esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Rules at Road Intersections Controlled by Stop Signs  
We earlier investigated the various types of road intersections controlled by stop signs. From Section 3, we can classify the stop sign road intersections into two scenarios. In the first scenario, a stop sign controls all input traffic flows at the intersection. The second scenario is where the stop signs are only installed on some input traffic flows, and others are left with no control [22]. In both scenarios, the existing stop sign at any input traffic flow  
Figure 3: Crossed junctions and stop signs.  
indicates that all arriving vehicles on that input traffic flow have to come to a complete stop before passing through the intersection. This allows the drivers to check the competing vehicles in the conflicting traffic flows and follow the driving priorities to traverse the intersection safely [23].  
The first vehicle that arrives at the intersection has the highest priority to proceed through the intersection among the competed vehicles (i.e., arriving from conflicted input flows). This means when each vehicle arrives at the intersection, its driver has to check its competed vehicles first. The arrival time of each competed vehicle determines which vehicle has arrived first. Then, the driver can determine the priority of passing through the intersection (i.e., if it can proceed directly or if it has to wait for other vehicle/s to proceed first). The time and speed to traverse the road intersection are controlled by the driver based on his/her skills and experience and according to the real-time traffic distribution on the road intersection.  
On the other hand, arriving vehicles can proceed directly through the intersection without stopping if no stop sign exists on the contained traffic flow. However, if vehicles want to take the left direction at the intersection, they must come to a complete stop and ensure a safe gap between vehicles on the other main road before proceeding through the intersection [22].
# On Finding Optimal (Dynamic) Arborescences
## 2 Optimal Arborescences
### Edmonds' Algorithm
#### 2.1.1 Contraction Phase  
The algorithm proceeds by selecting vertices in \(G_{i}\), which are not yet in \(D_{i}\). If such a vertex exists, then it is added to \(D_{i}\), and the minimum weight incident edge on it is added to \(E_{i}\). The algorithm stops if either a cycle is formed in \(E_{i}\) or if all the vertices of \(G_{i}\) are in \(D_{i}\).  
If \(E_{i}\) holds a cycle, then we add the edges forming the cycle to \(Q_{i}\), and we build a new graph \(G_{i+1}\) from \(G_{i}\), where the vertices in the cycle are contracted into a single super vertex \(v^{i+1}\). The edges \((u,v)\in G_{i}\) are added to \(G_{i+1}\) and updated as follows:  
1. Loop edge removal: If \(u,v\in Q_{i}\), then \((u,v)\
otin G_{i+1}\).
2. Unmodified edge preservation: If \(u,v\
otin Q_{i}\), then \((u,v)\in G_{i+1}\).
3. Edges originating from the new vertex: If \(u\in Q_{i}\cap v,\
otin Q_{i}\), then \((v^{i+1},v)\in G_{i+1}\).
4. Edges incident to the new vertex: If \(u\
otin Q_{i}\cap v\in Q_{i}\), then \((u,v^{i+1})\in G_{i+1}\), and \(w(u,v^{i+1})=w(u,v)+\sigma_{Q_{i}}-w(u^{\prime},v)\).  
Here, \(w(u,v^{i+1})\) is the weight of the edge \((u,v^{i+1})\) in \(G_{i+1}\), \(w(u,v)\) is the weight of \((u,v)\) in \(G_{i}\), \(\sigma_{Q_{i}}\) denotes the maximum edge weight in the cycle \(Q_{i}\), and \(w(u^{\prime},v)\) is the weight of the edge in the cycle incident to vertex \(v\). After the weights are updated, the algorithm continues the contraction phase with the next iteration \(i+1\).  
The contraction phase ends when there are no more vertices to be selected in \(G_{i}\) for some iteration \(i\).
