esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Dataset Description  
The ExDark dataset [1] was used for both training and testing. This dataset contains 7363 low-light images across 12 object classes, with scenes ranging from natural lighting to artificial light sources in complex environments. The dataset was split into training and testing sets at an 80:20 ratio. The detailed class distribution is as follows: - Bicycle: 652 images (8.9%) - Boat: 438 images (5.9%) - Bottle: 266 823 images (11.2%) - Bus: 297 images (4.0%) - Car: 1201 images (16.3%) - Cat: 399 images 267 (5.4%) - Chair: 681 images (9.2%) - Cup: 541 images (7.3%) - Dog: 524 images (7.1%) - 268 Motorbike: 463 images (6.3%) - People: 1052 images (14.3%) - Table: 292 images (4.0%) 269. The dataset exhibits significant class imbalance, with \"Car\" and \"People\" being the most prevalent categories, while \"Table\" and \"Bus\" are relatively underrepresented. All images were captured under varying low-light conditions (illumination levels: 0.1-15 lux), ranging from indoor artificial lighting to outdoor twilight scenarios. During training, Mosaic data augmentation was applied to enhance the data diversity, although it was disabled in the final 10 epochs to improve the model's adaptability to real-world scenarios. All input images were resized to 640 \(\times\) 640 pixels.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### Tethering, Docking, and Fusion with the Golgi  
Rab family GTPases together with their regulators define the structural and functional identity of intracellular organelles [54]. Rab11 and Rab6a' are implicated in the endosome-to-Golgi transport of STxB/STx1B [40; 41; 42]. Antibody against Rab11 or overexpression of wild-type, GTPase-deficient, or dominant-negative Rab11 inhibits the delivery of STxB/STx1B from endosomes to the Golgi [40; 41]. Antibody or siRNA-mediated inhibition of Rab6a' or overexpression of dominant-negative Rab6a' also leads to the accumulation of STxB/STx1B in the endosomal compartment [41; 42]. Additionally, a series of Rab GTPase-activating proteins (EV15, RN-tre, TBC1D10A-C, and TBC1D17) are reported to play a role in the transport of STxB/STx1B to the Golgi, but whether these proteins, and their associated Rabs, are _bona fide_ regulators of toxin transport at the early endosome-to-Golgi transport step is unclear [55].  
Golgins are long coiled-coil proteins that are anchored to the Golgi membrane through the C terminus [56]. These highly flexible proteins are not only responsible for linking Golgi cisternae and ministacks but also tethering vesicles and Rab effectors [56]. Early endosome-to-Golgi transport of STxB/STx1B requires at least four golgins: golgin-97, golgin-245, GCC185, and TMF [43; 44; 45; 46]. All four localize to the _trans_-Golgi network, with golgin-97, golgin-245, and GCC185 recruited by Arl1 and TMF by Rab6b [56]. RNA interference of Arl1 or any of these golgins causes defects in the retrograde transport of STxB/STx1B from early endosomes to the Golgi [43; 44; 45; 46]. The GARP complex is another tethering factor required for the transport of STxB/STx1B to the Golgi. In cells depleted of GARP by siRNA, STxB/STx1B fails to traffic to the Golgi and accumulates in a population of small endosome-like structures [47].  
SNARE proteins are transmembrane proteins that mediate fusion of vesicles with target membranes [57]. The early endosomes-to-Golgi transport of STxB/STx1B relies on two SNARE complexes [22]. One is composed of syntaxin 5, Ykt6, GS15, and GS28, and the other of syntaxin 6, syntaxin 16, Vti1a, and VAMP3/4 [41; 48]. Antibodies against syntaxin 5, Ykt6, GS15, or GS28 inhibit the early endosome-to-Golgi transport of STxB/STx1B in a permeabilized cell assay [48]. Soluble cytosolic domains of syntaxin 6, syntaxin 16, or VAMP4 also block STxB/STx1B transport from early endosomes to the Golgi [41].
