esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Physical Security  
In [45], the researchers present a hybrid watermarking algorithm for protecting the integrity of an object while it is being transmitted through the Long-Term-Evolution (LTE) physical layer. This algorithm starts by classifying the object based on the use of the Support Vector Machine (SVM) in order to discover different important and unimportant regions. Following that, the DWT and SVD techniques are used to embed the scrambled data into a number of coefficients. Finally, the output of the embedding data is embedded into unimportant regions. Nonetheless, the algorithm shows that the improvement is made for the quality of an object when the complexity is high. A similar way for using the watermark technology in the WSN physical layer is investigated in [82] where a new protocol, namely, the 'Watermarked-based Blind Physical Layer Security (WBPLSec) protocol is proposed in order to secure the encountered communication among existing nodes. In fact, this protocol combines a blind watermark algorithm with the jam receiver over an acoustic channel in order to interchange a key of 128 bits with an adjacent equipment. The process starts by modulating the message with the Direct Sequence Spread Spectrum (DSSS) technique and embeds this message with a shifting key to represent a watermarked segment. After that, the segment is encoded in a waveform audio file format and is transmitted by an amplifier. The receiver can restore the cleaned code by using the information in the watermark. Nevertheless, this proposal requires a private and covert channel among existing nodes where this reduces the wireless bandwidth. In [112], the researchers merge the MAC layer parameters within a watermark technique in order to improve the attack detection in the WSN. The MAC address and CSMA/CA collision time are combined together to act as a watermarking key. The watermarking key is added to the DCT and DWT to embed the WSN data. Finally, the watermarked data are sent to the edge-router and edge-route, which reverses the watermarking process.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 2 Gravitational Search Algorithm  
### Physics Foundation of GSA  
The law of universal gravitation is one of the four basic forces in nature. It is one of the fundamental forces in nature. The gravitational force is proportional to the product of the mass, and is inversely proportional to the square of the distance. The gravitational force between two objects is calculated by:  
\[F=G\frac{M_{1}\cdot M_{2}}{R^{2}} \tag{1}\]  
where, \(F\) is the gravitational force between two objects, \(G\) is the gravitational constant, \(M_{1}\) and \(M_{2}\) are the masses of the objects 1 and 2 respectively, \(R\) is the distance between these two objects. According to the international unit system, the unit of \(F\) is Newton (N), the unit of \(M_{1}\) and \(M_{2}\) is kg, the unit of \(R\) is m, and the constant \(G\) is approximately equal to \(6.67\times 10^{-11}N\cdot m^{2}/kg^{2}\).  
The acceleration of the particle \(a\) is related to its mass \(M\) and of the gravitational force \(F\), which is calculated by the following equation.  
\[a=\frac{F}{M} \tag{2}\]  
According to the Equations (1) and (2), all of the particles in the world are affected by gravity. The closer the distance between two particles, the greater the gravitational force. Its basic principle is shown in Figure 1, where the mass of the particles is represented by the image size. Particle \(M_{1}\) is influenced by the gravity of the other three particles to produce the resultant force \(F\). Such an algorithm will converge to the optimal solution, and the gravitational force will not be affected by the environment, so the gravity has a strong local value.  
The gravitational search algorithm should make the moving particle in space into an object with a certain mass. These objects are attracted through gravitational interaction between each other, and each particle in the space will be attracted by the mutual attraction of particles to produce accelerations. Each particle is attracted by the other particles and moves in the direction of the force. The particles with small mass move to the particles with great mass, so the optimal solution is obtained by using the large particles. The gravitation search algorithm can realize the information transmission through the interaction between particles.
