esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 What Is a Generative Adversarial Network?
### Types and Applications of Generative Adversarial Networks
#### 2.2.2 Applications of Generative Adversarial Networks for the Creation of Synthetic Images  
In recent years, the popularity of GANs has surged due to their remarkable image synthesis capabilities. GANs are commonly employed in image augmentation and image synthesis. Image augmentation involves creating new training examples through transformations or variations applied to existing images. In parallel, GANs are adept at generating synthetic data that closely resemble real images, thereby expanding training datasets, enhancing generalization, and improving the performance of machine learning models. Furthermore, GANs exhibit notable proficiency in image synthesis, enabling the creation of entirely new images based on specific constraints or specifications. Their prowess in both image augmentation and synthesis has unlocked diverse opportunities across fields such as computer vision, graphics, and data augmentation for machine learning tasks. By harnessing the potential of GANs, researchers, and practitioners can elevate both the quality and quantity of available data, ultimately enhancing performance in various image-related applications.  
The successes of GANs extend across diverse domains. They have been effectively applied in domains such as medicine, pandemics, image processing, face detection, texture transfer, traffic control, and sequential data generation (financial or musical data). Notably, successful GAN applications encompass data modeling (image generation), image super-resolution, image synthesis, image segmentation, text generation, text-to-image synthesis, medical image synthesis, image and audio signals, malware detection, climate data analysis, music generation, voice conversion, video generation, video super-resolution, etc. [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38].  
Figure 3 showcases the manifold application domains of GANs.  
In essence, the potential applications of GANs are virtually limitless, bounded only by the creative capacity of developers who leverage their capabilities.  
These domains represent the specific areas in which GANs have demonstrated their usefulness and versatility, showing how this innovative approach has been applied in a variety of contexts and sectors.  
GANs have also been used in a variety of other domains such as chess game playing, mobile user profiling, data augmentation, autonomous driving, fires, and earthquakes.  
Figure 2: The frequency of the main architectures of the literature review.
## 4 Simulation Results
### Experimental Results  
The evaluation of performance metrics demonstrates the improvement and strength features of our design protocol compared with CS data collection, EECDA and LEACH-Hetero protocols. Here, we present the obtained performance results by the simulation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Parameter** & **Value** \\ \hline Number of nodes & \(N\) & 100 \\ Transmission ranges & \(r_{p}\) & \(10,\ldots,80\,m\) \\ Proportion of advanced nodes & \(m\) & 0.2 \\ Proportion of super nodes among advanced nodes & \(m_{0}\) & 0.3 \\ Energy factor for advanced nodes & \(\upalpha\) & 3 \\ Energy factor for super nodes & \(\upbeta\) & 1.5 \\ Initial energy level of normal nodes & \(E_{0}\) & 0.5 J \\ Location of the BS & BS & (50, 50) \\ Data packet size & \(L\) & 4000 bits \\ Network area & \(R\times R\) & \(100\times 100\,m^{2}\) \\ Radio Electronics Energy & \(E_{elec}\) & \(50\,\,nJ/bit\) \\ Transmit amplifier if \(d_{BS}\leq d_{0}\) & \(\epsilon_{fs}\) & \(10\,pJ/(bit*m^{2})\) \\ Transmit amplifier if \(d_{BS}\geq d_{0}\) & \(\epsilon_{mp}\) & \(0.0013\,pJ/(bit*m^{4})\) \\ Threshold distance for swapping amplification model & \(d_{0}\) & \(87.7058\,\,m\) \\ Number of nonzero coefficients & \(K\) & 10 \\ Number of measurements & \(M\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 5: Sparsity of networked data in a Distributed Wavelet Transform (DWT) basis.
## 4 Discussion
### Analysis Regarding Devices Working with a Fixed Eirp from the ARCM  
The ETSI EN 300 328 V2.2.2 (2019-07) is a harmonized standard with a directive 2014/53/EU (known as the RED Directive) for data transmission equipment operating in the 2.45 GHz ISM band [22; 23]. According to this standard, the use of such devices is permitted without special administrative permission (e.g., in Poland, issued by the Office of Electronic Communications (UKE)), as long as the eiry emitted from the antenna does not exceed 100 mW (Wi-Fi 2G, Bluetooth class 1) [22; 23]. Due to the regulations and the antenna gain variation shown in Figure 5 (up to 5 times variability between cases with fixed eiry from ARCM) among the considered exposure scenarios, it is important to analyze the levels of biophysical effects of EMR on the human body (characterized by 10g-SAR in the user's body), also in terms of the eiry level. Figure 8 shows the relative differences between the 10g-SAR values obtained at the fixed eiry emitted from a normalized ARCM and values obtained with fixed input power to the ARCM for the considered exposure scenarios.  
