esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 3 Scene Text Segmentation Image Generation  
### Overview of the Architecture  
CycleGAN [17] is a ring-shaped structure, as shown in Figure 1a, mainly consisting of two generators \(G_{A}\), \(G_{B}\) and two discriminators \(D_{A}\), \(D_{B}\). \(a\) denotes images in domain \(A\), and \(b\) denotes images in domain \(B\). There are two direction processes in CycleGAN. As shown in Figure 1b, the original image of domain \(A\) is transferred to domain \(B\) by the generator \(G_{A}\), and then it is re-transferred to domain \(A\) by generator \(G_{B}\). Figure 1c shows the other direction's process. In order to ensure the style transfer of the image, the discriminators \(D_{A}\) and \(D_{B}\) play a discriminating role.  
We use CycleGAN to complete text segmentation of scene images. Images in domain \(A\) are scene text images with a complex background; images in domain \(B\) are text images without background and can be regarded as images with semantic labels. However, images in domains \(A\) and \(B\) are not paired. In this way, we convert the task about scene image text segmentation into a supervised image style transfer task.  
Figure 1: Bidirectional circulation of CycleGAN.A indicates the image in domain A, and B indicates the image in domain B. (**a**) shows the cyclic structure of CycleGAN; (**b**) shows the generation process of transforming an image in domain A into an image in domain B and then restoring it to an image in domain A; (**c**) shows the generation process of transforming an image in domain B into an image in domain A.  
In this paper, the generation process of the scene text segmentation network is shown in Figure 2. The scene text image \(a\) with a complex background is converted by the generator \(G_{A}\) to \(\hat{b}\) with the background image removed, and then it is reconstructed back to a complex background image \(\hat{a}\) by the generator \(G_{B}\). The discriminator \(D_{B}\) is used to determine whether the generated image is a text image with the background removed. The image \(b\) without background generates image \(\hat{a}\) with complex background through the generator \(G_{B}\), and then it restores it to the image \(\tilde{b}\) without background through the generator \(G_{A}\). The discriminator \(D_{A}\) is used to determine whether generated images are complex background images. The text segmentation images generated using CycleGAN are less effective in terms of details, so we change the structure of the network to enhance the detail of the segmented images by introducing the ASPP module in the generator.  
After model training is completed, we need to use the generator \(G_{A}\) for text segmentation of the scene image. Inputting the scene text image in domain \(A\) into the generator \(G_{A}\) will generate text image without background and complete scene text segmentation.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Neuronal Network's Features  
Our classification model to predict the LQE was trained on an original dataset with the following features: the transmission rate x\({}_{1}\), the transmitter power x\({}_{2}\), the distance  
Figure 1: Experimental scenario, workshop TecMM, Zapopan, México.  
from the transmitter to the receiver x\({}_{3}\), the payload x\({}_{4}\), and the bit error rate x\({}_{5}\). Table 1 shows statistical information of the input data. The data rate, power, and payload were collected from the transceiver configuration. Distance data were collected by locating the sensor node at different positions in the experimental scenario and the bit error rate (BER) was calculated with (1), where \(f\) = _8n_ is the length of the packet in bits, and n is the data payload [40].  
\[\text{BER}=1-\text{PRR}^{(f^{-1})} \tag{1}\]
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 5 Earthquakes  
Earthquake EW systems can also benefit from the Internet of Things and related technologies. Sensors and sensing units can monitor vibrations and ground movement to create alarms and to alert people in certain places before the earthquake waves reach them. Even a few seconds or minutes of warning can prove essential in saving lives. The most common method to detect an earthquake is P-wave detection. When an earthquake occurs, compression P waves and transverse S waves radiate from the epicenter. Since P-waves travel faster but are have less destructive effects, if detected they can be used to forward warnings before the more destructive S waves reach a location. Figure 3 illustrates a generic implementation of an earthquake Early Warning System based on P-Wave detection.  