###### Abstract  
Medical imaging is crucial for disease diagnosis, but noise in CT and MRI scans can obscure critical details, making accurate diagnosis challenging. Traditional denoising methods and deep learning techniques often produce overly smooth images that lack vital diagnostic information. GAN-based approaches also struggle to balance noise removal and content preservation. Existing research has not explored tumor detection after image denoising; instead, it has concentrated on content and noise learning. To address these challenges, this study proposes the Adversarial Content-Noise Complementary Learning (ACNCL) model, which enhances image denoising and tumor detection. Unlike conventional methods focusing solely on content or noise learning, ACNCL simultaneously learns both through dual predictors, ensuring the complementary reconstruction of high-quality images. The model integrates multiple denoising techniques (DnCNN, U-Net, DenseNet, CA-AGE, and DWT) within a GAN framework, using PatchGAN as a local discriminator to preserve fine image textures. The ACNCL separates anatomical details and noise into distinct pathways, ensuring stable noise reduction while maintaining structural integrity. Evaluated on CT and MRI datasets, ACNCL demonstrated exceptional performance compared to traditional models both qualitatively and quantitatively. It exhibited strong generalization across datasets, improving medical image clarity and enabling earlier tumor detection. These findings highlight ACNCL's potential to enhance diagnostic accuracy and support improved clinical decision-making.  
Keywords:tumor detection; adversarial learning; image denoising; GAN-based networks; deep learning; content-noise learning
## 2 Background Knowledge  
### Multiple Instance Classification  
Multiple instance learning (MIL) provides an elegant framework to address the weakly supervised learning problem with inexact supervision. Different from the general fully supervised problem that assumes each sample has a label, training samples \(\mathrm{D}=\left\{\left(X_{1},y_{1}\right),\left(X_{2},y_{2}\right),\ldots, \left(X_{m},y_{m}\right)\right\}\) in MIL are composed of bags, where each bag \(X_{i}\) bears a label \(y_{i}\) and consists of a set of instances \(\left\{x_{i,1},x_{i,2},\ldots,x_{i,m_{i}}\right\}\). Bag \(X_{i}\) is assigned a positive label if there exists at least one positive instance, while it is assigned a negative label if it only contains negative instances. The goal of the classification task in MIL is to predict the labels for unseen bags, where bag labels are observed and instance labels are not observed in the training dataset.  
\[y_{i}=\left\{\begin{array}{l}1\;\exists x_{i,k}:\;x_{i,k}=1\\ 0\;\forall x_{i,k}:\;x_{i,k}=0\end{array}\right. \tag{1}\]  
The MIC framework is naturally suitable for handling high-resolution pathological image classification tasks. For example, an alternative mapping is to regard the pathological images for malignant/benign patients as positive/negative bags and patches (or features of patches) extracted from the same image as instances in each bag. All the patches extracted from a pathological image with the negative label can be considered to contain benign tissue information. On the other hand, in a pathological image with the positive label, there must exist at least several patches containing malignant tissue information. The properties of pathological image classification are consistent with the assumption of the MIC framework.
# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 6 Experiment  
### Comparison Using Vision  
The first experiment involved driving under foggy conditions in a comparison test of a vision-based system and LRF to demonstrate that the vision-based system is influenced by fog whereas LRF is not. The first picture in Figure 11a shows the test environment on a foggy road. The picture beneath it shows the vision-based processing. Figure 11b shows an onsite LRF-scanned 3D road map.  
In order to extract the lane-related information from the image obtained using the vision-based system, various filters or considerable computations are required. We extracted the lane information using SOBEL after changing the image into a gray-scaled one. As shown in the picture above, the front lane is partially visible as a blurred image, but the overall visibility for extracting the lane information is very limited. Moreover, under more severe foggy conditions, it would be a huge challenge to find lanes using vision. In contrast, LRF was not influenced by the fog at all, demonstrating the same effect as that  
Figure 10: The architecture of the tracking controller.  
on a clear day. Figure 11b shows a 3D road map built using the LRF scan data around the center line of the lane. The robot was instructed to navigate at the velocity of 1 m/s.  
In Figure 12, the lane feature points extracted from the 3D road map shown in Figure 11b by using the feature point extraction algorithm are highlighted with yellow dots. The robot was instructed to navigate along the right-hand-side boundary line. The errors in the case of the real lane width and the case of the scanned lane width with respect to the right-hand-side boundary line were compared.  
