esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Azimuth Thruster
#### 2.1.3 Data Selection  
The data sets in this study come from two thrusters that are named as thruster 1 and thruster 2. The data set of thruster 1 is collected from a 515-day period, whereas the data set of thruster 2 covers 466 days. ISO certified vibration analysts from the manufacturer analyzed the data sets and labeled the condition of the thrusters in monitoring reports covering different periods of operation. In addition, both thrusters were videoscope inspected, verifying damage in the components. The thrusters had inner race defect in the monitored bearing and the pinion and wheel gears had visible wear on contact surfaces. The data from the operation labeled as fault-free were used to train the algorithm. The identification data of thruster 1 consisted of a 150-day period with 207 samples. For thruster 2, a 101-day period with 162 samples was selected.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 2 Problem Statement and Related Work
### Problem Statement
At \(t=i+1\), a regular packet joins the output queue of the same node that is either coming from one of its children or generated by node \(F\) itself. Considering the LIFO policy for this case, at \(t=i+2\), the emergency packet transmission is blocked due to the transmission of the recently arrived regular packet, which may cause the emergency packet to miss its deadline. A similar situation would happen if we consider the FIFO policy. Therefore, for such applications, a proper packet scheduling method is needed.
## 4 Seismic Performance of Isolation Systems Equipped with SLDTFPB and SLDRFPB
### Seismic Response of Isolation Layers Equipped with SLDRFPB and SLDTFPB
Both maximum displacement and maximum theoretical residual displacement increase with equivalent radius, and therefore, small equivalent radius together with deep isolation layer should be adopted in the design strategy in the sense of reset capability of the isolation layer. Figure 19 describes peak bearing reactions of SLDRFPB and SLDTFPB, in which vertical axis represents the ratio between bearing reaction under earthquake and that under static state. When the bearing reaction is greater than zero, the bottom of the column is always under compression, and the isolation layer has the ability to resist overturning. As shown in Figure 19, the bearing reactions of structures with SLDRFPB or SLDTFPB almost remain unchanged when subjected to rare and very-rare earthquakes. Superior overturning resistance and large vertical carrying capacity ensure seismic  
Figure 17: Maximum displacements of isolation layers equipped with SLDTFPB or SLDRFPB. performance along the vertical direction. In general, SLDTFPB shows better isolation effect and better anti-overturning ability than SLDRFPB in case of similar equivalent radius and the same friction coefficient of the sliding surface.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 3 Materials and Methods
### GNN-Based Optimization  
In this study, a graph neural network (GNN)-based optimization technique is developed to address truss size optimization problems. In the proposed technique, a trained GNN is employed to approximate the values of constraint functions for various structural designs during most of the iterations in the PSO algorithm. Specifically, the GNN model directly approximates the nodal displacements in Equation (1), which subsequently leads to the calculation of the element stresses. By doing so, the trained GNN eliminates the need for analyzing the structural responses via FEM. This significantly improves the efficiency of the optimization process. However, given that neural network predictions may contain some degree of error, FEM analysis is used in the final iterations. In this manner, the developed technique achieves a significant enhancement in computational efficiency without compromising the reliability of the final optimal results.  
A flowchart of the proposed GNN-based optimization technique is depicted in Figure 1. As shown, the technique consists of the following three main stages:* **Data Preparation**. Generating a dataset of trusses with their members assigned a random sample set of cross-sectional areas, **A**, which is then analyzed and transformed into graphs to be used as input to the GNN for training and validation.
* **Surrogate Model**. Building and training the GNN as a surrogate model for the constraints.
* **Optimization**. Using a PSO algorithm integrated with the trained GNN to attain the optimal cross-sectional areas of the truss members.  
Details of the three stages are given below.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 4 Model Building
### Specification of Trace Component  
We present the definition of DFA, which we employ to define trace components of the workflow \(\mathcal{W}\).  
**Defintion 2** (Deterministic finite automata).: _A DFA \(\mathcal{A}\) is a tuple \((Q,\Sigma,\delta,Q_{0},Q_{F})\), where_  
* \(Q\) _is a set of locations;_
* \(\Sigma\) _is an alphabet;_
* \(\delta:Q\times\Sigma\to Q\) _is a transaction function;_
* \(Q_{0}\subseteq Q\) _is a set of initial locations;_
* \(Q_{F}\subseteq Q\) _is a set of accept locations._  
Throughout the paper, we let the alphabet of \(\mathcal{A}\) be the power set of atomic propositions in a workflow \(\mathcal{W}\), namely \(\Sigma=2^{AP}\). Based on a transition system \(\mathcal{W}\) and a DFA \(\mathcal{A}\), we can define a product transition system that models the behaviour of a trace component.  
