esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Integrative Example
### The Catalog Service  
For our game stream profile to work, we characterized a BBCP with the information from the Catalog Service. We gave the profile a deliberate Profile Evaluation Rate (_PER_) equal to 1/600 Hz (an evaluation every 10 min). The run and stop probabilities were set equally to 0.5 to establish an unbiased baseline. We set the scale to 7 to simulate a week, and created two cycles. The first cycle takes place during the weekdays (Monday through Friday) when the service's customers' spare time is reduced, while the second cycle takes place during the weekends when game streaming services are used the most due to the increase in the available time of its customers.  
To complement the cycles, we created three timed expectations for each of them, available in Table 4, which are meant to divide the day in three different time segments: work time, spare time, and nighttime. The behavior was further constrained using elasticity for each timed expectation.  
The values used for the elasticity of each timed expectation are available in Table 5. We concluded the characterization of the Catalog Service expectations by adding run constraints to each timed expectation (see Table 6) to simulate the deliberate time restrictions that the users demoing the product would encounter. A Minimum Run Time equal to 0 stands for no minimum threshold of time running. Such limitations are usually set in place by subscription business models to entice the user to become a member, removing these restrictions. As seen in the timed expectations for both cycles, the execution probability tends to increase into the latter part of the day, as well as the minimum and maximum time of consumption. This is due to the amount of spare time a user tends to have for this type of application, common across leisure activities.  
For this profile, we decided to add a single operation with no actual resource consumption or operational time constraints, available in Table 7. The goal of this operation was to define an operational trace between the Catalog Service and the Game Stream Service, where the catalog selection functions as a trigger for the Game Stream Service each time it runs. This relationship will become more evident further on with the definition of the Game Stream Service in Section 4.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cycle** & **Timed Expectations** & **Time Range** & **Interpretation** \\ \hline
1 & 1 & [1; 2; 3; 4; 5; 6; 7; 8] & Early in the morning \\ \hline
1 & 2 & [9; 10; 11; 12; 13; 14; 15; 16] & Work schedule \\ \hline
1 & 3 & [17; 18; 19; 20; 21; 22; 23] & After work \\ \hline
2 & 1 & [1; 2; 3; 4; 5; 6; 7; 8; 9] & Early in the morning \\ \hline
2 & 2 & [10; 11; 12] & Morning to mid-day \\ \hline
2 & 3 & [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] & Evening to late-night \\ \hline \hline \end{tabular}
\end{table}
Table 4: Catalog Service: timed expectations per cycle.
## 4 Analysis
### Analysis of Luminous Flux Utilization  
#### 4.2.1 Effect of FOV  
In applications, the incident light in SBFTIS is typically composed of spherical waves, with the maximum allowable incident angle being the FOV of the system. The aperture stop of the SBFTIS is dependent on the FOV, and thus the luminous flux is limited by it. For different FOVs, the aperture stop varies, leading to different luminous flux utilizations.  
To determine the aperture stop size and luminous flux utilization for different FOVs, calculations were performed using a point source of spherical waves positioned on the vertical line of the prism surface, with a fixed prism spacing of \(s\) = 0 mm. The spherical waves were then simulated for different FOVs ranging from 0\({}^{\circ}\) to 10\({}^{\circ}\), such that the outgoing light just exits from the edge of the rear surface of the DWP. The range of the aperture stop of the system under different FOVs is depicted in Figure 15.  
In Figure 15a-e, the black solid line rectangle represents the size of the prism, and the red dotted line rectangle and the blue dotted line rectangle represent the aperture stops of the eooe and oeeo lights, respectively, while the gray solid rectangle in the figure shows the total aperture stop of the system. As the FOV increases from 0\({}^{\circ}\) to 10\({}^{\circ}\), the aperture stop size reduces from 377.48 mm\({}^{2}\) to 293.44 mm\({}^{2}\), and, consequently, \(\eta\) exhibits a linear decrease from 94.37% to 73.36%, as depicted in Figure 15f.  
