esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Thermal Parameters of the Sensor Integrated Tool  
Heating with the help of the hot air soldering station causes a large-area heat input of the mold cutting edge, resulting in a uniform heating of the various temperature sensors. The temperature curves shown in Figure 7 are as follows. When the heat is applied at about the 25 th second, all three sensors are almost similarly heated for 60 s. Afterwards, the tool is left to cool down to room temperature.  
To optimize the thermal parameters, the heat transfer coefficient was adjusted in order to maintain the temperature distribution in the cutting insert but also to match the sensor temperatures measured in the experiment. The change of the heat transfer coefficient is only made in preparation for the cooling simulation and is further not considered. This results in the heating curve shown in Figure 8a. With a heat transfer coefficient of \(\alpha=572.3\) W/m\({}^{2}\)K, the tool is heated to the corresponding temperature. The linear behavior of the T1-3 opt simulation curves is due to the neglected convection. The heat is stored in the tool and only dissipated by a small amount of heat radiation on the outer surfaces of the cutting insert. To eliminate the influence of the working distance or the temperature control of the soldering station in the optimization step, the cooling curve is considered in more detail. Subsequently, the heated cutting tool is cooled to room temperature. For the cooling  
Figure 5: Schematic of the experimental setup for measuring the temperatures in the process zone using a thermocouple.  
Figure 6: Analytical functions describing the temperature distribution on the edge of the cutting insert.  
simulation, the temperature evolution at the cutting edge can be read via a predefined field as thermal boundary conditions, or the generated result file can be accessed by the model. With the resulting temperature curves, an optimization of the thermal conduction, the heat transfer coefficient due to convection and the specific heat capacity can be performed. The optimized parameters in Table 6 can be found as results from the cooling simulation.  
As shown in Figure 8b, the temperature curves in the simulation fit relatively well with the experimentally determined temperatures. The parameters determined in this way are quite close to the literature values given in Table 1, except for the heat transfer coefficient, which is given a value of \(\alpha=22\) W/m\({}^{2}\)K in the literature [16]. The cutting insert therefore releases the thermal energy more quickly to the environment.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 3 Results
### Dataset Description  
The experimental data in this paper are the optical images of the noncooperative target in space. To collect the optical image data of space targets under different illumination and different noise interference, we built a semiphysical simulation platform to simulate the space environment. The semiphysical simulation platform is shown in Figure 9. The experimental system includes a three-axis turntable, a three degree of freedom guide rail, a camera suitable for the micro-nano satellite platform in space, a real 3U CubeSat, a solar illumination simulator, and the Earth background. In terms of illumination simulation, we use the solar simulator to simulate different illumination intensities and control the movement of the target by rotating the three-axis turntable so as to simulate the situation of different incidence angles of sunlight, and the images of different light intensities and different incidence angles of sunlight are collected by the camera. In addition, the three degrees of freedom guide rail can also control the distance between the target and the tracker. In general, the platform can simulate and collect the pose of the CubeSat in different space environments.  
The dataset contains 21 images with a size of 640 \(\times\) 480, which simulates 7 images of CubeSat each under high brightness, low brightness, and noise interference. As shown in Figure 10, we list some of the images collected in the micro-nano satellite line segments (MNS-LS) dataset. Figure 10a represents a high-brightness image. We control different illumination intensities on the basis of maintaining high brightness and adjust the distance and angle between the target and the camera to control the local or global high brightness of the target. Figure 10b represents the low-brightness image, and Figure 10c represents the image containing noise. We carefully annotated the dataset with the annotation software and obtained the ground truth line segment of each image in the MNS-LS dataset. In this paper, the performance of ST_LSD is verified by using the MNS-LS dataset.
## 4 Results and Discussion
### Case Study 1
#### 4.1.3 Comparison of RTO with the GA Algorithm  
The results obtained by using the RTO algorithm are compared with the genetic algorithm, as given in Table 3. The RTO outperforms the genetic algorithm for obtaining the optimum TMS values and gives the advantage of 5.20198 s over the GA with the same initial conditions as supposed for this case study. The proposed RTO performs outstandingly over GA, gives an advantage in total net time gain, minimizes the total operating time up to the optimum value, and maintains proper coordination during a fault condition. In this condition, the results obtained by RTO for all the relays will satisfy the coordination constraints. Furthermore, no violation has been found regarding the coordination constraints.
