esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Radiometric Properties
The same occurred in the yellow and colorless EPCs, where, despite presenting high transmission percentages (Table 9), their behavior was highly homogeneous throughout the entire spectral region of NIR. The difference between the first and the last two was due to the use of additives in the grey EPC to prevent the passage of NIR radiation. Regarding reflectance, it can be seen in Figure 1 that the blue EPC presented the highest value in the NIR region (around 30%), the grey EPC had an intermediate value (around 20%) and for the yellow and colorless EPCs, the value was lower than 10%. Both the blue and grey EPCs can be considered NIR reflectors and are therefore adequate for warm climates [48]. [PERSON] et al. [51] compared a conventional control plastic (similar to the colorless EPC) with a plastic with pigments that reflected NIR, showing a 15% reduction in the PAR of the former compared to the latter. Regarding the PAR/NIR ratio (Table 3), values above 1 would indicate that the plastic film is able to transmit more PAR than NIR, which would be ideal in hot climates where very high temperatures are reached during the summer [52]. In our case, the grey, yellow and colorless EPCs showed values close to 1, i.e., they transmitted the same amount of PAR as NIR. The worst in this respect was the blue EPC (0.52), which transmitted considerably less PAR relative to NIR. Regarding the R/FR ratio, the values of the grey, yellow and colorless EPCs were all 1, while the blue EPC showed a ratio of 0.56. The R/FR ratio is of vital importance for phytochrome response [53]. Low values of this ratio can cause a priori fewer desirable characteristics such as stem elongation, an enhancement of apical dominance, a reduction in branching and an inhibition of flowering [54]. Looking at the B/R ratio (Table 3), our results showed values close to 1 for the grey, yellow and colorless EPCs, while the blue EPC showed a high value (1.71). [PERSON] [55] found that high radiation levels within the blue spectrum relative to those in the red spectrum have various morphological effects. [PERSON] et al. [56] observed how for a crop of _Melissa officinalis_ L., high values of this ratio caused increases in the plant height, leaf area and chlorophyll content. After analyzing the behavior of diffuse radiation (Table 4), it was found that none of them exceeded 50% diffusion, which is the minimum value that must be reached to confirm that a plastic film has special additives in its composition for this purpose and can be considered as highly diffuse [7]. Despite presenting values below 50%, according to ASTM D1003, it is sufficient to have a percentage >30% in order to be considered a diffusing coating, and all the EPCs analyzed in this study can be considered as such. The yellow EPC demonstrated asymmetry between its sides. This fact could be due to the nickel-quencher-type photostabilizer additive used in the composition of the yellow EPC in the external layer of the film, which, by absorbing UV radiation to prevent degradation, also absorbed part of the PAR region [9].
## 2 System Model
### Transmitted Signal Pattern  
A single carrier phase shift keying (PSK) modulation with channel coding is taken into consideration here. For the \(k\)-th frame transmitted signal, the bit stream \(\mathbf{b}_{k}\) of baseband is first encoded by a recursive system convolutional (RSC) decoder and interleaved by a random interleaver, and the size of produced binary bit stream \(\mathbf{c}_{k}\) is generally different from \(\mathbf{b}_{k}\). Assuming that a \(q\)-order modulation is exploited, the symbol constellation contains \(2^{q}\) pots, i.e., the constellation set \(S=\{\gamma_{1},\cdots,\gamma_{2^{q}}\}\). The relevant bit pattern of the constellation pot \(\gamma_{i}\) is \(s_{i}=[s_{i}^{1},\cdots,s_{i}^{q}]\), where \(s_{i}^{q}\in\{+1,-1\}\). Then, \(\mathbf{c}_{k}\) is divided into segments according to the adjacent \(q\) bits with \(\mathbf{c}_{k,n}=[c_{k,n}^{1},\cdots,c_{k,n}^{q}]\). Finally, the grouped bits are mapped to the constellation, resulting in the transmitted baseband signal \(\mathbf{x}_{k}\). Under the assumption that the correlation between neighbouring frames is independent, we omit the subscript character \(k\). Therefore, the passband transmitted signal of the \(n\)-th symbol is given as [38]:  
\[\hat{x}_{n}(t)=\mathfrak{R}\{x_{n}\text{exp}\{j2\pi f_{c}t\}g(t-nT_{c})\} \tag{7}\]  
where \(\mathbf{x}=[x_{1},\cdots,x_{n},\cdots,x_{N}]^{T}\), \(f_{c}\) is the carrier frequency, \(g(t)\) is the pulse shaping filter, \(T_{c}\) is the symbol period. For flatness in passband, the raised cosine rolling-off shaping filter is commonly used in UWA communication systems due to the severe frequency selective fading.
