esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Tool Geometry in Hard Turning  
Hard turning is typically a finishing process characterized by high cutting speeds, small depths of cut, and low feed rates [11; 12; 13]. The previously developed mathematical model of tool geometry in hard turning showed that the cutting action is confined to the tool-nose radius [25]. Therefore, the local tool-geometry parameters and the undeformed chip thickness of the tool-nose radius elements engaged in the cutting process are determined as shown in Figure 2, using the following input parameters: cutting-edge angle \(K_{r}=60^{\circ}\), inclination angle \(\lambda=-\)\(6^{\circ}\), rake angle \(\gamma=-\)\(6^{\circ}\), tool-nose radius \(r=0.8\) mm, chamfer angle \(=-25^{\circ}\), and chamfer width \(=1.5\) mm. The equations of the proposed model are presented as [25]:  
- The portion of the nose radius involved in the cutting process:  
\[\theta_{A}=K_{r}-cos^{-1}\Big{(}\frac{r-d_{w}}{r}\Big{)}\]  
\[\theta_{C}=K_{r}\]  
\[\theta_{D}=K_{r}+sin^{-1}\Big{(}\frac{f}{2r}\Big{)}\]  
\[\theta_{B}=K_{r}-tan^{-1}\Big{(}\frac{rsin(K_{r}-\theta_{A})-f}{r-d_{w}}\Big{)}\]  
- Local rake angle:  
\[\gamma^{j}=tan^{-1}\Big{(}sin\Big{(}\frac{\pi}{2}-\theta^{j}\Big{)}tan\gamma+ cos\Big{(}\frac{\pi}{2}-\theta^{j}\Big{)}tan\lambda\Big{)}\]  
- Local undeformed chip thickness:  
\[\text{Zone 1}:\;\theta_{A}\leq\theta^{j}<\theta_{B}\]  
\[t_{1}(\theta^{j})=r-\frac{r-d_{w}}{\cos{(K_{r}-\theta^{j})}}\]  
\[\text{Zone 2}:\;\theta_{B}\leq\theta^{j}\leq\theta_{D}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**Factors**} \\ \cline{2-7}  & \(\mathbf{K_{r}}\) & \(\mathbf{\gamma}\) & \(\mathbf{\lambda}\) & \(\mathbf{No.}\) & \(\mathbf{K_{r}}\) & \(\mathbf{\gamma}\) & \(\mathbf{\lambda}\) \\ \hline
1 & \(+1\) & \(-1\) & \(-1\) & \(11\) & \(0\) & \(-\alpha\) & \(0\) \\
2 & \(-1\) & \(-1\) & \(-1\) & \(12\) & \(0\) & \(+\alpha\) & \(0\) \\
3 & \(+1\) & \(+1\) & \(-1\) & \(13\) & \(0\) & \(0\) & \(-\alpha\) \\
4 & \(-1\) & \(+1\) & \(-1\) & \(14\) & \(0\) & \(0\) & \(+\alpha\) \\
5 & \(+1\) & \(-1\) & \(+1\) & \(15\) & \(0\) & \(0\) & \(0\) \\
6 & \(-1\) & \(-1\) & \(+1\) & \(16\) & \(0\) & \(0\) & \(0\) \\
7 & \(+1\) & \(+1\) & \(+1\) & \(17\) & \(0\) & \(0\) & \(0\) \\
8 & \(-1\) & \(+1\) & \(+1\) & \(18\) & \(0\) & \(0\) & \(0\) \\
9 & \(+\alpha\) & \(0\) & \(0\) & \(19\) & \(0\) & \(0\) & \(0\) \\
10 & \(-\alpha\) & \(0\) & \(0\) & \(20\) & \(0\) & \(0\) & \(0\) \\ \hline \end{tabular}
\end{table}
Table 2: Coded experimental factor matrix.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cutting Speed \(v\) (m/min)** & **Feed Rate \(f\) (mm/rev)** & **Depth of Cut \(dw\) (mm)** \\ \hline
150 & 0.08 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: Fixed machining parameters in the study.  
From Figure 2c,d, the cutting process occurs from \(\theta^{A}=18.59^{\circ}\) to \(\theta^{D}=62.59^{\circ}\). At the cutting-edge element \(\theta^{D}=23.59^{\circ}\), the largest uncut chip thickness \(t(\theta^{D})=0.05\) (mm).