In Table 5, Given the land width of 150 mm, the average feature point coordinates did not deviate from those of the real lane width. Further, the maximum error was below 20 mm, not largely deviating from the real lane width. Nor did the robot deviate considerably from the lane during navigation because of the data error.  
Figure 11: Lane detection using vision in foggy weather.  
Figure 12: Feature point extraction.  
Furthermore, we conducted the same experiment on a dark road at night without street lamps and obtained the same results as those shown in Figure 11b. The results of this experiment verified that LRF could recognize lanes irrespective of the weather or light conditions by yielding the same results on foggy or dark roads as those on clear and bright roads.
## 5 Conclusions  
The following conclusions could be derived from the performed examination and analysis of the results:  
* Texture analysis can be very useful in the determination of the areas which are prone to crack initiation and affect formability properties of automotive profiles. Apart from the typical analysis of the orientation and grain size analysis, Taylor factor maps should be constructed for the observation of areas with high differences in respective values.
* A plausible scenario for crack initiation in automotive profiles involves a subsurface region where the convergence of slip lines and the presence of grain boundaries with relatively high susceptibility to crack initiation and propagation may promote this phenomenon. The outer surface, however, may not be the most likely site of crack initiation. Misorientation maps can be used for observation of grain boundaries which are based on their types (HAGBs, LAGBs, and SGBs) which are related with different propensity values for precipitation. On HAGBs, coarser precipitates are formed, and this is accompanied by the formation of PFZs which are visible with optical microscopy.
* Slow cooling is related with unacceptable formability, as it can lead to substantial grain boundary sensitization and early fracture in mechanical tests due to coarse Mg2 Sinon-coherent particles formation. In addition, the strength of the material is lower due to a lower alloying elements amount available for precipitation hardening by artificial aging.
* Step cooling can lead to mechanical performance which are comparable with water quenching and therefore leaves space for further investigation, as it is directly related with the extrusion process and the cooling method setup.  
Conceptualization, G.S. and A.V.; methodology, A.V.; software, A.V. and S.P.; validation, F.A., E.S. and A.R.; formal analysis, F.A., E.S. and A.R.; investigation, A.V. and G.S.; resources, A.V. and S.P.; writing--original draft preparation, A.V. and S.P.; writing--review and editing A.V.; visualization, A.V. and S.P.; supervision, A.V. and G.S.; project administration, S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are available.  
The authors would like to express their gratitude towards the ELKEME SA and ETEM GESTAMP SA managements.  
The authors declare no conflict of interest.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 4 The Methodology
### Algorithm Pseudocode
#### 4.2.4 Find Paths in DM-Based Subgoal Graphs
\begin{table}
\begin{tabular}{|l|} \hline
**FindHighLevelPath(subgoals \(V\), edges \(E\), safetyRadius \(R\), startCell s, _endCell s*_)** \\
77. \(ConnectToGraph(\(V\),\(E\),\(R\),\(s\)) \\
78. \(ConnectToGraph(\(V\),\(E\),\(R\),\(s^{\prime}\)) \\
79. \(\Pi\leftarrow\) A*(\(V\),\(E\),\(s\),\(s^{\prime}\)) \\
80. _restore original graph_ \\
81. _return_\(\Pi\) \\ \hline
**ConnectToGraph(subgoals \(V\), edges \(E\), safetyRadius \(R\), cells \(s\))** \\
82. **if**\(s\)\(\in\)_V_ **then** _return_ \\
83. \(Vs\gets Vs\bigcup\{s\}\) \\
84. \(S\leftarrow\) GetDirectHReachable(\(V\),\(R\),\(s\)) \\
85. **for each**\(s^{\prime}\)\(\in\)**do** \(Es\gets Es\bigcup\{(s,s^{\prime})\}\) \\ \hline \end{tabular}
\end{table} 
Table 5: The pseudocode for searching paths in DM-based subgoal graphs. real total cost, denoted by \(h^{*}\)(s',s') (line 90). Finally, the agent moves by changing its current position towards the most promising cell discovered in the planning phase (line 91). A drawback that prevents the application of LRTA* is that agents who adopt this strategy could possibly be trapped in local minima [37]. As shown in Figure 8a, local minima often exist around certain terrain patterns such as concave regions, long distance barriers, and so on. Agents who use LRTA* to search paths in these patterns would meaninglessly move back and forth, visit the same cells several times to correct their heuristic values before escaping from these regions (As shown in Figure 8c). However, the high-level path planned from the DM-based subgoal graph can provide an agent with waypoints which circumnavigate the collision regions (As shown in Figure 8b). Therefore, it can efficiently evade local minima by iteratively popping subgoals from the high-level path as the next waypoint to head for (as shown in Figure 8d).
