esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Development of Composite Bicycle Frame
## 3 Experimental Results and Discussion
### Structural Strength Test Results
#### 3.3.3 Summary of Experimental Results for the FBG Sensors  
A summary of the results of the experimental analysis performed on the composite bicycle frame using the FBG sensors and strain gauges is shown in Table 2. It is obvious that when testing on the ergometer, higher values of the indicated deformations were achieved than in any of the cases defined in the ISO test standard.  
If we compare the corresponding load cases, i.e., the horizontal forces test case and the frame structural strength test (both for a pushing load case direction), the indicated values of mechanical strain in the DT and HT joint area are at the level of 10-13% of the maximum strain value, while in the case of the TT and HT joint it is 5-7%.  
The ultimate mechanical strain measured at the TT and HT joint reaches 35% of the ultimate mechanical strain found during the tensile strength test of unidirectional filaments ud1 and ud2. This means that these reinforcing strips are not a limiting element of the joints of individual tubes in the case of tensile strain.  
The highest and lowest values of mechanical strain, measured by FBG sensors in ISO test cases (580 \(\upmu\)m/m for FBG DT, \(-\)480 \(\upmu\)m/m for FBG TT), are in the range of 27-37% and 28-44% of the limit mechanical strain found during the frame structural strength test at the point of FBG DT and at the point of FBG TT, respectively.  
The highest and lowest values of mechanical strain, measured by FBG sensors during the ergometer test (963 \(\upmu\)m/m for FBG DT, \(-\)743 \(\upmu\)m/m for FBG TT), are in the range of 45-61% and 44-68% of the limit mechanical strain found during the frame structural strength test at the point of FBG DT and at the point of FBG TT, respectively.  
If we compare the mechanical strain values from the FBG sensors (in the FBG DT and FBG TT locations) and the values from the strain gauges (in the SG DTF34 and SG TT34 locations), the stresses inside the DT to HT and the TT to HT joints are higher than on the frame tubes. This is especially true for load cases corresponding to the normal operation of the bike frame, i.e., the pedal forces test case, horizontal forces test case and the ergometer test. Other experimental and computational work therefore focused in more detail on the head tube joint.  
Figure 15: Mechanical strain indicated by strain gauges vs. loading force for the DT (**top**) and the TT (**bottom**) (frame specimens 1, 2, 4, 5, 6 and 7).
## 3 Results
### Subjective Evaluation  
#### 3.2.1 Results and Analysis of Tone Accuracy  
Tone accuracy is an important factor to analyze the enhancement of continuous Mandarin EL speech. The accuracy of all tones is listed in Table 5.  
From Table 5, for the EL-FT, the accuracy of Tone2, Tone3 and Tone4 is not zero. This is because listeners would inevitably correct the tone according to what they have heard. In addition, in one utterance, the first tone sounds like Tone2 and the last tone sounds like Tone4. This is because the speaker pressed the EL button at the beginning of pronouncing and released the button at the end. The process is shown in Figure 4a. For EL-VT, after being enhanced using the CLDNN-based approach, the accuracy of Tone1 and Tone4 decreases and the accuracy of Tone2 and Tone3 increases. After being enhanced using our proposed approach, the accuracy of Tone2, Tone3 and Tone4 increases while the accuracy of Tone1 decreases slightly. For EL-FT, the average tone accuracy increases by 29.55% through the CLDNN-based approach, by 39.67% through the V3-based approach and by 42.38% through our approach. For EL-VT, the improvement of different tones is not stable through CLDNN-VT and CycleGAN-VT. For CLDNN-VT, the accuracy of Tone1 and Tone4 decreases dramatically, while for VC3-VT and 2C1T2C-VT, only the accuracy of Tone4 decreases slightly. For EL-VT, the average tone accuracy increases 2.29% through the CLDNN-based approach, 11.16% through the V3-based approach and 12.12% through our  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Speech Types** & **Tone1** & **Tone2** & **Tone3** & **Tone4** & **Mean** & **STD** \\ \hline EL-FT & 95.44\% & 10.37\% & 0.61\% & 13.14\% & 29.89\% & 0.3813 \\ CLDNN-FT & 77.90\% & 49.03\% & 44.31\% & 66.50\% & 59.44\% & 0.1349 \\ VC3-FT & 84.22\% & 65.33\% & 53.32\% & 75.38\% & 69.56\% & 0.1151 \\
2C1T2C-FT & 92.15\% & 68.22\% & 53.86\% & 74.85\% & 72.27\% & 0.1583 \\ EL-VT & 87.64\% & 55.49\% & 56.04\% & 87.20\% & 71.59\% & 0.1583 \\ CLDNN-VT & 83.25\% & 66.47\% & 67.28\% & 78.50\% & 73.88\% & 0.0720 \\ VC3-VT & 88.75\% & 76.58\% & 78.74\% & 85.32\% & 82.35\% & 0.0490 \\
2C1T2C-VT & 91.00\% & 77.84\% & 79.96\% & 86.05\% & 83.71\% & 0.0518 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The tone accuracy of unenhanced/enhanced continuous Mandarin EL speech.  