**Defintion 3** (Product transition system).: _Given a workflow \(\mathcal{W}\) and a DEA \(\mathcal{A}\), a product transition system \(\mathcal{W}\otimes\mathcal{A}\) is the tuple \((W\times Q,\{w_{\mathrm{ini}}\}\times Q_{0},\longrightarrow_{\otimes})\) where the transition relation \(\longrightarrow_{\otimes}\subseteq(W\times Q)^{2}\) is the smallest relation defined by the following rules:_  
\[\frac{w\longrightarrow w^{\prime}\wedge\delta(q,L(w^{\prime}))=q^ {\prime}\
ot\in Q_{F}}{\langle w,q\rangle\longrightarrow_{\otimes}\langle w^{ \prime},q^{\prime}\rangle}\text{ and }\] \[\frac{\langle w,q\rangle\longrightarrow_{\otimes}\langle w^{ \prime},q^{\prime}\rangle\wedge q\in Q_{F}}{w=w^{\prime}\wedge q=q^{\prime}}\]  
Clearly, traces of \(\mathcal{W}\otimes\mathcal{A}\) are also traces of \(\mathcal{W}\). This justifies \(\mathcal{W}\otimes\mathcal{A}\) as a trace component of \(\mathcal{W}\). The DFA is a very expressive formalism used to specify temporal patterns for traces. Figure 3(a),b presents two DFA examples \(\mathcal{A}_{1}\) and \(\mathcal{A}_{2}\). The production transition systems of \(\mathcal{W}_{SF}\otimes\mathcal{A}_{1}\) and \(\mathcal{W}_{SF}\otimes\mathcal{A}_{2}\) model the two trace components of SimpleFlow in Section 2.1.  
Note that Definition 3 is slightly different from the standard definition of production transition systems. For technical purposes, we encode absorbing states in the production transition system with the second rule of transition relations.
## 1 Introduction
Unfortunately, due to the influence of the laser phase noise and the random variation of the polarization state, the OPCR measuring distance was only 5.4 km [21; 22]. In summary, OFDR and OPCR are not suitable for applications in optical fiber communication systems, especially for cost-sensitive PONs. This paper proposes a novel OTDR method, called digital LFM OTDR (DLFM-OTDR), which can be considered a new pulse coding technique, but it has different performance than the previous pulse coding technique. It combines the advantages of the conventional OTDR and OFDR. Based on the pulse coding technique with the modulated signal as a digital LFM signal, the probe signal is directly modulated at the transmitting end. There is no need for additional optical equipment, which is bulky and unsuitable for portable OTDRs, to modulate the frequency of the signal light as with OPCR. Additionally, direct detection at the receiving end is adopted. Thus, the DLFM-OTDR can overcome the shortcomings of OFDR and OPCR. The measurement curve is affected neither by the phase noise and coherence length of the laser nor by the changes in the polarization state of the backscattered light. Moreover, DLFM-OTDR can simultaneously overcome the contradiction between the spatial resolution and measurement distance of the conventional OTDR by using the digital LFM probe signal and the short-time fractional Fourier transform (STFrFT) [23; 24]. In the demo experiments, testing the proposed method on a conventional OTDR development board for comparison, we achieve a 7-dB improvement in the dynamic range and an approximately 10-times improvement in spatial resolution.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Effect of Wind Power Generation on Transient Stability
#### 4.1.1 For System with One WF at Bus 1  
In this case, it can be seen clearly from the results for generator speeds in Figure 5 that the system is stable after a short time period of transients. The results in Figure 5 show that the first peak of generator speeds during the disturbance almost approaches 1.01 p.u. and settles down in 8 s. Now, if we look at the generator angle response in Figure 6, generator 2 has the maximal positive angle peak at 0.6 radian and generator 4 has the minimal negative angle peak at \(-1\) radian, and all the generator angles are static at 20 s.  
