esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Obstacle Detection Method Based on Improved YOLOv5
## 4 Results  
### Datasets  
Most existing datasets for road target detection primarily focus on vehicles and pedestrians, leaving a shortage of suitable datasets for this study. To address this gap, a custom dataset was created for both training and testing purposes. The process began with filtering images sourced from the internet and offline collections, ultimately resulting in 1869 valid images that met the necessary criteria. These images were annotated using the LabelImg tool (as illustrated in Figure 7). The dataset was subsequently divided into training, validation, and test sets in an 8:1:1 ratio. Specifically, the test set comprises 173 images, while the validation set includes 174 images. To tackle the challenge of limited training data, data augmentation techniques were employed on the original training set. This approach not only balanced the number of images across different categories but also improved the algorithm's generalization and robustness, enabling the model to effectively learn the distinctive features of each category. Following data augmentation, the training set expanded to 4921 images. The details of the dataset are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Obstacle** & **Label** & **Number of Labels** & **Number of Pictures** \\ \hline Stone & stone & 5281 & 1288 \\ Tree & tree & 3181 & 2040 \\ Pothole & pothole & 4397 & 1593 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets and labels.  
Figure 6: Structure of YOLOv5-EC3F algorithm.  
Figure 7: Examples of obstacle labels: (**a**) stone, (**b**) tree, and (**c**) pothole.
## 4 Results
### Geological Controls on Fluoride Occurrence  
Fluoride concentrations were lowest in the southeast where the dominant lithology is charnockitic gneiss and granulite, becoming progressively more elevated northwest where the highest fluoride concentrations were located within alkaline meta-igneous rocks (augen gneiss). This lithology contains all fluoride concentrations \(>\)3 mg/L. Concentrations then decrease slightly further northwest where the dominant lithology returns to Charnockitic gneiss and granulite, with interbedded hornblende-biotite  
Figure 5: Plot of calcium versus fluoride showing equilibrium for the mineral fluorite (CaF\({}_{2}\)). The fluorite equilibrium curve was calculated from \(\{\text{F}\}=(K_{\text{eq}}/(CaI))^{0.5}\) that assumes equality of the ion activity product (IAP) with the equilibrium constant (\(K_{\text{eq}}\)) (\(3.7\times 10^{-11}\)).  
Figure 6: Gibbs diagram of study area groundwater samples showing dominance of rock dissolution. Calculations are shown on axes.  
gneiss (Figure 7). No data were available for perthitic sysenite, calc-silicate marble or superficial deposits so their (fluoride) hydrochemical profile remains unknown.  
A box and whisker plot to investigate the specific lithology-fluoride relationships (Figure 8) shows Charnockitic gneiss and granulite producing the lowest groundwater fluoride with only 20% of samples exceeding the WHO standard for fluoride in drinking water of 1.5 mg/L. A total of 10% of groundwater samples in this lithology exceeded 2 mg/L and none exceeded 3 mg/L. Augen gneiss (alkaline composition) on the other hand had 69% of samples exceeding the WHO standard. This lithology also possesses the highest fluoride concentrations with 56% of samples exceeding 2 mg/L and 25% exceeding 3 mg/L (Table S1--Supplementary Materials). Hornblende-biotite gneiss sits between those lithologies with respect to fluoride concentrations. Low sample numbers for this lithology prevent definitive conclusions, although the expectation would not differ much from those measured values as it contains more fluoride-bearing minerals (relatively) than charnockitic gneiss (biotite and hornblende) and less sodium than the augen gneiss (contains an abundance of Na-plagioclase megacrysts) (Figure 4) which makes it a fluoride source candidate. It has been provisionally included in the (later) risk map for this reason, recognising the need for greater sample numbers.  
