esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 6 Results
### Cajazeiras Lagoon  
The first official autonomous mission occurred in the Cajazeiras Lagoon (Latitude: \(-3.81090833333\), Longitude: \(-38.50621\)) on 15 July 2017 (ID Mission: 20170715180423). The paths configured and traversed by the boat are displayed in Figure 9.  
The mission began at 9:50 am and ended at 9:59 am, totaling 35 collections. The ambient temperature had a minimum value of 30.25 \({}^{\circ}\)C and a maximum of 32.00 \({}^{\circ}\)C, reaching a mean value of 30.93 \({}^{\circ}\)C, with a standard deviation of 0.46 \({}^{\circ}\)C.  
Figure 10 shows the temperature variations during the course of the 35 waypoints, where the one with the highest value was at waypoint 1 and the one with the lowest value was at waypoints 7 to 10.  
Figure 7: Distance and bearing angle.  
Figure 8: A general flowchart of the boat’s software.  
With respect to water temperature, the minimum value was 27.50 \({}^{\circ}\)C, and the maximum was 27.75 \({}^{\circ}\)C, reaching an average of 27.54 \({}^{\circ}\)C, with a standard deviation of 0.46 \({}^{\circ}\)C. The temperature remained constant throughout most of the route, with slight variations at the first and last waypoints, as shown in Figure 11.  
The value of Dissolved Oxygen (DO) had a minimum value of 8.93 ppm and a maximum of 9.14 ppm, reaching a mean value of 9.10 ppm and a standard deviation of 0.05 ppm. During the route, there was a discrete growth reaching the maximum value of 9.14 ppm at the waypoints 19 to 21 and 23 to 28, as shown in Figure 12.  
Figure 11: Water temperature in the Cajazeiras Lagoon.  
Figure 10: Ambient temperature in the Cajazeiras Lagoon.  
Figure 9: Data collection in the Cajazeiras Lagoon. Source: Adapted by Google Maps, 2017.  
As for the Oxidation-Reduction Potential (ORP), dispersed values were obtained in certain regions of the lagoon, with a minimum value of 100.50 mV at waypoint 33 and maximum of 113.30 mV at waypoint 6, with an average of 107.82 mV and a standard deviation of 2.77 mV, as shown in Figure 13.  
Regarding the Potential of Hydrogen (pH), there were slight variations during the course, reaching a minimum value of 7.80 at waypoints 22, 26, and 33, and a maximum value of 8.10 at waypoint 1, with a mean value of 7.89 and a standard deviation of 0.07, as shown in Figure 14.  
Figure 14: pH graph for the Cajazeiras Lagoon.  
Figure 12: Dissolved Oxygen (DO) values for the Cajazeiras Lagoon.  
Figure 13: The Oxidation–Reduction Potential (ORP) graph for Cajazeiras Lagoon.
## 1 Introduction
Inspired by the aforementioned research, this paper proposes a Self-Supervised Learning SEI (SSL-SEI) method. The method fully leverages unlabeled auxiliary datasets to train the RFFs extractor and achieves high-performance emitter identification by fine-tuning the RFFs extractor and classifier using a limited number of labeled samples. The main contributions of this paper are as follows:  
* We propose a novel SSL-SEI framework that integrates self-supervised contrastive learning with supervised fine-tuning. This framework effectively leverages unlabeled signals to learn generalizable RFFs and adapts to downstream SEI tasks using only a small number of labeled samples, thereby overcoming the limitations of dependence on labeled samples and achieving high-accuracy emitter identification. * We design specialized data augmentation strategies for satellite signals and design a hybrid encoder combining Transformer [36] and ResNet [37] modules to enhance RFF extraction. In the fine-tuning phase, we introduce VAT [38] as a regularization technique to further improve model robustness under label-scarce conditions. * We validate the proposed method on the real-world Beidou Navigation Satellite System (BDS) signal datasets with 10 emitter classes. Experimental results show that our approach significantly outperforms the competing methods across various settings with limited labeled samples, demonstrating its effectiveness and generalizability.
# Fuzzy Frankot-Chellappa Algorithm for Surface Normal Integration
## 5 Experimental Results
### Impact of Symmetric Perturbations  
At first, we considered as in [11] that the modifications applied to the input data are symmetric around the original value. Consequently, the weighted average (which ultimately yields the fuzzy output) closely approximates the original value.  