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 3 Antenna Radiation Properties
### Radiation Pattern  
The simulated results of the spherical gain deviation of the proposed antenna are plotted in Figure 8. For brevity, the representative radiation patterns at the four different frequencies of 2.0, 2.6, 3.2, and 3.6 GHz are presented. The maximum gain deviations are 6.3, 8.5, 7.5, and 9.2 dB for the entire space at each frequency. From a practical perspective, obtaining the measured performance expected from the simulation is challenging due  
Figure 6: Parameter study results. (**a**) Effects of \(r_{c}\). (**b**) Effects of \(h\). (**c**) Effects of \(\alpha\).  
Figure 7: Reflection coefficient and gain deviation of the final design.  
to the unbalanced conditions that can occur from the vertical current distribution of the feeding coaxial cable. Therefore, to check the impact of the ferrite beads surrounding the coaxial cable, we carried out a simulation for three different types: one with the ideal port excitation, one with the coaxial cable without the ferrite beads, and one with the coaxial cable covered by the ferrite beads.  
The gain deviation performance results from the various excitation methods are plotted in Figure 9. First, we can see that the ideal port without the coaxial cable shows the broadest operating frequencies. The case of the feeding cable with ferrite beads shows a narrower bandwidth than that of the ideal case. Nevertheless, it still shows the comparable bandwidth closest to the ideal port case with a 10 dB gain deviation criterion. This implies that the undesirable vertical current component on the coaxial cable is effectively dissipated by the ferrite beads. In this work, we used ferrite beads of part number 2661000301 from Fair-Rite [41].  
Figure 8: Simulated spherical gain deviations of the proposed antenna: (**a**) 2.0 GHz, (**b**) 2.6 GHz, (**c**) 3.2 GHz, and (**d**) 3.6 GHz.  
Figure 9: Simulated gain deviation of the proposed antenna with respect to excitation methods.  
Lastly, even when the cable is used without ferrite beads, the gain deviation worsens in terms of bandwidth as well as gain deviation. From this, we can see the effect of the vertical current along the outer conductor of the coaxial cable on the gain deviation of the quasi-isotropic antennas. The antenna performance in the built prototype would be sensitive to the feeding cable.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Simple Trajectory Gesture Recognition
#### 3.1.4 Gesture Trajectory Recognition
The state transition diagram of the finite-state machine is depicted in Figure 13. The threshold for Gesture Activated is set to 0.2 m based on actual daily requirements, while the threshold for Gesture Deactivated is set to 0.25 m to prevent state oscillation when the gesture is close to 0.2 m.  
Assessing the linearity of the trajectory is to determine whether the current gesture contains linear motion, thereby identifying whether the current motion constitutes an effective gesture action. To analyze the trajectory, we use a sliding window to segment it into parts, where the length of each segment is determined by a predefined threshold, such as five points per segment. The method of assessment involves calculating the ratio of the segment length to the entire trajectory length. If this ratio exceeds 0.9, the trajectory is classified as a straight line; otherwise, it is classified as a curve. Judging the direction of the trajectory involves determining the direction of the current action, aiming to distinguish the upward, downward, leftward, and rightward directions of the gesture action. The method of judgement includes projecting the three-dimensional trajectory of gesture motion onto the \(xy\) plane, \(yz\) plane, and \(xz\) plane separately. Then, determine the direction of gesture motion by calculating the ratio of trajectory projections and analyzing the trend of trajectory changes on the three coordinate planes.