Figure 7: Map showing fluoride data in the study area plotted onto local geology.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## Nomenclature  
LAS Laser absorption spectroscopy  
DAS D diode absorption spectroscopy  
WMS Wavelength modulation spectroscopy  
TDLAS Tunable diode laser absorption spectroscopy  
LOS Line-of-sight  
SNR Signal-to-noise ratio  
\(I_{t}\) [W/cm\({}^{2}\)] Transmitted light intensity  
\(I_{0}\) [W/cm\({}^{2}\)] Incident light intensity  
\(\omega_{v}\) Absorbance  
\(k_{v}\) [1/cm] Spectral absorption coefficient  
\(L\) [cm] Absorption pathlength  
\(n\) [molecule/cm\({}^{3}\)] Number density of the absorbing species  
\(\alpha_{v}\) [cm\({}^{2}\)/molecule] Absorption cross- section  
\(S\) [cm\({}^{-2}\)/atm] Absorption linestrength  
\(\phi_{v}\) [cm] Frequency-dependent lineshape function  
\(P\) [atm] Pressure  
X\({}_{i}\) & Mole fraction of the absorbing species \(i\)  
\(\
u\) & Light frequency  
NEA Noise equivalent absorbance  
DFBs Distributed feedback lasers  
VCSELs Vertical-cavity surface-emitting lasers  
FDML Fourier-domain mode locked  
QCLs Quantum cascade lasers  
ICLs Interband cascade lasers  
LM Levenberg-Marquardt  
AI Advanced integrative  
HITRAN High Resolution Transmission  
HITEMP High Temperature  
R2f & 2f peak ratio  
T Temperature
## 3 Machine Learning Models
### Support Vector Machines (SVM)
#### 3.1.5 Utilities  
The research of [PERSON] et al. [25] on power grid load forecasting concludes that the SVM model can predict the energy consumption profile pattern and achieve higher accuracy than ARIMA models. However, SVM failed to predict the magnitude of consumption peaks in some cases, which is related to unpredictable changes in consumer behavior. The comparison of the two models also highlights the fact that SVM performs better in the case of non-linear consumption patterns, while ARIMA exhibits better behavior in the linear load approximation. Finally, in the work of [PERSON] et al. [26], ARIMA, Back-Propagation Neural Network, SVM, and hybrid EEMD-ARIMA, EEMD-BP, and EEMD-SVM (ensemble empirical mode decomposition) models were compared in predicting hourly water consumption in Shanghai city in China. This work shows that the SVM model outperforms the ARIMA and that the hybrid EEMD-SVM model has the best performance (based on various metrics), while it appears that the use of EEMD decomposition improves the prediction accuracy of the algorithms. The research also concludes that the optimal length of the training time series for hybrid algorithms is over one week.
## 5 Experiments and Discussion
### Ablation Experiment  
In all the conducted ablation experiments, the number of training iterations and hyperparameters remained constant, while the input image resolution of the drill chuck was set to 640 \(\times\) 640 pixels. The self-built coal mine drill pipe dataset was employed for conducting the experiments, enabling the assessment of the functionality and effectiveness of each module. The experimental findings and results are presented in Table 1, providing valuable insights into the performance of the proposed approach.  
It is shown in Table 1 above that after the changes of different modules on YOLOv7, the accuracy and speed have been improved to varying degrees.  
1. In order to reduce the parameters of the network, the ELAN module in the original backbone network was replaced with the GhostNetV2 module, the weight of the model was reduced by 6.3 M, the number of parameters was reduced by 8.41 \(\times\) 10\({}^{5}\), and the inference speed was increased by 23 frames/s.
2. In order to improve the expression ability of drilling rigs in complex backgrounds, such as coal dust and strong light, after using the lightweight FCA with integrated attention, the average accuracy is increased by 1.8%, the weight of the model is reduced by 21 M, and the number of parameters is reduced by 1.484 \(\times\) 10\({}^{6}\). The inference speed increased by 15 frames/s.