Figure 4: Modified IEEE-14 bus system with wind farms.  
Figure 5: Generator speeds for WF at bus 1.
## 3 Results
### Sensitivity Analysis of Scenario Weights and Rank Stability  
To evaluate the robustness of the ranking obtained by the hybrid approach proposed by [PERSON] et al. [12], sensitivity analysis was conducted by testing four distinct weight configurations corresponding to different decision-maker emphasis on severity (S), occurrence (O), and detection (D). These weight vectors are:  
- Scenario 1: (0.2, 0.2, 0.6)--emphasis on detection;  
- Scenario 2: (0.2, 0.6, 0.2)--emphasis on occurrence;  
- Scenario 3: (0.33, 0.33, 0.33)--equal weighting;  
- Scenario 4: (0.6, 0.2, 0.2)--emphasis on severity.  
The ranking results for each CF under these scenarios were compared using Kendall's Tau correlation to access to assess the consistency of rankings across different weighting schemes. Kendall's Tau is particularly suitable for this analysis, as it measures the ordinal association between ranked variables, making it ideal for evaluating the similarity of prioritizations derived from varying weighting strategies, without assuming any linear relationship between them [36].  
Table 10 presents the Kendall's Tau correlation coefficients, which quantify the consistency of rankings across different weighting scenarios. Among the scenarios, Scenario 3--which applies equal weighting to severity, occurrence, and detection--exhibits consistently high correlations with all other scenarios (\(\tau\geq 0.679\)), suggesting that a balanced approach results in stable and representative rankings of the CFs. The strongest agreement is observed between Scenario 1 and Scenario 3 (\(\tau=0.838\)), indicating that even when detection is emphasized, the resulting rankings remain closely aligned with those from a balanced weighting. Conversely, the lowest correlation occurs between Scenario 2 and Scenario 4 (\(\tau=0.439\)), a finding that aligns with the distinct emphasis each scenario places on different criteria--occurrence in Scenario 2 and severity in Scenario 4--which are likely to prioritize different CFs. Overall, the application of the hybrid approach proposed by [PERSON] et al. [12] demonstrates moderate to strong rank stability across various decision-making preferences, confirming its robustness and adaptability for practical applications.
###### Abstract
Finally, simulation results show that compared to five other algorithms, the proposed algorithm can effectively reduce the energy consumption of the network, prolong the network lifetime, and decrease the packet loss ratio. The remainder of the rest of the paper is as follows. The network model and energy consumption model are presented in Section 2. Section 3 proposes the improved ACO algorithm. The proposed clustering routing algorithm is given in Section 4. Simulation results and analyses are provided in Section 5. Section 6 draws the conclusion.
## 3 Methodology
### Short-Time Fourier Transformation (STFT) and Spectrogram  
In this research, hammering sounds were firstly transformed into spectrograms because of the Inception-ResNet-v2's strength in image identification. Hence, the STFT was utilized to process the sounds, as follows:  
\[S_{x}(n,\omega)=\sum_{-\infty}^{\infty}x(m)w(n-m)e^{-\mu_{i}m} \tag{1}\]  
where \(w(n)\) represents the window function, and \(\omega\) is the frequency in radians [30]. In an STFT process, it is important to determine the size of the window. A large window leads to poor time resolution, while a small window leads to a poor frequency resolution. Either window size will degrade the quality of a spectrogram. Moreover, during programming, the sampling frequency _fs_, the time lapse \(L\), and the frequency discretization \(N\) also affect the resolution of the spectrograms. However, it is difficult to determine the exact parameters in different cases. In this research, we found out a set of parameters through repeated experiments to make the spectrograms look clear.  
Spectrograms generated by the STFT are presented in Figure 4. In a spectrogram, there are three dimensions that correspond to frequency (vertical axis), time (horizontal axis), and sound pressure (color gradient) or power spectral density (PSD). In the spectrograms example presented in Figure 4, yellow represents high sound pressure, and blue represents low sound pressure. It is easy to distinguish how sound pressures are distributed along with the frequencies in a certain period, how the sound pressures change over time in a certain frequency range, and how the principal frequency changes over time. Specifically, Figure 4a corresponds to the hammering sound of a rock with low strength, Figure 4b corresponds to a rock with moderate strength, and Figure 4c corresponds to a rock with high  
Figure 3: Process of fine-tuning a deep learning network.  