Our experiments are outlined as follows:  
* Fuzzy perturbation of gradient data: In the simple implementation considered [11], the gradient data were perturbed to form three sets:\[p_{1}=p-\delta,\quad p_{2}=p,\quad p_{3}=p+\delta,\]  
representing a symmetric triangular fuzzy number \(\tilde{p}\) characterized by the triplet \((p_{1},p_{2},p_{3})\). The same formulation applies to \(\tilde{q}\).
* Comparison of classical and fuzzified methods: To evaluate the accuracy of the classical and fuzzified solutions, we computed their deviation from the exact solution. For every \((i,j)\in\partial\Omega=\{0,m\}\times\{0,n\}\), where \(\Omega=[0,m]\times[0,n]\), the root mean square error (RMSE) of \(z_{i,j}\) was calculated. The classical solution was obtained using a sine discrete transform, whereas the defuzzified solution, denoted as \(d(\tilde{z}_{i,j})\), was derived using a fuzzy sine discrete transform.
* Defuzzification method: The defuzzification method used was \(d(\tilde{z})=0.5(z^{-}+z^{+})\), assuming \(w_{l}=0.5\), \(w_{m}=0\), and \(w_{u}=0.5\) in Equation (13).  
Table 1 presents a comparison between the exact function and in Figure 1, the discrete sine transform approximation, and the fuzzy discrete sine transform approximation for several surfaces:  
\[z_{1}=\sin(uv)+u^{2}-v^{2},\quad z_{2}=u^{2}+v^{2}\]  
\[z_{3}=(1-\frac{u^{2}}{\sigma^{2}}-\frac{v^{2}}{\sigma^{2}})e^{-\frac{u^{2}+v^ {2}}{2v^{2}}},\quad z_{4}=e^{(u+0.25v)}\]  
The reconstruction is carried out under homogeneous Dirichlet boundary conditions, and we center the reconstructed surface by subtracting the mean height, if needed, to align it visually with the reference. This ensures fair visual and quantitative comparison despite the mismatch in boundary conditions.  
Figure 1 illustrates 3D surface reconstructions obtained using the fuzzy FC method, which is better at suppressing noise than classic FC methods, as we can see their difference between errors. If the noise were of even higher frequency (or if we increased the fuzziness level to aggressively filter out more detail), the advantage of the fuzzy approach would likely become even more pronounced. Conversely, if the noise were very low frequency, both methods would reconstruct it similarly. Our results here focus on demonstrating that the fuzzy approach does no harm to the solution (the accuracy remains essentially the same for clean data).
## 4 Results and Discussion
### Dataset and Experiment Results  
The Cityscapes dataset [31] is widely used for semantic segmentation studies. This dataset contains 5000 street scenes images collected from 50 different cities. These are divided into 2975 images for training, 500 images for verification, and 1525 images for testing. The Cityscapes dataset contains 19 categories, and all images have a resolution of \(2048\times 1024\) pixels. We use images with reduced resolution for training to reduce the training time, but we use images with the original resolution for evaluation.  
We compare several conventional methods and the EAR-Net in terms of accuracy (MIoU) and number of parameters. Table 2 shows the accuracy and number of parameters of the EAR-Net and the other methods used with the Cityscapes dataset. The EAR-Net achieves an MIoU of approximately 72.3%, which is higher than that of the other methods. EAR-Net improves MIoU by approximately 16.5%, compared with U-Net, and shows the same value as DeepLabv3+. This proves that residual learning and ASPP used in EAR-Net contribute to the improvement in accuracy.  
Figure 8 shows the comparison of the segmentation results of the EAR-Net and the U-Net in complex scenes using the Cityscapes dataset. The conventional U-Net lacks the features necessary to classify object categories, resulting in low segmentation accuracy in various objects such as people and traffic structures. In particular, a part of the object is not divided. However, the EAR-Net shows more precise segmentation results, compared to the U-Net because additional features are extracted, using residual learning and the ASPP. Furthermore, in the orange box area in Figure 8, the missing pixels of various objects are minimized and divided.  
