esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You Only Look Once) algorithm, this paper proposes a lightweight target detection algorithm with enhanced feature extraction of bridge structural damage. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion, which enhances the ability to extract damage features of bridge structures, and uses EFL (Equalized Focal Loss) to optimize the sample imbalance processing mechanism, which improves the accuracy of bridge structure damage target detection. The evaluation test of the model has been carried out in the constructed BDD (Bridge Damage Dataset) dataset. Compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S models, the [EMAIL_ADDRESS] of the BE-YOLOv5S model increased by 45.1%, 2%, and 1.6% respectively. The analysis and comparison of the experimental results prove that the BE-YOLOv5S network model proposed in this paper has a better performance and a more reliable performance in the detection of bridge structural damage. It can meet the needs of bridge structure damage detection engineering with high requirements for real-time and flexibility.  
healthy structure detection; bridge structure; deep learning; lightweight convolutional neural network; YOLOv5S
## 2 Numerical Analysis
### Grid Generation  
For numerical analysis, the model representing the fluid zone of the micro tubular propeller turbine system was constructed with a grid system. The pipe part was composed of a mixture of structured and unstructured mesh using ANSYS Mesh, and the runner part was composed of structured mesh using ANSYS TurboGrid. To resolve the problem of node mismatch at the interface between pipe and runner, the grid at the interface was densely generated. Y-plus is the dimensionless wall distance, which can be calculated as \(y^{+}=\frac{\rho y\mu_{\tau}}{\mu}\) where \(\rho\) is the working fluid density, \(\mu\) is the working fluid dynamic viscosity, \(y\) is the wall distance, \(u_{\tau}=\sqrt{\frac{\tau_{w}}{\rho}}\) is the wall shear velocity, and \(\tau_{w}\) is the wall shear stress. In this study, y-plus was limited below 2 to consider the shear stress on the wall, such as blades.  
A grid dependency test was performed to minimize the effect of the grid size on the results, and consider the economics of the numerical analysis time. Figure 3 shows the results of the variation in the power, head, and efficiency represented in Equations (1)-(3) by the number of elements. The results show that those values do not change greatly from more than about 5 million grid elements. Therefore, the number of grid elements used in this study was set to approximately 5 million.
# Through-Life Maintenance Cost of Digital Avionics
## 2 Review
They assessed the cost associated with false alarms and intermittent faults. Based on the analysis of previously published studies, the following conclusions can be drawn. In the maintenance models presented in studies [9; 10; 11; 12; 13; 14; 16; 17], the impact of intermittent faults and in-flight false-positives on the maintenance cost was not considered. The models proposed by [PERSON], [PERSON], and [PERSON] in [15; 19] can be used only for systems with a continuous mode of operation. Onboard electronic systems have a discontinuous operation due to the rotation of flights and landings. Consequently,the models are not acceptable for describing avionics systems. The study [20] shows the relevance of assessing the NFF impact on maintenance cost; however, the authors do not propose mathematical or simulation models. The Markov model developed by [PERSON] et al. [21] may not be used for modeling the intermittent faults in avionics systems since transient faults usually occur in electrical power systems. [PERSON] et al. [23] proposed empirical equations that can only be used with the specified percentage distributions of confirmed and unconfirmed failures. [PERSON] and [PERSON] proposed in [24; 25; 26] avionics maintenance models considering permanent failures and intermittent faults. However, in the studies [24; 25], the models require to know the conditional probabilities of in-flight occurrence and non-occurrence of intermittent faults, which are known for exponential time distribution and not easy to derive for any other distribution. The model considered by [PERSON] and [PERSON] in [27] is valid only for continuous monitoring with the help of analog sensors. Thus, from the conducted analysis of recently published studies follows that the combined impact of the in-flight false-positives, permanent failures, and intermittent faults on the maintenance cost of digital avionics has not been considered.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 4 Target Distortion Optimization
### Problem Solution  
Since the power scaling factor \(g_{i}\) is continuous, while the bandwidth usage variable \(K\) is discrete, these two subproblems are considered mixed-integer nonlinear programming (MINLP) problems that are NP-hard to solve optimally. Therefore, greedy search algorithms are proposed to approximate the resource allocation for power and bandwidth distortion problems.