## 4 Training Methodology
### Baseline Models  
Linear regression (LR) and Random Forest (RF) [37] models are frequently used as baseline models in solar forecast tasks [38; 39; 40; 41; 42; 43].  
Linear regression seeks to establish a linear relationship between the input variables and the output variable by employing a linear function to predict the values of the target variable based on the input characteristics; its interpretation is easily comprehensible.  
Random Forest is an ensemble model that combines multiple decision trees to make predictions. Each tree is trained on a random sample of features and examples from the dataset. To make the prediction, the average of the tree predictions is calculated. Random Forest has proved its resilience against overfitting and its ability to effectively manage linear data and categorical features. In this study, these models serve as a benchmark for evaluating the performance of the models proposed in Section 3.  
Parameter Estimation  
The dataset was split into two portions: training and testing. CyL-GHI data from the first 18 years were used for training, while the data from the final year were used for testing.  
To identify the best parameters for the models, a 5-fold cross-validation was carried out. In the cross-validation, multiple iterations were performed, and the training set was divided into training and validation folds using the \"TimeSeriesSplit\" [44] method (which ensures that the time series maintain their time index when the cut is performed). During the preprocessing, the data were normalized between 0 and 1 using min-max normalization, which was then applied to both the training and validation sets.  
The training consisted of several training operations for each model, with each iteration featuring a different combination of specified hyperparameters. The process considers several hyperparameters, such as the number of nodes (values of 10, 25, 50, 100), epochs (values of 20, 50, 100, 200), and batch size (values of 32, 64, 128, 256).
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 2 Experimental Setup  
Obsuring by acoustic shadow in an ultrasound image is somewhat similar to the phenomenon of an eclipse, whereby one object to be imaged is shielded by the shadow of another. A forward model study is carried out to demonstrate the effects of acoustic shadowing on the ultrasound image.
## 3 Graph Attention Network Categories
### Graph-Embedding GATs
GAT-GO (Graph Attention Networks for Gene Ontology) is a graph attention network (GAT) method designed to substantially improve protein function prediction by leveraging predicted structure information and protein sequence embedding. Traditional computational methods for protein function prediction can be fast, but they often lack satisfactory accuracy. GAT-GO aims to address this limitation by taking advantage of recent breakthroughs in protein structure prediction and protein language models. The GAT-GO method [48] integrates both structural information and sequence embedding to better predict protein functions. By combining these two sources of information, GAT-GO is able to capture both local and global features of proteins, which, in turn, enhances its predictive power. The graph attention mechanism in GAT-GO helps the model to focus on the most relevant parts of the protein structure and sequence, effectively improving the accuracy of the predictions. The KGANCDA (Knowledge Graph Attention Network for CircRNA-Disease Association) model [49] presents a novel computational approach to predicting circRNA-disease connections, capturing both low-level and high-level neighbor information from diverse associations. Its performance surpasses existing methods, as demonstrated by cross-validation results and a case study. In the HGATLDA (Heterogeneous Graph Attention Network for lncRNA-Disease Association) framework [50], the authors leverage node features, relationships in the network, heterogeneous topological structures, and semantic information from metapaths to accurately predict lncRNA-disease associations, showcasing the framework's effectiveness. The study [51] proposes a novel Heterogeneous Relational Graph (HRG) and a Multiplex Relational Graph Attention Network (MRGAT), along with a connecting embedding (ConnectE) model for the Knowledge Graph Entity Typing (KGET) task. This approach leads to improved entity type predictions and better integration of entity typing tuple with entity relation triples for enhanced entity classification. Finally, MGA-Net [52] introduces a novel few-shot learning (FSL) model that combines data augmentation, embedding network, and graph attention network to address the issue of insufficient data for Synthetic Aperture Radar (SAR) target classification.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 2 Materials and Methods
### GNSS Data Collection in NEP  
We installed a data acquisition system on the M/V TIANHUI, including a low-cost U-blox ZED-F9P GNSS receiver and a GNSS antenna, to evaluate the RT-PPP positioning performance in the NEP. Figure 2 shows that TIANHUI is a cargo ship with a length of 190 m and a width of 29 m. The GNSS antenna is placed on the ship deck, and the power supply and receiver are installed in the cabin equipment room.  