Up to 58 and up to 18 times higher 10g-SAR values were found in the APD and CWED exposure scenarios, respectively, when the results were based on a fixed eiry level from the ARCM. These values are significantly higher compared to those obtained in exposure scenarios with fixed input power to the ARCM. It can be concluded that at a particular level of eiry from the ARCM, compliance with the SAR limits should be demonstrated on a case-by-case basis. Furthermore, the relative difference in the 10g-SAR values in exposure  
Figure 7: The relative difference between antenna functionality parameters (antenna gain; S11 parameter; and 10g-SAR values) calculated in the WIT user simplified [S] versus anatomical [A] models of the head, arm, forearm, and chest, in the APD and CWED 2.45 GHz exposure scenarios, at 100 mW (20 dB) input power to the ARCM (whiskers—standard uncertainty of relative value determination \(\pm\) 35%).  
scenarios with anatomical [A] models compared to simplified [S] ones does not exceed \(\pm\)20% and \(\pm\)12% for the APD and CWED exposure scenarios, respectively. These variations were found to be within the standard uncertainty of their determination (\(\pm\)(30-35)%). The exceptions are the 110% lower 10g-SAR values found in exposure scenarios with the simplified [S] model of a head compared to the anatomical [A] model of a head, resulting from the large variability in antenna gain values. This suggests a possible overestimation of 10g-SAR values in simplified [S] models of body parts with a very diverse internal structure in exposure scenarios, when the EMR source is located at a distance of less than a few millimeters from the body. This overestimation leads to a possibly more conservative evaluation of electromagnetic impact on the user of wearable devices when using simplified [S] models, which is usually welcome in safety studies.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 4 Error Recovery in FTT-CAN
### Server Capacity Computation  
Message retransmissions require bus time, thus having impact on the response time of the remaining messages. Due to the unpredictable nature of errors' occurrence, it is necessary to bound such interference, at least when message timeliness guarantees are a system requirement. We decided to use a server to manage the retransmissions because, in addition to this functionality, a server:  
* Is resource-efficient, since it consumes bandwidth only when activated, i.e., in the presence of errors;
* allows controlling the reactivity to errors via its associated priority and budget/period;
* is predictable and analyzable.  
The analysis that follows allows computing the server capacity, extending the one presented in [12], by taking into account the need for multiple retransmissions to achieve a desired reliability level, as discussed previously.  
Equation (5) allows for computing the probability of having \(k\) faults over any given time interval \(\tau\). It is however useful to use as system design metric the probability of non-recovery, in the server period, designated \(pe_{S}\), which can be computed with Equation (15) for a Poisson process. \(P_{\lambda}\) (\(\geq\)n; \(\tau\)) is plotted in Figure 6 with \(\tau=1/\lambda\) and an Agressive environment. Based on this equation, it is possible to determine the number of errors that must be accommodated by the error-handling mechanism, and, from this, compute the minimum server capacity for its specified period \(T_{S}\).  
\[P_{\lambda}(\geq n_{errors};\ T_{S})<pe_{S} \tag{15}\]For example, considering one server period (\(T_{S}=\tau\)), \(\tau=1/\lambda\) and a target of \(p\varepsilon_{S}=10^{-10}\), the minimum value of \(n_{errors}\) that satisfies Equation (15) is 13 (see Figure 6). \(T_{S}\) was chosen heuristically, to match a mean expected number of errors of one. It was shown in [12] that the server bandwidth increases as the server period decreases, so there is no advantage in attributing smaller values to \(T_{S}\). Noting that it is not possible to foresee which messages will be affected by errors, the worst-case situation happens when all of them must be transmitted in the following EC, implying scheduling several replicas at once, as discussed previously. Assuming \(z\) replicas per error, the server must then have a capacity equal to 13 \(\times\)\(z\) maximum length messages. For instance, if the maximum replica level is equal to 3 (i.e., 3 replicas allow attaining the desired reliability) and LEC is equal to 2.5 ms, then the recovery mechanism uses 0.14% of the system bandwidth (\(C_{MAX}=135\)\(\upmu\)s and \(T_{S}=1/\lambda=3.85\) s), in the worst case, which represents a negligible fraction of the available bandwidth. Nevertheless, as we will see in the following section, the interference caused by the server execution on the scheduling of the remaining messages is non-negligible, and must be accounted for in the schedulability analysis.
