For all four models, FID scores are close to each other at the 200 th epoch.  
We also train the model with different batch sizes (of training data): 16, 32, 64, and 128 (see Figure 11). When the batch size is small, the learning curves _Loss_D_ and _Loss_G_ are smoother and the discriminator learns more easily However, FID score converges clearly best with batch size = 64.  
Figure 11: Learning curves for our model trained with different batch sizes (of input data): 16, 32, 64, and 128.  
Figure 10: Learning curves for our model trained with different learning rates: 0.001, 0.0005, 0.0002, and 0.0001.  
Figure 9: Sequences of action “standing” generated by the models trained (**a**) with non-TSSI pseudo-images (no reordering of joints) and (**b**) with TSSI-reordered pseudo-images.  
Finally, we investigate the influence of the dimension dim_z of the latent space z. In Figure 12, _Loss_D_ and _Loss_G_ are not affected by the choice of dim_z while FID scores vary significantly. With the increase of dim_z, FID scores tend to converge faster. For the model with dim_z = 5, the FID score shows the decreasing trend along 200 epochs. For the model with dim_z = 10 and 30, the FID scores both reach the minimal value near the 150 th epoch and then go up. The model with dim_z = 100 gets the lowest point of FID at the 130 th and 180 th epochs, much smaller than the results of the others. However, further training epochs are needed to figure out whether FID for dim_z = 5 and 100 would continue decreasing.  
To sum up, when training the model at 200 epochs, the original choice of the learning rate and batch size is already a relatively good setup. Regarding the optimal dimension of latent space, further study is needed to determine it robustly.
## 3 Results
### First-Order Interactions between Parameters  
The full factorial design allows for determination of the first-order interactions between parameters. Figure 5 shows 154 coefficients that describe all influences on Response 1, including the main effects and the interactions. Globally, the interactions between implant parameters have less influence on the response than the main effects. Interactions between the different implant geometry parameters are minimal. Notable interactions are those between implant geometry parameters (type, diameter and length) and cortical bone thickness. For example, the smaller the thickness of the cortical bone, the greater the influence of the implant diameter on stress. The most dominant interaction was between cortical bone thickness and cancellous bone modulus. The interaction between implant angle and the [PERSON]'s modulus of the cancellous bone was also slightly significant.  
Figure 4: Main effects of parameters on Response 2 (stress jump between cortical bone and implant).  
Figure 3: Main effects of parameters on Response 1 (average stress in the peri-implant cortical bone).  
Considering Response 2, several interactions have a high influence, as shown in Figure 6. Compared to the main effects, these interaction effects have a greater influence on this response than Response 1. First of all, the interactions between the geometrical parameters of the implant and its Young's modulus as well as the interaction between the Young's modulus of the implant and the bone parameters emerge. In addition, as with Response 1, the interactions between implant geometry and cortical thickness are also important. Finally, the interaction between implant length and implant angle can be highlighted.  
Figure 5: Main effects and first-order interactions between parameters on Response 1.  
Figure 6: Main effects and first order interactions between parameters on Response 2.
## 5 Experiments
### Experimental Settings  
#### 5.1.1 Datasets  
The experiments are performed on two public datasets DSWE 1 and NREL, released by [31; 32]. Each dataset offers geographical information, such as longitude and latitude. The basic structures of the two datasets are illustrated in Table 1.  
**DSWE 1**: In the DSWE 1 dataset, wind speed data from 120 turbines are collected. The dataset ranges from 2009 to 2010. The data collection frequency is 1 h. The linear interpolation is adopted to fill in the missing values.  
**NREL**: The dataset comes from the Wind Integration National Dataset, published by the National Renewable Energy Laboratory (NREL). The Wind Integration National Dataset gathers wind speed observations for over 120,000 turbines from the USA, covering from 2007 to 2013. In our experiments, we choose a \(10\times 10\) wind turbine array in Wyoming, spanning from 1 January 2012 to 31 December 2012. The data collection frequency is 1 h.