## 3. Shingle-Based Retrieval Scenario
In the next step, \(\mathbf{S}^{\text{Q}}\) is compared with all shingles from the set \(\mathbb{S}^{\text{D}}\). The comparison between Q and D is achieved by first transforming the shingles to vectors by a function  
\[f:\mathbb{R}^{F\times L}\rightarrow\mathbb{R}^{K} \tag{3}\]  
for some \(K\in\mathbb{N}\). In the brute-force case, \(f\) just flattens a matrix by concatenating all columns (i.e., \(K=FL\)). Using shingle embedding methods, as explained later, \(f\) performs a dimensionality reduction (typically \(K\ll FL\)). Given two shingles \(\mathbf{S}^{(1)}\) and \(\mathbf{S}^{(2)}\), we compare them in the embedding space using a distance function  
\[d:\mathbb{R}^{K}\times\mathbb{R}^{K}\rightarrow\mathbb{R}_{\geq 0}. \tag{4}\]  
In the following, we use the squared Euclidean distance:  
\[d(\mathbf{x}^{(1)},\mathbf{x}^{(2)})=\sum_{k=1}^{K}(x_{k}^{(1)}-x_{k}^{(2)})^{2}, \tag{5}\]  
where \(\mathbf{x}^{(1)}=f(\mathbf{S}^{(1)})\) and \(\mathbf{x}^{(2)}=f(\mathbf{S}^{(2)})\). Given a query Q (in the form of a shingle \(\mathbf{S}^{\text{Q}}\)) and a database document D (in the form of a set of shingles \(\mathbb{S}^{\text{D}}\)), we compute the distance between Q and D by  
\[\delta_{\text{D}}(\mathbf{S}^{\text{Q}}):=\min_{\mathbf{S}\in\mathbb{S}^{\text{D}}}d \big{(}f(\mathbf{S}^{\text{Q}}),f(\mathbf{S})\big{)}. \tag{6}\]  
Finally, for Q and a data collection \(\mathbb{D}\) containing \(|\mathbb{D}|\) documents, we compute \(\delta_{\text{D}}(\mathbf{S}^{\text{Q}})\) between Q and all \(\text{D}\in\mathbb{D}\) and rank the results by ascending \(\delta_{\text{D}}(\mathbf{S}^{\text{Q}})\). Previous work [12] used maximum cosine similarity instead of minimum Euclidean distance. We use the squared Euclidean distance because this distance naturally occurs in both dimensionality reduction methods, as explained in Section 4. Note that, since each feature vector of the shingles is normalized, Euclidean distance and cosine similarity lead to similar retrieval results. The relation between the squared Euclidean distance of \(\ell^{2}\)-normalized vectors and their cosine similarity is given by: \(d(\mathbf{x}^{(1)},\mathbf{x}^{(2)})=2\big{(}1-\cos(\mathbf{x}^{(1)},\mathbf{x}^{(2)})\big{)}\).
# Finding Patterns in Signals Using Lossy Text Compression  
[PERSON]  
1 Robotics & Big Data Lab, Computer Science Department, University of Haifa, Haifa 3498838, Israel; [EMAIL_ADDRESS] (S.  
remote controllers usually have no published communication protocol. While a few of them might be found in the internet, they change frequently from model to model.  
Unfortunately, most commercial, low-cost (<$50) toy robots (cars, quadcopters and humanoids), do not have published protocols. Moreover, their protocols frequently change over time without notice. In fact, many times we ordered a few copies of exactly the same toy robot from Amazon.com and each one of them had a different protocol. This was also the case with toy helicopters in our experimental results section.  
Our goal in this study was to take these toy robots and make them autonomous. To this end, we had to solve the to above-mentioned problems. Problem (i) was already handled by developing low-cost tracking systems based on web-cameras, or on-board analog cameras [1]. In this paper we handle Problem (ii): how to automatically reverse engineer the communication protocol of the robot.  
Once this protocol is known, we can imitate the remote control by producing the commands using a mini-computer, such as Arduino [2] or Raspberry Pi [3] that is connected to a transmitter or a few IR (Infra-Red) LEDs. Instead of a human with a remote control, an algorithm can then send hundreds of commands per second to the robot to result in a much more stable and autonomous robot. Such robotic, low-cost systems, that are based on this paper, can be found, e.g., in [1].  
