esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
## 2 Materials and Methods
### Freeze-Thaw Cycle Test  
In this study, freeze-thaw cycle tests were conducted on granite specimens with two distinct grain sizes: coarse-grained and fine-grained. Prior to the tests, the initial 1D gas permeability of the oven-dried specimens (at 105 \({}^{\circ}\)C) was measured using a 1D gas permeation apparatus. Based on the initial gas permeability results obtained at room temperature, each granite type was categorized into five groups, with each group comprising two standard cylindrical specimens. These grouped specimens were then subjected to a series of freeze-thaw cycling tests. The freeze-thaw cycle test was conducted using the B-DW-120W118 low-temperature preservation chamber manufactured by Shanghai Boyi Test Equipment Co., Ltd. (Shanghai, China) The internal dimensions of the chamber were 500 mm \(\times\) 400 mm \(\times\) 560 mm, and the temperature setting range spanned from \(-\)20 \({}^{\circ}\)C to \(-\)150 \({}^{\circ}\)C.  
Based on previous studies [6; 11; 31] and practical considerations, a freezing rate of 0.5 \({}^{\circ}\)C/min and a freezing duration of 4 h were selected. The freeze-thaw cycle testing procedure was conducted as follows:  
1. Specimens were saturated in an ultra-high-pressure vacuum saturation apparatus for 72 h to ensure full water saturation.
2. Freezing was performed at target temperatures of \(-\)20 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C, \(-\)60 \({}^{\circ}\)C, \(-\)90 \({}^{\circ}\)C, and \(-\)120 \({}^{\circ}\)C, with a controlled cooling rate of 0.5 \({}^{\circ}\)C/min. Once the desired temperature was reached, specimens were maintained at the set temperature for 4 h using a cryogenic freezer.
3. Thawing was conducted under natural ambient conditions.
4. Steps (1) through (3) were repeated for a total of 20 freeze-thaw cycles.  
Following completion of the freeze-thaw cycling, the granite specimens were oven-dried and subjected to 1D gas permeability testing.
# SS-YOLOv8: A Lightweight Algorithm for Surface Litter Detection
## 2 Related Works  
### Surface Litter Detection  
The conventional methodologies employed for the detection of water surface litter primarily entail the construction of templates through the enumeration of the texture, hue, and contour characteristics of water surface entities. Subsequently, the pertinent classifiers are utilized to ascertain the categories of water surface litter.  
Scholars currently employ deep learning techniques for the purpose of object detection [13; 14]. The primary distinction between these two approaches is whether they employ a single or double stage. In order to achieve real-time deployment, the single-phase you only look once (YOLO) series [15; 16] model is commonly utilized in the edge segment. [PERSON] et al. [17] employed the MobileViTv3 as a feature extraction network to encode the entire visual field, thereby reducing the parameters of the YOLO model and expanding its scope of application. [PERSON] et al. [18] employed a modified YOLOv7 with ACanny PConv-ELAN and multi-scale gated attention for adaptive weight allocation (MGA) attention to enhance the precision of target detection for small, challenging objects.  
While these methods are valuable for water surface litter detection, they still present several challenges. (1) Although numerous methodologies exhibit augmented detection precision in specific scenarios, they frequently necessitate larger model parameters and elevated computational complexity, which constrains their applicability for deployment at the edge. (2) Some existing lightweight models reduce the number of model parameters but fail to achieve the best match point between accuracy and model size. In this paper, we propose a convolutional module based on Sobel convolution, which is designed to facilitate the recognition of target edges, facilitate the separation of target and background, and thereby enhance the accuracy of the model. Furthermore, the model is pruned in order to reduce its overall size. This method achieves the best match point between model size and accuracy.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion
### SEM Test Result  
The micro images of basalt fiber and lignin fiber are shown in Figure 9, and the micro images of fiber-reinforced mixtures are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mixture Type** & **Aging Degree** & **Marshall Stability (kN)** & \
\begin{tabular}{c} **Immersed Marshall** \\ **Stability (kN)** \\ \end{tabular} &
\begin{tabular}{c} **Residual Marshall** \\ **Stability (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{BFSMA-13} & Unaged & 12.12 & 11.01 & 90.84 \\  & Short term aged & 12.62 & 11.38 & 90.17 \\  & Long term aged & 12.94 & 11.43 & 88.33 \\ \hline \multirow{3}{*}{LFSMA-13} & Unaged & 10.21 & 9.2 & 90.11 \\  & Short term aged & 9.94 & 8.83 & 88.83 \\ \cline{1-1}  & Long term aged & 9.88 & 8.56 & 87.24 \\ \hline \end{tabular}
\end{table}
Table 7: Water stability indexes.  