## 2 Methods  
### Surveying Approach  
Borehole imaging allows for the direct observation of the internal structural planes in a rock mass. In the present study, the borehole images were captured by a 28-mm panoramic digital borehole camera (RBIT-30), which comprises a panoramic probe, a depth measuring device, a control box and video recorder, cables, and measuring rods [35]. To operate the camera, a 28-mm-diameter borehole is first drilled, and a cable-connected probe is slowly inserted into the borehole to serve as a depth-measuring device. The control box and video recorder are designed to store videos or images acquired along the borehole.  
To acquire images, the panoramic probe was mounted above the roller measuring rods to push the drilling at 2 m per minute. The video data were recorded at a rate of 25 frames per second and transmitted to the control box and video recorder through the cables. During data acquisition, the depth-measuring device recorded the depth of the camera in the borehole, and the electronic orientation instrument inside the probe recorded the direction. The panoramic technology served to acquire two-dimensional representations of the borehole wall (360\({}^{\circ}\)), and an expanded image was formed by superimposing the azimuthal information. An expanded image of the borehole wall was then used to calculate the properties of various structural planes such as their aperture, strike, and dip, which are often used to judge the quality of surrounding rock.  
Over the last eight years, this equipment has been used in 30 coal mines in China, allowing numerous borehole images to be collected (Figure 1). The total length of the borehole images is 4.5 km (approximately 600 borehole images), and each test borehole was photographed in RGB color and saved in JPEG format as matrices of 1186 \(\times\) 194 pixels. While the training boreholes were drilled to variable depths ranging from 7 to 10 m, the validation boreholes in Huahong Mine maintained a consistent 8 m depth.
## 5 Experimental Tests
### Real Work Scenarios
#### 5.6.1 Handling of Trailers  
As for the handling of trailers, two different conditions were considered: a 0-40 km/h acceleration test with a fully loaded trailer (6000 kg mass) and road transportation of an empty trailer. Figure 13 shows the experimental data for the road transportation test of an empty trailer, while Figure 14 shows the experimental data for the 0-40 km/h acceleration test with a fully loaded trailer. Both tests were conducted on a flat, paved road. At the beginning of both tests, the vehicle was in neutral gear. As for the empty trailer test, the acceleration phase was characterized by three gear changes around 6, 11 and 21 s. Additionally, in the case of the test with the fully loaded trailer, the same three gear ratios were used, with shifting at approximately 4, 10 and 18 s during the acceleration phase. During both tests, the PTO was disengaged. It can be stated that the proposed work scenarios were characterized by sudden variations in the engine load due to acceleration, deceleration and gearshifting phases. Thus, the authors deemed that these profiles were suitable for testing the system in order to evaluate the capacity of the load unit to reproduce the power demand of the work cycles and to analyse the behaviour of the EMS in terms of the power split between the emulated FC and the batteries.  
However, it is necessary to point out that since the load profiles were extrapolated by monitoring a diesel-powered agricultural tractor, no regenerative braking was considered since the minimum value for the external load power was equal to zero. Figures 15 and 16 show the results of the proposed work scenarios on the test bench. Additionally, in this case, the total load power corresponds to the sum of the external load and the FC system auxiliaries' power absorptions. Analysing the results, the following elements were outlined:  
* The load unit successfully emulated the power demand with a good level of accuracy;
* The proposed EMS was able to reduce the fluctuations to the power output of the emulated FC.  
Figure 14: Experimental data for the 0–40 km/h acceleration test with a fully loaded trailer (6000 kg mass).  
Figure 13: Experimental data for the road transportation of an empty trailer.
