esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Technical Overview  
The electromagnetic environment consists of natural radiation and manmade EMFs that are produced either intentionally or as by-products of the use of electrical devices and systems. Based on this, all populations are now exposed to varying degrees of EMFs, and the levels will continue to increase as technology advances.  
EMFs are invisible areas of energy, often referred to as radiation, that are associated with the use of electrical power and various forms of natural (_i.e._, electrical discharges in the earth's atmosphere and radiation from sun and space) and manmade lighting [8]. These waves consist of oscillating electric and magnetic fields that influence each other and affect in different ways the biological systems (_i.e._, cells, plants, animals and human beings). To better understand this reciprocal influence it is useful to start with an overall description of the physical properties of electromagnetic waves (EMWs).  
EMWs are two waves in one: (i) an electrical wave creating an electrical field that moves along one plane; and (ii) a magnetic wave creating a magnetic field that moves perpendicularly to the electrical one.  
EMWs can be characterized by their wavelength, frequency or energy, which are directly related to each other. The frequency of an EMW is defined as the number of times the electric or magnetic fields change their sign, at a given point, per time unit and it is measured in Hertz (Hz). The wavelength of an EMW is inversely proportional to the frequency of the wave, proportional to the speed of the wave in the medium (usually the medium is considered to be the vacuum, where the speed corresponds to the speed of light \(c\) = 3 \(\times\) 10\({}^{8}\) m/s). As an example, a typical microwave furnace emits a wave of 2.54 GHz, corresponding to a wavelength of 12 cm.  
As a function of the frequency (or wavelength), the EMWs are usually classified into one of the two radioactive categories: (i) electromagnetic fields or non-ionizing radiations: Low-level radiation which is generally perceived as harmless to humans; and (ii) electromagnetic radiations or ionizing radiation, _i.e._, high-level radiation which has the potential for cellular and DNA damage. The ionizing radiations have a frequency high enough to produce the ionization of molecules and atoms through the breaking of the chemical bonds of the molecules. Examples of these radiations are the X-rays and radiations produced by radioactive sources. The non-ionizing radiations (NIR) correspond to those waves of the electromagnetic spectrum whose energy is too low to break the atomic bonds. Among these radiations are the ultraviolet and visible light, the infrared radiation, the radiofrequency and microwave emissions, the extremely low frequency fields and the static magnetic and electric fields. The NIR, even at very high intensity, cannot produce ionization in a biological system. Nevertheless, these radiations have been proven to produce other biological effects, such as heating, alteration of some chemical reactions or induction of electric currents on tissues and cells [9].
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 3 Branch-and-Bound Algorithm
On this basis, relaxed problems \(W_{n}^{1}\) and \(W_{n}^{2}\) at nodes \(N_{n}^{1}\) and \(N_{n}^{2}\) are formed as follows  
\[W_{n}^{1}:\min\Bigl{\{}f(x):x\in\wp_{n}^{1}\Bigr{\}}, \tag{15}\]  
\[W_{n}^{2}:\min\Bigl{\{}f(x):x\in\wp_{n}^{2}\Bigr{\}}. \tag{16}\]  
In the end, \(W_{n}^{1}\) and \(W_{n}^{2}\) are placed in \(\Psi\). This completes one iteration of the B&B algorithm. The process is repeated until there are no node problems remaining in \(\Psi\), at which point the global optimal solution to the original problem can be obtained. In this paper, the branch selection policy of the B&B algorithm randomly selects the discrete variable that deviates the most from its integer value. The node selection policy employs a breadth-first exploration policy. The pruning policy prunes a node only if one of the following conditions is met:  