## 2 Literature Review
### Video Super-Resolution Reconstruction Technology  
Super-resolution reconstruction is divided into single image (SR) and VSR, among which VSR is more suitable for dynamic monitoring of gas diffusion through multi-frame time series processing. [PERSON] et al. [17] divided deep learning-based VSR methods into the following two categories: alignment and non-alignment. Alignment methods use inter-frame motion information for feature alignment, such as deformable convolution; non-alignment methods directly fuse spatiotemporal features, such as end-to-end deep neural network [18]. In response to the high complexity of traditional VSR models, [PERSON] et al. [19] proposed BasicVSR, which mitigates local propagation constraints and single-line imbalance via bidirectional propagation for efficient reconstruction. Its enhanced version, BasicVSR++ [4], combines second-order mesh propagation and flow-guided deformable alignment to achieve improved performance. The UUT gas detection method adopts BasicVSR++ due to its end-to-end efficiency, multi-scale feature fusion, robust spatiotemporal integration, and compatibility with LSTM-based joint modeling in VSR tasks.  
VSR enhances video quality and recognition accuracy, with applications in medical imaging [20; 21], satellite remote sensing [22; 23], 3D reconstruction [24], VR [25], and surveillance. However, existing VSR methods primarily target visible-light datasets, with limited adoption in thermal imaging and civil engineering applications. The low-resolution infrared equipment used in UUT systems further degrades imaging quality in dark environments, making VSR a crucial research focus for UUT operation and maintenance management.
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Anticancer Activity  
_C. vulgaris_ has demonstrated promising anticancer effects, particularly through its capacity to induce apoptosis in cancer cells and inhibit tumor proliferation. Bioactive compounds such as polysaccharides and carotenoids--especially lutein and beta-carotene--have been shown to suppress carcinogenesis by protecting against DNA damage and blocking pathways that encourage tumor growth [18]. Additionally, chlorophyll contributes to cancer prevention by detoxifying carcinogens and hindering tumor development [4]. The polysaccharides derived from _C. vulgaris_ exhibit immunomodulatory and antioxidant properties, which further aid in reducing the risk of cancer. These compounds help mitigate oxidative stress and prevent DNA damage, both of which are critical factors in cancer progression [86; 87].  
Carotenoids, particularly lutein and beta-carotene, are prominent in _C. vulgaris_ and have been linked to anticancer effects. These compounds act as potent antioxidants, neutralizing free radicals and thereby reducing oxidative damage to cellular components, including DNA [88; 89]. Studies indicate that carotenoids extracted from _C. vulgaris_ can inhibit the proliferation of various cancer cell lines, including colon cancer cells, by inducing cell cycle arrest and apoptosis [88; 90].  
The presence of lutein, the major carotenoid in _C. vulgaris_, has been specifically noted for its ability to suppress cancer cell growth, making it a valuable component in cancer-prevention strategies [89]. Additionally, chlorophyll, another significant component of _C. vulgaris_, plays a role in detoxifying carcinogens and preventing tumor development. Chlorophyll has been shown to bind to potential carcinogens, facilitating their excretion from the body and thereby reducing the risk of cancer [91; 92].  
The detoxifying properties of chlorophyll, combined with its ability to enhance the antioxidant capacity of the body, contribute to the overall anticancer potential of _C. vulgaris_[55; 92]. In summary, the anticancer activity of _C. vulgaris_ is primarily mediated through its secondary metabolites, including polysaccharides, carotenoids, and chlorophyll. These compounds work synergistically to induce apoptosis in cancer cells, inhibit tumor growth, and protect against DNA damage, highlighting the potential of _C. vulgaris_ as a functional food in cancer prevention and management.
# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 3 Methodology
### Architecture of MobileNet for Classification of Parcel Boxes  
In order to recognize the damaged parcel boxes under ULS, architecture research is conducted using CNN MobileNet. Figure 5 shows a flow chart for classification of parcel boxes using MobileNet. MobileNet has the advantage of being faster than other image classification models because it approaches each response channel with a separate filter [20]. For accurate classification of parcel boxes, two convolutional layers are composed in the MobileNet algorithm. One layer is a depthwise convolution that can apply a single-input convolution at once in the filter stage, and the other layer is a pointwise convolution that can perform a linear combination output for the depthwise convolution. By placing the batch normalization and ReLU layers after each convolutional layer, it is possible to reduce the computational cost by allowing the classification to be performed faster than a general CNN model through downscaling of the model size and simplification [21].
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 1 Introduction
With this device, the forward chamfer and side chamfer produced vertical force on the seeds, which slowed down the time for the seeds to leave the metering plate and improved the metering performance. [PERSON] et al. [19] researched the influences of shaped hole diameter, actual working length, and rotational speed on the filling rate and seeding uniformity of the alfalfa seed metering device. The results indicated that the seeding performance is better when the diameter of the hole, the working length of the metering wheel, and the rotational speed of the metering wheel are 6.0\(\sim\)8.0 mm, 15.0\(\sim\)25.0 mm, and 20.0\(\sim\)40.0 r/min, respectively. [PERSON], [PERSON] et al. [20] researched the influence of cone taper of seed tray-type holes on seeding quality. As indicated from the results, the missed seeding index and replay seeding index was 1.3% and 4%, respectively, when the seeding disc velocity, vacuum degree, and cone angle were 0.42 m/s, 2 kPa, and 120\({}^{\circ}\). [PERSON] et al. [21] researched the effects of seed metering devices with different numbers of holes on cotton and maize seeding plate. [PERSON] et al. [22] used electronic control metering system to monitor and give feedback on the seeding process of the precision metering devices in real-time, which reduced the miss-seeding rate and improved the seeding quality. As revealed from the mentioned research, optimizing the hole structure of seed metering devices improves seed filling and seed metering performance effectively. Mechanized planting technology for small grain crops such as rice and maize has mainly been mature, However, research on industrial hemp seeding devices is extremely rare. In this paper, according to the technical requirements of industrial hemp planting. A self-propelled industrial hemp seeder was designed. The structure, working principle, and key components of the seeder were introduced. We studied the design parameters of the rotational speed of the metering wheel, the hole diameter, and the hole depth, and identified these as the main influencing factors of our hypothesis. We then carried out the parameter optimization test.
## 4 Proposed Methodology of Hybrid Channel Modeling
### Sub-Gallery Room-and-Pillar Model  
The interior area of a mine is usually large and irregular, which causes reflection for the transmitted signals. The impact of reflected signals from the vertical sidewalls is nominal but the reflection due to the ceiling/floor is significant. A specific approach is adopted in this study to model this structure as a planar air waveguide and the same procedure is used to compile a multimode model as used in the mine gallery [30].  
It is observed that the scenario of images and reflected rays are still considerable here, as only the \(y\)-coordinate is affected, and in this case, the incident angle is observed to be \(0^{\circ}\). The reflection coefficient is \((1-\sqrt{k_{h}})/(1+\sqrt{k_{h}})\) for the distributed field of \(X\)-polarized and \((\sqrt{k_{h}}-1)/(\sqrt{k_{h}}+1)\) for the field of \(Y\)-polarized. If the transmitting node and reception node is placed at the height of \(y_{o}\) and \(y\), respectively, then the EM field at the reception node can be computed as  
\[E^{Rx}=E_{o}\sum_{q}\left[\frac{exp(-jky_{q}(y))}{y_{q}(y)}\right]\left(\frac{ 1-\sqrt{k_{h}}}{1+\sqrt{k_{h}}}\right)^{|q|} \tag{28}\]where \(y_{q}(y)\) denotes the distance between the reception node and \(I_{q}\). The following expression can be used to calculate the value of \(y_{q}(y)\)  
\[y_{q}(y)=\begin{cases}|2 qb-y_{0}-y|,&\text{if $q$ is odd}\\ |2 qb+y_{0}-y|,&\text{if $q$ is even}\end{cases} \tag{29}\]  
Please note that the Eigen-function for \(X\)-polarized modes is obtained from [34]. The mode intensity \(C_{n}\) is simplified from Equation (28) using the Poisson sum method and the expressions are given below  
\[E_{n}^{x}(y)=E_{o}.\cos\left[\bigg{(}\frac{n\pi}{2b}-j\frac{n\pi}{2b^{2}k} \frac{k_{h}}{\sqrt{k_{h}-1}}\bigg{)}y+\varphi_{y}\right]\cong E_{o}\cos\frac{ n\pi}{2b}y+\varphi_{y} \tag{30}\]  
where \(\varphi_{y}=\pi/2\) if \(n\) is even; \(\varphi_{y}=0\) if \(n\) is odd.  