To experimentally verify the results, interferograms of various FOV were captured, and the FOV was limited to a range of 5-10\({}^{\circ}\) due to structural constraints of the optical system. Interferograms were captured for FOVs of 5\({}^{\circ}\), 7.5\({}^{\circ}\), and 10\({}^{\circ}\) with \(s\) = 0 mm, as shown in Figure 16.  
As the FOV increases from 5\({}^{\circ}\) to 10\({}^{\circ}\), the aperture decreases slightly, and its utilization decreases by 10.19%, but the amount of light entering the system doubles. Therefore, for the same exposure time, a larger FOV means a higher luminous flux. As a result, the brightness of the captured picture is increased, and the contrast of the interference fringes is improved.  
Figure 15: When \(s\) = 0 mm, FOV = 0–10\({}^{\circ}\): (**a**–**e**) Effective incident aperture of DWP; (**f**) Variation in luminous flux utilization with FOV.  
It should be noted that the interference fringes will be seriously bent if the FOV is too large, and the polarization information of the target may be affected by the telescopic system, which will affect the spectral restoration; thus, the FOV cannot be increased without limit.
## 3 Experimental Results
### Performance Metrics for MF Class Using Balanced Dataset without Feature Normalization  
In order to examine the classification performance for a balanced dataset for the machine failure label, we generated a balanced dataset which consisted of 330 rows of machine failure data and 402 rows that were randomly selected from non-failure labeled data in the dataset with 100 different repetitions. In addition to classical machine learning methods such as DTs, SVMs, and k-NNs, three types of deep learning architectures including 1D-LeNet, 1D-AlexNet, and 1D-VGG16 were utilized with 100 different balanced datasets and the maximum classification performance scores are displayed in the Table 4. The classification studies for the MF label were repeated on the balanced dataset without feature normalization.  
The performance results of classification with a balanced dataset, without normalization, for both SML and DL models, were separately added to the existing Table 4. The performance metric values, especially recall, precision, and F1-score, are better, with higher results in both shallow and deep machine learning algorithms when compared to the results obtained in the previous section. It can be clearly said that improvement in these classification performance metrics for shallow machine learning algorithms is better than for deep learning architectures.  
Figure 7 shows the comparison of fault classification performance metrics for SML and DL models on a balanced dataset without normalization.  
Figure 6: Performance metrics comparison for different models on imbalanced dataset without feature normalization.  
Figure 7: Performance metrics comparison for different models on balanced dataset without feature normalization.
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 3 Results and Discussion
### Volatile Compounds
Some of the volatile compounds, formed during the degradation of branched-chain amino acids and associated with this browning process, like 2-methylpropanal, 3-methylbutanal, 2-methylbutanal, but also methylpyrazines, were developed and partially transferred to the oil [80]. [PERSON] et al. (2022) reported the presence of these compounds in significantly higher content in mealworm larvae dried in a rack oven at 60 \({}^{\circ}\)C [77]. Pyrazines detected in TM oil's volatile profile are compounds formed in the Maillard reaction and are considered to provide cooked flavor [16; 34]. Among seven pyrazines, the relative abundance of 2,5-dimethyl pyrazine dominated. This compound is responsible for burning aroma [16]. Terpenes and terpenoid structures have been detected as secondary metabolites in plants, animals, and microorganisms. Some of them include highly odor-active compounds. Among the terpen identified, 3-carene and D-limonene are the most relevant. The odor of 3-carene is associated with resinous, coniferous forest odor [82]. D-limonene is one of the most common terpen in nature, occurring in citrus and a wide variety of other plant species, and has a pleasing orange scent [83]. Small amounts of this terpene were previously detected in TM edible oil. Summing up, the volatile profile of analyzed TM oil samples was characterized mainly by the distinguished abundance of aroma-dependent hexanal and other aliphatic aldehydes resulting from the oxidation of PUFAs. The second important volatile compounds were Strecker aldehydes and pyrazines, which may provide a wide range of aromas (roasted, fatty, toasty, and roasted). Some volatiles have not been associated with aroma attributes such as hydrocarbons; they may be important as an indicator of the quality of TM oil.