As can be observed in Figure 9, basalt fibers are rod-shaped and have a smoother surface. Lignin fibers, on the other hand, are curled and interwoven and have a rougher surface, which results in a much larger specific surface area than basalt fibers. The microscopic morphology of basalt and lignin fibers determines that they have different roles in asphalt mixtures; the role of lignin fibers in SMA is to absorb excess free asphalt. Although the asphalt-absorption capacity of basalt fibers is not as good as that of lignin fibers, their larger modulus of elasticity allows basalt fibers to better transfer the stresses in the mixture, thus improving the strength and toughness of the asphalt mixture.  
As shown in Figure 10, the basalt fibers can form a three-dimensional structure in the mixture [41]. The basalt fibers interweave with each other, however, the fiber itself does not curl (as is presented in Figure 10a). The three-dimensional structure formed by the basalt fibers produces stress caused by the traffic load that is spread more effectively in the mixture, and this structure enhances the weak area (uneven parts formed in the paving process). Lignin fibers also form a three-dimensional structure in the mixture, yet, this structure mainly contributes to the lignin fiber absorbing the asphalt in mixtures. The ability of lignin fiber to bear some of the stress in the mixture is weak because the fracture strength of lignin fiber is much lower (as is illustrated in Table 3). Generally, due to the superior physical and chemical properties of basalt fiber, the ability of basalt fiber to enhance the asphalt mixture is better than that of lignin fiber.  
Figure 10: SEM images of fiber reinforced mixtures: (**a**) BFSMA-13; (**b**) LFSMA-13.  
Figure 9: SEM images of fibers: (**a**) basalt fiber; (**b**) lignin fiber.
# A Brief Survey of Fixed-Parameter Parallelism
## 7 Parallel Algorithms Using FPT Methods
### Bounded Search Trees
The master then performs \(FPT\)-time branching and checks after \(k\) steps whether a solution exists or not by invoking a similar procedure in the slave nodes. If a set is returned, then the solution is incorrect and should be discarded; otherwise, if an empty set is returned, the solution is correct and should be returned. The algorithm can be easily modified for maximization problems. If the solution size reaches \(k\), return the solution. Otherwise, query for a set to branch on, and if there is no set returned, then the current branch does not entail a valid solution. The whole procedure of querying the input--for a set to branch on and branching on this set once found--is \(FPP\). This approach, as described, applies to problems where the branching set size is bounded by a constant, to allow the usage of polynomial number of processors to cover all sets. Problems that fall into this category include the \(d\)-Hitting Set problem (when \(d\) is constant), which brings to mind the Vertex Cover problems as a special case. In addition, problems on \(d\)-degenerate graphs (\(d\) constant), often have a branching set equal to \(d+1\), such as Independent Set and Dominating Set. Independent Set on planar graphs also lies in this category as planar graphs are 5-degenerate. Another family of problems for which this approach is applicable consists of problems where the branching set \(S\) is some (constant-size) forbidden structure. A typical example is the Cluster Editing problem, which requires transforming a given input graph into a disjoint union of cliques by performing a number of edge editing (addition/deletion) operations that does not exceed some parameter \(k\). In this case, the branching set consists of any three vertices that induce a path of length two (known as a _conflict triple_), as described in [30]. Our approach thus proves that Cluster Editing is \(FPP\). It would also be interesting to extend this algorithm further to other variants of Cluster Editing (see, for example, [31; 32; 33]).
