esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application in High-Precision Optical Fabrication  
The processing steps are as follows: (1) Select a piece of flat glass with poor flatness, mark the direction of the glass and detect its initial surface form with an interferometer; (2) calculate the dwell time using the iterative deconvolution method based on the surface form and the TIF without considering edge effects; (3) calculate the running speed of the robot according to the processing path and dwell time, and perform processing and detection; (4) if the surface form meets the requirements, complete the processing, and if it does not meet the requirements, proceed to the next round of processing. The processing parameters are shown in Table 3, and the processing device is shown in Figure 8.  
The processing results are as follows: the form error maps after processing are shown in Figure 9, and the convergence curves of the form error are shown in Figure 10. In the first processing of 95 min, the form error converged to PV-0.857\(\lambda\), RMS-0.129\(\lambda\), and PV and RMS converged by 40% and 58%, respectively. In the second processing of 64 min, the form error converged to PV-0.256\(\lambda\), RMS-0.049\(\lambda\), and PV and RMS converged by 70% and 62%, respectively. In the third processing of 18 min, the form error converged to PV-0.173\(\lambda\), RMS-0.013\(\lambda\), PV and RMS converged by 32% and 73%, respectively. In the fourth processing of 9 min, the form error converged to PV-0.144\(\lambda\), RMS-0.009\(\lambda\), PV and RMS converged by 17% and 31%, respectively. After 4 times of processing for about 3 h, PV and RMS have converged by 90% and 97%, respectively. The form accuracy achieved the expected target of RMS-0.02\(\lambda\), which proves that the SWPT has the characteristics of high convergence rate  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline
**Rotating speed** & 2 rps \\
**Polishing pressure** & 20 N \\
**Processing path** & Raster \\
**polishing powder** & Cerium oxide \\
**Particle size** & 1.5 \(\upmu\)m \\
**Solid-liquid ratio** & 0.2 \\
**Workpiece** & \(\upvarphi\)100 mm K9 class \\ \hline \hline \end{tabular}
\end{table}
Table 3: Processing parameters.  
Figure 7: TIFs of the theory and experiment. (**a**) Section profile in the x-direction, (**b**) section profile in the y-direction.  
and high fabrication accuracy. The SWPT has a broad application prospect in the field of high-precision optical fabrication.  
Figure 8: Processing device.  
Figure 9: Form error maps after processing. (**a**) Initial form error map, (**b**) first form error map after processing for 95 min, (**c**) second form error map after processing for 64 min, (**d**) third form error map after processing for 18 min, (**e**) fourth form error map after processing for 9 min.
## 4 Experimental Results and Discussions
### Experimental Setting and Parameter Configuration  
In this study, the proposed approach was implemented on a workstation equipped with an Intel Core i7-12700 KF CPU (manufactured by Intel Corporation, Santa Clara, CA, USA), an NVIDIA GeForce RTX 3080 Ti GPU (manufactured by NVIDIA Corporation, Santa Clara, CA, USA), and 64 GB of RAM (sourced from Kingston Technology, Fountain Valley, CA, USA), running Windows 10 with Python 3.8. XGBoost was adopted for leakage detection. Several key hyperparameters were configured as follows: the number of decision trees, representing the number of boosting iterations, was set to 100; the learning rate was set to 0.3, regulating the update step in each iteration; and the maximum tree depth was specified as 6, which denotes the maximum splitting depth for each node. This setup strikes a balance between computational overhead and detection accuracy.
# Emotion Identification in Movies through Facial Expression Recognition
## 2 Related Work
### Facial Expression Recognition
#### 2.2.2 FER Methodologies
Directly using these standard feature extractors and fine tuning the _softmax_ layer can contribute to softening the FER's small dataset problem. However, it creates a bottleneck because it relies on a predefined feature space. This issue is commonly tackled by using multistage fine-tuning strategies based on different combinations of the training dataset to enhance their performance [54] or by using facial recognition feature extractors and regularizing them with facial expression information [55]. To increase the power of representations for FER, several works have proposed novel architectures for increasing the depth of multi-scale features [56] or increasing the level of supervision in embedded representations [57]. Additionally, common limitations associated with _softmax_ are caused by inter-class similarity, and are tackled with novel loss functions that drive the extractor towards more separable representations [39; 40; 58; 59; 60]. Effectively training these novel architectures fails with insufficient amount of data. As observed in the previous analysis, FER datasets have a reduced size. Therefore, these limitations opened a new research direction in the context of FER, which is based on network ensembles and on the fusion of different face related tasks (e.g., facial landmark location and face recognition) [61]. In conclusion, the current datasets in the movie domain are still not large enough to allow traditional feature extractors to obtain the desired results. Additionally, physiological variations (such as age, genre, cultural context or levels of expressiveness) and technical inconsistencies (such as people's pose or lighting) are other challenges currently being addressed [61].
