esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Recent Trends and Applications of IoT in Additive Manufacturing
## 1 Introduction
It also eliminates some of the problems related to integration and data handling that are countered in AM processes. This creates an environment that is networked and comprised of \"networks of networks\" for the real-time management of production systems. These are the systems in which products are created, manufactured, and delivered. The Internet of Things encourages more individualization, the most efficient use of materials, and  
Figure 1: The network architecture of IoT-enabled applications [27]. speedy manufacture using AM processes [14; 28]. When IoT devices are integrated into additive manufacturing machines, a decentralized production environment is created. In this environment, data may be sent in real time between the maker and the end-user. The final product is built according to the demands of the client, and if the customer wishes to make any modifications to the parameters of the design, they may update the data stored in the cloud. The modifications are retrieved by the AM machine, and then they are applied to the product before it is created. This results in more efficient use of raw materials and a reduction in the overall amount of time spent on the production process [14]. Thus, making it a flexible approach for rapid prototyping and creating a greater impact on the whole production scenario by integrating various levels of manufacturing. This trend creates smart and connected manufacturing which is aimed at customization, efficiency, performance, increased innovation, reduction of errors, and better management and safety. In recent years, there have been several improvements in the manufacturing industry, including IoT, 3D printing, and digitalization. Additive manufacturing is one of the fastest-growing technologies to apply for the production of various components. However, there is a lack of skilled engineers to deal with this process. In order to solve this problem, with limited AM experts' production can be continued using IoT. However, there is no collective data and review material to understand the potential of the IoT and its applications. Hence, the present study, a critical review, was conducted to examine some of the recent trends and advancements of IoT-integrated AM technologies that exhibit a higher scope of future ruling applications. The following sections also explain the structural operations and their emerging applications in various sectors, giving both the advantages and limitations, which indicate that for tomorrow's smart factories, AM with integrated IoT has potential.
## 7 Conclusions  
In this paper, we investigated the performance of complex light structured patterns under human-made jamming threats. Two widely used laser mode families have been utilized to generate five modulation schemes. These are the 8-ary LG, 8-ary Mux-LG, 16-ary HG, 16-ary LG and Mux-LG, and 32-ary LG and Mux-LG and HG. Direct detection-based free space receivers with ML-based algorithm have been used to identify the attacked modes. This study showed that standard LG modes are highly affected by jamming and is not recommended for data transmission at low SJR (i.e., less than 0 dB). This can be attributed to the structural similarity of the standard Gaussian jammer and the LG profiles as opposed to Mux-LG and HG structures. Moreover, the CNN-based algorithm can identify the jammer DoA with 100% accuracy under sever jamming conditions. The work in this paper can be extended to study and investigate human-made eavesdropping of structured light signal in the coexistence of atmospheric distortions.  
Conceptualization, A.M.R. and S.A.A.; methodology, A.M.R. and S.A.A.; experiment, A.M.R.; software, W.S.S. and S.A.A.; validation, A.M.R. and W.S.S.; investigation, A.M.R., W.S.S., and S.A.A.; writing--original draft preparation, A.M.R. and W.S.S.; writing--review and editing, A.M.R. and S.A.A.; supervision S.A.A.; funding acquisition, S.A.A. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Jammer DoA identification using (**a**) CNN-based and (**b**) LDA-based algorithms.  