approach. In summary, our approach performs better than others in enhancing the tone variation of continuous Mandarin EL speech.
## 4 RFGG-FG-FFT Algorithm  
The impedance matrix \(Z^{C}\) consists of the near field interactions \(Z^{C-near}\) and the far-field interactions \(Z^{C-far}\)[6; 10]. In general, \(\gamma\in[0,1]\) is selected as 0.5. In this way, CFIE can avoid internal resonance, thus ensuring high accuracy and small matrix condition number.  
\[\begin{array}{ll}Z^{C}&\approx(Z_{Mod}^{C-near}-Z_{FFT}^{C-far})+Z_{FFT}^{C -far}\\ &=[\gamma(Z_{Mod}^{E-near}-Z_{FFT}^{E-far})+(1-\gamma)(Z_{Mod}^{M-near}-Z_{ FFT}^{M-far})]+\gamma Z_{FFT}^{E-far}+(1-\gamma)Z_{FFT}^{M-far}\end{array} \tag{14}\]  
In Equation (14) \(Z_{Mod}^{C-near}\) is near field interaction part computed by MoM, \(Z_{FFT}^{C-far}\) is the approximation by FFT-based algorithm in near field. \(Z_{Mod}^{C-near}-Z_{FFT}^{C-far}\) is precorrected matrix. \(Z_{Mod}^{C-near}\) and \(Z_{FFT}^{C-far}\) are stored as sparse matrices. \(Z_{FFT}^{E-far}\) and \(Z_{FFT}^{M-far}\) are the calculated by FFT-based algorithm expressed as:  
\[Z_{FFT}^{E-far}=jk\eta_{0}\prod\cdot G\cdot\prod^{T}-j\frac{\eta_{0}}{k}\prod _{d}\cdot G\cdot\prod^{T}_{d} \tag{15}\]  
\[Z_{FFT}^{M-far}=\prod_{g}\cdot G\prod^{T} \tag{16}\]  
where \(\prod\), \(\prod_{d}\) and \(\prod_{g}\) are sparse coefficient transformation matrices. \(G\) are triple Toeplitz matrices (called numerical Green functions) formed by Green functions between spatial grid points. Superscript \(T\) represents the transpose of a matrix. In this way, FFT can accelerate the matrix vector products of each iteration.  
Let \(M\) be expansion order, \(C_{m}\) be fitting cube. \(C_{m}\) consists of \((M+1)^{3}\) grid nodes. The center of \(C_{m}\) is \(c_{m}\). \(h_{x},h_{y},h_{z}\) are Cartesian grid spacing in three coordinate directions. \(\bar{S}_{m}\) is testing spherical surface, which's center is \(c_{m}\) and radius is \(R_{m}=r_{m}+0.05\lambda\).  
is the minimum radius of sphere surrounding \(C_{m}\). \(\{\overrightarrow{p}\}\) are testing points located on \(\bar{S}_{m}\). Therefore, \(G(\overrightarrow{r},\overrightarrow{r}^{\prime})\) and \(\
abla G(\overrightarrow{r},\overrightarrow{r}^{\prime})\) can be expressed as:  
\[\begin{bmatrix}RG(\overrightarrow{p},\overrightarrow{q})\\ IG(\overrightarrow{p},\overrightarrow{q})\end{bmatrix}=\sum_{ \overrightarrow{v}\in\mathbf{C}_{m}}\pi_{\overrightarrow{v},C_{m}}^{ \leftarrow}\begin{bmatrix}RG(\overrightarrow{p},\overrightarrow{v})\\ IG(\overrightarrow{p},\overrightarrow{v})\end{bmatrix} \tag{17}\]  
\[\begin{bmatrix}\
abla RG(\overrightarrow{p},\overrightarrow{q})\\ \
abla IG(\overrightarrow{p},\overrightarrow{q})\end{bmatrix}=\sum_{ \overrightarrow{v}\in\mathbf{C}_{m}}\hat{\xi}_{\overrightarrow{v},C_{m}}^{ \leftarrow}\begin{bmatrix}RG(\overrightarrow{p},\overrightarrow{v})\\ IG(\overrightarrow{p},\overrightarrow{v})\end{bmatrix} \tag{18}\]  
where, \(\{\pi_{\overrightarrow{v},C_{m}}^{\overrightarrow{v}}\}_{u=1}^{N_{c}}\) and \(\{\overrightarrow{\xi}_{\overrightarrow{v},C_{m}}^{\overrightarrow{v}}\}_{u=1}^ {N_{c}}\) are fitting coefficients. The real and imaginary part of \(G(\circ)\) are \(RG(\circ)\) and \(IG(\circ)\).