## 6 Experimentation  
### Experimental Setup  
The proposed ACC2 PID control scheme is tested on a LMs-driven X-Y-Y stage, as shown in Figure 5. In this experimentation, the x-axis is actuated by a long-travel LM stage, while the \(y_{1}\)-axis and \(y_{2}\)-axis are actuated by a series type dual LMs. All the adopted LMs are manufactured by Hiwin technologies corporation. The travel ranges of the movers in \(x\)-axis and \(y\)-axis are 580 mm and 400 mm, respectively. Linear optical encoders with the resolution of 50, 10, and 10 \(\upmu\)m were, respectively, used for the position sensors of measurement \(x\), \(y_{1}\), and \(y_{2}\). Moreover, a digital signal processor (DSP) attaching a floating-point PowerPC 400-MHz processor and a VxWorks real-time operating system was used as the control core. The DSP was equipped with a 32-b resolution encoder interface and 16-b resolution digital-to-analog converters (DACs) to carry out the closed-loop control architecture. Furthermore, the gains of the input control signals to the output drive currents of the servo drivers mega-fabs MD-36-S2 were set as 0.85 A/V. The main parameters of the developed LMs-driven X-Y-Y stage are given in Table 1.  
In the developed control system, the software developed for the DSP-based real-time control comprises a main program and an interrupt service routine (ISR). In the main program, the IABC algorithm was implemented to optimize the control parameters of the ACC2 PID control system. Furthermore, the ISR with a 0.5 millisecond sampling interval was executed for reading the mover positions from the encoder interfaces, realizing the ACC2 PID control laws, and sending control signals to the servo drivers through DACs. Then, the drive currents generated by the servo drivers drove the movers to track the contour commands accurately. To avoid performance degradation due to the use of immature parameters, the parameters were tested by a virtual m  
Figure 4: The contour tracking control configuration of the LMs-driven X-Y-Y stage using the proposed auto-tuning cross-coupled (ACC)2 PID control system.  
The control parameter candidates with the highest fitness value were selected as the final parameters for the LMs-driven X-Y-Y stage afterwards.
## 4 Structure of the Network and Training Scheme  
### Structure of the Network  
The network consists of two identical encoder and decoder layers within the encoder and decoder modules, respectively. As illustrated in Figure 1, the CSI matrix is initially fed into the multi-head attention layer of the encoder. Subsequently, the input and output of the multi-head attention layer are sent into the add and norm layer, which combines them by skip connections. Next, the output of the add and norm layer enters the feed-forward layer. Similarly, another add and norm layer combines the input and output of the feed-forward layer. After transmitting the output of the second encoder layer into the fully connected layer, which compresses the matrix at a fixed compression rate, we obtain the output of the entire encoder. Compared to a Transformer encoder, a Transformer decoder starts with an additional masked multi-head attention layer and an add and norm layer. Notably, the multi-head attention layer of the decoder receives two inputs, the output of the first add and norm layer and the output of the fully connected layer of the decoder. Finally, processed by two decoder layers, the output is sent into the linear layer and the softmax layer. The network's improvement in feedback accuracy is primarily attributed to the multi-head attention mechanism and the optimized training strategy.