# Hardware-in-Loop Modules for Testing Automated Ventilator Controllers
## 2 Materials and Methods
### Characterization of the MechVent Module  
A comprehensive series of tests was developed to characterize the system's response; these focused on variations in MV, R\({}_{\text{aw}}\), and CL. The calculated CL values and the corresponding expiratory tidal volume (VTE) were recorded using various loads in the CL compartment ranging from 200 to 1000 g. The calculated R\({}_{\text{aw}}\) values and corresponding VTE and inspiratory tidal volume (VTI) were also recorded at each R\({}_{\text{aw}}\) setting. A baseline CL load of 500 g and an 'Open' R\({}_{\text{aw}}\) level were used for all EtCO\({}_{2}\) control testing. The system performance was evaluated across RRs ranging from 6 to 30 breaths per minute (BPM), and each test ran for 10 min, generating 600 data points per scenario. Pearson's correlation values between the controller parameters and responses were calculated, as were the statistical significances for each correlation. Although the ventilator did not support Continuous Mandatory Ventilation (CMV), the system was tested using Intermittent Mandatory Ventilation (IMV) modes. This resulted in minor discrepancies between the VTI and VTE, but the testing protocol still facilitated the collection of critical data, enabling the simulation of real-world ventilatory conditions and contributing to system refinement.  
In addition, a series of proof-of-concept tests were conducted on the MechVent module to confirm its usability for evaluating automated ventilation controllers. A simple decision-table-based controller following a modified version of the Acute Respiratory Distress Syndrome Network (ARDSNet) protocol (Figure 2A) [35] was used to bring the system within the target range of 30-40 mmHg. Test runs were conducted at 60 s sampling rates and included scenarios with either low (-20 mmHg) or high (-60 mmHg) starting EtCO\({}_{2}\) values. To achieve the different baseline EtCO\({}_{2}\) levels while holding the initial settings of the system constant (7 L/min, MV and 5 cm H\({}_{2}\)O, PEEP), a solenoid valve was used in place of the custom choke, with the duration of valve opening controlling the CO\({}_{2}\) supply (Figure 2B). The simulated patient's weight was defined as 70 kg in order to provide sufficient V\({}_{\text{T}}\) overhead for the upper MV range of the ARDSNet protocol. As mentioned above, MV is the product of RR and V\({}_{\text{T}}\), either of which can be changed independently to produce the same net change in MV.
## 1 Introduction  
Graphene--a novel low-dimensional nano-material where carbon atoms are arranged in a honeycomb-structure--is formed by a flat monolayer of carbon atoms [1]. Due to its outstanding mechanical and electrical properties [2; 3; 4; 5], graphene has broad application prospects, such as micro-nano devices [6; 7; 8], reinforcing materials, and photoelectric detection. In 2011, [PERSON] et al. [9] investigated a fast and reliable deoxyribonucle acid (DNA) sequencing device. The feasibility of DNA sequencing using a fluidic nanochannel functionalized with a graphene nanoribbon was theoretically demonstrated. In 2014, [PERSON] et al. [10] found that the electron transmission of a graphene nanoribbon on which a molecule is adsorbed shows molecular fingerprints of Fano resonances, which can be used to devise an ultrasensitive Fano-resonance-driven DNA sequencing method. In addition, one research hotspot is the high frequency NEMS (nanoelectromechanical systems) graphene resonator, which shows potential for mass, charge, and force sensitivity [11; 12]. In 2007, [PERSON] et al. [6] fabricated a graphene-based resonator via a suspended graphene nanoribbon above predefined trenches etched into a SiO\({}_{2}\) surface. The resonant properties of the monolayer and multilayer graphene were analyzed. The graphene sample showed few defects when it was exfoliated from graphite [13]. Recently,graphene was chemically synthesized with no defects [14]. However, in most cases, a variety of imperfections--including vacancy defects [15,16], topological defects [17,18], adatom [19,20] and grain boundaries [21]--are inevitably produced during material processing. Because the mechanical and electrical properties of graphene are very sensitive to lattice imperfections [22], it is critically important to study defects in graphene [23]. In 2008, [PERSON] et al. observed graphene membranes with single atomic vacancy defects and edge defects through transmission electron microscopy, as is shown in Figure 1[24].  
