esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Training and Inference  
### Loss Function  
The output of our network includes the start/end boundary \((d_{t}^{s},d_{t}^{s})\) and class probability \(C_{t}\), and we used the following loss function to optimize the model:  
\[\mathcal{L}=\mathcal{L}_{cls}+\lambda\mathcal{L}_{rgg} \tag{11}\]  
where \(\lambda\) is a hyper-parameter, used to adjust the weight of the classification and regression losses, and we treated these two losses equally and set \(\lambda\) = 1. Our classification loss uses the-focal loss [43] function, which can effectively solve the problem of the imbalance between the foreground and background  
\[\mathcal{L}_{cls}=\frac{1}{T_{j}}\sum_{t=0}^{T_{j}}\sum_{i=0}^{N}-\alpha_{t}^{i} \Big{(}1-p_{t}^{i}\Big{)}^{\gamma}\log\Big{(}p_{t}^{i}\Big{)} \tag{12}\]  
where  
\[p_{t}^{i}=\left\{\begin{array}{ll}C_{t}^{i}&\text{if }y_{t}^{i}=1,\;\;i=0,1,2,\cdots,N\\ 1-C_{t}^{i}&\text{otherwise, }i=0,1,2,\cdots,N\end{array}\right. \tag{13}\] \[\alpha_{t}^{i}=\left\{\begin{array}{ll}\alpha&\text{if }y_{t}^{i}=1,\;\;i=0,1,2,\cdots,N\\ 1-\alpha&\text{otherwise, }i=0,1,2,\cdots,N\end{array}\right. \tag{14}\]  
In the above, class label \(y_{t}\in R^{N},y_{t}^{i}\in\{0,1\}\), indicates whether the temporal location \(t\in[1,T]\) belongs to action (\(y_{t}^{i}=1\)) or background (\(y_{t}=\{0\}_{i=0}^{N}\)), the predicted classification score \(C_{t}=\{C_{t}^{1},C_{t}^{2},\cdots,C_{t}^{N}\}\). \(T_{j}\) is the length of the full sequence in the j-th stage, and N represents the total number of action categories. \(\alpha\) and \(\gamma\) are hyper-parameters, which are specified as 0.25 and 2 in the experiments. \(\mathcal{L}_{reg}\) is the intersection over union (IOU) loss [44] between predicted boundaries \(\hat{\Omega}_{t}=\big{(}\hat{\theta}_{t},\xi_{t}\big{)}\) and the corresponding ground truth \(\Omega_{t}=(\theta_{t},\xi_{t})\):  
\[\mathcal{L}_{reg}=\frac{1}{T_{p}}\sum_{t}\mathbb{I}(y_{t}\geq 1)\Bigg{(}1- \frac{|\hat{\Omega}_{t}\cap\Omega_{t}|}{|\hat{\Omega}_{t}\cup\Omega_{t}|}\Bigg{)} \tag{15}\]  
where \(T_{p}\) represents the total number of positive samples. The function \(\mathbb{I}\) is used to indicate that the location \(t\in[1,T]\) is inside (\(\mathbb{I}=1\)) or outside (\(\mathbb{I}=0\)) an action instance.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Methods Deployed Across Studies
#### 3.3.3 Optimization  
Many studies employed optimization models to minimize costs [6; 8; 11; 13; 35; 44; 47; 59; 62; 64]. These included electricity demand costs, generation costs for the system, and the total system costs in general. Other optimization models optimized (minimized) values such as power demand [7; 23; 35; 36]. An interesting technique employed by researchers involved minimizing variations in demand from the system mean value to flatten the load curve [13; 28; 36]. The authors of [55] included various measures in their cost considerations. They maximized overall profits, which were functions of feed-ins from the grid, energy costs, and electricity usage. Finally, the authors of [44] optimized costs but also minimized carbon emissions [28], maximized social welfare, and [6] maximized production of the facility under study.  
In terms of the constraints employed for optimization models, researchers have included constraints that represented system limits [7; 8; 11; 13; 35; 36; 59; 62; 64] and technology limits [6; 7; 8; 13; 28; 35; 36; 44; 47; 56; 64]. System constraints include the supply-demand balance [11; 59; 64], building temperature requirements [35], power flow equations and grid infrastructure limits [8; 64], minimum lighting requirements [36], industrial process flows [7; 56; 67], and ramping constraints [8; 47].