## 3 Bearing Fault Recognition Based on SSL-VCST Method
### Experimental Analysis  
From the above three experimental results, it can be seen that under the three operating conditions, the accuracies of the testing sets using CNN, Swin-T, and VCST decrease by an average of 0.07, 0.069, and 0.059, respectively, indicating that the variational attention mechanism can alleviate overfitting to a certain extent. In order to adapt to the situation of insufficient labeled samples, SSL is introduced to supplement training with unlabeled data. The accuracies of the testing set using SSL-CNN, SSL-Swin-T, and SSL-VCST decrease by an average of 0.088, 0.087, and 0.016, respectively. It can be seen that compared to the accuracies before the introduction of the SSL mechanism, the accuracies of the testing sets of SSL-CNN and SSL-Swin-T have further decreased slightly. It is considered that the process of assigning pseudo labels to unlabeled data by SSL depends on the training situation of the network itself, which affects the fitting effect in subsequent classification. However, SSL-VCST significantly improved the average accuracy compared to VCST, thus ensuring diagnostic accuracy. After comprehensively balancing variational attention and SSL mechanism, as well as considering the advantages of CNN and Swin-T, the SSL-VCST method proposed in this study has advantages in suppressing overfitting and improving fault diagnosis accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Sample Type** & **Normal** & \begin{tabular}{c} **Inner Race** \\ **Fault** \\ \end{tabular} & \begin{tabular}{c} **Outer Race** \\ **Fault** \\ \end{tabular} \\ \hline T1 & Training sets: & 100 & 12 & 12 \\ T2 & Labeled samples & 100 & 27 & 27 \\ T3 & \
\begin{tabular}{c} \\ Unlabeled samples \\ \end{tabular} & 100 & 45 & 45 \\ \hline T1-T3 &
\begin{tabular}{c} Training sets: \\ Unlabeled samples \\ \end{tabular} & 100 & 100 & 100 \\ T1-T3 & Testing sets & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental dataset setup of vibrating screen under constant speed conditions.  
Figure 12: Comparative results of four models under constant speed conditions of vibrating machinery.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 5 Discussion  
In order to solve the existing problems in the field of visual ranging based on object detection, an object detection bounding box method and a similar triangle ranging method based on height difference are proposed in this paper. The former aims to solve the problem of ranging error caused by pixel spacing when the bounding box and the object are not fitted, and the latter provides a new method to measure the distance to an object of unknown size for monocular visual ranging.  
In order to validate the feasibility of the aforementioned methods, experiments were conducted in this study. Firstly, both methods are based on object detection. To better assess the effectiveness of these methods, data augmentation techniques were employed to improve the detection accuracy of the YOLOv5s model. The experimental results demonstrate a significant improvement in the mean average precision (mAP) value, which increased from 77.1% to 99.5%. Secondly, utilizing object detection, the proposed bounding box detection optimization method is tested, and the width optimization value (WOV) and height optimization value (HOV) are proposed to quantitatively evaluate the results. The results show that the pixel width and pixel height of bounding box were optimized by 1.19-19.24% and 0-12.14%, respectively, which shows that the method can eliminate the pixel spacing, improve the fit degree between the detection bounding box and the object, and improve the ranging accuracy. Finally, utilizing optimized bounding boxes, the proposed similar triangle ranging method based on height difference was verified experimentally. The experimental results show that the relative distance error within 6 m range is 0.7-2.47%, which meets the requirements of some ranging scenarios, and verifies the effectiveness of the ranging method. In addition, we compare the ranging results before and after the object detection bounding box optimization, and these results also demonstrate that the proposed object detection bounding box optimization method can improve the ranging accuracy.
## 3 Results  
### Creating the Model  
We used the Snoopy tool [14] and the biological context detailed in Section 2.4 to develop a Petri net-based model of a drug-disease network for the synthesis of SMN protein from _SMN2_. A Petri net model incorporates discrete, continuous, hybrid, stochastic, and fuzzy parameters and aspects. This model captures all the essential interactions among biological components within the framework of mass action kinetics [27]. We meticulously defined the reaction rates by anchoring them to a baseline transcription rate of 1, and then expressing the rates of other processes relative to this transcription rate. The kinetic rates applied in our study are consistent with those reported in [10].  
The Petri net model is illustrated in Figure 4, incorporating features from discrete, continuous, hybrid, and stochastic Petri nets. In this model, discrete places represent the inclusion or exclusion of various drug candidates, while continuous components manage the flow of tokens, represented by real numbers, across the network. The discrete places include Dacinostat, TSA, Resveratrol, VPA, AZA, PTMKSMA1, and Indole, while the other 11 places are continuous, corresponding to genes and gene products whose concentrations change gradually over time. The model features 25 transitions, 2 read arcs, 7 inhibitory arcs, and 32 regular arcs, with no guard function assigned to the transitions.  