**Funding:** This research was funded by Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia project number DRI-KSU-1088.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Materials and Methods  
Schematic illustrations of the LED structure are shown in Figure 1a,b. All samples were grown on \(c\)-plane flat sapphire substrates (FSS) with either a sputtered-AlN buffer layer or a conventional low temperature GaN buffer layer. For deposition of the sputtered-AlN buffer layer (Figure 1a), a sintered AlN target was placed \(\sim\)85 mm from the substrate. After the chamber was evacuated to \(<\)5.5 \(\times\) 10\({}^{-5}\) Pa, an Ar-N\({}_{2}\) gas mixture was introduced as the sputtering gas. By controlling the time at 450 W of RF power, a 20-nm thick sputtered-AlN layer was deposited on the FSS at 600 \({}^{\circ}\)C. By contrast, for deposition of the low temperature GaN buffer layer (Figure 1b), a 30 nm thick GaN buffer layer was grown on FSS in MOCVD at 470 \({}^{\circ}\)C. The epitaxial structure of the green LEDs are identical to each other except for the buffer layer, and was composed of a 4 \(\upmu\)m thick \(u\)-GaN template layer grown at 1050 \({}^{\circ}\)C, a Si-doped \(n\)-Al\({}_{0.02}\)Ga\({}_{0.98}\)N layer, 10 pairs of Ga\({}_{0.93}\)In\({}_{0.07}\)N/GaN (3 nm/3 nm) SLs, and the MQWs consisting of five pairs of 3 nm thick Ga\({}_{0.77}\)In\({}_{0.23}\)N QWs sandwiched by 10 nm thick GaN barriers. On top of the MQWs, a Mg-doped \(p\)-GaN layer was grown. The indium composition in the MQWs was estimated by X-ray diffraction of both samples. Although we run these samples separately, significant differences cannot be observed in terms of epi-layer thickness and alloy composition between samples.  
Figure 1: Schematic illustrations of (**a**) GaInN-based green light-emitting diode (LED) with a sputtered-AlN buffer layer (LED A) (**b**) GaInN-based green LED with a conventional LT-GaN buffer layer (LED B). The inset of Figure 1a is the top-view image of both samples and the inset of Figure 1b is the top-view image of electroluminescence (EL) emission for both samples.  
The chip size was 400 \(\times\) 400 \(\upmu\)m with a conventional lateral electrode structure as shown in the inset of Figure 1. The chip was mounted on a STEM-type metal package without epoxy molding for measuring electrical and optical properties. Hereafter, we refer to green the LEDs grown on a sputtered-AlN buffer layer in Figure 1a, as LED A, and the green LEDs grown on a conventional low temperature GaN buffer layer in Figure 1b, as LED B.  
The I-V and capacitance-voltage (C-V) characteristics were measured using an Agilent semiconductor parameter analyzer and an Agilent impedance analyzer, respectively. The C-V characteristics were measured at a fixed modulation frequency of 1 MHz with an amplitude of 10 mV under a reverse bias range. The optical properties including light output power and spectrum were measured by a Si photo-diode and a spectrometer under the pulsed-current driving condition (pulse period = 100 \(\upmu\)s and duty cycle = 1%) in order to avoid self-heating, respectively.
## Instrument Description
### Offline Signal Processor  
This section describes the two main post-processing steps applied to the SPIR raw data set to obtain waveforms, which are the main observables in GNSS-R: a beamformer and a correlator. It is worth mentioning two aspects at this point:  
* We only consider here the case of an up-looking antenna tracking a given GNSS satellite and a down-looking antenna tracking the corresponding specular point of reflection over the Earth's surface. However, other options could be also envisaged with SPIR, such as the observation of areas far from the specular one with the down-looking antenna.
* The description on how to obtain waveforms from the beamformed signals is based on the interferometric approach or iGNSS-R (i.e., direct cross-correlation between direct and reflected signals) [2,3], but other processing approaches, such as the GNSS-R clean-replica e.g., [6], partial interferometry [13] or semi-codeless [14], can be also attempted with the SPIR raw dataset.
# Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
## 3 Stochastic Models for Grasping
### Grasping Model of the Anchoring Mechanism  
Whether due to the asteroid robot's leg stepping action, adjustment of the posture of the fuselage, or a disturbance, there is an external force acting on the anchoring mechanism that grasps the asteroid surface. Previous studies have focused on the analysis of the grasping model of a single microspine. In this section, the grasping model of the centrosymmetric anchoring mechanism is established.  
The microspine array is pulled by a cable that behaves similarly to animal's tendon. The cable converts the lifting force of the drive disk into tension and acts on the microspine array. Because there is no energy consumption due to deformation, the inextensible cable improves the pulling efficiency.  