Figure 2: Diagram of a residual network unit.  
strength. It is obvious that in Figure 4c the proportion of high frequency is the largest, the attenuating speed is the slowest, and the differentiation of different frequency bands is the clearest.  
Additionally, we found that there were no differences in parts of the spectrograms that had frequencies greater than 5 kHz, no matter for high-strength rocks or for low-strength rocks; thus, the parts with high frequency do not contribute to the analysis. Moreover, research has shown that human's most sensitive sound frequency is 2 kHz-5 kHz [31]. Therefore, only the 0-5 kHz part of the spectrograms was used for identifying strengths.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 3 Panoramic Texture Generation Method
### GAN-Based Panoramic Texture Translator
#### 3.2.2 Discriminator  
The discriminator D was trained to discriminate between the texture produced by generator G and the actual input texture. As shown in Figure 9, the unwrapped textures from the dataset and the generated textures generated by the generator are combined in pairs to form fake samples. The unfolded texture from the dataset is combined with its corresponding panoramic texture to form a true example. The real and fake examples are the input to the discriminator, both supplied for discrimination. The discriminator classifies whether an input example is true or false. The discriminator consists of several convolutional layers. In this work, we adopt the PatchNet structure in pix2 pix [32]. Finally,  
Figure 9: Structure of the texture translator network.  
owing to generative adversarial training, the discriminator cannot discriminate between the panoramic texture in the dataset and texture generated by the generator, which indicated that the textures generated by the generator is sufficiently accurate and realistic. The generative adversarial loss is defined as:  
\[\mathcal{L}_{cGAN}(G,D)=\mathrm{E}_{x,y}[\log D(x,y)]+\mathrm{E}_{x,z}[\log(1- D(x,G(x,z)))] \tag{3}\]  
where D is discriminator, G is generator. x,y,z is same as described in Section 3.2.1. The final antagonistic learning objective \(G^{*}\) is expressed as follows:  
\[G^{*}=\arg\min_{G}\max_{D}\mathcal{L}_{GAN}(G,D)+\lambda\mathcal{L}_{L_{1}}(G) \tag{4}\]
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### GPS Time Series Tendency and Seasonality Analysis
#### 3.3.2 Sifting Process  
The algorithm allows for finding the IMFs from a time series of data, which we will call \(x(t)\)[49]. First, we searched for local extrema and classified them into relative maxima or minima. Second, two time series were assembled, one with each of them. To form the upper envelope \(\mathrm{e_{max}}\) (t), the maxima are connected with a cubic spline., and the minima are connected to form the lower \(\mathrm{e_{min}}\) (t). Next, the mean \(\mathrm{m_{1,1}}\) is calculated between the two envelopes. Finally, the first proto-mode (h) is computed by subtracting the mean from the original series as Equation (1) shows:  
\[h_{1,1}=x(t)-m_{1,1} \tag{1}\]  
The procedure is repeated k times starting from Equation (1) until the following equation is obtained:  
\[h_{i,k}=x(t)-m_{i,k} \tag{2}\]  
The purpose of Equation (2) is to eliminate background signals on which the IMF is built, and then bring it closer to the requirements of its definition. The first IMF component is taken as Equation (3), which should contain the short period variations of the original signal x(t).  
\[c_{i}=h_{i,k} \tag{3}\]  
Finally, the residual is calculated as the difference between the input signal and the found IMF (Equation (4)). This residual is treated as the new input data set and the procedure is repeated until a cut-off criterion is satisfied [50]. Note that the criteria for stopping the iterative process are satisfied when the cut-off parameter is below a set value.  
\[r_{i}=x(t)-c_{i} \tag{4}\]  
The input data can be thought of as the sum of \(n\) intrinsic mode functions plus a residual (Equation (5)):  
\[x(t)=\sum_{i=1}^{N}c_{i}(t)+r_{N}(t). \tag{5}\]
## 4 Multi-Objective Segmentation and Localization Method for Tomato Plants  
### Based on VGG16-UNet Tomato Plant Segmentation Model  
Due to its excellent segmentation performance and fast training speed on small sample datasets [16; 17; 18; 19], the UNet network is adopted by this paper as the basic model for the segmentation task. The UNet architecture consists of an encoder and a decoder. The  
Figure 4: Data augmentation.  