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion  
### Video Frame Interpolation Module Evaluation  
#### 4.1.1 MTVFI Model Evaluation  
Table 2 presents a performance comparison of our proposed MTVFI model and advanced video frame interpolation techniques, Super-SloMo and IFRNet, on the test set of Dataset 3. The performance evaluation was based on three key metrics: loss value, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The MTVFI model performed the best in the SSIM metric, indicating that its generated intermediate frames have a higher similarity to the ground truth and produce more accurate temperature distributions. In terms of the PSNR metric, the IFRNet model achieved the best results, while the MTVFI model demonstrated a suboptimal performance. This may be due to the MTVFI model's insufficient attention to the external areas of the melt pool when deeply learning the motion characteristics of the melt pool, leading to biases in temperature estimation, affecting the PSNR performance. Nevertheless, the MTVFI demonstrated a unique advantage in capturing the dynamics of the melt pool, which is crucial for improving the accuracy of melt pool monitoring.
## 2 Experimental Validation
### Experimental Test 4  
The experimental tests carried out by the authors [7] differed in several parameters, such as test scale, model constitution, type of materials and dimensions. Thus, of the six experimental tests carried out, four were carried out on a reduced scale and two on a full scale. All models consider a constructive solution in wood with gypsum board. The gypsum board used by [PERSON] and [PERSON] [7] were types C and X, with a density at room temperature of 732 and 648 kg/m\({}^{3}\), respectively. Additionally, it was standardized, in all models, that gypsum board type C had a thickness of 12.7 mm and type X had a thickness of 15.9 mm. Regarding wood material, the authors do not define the type of wood considered. However, they generally present two types of wood \"SPF\" and \"Douglas fir\", with a density at room temperature of approximately 480 and 550 kg/m\({}^{3}\), respectively.  
According to this, to compare the numerical solution proposed in this work, Test 4 [7] was used with the dimensions represented in Figure 1. The wooden material was considered in gilam GL24H and the gypsum board as type A with a density equal to 648 kg/m\({}^{3}\), both with approximation of the values to the referenced Test 4 [7]. Previously in other publication [19] similar properties were considered in the numerical model validated with Test 6 from [PERSON] and [PERSON] [7].  
The temperature fields obtained at different points measured by the authors [7] were analyzed, with a special interest in the interface between the wood and the gypsum board (point A), another point between the air cavity and the wood element (point F), and another point in the middle of the wooden beam (point E). Simultaneously, the residual wood cross-sections will be compared for periods equal to 90, 100, and 110 min. Finally, the thermal insulation criterion, defined by the standard EN 1363-1:2020 [20], was used to measure and compare the fire resistance between the experimental and numerical models.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 2 Time-Varying Meshing Stiffness Calculation Model
### The TCA Environment
#### 2.1.1 The Principle of Straight Bevel Gear Tooth Surface Generation  
As shown in Figure 1, generating surface C of a straight bevel gear is a circular subset of a plane, with its center coinciding with the top O of the base cone and its radius equal to the base cone distance \(R\). The generating surface C is tangent to the cone generatrix length, and the tangent line is \(A^{\prime}A\). When the generating surface C undergoes pure rolling around the base cone, the tangent line \(A^{\prime}A\) sweeps to form a curved surface called an involute cone, the theoretical tooth surface of a straight bevel gear. A spherical surface is created with the cone top \(O\) as the center and the cone distance \(R\) as the radius. The intersection of the spherical surface and the involute cone results in the spherical involute. Straight bevel gears' theoretical tooth surface can also be composed of spherical involutes with different radii centered on the cone top. For example, the small end spherical involute is a spherical involute with a radius of \(OA^{\prime}\) as the spherical radius.  