# An Empirical Evaluation of Document Embeddings and Similarity Metrics for Scientific Articles
## 4 Embeddings and Measures
### Similarity Measures
Apart from WMD, the authors propose a relaxed version of the Word Mover's Distance (RWMD), which we also tested. The difference from WMD lies in the elimination of the second constraint. Thus, the problem becomes reduced to the search of the minimum cost of travel for each word. This approach can be performed as a Nearest Neighbour Search (NNS) in the Euclidean word2 vec space. [PERSON] et al. [35] assess that EMD is a metric because it uses a metric for the calculation of the cost, i.e., the Euclidean distance. WMD, as a special case of EMD, is also a metric itself, unlike CS and SCS. The comparison with other relevant methods performed by the authors indicates the general superiority of WMD with many datasets for a k-Nearest Neighbour (kNN) task. **Normalized Relative Compression** (NRC) is a measure of distance between two files based on a model constructed for a reference file and the compression of the target file. It works trying to reconstruct the target file using the reference, and this \"possibility of reconstruction\" is the degree of similarity or the distance. Like CS and SCS, but unlike WMD, NRC is a general-purpose measure, as it is based in the Normalized Compression Distance [3], which can be applied to any sequence. NRC can be computed using the following expression:  
\[NRC(x,y)=\frac{C(x\|y)}{|x|} \tag{2}\]  
where \(C(x\|y)\) is the compression of \(x\) relative to \(y\). The relative compression is performed in two steps. First, given a reference file \(y\), the compressor builds a representation of it using finite context models. Second, it builds a representation of \(x\), i.e., the target, based on the representation of \(y\). With these two steps,NRC measures how much the target can be built using the reference. For this project, we use the relative compressor developed by [PERSON] et al. [36]. NRC, unlike the previously presented measures, works with the size of the documents and its compression, performing a relative compression. Clearly, this is an entirely different distance calculation approach. As a normalized measure, it ranges from 0 to 1, and the result depends on the \(C(x\|y)\) factor, which can be:  
* \(C(x\|y)\approx 0\) if, and only if, \(x\) is very similar to \(y\);
* \(C(x\|y)\approx|x|\) if, and only if, \(C(x|y)\approx C(x)\);  
where \(C(x)\) is the compressed file size and \(C(x|y)\) is the conditional compression of \(x\) given \(y\). NRC is asymmetric and does not preserve the triangle inequality [10]. Thus, it is not a metric. Recent results obtained in its use for authorship attribution [36] and in the visual analysis of research papers [10] suggest that NRC is a good measure for many NLP tasks.
# Study on Penetration Sensing Method Based on Pool Oscillation and Arc Voltage during Pulsed GMAW
## 2 Materials and Methods  
### Experimental System and Principle of Measurement  
The experimental system is shown in Figure 1. A high-speed camera and image analysis system was used in this study with 10,000 fps, synchronized with the monitoring of electrical signals. The background light source was a light-emitting-diode (LED) with a wavelength of 850 nm and a luminous power of 3 W. An 850-nm near-infrared filter was equipped on the camera to obtain clear photos without arc light. Fronius TPS5000 (FRONIUS, Austria) was selected as power sources of the welding system. The welding position was PA (Flat position, as per ISO 6947).  
To obtain the coordinate information about the weld pool dynamic behavior in high-speed camera photographs, an image processing program was built based on LabView in this paper. The processing workflow is as follows: (1) To eliminate the noise and extract the contour coordinates of the welding pool surface and wire tip in the picture, the original images obtained should be binarized and median filtering, as shown in Figure 1. (2) To quantitative analysis the weld pool motion, two reference points were defined on the weld pool surface. Point A was on the surface of the pool just below the wire, as shown in Figure 2. The distance between Point A and Point B was 1.8 mm (1.5 times wire diameter). The Y-coordinate of the reference point was measured as a function of time. (3) The vertical distance (L\({}_{\rm V}\)) and the minimum distance (L\({}_{\rm min}\)) from the end of the wire to the surface of the pool were calculated by the image processing program, as shown in Figure 2.  
Figure 1: Experimental system and image processing system.