It is noted that the experimental methods for the study of the vibrational properties of graphene sheets are very difficult. In order to be observed, graphene should be adsorbed on a substrate, which affects the original characteristic of graphene. However, the use of computer numerical simulation based on molecular dynamics (MD) [25] can overcome the difficulties of experimentation. [PERSON] et al. [26] investigated the intrinsic loss mechanisms of monolayer graphene nanoresonators undergoing flexural oscillations via classical MD simulations. [PERSON] et al. [27] investigated edge effects on the quality factor of graphene nanoresonators with different edge configurations and of various sizes by MD. [PERSON] et al. [28] carried out a fundamental study of the vibrational performance of Ag nanowires (NW) by MD simulation. In this communication, MD has been employed to study the resonance pattern of graphene sheets with vacancy defects, based on a micro-resonator. The effects of defect location, stress, and environmental temperature of graphene on the resonance properties are discussed.
## 3 Theory
### Point Spread Function  
PSF is defined as the imaging system response to an ideal, point-like source and constitutes the basic unit forming the image [27; 28; 42; 43] Thus, it represents the optical system's fidelity in reproducing objects through their images. In a perfect imaging system, the intensity coming from a point source (in the object plane) would be concentrated at a single point (in the image plane), the ideal image point. In real systems, however, optical imperfections result in an intensity distribution broadening around the ideal image point, producing blurred images. The PSF provides an estimation of this blurring effect [42; 43].  
Considering a linear system, the output formed by a sum of inputs is equal to the sum of the outputs corresponding to each input acting separately. Mathematically, if an object \(o(x,y)\) is imaged with an imaging system having a response \(PSF(x,y)\), the observed image \(i(x,y)\) is given by:  
\[i(x,y)~{}=~{}o(x,y)*PSF(x,y) \tag{2}\]  
where \(x\) and \(y\) are plane coordinates of the imaging system, perpendicular to the propagation beam axis and \(*\) denotes convolution operation [27]. As \(i(x,y)\) is the acquired image, Equation (2) can be inversely used in order to calculate \(o(x,y)\), knowing the system \(PSF(x,y)\). PSF of the imaging system is therefore used for improving image quality. Moreover, in a typical TPI system, the THz pulse is broadband, and thus it cannot be treated as a monochromatic beam. Therefore, the PSF is a frequency-dependent quantity: for including the full spectrum, the PSF is reconstructed by superposition of monochromatic beams in the frequency band.  
PSF can be evaluated by imaging objects such as tiny reflecting balls [28] and sub-wavelength holes for reflection and transmission setup geometries, respectively.
## 2 Materials and Methods
### Analysis of Network Simulations
#### 2.2.5 Statistical Analysis  
Statistical analyses were performed using Python 3.7, employing the SciPy (1.9.3), statsmodels (0.13.5), and scikit-posthocs (0.8.1) packages. The data are expressed as mean \(\pm\) standard deviation (SD) unless otherwise indicated. The boxplots report the first and third quartiles (lower/upper line of the box), the mean of the distribution (horizontal line in the box), and the whiskers (5 th and 95 th percentiles). The data were checked for normal distribution and homogeneity of variance using Shapiro-Wilk test and Levene test, respectively. According to the results, either parametric (e.g., one-way ANOVA and \(t\)-test) or non-parametric (e.g., Kruskal-Wallis H-test and Dunn's test) tests were performed. \(p\) values were adjusted for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate. Multiple one-sample \(t\)-tests were conducted with Bonferroni-Holm \(p\)-values correction to test the impact of each local manipulation compared with the control. Linear correlation between the metrics was computed with the Pearson correlation coefficient and its associated two-sided \(p\)-value. We conducted simple and multivariate linear regressions, calculating the coefficient of determination (R\({}^{2}\), and R\({}^{2}_{\text{adjusted}}\) to account for multiple regressors) and the \(p\)-value of the F-statistic. Additionally, we computed the slope coefficients of the regressors and their corresponding two-sided 95% confidence intervals (95% CI). For all statistical analyses, alpha = 0.05.