Then, the \(A^{\prime}A\)\(K^{\prime}K\) equation is as follows [28]:  
\[r_{f}=\left\{\begin{array}{l}x(R_{i},\varphi)=R_{i}[\cos(\varphi\cdot\sin \delta_{b})\cdot\sin\delta_{b}\cos\varphi+\sin(\varphi\cdot\sin\delta_{b}) \cdot\sin\varphi]\\ y(R_{i},\varphi)=R_{i}[\cos(\varphi\cdot\sin\delta_{b})\cdot\sin\delta_{b}\sin \varphi-\sin(\varphi\cdot\sin\delta_{b})\cdot\cos\varphi]\\ z(R_{i},\varphi)=R_{i}\cos(\varphi\cdot\sin\delta_{b})\cdot\cos\delta_{b} \end{array}\right. \tag{1}\]  
Figure 1: Principle of tooth surface formation for straight bevel gears.  
where \(\delta_{b}\) represents the base cone angle, \(\varphi\) denotes the angle rolled by the occurring surface, and \(\psi\) represents the angle between \(OK\) and the instantaneous axis of rotation \(ON\), \(\psi=\varphi\)\(\sin(\delta_{b})\), \(R_{i}\in(R-A^{\prime}A,R)\). According to the tooth surface equation, the normal vector of the tooth surface is obtained as:  
\[n_{f}=\left\{\begin{array}{l}x(R_{i},\varphi)=\cos(\varphi)R_{i^{\prime}}( \sin(\varphi\cdot\sin(\delta_{b}))-\sin(\varphi\cdot\sin(\delta_{b}))\cdot \sin(\delta_{b})^{2})\\ y(R_{i},\varphi)=\sin(\varphi)R_{i^{\prime}}(\sin(\varphi\cdot\sin(\delta_{b} ))-\sin(\varphi\cdot\sin(\delta_{b}))\cdot\sin(\delta_{b})^{2})\\ z(R_{i},\varphi)=-R_{i^{\prime}}\sin(\varphi\cdot\sin(\delta_{b}))\cdot \cos(\delta_{b})\cdot\sin(\delta_{b})\end{array}\right. \tag{2}\]
## 2 Proposed CPM-GFDM Transceiver
### CPM-GFDM Receiver  
#### 2.2.1 Received Signal and the Channel  
The received signal can be represented in matrix form as  
\[\mathbf{r}=\mathbf{H}\mathbf{x}_{\mathbf{CP}}+\mathbf{n} \tag{11}\]  
where \(\mathbf{H}\) is the channel matrix and \(\mathbf{n}\) is the additive white Gaussian noise with zero mean and \(\sigma^{2}\) variance. The proposed CPM-GFDM receiver is shown in Figure 3. After removing the CP, the received signal goes through the equalizer, assuming known \(\mathbf{H}\). The equalization can be expressed mathematically as in [42]  
\[\mathbf{r}_{\mathbf{eq}}=\mathbf{H}^{-1}\mathbf{H}\mathbf{A}\mathbf{D}+\mathbf{H}^{-1}\mathbf{n}=\mathbf{A}\mathbf{D}+ \widetilde{\mathbf{n}} \tag{12}\]  
where \(\widetilde{\mathbf{n}}\) represents the colored noise.  
Figure 3: Proposed CPM-GFDM Receiver.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Main Error
#### 2.1.1 Control Errors at Transmitters and Broadcasters  
A transmitter control error refers to discrepancies at the eLoran time station, and specifically the difference between the time broadcast by the eLoran station and standard time, UTC. These errors can be categorised into three main types: synchronisation errors between the reference time of the transmitter and that of the UTC, signal control errors, and antenna phase-centre measurement errors.  
The first type of broadcast control error is the synchronisation error between the reference time of the transmitter and the UTC. Variations in the method used by each station to synchronise with the UTC can result in significant errors. Methods such as optical fibres, GNSSs, bidirectional satellites, and satellite co-vision were used to keep the eLoran time correction synchronised with the UTC.  
The other two components, viz., the signal control error and antenna phase centre measurement error, are primarily hardware-related and challenging to separate. The errors caused by equipment and cable delays can be controlled to within 10 ns if the long-wave timing station is accurately calibrated.