3. For the target of the downhole drilling rig, the positioning accuracy improved. After using the NWD loss, the accuracy increased by 0.9%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **FPS** & **Model** & **Parameter** \\
**Type** & & & **Weight/MB** & **Amount/(10\({}^{6}\))** \\ \hline YOLOv7 & 96.4\% & 47 & 71.8 & 9.319 \\ +Ghost & 95.8\% & 70 & 65.5 & 8.478 \\ +FCA & 98.2\% & 62 & 50.8 & 7.835 \\ +NWD & 97.3\% & 47 & 71.8 & 9.319 \\ +Ghost + FCA + NWD & 99.5\% & 80 & 54.3 & 6.994 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compared the effectiveness of different modules on the original YoloV7 algorithm.
## 4 Evaluation of Biological Properties
### Anti-inflammatory Activity  
Using in vivo inflammation experiments (e.g., xylene-induced ear edema and carrageenan-nan-induced paw), edema could be mediated by inflammatory factors [118], such as serotonin, histamine, prostaglandandins, and bradykinin. [PERSON] et al. [119] investigated the anti-inflammatory activity of two sterols and two triterpenes extracted from _Pyrus bretschneideri_ Rehd., including daucosterol, \(\beta\)-sitosterol, ursolic acid, and oleanolic acid. Mice treated orally with the isolated compounds showed significant dose-dependent inhibition of edema compared to control mice [119].  
Daucosterol isolated from the leaves of _Liriodendron chinensis_ could be a natural anti-inflammatory agent due to its strong inhibitory effect on the activated inflammatory cells. As observed in the experiment performed by [PERSON] et al. [43], this compound could significantly decrease the NO content of LPS-induced rat peritoneal macrophages [43].  
In another study, [PERSON] et al. [120] used LPS-treated RAW 264.7 macrophage cells to assess the anti-inflammatory action of phytochemicals, including daucosterol, isolated from fermented bark of _Acantlophanax sessiliforus_ (FAS). The anti-inflammatory responses to FAS revealed decreased NO production and inhibition of COX-2, iNOS, collagenase, and proinflammatory cytokines (TNF-\(\alpha\) and IL-6) (Figure 4) [120]. Additionally, [PERSON] et al. tested the anti-inflammatory activity of bioactive compounds, including daucosterol, extracted from _Sanchezia specios_ [PERSON]'s leaf ethanol extract. This research reported that daucosterol might have anti-inflammatory action against heat-induced protein denaturation, with moderate inhibition (IC\({}_{50}=245.59\pm 3.17\)\(\mu\)g/mL) compared to 3-methyl-1 Hbenz[f] indole-4,9-dione, which exhibited the strongest inhibition (IC\({}_{50}=193.70\pm 5.24\)\(\mu\)g/mL) [121].  
The pharmacological effects of daucosterol as an anti-inflammatory agent isolated from the leaves and root bark of _Alchornea cordiifolia_ was also tested using a mouse ear edema model in the study carried out by [PERSON] and collaborators. At a dose of 90 g/cm\({}^{2}\), daucosterol was found to be more active (50% inhibition)--along with acetyl aleuritolic acid, N1, N2 disopentenyl guanidine, and N1,N2,N3-trisopentenyl guanidine--than indomethacin. However, \(\beta\)-sitosterol and di(2-ethylhexyl) phthalat  
Figure 4: Anti-inflammatory mechanisms of Daucosterol. The arrow indicates increasing, decreasing or inhibiting.  
In addition, daucosterol could suppress dextran sulfate sodium (DSS)-induced colitis in mice. Colitis is an inflammatory response of the large bowel, which may be of infectious or autoimmune origin [122]. [PERSON] et al. [31] found daucosterol decreased DSS-induced ROS production and the expression of pro-inflammatory cytokines, including TNF-\(\alpha\), IL-6, and IL-1\(\beta\)[31] (Figure 4). These outcomes indicate that daucosterol may be useful as a candidate in folk medicine and for adopting new therapeutics as an alternative for treating several inflammatory diseases associated with excessive NO production.