# Parameterized Algorithms in Bioinformatics: An Overview
## 2 Genome Comparison and Completion
### Genome Completion
#### 2.3.1 Scaffold Filling  
**Problem Description.** A scaffold is a partial representation of the genome, obtained after creating and ordering contigs from reads. Errors during DNA sequencing, low coverage, or sequence repetitions yield unavoidable gaps in the genome assembly (see Section 3). One solution to obtain an approximation of the original genome is to use a better-known genome to infer the missing genes. A _completion_ of genome \(G_{1}\) with respect to \(G_{2}\) is a supersequence \(G_{1}^{\prime}\) of \(G_{1}\) such that \(G_{1}^{\prime}\) and \(G_{2}\) are  
Figure 4: Illustration of the CMSR problem, recovering the strips bd, ef, hij, mpr, and yz by deleting \(k=4\) characters in the strings abcdefghijpmrxyz and hijbdgyzefamcprx.  
balanced. The quality of the completion is evaluated by a genomic distance (DCJ, breakpoints, etc.) denoted by \(d\) in the following.  
\(d\)-ScaffoldFilling(\(d\)-SF)  
**Input:** genomes \(G_{1},G_{2}\) (strings or permutations), and some \(k\in\mathbb{N}\)  
**Question:** Is there a completion \(G_{1}^{\prime}\) of \(G_{1}\) wrt. \(G_{2}\), such that \(d(G_{1}^{\prime},G_{2})\leq k\)?  
**Results.** In the signed permutations model, \(d\)-SF can be solved in polynomial time when the considered distance \(d\) is the DCJ distance [37]. On the other hand, for the string model, only weaker distances have been considered: the problem turns out to be NP-hard even for the common adjacencies measure, but is FPT for this measure as parameter [38]. The string algorithms can be generalized to the variant where one seeks extensions of both input genomes at the same time (the \"two-sided\" case).  
**Open Problems.** What is the parameterized complexity of MCSP-SF on strings? As MCSP is NP-hard, this problem must be too, but it may admit efficient algorithms parameterized by the MCSP distance and/or the parameter occ. Another open question is the DCJ algorithm can be fully generalized to the two-sided case.
# The Housing System Contributes to Udder Health and Milk Composition
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multicolumn{2}{c}{**Housing System (HS)**} & \multicolumn{3}{c}{**SCC Class (ths mL\({}^{-1}\))**} & \multirow{2}{*}{**SE**} & \multicolumn{3}{c}{**_p_-Value**} \\ \cline{2-2} \cline{5-10}  & **Tie-Stall** & **Free-Stall** & \(\leq\)**200** & **201–400** & **401–1000** & \(>\)**1000** & & **HS** & **SCC** & **HSxSCC** \\ \hline SCFAs & 8.68 & 8.75 & 8.74 & 8.65 & 8.59 & 8.63 & 0.068 & 0.235 & 0.745 & 0.923 \\ MCFA & 61.11 & 61.91 & 61.25 \({}^{\text{b}}\) & 61.52 \({}^{\text{b}}\) & 61.68 \({}^{\text{b}}\) & 62.56 \({}^{\text{a}}\) & 0.321 & 0.465 & 0.037 & 0.878 \\ LCFAs & 30.29 & 29.29 & 29.97 \({}^{\text{a}}\) & 29.94 \({}^{\text{ab}}\) & 29.75 \({}^{\text{ab}}\) & 28.73 \({}^{\text{b}}\) & 0.336 & 0.623 & 0.049 & 0.897 \\ SFAs & 66.48 & 67.55 & 66.89 & 66.94 & 67.14 & 68.02 & 0.395 & 0.515 & 0.325 & 0.911 \\ UFAs & 33.43 & 32.45 & 33.11 & 33.14 & 32.66 & 31.73 & 0.432 & 0.478 & 0.233 & 0.892 \\ MUFAs & 29.67 & 28.85 & 29.55 \({}^{\text{a}}\) & 29.37 \({}^{\text{a}}\) & 29.12 \({}^{\text{ab}}\) & 