# Designing a Reliable UAV Architecture Operating in a Real Environment
###### Abstract  
The article presents a method of designing a selected unmanned aerial platform flight scenario based on the principles of designing a reliable (Unmanned Aerial Vehicle) UAV architecture operating in an environment in which other platforms operate. The models and results presented relate to the medium-range aerial platform, subject to certification under the principles set out in aviation regulations. These platforms are subject to the certification process requirements, but their restrictions are not as restrictive as in the case of manned platforms. Issues related to modeling scenarios implemented by the platform in flight are discussed. The article describes the importance of Functional Hazard Analysis (FHA) and Fault Trees Analysis (FTA) of elements included in the hardware and software architecture of the system. The models in Unified Modeling Language (UML) used by the authors in the project are described, supporting the design of a reliable architecture of flying platforms. Examples of the transformations from user requirements modeled in the form of Use Cases to platform operation models based on State Machines and then to the final UAV operation algorithms are shown. Principles of designing system test plans and designing individual test cases to verify the system's operation in emergencies in flight are discussed. Methods of integrating flight simulators with elements of the air platform in the form of Software-in-the-Loop (SIL) models based on selected algorithms for avoiding dangerous situations have been described. The presented results are based on a practical example of an algorithm for detecting an air collision situation of two platforms.  
Unmanned Aerial Vehicle (UAV); collision avoidance; safety procedures; reliable architecture; Unified Modeling Language (UML) +
Footnote †: journal: Applied Sciences  
1 The Institute of Automatic Control and Robotics, Warsaw University of Technology, 02-525 Warsaw, Poland 1  
2
# Rapid Laser Reactive Sintering Synthesis of Colossal Dielectric CCTO Ceramics
## 4 Conclusions  
Polycrystalline CCTO ceramics were successfully prepared using a method of rapid laser reactive sintering. The crystalline quality, microstructure, dielectric properties, and impedance response were investigated in detail with increased laser power. Through adding the appropriate amount of PVA, the obtained CCTO quality was improved. Laser power and irradiation have a great impact on the synthesized samples and we lay emphasis on the study of the physical properties with laser power changes. The results revealed that pure CCTO could be obtained at 85 W for 30 s, with high dielectric permittivity and moderate dielectric loss. The highest permittivity value reached \(10^{5}\) in a low frequency range. The dielectric permittivity maintains an almost constant value in a broad range of \(10^{2}\) Hz to \(10^{7}\) Hz, which demonstrates good frequency stability. The impedance mechanisms were discussed according to the impedance spectroscopy analysis, and the results showed that the grain and grain boundary resistance decreased with rising laser power. Based on equivalent circuit consisting of resistors and capacitors for semiconducting grains and insulating grain boundaries, we calculated the activation energy for the grain boundaries using the slope of ln\(\sigma\) versus \(1/T\) and found it to be in the range 0.53-0.63 eV. Compared with conventional solid-state reaction method in the furnace we offer a route to prepare CCTO with cleaner and more efficient features. The CCTO synthesized by rapid laser reactive sintering was shown to be more competitive for practical applications with lower energy and greatly shortened sintering times.  
Figure 8: ln\(\sigma\) vs. temperature for CCTO samples in the form of the Arrhenius plots.  
**Author Contributions:** Conceptualization, J.H. and H.Z.; Data curation, J.H.; Formal analysis, Y.H.; Funding acquisition, H.Z.; Investigation, S.S.; Methodology, Y.H. and H.Z.; Project administration, M.Y.; Resources, G.F.; Supervision, Y.H.; Validation, J.H., Y.H. and G.F.; Visualization, J.H.; Writing--original draft, J.H.; Writing--review & editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Fundation of China (11974112, 61504034, 61875243, 11804100).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Ultrasonic Fault Detection Methodology
### Ultrasonic Dimensionality Reduction and Visualization Techniques  
Dimensionality reduction in ML is a technique used to reduce the number of input features or variables in a dataset. This process simplifies the dataset by transforming it into a lower-dimensional space, while preserving as much of the important information as possible. The main reasons for using dimensionality reduction includes improving model performance, reducing overfitting, and enhancing data visualization [34; 35]. Methods include the following:  
* Principal component analysis (PCA) reduces the dimensionality of a dataset by transforming it into a set of orthogonal components. These components capture the most variance from the original data [36].