**Compression or learning?** As explained above, the motivation for this study was to learn a communication protocol based on given recording of sampled signals. That is, to reverse-engineer the protocol. However, this problem in principle is very related to the problem of compressing signals. This is because an efficient compression algorithm for a specific protocol is expected to use the repeated format of this protocol. For example, machine learning is used to extract a small (compressed) predictive model from a large sample data, which is used in, e.g., video compression protocols to compress real-time video, where the decoder is expected to predict the next frame via the model, and only the differences (the fitting errors of the model) are being sent by the encoder. Similarly, the results in this paper can be used to learn a protocol, to compress a message efficiently based on a given protocol, or for noise removal. The theoretical optimization problem is very similar, as explained in the next sections.
# Limiting Conditions for Droplet Fragmentation of Stabilized Suspension Fuels
## 5 Conclusions  
1. [label=()]
2. Soy lecithin and sodium lignocellulosic are the most promising stabilizing additives to slurry fuels. The conditions for the intense droplet breakup were not observed when analyzing the parameter \((D/D_{0})^{2}\). The value of this parameter did not exceed 1. The transition between slurry droplet breakup regimes depends most heavily on the addition of a flammable liquid (diesel fuel or rapeseed oil) as well as replacement of filter cake with coal. The parameter \((D/D_{0})\) reached 1.7 during CWSP droplet breakup.
3. The slurry droplet breakup delay time was most significantly affected by the addition of a flammable liquid: diesel fuel or rapeseed oil. The shortest breakup delay times were recorded for the compositions containing diesel fuel as the flammable liquid. In the initial droplet size range from 0.95 to 1.15 mm, the breakup delay times did not exceed 10 s. The properties of child droplets were most significantly influenced by the addition of a flammable liquid: diesel fuel or rapeseed oil. CWSP droplets based on diesel exhibit higher child-droplet velocities and smaller size than droplets based on rapeseed oil. The sizes of child droplets fell into a range of 10 to 140 \(\upmu\)m, and their velocities ranged from 0 to 50 m/s.
4. A comparative analysis of slurry fuel droplet stabilization and breakup conditions revealed the most promising conditions of their preparation for storage, transportation, and primary and secondary atomization. The efficiency factor values were calculated for the secondary atomization of promising fuel droplets induced by microexplosive breakup. These values ranged from 0.25 to 1. The maximum values (0.95-1) corresponded to water/kerosene droplets.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122312271/s1](https://www.mdpi.com/article/10.3390/app122312271/s1)]([https://www.mdpi.com/article/10.3390/app122312271/s1](https://www.mdpi.com/article/10.3390/app122312271/s1)), Video S1: Supplementary material A, Video S2: Supplementary material B.  
Conceptualization, G.V.K.; data curation, D.S.R.; funding acquisition, G.V.K.; investigation, D.V.A. and D.S.R.; writing--original draft, D.V.A.; writing--review and editing, G.V.K. and D.V.A. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the Tomsk Polytechnic University (TPU) development program, Priority 2030 (Priority-2030-NIP/EB-038-1308-2022).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Short Name** & **Viscosity at \(\gamma\) = 100 s\({}^{-1}\) (mPa\(\cdot\)s)** \\ \hline K50W49 LI1 & 337.4 \\ K50W49D1 & 640.8 \\ K50W49R1 & 735.4 \\ K50W49 LE1 & 431 \\ K50W50 & 324.1 \\ C50W49 LI1 & 285.6 \\ C50W49D1 & 277.4 \\ C50W49R1 & 421.9 \\ C50W49 LE1 & 589.3 \\ C50W50 & 236.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Viscosity measurements of slurry fuels used in the experiments.
## 3 Implementation
### Encoding and Decoding Strategies for Data Normalization and Retrotranscription
#### 3.3.2 Leaf Values  
To maintain the readability of decoded genotypes, each parameter type uses common values (for example, pitch is represented with standard MIDI numbers). These convenient numeric ranges are converted to normalized values following these criteria:  
* A wide range of values is covered (for instance, for parameters regarding rhythm and articulation, very short and long durations are available).