## Appendix A  
The general solutions for wave functions \(\Psi_{A}\), \(\Psi_{B}\), and \(\Psi_{C}\) obtained from Equation (8) are given below:  
\[\Psi_{A}(x)=C_{1}e^{ik_{0}x}+C_{2}e^{-ik_{0}x} \tag{1}\]  
\[\Psi_{B  
where \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\), and \(C_{6}\) are solution coefficients. Constants \(k_{o}\) and \(k_{b}\) can be further expressed as follows:  
\[k_{o}=\frac{\sqrt{2m_{e}E}}{\hbar}\] (A4)  
\[k_{b}=\frac{\sqrt{2m_{e}(U-E)}}{\hbar}\] (A5)  
In the present case, the second term in \(\Psi_{C}(x)\) (Equation (A3)) can be ignored because there is no interface to induce reflection; the particle is moving unidirectionally to the right. Additionally, the solution coefficient \(C_{1}\) associated with the probability density of particle incident on the barrier is one. In the matrix region, denoted by B, the wave function exhibits a decaying behavior, as represented in Figure A1. Upon enforcing \(C^{1}\) continuity at the two fiber matrix interfaces (x = 0 and x = a where 'a' represents the barrier size), a deterministic system of equations involving four unknowns is obtained.  
At the incident interface (x = 0):  
\[1+C_{2}=C_{3}+C_{4}\] (A6)  
\[ik_{o}(1-C_{2})=k_{b}(C_{3}-C_{4})\] (A7)  
At the transmitted interface (x = a):  
\[C_{3}e^{k_{b}a}+C_{4}e^{-k_{b}a}=C_{5}e^{ik_{o}a}\] (A8)  
\[k_{b}\left(C_{3}e^{k_{b}a}-C_{4}e^{-k_{b}a}\right)=ik_{o}C_{5}e^{ik_{o}a}\] (A9)  
Solving Equations (A6)-(A9), the coefficient \(C_{5}\), which is linked to the tunneling probability, can be obtained. Subsequently, the transmission coefficient \(C_{T}\) is computed using the following equation:  
\[C_{T}=\frac{P_{t}}{P_{i}}\] (A10)  
where \(P_{t}\) and \(P_{i}\) are the probability density of finding the particle as it exits the barrier in region C and the particle incident on the barrier in region A, respectively. \(P_{t}\) can be further expressed as a product of the wave function \(\Psi_{C}\) and its complex conjugate \(\Psi_{C}^{*}\) using the following expression:  
\[P_{t}=\Psi_{C}\,\Psi_{C}^{*}\] (A11)  
Upon further simplification, \(P_{t}\) is found equivalent to \(C_{5}^{2}\). Given that \(C_{1}\) equals one, \(P_{i}\) also equals one, resulting in a streamlined tunneling probability formulation and yielding the ultimate tunneling coefficient as shown below:  
\[C_{T}=\left(1+\frac{\sinh^{2}(k_{b}a)}{4\eta(1-\eta)}\right)^{-1}\] (A12)where \(\eta\) represents the ratio of the particle's energy (\(E\)) to the barrier potential energy (\(U\)).