## 7 Future Research Directions
### Security and Cybersecurity  
Autonomous and remote-controlled ships must be well protected from cyber attacks. Connections in the architecture provide enlarged attack surfaces compared to conventional ships, where many of the critical systems cannot be accessed at all from outside. End-to-end cybersecurity procedures are essential and the development of authenticated gateways described in this paper requires more work. For example, scalability and heterogeneity [64] create complexity and numerous possible threats to autonomous ships. The importance of protecting AIS, ECDIS, and GMDSS was recently highlighted in [19].  
An important part of the safety system is fail-safe procedures, e.g., in case that communication is lost due to hijacking and sabotage of equipment. Fail-safe procedures incorporate various techniques to mitigate losses due to system or component failures. They include, for example, alerting operator personnel and providing specific instructions on subsequent steps to take (e.g., do nothing, reestablish system settings, shut down processes, restart the system). This can be prevented partly by inclusion of redundant communication equipment that are not easily found but provide at least reduced communication capacity to the ROC. The system may be preprogrammed to automatically activate an emergency position including a radio beacon (EPIRB) and search and rescue transponder (SART) if the communication is totally lost for a certain period of time. This would help locating the ship in need of help. However, these systems have been designed and are used for specific purposes. Thus, there may be a need to develop a separate emergency system for autonomous ships. Or perhaps the existing systems could be modified to support the needs of the emerging maritime industry? In addition, the ship may start running automatically in circles when hijacked so that the cargo cannot be taken to wrong destinations.
## 2 Optical Angle Nano-Positioning Mechanism Configuration
### Kinematics Analysis  
Dynamic characteristics are critical benchmarks for the optical angle nano-positioning mechanism. The inclusion of a flexible structure means that design parameters significantly affect the mechanism's inherent frequency. To enhance the mechanism's overall anti-vibration capabilities, it is crucial to optimize the design of parameters that influence dynamic stiffness. In this configuration, the flexure hinge functions as a rotating spring, while other structural elements are considered rigid bodies. The dynamic model of the optical angle nano-positioning mechanism is illustrated in Figure 5.  
Figure 5: Dynamic model diagram of the mechanism. Flexure hinges are reduced to springs.  
When the output angle is sufficiently small, the movement of the moving platform can be approximated as translational motion. Under these conditions, the kinetic energy, denoted by \(T\), in the direction of motion is defined as follows:  
\[T=\frac{1}{2}{M_{1}}^{2}+4\times\frac{1}{2}{\phi}^{2} \tag{9}\]  
where \(M_{1}\) represents the mass of the moving platform of the optical angle nano-positioning mechanism and \(J\) signifies the moment of inertia of the rigid rod with respect to the endpoints in the direction of motion. Here, \(M_{2}\) is the mass of the rigid rod and \(l_{0}\) denotes the length of the rigid rod:  
\[\left\{\begin{array}{c}J=\frac{M_{2}l_{1}^{2}}{3}\\ M_{2}=\rho(l_{1}-2r)bt\\ l_{0}=l_{1}-2r\end{array}\right. \tag{10}\]  
Since the output angle is small enough, there is \(\varphi\approx\frac{A}{l_{1}}\). By plugging in Equation \(l_{1}\), we can get:  
\[T=\frac{M_{1}l_{1}^{2}}{2}{\phi}^{2}+\frac{2\rho(l_{1}-2r)bll_{1}^{2}}{3}{\phi }^{2} \tag{11}\]  
Considering that the optical angle nano-positioning mechanism consists of eight identical hinges, the potential energy, denoted by \(U\), in the direction of motion is defined as follows:  
\[U=8\times\frac{1}{2}{k_{\alpha}}{\varphi}^{2}=4{k_{\alpha}}{\varphi}^{2} \tag{12}\]  
For a system with \(n\) degrees of freedom, the motion under the influence of an external force \(F\) adheres to the Lagrangian equation, which is defined as follows:  
\[\frac{\mathrm{d}}{\mathrm{d}t}(\frac{\partial T}{\partial{\dot{q}}_{j}})-\frac {\partial T}{\partial{q}_{j}}+\frac{\partial U}{\partial{q}_{j}}=F\quad(j=1,2 \cdots n) \tag{13}\]  
where \(q_{j}\) represents the generalized coordinates adopted by the system. By considering \(\varphi\) as the generalized coordinates, we can derive the following by substituting Equations (10) and (11):  
\[(\frac{M_{1}}{2}+\frac{2\rho(l_{1}-2r)bh}{3})l_{1}^{2}\ddot{\alpha}+4{k_{ \alpha}}{\alpha}=0 \tag{14}\]  
Moreover, the first-order inherent frequency, denoted as \(f\), of the optical angle nano-positioning mechanism can be calculated as follows:  