## 4 Results of the Experiment and Discussion  
The validation experiment consisted of 15 participants that already had experience in the job, but not more than 5 years. They were not older than 30 years. The volunteers of the validation experiment did not participate in the previous research, including the survey.  
The occurrence of significant errors on the job was assigned if the train traffic controller was characterized as follows:  
* Committing safe errors and uncertain performance for at least 25% of the conducted trains, where the uncertainty was identified by the eye-tracking image processing algorithm;
* Committing safe errors in at least 50% of the trains in the absence of unambiguous uncertainty in the performed activities, where the uncertainty was identified by the eye-tracking image processing algorithm;
* Making at least one unsafe error.  
The test validation results are presented in Table 3.  
Upon analyzing the data in Table 3, it should be noted that the respondents correctly performed the tasks in the experiment in 11 out of 15 cases, which indicates that the effectiveness of the proposed method is \(p_{1}=\frac{m_{1}}{n_{1}}=\frac{11}{15}=73\%\). After changing the scenario of the experiment to be adequate for the second preference of the post and conducting the experiment again, a total of 13 out of 15 cases showed a correct match to the post. This indicates the effectiveness of the method at \(p_{2}=\frac{m_{2}}{n_{2}}=\frac{13}{15}=87\%\).  
Figure 8: Eye-tracking camera footage—setting of the turnout.  
The gathered results have been analyzed statistically to answer the following questions:  
* Does the second step improve the effectiveness?
* Is the effectiveness after one or two steps at least 90%?  
To answer the questions, two parametric tests were performed. The first was the parametric test for two proportions. The z-value was calculated according to:  
\[z=\frac{p_{1}-p_{2}}{\sqrt{\frac{\frac{m_{1}+m_{2}}{m_{1}+m_{2}}\cdot\left(1- \frac{m_{1}+m_{2}}{m_{1}+m_{2}}\right)}{\frac{m_{1}+m_{2}}{m_{1}+m_{2}}}}} \tag{4}\]  
where the critical region for the significance level \(\alpha=0.05\) has a range of \((-\infty;\,-1.65)\). The calculated z-statistics is equal to \(-0.96\).  
Second, a parametric test for the proportion was performed, investigating the equality to 90% for the proportion after one step and for the proportion after two steps. For this, the formula was used.  
\[z=\frac{p-p_{0}}{\sqrt{\frac{p_{0}\cdot(1-p_{0})}{n}}} \tag{5}\]  
where the critical region for the significance level \(\alpha=0.05\) has a range of \((-\infty;\,-1.65)\). The calculated z-statistics are \(z_{1}=-2.19\) and \(z_{2}=-0.39\).  
Analyzing the results the following conclusions can be made:  
* The second step improves the effectiveness of the method (according to the second parametric test);
* The effectiveness after the first step is statistically less than 90%;
* The effectiveness after two steps is statistically at least 90%.  
At the current stage of research, no correlation between method errors and specific employee groups could not be found.
# Mask R-CNN with New Data Augmentation Features for Smart Detection of Retail Products
## 3 Experimental Results
### C. Experimental Tools  
In terms of preprocessing, the image is adjusted to 640 \(\times\) 640 before DA algorithms and DWT are used to filter noise. DA uses Imgaug, which is a Python library for image  
Figure 8: Data distribution for the Snacks dataset.  