Figure 3: Marking the axillary bud, main branch, and lateral branch.  
encoder extracts features from the input image to obtain four layers of feature maps, while the decoder performs layer-by-layer upsampling through deconvolution operations, accurately locating features and fusing them with the corresponding feature maps obtained at each level by the encoder. At the same time, the VGG16 model demonstrates robust image feature extraction and analysis capabilities. The use of large datasets for pretraining through transfer learning can effectively improve model performance and generalization ability. Therefore, the encoder part of the UNet model is replaced by the first 13 convolutional layers of VGG16, resulting in the VGG16-UNet model, as shown in Figure 5.  
In this paper, tomato plant segmentation is performed using the VGG16-UNet semantic segmentation model, and the parameter variations of each layer of the segmentation model are shown in Figure 5. The first 13 convolutional layers of VGG16 serve as the feature extraction network in the encoding structure. Each convolutional layer used a 3 \(\times\) 3 kernel size, with the number of kernels being 64, 128, 256, and 512, respectively. The activation function used is ReLU. The encoder uses a stacking operation of convolution and pooling to perform downsampling, progressively enlarging the receptive field and compressing the image from an input size of 512 \(\times\) 512 \(\times\) 3 to a size of 32 \(\times\) 32 \(\times\) 512 after four rounds of downsampling, thereby condensing the features of the tomato plant images. In the corresponding decoding part, upsampling and feature fusion are used to achieve tomato plant segmentation. Upsampling is performed using bilinear interpolation to increase the size of the feature map, which speeds up the training of the tomato plant segmentation model. Through continuous upsampling and convolutional stacking, the decoder reconstructs the tomato plant map, restoring the size of the final output layer to 512 \(\times\) 512. It then outputs the binary segmentation map of the tomato plant morphology. The VGG16-UNet semantic segmentation model uses four rounds of feature fusion to  
Figure 5: VGG16-UNet architecture.  
perform plant segmentation. The convolutional feature map in the encoder is fused with the corresponding upsampled feature map in the decoder. Using Skip-Connection cascade fusion, shallow position information and deep semantic information of tomato plants are fused across multiple channels to achieve the pixel-level segmentation of tomato plants.
# Development of a New Eye Movement Measurement Device Using Eye-Tracking Analysis Technology
## 2 Materials and Methods
### Device and Measurements  
We developed a new measurement device (\"EyeScore\", Figure 1) by modifying the commercially available eye-tracking device ORTe EYENAC (NAC Image Technology Inc., Tokyo, Japan). The EyeScore presents stimuli on an internal liquid crystal display (LCD) monitor and simultaneously measures the gaze of both eyes. With separator, EyeScore presents images to the left and right eyes independently (Figure 1e). The size of the internal monitor was 6 inches, with a resolution of 2560 \(\times\) 1440 pixels. The image on the LCD monitor was designed to be viewed through the eyepiece at 33 cm. EyeScore has  
Figure 1: **(a,b)** The newly developed device “EyeScore”. When the participant looked into the device, the target was displayed on an internal monitor. **(c)** Examples of visual targets displayed on the internal monitor. **(d)** Images obtained by the eye cameras correspond to the target positions shown in the upper panel. The eye position of each participant was determined based on the pupil center and corneal reflex. **(e)** The scheme of the internal structure of EyeScore. The images for the left and right eyes are separated by a separator.  
a built-in computer that generates visual stimuli, detects eye position, and records data (Figure 2). In addition, there are two cameras that record the right and the left eye at the same time, and both eye positions are detected independently. The sampling rate for eye position measurement was 30 Hz. There was a delay of 3-5 frames (mean 4.5) between the stimulation subsystem and eye-position-measuring subsystem. Accordingly, eye position data were delayed by approximately 0.15 s to the visual target presentation.  
The stimulus was a circle, and the visual angle of the circle was 0.775 deg. The target generated regular triangular wave-like movements in the horizontal or vertical direction within a viewing angle of 20 deg for 20 s. The target speeds were 20, 40, 60, 80, and 100 deg/s for both horizontal and vertical movements. For every target speed, a pair of trials was performed in the horizontal and vertical directions. In each trial, participants were instructed to follow the target as smoothly as possible. The eye-tracking and calibration method was the same as that of ORTe EYENAC [15].