* For each parameter type, a central value of 0.5 is assigned to a midpoint among the typical values, interval \([0.2,0.8]\) covers the usual range, and values \(<0.2\) and \(>0.8\) are reserved for extreme values rarely used in common music scores.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property Name** & **Description** & **Example** \\ \hline  & & output type of the function, \\  & a property used to create a \\ funcType & pool of expressions that can be & ’lmdipitchF’ \\  & referenced as an argument for \\  & other functions & \\ encGen & encoded genotype & [1,0.506578,0.53,0.404723, \\  & decoded genotype; that is, & 0.53,0.458756,0] \\ decGen & the expression of the & ’lm(60,62)’ \\  & evaluated function that & \\  & returns itself & \\ encPhen & encoded phenotype & [0.404723,0.458756] \\  & specific properties generated & \\ (other optional properties) & after evaluation, containing & \\  & useful information for the & (not returned by function lm) \\  & parent function & \\ \hline \end{tabular}
\end{table}
Table 1: Properties returned by a genotype function after evaluation, using as an example the expression ’lm(60,62)’, which represents a list of two MIDI pitches.  
* To favor the predominance of ordinary values, a previous conversion similar to the lognormal function is applied to each encoded parameter value \(x\) supplied by a germinal vector: \[f(x)=\frac{1}{2}+\frac{1}{14}\ln\biggl{(}-\frac{1}{1-x}\biggr{)}.\] (11)  
As an example, Figure 3 illustrates the equivalence among encoded and decoded values for pitch. The curve combines a simple linear map and the lognormal-alike function shown in Equation (11).  
So, by randomly generating germinal vectors, uniformly distributed values \(\in[0,1]\) are remapped to a Gaussian distribution, introducing a desired bias to produce musical scores with common characteristics, without limiting the possibilities to the generation of specimens exhibiting more extreme features.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Performance
Despite the good performance on the Gaze360 dataset, the proposed method still shows a certain performance gap in comparison with some state-of-the-art methods. Figure 7: Leave-one-out validation of the baseline and our model on the EyeDiap dataset. To validate the rationality of using ConvNext as the backbone, Figure 8 presents the experimental results under different network architectures. Compared to models using ResNet as the backbone, the proposed model achieves error reductions of 0.24\({}^{\circ}\), 0.33\({}^{\circ}\), and 0.61\({}^{\circ}\) across the three datasets, demonstrating the advantages of ConvNext in feature extraction and overall model performance. Similarly, when compared to models using MobileNet [51] as the backbone, the proposed model reduces errors by 0.27\({}^{\circ}\), 0.39\({}^{\circ}\), and 0.94\({}^{\circ}\), further highlighting the superior performance of ConvNext across multiple datasets. These comparative experiments provide strong evidence that selecting ConvNext as the backbone significantly enhances model accuracy and robustness across different datasets. To validate the effectiveness of the improved JointCBAM module, we compared it with different feature fusion modules in the experiment to fully demonstrate the improvements. We selected the classic SE [52] module and the original CBAM module as baselines. The experimental results, as shown in Table 2, indicate that the improved JointCBAM module outperforms others in gaze estimation tasks. Figure 9 presents the gaze estimation visualization results on facial images from different datasets, demonstrating the model's performance across various scenarios and highlighting its ability to generalize across individual differences, lighting conditions, and head pose variations. By comparing the predicted gaze directions with the ground truth annotations, the figure visually demonstrates the model's accuracy and robustness, further proving that the method can effectively adapt to changes in facial features and viewing angles, enhancing its effectiveness in real-world applications. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MPIIFaceGaze** \\ \hline MSMI-SE & 4.19 \\ MSMI-CBAM & 3.97 \\ MSMI-Net & 3.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature fusion module comparison experiment: MSMI-SE uses SE as the feature fusion module; MSMI-CBAM uses CBAM as the feature fusion module. Figure 8: Comparison of Backbone Networks, where MSMI-Net(R) represents the model using ResNet as the backbone, and MSMI-Net(M) represents the model using MobileNet as the backbone.