A very simple IoT system based on this method is the one proposed by [PERSON] et al. [59], which uses accelerometers connected to micro-controllers to collect and process ground vibration measurements, and then sends the data using Zigbee to a receiver connected to a PC which is used to forward warnings to users. GSM modules can also be used to send warnings to a base transceiver station which will then alert mobile phone users.  
EW systems for Earthquake monitoring are already developed in many countries, but there are still efforts to optimize sensor networks, provide reliable and low latency communications, and decrease the data processing latency, which is usually the most important factor contributing to the overall delay of an alert system for earthquakes [60]. Once an Earthquake EW message is generated by the network or processing hub, every millisecond delay to send the message corresponds to an increase of about 8 m of the radius of the area reached by the earthquake [35]. This means that decreasing latency is an essential requisite in earthquake EW and precise time constraints must be considered. Warning times and the collection of geophysical data should therefore be seconds to minutes. Since the most damage caused by an earthquake is usually localized within a certain perimeter around the epicenter, the most critical objective of an Earthquake EW system is to provide alarms within seconds to this area, while more accurate alerts to larger regions can then be sent in tens of seconds [6]. With optimized data processing algorithms and data latency reduced to 1 s, it is possible to obtain possible peak performance of 3-6 s to generate and disseminate the Earthquake EW alert [61].
## 4 Results
### Characteristics of Variables  
The variables of the height of the measured place above the target (X\({}_{1}\)) and the tube radius (X\({}_{2}\)) are independent discrete variables. The variable X\({}_{1}\) takes on values from 305 mm to 425 mm with a difference of 30 mm (X\({}_{11}\) = 305 mm, X\({}_{12}\) = 335 mm, X\({}_{13}\) = 365 mm, X\({}_{14}\) = 395 mm, X\({}_{15}\) = 425 mm). The variable X\({}_{2}\) takes on values from 100 to 140 mm with a difference of 10 mm (X\({}_{21}\) = 100 mm, X\({}_{22}\) = 110 mm, X\({}_{23}\) = 120 mm, X\({}_{24}\) = 130 mm, X\({}_{25}\) = 140 mm). The variables (angle of grain growth, thickness of the deposited layer, adhesion in the direction of grain growth, adhesion against the direction of grain growth, and roughness of the deposited layer) are continuous variables. The resulting values are the arithmetic mean of five measurements. The description of the variables is in Table 2.  
When analyzing the variables, we first of all determined whether there was a dependency between them, i.e., whether the variables were correlated. Since the variables were measured in different units, the relationships between them were identified using a correlation matrix (Table 3), while the relationship between two variables was determined through the correlation coefficient r.  
The resulting correlation matrix shows that there is a very strong positive correlation between variables Y\({}_{4}\) and Y\({}_{2}\) (r = 0.97) and a strong negative correlation between variables Y\({}_{2}\) and X\({}_{1}\) (r = \(-\)0.963). Moderate negative correlation is, for example, between variables X\({}_{1}\) and Y\({}_{1}\) (r = \(-\)0.755), Y\({}_{1}\) and Y\({}_{3}\) (r = \(-\)0.779), X\({}_{2}\) and Y\({}_{3}\) (r = \(-\)0.623).  
The measured values show that an increase in the height of the measured place above the target leads to a decrease in the grain growth angle (negative correlation, r = \(-\)0.755) and, conversely, an increase in the tube radius leads to an increase in the grain growth angle (positive correlation, r = 0.602). A graphic representation of the values using a boxplot is shown in Figure 7.  
By increasing the height of the measured location above the target, the value of the grain growth angle (Y\({}_{1}\)) decreases, and the thickness of the deposited layer (Y\({}_{2}\)) decreases.  
By increasing the radius of the tube, on the one hand, the grain growth angle increases; on the other hand, the thickness of the deposited layer decreases, and adhesion decreases in both directions.  