Drug treatments are simulated across four scenarios (A to D), detailed in Section 2.4, by introducing an inhibitory arc from a discrete place to a transition. Each drug candidate has a corresponding discrete place connected to its transition via an inhibitor arc. A treatment is activated when the discrete place is empty and is deactivated otherwise. For combination treatments, the related discrete places are kept empty. A Boolean variable is used to monitor the presence or absence of drug treatment for each candidate. For stochastic simulations, stochastic rates are applied as delays for stochastic transitions. We use read arcs to model the constant presence of transcription factor and _SMN2_ gene in DNA. For this reason, the input place for the TF_producer transition maintains a constant value of 1 and is connected to the transition via a read arc. Similarly, the input place for the SMN2_gene transition also has a constant value of 1 and is linked to the transition with a read arc. As a result, neither the transcription factor nor _SMN2_ is consumed within the system.
## 1 Introduction  
Root canal treatment is a complex practice whose success is based on diagnosis and treatment planning, knowledge of the anatomy and configuration of the canal system, a correct debridement, deep disinfection, and a three-dimensional filling of the tooth [1]. Scientific evidence shows that most of the endodontic therapy failures are caused by microorganisms involved in intraradicular and extraradicular infections [2]. Even so, according to several studies, root canal systems cannot be completely disinfected and cleaned [3]; an appropriate pulp canal filling may largely prevent possible filtration [4]. Thus, the purpose of obturation is to provide an impermeable, three-dimensional, and hermetic seal to prevent coronal and apical leakage of microorganisms [4].  
Bioceramic sealers have been available for the last thirty years for endodontic use. A great number of these endodontic sealers contain calcium phosphate, which enhances its setting properties and results in a crystalline structure and chemical composition similar to hydroxyapatite [5], improving the bonding of the root dentine to the sealer [5].  
TotalFill BC Sealer(r) is a bioceramic sealer recognized for its physicochemical and biological properties: biocompatibility, antibacterial activity, and bioactivity. This sealer uses the humidity of the tubular dentine for its setting reaction, which extends from 4 h to more than 10 h in a very dry environment [6].  
Because of the need to demonstrate the tubular infiltration of the bioceramic sealers, many studies have used laser confocal microscopy (CLSM) to visualize this penetration into the dentinal tubules [7; 8; 9]. Thus, CLSM is a useful method for assessing sealer penetration into dentinal tubules [7]. However, to promote an adequate vision, fluorophores associated with endodontic sealers must be used [8]. Fluorophores are stains used to see sealers clearly under CLSM. Most studies have evaluated the penetrability of bioceramic sealers within tubular dentine using CLSM and Rhodamine B as a fluorescent stain [9]. This fluorophore is soluble in water and when diluted it can produce fluorescence [8]. Thus, dentine penetration results could probably be overestimated if Rhodamine B is used, due to the hydrophilic nature of calcium silicate-based sealers [8]. Fluo-3 is another fluorescent stain that could be used for assessing calcium silicate sealers' penetration into the tubular dentine. It is an indicator that significantly increases fluorescence after mixing with calcium [8]. Calcium in calcium silicate-based sealers joins to Fluo-3 and promotes the sealer fluorescence observed in CLSM [10].  
Due to the high dissolution of Rhodamine B in water and the behavior of Fluo-3 with calcium ions from bioceramic sealers, it is appropriate to investigate the real behavior of calcium silicate sealers associated with these two fluorophores.  
Therefore, the aim of this study was to compare the percentage of sealer penetration and maximum sealer penetration of a bioceramic sealer when associated with two different fluorescent staining methods (Rhodamine B or Fluo-3) through the use of CLSM.