Figure 9: Format for the Snacks dataset.  
augmentation in ML from the Python package index (PyPI). DWT uses PyWavelets, which is a wavelet transform software for Python using PyPI.  
For the mask R-CNN, a ResNet-101 is used with a FPN. The results of previous studies [5], as shown in Table 3, show that ImageNet and ResNet-152 have the smallest error rate for the nine models. The mask R-CNN for this study only supports ResNet-50 and ResNet-101, so ResNet-101 is used because it has the second lowest error rate.  
In contrast to previous image pyramids, FPN uses a bottom-up pathway and a top-down pathway. This method of feature extraction retains more detailed features, as shown in Figure 10. A previous study [9] determined the effectiveness of an FPN using a faster R-CNN as the understructure and RetNet50 as the backbone with the COCO minival dataset and showed that models with an FPN give high AP values, as shown in Table 4, so this study uses an FPN.  
Computations for this study use an NVIDIA RTX2060 GPU with 6 GB VRAM. The DL frameworks are TensorFlow1.15 and Keras2.2.5. The training process in Table 5 involves three stages: stage 1 trains the RPN and mask heads for 100 epochs using a learning rate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Faster R-CNN** & **AP** & **AP\({}_{\mathbf{s}}\)** & **AP\({}_{\mathbf{m}}\)** & **AP\({}_{\mathbf{t}}\)** \\ \hline Baseline on conv4 & 31.9 & 13.9 & 36.5 & 45.5 \\ \hline Baseline on conv5 & 28.8 & 11.9 & 32.4 & 43.4 \\ \hline FPN & **33.9** & **17.8** & **37.7** & **45.8** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Differences using the FPN.  
Figure 10: Framework for the FPN [9].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Top-1 Err.** & **Top-5 Err.** \\ \hline VGG-16 & 28.07 & 9.33 \\ \hline GoogLeNet & - & 9.15 \\ \hline PReLU-net & 24.27 & 7.38 \\ \hline ResNet-34A & 25.03 & 7.76 \\ \hline ResNet-34B & 24.52 & 7.46 \\ \hline ResNet-34C & 24.19 & 7.40 \\ \hline ResNet-50 & 22.85 & 6.71 \\ \hline ResNet-101 & 21.75 & 6.05 \\ \hline ResNet-152 & **21.43** & **5.71** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of error rates for nine models.  
(LR) of 0.001; stage 2 trains ResNet101 and the FPN for 200 epochs; and stage 3 trains all layers for 300 epochs using a LR of 0.0001. Dividing the process into three stages allows the use of greater LR, so the loss rate rapidly converges to the bottom of the gradient. Stage 3 uses a y smaller LR so the entire loss rate converges more precisely.
## 2 Materials and Methods  
### Data Generation  
We used the lid-driven cavity problem [31; 32; 33; 34] to generate data for the models. Lid-driven cavity flow is well known in the CFD research field because it provides complex flow structures and interactions and can be run in both laminar and turbulent flow regimes. For our cases, Reynolds numbers of 1000, 2000, 4000, 6000, 10,000 (test dataset), 14,000, 16,000, 18,000 and 20,000 were considered by the transient incompressible RANS solver (pisoFoam), which is part of the OpenFOAM CFD package [35; 36]. The Re = 8000 and 12,000 cases were not used, aiming to leave a reasonable gap between the training and testing datasets. The spatial domain was divided into a 128 \(\times\) 128 mesh, in a 0.1 \(\times\) 0.1 m\({}^{2}\) cavity. This research work adopted the present resolution for the sake of computational load, which also impacts the tested ML models. Specific cavity lid velocity, in the range of 0.1 to 2.0 m\(\cdot\)s\({}^{-1}\), established predefined Reynolds numbers. For calculation convenience, the kinematic viscosity was taken as 10\({}^{-5}\) m\({}^{2}\cdot\)s\({}^{-1}\), which is near the value for air.  
Although the simulation time step was 5 \(\times\) 10\({}^{-4}\) s, the flow variables were recorded at every 0.1 s interval. The solution started with a fully static fluid and proceeded to simulate the flow up to 20 s, which was sufficient to reach a steady state for all the cases, before using the solution for the models.  