## 5 Results
### CSF and Plasma Concentrations of Caffeic Acid, Homovanillic Acid and 3-hydroxyphenyl Acetic Acid  
Figure 3 shows the mutual comparisons of concentrations of CA, HVA, and 3 HPAA in CSF and plasma samples of the whole group of studied patients. 3 HPAA had the highest mean CSF concentration (7.96 umol/L), which was about 40- and 400-times higher (\(p<0.05\)) than that of HVA and CA, respectively. Mean CSF concentrations of 3 HPAA and HVA were 11- and 2-times higher (\(p<0.05\)) respectively than those in plasma, while the levels of CA were similar in these body fluids (Figure 3). Patients with multiple sclerosis (MS) (\(n=15\)) did not differ from other neurological disorders (OND) subgroup (\(n=13\)), in respect to CSF concentrations of the afore-mentioned 3 phenolics (Table 3). Similarly, plasma levels of HVA and CA were similar in these subgroups. In the MS subgroup, plasma HVA and CA were 0.09 \(\pm\) 0.02 (0.08; 0.03) umol/L and 0.03 \(\pm\) 0.01 (0.03; 0.01) umol/L, while in the OND subgroup, they reached 0.11 \(\pm\) 0.01 (0.12; 0.07) umol/L and 0.03 \(\pm\) 0.01 (0.03; 0.01) umol/L, respectively.  
Figure 3: Concentrations of 3-hydroxyphenyl acetic acid (**A**), homovanillic acid (**B**) and caffe acid (**C**) in cerebrospinal fluid (CSF) and matched plasma samples obtained form 28 studied patients. Results are expressed as mean \(\pm\) SD and median; IQR (interquartile range). * versus corresponding value in plasma, \(p<0.05\). \(\dagger\) versus blood-cerebrospinal fluid (CSF) concentrations of homovanillic acid and caffe acid, \(p<0.05\). \(\ddagger\) versus CSF concentration of caffe acid, \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenolic Acid (\(\mu\)mol/L)** & **Multiple Sclerosis** & **Other Neurological Disorders** \\ \hline
3 HPAA & \(8.12\pm 10.08\) (4.50; 5.39) & \(7.78\pm 7.18\) (4.20; 8.32) \\ HVA & \(0.19\pm 0.13\) (0.16; 0.15) & \(0.22\pm 0.13\) (0.19; 0.17) \\ CA & \(0.02\pm 0.01\) (0.03; 0.01) & \(0.02\pm 0.01\) (0.02; 0.00) \\ \hline \hline \end{tabular}  
No significant differences between subgroups were found (\(p>0.05\)).  
\end{table}
Table 3: Concentrations of 3-hydroxyphenyl acetic acid (3 HPAA), homovanillic acid (HVA) and caffe acid (CA) in cerebrospinal fluid of patients subgroup with multiple sclerosis (\(n=15\)) and subgroup with other neurological disorders (\(n=13\)).
# Super-Frequency Sampling for Thermal Transient Analysis
## 4 Results and Discussion
### Overall Validity  
The \(11\times 11\) area in the center of the diode is chosen rather arbitrarily as it seems that the temperature distribution in this area is quite homogeneous (Figure 4, left). To investigate the influence of the area's size on the quality of the result, the analysis is repeated for all possible sub-areas within the \(11\times 11\) area, i.e., in total 121 (\(1\times 1\)) areas, 100 (\(2\times 2\)) areas, 81 (\(3\times 3\)) areas, 64 (\(4\times 4\)) areas, 49 (\(5\times 5\)) areas, 36 (\(6\times 6\)) areas, 25 (\(7\times 7\)) areas, 16 (\(8\times 8\)) areas, 9 (\(9\times 9\)) areas, 4 (\(10\times 10\)) areas, and 1 (\(11\times 11\)) area, with each of these 506 test areas having a different \(\tau_{\rm true}-\tau_{\rm set}\). The cumulative distribution of these \(\tau_{\rm true}-\tau_{\rm set}\), grouped by the area's size, is shown in Figure 8 (left). It can be observed, for instance, that for the \(1\times 1\) sized areas, approximately 40 % of the sampling periods have the same value as the period of the single \(11\times 11\) area. For areas equal or larger than \(8\times 8\), the algorithm converges to the same value. It should be noted that, in all cases, the true sampling period, \(\tau_{\rm true}\), is larger than the set sampling period, \(\tau_{\rm set}=20\,\rm s\ +\ 1\cdot 10^{-4}\) s. This indicates that the internal clock of the signal generator is \(3.9\cdot 10^{-4}\) s slower than that of the camera system.  