Figure 9 shows the comparison of the segmentation results of EAR-Net and U-Net in multiple objects. The U-Net shows low segmentation accuracy in various objects, such as buses, people, and trucks. In particular, some of the objects are classified into different classes. However, EAR-Net shows high accuracy by completely dividing various objects. In Figure 9, buses, people, and trucks are partitioned with few missing pixels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params (M)** & **MIoU (\%)** \\ \hline FCN [8] & 35.3 & 65.3 \\ U-Net [9] & 31.0 & 55.8 \\ SegNet [32] & 29.5 & 57.0 \\ ENet [21] & 0.4 & 57.0 \\ ESNet [33] & 1.7 & 69.1 \\ LEDNet [34] & 0.9 & 69.2 \\ DeepLabv2 [14] & 262.1 & 70.4 \\ ICNet [22] & 26.5 & 70.6 \\ FasterSeg [35] & 4.4 & 71.5 \\ FRRN [36] & - & 71.8 \\ DeepLabv3 [15] & 58.0 & 72.0 \\ STDC1 [37] & 8.4 & 72.2 \\ DeepLabv3+ [16] & 54.7 & 72.3 \\ \hline EAR-Net & 26.8 & 72.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results on Cityscapes dataset. “-” means the result is unavailable.
## 3 Background of the Methods Used
### Extreme Gradient Boosting Machine (XGBoost)  
Based on the ideas of a gradient boosting machine [51; 52], [PERSON], [PERSON] [53] improved and introduced the XGBoost algorithm as a robust decision tree. Unlike the gradient boosting machine, XGBoost can  
Figure 3: The particle swarm optimization (PSO) pseudo-code for the optimization process [50].  
run in parallel based on the constructed boosted trees. It can handle complex data at high speed and accuracy. The XGBoost algorithm can be described as follows [54]:  
Given a dataset with \(n\) examples and \(m\) features \(D=\left[(x_{i},y_{i})\right]\) (\(|D|=n,x_{i}\in R^{m},y_{i}\in R\)), \(K\) additive functions will be used to predict the output values of a tree ensemble model as follows:  
\[\hat{y}_{i}=\phi(x_{i})=\sum_{k=1}^{K}f_{k}(x_{i}),\ f_{k}\in F \tag{3}\]  
where \(F\) is the regression trees space. It is calculated as:  
\[F=\left\{f(x)=\omega_{q(x)}\right\}(q:R^{m}\to T,\ \omega\in R^{T}) \tag{4}\]  
where \(q\) denotes for the structure of each tree; \(T\) denotes for the number of leaves in the tree, and \(f_{k}\) is a function that corresponds to an independent tree structure \(q\) and leaf weights \(w\).  
To reduce errors of ensemble trees, the objective function is calculated in the XGBoost model:  
\[L^{(t)}=\sum_{i=1}^{n}l(y_{i}\hat{y}_{i}{}^{(t-1)}+f_{t}(x_{i}))+\Omega(f_{t}) \tag{5}\]  
where \(l\) is a differentiable convex objective function to determine the error between predicted and measured values; \(y_{i}\) and \(\hat{y}_{i}\) is regulated and predicted values, respectively; \(t\) denotes the repetitions in order to minimize the errors; and \(\Omega\) is the complexity penalize with the regression tree functions:  
\[\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\|w\|^{2} \tag{6}\]
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 6 Scanning Patterns
### Raster Scanning  
Raster scanning (also called serpentine scanning) is a commonly used scanning pattern. In this case, the scanning image is subdivided into scan lines in one direction. The beam sweeps the sample in one direction along a line, comes back to other side, and again starts scanning along the next line. Thus, it follows a rectangular scan pattern. The axis of the line scan is called the fast axis (scanned rapidly), while the other axis is called the slow axis (scanned slowly). Usually, the fast axis is driven by oscillating the light beam at resonance, while the slow-axis scan is performed using a non-resonant scan [127]. Typically, the raster scan pattern is obtained using gimbal-mounted micromirrors or optical fiber scanners.  