1. Prune by bound: The solution \(v_{n}^{*}\) of the \(W_{n}\) is a non-integer solution but greater than or equal to \(c^{*}\). Since \(\partial\subseteq\wp_{n}\) and \(\wp_{n}\subseteq\wp\) (\(\wp_{n}\) is the feasible domain of all the children node of \(N_{n}\)), the value of \(v_{n}^{*}\) is a lower bound on the solution of \(W_{n}\) and the optimal solution of its children cannot be the global optimal solution to the original problem. 2. Prune by infeasibility: The relaxed problem \(W_{n}\) is infeasible, i.e., unsolvable. It is considered as \(v_{n}^{*}=+\infty\). 3. Prune by integrality: \(x_{n}^{*}[a],\forall a\), is an integer, we have already found an optimal solution to \(W_{n}\). Therefore, there is no longer a need to search for the children of \(N_{n}\). We develop the B&B algorithm for the problem, as presented in Algorithm 1. ```
1. problem list \(\Psi=\{\ \}\), node number \(n=0\), upper bound \(UB=c^{*}=+\infty\);
2. \(N_{1}\to W_{1}\rightarrow\Psi\), \(\Psi=\{W_{1}\}\);
3. **while**\(\Psi\
eq\emptyset\)do
4. \(n=n+1\);
5. Pop the node relaxed problem \(W_{n}\) from \(\Psi\);
6. \(W_{n}\to x^{*}{}_{n}\), \(v_{n}^{*}\);
7. **if**\(x_{n}^{*}[a],\ \forall a\), is an integer then
8. \(UB=c^{*}=\min(UB,\ v_{n}^{*})\);
9. else
10. **if**\(v_{n}^{*}<UB\)then
11. Select the non-integer variable \(x_{n}^{*}(a)\) to round up and down;
12. Cutting feasible region \(\wp_{n}\);
13. Two children nodes of \(N_{n}\rightarrow\left(N_{n}^{1},\ N_{n}^{2}\right)\rightarrow\left(W_{n}^{1}, \ W_{n}^{2}\right)\rightarrow\Psi\);
14. **endif**
15. **endif**
16. **end while**
```  
**Algorithm 1** The branch-and-bound (B&B) algorithm  
The computation time of the B&B algorithm is directly proportional to the size of the search tree, which grows exponentially with the number of discrete variables. This characteristic is particularly pronounced in UC problems, where the extensive search space makes it challenging for the B&B algorithm to achieve satisfactory solutions within a reasonable timeframe. Therefore, the ability to effectively reduce the size of the search tree is critical to the feasibility of the B&B algorithm in UC problem practical applications.
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 2 Materials and Methods
### Cam
#### 2.2.2 Computer Numerical Control (CNC) Programming Language  
Turning a digital design into a physical object requires defining and converting process parameters into machine instructions, which are then exported--most commonly--as G-code. G-code is a standardized and widely adopted language used to control machine operations such as movements, temperatures, and tool actions. It serves as the link between the virtual model and physical execution. Even though G-code is standardized (based on ISO 6983 [45]), there are many variants--such as Marlin, RepRep, and Fanuc--especially for FFF technologies, where these custom commands and syntax are specific to the machine firmware or controller.  
In subtractive manufacturing, CAM software generates these instructions from settings such as the tool paths, cutting depths, and spindle speeds for CNC machines. In AM, so-called'slicers' perform a similar role, using parameters such as the layer height, extrusion width, print speed, temperature, and many others to produce instructions for 3D printers. There are various slicing programs, many of which are free (such as Cura, PrusaSlicer, Slic3r, and OrcaSlicer) that process the 3D model, divide it into layers, and generate optimized G-code. These tools offer fine control over printing strategies, helping balance the quality, time, and material use.
## 3 Experimental Setup  
The experimental configuration is shown in Figure 2. A linear cavity was adopted for BGSe-OPO. The pump source was an electro-optical (EO) Q-switched Nd:YAG oscillator at 1.064 \(\upmu\)m at 10 Hz (LPS-1064-S-200 mJ). The maximum pulse energy was 200 mJ, with a pulse duration of 10 ns. Firstly, an optical isolator (ISO) was employed to avoid possible  
Figure 1: Calculated phase-matching characteristics of BGSe-OPO pumped by 1.064 \(\upmu\)m laser. (**a**) Phase-matching angles versus OPO signal and idler wavelength at the \(x\)-\(z\) principal plane and \(y\)-\(z\) principal plane of BGSe crystal. (**b**) The OPO effective nonlinear optical coefficients \(d_{\mathit{off}}\) versus phase-matching angles at the \(x\)-\(z\) and \(y\)-\(z\) principal planes.  