## 4 Experimental Results and Analysis
### Typical Results of Multi-Satellite Scheduling Based on Real-Coding PBIL Algorithm  
The average results of the 10 experiments are shown in Table 2, and one of the typical results is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Scene Settings** & \multicolumn{2}{c}{**Average Optimal Fitness Value**} \\ \hline Objective function & Number of targets & Average optimal fitness value & Average running time (s) \\ Q\({}_{1}\)\(=\max\,(\,\sum V_{i}x_{i})\), \(i\in\,1\) & 50 & 7436 & 7046 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of multi-satellite scheduling.  
Figure 8: The distribution of the targets in STK.  
Figure 10: This is the typical simulation results of multi-satellite scheduling based on a real-coding PBIL algorithm: (**a**) Evolution curve of the algorithm, a description of the optimization process, in which x-coordinate is iterations, and y-coordinate is fitness (corresponding to the scheduling results); (**b**) Gantt chart of multi-satellite scheduling results, in which x-coordinate is time, and y-coordinate is satellite ID, each colored rectangle represents a satellite mission, with a number next to it is the ground targets ID observed by that mission.  
Figure 9: The distribution of the ground stations in STK.  
As shown in the table and figure above, it can be seen that the real-coding PBIL algorithm can effectively solve multi-satellite scheduling problems. As shown in the evolution curve in Figure 9a, it converges around generation 80 and has high computational efficiency.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 1 Introduction
from Cranfield University to experimental testing of a path manager [13]. While [PERSON] et al. introduced an intelligent navigation algorithm to improve motion control and path following of the USV [14]. [PERSON] et al. conducted the adaptive non-linear control for the USV [15], and more sophisticated, robust control with Fuzzy Logic guidance is applied by [PERSON] et al. for improving the precision of USV maneuvering [16]. [PERSON] et al. adopted a high precision Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) to evaluate steering precision [17]. Even the Nonlinear Predictive Controller (NMPC) based on Convolutional Neural Network (CNN), and Ant Colony Optimizer (ACO) is proposed by [PERSON] et al. from Wuhan University of Technology [18]. According to those works, in most research, to develop a reliable USV that follow the command autonomously, a sophisticated controller, such as a non-linear, Fuzzy Logic control, and complex algorithm for path following design is needed, several researchers also propose the Internet of Things (IoT) concept to bridge the communication between the USV and Ground Control Station (GCS). Furthermore, expensive hardware to support the USV is also necessary. For these reasons, the development of USV is exceptionally not affordable and not prevalent to be studied in undeveloped countries. However, in this research, the traditional controller (PID combined Feed Forward) integrated to a simple algorithm, applied in affordable hardware, and using an open-source ground control station is implemented. The mechanical structure of a USV, which is composed of a thruster and rudder, is quite complicated to build. The rudder frequently uses plastic as its primary material, which can be easily damaged by the wear of gear during the steering process. The energy conversion rate of the rudderless USV is higher than USV with rudder [19]. This study employs rudderless and double thrusters as the main actuators. The USV velocity can be controlled by increasing or decreasing the speed thrusters, while the steering of the USV is generated from the differential thrust control from the left and right thrusters.
## 3 Results  
In addition, the Arduino communication system is used as a data acquisition and communication system. Arduino is used to collect data from the sensor side and send information such as temperature, moisture, soil pH, etc., to the database of the Raspberry Pi device via a USB connection. Based on the collected data, Raspberry Pi builds a data learning model. In the modeling of data processing, the data are learned from different factors, and the system automatically compares them based on different vector factors and proposes appropriate suggestion results. For the MQTT participant, the Raspberry Pi machine will compare the collected data with the received data automatically. The simulation architecture of our experimental scenario is shown in Figure 3.  