# Research on a Comfort Evaluation Model for High-Speed Trains Based on Variable Weight Theory
###### Abstract
[16] identified the priority of the influencing factors of cabin ride comfort by collecting feedback from a large number of passengers and using a fuzzy language decision-making method. [PERSON] et al. [17] proposed a machine learning evaluation model for evaluating passenger comfort. These studies further optimized the fuzzy relationship with fuzzy logic using a large amount of training and reduced the logical break in judgment. However, this research process is too cumbersome, and its workload is too large. Therefore, it is not suitable for the high-efficiency requirements of current evaluation. The literature research shows that the trend in studies of the realistic demand for comfort is to carry out a reasonable overall comfort evaluation on the basis of comprehensive influencing factors. In this case, the practical significance and importance of creating a comfort evaluation model are highlighted. The comfort evaluation model can take into account all the multi-dimensional influencing factors and can show the whole evaluation process more intuitively. At the same time, it can effectively reduce the evaluation workload and meet the high-efficiency needs of actual cases. However, when the existing comfort evaluation method determines the index weight, it is often set so that the weight value of each influencing factor does not change in different instances. This leads to a significant difference between the weight value and the state value of the index factors. The final judgment results cannot truly reflect some extreme situations, and it is difficult to ensure the authenticity and accuracy of the comfort evaluation results. Therefore, an optimization method to solve the above problems is urgently needed to scientifically and accurately evaluate the comfort. This paper introduces the variable weight theory for the evaluation of train comfort. A high-speed train comfort evaluation model based on variable weight theory was established. This work considered the physical feelings, physiological feelings, and psychological feelings of passengers, combined with the data obtained from a track inspection vehicle, a passenger survey, and noise monitoring under actual working conditions. The variable weight theory was used to dynamically optimize the weight of each factor. As a result, evaluation results closer to the actual situation were obtained.
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 1 Introduction
The system deploys data acquisition and processing algorithms in a data acquisition and control system, adopts a highly reliable redundant communication scheme to achieve reliable transmission of tsunami data, and conducts sea trials in the South China Sea to verify the effectiveness and feasibility of the system designed in this study.
# Power Management Strategy for an Autonomous DC Microgrid
## 3 Power Management Strategy
### Case 1, 2, 3, 4  
When PV power is greater than user demand, there is excess PV power after fully supplying the load.  
Figure 3: Flow chart of the power management strategy.  
If both the SC and the battery bank are fully recharged, PV power is limited to only supplying the load and stabilizing the DC bus, and the system operates as given in case 1 of Figure 3. If the electrochemical battery bank is not full, excess PV power is injected into the battery bank. However, if the excess PV power is greater than the battery bank's maximal recharging power, PV power is limited to supplying only the load and the battery bank's maximal recharging power and the system works as given in case 2. Otherwise, PV power is not limited and the system runs as given in case 3. Conversely, if the SC is not in its saturation state, i.e., \(SOC_{SC}\leq SOC_{SC\_MAX\_MIN}\), then excess power will first be injected into the SC until \(SOC_{SC}\) reaches \(SOC_{SC\_MAX\_MAX}\) and the system will operate as given in case 4.
# Antimicrobial Peptide Screening from Microbial Genomes in Sludge Based on Deep Learning
###### Abstract  
As the issue of traditional antibiotic resistance continues to worsen, exploring new antimicrobial substances has become crucial to addressing this challenge. Antimicrobial peptides (AMPs), recognized for their low resistance levels and minimal bacterial mutation frequencies, have garnered significant attention from researchers. However, traditional screening methods for AMPs are inefficient and costly. This study proposes a combined AMP screening model based on long short-term memory (LSTM) neural networks with an attention mechanism. By analyzing the characteristics of peptide segments, which are simulated enzymatic hydrolysis products of proteins expressed in sludge microbial genomes, the model accurately identifies peptide segments with potential antimicrobial activity. Molecular docking and dynamic simulation results validate three potential antimicrobial peptide candidates: LLPRLLARRY, GVREIHGLNPGGCLLHTVRLVCR, and FRTTLAPHYLTRLLAPCW. These candidates exhibit high binding stability and affinity with target proteins, confirming the efficiency of the proposed AMP screening model.  
machine learning; LSTM; microbial genome; antimicrobial peptides +
Footnote †: journal: Applied sciences