Computers_**2015**, \(4\), 283-292; doi:10.3390/computers4040283  
_Computers_  
**ISSN 2073-431X**  
www.mdpi.com/journal/computers  
_Article_  
**Hop-by-Hop Worm Propagation with Carryover Epidemic Model in Mobile Sensor Networks**  
**[PERSON]**  
Department of Information Security, Seoul Women's University, 621 Hwarangro, Nowon-Gu, Seoul 139-774, Korea; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-970-5607; Fax: +82-2-978-7931  
Academic Editor: [PERSON]  
_Received: 15 June 2015 / Accepted: 15 October 2015 / Published: 20 October 2015_  
**Abstract:** In the internet, a worm is usually propagated in a random multi-hop contact manner. However, the attacker will not likely select this random multi-hop propagation approach in a mobile sensor network. This is because multi-hop worm route paths to random vulnerable targets can be often breached due to node mobility, leading to failure of fast worm spread under this strategy. Therefore, an appropriate propagation strategy is needed for mobile sensor worms. To meet this need, we discuss a hop-by-hop worm propagation model in mobile sensor networks. In a hop-by-hop worm propagation model, benign nodes are infected by worm in neighbor-to-neighbor spread manner. Since worm infection occurs in hop-by-hop contact, it is not substantially affected by a route breach incurred by node mobility. We also propose the carryover epidemic model to deal with the worm infection quota deficiency that might occur when employing an epidemic model in a mobile sensor network. We analyze worm infection capability under the carryover epidemic model. Moreover, we simulate hop-by-hop worm propagation with carryover epidemic model by using an ns-2 simulator. The simulation results demonstrate that infection quota carryovers are seldom observed where a node's maximum speed is no less than 20 m/s.  
**Keywords:** hop-by-hop worm propagation model; carryover epidemic model; mobile sensor network
## 3 Proposed Method for Comparing between Original and Manipulated Videos
### Description of Image Processing, Optimization Search Method, and Comparison Algorithm
#### 3.2.2 Optimized Comparison Search Technique for Original and Manipulated Videos  
It can take a long time to compare a reference video with the videos in a suspects' mobile devices because the number of videos is large. Therefore, an optimized comparison search technique is proposed in this study for finding equal videos within an optimal time. We summarize the definition of the optimization as follows. * Reduce the number of images: We reduce the total number of analytical images to be compared by predetermining the number of images (frames) extracted based on the runtime of the video (Table 1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Run Time (min)** & **Frames per Second** & **Number of Frames to Extract** \\ \hline
0.1–4.59 & 3 & 3–897 \\
5–7.69 & 2 & 600–958 \\
8–14.59 & 1 & 480–899 \\
15–19.59 & 0.5 & 300–599 \\
20–\(\infty\) & 0.334 & 400–\(\infty\) \\ \hline \hline \end{tabular}  
* Reduce the search time: We reduced the search time of the representative image determined by the investigator by referring to the quicksort and quickselect method (Table 2).  
\end{table} 
Table 1: Experimental datasets. \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Frames Extracted** & **Number of Continuous Frames (k)** \\ \hline
0–200 & 20 \\
200–400 & 50 \\
400–600 & 100 \\
600–800 & 150 \\
800–\(\infty\) & 200 \\ \hline \hline \end{tabular}  
* Reduce the misuse detection rate: We quantitatively predefined the attribute information of the crime video and reduced the misuse detection rate by setting each threshold for the filming properties (Table 5).  