Before discussing the influence of external force on the anchoring mechanism, opposed-direction microspines are modeled here. After two opposite microspines are engaged to the asperities, the external force will cause the microspine on one side to provide a greater shear force, while the microspine on the opposite side tends to break away from the asperity.  
\[\left\{\begin{array}{l}F_{es}=F_{es1}-F_{es2}\\ F_{en}=F_{en1}+F_{en2}\end{array}\right. \tag{21}\]  
For comprehensively evaluating the grasping force of the anchoring mechanism, an experiment was carried out. The maximum rotation angle of the driving servo motor of the anchoring mechanism was 35\({}^{\circ}\), and the experiment was carried out every 5\({}^{\circ}\). The angle between the vector of the external force and the vector of the surface ranged from 0\({}^{\circ}\) to 90\({}^{\circ}\), and the experiment was carried out every 10\({}^{\circ}\). In order to ensure the validity of the experimental data, 100 groups of experiments were carried out for each permutation and combination. The experimental results are shown in Figure 8. The maximum adhesion of the anchoring mechanism continued to decrease as the direction of the external force  
Figure 7: Model and analysis of the microspine.  
deviated from the direction of the normal vector of the ground. This phenomenon was particularly obvious at the maximum rotation angle of the driving motor. This showed that the ability of the anchoring mechanism to resist the external force in the horizontal direction was weak. When planning the motion of the robot, the excessive external force acting on the horizontal direction of the anchoring mechanism should be avoided.
## 2 Materials and Methods  
### Leaf-Scale Measurement System Setup  
We developed a leaf-scale measurement system to concurrently measure CO\({}_{2}\) assimilation, active ChIF yields, and passive ChIF radiances from both adaxial and abaxial leaf sides, which was similar to the instrument in [PERSON] et al. [31]. A gas exchange system was modified to integrate one external LED light source, one PAM fluorometer, and four spectrometers (Figure 1). The main components included: (1) an LI-6800 portable gas exchange system with a clear-top leaf chamber (LI-COR Biosciences, Lincoln, NE, USA), (2) a DUAL-PAM-100 portable fluorometer (Heinz Walz GmbH, Effeltrich, Germany) measuring active ChIF yields, (3) two QEpro spectrometers (Ocean Optics, Dunedin, FL, USA; 630-860 nm, 0.24 nm spectral resolution) measuring backward and forward ChIF emissions in the 640-850 nm region, and (4) a white LED fiber optic O-ring light (S5000, Nanjing Hecho Technology Co., Ltd., Nanjing, China) with a 625 nm short-pass filter (Edmunds Optics, Barrington, NJ, USA).  
The top of the leaf chamber was equipped with two vertical airtight slots (Figure 2). One was installed on the top of the leaf chamber, fitting the bundled fiber optic for two spectrometers (backward spectral radiance) and housing the fiber optic of the PAM fluorometer; the second slot was on the bottom of the leaf chamber to fit another bundled fiber optic for the two spectrometers (forward spectral radiance). The top and bottom fibers  
Figure 1: Photograph of the practical synchronous measurement system. The picture demonstrates a custom-made leaf chamber system for concurrent fluorescence measurements on a leaf sample under various environmental factors. It shows the main components in this system, including an LED light source with a short-pass filter, a PAM fluorometer, an LI-6800 gas exchange system, and four spectrometers with two bundled fibers.  
were aligned to capture the exact same footprint from both sides of a leaf and remained stable during the measurements. The O-ring LED light was horizontally placed above the leaf chamber such that the entire leaf surface was illuminated. A short-pass filter could be inserted into the connector between the LED light sources and the O-ring fiber optic that blocks the incoming light above 625 nm. Using this system, we could simultaneously measure leaf-scale passive and active ChIF, reflected and transmitted radiance, and gas exchange measurements under controlled environmental conditions.