## 4 Bigram and Traditional Results
### Assessing Water Quality and Potability
#### 4.2.2 Traditional Analysis
The results can improve irrigation water quality and assist farmers in crop planning and irrigation management. Finally, in [61], a method for accurate water quality monitoring in inland waters was proposed. This study introduces proximal remote sensing using a high-resolution hyperspectral imager for long-term monitoring. Machine learning algorithms were developed and validated with in situ data from three sites featuring varying water quality. The back-propagation neural network model achieved over 80% accuracy for TN and over 90% for TP and COD, demonstrating the potential of combining proximal remote sensing and machine learning for inland water quality monitoring.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets
### Marking-Based Construction
#### 4.2.1 Boundedness  
The boundedness is an essential characteristic to ensure avoiding an overflow in the system behavior. In the Petri net theory, we can express this characteristic as follows.  
\[M_{k}(P_{i})\leq U_{i},\forall i,k \tag{33}\]  
where \(U_{i}\) is the upper bound for \(P_{i}\). We can convert the constraint into a binary quadratic net and represent it as the energy function under the one-hot constraint (32):  
\[H_{boundedness}(\mathbf{M})=\sum_{k=0}^{K+1}\sum_{i=1}^{|P|}(\sum_{n=0}^{N} nM(\hat{p}_{i,n}^{k})-U_{i})^{2}, \tag{34}\]  
Function (34) is sufficient for the equality constraint \(M_{k}(P_{i})=U_{i}\), but not for the upper bound. Therefore, we improve the function by introducing ancilla places, \(\hat{u}_{i,m},i=1,2,\ldots,|P|\), \(m=0,1,\ldots,U_{i}\). This technique is well known in the Ising model formulation [6; 14].  
\[H_{boundedness}(\mathbf{M})=\sum_{k=0}^{K+1}\sum_{i=1}^{|P|}(\sum_{n=0}^{N} nM(\hat{p}_{i,n}^{k})-\sum_{m=0}^{U_{i}}m\hat{u}_{i,m})^{2}+(\sum_{m=0}^{U_{i}} \hat{u}_{i,m}-1)^{2} \tag{35}\]  
The upper bound constraint appears in numerous optimization problems. The knapsack constraint is a well-known example of this constraint for the knapsack place. We can also express the boundedness of specific places \(P_{i}\) by removing the other places from the function.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 5 Results and Discussion
### Message Delivery Ratio (MDR)  
The TARS protocol shows a high MDR, as compared to the DBSR and POVRP with different numbers of vehicles. When the number of vehicles increases, then the rate of messages also increases. If the RSU and vehicles both transmit the messages, then heavy congestion in the channel will result, the frequency of transmitting messages gets reduced, and the messages cannot be delivered to the neighboring vehicles. Moreover, due to the unsuccessful reception of messages, there are a lot of chances of the data packet loss and due to this packet loss some safety messages also experience loss and cannot reach the specified destination. In order to overcome this challenge, the TARS protocol plays a very important role and shows high PDR, meaning the protocol builds a reliable connection between the two vehicles as well as the connection between the RSU and vehicle, with minimum packet and message loss [39; 40; 41; 42; 43]. The RSU transmits the warning message up to one hop neighbor (which lies in its transmission range) and the direction based forwarding mechanism minimizes the packet loss chances, which also helps to improve the packet delivery ratio [44]. The protocol shows the successful packet transmission of warnings as well as safety messages with maximum reach, and the ability to improve driving safety through more reliable delivery of BSMs in VANETs, as shown in Figure 13. Furthermore, the MDR achieves successful transmission to the specified destination; for the MDR required, 7, 8, and 9 out of 10 messages per second must be calculated and successfully received from the other vehicles or RSU.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## Nomenclature  