A complete Direct Numerical Simulation (DNS) flow solution was generated employing OpenFOAM. This approach required large time and computer resources, when compared with the simpler RANS k-\(\epsilon\) turbulence model for a minimal gain on the proof of concept for our case. Therefore, we proceeded with the analysis, employing the classical RANS k-\(\epsilon\) model for validation, which was carried out using the data from [37], whose authors implemented a numerical method for 2D Steady Incompressible Navier-Stokes equations in streamfunction and vorticity formulations. For this task, the authors solved the lid-driven cavity flow to Reynolds numbers in the range of 1000-21,000. The authors provided the normal velocity components along a vertical and a horizontal line in the middle of the domain, i.e., the x component on the vertical line and the y component on the horizontal line. They used a high-order simulation to generate highly accurate data for the investigated Reynolds numbers. This approach permits taking values at critical points, which present highly variable shear stresses and vorticity.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 4 System Design and Implementation
### System Deployment
#### 4.2.2 Control and Management Layer  
The implementation of the control and management layer of the proposed system is described below:  
1. Edge Rule-Based Monitoring: A program was developed using Python to compare the readings collected from the sensors in the physical layer to preset values to detect irregular changes in acceleration, approaching nearby objects, and physical impacts on the bike. This program was deployed on the RPi 4 discussed in the physical layer to ensure immediate action is taken, such as sending warnings to the rider's mobile phone or contacting emergency contacts in case of accidents.
2. Application Servers: The application services were developed as Java-based microservices. The microservices were classified as riders' management, vehicle management, and user management, and each of them were deployed on a cloud-based server. They received data from the edge computing device, processed events, and facilitated communication between different components of the system. Additionally, they provided user management, rides management, vehicle management, and decision-making functionalities.
3. NoSQL Database System: A NoSQL database system was utilized to store and retrieve various data related to trips, sensor faults, registrations, reports, emergency contacts, and user information. The database system was used to store the rides, vehicles, and users' databases.
4. Machine Learning Server: The dedicated machine learning server integrated machine learning capabilities into the system. The machine learning model was trained and integrated into a Flask web service deployed on a cloud. It processed the sensor data collected during trips and classified maneuvers based on predefined patterns and algorithms. By analyzing driving behavior, the machine learning server identified risky instances, such as sudden acceleration or harsh braking, contributing to the decision-making process.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 11 Sample Outdoor Localization Results
### Observed RTT Distance Measurement Errors  
We next consider the errors in the RTT measurements by comparing the reported RTT distance with the actual distance between the UE and the AP.  
In Figure 6, the vertical axis is the RTT measured distance while the horizontal axis is the actual distance. This shows that the RTT distance can be considered to be the true distance plus some form of measurement error (and a small offset). There are, however, also quite a number of large outliers (including some negative values).  
Figure 4: Ground truth (blue), GNSS (red) and RTT (green) positions with outlines of buildings in black. The path folds back on itself and traverses some sections twice. The large outer rectangle (210 \(\times\) 120 m) delineates the area covered by the Bayesian grid.  
Figure 5: Histogram of errors in the position of the UE (in meters) based on (i) RTT measurements and known positions of APs; and (ii) the smartphone’s GNSS measurements. The mean and RMS error are smaller for the RTT based results than for the smartphone’s GNSS.  
We can present this information in another way by plotting the difference between the RTT measurement and the true distance (i.e., the measurement error) versus distance as shown in Figure 7. Note that the vertical spread is fairly independent of distance.  
Overall, a histogram of RTT error, as in Figure 8, looks approximately Gaussian with standard deviation \(\sigma\approx 3\) m (aside from some long tails). This probability distribution was used as the observation model in the BGU method both for finding the APs from RTT measurements and then also for finding the UE from RTT measurements (Section 7).  
Figure 6: Scattergram of RTT distance (vertical axis: meters) versus actual distance (horizontal axis: meters). The scatter is relatively large, in part because of the small bandwidth (20 MHz). The dashed line has slope one. There are many outliers far above and below the dashed line.  