# Imitating Human Go Players via Vision Transformer
## 3 Materials and Methods  
### Dataset  
#### 3.1.1 Dataset Formation  
The dataset used in this study was collected from the KGS Go server, spanning from 2004 to 2019. To maintain a consistent level of gameplay quality, we excluded handicapped games and focused solely on matches involving players ranked between 4 th dan and 9 th dan. This decision was made to ensure that the model would learn from expert-level gameplay, as these ranks represent a high degree of skill and strategic depth, avoiding the potential noise or suboptimal moves that might arise from lower-ranked or handicapped games. The dataset provides a robust foundation for training a model aimed at imitating professional-level gameplay.  
The dataset was divided into a training set and a holdout set, with the training set comprising 26,078,877 positions and the holdout set containing 1,376,321 positions. Each position captures a snapshot of the board at a specific point during a game, representing the arrangement of stones for both players. These positions serve as the input data for the model, enabling it to analyze and predict the next move based on the current board configuration.  
One of the key properties of Go is its inherent symmetry. Due to the rules of the game, the information provided by each board position remains invariant under horizontal flips, vertical flips, and rotations. This characteristic, referred to as the symmetry property, allows significant data augmentation opportunities. In this study, we leveraged this property by applying all eight possible transformations to the board, including rotations of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), as well as horizontal and vertical reflections, as shown in Figure 4. These transformations effectively expand the dataset while preserving the original strategic context of each position.  
The labeling process for the dataset involved assigning a corresponding move to each board position. Specifically, the label for each position is the next move made by the human player in the game. This move is represented as a two-dimensional coordinate on the 19 \(\times\) 19 Go board. To make the data compatible with the model, these coordinates are flattened into a one-hot vector of length 361, corresponding to all possible board positions (excluding the pass move). In this vector, the position where the move was played is marked with a value of 1, while all other positions are marked with 0. This one-hot encoding method provides a straightforward and efficient representation of the target variable, enabling the model to treat the task as a multi-class classification problem.  
By focusing on expert-level gameplay and utilizing the symmetry property of Go, the dataset was designed to comprehensively reflect the strategic complexity and diversity of high-level matches. This approach ensures that the model is trained on data that closely mirror the patterns and decision-making processes of skilled human players.
###### Abstract  
The aim of this study was to determine whether the residual presence of eugenol in coronal dentin may compromise the bond strength of resin-based restorative materials. A search was performed on MEDLINE/Pubmed, Scopus, and by hand search for relevant papers. No restriction was applied for language and publication date. The studies selected for analysis tested specimens with reduced size (micro-shear bond strength (\(\upmu\)SBS) and micro-tensile bond strength (\(\upmu\)TBS)) of adhesive systems and resin-based restorative materials applied to coronary dentin \"contaminated\" with eugenol-based materials. The search provided 335 articles, but only 10 studies met the inclusion criteria. The pooled global analysis showed a significant influence of eugenol, as it negatively influenced the bond strength of resin-based restorations (5.79 (3.31-8.28) MPa, \(p<0.00001\)). The subgroup analyses for conventional etch-and-rinse (\(p\) = 0.003) and self-etch (\(p<0.0004\)) adhesive systems, as well as for \(\upmu\)SBS (\(p\) = 0.01) and \(\upmu\)TBS (\(p<0.0001\)), showed a negative influence of eugenol on the bond strength. Data were statistically heterogeneous. However, it was possible to observe that eugenol could negatively affect the bonding of resin-based restorative materials to dentin. Further evidence is necessary in order to acquire more accurate information about this issue and confirm that the residual presence of eugenol in dentin compromises the bond strength of resin-based materials.  
eugenol; dentin; adhesive; zinc oxide-eugenol cement; dental cements; dental bonding
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Estimated Performance Evaluation of Critical Latency  
The estimated performance of critical latency was evaluated by a visual analysis of the position and time between the collision point that can be caused by the designed route and the collision point that can be caused by turning. The estimated performance evaluation was carried out for each of the four scenarios.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 3 Remote Sensing Image Retrieval Algorithms Based on Scene Graph Similarity
### Similarity Calculation of the Remote Sensing Image of the Fusion Scene Map
#### 3.4.2 Feature Similarity Calculation Module  
The feature similarity calculation module primarily computes the similarity between the feature vectors Hq and Hs extracted by the scene graph feature extraction module for graphs Gq and Gs, respectively. This paper utilized a neural tensor network (NTN) [43] for feature similarity calculations.  