## 3 Results
### Compound 4-MSP Inhibits HSC Proliferation Through Blocking PDGF-BB-Induced MAPK and Akt Signalings  
Since the initiation of HSC proliferation is a critical stage in promoting liver fibrogenesis [7], we next evaluate the anti-proliferation effects of 4-MSP on HSCs. LX2 cells were treated with 0-20 \(\upmu\)M 4-MSP for 24-96 h, and the results showed that dose-dependent and time-dependent inhibition of LX2 cell proliferation was observed after 4-MSP treatment (Figure 2a). It had been reported that PDGF-BB is the main stimulus for HSCs proliferation [11]; we further examined the change of the activation of MAPK and AKT signaling pathway after 4-MSP treatment in LX2 cells. The results indicated that 4-MSP pretreatment decreased the PDGF-BB-mediated phosphorylation of MEK/ERK, JNK, and p38 (Figure 2b). Moreover, Akt and p7056K phosphorylation showed a decrease in the 4-MSP pretreated cells, compared with that of the control (Figure 2c). These phenomena were also found in the HSC-T6 cells. Treatment of 4-MSP significantly inhibited the PDGF-BB-mediated phosphorylationof MEK/ERK, JNK, Akt, and p70S6K (Figure 2d,e). To further elucidate whether the 4-MSP affected the expression levels of PDGF receptor (PDGFR) or influenced the phosphorylation of PDGFR, the western blot experiment was performed. The results demonstrated that treatment of 4-MSP did not affect the phosphorylation of PDGFR or the total expression levels of PDGFR in two HSCs cell lines (Figure 2f,g). Furthermore, we also performed the wound healing assay to analyze the effect of 4-MSP on the cell migration ability. The results showed that treatment of 4-MSP inhibited the PDGF-induced cell migration ability in LX2 cells (Figure 2h).  
Figure 2: Compound 4-MSP inhibits HSCs proliferation via inhibiting MAPK and AKT signaling pathways. (**a**) LX2 cells in the treatment of 4-MSP at 1.25–20 uM for indicated times, and then subjected to alamarBlue analysis. (**b,c**) Pre-treated LX2 cells with 5 uM of 4-MSP overnight, and then subjected to PDGF-BB (10 ng/mL) for indicated times. Phosphorylation and total indicated proteins were analyzed by western blot. (**d,e**) Pre-treated HSC-T6 cells with 5 uM of 4-MSP overnight, and then subjected to PDGF-BB (10 ng/mL) for 30 min. Phosphorylation and total indicated proteins were analyzed by western blot. (**f,g**) LX2 and HSC-T6 cells were treated with 5 uM of 4-MSP for 24 h. Western blot analyzed the protein levels of p-PDGFR, T-PDGFR, and \(\alpha\)-tubulin. (**h**) LX2 cells were treated with PDGF-BB (10 ng/mL) and 4-MSP for indicated times. The cell migration was documented by using a digital camera. Data are presented for at least three independent experiments, with similar results.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 2 System Model
### Uplink Power Control  
In [19], the Msg1 transmission adopted _a power-ramping scheme_ to increase the probability of preamble detection, which was not considered in this study. When a device re-attempts the RA procedure after back-off, it increases its Msg1 transmission power through a power-ramping. Let \(P_{msg1}\) denote the Msg1 transmission power, and \(P_{msg1}\) can be defined as follows [10; 17]:  
\[P_{msg1}=\min(P_{Cmax},PRTP+PL), \tag{2}\]  
where \(P_{Cmax}\)[dBm] is the maximum transmission power, \(PL\)[dB] is the path loss; and \(PRTP\)[dBm] is _the preamble received target power_, which is defined as follows:  
\[PRTP=PIRT+delta+(r-1)PRS. \tag{3}\]  
Here, \(PIRT\) is _the power initial received target_, \(delta\) is _the offset value_ depending upon the preamble format, and \(r\) is _the ramping number_ that is associated with the trial number of the device. Using open- and closed-loop power control [19], a device can estimate the \(PL\) and, therefore, can adjust its transmission power to meet the target received power at the eNB. In this study, we assume that the power control is performed perfectly, such that the received signal power depends only upon the ramping number.  
The Msg3 transmission power follows the PUSCH power control policy [18]. The Msg3 transmission power is affected by the modulation and coding scheme (MCS), resource blocks, and other factors. For the case of M2M communications, the data required is quite small, and the MCS is also low due to the low complexity. Hence, it is reasonable that the transmission power of Msg3 is identical for M2M devices. Consequently, we assume, for simplicity, that  
\[P_{msg3}=\min(P_{Cmax},P_{target}+PL), \tag{4}\]  
where \(P_{target}=10\log M+P_{0}+delta_{MCS}+f(i)\) is assumed to be a constant value. Here, \(M\) is the number of assigned resource blocks, \(delta_{MCS}\) is the change in value related to MCS, and the other values are defined by the high-layer.