It follows from the correlation matrix that as the height of the measured location above the target increases, the value of the thickness of the deposited layer decreases (Y\({}_{1}\), r = \(-\)0.963). The correlation (dependence) between roughness and other variables is not confirmed by the correlation matrix (in all cases, \(|\)r\(|\) < 0.29).  
\begin{table}
\begin{tabular}{l r r r r r r r} \hline  & **X\({}_{1}\)** & **X\({}_{2}\)** & **Y\({}_{1}\)** &
## 1 Introduction
However, the true extent of isotonitazene-related fatalities may be under-reported due to challenges in its detection and the limited awareness of its prevalence [12]. Forensic toxicological studies have identified isotonitazene in postmortem samples at varying concentrations, often in combination with other central nervous system depressants such as benzodiazepepines [2; 13; 14]. These findings suggest that polysubstance use significantly exacerbates its toxicity and contributes to the high mortality rates observed. Isotonitazene exhibits pharmacological effects typical of synthetic opioids, including analgesia, sedation, and euphoria. While these effects are desirable in a controlled medical context, the substance's high potency drastically increases the likelihood of adverse outcomes in non-medical use [2; 12]. Respiratory depression is the most significant and life-threatening adverse effect, often leading to fatalities when the drug is used in excess or combined with other depressants such as alcohol or benzodiazepepines. Cognitive impairment, drowsiness, and reduced motor coordination are also frequently observed, further contributing to its risk profile [2; 12]. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a gold-standard technique for the analysis of drugs and metabolites in biological matrices due to its high sensitivity, specificity, and precision. It is particularly suited for detecting low-concentration analytes in complex matrices such as serum or plasma. LC-MS/MS methods have been employed for the detection of various NPSs [15]. However, the validation of robust protocols specifically targeting flualprazol and isotonitazene remains scarce. Despite the increasing use and toxicity of flualprazol and isotonitazene, validated methods for their quantification in serum are limited. Existing studies have primarily focused on urine analysis, leaving a critical gap in understanding these substances' pharmacokinetics in blood matrices [16]. Therefore, this study aims to develop and validate a highly sensitive LC-MS/MS method for the detection of flualprazol and isotonitazene in serum, addressing the current analytical and clinical needs. To date, there is no validated method in the literature for the quantification of flualprazol and isotonitazene in serum samples. The increasing prevalence of substance use in recent years has underscored the urgent need for reliable toxicological tools. Developing a sensitive and reproducible analytical method is critical to addressing this need, as such methods can contribute significantly to public health and forensic investigations. A validated and robust method not only ensures the accurate identification and quantification of flualprazol and isotonitazene but also aids in monitoring substance use trends, supporting clinical diagnostics, and providing crucial evidence in legal case. The aim of this study was to systematically validate the accuracy of the LC-MS/MS method for quantifying flualprazol and isotonitazene in serum samples, thereby establishing its reliability and applicability in forensic and clinical toxicology.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Transportation Mode  
Researchers used publish/subscribe middleware to improve ITS across different transportation modes: aviation, land, and marine. This section discusses various systems that use middleware to achieve two main goals: ensuring passenger safety and providing the best travel experience, which includes improved comfort and convenience, faster and more reliable travel times, and real-time updates.
## 3 Simulations for Control Performances
### Dynamic Model and Parameters
In this simulation study, the input excitation force of the model is chosen as similar properties to real conditions, which have complicated road disturbances. The random step wave road and bump road shown in Figure 3 are used. Control parameters for three controllers used in this simulation are given in Tables 1-3. These values are appropriately chosen based on both the real seat suspension of a passenger car and the maximum damping force of MR damper [23]. It is noted that in the Compared Controller 1, the equations for coding program include Equations (14) and (15) and adaptation laws Equations (20)-(27), while in the Compared Controller 2, the Equations (14) and (28) and adaptation laws Equations (20)-(27) are used for coding program.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 6 Further Analysis and Discussion
### Effect of Shunt Wedge Error  
The width error of the shunt wedge may stem from machining and erosion wear, resulting in the actual width being expressed as  
\[l_{m}=l_{ms}+\Delta l_{m} \tag{32}\]  
where \(l_{ms}\) is the design value, and \(\Delta l_{m}\) is the width error.  