During data training and learning, we must select a communication protocol to verify the data training template. In our research, we use MQTT's communication protocol to establish a recording and communication mechanism. The MQTT communication protocol was made publicly available by QASIS in 2013. It provides a publishing-subscription transmission protocol and provides the status of the mutual transmission data in M2M/IoT mode.  
In this study, the model was built after the data are fed to the remote server by MQTT on the Raspberry Pi node. Along with another set of Raspberry Pi, hardware is used to check the training results. The audit results are sent to the cloud platform synchronously so that users can use them and see if the changing curves of different vegetable growth factors in different environments show different results.  
To make repetitive training results more accurate in a standard neural network, a probability representing the possibility of stopping each neuron is usually established in the exclusion layer. Its main goal is to stop that node and let another node conduct the computation (Figure 4).  
Figure 3: Experimental environment hardware and communication system architecture.  
Considering the data to avoid excessive simulation calculations and possible distortion in deep learning processing, the exclusion mechanism was added to construct each layer of the neural network and to fix the probability of elimination of the nodes in the neural network. Some content from each training course is extracted, giving the data back during the repeated workout and then extracting another portion of the data content to gradually increase the authenticity of its workout and to prevent overflow.  
The model structure using dropout had been defined as listed in Table 2.
## 3 Fecal VOC in Gastrointestinal DISEASES
### Necrotizing Enterocolitis  
Necrotizing enterocolitis is one of the leading causes of morbidity and mortality in neonatal intensive care units [51]. While the overall survival of extremely preterm infants is improving, incidence of NEC increases, varying from 2% to 7% in preterm infants with a gestational age <32 weeks and 5-22% in very low birth weight infants [51]. Mortality in high-income countries varies from 15.9% to 42% and 20-40% eventually require surgical interventions [51; 52]. Nowadays, establishment of the diagnosis is reached by a combination of clinical, radiographic and surgical features, where early diagnosis and initiation of therapy are of pivotal prognostic importance. Up until now, two studies focused on fecal VOCs as a noninvasive biomarker for preclinical detection of NEC.  
In 2009, [PERSON] et al. conducted a pilot case-control study analyzing fecal VOCs of six NEC and seven control infants by means of GC-MS [24]. Fecal samples of different time points prior to clinical manifestation of NEC were analyzed. The number of different extracted VOCs became more numerous as the infant matures (0.49 extra VOCs per day), while this trend was absent in NEC cases. This phenomenon likely reflects the increasing intestinal microbial diversity. In addition, up to four days prior to clinical manifestation of NEC, absence of four different ethers were noticed (Table 3). The non-NEC samples, mostly contained at least one of these esters. The authors hypothesized that the loss of some VOCs before clinical manifestation of NEC may reflect the change in intestinal physiology and intestinal microbiota. Another research group conducted a prospective multicenter case-control study including 13 NEC cases, 31 sepsis cases and 14 controls [14]. Fecal samples collected in thedays prior to clinical onset of NEC and sepsis were analyzed using an eNose (Cyranose 320(r)). It was possible to discriminate fecal VOC profiles of NEC cases from controls up to three days prior to clinical onset of NEC. In addition, the differences in VOC profiles of the two groups increased even further toward onset of NEC (Table 3).  