## 4 Simulation-Signal Test  
Through the comparative study of simulated signals, the efficiency of the LMD algorithm proposed in this paper is illustrated. The vibration signal \(x(t)\) is artificially synthesized by the frequency-modulation signal \(f_{1}(t)\), the amplitude-modulation signal \(f_{2}(t)\) and the sinusoidal signal \(f_{3}(t)\). Considering the noise interference of the mine microseismic signal, white noise \(n(t)\) is added to the simulation signal, where the signal-noise ratio is 3 and \(t\in[0,1]\), as shown in Equation (8). The \(x(t)\) sampling frequency is 400, and the time-frequency-domain waveform is obtained, as shown in Figure 3. Then, the original vibration  
Figure 2: Mine-microseismic-signal-identification process of LMD–PNN.  
signal is decomposed into multiple signal components with different resolutions through LMD and EMD, and then superimposed and compared.  
\[\left\{\begin{array}{l}f_{1}(t)=\sin(100\pi t)\\ f_{2}(t)=2.5\cos(40\pi t+\sin(160\pi t))\\ f_{3}(t)=0.8\cos(300\pi t)\\ n(t)=awgn(x,3)\\ x(t)=f_{1}(t)+f_{2}(t)+f_{3}(t)+n(t)\end{array}\right. \tag{8}\]  
It can be seen from Figure 3a that the distribution of the synthesized vibration signal in the time domain is disorderly and has no obvious signal-fluctuation characteristics. The synthesized-vibration-signal spectrum has obvious wave fluctuations at 20 Hz, 50 Hz, 60 Hz, 100 Hz and 150 Hz. It shows that the main frequency-band waveforms of this signal are distributed in 20 Hz, 50 Hz, 60 Hz, 100 Hz and 150 Hz. The signal is decomposed by EMD and LMD to obtain the frequency-spectrum diagram of each component, as shown in Figures 4 and 5.  
As can be seen from Figure 4, IMF2 obviously exhibits mode aliasing. In addition, multiple low-frequency components originally belonging to the same principal component are obtained. Due to the over-decomposition of the EMD algorithm, too many false modes are generated, which not only delays the calculation time, but also reduces the accuracy of feature extraction. In particular, the fit of IMF3-IMF6 components decomposed by EMD is even more deformed.  
It can be seen from Figure 5 that the components PF1 and PF2 obtained by the LMD algorithm correspond to the components above 50 Hz and 25 Hz in the original signal, respectively. Therefore, the decomposition of four components is sufficient to express the lower-frequency signal. It can be seen that the LMD algorithm saves calculation time and feature amounts when extracting microseismic-signal features, which are obvious advantages.  
Figure 3: Synthetic vibration signal and its frequency spectrum. (**a**) Synthetic vibration signal; (**b**) Synthetic-vibration-signal frequency spectrum.
# An Earthquake Early Warning Method Based on Bayesian Inference
###### Abstract  
Earthquake early warning (EEW) systems can give people warnings before damaging ground motions reach their site and can reduce earthquake-related losses. It has been shown that Bayesian reference can be used to estimate earthquake magnitude, location, and the distribution of peak ground motion using observed ground-motion amplitudes, predefined prior information, and appropriate attenuation relationships. In this article, we describe a Bayesian approach to earthquake early warning (EEW) systems for the estimation of magnitude (M) and peak ground-motion velocity (PGV) using the Guttenberg-Richter relation as a priori probability derived from statistical historical earthquake information from the Japanese KiK-net. Moreover, using the magnitude \(M_{\rm j}=4.5\) and PGV = 0.4 cm/s as the warning threshold, an earthquake hazard discrimination model was established. Following this, the proposed approach was compared with the traditional fitting method to analyze the earthquake hazard discrimination using an \(M_{\rm j}=4.3\) earthquake in the Mt. Fuji area and an \(M_{\rm j}=5.5\) earthquake in the Ibaraki area of Japan as examples. The results are as follows: (1) The probabilistic algorithm founded on the predictive model of the magnitude from the average period (\(\tau_{c}\)) and FGV from the displacement amplitude (\(P_{d}\)) from the initial 3 s of the P wave is able to provide a fast and accurate estimation of the final magnitude and location. (2) The Bayesian inference approach for the earthquake hazard discrimination model has a 2.94% miss rate and a 5.88% false alarm rate, which is lower than that of the traditional fitting method, thus increasing the accuracy and reliability of earthquake hazard estimation.  
earthquake early warning; Bayesian inference; magnitude prediction; PGV prediction  
(\(P_{\rm a}\)), initial P-wave velocity amplitude (\(P_{\rm v}\)) and PGV. However, accurately evaluating earthquake hazards on the basis of a single parameter proves to be a complicated task [9]. Methods used for earthquake hazard assessments should be based on multiple parameters. Thus, this paper applies Bayesian inference to obtain prior probability distributions using the Gutenberg-Richter (G-R) relationship and historical earthquake information from Japan [10]. Combined with the average P-wave period \(\tau_{c}\) and the displacement amplitude \(P_{d}\), the magnitude and subsequent PGV are predicted. The four-level division method is then applied to complete the comprehensive judgment of earthquake hazards. The earthquake warning given according to the earthquake hazard level can reduce the rate of missed and false alarms of earthquake warnings.