The deviation of \(l_{ms}\) will affect \(S_{f1}\), \(S_{f2}\), and \(S_{f0}\) in Equations (25)-(27); hence the balance pressure can be expressed as a function of \(\Delta l_{m}\).  
\[p_{r}=\frac{1}{S_{r}}\Big{[}2\phi b_{0}^{\prime}l_{c}^{\prime}u\eta_{0}^{2}- \int_{-l_{c}^{\prime}}^{l_{c}^{\prime}}\int_{-\frac{l_{ms}+\Delta l_{m}}{2}}^{ \frac{l_{ms}+\Delta l_{m}}{2}}p_{m}\exp\left(-\frac{\eta^{2}}{b_{p}^{2}}y^{2} \right)dydz\Big{]} \tag{33}\]  
This is represented in Figure 24.  
Therefore, the shunt wedge width has an approximately linear effect on the balance pressure. When the width error ranges from \(-5\)\(\upmu\)m to \(5\)\(\upmu\)m, the balance pressure drops by \(0.49\) MPa (\(7.4\%\)). By contrast, the deviation originating from the shunt wedge width error is remarkable. Hence, it is considered to be the main reason that the same valves differ in balance pressure.  
From Equations (25), (26), (28) and (32), the pressure gain can be calculated by  
\[K_{p}=\frac{P_{m}}{8\pi J_{f1}}\left[\int_{-l_{c}^{\prime}}^{l_{c}^{\prime}} \int_{-\frac{l_{ms}+\Delta l_{m}}{2}+x_{f}}^{0}\exp(-\frac{\eta^{2}}{b_{p}^{2} }y^{2})dydz-\int_{-l_{c}^{\prime}}^{l_{c}^{\prime}}\int_{0}^{\frac{l_{ms}+ \Delta l_{m}}{2}+x_{f}}\exp(-\frac{\eta^{2}}{b_{p}^{2}}y^{2})dydz\right] \tag{34}\]  
Figure 23: Drift of average pressure gain caused by installation error.  
For different deflector displacements, the correlation between the shunt wedge width error and the average pressure gain is depicted in Figure 25.  
Our calculation shows that when the error of the shunt wedge width ranges from \(-5\) um to \(5\) um, the pressure gain of the hydraulic amplifier drops by 6.93 MPa/mm (3.6%), hence exerting a greater influence on the dynamic performance than the installation error.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 4 Performance Evaluation
### Discussions  
From the experimental results, it can be confirmed that the proposed scheme has better performance than the existing schemes used as the reference model. In particular, we see a noticeable performance improvement of about 21% in Dataset #1 and Dataset #2 in low-bandwidth environments of the network. This is due to the mechanism of adapting neural network models to network environments by detecting QoE degradation in the proposed scheme.  
Figure 8: The degree of performance improvement according to time and number of threads of online learning in the dynamic network environment.  
Figure 9: Change of the normalized QoE according to change of threshold value, (**a**) Dataset #2, (**b**) Dataset #4, (**c**) Dataset #7.  
However, considerable time is required for performance improvement to adapt the neural network model to the network environment. Therefore, to increase the efficiency of the proposed scheme, it is necessary to shorten the learning time. Experimental results confirm that the proposed scheme reduced the performance improvement rate of neural network models by regulating the number of threads used for training. Users can expect better performance by appropriately adjusting the number of threads according to the computing power and the characteristics of the network environment in which streaming is performed.
###### Abstract  
Currently, the primary method for controlling red tides in the ocean involves spraying water solutions with special chemicals as solutes. High-pressure spraying results in the formation of typical jet structures. In this study, numerical simulation methods are employed to investigate the velocity variations, turbulent characteristics, and gas content distribution of jet flow fields under different initial jet flow pressures, cone angles, and nozzle diameters. Based on practical application scenarios, cluster analysis is used to explore the similarities and differences in jet equivalent diameters under different parameter conditions. The research findings indicate the following. (1) The difference of jet velocity distribution at the far field exit will be enlarged with the increase in the nozzle cone angle. When the nozzle cone angle is 4 mm, the velocity uniformity at the outlet is the best. (2) The TKE of the flow field has no consistent change law along the central axis. At the jet exit, the TKE shows an obvious multi-peak structure. (3) The gas content demonstrates a typical \"double-valley\" feature at the jet outlet cross-section. Increasing the initial pressure leads to a decrease in the gas content within the jet due to reduced entrainment, while the entrainment range remains largely constant. (4) Cluster analysis reveals that the similarity of jet flow width when it reaches the water surface is minimal compared to other operating conditions when the initial pressure is 0.36 MPa, the cone angle is \(115^{\circ}\), and the nozzle diameter is 2 mm. All conditions can be categorized into two or three groups to ensure jet effectiveness. The study results provide scientific guidance for selecting spray devices for controlling red tides in the ocean.  