* Independent component analysis (ICA) separates a multivariate signal into independent non-Gaussian components, assuming statistical independence. ICA excels in identifying independent sources and handling non-Gaussian data, making it useful for noise reduction, feature extraction, and source separation [37].
* Uniform manifold approximation and projection (UMAP) preserves the local and global data structure by optimizing a low-dimensional graph to reflect the high-dimensional graph. It is computationally efficient and scalable, suitable for large datasets [38].
* Linear discriminant analysis (LDA): This is a statistical method used in supervised classification problems. LDA aims to find a linear combination of features that best separates industrial faults. It projects high-dimensional acoustic data onto a lower  
Figure 5: k-fold CV algorithm structure to evaluate the ultrasonic diagnosis.  
dimensional space by maximizing the distance between the means of different classes and minimizing the variance within each class [39].  
By applying these algorithms, we can gain insights into the patterns and relationships within the ultrasonic signals, which can be crucial for identifying anomalies or understanding the underlying physical phenomena.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 3 Methodology and Experimental Procedure
### Step 1: Dynamic Platform Simulator  
A simulation tool has been used to emulate a real case scenario (similar position and number of anchors, same data rate, etc.). Using a simulator has been very helpful to obtain an initial characterisation of the EKF variables before carrying out the real tests. This simulator has been used to obtain an initial characterisation of the EKF variables before carrying out the real tests.  
Basically, the simulation tool permits to emulate the behaviour of a real RC car test platform by simulating its odometry and inertial data [18]. Thus, the user can get information about the orientation, the travelled distance, the real position or the velocity of the simulated vehicle at the desired rate. It should be pointed out that some inertial parameters, as the acceleration, are not provided by the simulator. Apart from simulating the odometry and inertial data, the RC car simulator allows the user to build an UWB ranging system. The user can set different UWB anchors and their positions, as well as the rate in which the ranging estimates will be generated. Once the UWB ranging system has been set, the RC car simulator provides ranging estimates at the configured rate containing the distance between the simulated car and the UWB anchors.
## 3 Results and Comparison
### Results for 3D VIV with FSI Using Different Modeling Methods
Meanwhile, it can be seen that the maximum von Mises stress occurs at the top end of the steel riser (Riser 1) due to large top tension force required to resist the extreme weight of the steel riser; on the contrary, the maximum von Mises stress of the composite risers (Risers 2 and 3) take place in the middle/at the fixed end of the risers due to the large bending moments. The overall performance and characteristics of composite risers can be obtained using EMM; however, it is impossible to analyze the stress distribution and failure criterion in every composite lamina by EMM. Therefore, LSM is employed to solve this problem. Figure 16 presents the maximum  
Figure 14: Maximum von Mises stress of the risers for the vortex-induced vibration. Figure 15: Stress distribution for: (**a**) Riser 1; (**b**) Riser 2; and (**c**) Riser 3.  
stress distribution including stress in fiber direction (S1), stress in transverse direction (S2) and stress in shear (S12) in every composite lamina for Risers 2 and 3. It is shown in Figure 16 that the stress distribution in each composite lamina in composite Risers 2 and 3 are totally different, since their designed lay-ups are diverse, that is, composite Riser 2 only contains fiber reinforcements in hoop and axial directions while composite Riser 3 includes fiber reinforcements in hoop, axial and inclined directions. Both composite Risers 2 and 3 are dominated by bending deformation under the combination of VIV, buoyancy, top-tension force and gravity. Generally, the stress in each composite lamina is larger in composite Riser 3 than in composite Riser 2, which means tailor-designed composite Riser 3 can take advantage of the fiber strength more efficiently. Comparing the stress distribution in every composite lamina for composite Risers 2 and 3, it is found that:  
1. Stress in fiber direction (S1): S1 is smaller in hoop reinforced fiber layers than that in axial reinforced fiber layers for composite Riser 2 while S1 is largest in axial reinforced fiber layers, medium in inclined reinforced fiber layers and smallest in hoop reinforced fiber layers for composite Riser 3. 2. Stress in transverse direction (S2): S2 is larger in hoop reinforced fiber layers than that in axial reinforced fiber layers for composite Riser 2, while S2 is largest in hoop reinforced fiber layers, medium in inclined reinforced fiber layers and smallest in axial reinforced fiber layers for composite Riser 3. 3. Stress in shear (S12): Riser 2 only contains orthorhombic fiber reinforcements, and therefore almost no shear stress S12 is produced; contrarily, composite Riser 3 includes shear stress S12 in the inclined composite layers.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