28.10 \({}^{\text{b}}\) & 0.343 & 0.579 & 0.045 & 0.965 \\ PUFAs & 3.75 \({}^{\text{a}}\) & 3.50 \({}^{\text{b}}\) & 3.63 \({}^{\text{b}}\) & 3.58 \({}^{\text{b}}\) & 3.67 \({}^{\text{ab}}\) & 3.77 \({}^{\text{a}}\) & 0.055 & 0.038 & 0.049 & 0.598 \\ n-3 PUFAs & 0.61 \({}^{\text{A}}\) & 0.49 \({}^{\text{B}}\) & 0.55 & 0.55 & 0.59 & 0.57 & 0.017 & 0.000 & 0.397 & 0.265 \\ n-6 PUFAs & 2.04 & 2.06 & 2.04 & 2.05 & 2.12 & 2.15 & 0.026 & 0.767 & 0.414 & 0.376 \\ n-6/n-3 PUFA ratio & 3.34 \({}^{\text{B}}\) & 4.21 \({}^{\text{A}}\) & 3.73 & 3.74 & 3.43 & 3.61 & 0.069 & 0.000 & 0.662 & 0.276 \\ SFA/UFA ratio & 2.01 & 2.11 & 2.05 & 2.04 & 2.07 & 2.16 & 0.041 & 0.467 & 0.656 & 0.893 \\ MUFA/PUFA ratio & 7.94 & 8.24 & 8.15 & 8.23 & 7.96 & 7.47 & 0.156 & 0.589 & 0.235 & 0.834 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline  & SEM—standard error of the mean; SCFAs—short-chain fatty acids; MCFAs—medium-chain fatty acids; LCFAs—long-chain fatty acids; SFAs—saturated fatty acids; UFAs—unsaturated fatty acids; MUFAs—monosuratrated fatty acids; PUFAs—polyunsaturated fatty acids. Means followed by different letters differ within rows (within the factor): A, B (\(p\leq 0.01\)); a, b (\(p\leq 0.05\)).  
\end{table} 
Table 5: The effect of the housing system and under health on the percentages and ratios of fatty acid groups in milk fat.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Evaluation Metrics  
We adopted standard evaluation metrics to quantify detection performance and compare results across models. These include Precision (P), Recall (R), and mean Average Precision (mAP) at various IoU thresholds, which collectively offer a comprehensive view of model accuracy and robustness. To comprehensively evaluate the detection performance, we employed standard metrics, including Precision (P), Recall (R), and mean AveragePrecision (mAP) across different Intersection over Union (IoU) thresholds--specifically, [EMAIL_ADDRESS], [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95. Precision measures the proportion of correctly predicted positive samples among all predicted positives, while Recall measures the proportion of actual positives that are correctly detected.  
The mean Average Precision (mAP) is the primary evaluation metric in object detection tasks. It is calculated by averaging the Average Precision (AP) across all object classes. In particular, [EMAIL_ADDRESS] evaluates AP at an IoU threshold of 0.50, whereas [EMAIL_ADDRESS]:0.95 provides a more rigorous measure by averaging AP values over IoU thresholds from 0.50 to 0.95 in increments of 0.05.  
The evaluation metrics are mathematically defined as  
\[P=\frac{TP}{TP+FP} \tag{23}\]  
\[R=\frac{TP}{TP+FN} \tag{24}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{25}\]  
where _TP_, _FP_, and _FN_ denote the number of true positives, false positives, and false negatives, respectively.  
For internal ablation studies, we report both Precision and Recall alongside _mAP_ to provide a detailed performance analysis. However, in comparison tables with prior works, only _mAP_ is reported, as most existing methods do not disclose consistent \(P\) and \(R\) values. This ensures a fair and standardized evaluation across models.