Both studies underline the potential of fecal VOC analysis in early detection of infants who develop NEC, as a non-invasive, low-cost and user friendly diagnostic mean. Furthermore, in clinical practice, it is yet found difficult to diagnose NEC at an early stage, whereas early diagnosis is correlated with a better outcome. Before clinical implementation of VOC analysis as a diagnostic mean, larger multicenter validation studies need to be carried out, by means of both GC-MS and eNose, preferably linked to longitudinal microbiota analysis to assess origin of the VOCs, increasing understanding of NEC pathophysiology.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 4 Resource Allocation Algorithm
### Sub-Problem Solution
Substituting the expression of \(\beta_{n}(t)\) into \(r_{n}(t)\). According to the nature of logarithmic function, we can get  
\[\log(K\frac{p_{n}(t)}{\sum_{i=1,i\
eq n}p_{i}(t)})=\log(K)+\log(p_{n}(t))-\log( \sum_{i=1,i\
eq n}p_{i}(t)).\]  
We can get the value of \(\log(\sum_{i=1,i\
eq n}p_{i}(t))\) in time slot \(t\), and \(\log(K)\) is a constant. Thus, the minimization problem can transform to  
\[\min_{p_{n}(t)}\widehat{f}_{n}(t)z_{n}(t)p_{n}(t)-I_{n}(t)z_{n}(t )\log(p_{n}(t)),\] \[\text{s.t.} \begin{cases}0\leq p_{n}(t)\leq p^{max},\\ z_{n}(t)=1,if\;I_{n}(t)>L.\end{cases} \]  
In time slot \(t\), we can get the values of energy queue occupancy \(J_{n}(t)\), data queue occupancy \(I_{n}(t)\), and \(z_{n}(t)\). They are constants in time slot \(t\). Thus, minimization of the optimal problem just needs to optimize the transmission power \(p_{n}(t)\), and the logarithmic function is concave. Thus, the problem is convex. Let the transmission power rate \(p_{n}^{*}(t)\) be the optimal solution, and we have:  
\[p_{n}^{*}(t)=[\log^{{}^{\prime}-1}(\frac{\widehat{f}_{n}(t)}{I_{n}(t)})]_{0}^ {p^{max}}. \tag{15}\]
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Registration  
#### 3.4.1 Techniques and Approaches  
Image registration is the process of aligning two or more images, often acquired from different modalities or at different time points, to facilitate comparison and analysis [129]. DL has been increasingly applied to image registration tasks, with CNNs and spatial transformer networks (STNs) being the most commonly used architectures [130]. Supervised learning approaches, such as using ground-truth deformation fields or similarity metrics as labels, have been employed to train deep registration models [131]. Moreover, unsupervised learning techniques, which do not require ground-truth correspondences, have been proposed to overcome the challenges of obtaining labeled data for registration tasks [132].
# Input-to-State Stability of Variable Impedance Control for Robotic Manipulator
## 3 ISS of Variable Impedance Model
### Update Rule of Stiffness Matrix
For example, if the joint velocity error of the manipulator and an external joint torque have the same signs, then the all the elements of the outer product of \(\dot{e}_{i}d_{j}\) for \(i=1,2,\cdots,n\) and \(j=1,2,\cdots,n\) will have positive signs in the matrix. It results in a hard motion of the manipulator by increasing the joint stiffness matrix. On the other hand, if they have the opposite signs, then the element of outer product of \(\dot{e}_{i}d_{j}\) will have negative, and it brings the soft motion of the manipulator by decreasing the joint stiffness matrix. For implementing the stiffness matrix update of Equation (26), the information of external joint torque is required, as an alternative, the momentum observer [16] can be used to estimate the external joint torque using the momentum dynamics. Since the variable impedance control of Equation (6) does not require any force/torque sensor attached to the end-effector of the manipulator, the momentum observer would be a good alternative. One of the leading roles of the proposed sufficient conditions in Theorem 2 and the subsequent update rule in Theorem 3 is to consider the external forces for stability analysis, and it brings the difference from the reference [7]. The external force is a necessary factor in determining the stability of the closed-loop control system since the variable impedance control is used in interactive situations with the environment and its update rules/profiles also work when external forces are detected. Theorem 1 in [7] demonstrated the stability of the control system under the assumption of a free (unconstrained) motion situation. It means that the update profiles for the stiffness and damping matrices cannot guarantee stability when the external forces affect the motion of the robotic manipulator. On the other hand, the proposed sufficient conditions and the update rule promise the ISS of the closed-loop control system even when the external forces are applied to the control system, and further, the asymptotic stability can be proven in the case of free motion (\(d=0\)).