## 2 Materials and Methods
### Analysis Methods  
The determination of soil organic carbon (SOC) content was conducted using the K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\)-volumetric method [29], a well-established technique in soil analysis. This method enables accurate quantification of SOC, providing essential insights into soil fertility and health. Additionally, the measurement of dissolved organic carbon (DOC) was performed using a 0.5 mol L\({}^{-1}\) K\({}_{2}\)SO\({}_{4}\) solution, which effectively extracts and quantifies DOC, crucial for understanding carbon dynamics within the soil [30]. Furthermore, the content of easily oxidized carbon (EOC) was assessed using a 333 mol L\({}^{-1}\) KMnO\({}_{4}\) solution, allowing researchers to measure the readily available carbon fractions that microorganisms can easily utilize [23]. The evaluation of microbial biomass carbon (MBC) was carried out via chloroform fumigation, a technique that estimates the carbon directly associated with the microbial community in the soil [31]. Finally, the measurement of particulate organic carbon (POC) was achieved using (NaPO\({}_{3}\))\({}_{6}\), indicating the carbon that remains within the soil matrix in particulate form [32].  
To enhance the understanding of soil carbon dynamics, several indices were calculated, namely the carbon pool active degree (CA), carbon pool active index (CAI), carbon pool  
Figure 2: Distribution of soil humus layers under different land use patterns. Note: The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize-soybean rotation (MSR), grass belt (GB), and forest belt (FB).  
index (CPI), and carbon pool management index (CPMI). These calculations followed established protocols based on previous research, providing a framework for assessing the active capacity of soil carbon and its management [23]. In this paper, we selected a soil sample collected from bare land in the same area, and in close proximity to each other, as the reference point for calculating the CAI, CPI, and CPMI. The choice of bare land as the reference is justified, as the levels of SOC in crop land are generally among the lowest observed in this study; this comparative analysis is crucial for understanding how land management practices impact soil carbon pools.  
\[\text{CA}=\text{Sample EOC}/\text{Sample (SOC}-\text{EOC)}, \tag{1}\]  
\[\text{CAI}=\text{CA}\text{in sample soil}/\text{CA}\text{in reference soil}, \tag{2}\]  
\[\text{CPI}=\text{Sample SOC}/\text{Reference SOC}, \tag{3}\]  
\[\text{CPMI}=\text{CPI}\times\text{CAI}\times 100\%. \tag{4}\]  
The difference between total and labile organic C is termed as the non-labile organic C, and the oxidation stability coefficient (_Kos_) is calculated by dividing the non-labile organic C content by the labile organic C content [23]:  
\[\text{Oidation stability coefficient (_Kos_) = Sample (SOC}-\text{EOC)}/\text{Sample (EOC)}. \tag{5}\]
## 3 Results and Discussion
### Impact of Simulated In Vitro Digestion on Antioxidant Activity of GCPs  
Studies have indicated that during simulated gastrointestinal digestion, the action of digestive enzymes may lead to the cleavage or hydrolysis of peptides, altering their structure and potentially affecting their antioxidant activity [32]. As shown in Figure 4, after undergoing the complete simulated in vitro digestion process, all active components isolated through chromatography exhibited varying degrees of decline in their antioxidant activity. Specifically, the DPPH radical scavenging rates dropped from their original values of 50.29%, 64.55%, and 38.63% to 9.68%, 12.43%, and 11.33%, respectively, representing the largest decrease among the tested indices. It was speculated that during gastrointestinal digestion, GCPs undergo further enzymatic hydrolysis into smaller peptide fragments and amino acids, and the antioxidant activity of these newly generated small molecules was significantly lower than that of the original active fragments of GCPs. Observations from gastrointestinal digestion experiments revealed that GCPs exhibited relatively stable ABTS radical scavenging activity and hydroxyl radical scavenging activity during the digestion process. Based on these findings, it was hypothesized that certain antioxidant functions of GCPs possess good digestive stability.  