The scanning beam passes over the surface of the target object at different times. Thus, each pixel on the reconstructed image corresponds to a different time point. This kind of scanning in time leads to in-frame and inter-frame motion artifacts. The former among these takes place in the case where there is a displacement or movement of the sample within the acquisition time. The inter-frame artifacts occur due to motion occurring between the successive frames or image sequences. The in-frame motion artifact can be reduced by changing the motion path or using image processing algorithms. The inter-frame distortions can be reduced by increasing the scanning speed by implementing a bidirectional scan [128]. [PERSON] et al. tested triangular, sinusoidal, and sawtooth scanning profiles and demonstrated that the triangular profile provides artifact-free images. The other two profiles display image distortion at the margins [129].  
The raster scanners require high operating voltages to get slow-axis scans and have difficulties incorporating them in small spaces, which make them less usable in clinical devices [130].
## 1 Introduction
This paper is organized as follows: Section 2 introduces the characteristics of the GPS L2C signal as an example, and the framework of GNSS receivers to indicate the crucial role of the proposed strategies. Next, Section 3 elaborates on the proposed resampling strategy and variable circular correlation time, in terms of the principle, realization, coarse and fine acquisition, as well as performance evaluation. Comparative experiments and performance analysis are performed on four sets of real GNSS signals in Section 4, to verify the effectiveness of the resampling strategy and variable circular correlation time. Conclusions are drawn in Section 5.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 1 Introduction
### Comparison with Directed Diffusion  
A comparison is made between adapted diffusion (AD) and directed diffusion (DD) in energy consumption when transmitting and receiving data. Adaptation occurs when the nodes learn the location of their neighbors in relation to the location of the network sink and source. We demonstrate that significant energy savings are achieved by use of the adapted model.
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 2 Vehicle Drift Motion Mechanism Analysis
### Vehicle Dynamics Model
#### 2.1.3 Roll Safety Analysis  
Vehicle wheels lift off from the ground more often than not as a result of large rolling motion under drift conditions. The vehicle has a risk of rollover when the vertical force on one side wheel equals zero, otherwise, there is no risk in [26; 27; 28]. Therefore, the vehicle roll model is classified into two conditions: before and after wheel lift-off as shown in Figure 2a,b.  
The roll dynamics of the vehicle is represented by two different equations as expressed in Equations (4) and (5) corresponding to Figure 2a,b:  
\[I_{x}\ddot{\phi}-I_{xz}\dot{\gamma}-m_{b}h_{b}\big{(}\dot{v}_{y}+v_{x}\gamma \big{)}=m_{b}\dot{g}h_{b}\sin\phi-K_{\phi}\phi-C_{\phi}\dot{\phi} \tag{4}\]  
\[I_{x\phi}\ddot{\phi}-I_{xz\phi}\dot{\gamma}-mh_{b}\big{(}\dot{v}_{y}+v_{x} \gamma\big{)}=m\dot{g}h_{b}\sin\phi-mg\frac{t_{b}}{2}\cos\phi \tag{5}\]  
Apparently, the relational expression between the safe roll angle \(\phi_{s}\), the safe roll rate \(\dot{\phi}_{s}\) and the safe roll angular acceleration \(\dot{\phi}_{s}\) in critical states can be obtained.
## 5 Hardware Deployment
### Machine Learning Compiler TVM  
Apache Tensor Virtual Machine (TVM) is an open-source machine learning compiler framework [38], which optimizes the code to deploy machine learning models for various hardware platforms, including CPUs, GPUs, FPGAs, and specialized accelerators. Apache TVM accepts inputs from ANN frameworks such as TensorFlow or PyTorch. TVM support for model training is under development [39], so, currently, TVM does not support the training of models similar to most ANN compilers which have only limited support as the emphasis is on inference and therefore focus is shifted to effectively deploying models to target hardware in the best possible optimization. It is, therefore, imperative to train the model with a different platform before loading the pre-trained model on the TVM frontend.  
Compiling the ANN model using TVM consists of multiple steps, as shown in Figure 5. The TVM compiler takes the high-level neural network model in the TensorFlow framework,  
Figure 4: PYNQ-Z2 Development Board [37].  
translates the model into a hardware description language (HDL), performs different stages of optimizations, and then generates a bitstream suitable for deployment into the FPGA.