The M/V TIANHUI set sail from Yangshan Port in Shanghai on 30 August 2019, crossed the Bering Strait into the NEP on 10 September, and entered the Norwegian Sea on 20 September, which is shown in Figure 3. The total voyage on the NEP was approximately 3000 nautical miles, and we collected approximately 11 days of GNSS raw observation data with a 1 s sampling rate. However, the GLONASS signal was shielded in the data acquisition process; as such, we only collected GPS, Galileo, and BDS observation data.  
Figure 1: Flow chart of RT-PPP method in Northeast Passage.  
Figure 2: M/V TIANHUI and location of GNSS equipment.  
Meanwhile, we received the CLK93 stream of the state-space representation (SSR) orbit/clock correction products, which contain the correction information for Multi-GNSS, via NTRIP Caster by BNC software, and the data period was from 10 September to 20 September 2019.
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
###### Abstract
They offer a more accurate and adaptable solution compared to traditional atlas-based methods [29; 30], especially in cases with significant anatomical variations or pathologies. The integration of DL techniques, in particular, has opened new horizons, enabling more precise, patient-specific, and versatile applications of pseudo-CT synthesis in both diagnostic and therapeutic realms. However, while these methods have shown progress, significant room for improvement remains, specially when moving to other areas than head and neck, and pelvic imaging. Specifically, pseudo-CT synthesis in knee imaging presents a critical challenge, as images usually present different field of view (FOV) and/or positioning of the knee between the MR and the CT scans due to the high degrees of freedom of the knee, hindering matching between MR and CT image structures. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method [31; 32], tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. In this context, the CycleGAN presents an innovative solution to address the problem of image synthesis between different modalities by translating images from one domain (MR) to another (CT), tackling with potential misregistrations between corresponding data pairs. The distinctive feature of the CycleGAN lies in the usage of adversarial training and the principle of cycle consistency. This integration enables the accomplishment of unpaired data training, ideally suited to tackle the intricate challenge of possible misalignments between CT and MR, as we will further see in Section 2. Beyond the technical intricacies, we also discuss the profound clinical implications of this advancement. To the best of our knowledge, this study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging.
## 3 Results
### Application: Multi-Modal and Multi-Temporal ARD for Crop Monitoring  
Vegetation has been proven to strongly absorb the blue and red bands of the visible segment of the electromagnetic spectrum and reflect the near-infrared wavelengths [65]; these characteristics form the basis for the detection of vegetation on the surface of the Earth from satellite sensor data. The Normalised Difference Vegetation Index (NDVI) [66], calculated as the ratio of the difference in reflectance between the near-infrared and the red band to the sum of reflectance between the near-infrared and the red band, as shown in Equation (1), has been defined in this context:  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{1}\]  
The NDVI ranges from \(-1\) to \(1\), with negative values indicating clouds and water, positive values close to \(0\) corresponding to no vegetation and dense vegetation corresponding to a value close to \(1\). The NDVI has been used to monitor the growth of crops and for the estimation of crop yield [67; 68]. An example of the variation of NDVI for a field of peas in Scotland for the period of 29 May 2020 to 12 August 2020 is shown in Figure 13. Figure 13 shows the time series of the mean NDVI along with sample RGB and NDVI cloud-free images; Figure 13b shows the mean of SAR VV and VH polarisation normalised radar cross-section (RCS) along with Sentinel-1 false colour composite images for the field for all available acquisitions in the period. Evident in Figure 13a is that NDVI values are lower before sowing (May-June 2020), viz. when the soil is bare, and after harvest (August 2020); the NDVI increases with crop growth (July-August 2020). A similar trend can be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Sentinel-2 Tile** & **Selection Time** & **Processing Time (s)** & **Total Time (s)** \\  & **Coverage** & **(s)** & **Time (s)** & **Total Time (s)** \\ \hline A & 1 & 1.2 & 267.8 & 269.0 \\ B & 2 & 2.2 & 343.4 & 345.7 \\ C & 4 & 3.4 & 442.7 & 446.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time requirement for the generation of ARD for regions of interest covering one, two and four Sentinel-2 tiles, shown as **A**, **B** and **C**, respectively, in Figure 12.  
Figure 12: Regions of interest covering one, two and four Sentinel-2 tiles shown by **A**, **B** and **C**, respectively. The Sentinel-2 tile grid is shown in black, with the tile ID at the centre of each tile. Base layer © OpenStreetMap contributors.  
observed in the SAR data shown in Figure 13b, illustrating the potential to facilitate further studies in these applications that combine Sentinel-1 and Sentinel-2 data [69; 70; 71].