\[f=\frac{1}{2\pi}\sqrt{\frac{4{k_{\alpha}}}{(\frac{M_{1}}{2}+\frac{2\rho(bh) }{3})(l_{0}+2r)^{2}}} \tag{15}\]
## 2 Background
### Vehicular Applications  
CHP2 was originally designed to provide robust PNT services to automated vehicular applications, both terrestrial and airborne; however, many emerging technologies enable similar capabilities through different RF techniques. These V2V positioning technologies include radar systems [42]; LiDAR [43; 44]; optical systems [45; 46]; and RFID [47]. Many RF systems have also been considered for collision avoidance, including radar systems for ground vehicles [48; 49] and UAVs [50; 51]; Li-Fi systems [52; 53]; and LiDAR systems [54; 55; 56]. Other related applications include ATM [57; 58] and asset tracking [59], which require cooperative positioning and communications services. In many applications, these technologies offer excellent performance but are limited in various respects, including flexibility, SWaP-C, interference, and resource consumption. We propose CHP2 not as a replacement for these systems but as a suitable alternative that offers efficiency and flexibility to adapt to future design challenges.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 2 Smart Substation Delay Analysis and TSN Technology Analysis
### Smart Substation Delay Analysis  
The IEC 61850 standard is widely used to construct smart substation network systems. This standard defines a communication interface model consisting of three layers, in which each layer adopts an open structure to facilitate network connection. The overall network architecture can be succinctly summarized as \"three layers and two networks\", as shown in Figure 1. In this model, the station control layer and the interval layer devices are connected to each other through the station control layer network, and the interval layer and the process layer devices are connected to each other through the process layer network. As the data traffic within the substation network increase, various network issues emerge, including network congestion, network storms and latency jitter.  
The smart grid requires a high QoS, especially for the security of relay protection within the grid. Currently, the general relay protection method used within the grid is still the differential protection method, in which Electronic Devices (IEDs) at the interval layer collect current data from multiple measurement points under the Ethernet network in the process layer and then send differential protection commands and recognize fault point information by analyzing these collected data. Hence, the reliability and real time of the communication network are good enough to promptly respond to the differential protection, namely, lower latency, slighter jitter and less packet loss. These all require the communication network of the smart grid to have low latency, a slight jitter and less packet loss, viz. time-critical sensitive streams. According to the IEC 61850, the fast-breaking stream and sensor data information stream require a delay of less than 3 ms (see Table 1).  
Figure 1: The three-layer two-network model.  
In order to meet the requirements of low latency and high reliability for the various traffic within the smart grid, TSN switches are needed to replace traditional Ethernet switches, and the Qbv scheduling mechanism of TSN switches is also utilized to satisfy the QoS of various traffic.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology
### Emission Modeling in VISSIM  
In assessing the environmental impact of autonomous vehicles (AVs) at urban intersections, accurately quantifying emissions across different traffic conditions is essential. This study employs the emission modeling features of PTV VISSIM, utilizing the Handbook Emission Factors for Road Transport (HBEFA) to simulate and evaluate pollutant emissions such as CO, NOx, and particulate matter (PM). VISSIM calculates emissions using a polynomial function that takes vehicle speed into account, allowing for detailed analysis of the pollutants generated under varying driving behaviors and traffic scenarios.  
\[E=\sum_{i=1}^{n}\Big{(}a+b\cdot v_{i}+c\cdot v_{i}^{2}+d\cdot v_{i}^{3}\Big{)} \cdot\Delta t \tag{11}\]  
The total emissions (\(E\)) of a specific pollutant (in grams) are calculated using a polynomial function, where \(v_{i}\) represents the speed of vehicle \(i\) (in kilometers per hour), and \(a,b,c\), and \(d\) are empirical coefficients (unitless) specific to vehicle types and driving conditions. The simulation time step \(\Delta t\) (in seconds) is used to account for emissions generated over time during the simulation. This allows for an accurate estimation of emissions based on real-time vehicle behavior within the VISSIM model.  