## 4 Discussion
### What Do Students Know and Believe About ASRs?  
Students' survey responses reveal that only 47.2% of students in our sample had heard of ASRs, despite it featuring on official university websites and being used by instructors university-wide. Among students who reported knowing what an ASR is, 65.4% had received an ASR during their academic career. Students tended to hold accurate knowledge of the system, such as that it is used by instructors to communicate a student's performance in a class and can be positive or negative. Students were also aware that multiple stakeholders are involved in the system, noting that the administrators, advisors, and parents use the information contained in ASRs. Students reported that parents who are given access to their academic portals may read ASRs, which is notable as the ASR system was not specifically designed with parents in mind as an audience. This result echoes that from a prior survey of advisors' use of an early alert system which found that advisors showed the interface to students during meetings even though it was designed to be viewed only by advisors [35]. These findings affirm the importance of investigating intended versus actual use of systems with multiple stakeholders.
## 3 Results and Discussion
### Effect of Electrode Length on NO Oxidation
The drift velocity \(v_{d}\) can be calculated from the solution \(f_{0}\) by Equation (8) [33]:  
\[v_{d}=-\frac{E}{3N}\sqrt{\frac{2\epsilon}{m}}\int_{0}^{\infty}\frac{\epsilon}{ Q}\frac{\partial f_{0}}{\partial\epsilon}d\epsilon \tag{8}\]  
where \(Q\) is the effective total momentum-transfer cross section. The simulation results are shown in Figure 9. Figure 9a shows the effect of E/N on mean electron energy. Obviously, the mean electron energy increases with the increase of E/N. Figure 9b shows the effect of the E/N on the efficiency for a particular electron collision process. The results show that the G-value of the reaction R1 for the generation of \(N_{2}(X,v)\) (which has been proven to be an important species for the generation of NO via R10 and R11 [34]) decreases rapidly with the increase of E/N. The G-values of the main reactions R4 and R5 for the generation of O radicals increase rapidly in the range of 100-250 Td. As a result, NO is transformed to NO\({}_{2}\) via R8 and R9, which causes the concentration of NO\({}_{2}\) increases, and the oxidation degree of NO\({}_{X}\) increases with the increase of SED at the initial discharge stage (as shown in Figures 6 and 7). The G-value of the reaction R3 for the generation of N radicals increases rapidly when E/N exceeds 250 Td. Although the G-value of the reaction R2 for the formation of \(N_{2}\Big{(}A^{3}\sum_{u}^{+}\Big{)}\) (which was considered as an important species to generate NO via R13 [35]) decreases when E/N exceeds 250 Td, the generation efficiency of NO is higher due to the higher reaction rate coefficient of N for NO generation than that of \(N_{2}\Big{(}A^{3}\sum_{u}^{+}\Big{)}\)[36; 35; 30]. As a result, the generation efficiency of O radical decreases and the generation of NO increases when the E/N exceeds 250 Td, which causes the oxidation degree of NO\({}_{X}\) to decrease with the increase of SED (as shown in Figures 6 and 7).