# Design of a 90 GHz SOI Fin Electro-Optic Modulator for High-Speed Applications
###### Abstract  
Introducing high speed networks, such as the fifth generation of mobile technology and related applications including the internet of things, creates a pressing demand for hardware infrastructure that provides sufficient bandwidth. Here, silicon-based microwave-photonics presents a solution that features easy and inexpensive fabrication through a mature platform that has long served the electronics industry. In this work, the design of an electro-optic modulator is proposed where the 'fin' structure is adopted from the domain of electronics devices, with emphasis on the high speed of operation. The proposed modulator is customized to provide a bandwidth of 90 GHz with a small phase shifter length of 800 \(\upmu\)m and an optical insertion loss of 4 dB. With such a speed, this proposed modulator fits high-speed applications such as modern tele-communications systems.  
CMOS; integrated optics; silicon photonics; modulators +
Footnote †: journal: Applied Physics Letters
## 3 Related Work on Correcting Typographical Errors
### Real-Word Errors  
We already reviewed techniques for detecting and correcting non-word typos. The other, far more difficult, group is the real-word errors. These are misspellings that result in other real words. Ironically, these errors are also caused by automatic spelling correction systems [78]. As it is harder to apply unsupervised methods such as the dictionary, there is also a challenge to build tools for different languages with different alphabets and rules [79].  
The detection of real-word errors can be done by searching every word in a confusion set and checking for a better alternative [66; 72; 80; 81]. The candidate population is usually done by the \(n\)-gram method, and others, as already discussed in Section 3.2. Some works employ natural language parsers that check grammar [82; 83] or look for words semantically unrelated to their context, that have semantically-related spelling alternatives [84]. Since the detection is similar to the selection of candidates here, the real-word error correction systems often do detection and correction at the same time.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Goal Oriented Error Estimates
#### 4.1.3 Timestep Controller  
Now, we want to choose timesteps as to keep the sum of the error estimates of Equations (21) and (24) at a given tolerance. For this we need to model the temporal evolution of the estimated errors. We linearise the second term in Equation (21) about \(\left(t_{n+1},\mathcal{N}^{t_{n}\Delta t_{n}}_{-}\mathbf{u}_{n}\right)\) and get  
\[\hat{\ell}^{\mathcal{J}_{h}}_{n}=\frac{\partial j\left(t_{n+1},\mathcal{N}^{t_ {n}\Delta t_{n}}_{-}\mathbf{u}_{n}\right)}{\partial\mathbf{u}}\hat{\ell}_{n}+\mathcal{ O}(\Delta t^{\hat{r}+2}_{n}). \tag{25}\]  
Assuming \(\hat{r}=\hat{r}\), we choose the leading terms of Equations (25) and (23) to define our model by  
\[m^{j}_{n} :=\left|\frac{\partial j(t_{n+1},\mathcal{N}^{t_{n}\Delta t_{n}} _{-}\mathbf{u}_{n})}{\partial\mathbf{u}}\mathbf{m}_{n}\right|+\left|\Delta t^{\hat{r}+1}_ {n}\varphi_{-}(t_{n},\mathbf{u}(t_{n}))\right|\] \[\stackrel{{\eqref{eq:q_n}}}{{=}}\Delta t^{\hat{r}+1}_ {n}\left(\left|\frac{\partial j(t_{n+1},\mathcal{N}^{t_{n}\Delta t_{n}}_{-}\mathbf{ u}_{n})}{\partial\mathbf{u}}\mathbf{\phi}_{-}(t_{n},\mathbf{u}_{n})\right|+\left|\varphi_{-}(t_{n}, \mathbf{u}(t_{n}))\right|\right),\]  
using the previous Model (9) for \(\mathbf{m}_{n}\). Assuming both principal error functions and the derivative term be to slowly changing, the error in the next step is  
\[m^{j}_{n+1} \approx\Delta t^{\hat{r}+1}_{n+1}\left(\left|\frac{\partial j(t_ {n+1},\mathcal{N}^{t_{n}\Delta t_{n}}_{-}\mathbf{u}_{n})}{\partial\mathbf{u}}\mathbf{\phi} _{-}(t_{n},\mathbf{u}_{n})\right|+\left|\varphi_{-}(t_{n},\mathbf{u}(t_{n}))\right|\right)\] \[=\left(\frac{\Delta t_{n+1}}{\Delta t_{n}}\right)^{\hat{r}+1}m^{j }_{n}\approx\left(\frac{\Delta t_{n+1}}{\Delta t_{n}}\right)^{\hat{r}+1}\left( |\hat{\ell}^{\mathcal{J}_{\mathcal{Q}}}_{n}|+|\hat{\ell}^{\mathcal{J}_{ \mathcal{Q}}}_{n}|\right).\]  
Aiming for \(|m^{j}_{n+1}|=\tau\) we get the deadbeat-type goal oriented controller  
\[\Delta t_{n+1}=\Delta t_{n}\left(\frac{\tau}{\left|\hat{\ell}^{\mathcal{J}_{ \mathcal{Q}}}_{n}\right|+\left|\hat{\ell}^{\mathcal{J}_{\mathcal{Q}}}_{n} \right|}\right)^{1/(\hat{p}+1)}. \tag{26}\]We thus constructed a timestep controller to control the local error contributions to the QoI, see Equation (2), based on the splitting in Equation (17).  