Figure 7: Scattergram of error in RTT distance (vertical axis) versus actual distance (horizontal axis). The mean (\(\mu\)) of each 5 m wide histogram bin is shown in red, while the blue lines go from \(\mu-\sigma\) to \(\mu+\sigma\), where \(\sigma\) is the standard deviation.  
We can use the formula \(\sigma\approx 2.45+0.035\ d\) if we wish to account for a slight increase in measurement error with distance \(d\) (in meters). Note, however, that there remain some large outliers that do not fit the Gaussian distribution model.
# 3D Shape Generation via Variational Autoencoder with
## 3 Proposed 3D-VAE-SDFRaGAN
### VAE, GAN, and VAE-GAN  
An extended version of an autoencoder network that imposes additional restrictions on latent variables is known as a variational autoencoder (VAE). The network is transformed into an algorithm that learns its input information from a latent variable model due to the restriction. The parameters that model the data from the probability distribution are learned by the VAE. The VAE network comprises encoder and decoder networks, with the encoder network compressing the input data over the latent distribution \(p(z)\) regulated by the prior into a latent representation, while the decoder network generates a new instance of input data from the latent representation. The VAE's weights are trained simultaneously by improving the reconstruction loss and Kullback-Leibler divergence between the learned latent distribution and the prior.  
On the other hand, a generative modeling algorithm called the generative adversarial network (GAN) utilizes a generator network and a discriminator network to generate new instances of data. The generator network transforms a random vector drawn from a Gaussian distribution into a data sample that is similar to the training dataset, while the discriminator network evaluates if the generated data sample is similar to the training dataset. The generator and discriminator networks train each other by competing against each other until the generator produces high-quality data.  
The integration of VAE and GAN to improve the quality of data generation is becoming increasingly popular. The combination is computationally more efficient and produces a better quality of data generation. For instance, [PERSON] et al. [44] proposed a combination of VAE and GAN for 2D image generation, which resulted in improved feature representation and similarity measures compared to using either VAE or GAN alone. [PERSON], [PERSON] et al. [6] combined 3D-VAE-GAN to generate a voxel-based 3D model from 2D images, while [PERSON] and [PERSON] [37] combined 3D-VAE-GAN to generate SDF from 3D point clouds. Another work by [PERSON] and [PERSON] [7] combined 3D-VAE-IWGAN to perform voxel-based 3D model generation, 3D model reconstruction, and 3D shape completion from a 2D image. In this work, the authors propose a similar framework to learn the latent spaces of 2D images and map them to their respective signed distance functions.  
Figure 1: Examples of 2D images and the transformation of 3D meshes into signed distance functions (SDFs).
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 5 Methodology
### Deploying the Micro-FL Framework  
Table 1 demonstrates the configuration of the Kubernetes cluster and the specifics of the Micro-FL deployment are detailed in Table 2. The Google Cloud Kubernetes Engine is used to deploy the Micro-FL frameworks, employing three Kubernetes nodes. This setup will be used to evaluate the fault tolerance behaviour of the microservices, maintaining system robustness in the face of potential faults in instances.  
For both Kafka brokers and Kafka Connect, a replication factor of three is implemented across all Kafka topics (which is recommended for the production level [58]). Additionally, the minimum in-sync replicas parameter was configured to two, providing resilience against any single instance failure. Three replicas for the ZooKeeper microservice are also established. In the case of Elasticsearch, indices were configured with a replication factor of 3. Kibana is used for data visualization tasks within the Micro-FL platform.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value** \\ \hline Machine Family & E2-Standard-16 \\ Number of Nodes & 3 \\ vCPUs & 16 \\ RAM & 64 GB \\ Image Type & Ubuntu With Containerd \\ Boot Disk Type & Balanced Persistent Disk \\ Boot Disk Size & 100 GB \\ Zone & us-central-c \\ GKE Ver. & 1.25.8-gke.500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the Micro-FL Kubernetes instance.  
Figure 4: Overview of the methodology to evaluate the fault tolerance of Micro-FL.