Figure 8 (right) shows the cumulative distribution of the medians, \(\Delta\tilde{\rm r}_{\rm r}\), for the different area's sizes. For some cases, the \(\Delta\tilde{\rm r}_{\rm r}\) are smaller than \(0.1\cdot 10^{-4}\) s. In these cases, the reconstructed points for one period are very close to each other and followed by a larger gap. This indicates that the reconstructed points are very unevenly distributed as the algorithm minimizes the difference of the temperature values of consecutive data points without considering their temporal difference.  
Figure 7: (**left**) Histogram of sampling periods within the reconstructed signal (on the basis of ten periods). (**right**) Cumulative distribution of the reconstructed sampling periods for a different number of periods included in the analysis.  
To measure the influence of noise on the sample period reconstruction, the signal-to-noise ratio (SNR) is calculated for each sub-area. The signals are filtered with a first-order Savitzky-Golay filter. Figure 9 illustrates the average SNR, which is given by the average of all corresponding sub-area SNRs. The error bar indicates the range between the 5 th and the 95 th percentiles, \(Q_{0.05}\) and \(Q_{0.95}\), of the signal-to-noise ratios of the input signals. For the \(8\times 8\) area, the reconstructions of all \(8\times 8\) sub-areas converge to the same sampling period, \(\tau_{\text{true}}\). The average SNR of the \(8\times 8\) area amounts to 5680. This threshold is indicated by the dashed line.
## 9 Estimation of Mechanical Parameters Based on RQD Value
### Estimation of Deformation Modulus Using RQD Value
#### 9.1.1 Linear Relationship  
Firstly, collecting several measured data, [PERSON] and [PERSON] [45] proposed the following linear equations to estimate the deformation moduli of the rock mass (\(\text{E}_{\text{m}}\)) from the deformation moduli of the intact rock (\(\text{E}_{\text{r}}\)) (see Figure 19):  
\[\text{E}_{\text{m}}=\text{\alpha}_{\text{E}}\ \text{E}_{\text{r}} \tag{32}\]  
\[\text{\alpha}_{\text{E}}=0.0231\ \text{RQD}-1.32\geq 0.15 \tag{33}\]The collected point with the suggested linear equation is presented in Figure 18 [45].  
The chart of the reduction factor of the elastic modulus in field tests compared to the elastic modulus in seismic tests for various dam sites and rock types is plotted in Figure 20 [2]. As shown in this Figure, the rock mass quality is represented by either the rock quality designation (RQD) or the ratio of seismic field velocities to laboratory sonic velocities (\(\rm V_{F}/V_{L}\)). In many cases, only one of these measures--either RQD or \(\rm V_{F}/V_{L}\)--was available for the data points, but not both. The generalized relationship becomes nearly flat when the reduction factor falls below 0.2 or when RQD is less than 60%. This flatness suggests a broad range of rock mass conditions in this region. Recognizing this, [PERSON] et al. [2] recommended further investigation, which was subsequently carried out by several researchers and practitioners.  
According to [PERSON] [46] this equation can be used if RQD > 57%. Additionally, it is incorporated into the Standard Specification for Highway Bridges by the American Association of State Highway and Transportation Officials (AASHTO) [47].  
Figure 19: Variation in \(\rm E_{m}/E_{r}\) after [45].  
Figure 20: Variation in reduction factor with rock quality [2].  
The RQD-(E\({}_{\rm m}\)/E\({}_{\rm r}\)) relations of [PERSON] and [PERSON] [45] and [PERSON] [42] have the following drawbacks, according to [PERSON] and [PERSON] [48]:  
1. Either the entire range of RQD \(<\) 60% is not covered, or merely a random E\({}_{\rm m}\)/E\({}_{\rm r}\) value is chosen.