**Remark 3**.: _One can, likewise, construct controllers using only the quadrature or time-integration error estimate by setting \(\hat{\ell}_{n}^{\mathcal{J}_{n}}\) resp. \(\hat{\ell}_{n}^{\mathcal{J}_{0}}\) equal to zero._
## 3 Theoretical Modeling and Research
### Mechanism of Roller Sticking to Rod in the Hot Rolling of Copper Rod  
In the process of hot rolling, when the reduction ratio is large, the plastic deformation of the surface layer of the copper rod causes the brittle oxide coating on the surface of the copper rod to fracture, and the copper rod is exposed directly without protection of the oxide layer. With an increase in rolling force, the contact point between the two metals experiences local deformation, resulting in the fracture of the brittle coating, an increase in the contact surface expansion rate, cracking of the coating and extrusion of the processing material [28]. At the micro-level, the sharp plastic deformation in the rolling zone leads to the copper bar matrix extruded oxide film cracking and forming a reaction film with the polar molecules of emulsion. When the reaction film is damaged under pressure, the extruded copper matrix directly contacts the roller surface, which are welded to each other by friction, as shown in Figure 5. To reduce the surface energy of two metals in close contact, the interface bond is formed by the combination of the surface lattice. With the sheer movement of the near-contact surface, the interface bond is strengthened continuously, resulting in the phenomenon of welding [29]. As the shear load continues to  
Figure 4: Schematic diagram of pits on the surface of the copper wire rod of the intermediate billet.  
act, the interface energy formed by the interface bond increases continuously. When the critical interface energy is exceeded, the two metals undergoing welding are destroyed. The size of the interface energy determines the location of the fracture point of the contact welding. As such, the macroscopic shear strength of the copper rod and roller can be characterized by the binding energy between micro atoms. This mechanism is consistent with the strong plastic flow of metal in the groove of the rolling mill, which promotes stronger adhesion to the surface of the groove [30; 31].  
The main component of the copper rod is the Cu atom and, the internal binding energy is (Cu-Cu); the main component of the roll is the Fe atom and, the internal binding energy is (Fe-Fe), and the interface energy between the copper rod and the roll surface is (Fe-Cu). Research shows that the interface energy of Fe-Cu is greater than that of metal, and the internal binding energy of Cu-Cu is the minimum [32]. Therefore, the fracture point of welding occurs on the copper matrix with the minimum internal binding energy, which is characterized by the copper sticking phenomenon. At the same time, the temperature, friction stress, and normal pressure of the surface layer and sliding layer of the contact surface between the roll and the rolled piece during the hot rolling process--in addition to the degree of deformation--have an important influence on the adhesion behavior of copper [33].
_Algorithms_**2015**, \(8\), 541-551; doi:10.3390/a8030541  
_article_  
**Target Detection Algorithm Based on Two Layers Human Visual System**  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
School of Electrical Engineering and Automation, Harbin Institute of Technology, No.2 Yi-Kuang Street Nan Gang District, Harbin 150001, China; E-Mails: [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (S.J.); [EMAIL_ADDRESS] (C.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-451-8640-3083.  
Academic Editor: [PERSON]  
_Received: 27 April 2015 / Accepted: 17 July 2015 / Published: 29 July 2015_  
**Abstract:** Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.  
**Keywords:** small target detection; human visual system; phase spectrum Fourier transform; support vector machine