nozzle; structural parameters; flow characteristics; high pressure jet; cluster analysis +
Footnote †: journal: Applied Sciences
# Identification of Novel Markers of Prostate Cancer Progression, Potentially Modulated by Vitamin D
## 3 Results
### The Vitamin D Regulatory Mechanism on the Six PCa Genes Selected  
In order to explain how Vitamin D plays a role in the modulation of the marker genes highlighted in our study, we analyzed a new dataset (GSE107438) composed of LNCaP cells transfected with control siRNA (siCT), siRNA for CYP27B1 (siCYP27B1) or siRNA for VDR (siVDR), followed by 100 nM of 25D\({}_{3}\) or 1,25D\({}_{3}\) treatment for a further 24 h. The analysis of genes belonging to the Vitamin D pathway such as CAMP, VDR, CYP27B1 and CYP24A1 demonstrated that in this LNCaP cell line, the 1,25D\({}_{3}\) reduced the expression levels of CYP27B1 and CAMP, and on the contrary, increased the expression levels of CYP24A1. No variation was observed for VDR expression levels (Figure 6A,B).  
These results showed accordance with the analysis previously effectuated (Figure 5, GSE64657). In addition, the 25D\({}_{3}\) LNCaP cell treatment strongly increases CAMP and CYP24A1 expression levels. The siCYP27B1 and siVDR LNCaP cells treatment have demonstrated that CYP24A1 and CAMP are regulated principally by 25D\({}_{3}\) in LNCaP cells via CYP27B1 and VDR. As regards the PCa genes highlighted in our analysis, we have demonstrated that all genes were sensitive to the effect of 1,25D\({}_{3}\) and 25D\({}_{3}\) at the dose of 100 nM for 24 h (were an exception the expression levels of MCC2) (Figure 6C,D). Furthermore, the siCYP27B1 and siVDR LNCaP cells treatment showed that the downregulation of KLK3 depends strictly on the CYP27B1 gene transcription. More mildly, the PRSS8 and SMYD2 genes expression also depends on the transcription of the CYP27B1 gene. Co-silencing of the CYP27B1 and VDR genes was determinant in the transcription of the CCNG2, PRSS8, SMYD2 and SOX4 genes (Figure 6C,D).  
Figure 5: The Vitamin D-modulated PCa related genes. (**A**) The expression of PRSS8, SOX4, SMYD2, MCCC2, CCNG2 and CD2 AP were significantly down-regulated in this LNCaP cell line at 24 h after exposition to 100 nM of 1,25D\({}_{3}\). (**B**) In addiction, the expression levels of Vitamin D metabolism genes were significantly modulated in LNCaP cells line under 1,25D\({}_{3}\) exposition (no significant modulation was observed for CYP27B1) (Data are expressed as expression levels and presented as vertical bars. \(p\) values \(<\) 0.05 were considered to be statistically significant \({}^{**}p\)\(<\) 0.005; \({}^{***}\)\(p\)\(<\) 0.0005; \({}^{***}\)\(p\)\(<\) 0.00005).  
We showed that all selected genes were modulated by 1,25D\({}_{3}\) and its precursor 25D\({}_{3}\). In addition, 25D\({}_{3}\) downregulates KKL3 and SMYD3 genes expression with CYP27B1 activity. Furthermore, 25D\({}_{3}\) modulates the expression levels of CCNG2, CD2 AP, PRSS8, SMYD2 and SOX4 via VDR transcription (Figure 6).