\begin{tabular}{l l} \(A\) & area (m\({}^{2}\)) \\ \(Ar\) & Archimedes number (-) \\ \(C_{D,b}\) & drag coefficient of a bubble (-) \\ \(c_{j}^{mass}\) & concentration of component j (kg/m\({}^{3}\)) \\ \(c_{j}\) & concentration of component j (mol/m\({}^{3}\)) \\ \(c_{p}\) & specific heat capacity (J kg\({}^{-1}\)K\({}^{-1}\)) \\ \(d_{sv}\) & Sauter diameter (m) \\ \(d\) & diameter (m) \\ \(\overline{E}\) & mean Error (-) \\ \(F_{0}\) & flow of fresh CaCO\({}_{3}\) (mol/s) \\ \end{tabular}  
\begin{tabular}{l l} \(F_{R}\) & flow of circulating CaO (mol/s) \\ \(F_{C}\) & flow of elemental carbon in fuel flow (mol/s) \\ \(g\) & acceleration of gravity (m/s\({}^{2}\)) \\ \(H\) & height (m) \\ \(\dot{H}\) & enthalpy flow (J/s) \\ \(h\) & enthalpy (J/kg) \\ \(h\) & reactor co-ordinate (m) \\ \(I\) & maximum number of chemical reactions (-) \\ \(J\) & maximum number of components (-) \\ \(K_{\rm{dB}}\) & mass transfer coefficient (m/s) \\ \(k\) & heat transfer coefficient (W m\({}^{-2}\)K\({}^{-1}\)) \\ \(k\) & empirical constant for sorbent deactivation (-) \\ \(k_{\rm{dint}}\) & elutriation rate constant (here in g/s) \\ \(LHV\) & lower heating value (MJ/m\({}^{3}\)) \\ \(M\) & mass (kg) \\ \(M\) & syngas module (mol/mol) \\ \(M\) & maximum number of cells in freeboard (-) \\ \(\dot{M}\) & mass flow (kg/h, g/s) \\ \(MU\) & make-up flow of fresh limestone (kg/h) \\ \(MW\) & molar weight (kg/kmol) \\ \(m\) & discretization cell in freeboard (-) \\ \(N\) & number of calcination-carbonation cycles (-) \\ \(N\) & maximum number of cells in fluidized bed (-) \\ \(n\) & discretization cell in fluidized bed (-) \\ \(n_{RZ}\) & parameter for descent rate of single particle in suspension phase \\ \(p_{j}\) & partial pressure of component j (-) \\ \(Q\) & heat flux (J/s) \\ \(Re\) & Reynolds number (-) \\ \(R\) & reaction rate (mol/s, kg/s) \\ \(r\) & reaction rate (mol s\({}^{-1}\)m\({}^{-3}\), kg s\({}^{-1}\)m\({}^{-3}\)) \\ \(S\)/C & steam-to-carbon ratio (mol H\({}_{2}\)O/mol carbon) \\ \(T\) & Temperature (K) \\ \(t\) & time (s) \\ \(u\) & superficial gas velocity (m/s) \\ \(u_{mf}\) & minimum fluidization gas velocity (m/s) \\ \({}^{u}_{b}\) & ascent velocity of bubble (m/s) \\ \(\dot{V}\) & volume flow (m\({}^{3}\)/h) \\ \(v_{p}\) & particle falling velocity (m/s) \\ \(WHSV\) & weight hourly space velocity (1/h) \\ \(X_{\rm{age}}\) & average CO\({}_{2}\) carrying capacity (mol CaCO\({}_{3}\)/mol Ca) \\ \(X_{N}\) & CO\({}_{2}\) carrying capacity after \(N\) cycles (mol CaCO\({}_{3}\)/mol Ca) \\ \(X_{r}\) & empirical constant for sorbent deactivation (-) \\ \(x\) & mass fraction (-) \\ \(y\) & volume fraction (-) \\ \(\alpha\) & fraction of mass exchange in calculation cell (-) \\ \(\delta\) & precision of numerical calculation \\ \(\varepsilon\) & porosity (-) \\ \(\
u\) & kinematic viscosity (m\({}^{2}\)/s) \\ \(\
u\) & stoichiometric coefficient (-) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\psi\) & sphericity of particles (-) \\ \(\omega_{j}\) & mass fraction of pyrolysis product j (-) \\ \end{tabular}
## 4 Simulation Results and Discussions
### Numerical Optimization of the SS-ELM Hyper-Parameters
Then, the selected parameters are \(\lambda=100\) and \(\mu=6\), because this combination shows the best overall results for the simulated configurations. Finally, considering that the total value of unlabeled data used to train each data frame is \(\mu=6\) times \(N_{symbols}=128\) times 4, the total value used is 3072 symbols; when we compare this number with the total number of labeled data available for the training (616 symbols), we can observe that we use approximately five times more unlabeled data than labeled data for the training procedure. Hence, we take advantage of the semi-supervised paradigm, where we have few labeled data and plenty of unlabeled data. Transforming this to the studied communication scenario, we have only four pilot subcarriers to equalize 52 data subcarriers, but we use unlabeled data to improve the performance. Figure 9: BER countour plot in terms of the regularization parameter \(C\), number of hidden neurons, and different \(E_{b}/N_{0}\) values for system configuration 2. Figure 11: BER contour plot of \(\lambda\) and \(\mu\) for different \(E_{b}/N_{0}\) values and system configuration 2. Figure 10: BER contour plot of \(\lambda\) and \(\mu\) for different \(E_{b}/N_{0}\) values and system configuration 1.