damage to the pump source, as caused by the reflected pump light. Then, the incident light was reshaped and collimated by a telescope system (TS) to a Gaussian beam with a spot diameter of 2 mm. Next, an attenuator, which consisted of a half wave plate (HWP) and 45\({}^{\circ}\) polarized beam splitter (PBS) at 1064 nm, was utilized to adjust the incident energy at vertical polarization, without altering the pulse duration and pump beam profile. A large aperture 6 \(\times\) 6 \(\times\) 18 mm\({}^{3}\) BGSe crystal was grown by the Bridgman-Stockbarger method. Both end facets of BGSe crystal were finely polished and coated with high transmittance (HT) via 1.064, 1.274, and 6.45 \(\upmu\)m lasers. The transmission spectrum of the uncoated BGSe sample, in the range of 0.2-20 \(\upmu\)m, is shown in the inset of Figure 2. It has better transmission in the range of 1-12 \(\upmu\)m and can transmit up to 18 \(\upmu\)m. The OPO cavity was constructed of two parallel-plane mirrors, i.e., M1 and M2, and the coating parameters were as follows: M1: 1.064 \(\upmu\)m HT, 1.274 and 6.45 \(\upmu\)m high reflectivity (HR); M2: 1.064 and 1.274 \(\upmu\)m HR, 6.45 \(\upmu\)m, with partial transmittance of 40%. The physical length of the cavity was about 25 mm. A germanium (Ge) wafer with a transmittance of 42% at 6.45 \(\upmu\)m was placed behind the output coupling (OC) mirror, M2, in order to block the residual pump and signal waves. An energy meter was placed behind the Ge wafer to detect the energy of the idler laser.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 1 Introduction
Therefore, they need cybersecurity protection studies for each security component because traditional techniques are applied for the first time. Because the SG system is a cyber-physical and communication system, the power is also exchanged [15]. A thorough review that studies components of the SG system security is required. Several reviews focus only on bad data detection and state estimation attacks [16; 17]. Moreover, both attacks' effects may be different, and all processes should terminate within a certain period. The aims and objectives of the research focus on this point. This paper presents a comprehensive review of security concerns in different system parts of smart grids, different types of attacks, standards, and available mitigation techniques to fill this gap. Due to its enormous impact, False Data Injection (FDI) attack and detection methods are discussed in detail. The rest of the paper is organized as follows: Section 2 introduces the cybersecurity concerns in SGs and discusses the mitigation requirements. Figure 2: SG Cyber-Security System Structure and Relationship Diagrams. Section 3 reviews different types of cyber-attacks and discusses the impacts of attacks on SG. Section 4 introduces false data detection methods and proposed efficiency analysis, and Section 5 concludes the paper. See Appendix A for abbreviations and meanings. Main contributions of this survey paper are as follows:  
* A thorough discussion on changing paradigms in power systems is presented. Different levels of communication and information exchange are discussed so that readers can grasp why smart grid cybersecurity became important in recent times. * Different communication standards used in power system communication are studied. Issues that are unique to each standard and the protocols it uses are presented. Benefits and drawbacks of using single or multiple standards in a system are presented. * A thorough review of SG attacks is performed so that readers can understand the types of attacks and their impacts on the system. Among these attacks, FDI attacks have significant potential to disrupt power system operation or cause damages. For this reason, a survey is performed on techniques developed to detect FDI attacks. * Based on the discussions and insights of this work, future research directions are provided.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 2 Materials and Methods
### Vmg  
VMG is defined as the speed that the boat reaches directly in windward and leeward courses, and it measures how fast the boat progresses in each of the courses and along the Rhumb line (Figure 2) [26]. This line is the straight line from the start point to the finish line, and it is the shortest distance to complete the course. VMG is a vector that measures a straight line to the next mark; therefore, in addition to speed, it also provides information about the distance between the boat and the destination point [27].