\[C_{n}(z)=\frac{E_{o}\pi}{bz\sqrt{1-(\frac{n\pi}{2b})^{2}}}\cos\Bigl{(}\frac{ n\pi}{2b}y_{o}+\varphi_{y}\Bigr{)} \tag{31}\]  
Now the field at any point can be calculated by computing the Eigen-function and intensity of each mode.
## 3 Results
### Acoustic Properties
\(L\), _RT_, and \(B\) = longitudinal, radial–tangential, and bending directions, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Mean** & **st. dev.** & **CV(\%)** \\ \hline \(V_{\text{USL}}\) (m s\({}^{-1}\)) & 5843 & 278 & 5 \\ \(MOE_{\text{USL}}\) (GPa) & 15901 & 2518 & 16 \\ \(Z_{\text{USL}}\) (MPa s m\({}^{-1}\)) & 2.71 & 0.34 & 13 \\ \(R_{\text{USL}}\) (m\({}^{4}\) kg\({}^{-1}\) s\({}^{-1}\)) & 12.72 & 1.28 & 10 \\ \(V_{\text{USRT}}\) (m s\({}^{-1}\)) & 1165 & 351 & 30 \\ \(MOE_{\text{USRT}}\) (GPa) & 681 & 429 & 63 \\ \(Z_{\text{USRT}}\) (MPa s m\({}^{-1}\)) & 0.54 & 0.16 & 31 \\ \(R_{\text{USRT}}\) (m\({}^{4}\) kg\({}^{-1}\) s\({}^{-1}\)) & 2.55 & 0.84 & 33 \\ \(V_{\text{FREQL}}\) (m s\({}^{-1}\)) & 5833 & 313 & 5 \\ \(MOE_{\text{FREQL}}\) (GPa) & 15857 & 2641 & 17 \\ \(Z_{\text{FREQL}}\) (MPa s m\({}^{-1}\)) & 2.71 & 0.35 & 13 \\ \(R_{\text{FREQL}}\) (m\({}^{4}\) kg\({}^{-1}\) s\({}^{-1}\)) & 12.70 & 1.31 & 10 \\ \(MOE_{\text{FREQL}}\) (GPa) & 15040 & 2512 & 17 \\ \(R_{\text{FREQL}}\) (m\({}^{4}\) kg\({}^{-1}\) s\({}^{-1}\)) & 12.37 & 1.23 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of the acoustic properties calculated in the L direction using both the ultrasonic method (_US_) and the frequency method (_FREQ_). 6.42 to 14.72 GPa. The results of this research produced an average \(MOE_{B}\) of 15.04 GPa, which is higher than that in the literature. The R must be high in resonance soundboard. The R ranges between 8.7 and 16.4 m\({}^{4}\) kg s\({}^{-1}\)[14]. The R of spruce is stabilized at 11.41 m\({}^{4}\) kg s\({}^{-1}\)[35; 37]. The average R results of this research were: 12.72 for the ultrasonic direct transmission method and 12.70 m\({}^{4}\) kg s\({}^{-1}\) for the resonance method--both inside the range shown in the literature by [PERSON] [14] but higher than that of [PERSON] [37]. The R in bending was slightly lower--12.37 m\({}^{4}\) kg s\({}^{-1}\). The \(Z\) is the main factor when the vibration energy passes from the strings to a soundboard and vice versa [33; 37; 39]. A low value of impedance corresponds to a high efficiency of energy transfer. In Norway spruce, the \(Z\) is fixed to 2.5 MPa s m\({}^{-1}\), and, when used as the wood for soundboards, the allowed range is defined between 1.2 and 3.339 MPa s m\({}^{-1}\)[14; 37]. Since the impedance is calculated by the velocity, \(Z\) was achieved based only on the L direction; both methods reached the same value of \(Z\): 2.71 MPa s m\({}^{-1}\).