# On Nash Equilibria in Non-Cooperative All-Optical Networks +
## 2 The Model
Each agent \(\alpha=\{x,y\}\) knows the instance \(I\) and the routing \(\mathcal{R}\), that is, the function \(price\) is not restricted. \begin{table}
\begin{tabular}{r l l} \hline \hline  & & The graph modelling the optical network, with \(V\) being the set of sites and \(E\), the set of bidirectional optical links between the sites. \\ \(\mathcal{P}\) & \(\triangleq\) & The set of all simple paths in \(G\). \\ \(\{x,y\}\) & \(\triangleq\) & A communication request between sites \(x\) and \(y\) (\(x,y\in V\)). \\ \(I\) & \(\triangleq\) & A multiset of requests defining a communication instance. \\ \(P\) & \(\triangleq\) & A path system. \\ \(W\) & \(\triangleq\) & The set of available wavelengths or colors. \\  & & \(\mathcal{R}(G,I)\) is a routing (i.e., a solution) for an instance \(I\) in \(G\); \\ \(\mathcal{R}(G,I)=(P_{\mathcal{R}},c_{\mathcal{R}})\) & \(\triangleq\) & \(P_{\mathcal{R}}\) is a path system for \(I\); \(c_{\mathcal{R}}\) is a function associating a color to each request. \\ \(p_{\mathcal{R}}(\{x,y\})\) & \(\triangleq\) & The path connecting \(\{x,y\}\) in \(\mathcal{R}\). \\ \(\omega_{\mathcal{R}}(G,I)\) & \(\triangleq\) & The number of colors used by the routing \(\mathcal{R}\) for \(I\) in \(G\). \\ \(\omega(G,I)\) & \(\triangleq\) & The minimum number of colors used by any routing for \(I\) in \(G\). \\ \(\pi_{\mathcal{R}}(G,I)\) & \(\triangleq\) & The maximum load of an edge in \(G\) induced by the routing \(\mathcal{R}\) for instance \(I\). \\ \(\pi(G,I)\) & \(\triangleq\) & The minimum maximum load of the routings for \(I\) in \(G\), \\ \(\alpha=\{x,y\}\) & \(\triangleq\) & A generic agent associated to request \(\{x,y\}\). \\ \(price_{\mathcal{R}}(\alpha)\) & \(\triangleq\) & The cost that agent \(\alpha\in I\) has to pay to the network provider in order to obtain the asked service if the routing \(\mathcal{R}\) is adopted. \\ \(f:W\to R^{+}\) & \(\triangleq\) & A function associating a cost to every color. \\ \(\mathcal{G}=(G,I,price)\) & \(\triangleq\) & A game among agents in \(I\) on the network \(G\). \\ \(p^{\alpha}\) & \(\triangleq\) & The set of connecting paths for agent \(\alpha\). \\ \(u(\alpha)\) & \(\triangleq\) & The utility of agent \(\alpha\). \\ \(\mathcal{N}\) & \(\triangleq\) & The set of Nash equilibria of a game. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation used in this paper. Clearly, even if not explicitly mentioned, in any level of information, for each agent \(\alpha\), \(price_{R}(\alpha)\) can depend also on the wavelength \(c(\alpha)\) and the path \(p(\alpha)\) assigned to \(\alpha\) in \(\mathcal{R}\). Recall that the players move sequentially and can choose, as their current strategy, only valid strategies with respect to the current routing. Notice also that even with minimal information, any agent is always able to compute a valid wavelength for the chosen path, so as to not interfere with any other agent, and thus not compromising the feasibility of the solution.
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
Another motivation behind exploiting multiple IRSs is its ability to control the communication environment by using the transmitted signal's phase and amplitude as a beam-forming to control and direct the reflection toward the receiver. IRS beam-forms the incident signals by nullifying the unwanted ones and strengthening the intended ones. IRS could also assist the BS transmission towards the cell-edge users, which represents a cost-effective solution as compared to other measures such as increasing the transmission power and placing extra BS at the cell boundaries. By controlling the communication environment, IRS could minimize the effect of channel randomness, boost the achievable capacity, and enhance the channel reliability. Nevertheless, this paper examines the performance of IRS-NOMA and proposes a number of solutions to boost its performance. First of all, it focuses on optimizing the number of reflecting elements in the IRS-NOMA system. The paper also proposes using multiple IRS-based NOMA systems to improve the quality of the received signal, and to overcome the limitations of a single IRS-based system. The EE-SE trade-off behavior is investigated to obtain a comprehensive view of the proposed solutions, along with the considered IRS-NOMA system, and all the comparisons throughout this paper included the orthogonal multiple access (OMA) system. The rest of this paper is organized by presenting the considered IRS model in Section 2. Section 3 presents the derivation steps of the proposed optimal number of IRS elements. The proposed multiple IRS-based NOMA is explained in Section 4. The performed simulations and the obtained numerical results are presented in Section 5, and Section 6 lists the conclusions of the paper.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 5 USVs' Missions  
A mission represents a set of geographical points, tuples of values representing the latitude and longitude. One of them is explicitly the starting point of a mission. Each mission has a specific identification in order to indicate which of the missions are requested to deploy. The group identification of the sensors to be used in the mission is also specified. Finally, a mission mentions which nodes are allocated to that specific mission.  