## 4 Experiment Design
### Model Performance Metrics  
For sake of completeness, various standard metrics were used to evaluate the model performance, namely the overall accuracy (OA), the F1 score, and the intersection over union (IoU). For the readers' convenience, the definition of the first three metrics are reported below.  
\[precision=\frac{tp}{tp+fp};\quad recall=\frac{tp}{tp+fn};\quad F_{score}=2\cdot \frac{p\cdot r}{p+r}. \tag{1}\]  
In the expressions above, \(tp\), \(fp\), \(fn\) refer to the number of true positive, false positive, and false negative cases, respectively. The IoU metric is defined as:  
\[IoU=\frac{target\cap detected}{target\cup detected}. \tag{2}\]  
Here, _target_ represents the set of building pixels from the ground truth, and _detected_ represents the set of pixels assigned to class \"building\" by the classifier. It is important to note that the number of building pixels is about one order of magnitude smaller than non-building pixels in the average considered image patch. In a segmentation setting with strong class imbalance, IoU is probably slightly more representative than the other measures, since it gauges the overlap rate of the detected target pixels and the labeled target pixels.
# In-Plane Monolithic Integration of Scaled III-V Photonic Devices
## 3 Hybrid III-V/Si Photonic Crystal Cavity Lasers
### Design and Fabrication
The smooth and straight side walls of both the III-V and Si rods are due to the mature Si processing technology using HBr, whereas the etching of III-V stacks, especially when it includes different compositions, generally results in greater surface roughness, which impacts the final device performance.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 4 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{Rotterdam} & \multicolumn{3}{c}{Boston} & \multicolumn{3}{c}{Antwerp} & \multicolumn{3}{c}{Hong Kong} \\ \cline{2-13}  & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE \\ \hline STGCN & 10.0471 & 3.3148 & 19.1571 & 2.7766 & 0.558 & 5.0216 & 2.2405 & 0.5603 & 3.7479 & 21.7833 & 1.7334 & 44.5805 \\ AGCRN & 4.0285 & 0.5375 & 23.3363 & 2.7057 & 0.5186 & 5.118 & **2.0334** & 0.5032 & 3.7522 & 24.765 & 1.4236 & 53.009 \\ DCRNN & 6.7308 & 0.7575 & 36.2379 & 3.6936 & 0.5506 & 9.0707 & 3.5909 & 0.7169 & 9.1206 & 23.4573 & 1.6995 & 45.0641 \\ STTN & 7.3341 & 0.7816 & 37.1314 & 2.8498 & 0.508 & 5.6422 & 3.4785 & 0.6061 & 8.2283 & 25.1063 & 1.6549 & 50.5299 \\ STMGAT & 4.0649 & 0.5334 & 20.0261 & 2.7224 & 0.5165 & 5.0336 & 2.0679 & **0.4968** & **3.6653** & 18.2011 & 1.5736 & 39.1712 \\ GWNET & 3.62 & **0.5294** & 19.4332 & 2.7968 & 0.4875 & 5.3694 & 2.2134 & 0.5158 & 4.1839 & 14.0173 & 0.8861 & 31.1028 \\ \hline GAGW & **3.3688** & 0.579 & **15.5115** & **2.6928** & **0.4753** & **5.0081** & 2.1446 & 0.5008 & 4.0861 & **13.3074** & **0.8242** & **29.6616** \\ \hline \multicolumn{13}{c}{Shanghai} & \multicolumn{3}{c}{Singapore} & \multicolumn{3}{c}{Incheon} \\ \cline{2-13}  & MAE & MAPE & RMSE & MAE & MAPE & RMSE & MAE & MAPE & RMSE \\ \hline STGCN & 21.8422 & 1.7012 & 45.8386 & 17.122 & 3.6693 & 36.7725 & 26.8699 & 1.8365 & 60.105 \\ AGCRN & 23.038 & 1.5282 & 47.7465 & 12.0759 & 0.9366 & 34.2086 & 21.9169 & 1.3855 & 44.5575 \\ DCRNN & 21.6196 & 1.8093 & 41.1723 & 14.5029 & 1.2927 & 39.0149 & 29.8365 & 1.6589 & 65.2048 \\ STTN & 22.5262 & 1.9444 & 44.5484 & 16.9381 & 1.2286 & 44.5798 & 23.6124 & 1.1277 & 59.0685 \\ STMGAT & 24.5565 & 2.6933 & 46.0391 & 6.9351 & 0.6712 & **19.7745** & **13.7049** & 0.6775 & **32.9118** \\ GWNET & 13.5886 & **0.9734** & 29.5113 & 6.9078 & 0.797 & 20.0405 & 15.5624 & **0.6453** & 40.8259 \\ \hline GAGW & **11.9513** & 1.0716 & **26.2667** & **6.7402** & **0.6319** & 20.937 & 14.9141 & 0.6781 & 39.1262 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment on spatiotemporal feature fusion module. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{_w/o_ **Feature Fusion**} & \multicolumn{3}{c}{_w/_ **Feature Fusion**} \\ \cline{2-7}  & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline STGCN & 13.7484 & 23.7532 & 0.9145 & **11.9819** & **21.2891** & 0.9319 \\ STTN & 21.6699 & 36.0566 & 0.8089 & **18.8857** & **33.1819** & **0.8384** \\ GWNET & **10.2954** & 20.1326 & 0.9402 & 10.3233 & **19.8169** & 0.943 \\ ASTGCN & 13.0184 & 22.8163 & 0.9229 & **11.3801** & **21.2428** & 0.9346 \\
**GAGW** & 10.9752 & 20.2516 & 0.9385 & **10.194** & **19.691** & **0.9428** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment of temporal and spatial layers.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 2 Different Approaches for a Wireless Power Transfer System
An assessment of variation in tissue electromagnetic properties was provided by [PERSON] et al. [48]. The recent reports on tissue electromagnetic properties are depicted in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Year** & **Tissue** & **Frequencies** & **Models/Methods** \\ \hline
[49] & 2020 & In vivo, ex vivo & - & FEM * \\
[50] & 2019 & Muscle, fat, skin & 50 MHz, 300 MHz, 700 MHz, & FDTD ** \\  & & & and 900 MHz & & \\
[51] & 2019 & Body & (0.5–26.5) GHz & Measured properties, \\
[52] & 2018 & Brain, liver & 200–1600 Hz & Measured properties \\
[53] & 2018 & Muscle, fat, skin & 915 MHz and 2 GHz & Measured properties \\
[54] & 2017 & Blood, liver, fat, brain & 10 kHz–10 MHz & model, measured \\  & & & & properties \\
[55] & 2016 & Muscle, bladder, & 128 MHz & Measured properties, \\  & & & & Cole–Cole \\
[56] & 2016 & Body/14 tissues & 2.1 GHz, 2.6 GHz & FDTD \\
[57] & 2016 & Head & (0.75–2.55) GHz & Phantom/ FEM \\
[58] & 2016 & Muscle & 500 MHz–20 GHz & Fricke \\
[59] & 2015 & Eye/6 tissues & (0.9–10) GHz & FDTD \\
[60] & 2015 & Skin & (0.8–1.2) THz & FEM \\
[61] & 2014 & Eye, head/14 tissues & (0.9–5.8) GHz & FDTD \\
[62] & 2010 & Head & & FEM \\
[63] & 2009 & Head/16 tissues & 50 MHz–20 GHz & Measured properties, \\  & & & & FDTD \\
[64] & 2006 & Eye, head/15 tissues & 900 MHz, 1800 MHz, & FDTD \\  & & & & 2450 MHz \\
[65] & 2004 & Body & 400 MHz, 900 MHz, 2400 MHz & Visible human, FDTD \\
[66] & 2004 & Body/51 tissues & 30 MHz–3 GHz & FDTD \\
[67] & 2002 & Head/10 tissues & 900 MHz, 1800 MHz & Visible human, FDTD \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of different approaches in analyzing tissue electromagnetic properties.