Figure 11 shows the flowchart of the feature similarity module. Initially, the node feature vectors Xq and Xs and edge feature vectors Eq and Es extracted from graphs Gq and Gs by the scene graph feature extraction module were fused to obtain graph feature vectors Hq and Hs. Then, Hq and Hs were passed into the NTN network to compute their feature similarity, giving the output of the similarity computation.  
Figure 10: The EGCN module.  
Figure 9: Example of the scene image construction method.
## 2 Grounded Gyrator-C-Based Tunable Active Inductor
### Basic Concept
Under the condition where \(\text{R}_{\text{P}}\gg\text{R}_{\text{s}}\) and \(\text{R}_{\text{s}}\) is considered negligible, the resonant frequency \(\omega_{0}\) and the pole frequency \(\omega_{\text{P}}\) can be approximated as nearly equal \(\omega_{\text{P}}=\sqrt{1/\text{L}_{\text{eq}}\text{C}_{\text{P}}}\simeq \omega_{0}\). Figure 2 presents the Bode plots of \(\text{Z}_{\text{in}}\), illustrating the frequency-dependent behavior of the lossy gyrator-C network. The network demonstrates resistive characteristics at frequencies \(\omega<\omega_{\text{z}}\), behaves inductively in the range of \(\omega_{\text{z}}<\omega<\omega_{0}\), and exhibits capacitive behavior for \(\omega>\omega_{0}\)[23]. An additional key parameter of an active inductor is the quality factor Q, which quantifies its efficiency. For linear inductors, including active inductors, Q is defined as the ratio of the imaginary to the real part of the input impedance. Based on the transfer function of \(\text{Z}_{\text{in}}\) in Equation (3), the quality factor for lossy active inductors can be expressed as:  
\[\text{Q}=\left(\frac{\omega\text{L}_{\text{eq}}}{\text{R}_{\text{s}}}\right) \left(\frac{1-\left(\text{R}_{\text{S}}^{2}\text{C}_{\text{P}}/\text{L}_{\text {eq}}\right)-\omega^{2}\text{L}_{\text{eq}}\text{C}_{\text{P}}}{1+\left(\text {R}_{\text{s}}/\text{R}_{\text{P}}\right)\left(1+\left(\omega\text{L}_{\text {eq}}/\text{R}_{\text{s}}\right)^{2}\right)}\right) \tag{6}\]  
Figure 3 shows the effect of variations in \(\text{R}_{\text{P}}\) and \(\text{R}_{\text{s}}\) on the quality factor of active inductors, using the parameter values \(\text{L}_{\text{eq}}=2.4\,\text{nH}\), \(\text{C}_{\text{P}}=140\,\text{fF}\), \(\text{R}_{\text{s}}=5\,\Omega\), and \(\text{R}_{\text{P}}=5\,\text{k}\Omega\). Enhancement in the quality factor is achieved by increasing \(\text{R}_{\text{P}}\) and reducing \(\text{R}_{\text{s}}\). Figure 2: Bode plot of input impedance \(\text{Z}_{\text{in}}\) for a lossy single-ended gyrator-C active inductor. When the values of series and parallel resistances are comparable, the parallel resistance \(\mathrm{R_{p}}\) has a more dominant influence on determining the quality factor \(\mathrm{Q}\). In advanced deep-submicron technologies, where the transconductance of transistors is significantly greater than their output conductance, the quality factor can be approximated by the following expression:  
\[\mathrm{Q}=\frac{\mathrm{R_{p}}}{\omega\mathrm{L_{eq}}} \tag{7}\]  
Regardless of whether the circuit inherently exhibits a high \(\mathrm{R_{p}}\) or achieves it through optimization techniques, the series resistance \(\mathrm{R_{s}}\) remains the primary limiting factor affecting the quality factor. Therefore, the quality factor can also be estimated using the following expression:  
\[\mathrm{Q}=\frac{\omega\mathrm{L_{eq}}}{\mathrm{R_{s}}} \tag{8}\]