## 3 Results
### New Shapes of Rehabilitation Devices
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area of Application/Improvement** & **Detailed Description** \\ \hline Improved data & By analyzing data from medical scans and patient metrics, AI can design products tailored to specific physical conditions or limitations, improving recovery outcomes and more precisely adapting to the next steps in the therapeutic process, improving patient guidance during therapy, or changing solution parameters over time. \\ \hline Improved personalization & AI enables 3D-printed rehabilitation solutions, such as braces, prosthetics, and orthotics, to be precisely tailored to a person’s unique anatomy, providing greater comfort and effectiveness. \\ \hline Optimized design processes: & AI algorithms streamline design iterations, enabling the rapid prototyping and development of complex geometries that are lightweight yet durable, ideal for rehabilitation needs. \\ \hline Predictive adjustments & ML models can predict how a patient’s condition will evolve and adjust 3D-printed solutions accordingly, reducing the need for frequent replacements or modifications. \\ \hline Increased accessibility & Automation of the design and manufacturing process lowers costs, making personalized rehabilitation devices more affordable and accessible to a wider population. \\ \hline Improved performance tracking & AI-based 3D-printed devices can incorporate sensors to monitor patient progress, providing healthcare providers with real-time feedback for adaptive treatment plans. \\ \hline Faster time to market & AI accelerates the design and testing phases, enabling the faster delivery of personalized solutions to patients, which is crucial for timely rehabilitation interventions. \\ \hline Error reduction & Advanced AI-based simulations help identify and correct potential design flaws before production, ensuring higher device reliability and safety \\ \hline Scalable personalization & AI enables efficient mass personalization, allowing healthcare providers to meet the unique needs of multiple patients without significantly increasing manufacturing complexity. \\ \hline Sustainability & By optimizing material usage and reducing waste, AI-based 3D printing contributes to environmentally sustainable practices for creating personalized rehabilitation tools. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transformative properties of introducing AI to the personalized AM of solutions for rehabilitation and physiotherapy (authors’ own elaboration).