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
## 2 Related Work  
### Camera Calibration  
In the process of acquiring images, camera lenses can cause distortion [13, 14, 15, 16] during shooting, with radial distortion and tangential distortion frequently occurring, especially with wide-angle lenses. Radial distortion can be further divided into barrel distortion and pincushion distortion. Barrel distortion causes the image to appear magnified as it moves away from the center, while pincushion distortion causes the image to appear compressed towards the center. To remove these distortions, camera calibration [17, 18] is necessary. This process involves measuring the camera's internal and external parameters and is primarily used to correct lens distortion. Camera calibration is the process of finding the parameters that describe the transformation relationship between the 3D space coordinates and 2D image coordinates. The internal parameters of the camera include the focal length, principal point, and skew coefficient. The external parameters of the camera describe the transformation relationship between the camera coordinate system and the real-world coordinate system and include information about the camera's position and orientation. There are various methods for performing camera calibration, but one of the most common approaches uses calibration patterns [19, 20, 21], which are geometric patterns such as chessboards or circular patterns. In addition to traditional methods, recent advancements have proposed approaches using deep learning models for camera calibration [22; 23; 24]. In the case of deep-learning-based methods, camera calibration is performed by training on large amounts of image data to predict camera parameters. The parameters obtained through calibration are then utilized for image rectification.
# Stochastic Weight Averaging Revisited
### Limitations  
As mentioned previously, there are two seemingly independent views on the role of weight-averaging: one is statistical, namely, the variance-reduction perspective; and the other is geometric, namely, the wider-optimum perspective. However, we were unaware of nature of the relationship between these two views, and how to reconcile them. After a detailed inspection of SWA [5], we discovered that its behavior resulted from a combined effect of several possible intertwined factors, namely the convergence rate of the SGD that processed before SWA, the CRC learning-rate schedule, the weight-averaging operation, and finally, the application of the momentum technique and weight-decaying. The common geometric view could not explain the specific role of each factor. For example, it could not answer the following questions:  
1. If we did not use the momentum technique in its backbone SGD, how would SWA behave?
2. If the SGD that processed before SWA could not converge or converged to a bad optimum, could SWA still work?
3. What was the actual function of the weight-averaging operation in SWA, either variance reduction, the identification of a wider optimum, or both?
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 4 The Proposed Scheme
### Algorithm of Texture Image Encryption  
#### 4.3.1 Arnold Transform Permutation Encryption  
Arnold transformation can realize the displacement of image pixel positions and is widely used in image scrambling and image encryption [31]. The narrow sense Arnold transformation formula is shown in Equation (12), and the generalized Arnold transformation formula is shown in Equation (13):  
\[\begin{bmatrix}x^{\prime}_{n}\\ y^{\prime}_{n}\end{bmatrix}=\begin{bmatrix}1&1\\ 1&2\end{bmatrix}\begin{bmatrix}x_{n}\\ y_{n}\end{bmatrix}mod(N) \tag{12}\]  
Figure 5: Flow chart of texture coordinate watermark extraction.  
\[\begin{bmatrix}x_{n}^{\prime}\\ y_{n}^{\prime}\end{bmatrix}=\begin{bmatrix}1&b\\ a&ab+1\end{bmatrix}\begin{bmatrix}x_{n}\\ y_{n}\end{bmatrix}mod(N) \tag{13}\]  
In Equations (12) and (13), \((x_{n},y_{n})\) is the position coordinate of the pixel point in the original image, \((x_{n}^{\prime},y_{n}^{\prime})\) is the corresponding coordinate of the transformed pixel point \((x_{n},y_{n})\), \(mod\) is the modulo operation, and parameters \(a\), \(b\) are positive integers. \(N\) is the end of the digital image matrix. The Arnold transformation in a narrow sense is also periodic. For an \(N*N\) image, the generalized Arnold transformation is not periodic, so the number of permutations in the algorithm can be selected between \(1\) and \(N\).