## 2 Backgrounds  
### K-Nearest Neighbors  
The K-nearest neighbors algorithm (KNN) is a non-parametric supervised learning method used for both classification and regression. In regression, the KNN outputs the weighted average of the KNNs. That is, for an input \(x\), the output estimate y can be expressed as \[\hat{y}(x)=\frac{1}{k}\sum_{i=1}^{k}Y_{i}(x) \tag{1}\]  
where \(Y_{i}(x)\) is the output of the \(X_{i}(x)\) nearest neighbor of \(x\). The equation above can be rewritten as  
\[\hat{y}(x)=\frac{1}{k}\sum\
olimits_{i=1}^{n}Y_{i}1_{x_{i}\in[X_{1}(x),\dots X _{k}(x)]}, \tag{2}\]  
where \(X_{i}(x)\) is the i-th nearest neighbor of \(x\). Using the Euclidian distance, this means that \(\|x-X_{1}(x)\|\leq\|x-X_{2}(x)\|\leq\dots\leq\|x-X_{n}(x)\|\), where \(n\) is the total number of inputs. The parameter choice plays a fundamental role in the algorithm performance. Oftentimes, a number of K values are assessed before selecting the best one for a specific task. Note that, in general, a very small K often models the noise, while with a very large K, the neighbors lead to high bias. Some implementations also use a distance-based voting scheme, where closer neighbors, which correspond to setting a dynamic weight, have more influence, as shown in Equation (2).
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 2 Background
### The Convolution Layer  
Convolution layers use a set of small parameterised filters, sometimes referred to as kernels, to perform convolution operations to produce different feature maps of their input [38]. Generally, in the state-of-the-art 2D models [18; 19; 39], the filter is based on a \(3\times 3\) matrix. Larger filters, such as a \(5\times 5\) matrix, can also be used in semantic segmentation to mitigate the contradiction between classification and localisation [40]. For example, in the classification problem, the model is required to be invariant to the transformation of the input image, while in the localisation problem, a model has to be transformation-sensitive to perform localisation [40].  
The convolution layers can be used in the form of a transposed convolution or in combination with an interpolation layer. Transposed convolution (i.e., deconvolution) is obtained by reversing the forward and backward passes of a convolution [41]. Unlike the fixed interpolation methods (e.g., nearest-neighbour, bilinear interpolation), the transposed convolution filter weights can actually be learnt. Transposed convolution is known to produce checkerboard artifacts [42] that can be completely avoided by combining interpolation and convolution layers [43]. When a convolution layer is combined with an interpolation layer it can also improve upsampling. Note that transposed convolution can also improve the upsampling process.  
A convolution layer with a stride greater than one (i.e., strided-convolution) can be used to replace a pooling layer (described in Section 2.3) to perform downsampling in CNN without loss in accuracy [44]. Here stride refers to the number of pixels the parameterised filter shifts at a time in a convolution operation.
## 1 Introduction  
The issue of land spatial utilization is a common problem faced with respect to urban areas in Taiwan's development. To effectively compete for space, there is a growing trend of existing buildings undergoing renovations or new buildings being constructed with increased above-ground floors, as well as a shift towards underground development. As a result, building foundations are being dug deeper, and the scale of deep excavation projects is increasing. In recent years, with the continuous updates and changes in analysis software, there has been an increase in the development of programs that combine and utilize drawing software to quickly establish analysis models, set parameters, and perform excavation simulation analyses at various stages, such as GTS and SoilWorks.  
Most studies on deep excavations have primarily focused on soft soil layers and sandy layers. Only a limited number of studies in Taiwan have explored the engineering characteristics of gravel layers. Deep excavation engineering is a complex task that involves evaluating the interaction between the soil and structures. The design analysis of deep excavations can make effective use of relevant knowledge and practical experience from the fields of geotechnical and structural engineering. The finite element method employs the concept of continuum mechanics to simulate the behavior of soil and retaining structures during construction, defining the stress-strain relationships and boundary conditions of the soil.  
In this study, based on the parameters used for the design analysis of deep excavations in the gravel layer of the Xindian district, the monitoring data collected in this excavation project were evaluated. Using the PLAXIS 2D and SoilWorks programs as research tools, the aim of this analysis was to explore the behavior of deep excavations in the gravel layers of this district. The findings provide recommendations for the analysis and design of deep excavations in similar gravel-layered settings.