\begin{table}
\begin{tabular}{c c c} \hline
**Items** & **GCP-II** & **GCP-II After Digestion** \\ \hline Gly & 7.032 & 0.169 \\ Lys * & 5.459 & 0.058 \\ Arg & 2.819 & 0.311 \\ Ser & 1.797 & 0.098 \\ Asp & 1.135 & 0.129 \\ Glu & 0.855 & 0.153 \\ Phe *\# & 0.815 & 0.104 \\ Thr & 0.641 & 0.071 \\ Val *\# & 0.639 & 0.075 \\ Leu *\# & 0.487 & 0.094 \\ Geu * & 0.419 & 0.068 \\ Ile *\# & 0.304 & 0.049 \\ Met *\# & 0.207 & 0.011 \\ His & 0.125 & 0.019 \\ Tyr & 0.025 & 0.038 \\ Pro * & 0.009 & 0.001 \\ Toatal & 22.768 & 1.448 \\ \hline \end{tabular} Note: * indicates essential amino acids for humans, and * indicates hydrophobic amino acids.  
\end{table}
Table 4: Analysis of amino acid component of GCP-II before and after digestion (g/100 g).
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 4 Experiments
### Dataset Collection and Processing
Specifically, we adopt two commonly used metrics, recall, and precision, together with the average precision (_AP_) that measures the detection accuracy of a detector. _AP_ computes the average precision for recall values ranging from 0 to 1. The general definition has the formula:  
\[\mathbf{AP}=\int_{0}^{1}\mathbf{p}(r)dr, \tag{3}\]  
where \(\mathbf{p}(r)\) is the precision value at the recall value \(r\). In practice, the equation is replaced with a finite sum over several recall values, such as the \(11\)-point interpolated _AP_ used in the Pascal VOC challenge [44] that is defined as the mean precision at a set of 11 equally spaced recall values ([0, 0.1, 0.2,..., 1]). We follow the new evaluation protocol of the Pascal VOC challenge where they use all data points, rather than interpolating only 11 equally spaced points [44] (we used the open-source evaluation tool Object-Detection-Metrics from: [[https://github.com/rafaelpadilla/Object-Detection-Metrics#interpolating-all-points](https://github.com/rafaelpadilla/Object-Detection-Metrics#interpolating-all-points)]([https://github.com/rafaelpadilla/Object-Detection-Metrics#interpolating-all-points](https://github.com/rafaelpadilla/Object-Detection-Metrics#interpolating-all-points)) Accessed 25 July 2019). The mean average precision (mAP) is the average of _AP_ over all classes or or \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Logo Class \& Group** & **Images** & **Logo Class \& Group** & **Images** & **Logo Class \& Group** & **Images** \\ \hline Ashley (**E**) & 83 & E (**D**) & 248 & Lays (**M**) & 64 & UPS (**D**) & 236 \\ \hline Atlas (**M**) & 52 & FedEx (**E**) & 1128 & OD (**D**) & 392 & US Foods (**M**) & 163 \\ \hline Budget (**M**) & 47 & HamburgQUD (**M**) & 63 & Opies (**D**) & 51 & Werner (**D**) & 142 \\ \hline CarrollFulmer (**M**) & 30 & HeartlandExpress (**M**) & 245 & Prime (**E**) & 48 & XTRA (**E**) & 489 \\ \hline Celadon (**E**) & 107 & heyl (**M**) & 50 & RBI (**D**) & 281 & YRC (**M**) & 53 \\ \hline Davis (**D**) & 95 & JNJ (**M**) & 168 & SouthernAG (**M**) & 174 & **Total** & 5,020 \\ \hline Dollar General (**E**) & 199 & Landstar (**E**) & 362 & Sunstate (**E**) & 50 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Logo distributions of the Annotated Logo Dataset. We use (**E**), (**M**), and (**D**) to represent logo classes in groups ‘easy’, ‘medium’, and ‘difficult’. categories. To decide whether a detection is correct, we calculate \(\mathbf{IoU}\) (Intersection over Union) between predicted logos and ground truth logos. If the \(\mathbf{IoU}\) is greater than a certain threshold (such as 0.5), the prediction is considered true; otherwise, it is considered false. The formula of \(\mathbf{IoU}\) is provided as follows:  
\[\mathbf{IoU}=\frac{|B\cap B_{gt}|}{|B\cup B_{gt}|}, \tag{4}\]  
where \(B\) and \(B_{gt}\) represents the predicted bounding box and its corresponding ground-truth, respectively.