In order for a node to initiate a mission, first it needs to have the information of the assignment. The USV stores all relevant mission data in order to correctly deploy the assignment when required and allowed it to do so. When a LoRa start mission packet reaches a USV, the USV selects the records of the specific mission from the database, checks if it is assigned to it and starts the mission at the specified time, which is included on the start mission packet payload.  
Because the mission's data is usually extensive, it is not appropriate to send it through LoRa, where the maximum length of a LoRa packet is 255 bytes, as mentioned. Instead of using LoRa technology to inform the USV about the available missions, so it can populate its database with data records of the missions of interest, the MC sends a _Request Update Mission_ Message through LoRa, to notify the USVs that they need to update their missions' data. This request contains also the location of the DTS.  
Once a USV reaches the area where the DTS is placed, it tries to communicate with the DTS through Wi-Fi using \(mOVE\). The USV gathers the data from the DTS and the same shares the information with the other USVs in reach. This is done in order to avoid all USVs to proceed to the DTS geographical area. By the time the mission information reaches the USVs, it is stored in their databases. The DTS retrieves the missions' data from the server so that it can send it to the USVs. When a DTS discovers a new neighbor node through the \(mOVE\) module, if it does not contain the mission, the missions' data (the _Start Mission_ Message) is fetched through a HTTP GET Request to the server, which comes in a JSON format. Then, this information is sent to the USV through Wi-Fi. This process is illustrated in Figure 10.  
With the mission information received, the USV will start the mission at the time specified in the payload of the mission. Once the mission starts, the USVs will start the correspondent mission at the specified start position. From there, they will travel through a set of points defined by the mission, collecting environmental and aquatic data just after it reaches a point of interest and send it to the DTS using LoRa or Wi-Fi (through \(mOVE\)) technology. This process is illustrated in Figure 11.
## 4 Numerical Simulation of a Vehicle Collision with Column
### Finite Element Models of Vehicles and Reinforced Concrete Columns
Using the axial load compatibly criteria, the following equation can be developed to determine the stress on the concrete (\(\sigma_{c}\)):  
\[\sigma_{c}=\frac{P}{A_{c}(1+n\rho_{s})} \tag{4}\]  
where \(P\), \(A_{c}\), \(\rho_{s}\), and \(n\) are the applied axial force on the column, the cross-section area of the concrete, the steel ratio of the section, and the modular ratio (\(E_{s}/E_{c}\)). The majority of the analysis in this study was done using two sizes of columns: 300 \(\times\) 300 mm\({}^{2}\) (C300) and 400 \(\times\) 400 mm\({}^{2}\) (C400). Two other column sizes, 500 \(\times\) 500 mm\({}^{2}\) (C500) and 600 \(\times\) 600 mm\({}^{2}\) (C600), were also studied in FE analysis for comparison purposes. The columns were reinforced with 2.6% of the concrete area. The spacing of shear reinforcement was 150 mm, and the diameter of shear reinforcement was 10 mm. The material properties of reinforcing bars were the same as the validated column model. However, the concrete compressive strength used in the model was 40 MPa.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Simulation Parameters  
For optical simulations, Monte Carlo ray tracing in _Optic Studio Zemax 17_ is used. For each simulation, \(5\cdot 10^{5}\) rays are traced. Thereby, polycarbonate lenses are used with a refractive index of \(n_{1}=1.57\) at \(\lambda=940\,\mathrm{nm}\). We especially investigate Fresnel-reflections which lead to direct optical crosstalk \(P_{\text{PD cross}}\) if the reflected rays reach the PD. The detector is a circular shaped PIN-PD with a radius of \(100\,\mathrm{\SIUnitSymbolMicro m}\). The LD has an output power of \(P_{\text{TX}}=4\,\mathrm{mW}=6\,\mathrm{dBm}\) at \(\lambda=940\,\mathrm{nm}\). First, the transmitter part is considered. Then, the full channel is characterized. In order to give a comparable result to other rotary communication technologies, the link distance is set to \(12.5\,\mathrm{mm}\).  
Conventionally, optical concentrators are characterized by estimating the gain over the angle of incidence by assuming parallel light [53]. However, Figure 6a shows a large divergence of the incident rays. Thus, the assumption of parallel rays does not apply for the present arrangement. The RX performance depends directly on the TX characteristics. Therefore, it is evaluated by the full-channel simulation.