The coefficients \(a,b,c\), and \(d\) are derived from the HBEFA model and are specifically tailored to represent the emission characteristics of different vehicle categories, including both autonomous and human-driven vehicles. These coefficients play a crucial role in modeling the effects of various driving behaviors and traffic conditions on emission levels. They differ based on vehicle type, engine properties, and driving patterns, enabling a realistic evaluation of emissions across different traffic scenarios. The following is a breakdown of these coefficients.  
Coefficient \(a\) represents the baseline emissions produced when vehicles are idling or moving at low speeds. It tends to be lower for efficient or less aggressive driving behaviors, such as AV platooning, which minimizes idle time and low-speed operations. Coefficient \(b\) establishes a linear relationship between speed and emissions, with higher values typically associated with more aggressive driving styles, like AV aggressive, where emissions increase at higher speeds. Coefficients \(c\) and \(d\) account for the nonlinear effects  
Figure 12: Comparison of estimated and simulated average travel time for all directions. Source: processed by authors.  
of speed on emissions, highlighting the significant impact of rapid speed fluctuations, such as during aggressive acceleration or deceleration. These coefficients reflect how dynamic driving behaviors influence overall emissions output.
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies  
In additive manufacturing, new technologies are changing many industries. AM enables the creation of intricate components in robotics, enhances the design and maintenance of complex systems through digital twins, augments the immersive experience in virtual reality (VR), and streamlines automation processes. These advancements demonstrate the enormous potential of new additive manufacturing technologies to impact robotics, automation, digital twins, and virtual reality (VR) in the future [46].  
Figure 3: Hybrid AM processes.
# GAN Data Augmentation Methods in Rock Classification
###### Abstract  
In this paper, a data augmentation method Conditional Residual Deep Convolutional Generative Adversarial Network (CRDCGAN) based on Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to address the problem that the accuracy of existing image classification techniques is too low when classifying small-scale rock images. Firstly, Wasserstein distance is introduced to change the loss function, which makes the training of the network more stable; secondly, conditional information is added, and the network has the ability to generate and discriminate image data with label information; finally, the residual module is added to improve the quality of generated images. The results demonstrate that by applying CRDCGAN to the augmented rock image dataset, the accuracy of the classification model trained on this dataset is as high as 96.38%, which is 13.39% higher than that of the classification model trained on the non-augmented dataset, and 8.56% and 6.27% higher than that of the traditional dataset augmented method and the DCGAN dataset augmentation method, respectively. CRDCGAN expands the rock image dataset, which makes the rock classification model accuracy effectively improved. The data augmentation method was found to be able to change the accuracy of the classification model to a greater extent.  
GAN; rock image generation; data augmentation; condition information; rock classification +
Footnote †: journal:  
SinGAN, a rock image generation network based on generative adversarial networks, and the experimental results showed that the method can automatically generate rock images with diverse features and effectively retain the image feature detail information, but the generated images cannot effectively improve the accuracy of the rock thin section image classification model [11].  
In this paper, we propose a CRDCGAN data augmentation method based on Generative Adversarial Network (GAN) by analyzing the features of rock image data in a rock classification task scenario. CRDCGAN starts by adding conditional information to enable the generative network to generate matching sample label pairs and generates multi-class rock image data based on label information and noisy data. At the same time, the residual module is introduced, and the loss function is changed to solve the problems of poor quality of generated rock images, redundancy of generated rock image feature information, and instability of training of the Generative Adversarial Network. By augmenting the rock image data with CRDCGAN and generating a large amount of various rock image data with labels, the accuracy of the classification model is greatly improved, and better results are also obtained in other evaluation methods.