## 2 Proposed Static Sound Event Localization and Detection Using Bipartite Matching Loss
The composite loss equation that incorporates these elements might be represented as follows:  
\[\mathcal{L}_{\text{hungarian}}(y,\hat{y})=\sum_{i=1}^{N}\Bigl{[}-\log\hat{p}_{ \hat{\sigma}(i)}(c_{i})+\mathbb{1}_{\{c_{i}\
eq\emptyset\}}\mathcal{L}_{\text {loc}}(l_{i},\hat{l}_{\hat{\sigma}(i)})+\mathbb{1}_{\{c_{i}\
eq\emptyset\}} \mathcal{L}_{\text{det}}(d_{i},\hat{d}_{\hat{\sigma}(i)})\Bigr{]}, \tag{2}\]  
where \(\hat{\sigma}\) is the optimal assignment computed from the Hungarian algorithm. Here, when \(c_{i}\) corresponds to \(\emptyset\) (no objects), it is common practice to assign a smaller weight to such instances to avoid the model biasing towards predicting \(\emptyset\) (no objects). In DETR [20], they may assign a weight as low as one-tenth for \(\emptyset\) (no objects); however, this paper opts for a relatively larger weight, specifically half that of the other classes. This adjustment is due to the DETR model typically setting \(N\) to around 100, which results in a significant number of \(\emptyset\) (no objects) predictions. Contrarily, by setting \(N\) to 15, this paper limits the number of \(\emptyset\) (no objects) instances and therefore experimentally employs a higher weight for these instances. The justification for this approach is based on the reduced relative frequency of  
Figure 1: Overview of proposed sound event localization and detection method using bipartite matching loss. Our method uses the ResNet-50 model within a CNN framework and infers a fixed-size set of audio predictions. This method is enhanced by employing bipartite matching through the Hungarian algorithm, which aligns audio predictions with their corresponding actual audio objects. It effectively manages classification, horizontal localization, and the detection of the beginning and end of audio events. \(\oscal\) (no objects) cases, allowing for a slight increase in weight without overwhelming the learning process with an excessive focus on the \(\oscal\) (no objects) class. The overall architecture is surprisingly simple and illustrated in Figure 1. In the proposed architecture, a straightforward feed-forward network (FFN) is appended to the fundamental structure of a ResNet-50. This enhancement is designed to process and refine the features extracted by the ResNet-50 for the task at hand, enabling a more sophisticated interpretation of the data pertinent to the objectives of the neural network, specifically for static sound event localization and detection. For the training of the neural network, the Adam optimizer was employed with the learning rate set to 0.001 [24]. Upon observing that the validation loss stabilized and plateaued, we manually reduced the learning rate by a factor of 10 and resumed the training process. The batch size was configured to 64, and within the CNN architecture, a dropout rate of approximately 0.1 was utilized to facilitate training [20,22]. This approach is consistent with regularization strategies that prevent overfitting and encourage the model to learn more robust features.
## 2 Stability Analysis of Weak Slope under the Protection of Polymer Curing Agent
### Mohr-Coulomb Failure Criteria  
In the non-rain period, weak rock is usually unsaturated. In addition, suction exists in the weak rock, which maintains the stability of slope. However, as the rain permeates, the water content of soil mass increases. The suction gradually decreases and cohesion is reduced, which leads to the decrease in the shear strength of the rock mass [23; 24; 25; 26]. When the shear stress of the rock mass exceeds the shear strength of soil mass itself, the soil will slide along one of its sliding surfaces, thus causing the soil to lose overall stability. This is according to the Mohr-Coulomb strength theoretical formula, \(\left|\tau_{f}\right|=c^{\prime}+\sigma^{\prime}tang^{\prime}\), where the cohesion, \(c^{\prime}\), effective normal stress, \(\sigma^{\prime}\), and the internal friction angle, \(\varphi^{\prime}\), are main influencing factors.
## 2 Materials and Methods
### Partial Least Square Regression (PLSR) Analysis  
The spectral data obtained by the compact FT-NIR spectral sensor were evaluated using multivariate analysis software (Pirouette(r) 4.5, Informetric Inc., Bothell, WA, USA). The spectral data were prepared for analysis via mean-centering, normalization, and taking the 2 nd derivative (Savitzky-Golay 35-point window) to enhance the spectral features through baseline shift correction and resolving the variability between replications [17]. The data were randomly divided into two groups: a calibration/training set (80% of the total sample set) and an external validation/test set (the remaining 20%). PLSR analysis was applied to the calibration set to correlate the reference sugar content (individual and total) from the HPLC with their corresponding spectral data to generate the multivariate quantitative models. PLSR analysis develops prediction algorithms by combining the features of both principal component analysis (PCA) and multiple linear regression (MLR) [35]. PLSR aims to predict the dependent variables (sugar content) through the independent variables (spectral data-wavelength) by extracting a number of orthogonal factors or latent variables with the best predictive power from the independent variables [35]. Model performance was assessed by latent variable/factor numbers, standard error of cross-validation (SECV), the correlation coefficient of calibration (R\({}_{\text{CV}}\)), standard error of prediction (SEP), the correlation coefficient of prediction (R\({}_{\text{Pre}}\)), and outlier diagnostics.