## 3 Simulation Results
### Straight-Line Scanning
The results of PSNRs are displayed in Table 1. Results show that the TV-GPEF acquires the highest PSNRs compared to those of other two algorithms with the average of 11 dB higher than that of the TV-GD and 7 dB higher than that of the TV-VB. From the results of PSNR, we can see that the TV-GPEF algorithm is superior to the other two algorithms. Figure 4: The reconstructed images from the straight-line scanning by the TV-GD (TV-based algorithm solved by gradient descent method), TV-VB (TV-based algorithm solved by the variable splitting and Barzilai–Borwein based method) and TV-GPEF (TV-based algorithm incorporated with Gerchberg-Papoulis-based FFT-utilized compensation method) algorithms: the first row refers to the results of the TV-GD (**a–c**); the second row refers to the results of the TV-VB (**d–f**); and the third row refers to the results of the TV-GPEF (**g–i**). The first to third columns refer to the results from: 50-points (**a,d,g**); 20-points (**b,e,h**); and 10-points (**c,f,i**) sampling, respectively. The number of the estimated detectors is another factor which need to consider for TV-GPEF algorithm. We have represented the PSNRs versus the number of estimated detectors for the straight-line scanning in the line chart in Figure 2.0 It is found that properly increasing the number of estimated detectors elements would improve the results to some extent. However, as the number of estimated detectors continues to increase, the trend of improvement would tend to mitigation. When the increase comes to a certain degree, there would be almost no improvement for the results. Meanwhile, the calculation time would increase greatly. Considering the factors of PSNR and calculation time comprehensively, we choose the number of the estimated detectors of 80, 50, and 30 for 50-points, 20-points and 10-points scanning, respectively.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 2 Methodology
### Electro-Optical Performance Model of a Space-Based Infrared System  
The infrared detection system mainly depends on the temperature difference between target and background from the image sequence to realize detection, recognition and tracking. The core technical difficulty of AVD is to extract moving targets in a complex background. In recent years, large-scale staring infrared focal planes have become the mainstream of moving target detection systems due to their temporal and spatial advantages. Such systems can form wide-area coverage with large-scale detectors and achieve continuous observation through high frame rates, which meets the requirements of moving air targets better.  
The sensitivity of the AVD system can be described by a series of noise equivalent sensitivity coefficients. The noise equivalent sensitivity coefficient is the smallest noise step that one system can distinguish, and it is represented at various positions along the imaging chain [31]. System sensitivity generally refers to the ability of the system to detect targets, and is usually defined as the change in radiation power when the output \(SNR\) of the system is 1. As shown in Figure 2, at the target and background level we have noise equivalent target radiant intensity (\(NET\)), clutter equivalent radiant intensity (\(CET\)) and noise equivalent temperature difference (NEDT); at the aperture through atmosphere transmission, there is system-level noise equivalent irradiance (NELsys); at the surface of the focal plane, there is a detector-level noise equivalent irradiance (NELdet); after the electro-optical conversion, there is noise equivalent charge (\(NEC\)); at the signal processing level, there is noise equivalent voltage (\(\mathrm{NEV}\)); and finally, at the image level, we have noise-equivalent gray-scale (\(\mathrm{NEG}\)). In this paper, two levels of noise equivalent sensitivity coefficients are highlighted, which are \(NET\) and \(CET\), and \(NEC\).  
Figure 2: Noise equivalent sensitivity coefficient along the imaging chain.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
###### Abstract  
Edge computing, characterized by its proximity to users and fast response times, is considered one of the key technologies for addressing low-latency demands in the future. An appropriate edge server deployment strategy can reduce costs for service providers and improve the quality of service for users. However, most previous studies have focused on server coverage or deployment solution consumption time, often neglecting the most critical aspect: minimizing user-request response latency. To address this, we propose an edge deployment strategy based on the queuing search algorithm (QSA), which models the edge deployment problem as a multi-constrained nonlinear optimization problem. The QSA mimics the logic of human queuing behavior and has the ability to perform faster global searches while avoiding local optima. Experimental results show that, compared to the genetic algorithm, simulated annealing algorithm, particle swarm optimization, and other recent algorithms, the average number of \"hopping\" iterations in QSA is 0.1 to 0.6 times fewer than in the other algorithms. Additionally, QSA is particularly suitable for edge computing environments with a large number of users and devices.  
Keywords:edge computing; edge server deployment; queue search algorithm; cloud computing; low
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 6 Digital Transmission  
Digital sensors require digital transmission systems to transmit information between each other in order to form a higher level of integration such us the Internet of Things (IoT). Although Artificial Intelligence (AI) seeks decentralized autonomous systems that can learn and take independent decisions, AI also seeks a shared and coordinated learning between agents.  
Digital transmission systems are used in the Smart Infrastructure with two main technologies. Wired communications use physical cables to provide security and higher data rates, however, sensors are fixed to the location. Wireless communications provide user mobility and flexibility with the drawback of a lower data transmission rate and security.