2. E\({}_{\rm m}\) is taken to be equivalent to E\({}_{\rm r}\) for RQD = 100%. In terms of design practice, this is clearly dangerous because RQD = 100% does not imply that the rock is intact. Even when RQD = 100%, there may be discontinuities in the rock masses, which means that E\({}_{\rm m}\) might be smaller than E\({}_{\rm r}\).
## 3 Related Work and Methodology
### Global and Local Moran's I
#### 3.3.2 Global Moran's I (GMI)  
Global Moran's I (GMI) is a statistical measure used to assess spatial autocorrelation in geospatial data. Spatial autocorrelation refers to the degree to which a set of spatial features and their associated data values are correlated with themselves in space. In essence, it evaluates whether the pattern expressed is clustered, dispersed, or random [39]. The Global Moran's I is mathematically defined as [43]:  
\[I=\frac{N}{\sum_{i}\sum_{j}w_{ij}}\frac{\sum_{i}\sum_{j}w_{ij}\big{(}X_{i}- \overline{X}\big{)}\big{(}X_{j}-\overline{X}\big{)}}{\sum_{i}\big{(}X_{i}- \overline{X}\big{)}^{2}} \tag{6}\]  
where \(N\) is the number of spatial units indexed by \(i\) and \(j\), \(X_{i}\) and \(X_{j}\) are the values of the variable of interest for spatial units \(i\) and \(j\), \(\overline{X}\) is the mean of \(X\), and \(w_{ij}\) is the spatial weight between units \(i\) and \(j\).  
This statistic falls in the range of \([-1,1]\), where a value close to \(+1\) indicates clustering, \(-1\) indicates dispersion, and a value around \(0\) implies randomness.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 5 Evaluation
### Vulnerability-Reproducing Capability (RQ1)  
The main goal of directed fuzzing is to reproduce software vulnerabilities effectively. Therefore, we compare SEZZ with existing state-of-the-art directed fuzzers to evaluate its capability in vulnerability reproduction. Figure 3 is a heatmap that displays the result of the time to expose vulnerabilities. As evidenced by the color gradient in the figure, the SEZZ row consistently exhibits lighter-toned green blocks compared with the other methods, indicating superior efficiency in vulnerability reproduction. The detailed results are recorded in Table 2, where TTR represents the average time to successfully reach the target location across five experiments, TTE denotes the average time to successfully trigger the vulnerability, Factor indicates the ratio of the triggering time compared with SEZZ, and \(p\)-value represents the Mann-Whitney U test value for the TTE. The experimental results show that SEZZ can reach the target location and trigger vulnerabilities faster in most target vulnerabilities, with a \(p\)-value less than 0.05. Compared with AFLGo, Beacon, WindRanger, and SelectFuzz, SEZZ is on average 13.7\(\times\), 1.50\(\times\), 9.78\(\times\), and 2.04\(\times\) faster, respectively. Additionally, although Beacon is comparable with SEZZ in speed, SEZZ triggers five more vulnerabilities than Beacon. SEZZ discovers 32 vulnerabilities, while the other methods can only find up to 27. Moreover, only SEZZ can trigger PDF618, TIF005, and XML001 within the specified time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Programs** & **Version** & **Size** & **Format** & **Number of CVE** \\ \hline Libpng & 1.6.38 & 95K & PNG & 3 \\ Libsndfile & 1.2.0 & 83K & Various & 7 \\ Libtiff & 4.1.0 & 95K & TIFF & 7 \\ Libxml2 & 2.9.10 & 320K & XML & 4 \\ Openssl & 3.0.0 & 630K & Binary & 3 \\ Poppler & 0.88.0 & 260K & PDF & 5 \\ Sqlite3 & 3.32.0 & 387K & SSL & 3 \\ PHP & 8.0.0-dev & 1.6M & Various & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of the Magma dataset included in the experiment. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{** \begin{tabular}{}** **AFLGo** \\ \end{tabular}