\end{table} 
Table 2: Frame continuity unit according to the number of frames of a video in comparison. beginning, and the end and repeats the process, excluding the searched frames, until the whole video is scanned. The process refers to the quicksort [21] method. The process of comparing a query video with a comparison video is expressed in the pseudocode in Figure 3. The first to last \(M\) frames of a query video are labeled \(Q_{1}\), \(Q_{2}\), \(Q_{3}\),..., \(Q_{m}\), respectively. The first to last \(N\) frames of a comparison video are set to \(A_{1}\), \(A_{2}\),..., \(A_{n}\), respectively. The first query frame \(Q_{1}\) is compared with the middle of the comparison frame, or \(A_{n/2}\). The search is processed as many times as the continuous frame units (\(k\) frames) in Table 1. If the frames are not concluded to be similar based on the results of the search, the comparison frame is changed to \(A_{1}\), and \(k\) frames are compared again. From the middle frame, \(k\) frames are continuously compared, and then from the first frame, \(k\) frames are continuously compared. Thus, the search sequence is determined. The pseudocode is described in detail as follows:
## 5 Numerical Examples
### Nonstationary Wind Fields  
For the nonstationary wind field, the target auto-EPSD is assumed to be the extended Kaimal spectrum, which can be obtained by replacing the constant mean wind velocity as the time-varying one \(\hat{U}(t)\) in Equation (34). The time-varying mean wind velocity \(\hat{U}(t)\) is generally given by  
\[\hat{U}(t)=d(t)U \tag{36}\]  
where \(d(t)=\) time modulation function with three parameters [38], i.e.,  
\[d(t)=\alpha_{0}t^{\beta_{0}}e^{-\lambda t},\alpha_{0}>0,\beta_{0},\lambda\geq 0 \tag{37}\]  
in which the parameter \(\alpha_{0}=\lambda^{\beta_{0}}/\beta_{0}^{\beta_{0}}e^{\beta_{0}}\) and assume the maximum value of this function occurs at \(t_{\max}=\beta_{0}/\lambda\). In this simulation, the parameters \(t_{\max}=300\) and \(\beta_{0}=2\) are adopted. Assume that each simulation point has the same auto-EPSD.  
The time-invariant coherence function adopts Equation (35). The time-varying coherence function is obtained by extending the [PERSON]'s model, i.e., [37]  
\[\gamma_{jk}(\omega,t)=\exp\left(-\frac{C_{y}\big{|}y_{j}-y_{k}\big{|}\omega}{ 2\pi U(t)}\right)\exp\left(-i\frac{(y_{j}-y_{k})\omega}{v_{app}^{jk}(t)}\right) \tag{38}\]  
where \(U(t)=\big{[}U_{j}(t)+U_{k}(t)\big{]}/2=\) averaged time-varying mean wind velocity and adopts the following simplified form:  
\[U(t)=U\cdot\big{[}1+\gamma\cos\left(\omega^{\prime}t\right)\big{]}=40[1+0.3 \cos\left(2\pi/1200\cdot t\right)] \tag{39}\]  
and the apparent wave velocity \(v_{app}^{jk}(t)\) is given by [37]  
\[v_{app}^{jk}(t)=\frac{2\pi U(t)}{C_{\theta}} \tag{40}\]  
in which \(C_{\theta}=\) a coefficient obtained by experiments and generally takes 5.5.  
Figure 6: Estimated correlation functions by ergodic stationary wind fluctuation samples: (a) \(R_{21,21}(\tau)\); (b) \(R_{1,21}(\tau)\).  
In the following, two nonstationary wind fields with time-invariant coherence and time-varying coherence will be simulated based on the two phase schemes to study the influence of the phase on the nonstationary simulation. The time and frequency parameters used to the nonstationary wind field are the same as the above non-ergodic stationary simulation.
# Scaling of FAPAR from the Field to the Satellite
## 1 Introduction
Secondly, the logistic model was used to interpolate the ground measurements to match the dates of satellite data. Thirdly, the reference FAPAR values at 15 m were estimated from fine-resolution satellite data to bridge the spatial discrepancies between _in-situ_ data and coarse-resolution satellite product. The reference FAPAR values at coarse resolution were finally generated through spatial aggregation as spatially and temporally comparable to coarse-resolution FAPAR products. The study is performed in Heihe River Basin, China, where arid and semi-arid climate dictates. It is a part of the Heihe Watershed Allied Telemetry Experiment Research (HiWATER), which was implemented in 2012 and collected a large volume of field-measured biophysical parameters [32]. HiWATER provides valuable and reliable field FAPAR measurements for this study. The MODIS FAPAR product MOD15A2 C5 [33] was taken as a study case for coarse-resolution satellite data for its popularity in eco-environmental applications. The definition of FAPAR within MODIS product includes absorption of PAR from both direct solar radiation and diffuse skylight, which is in accordance with the ground measurements and is a particularly typical case for temporal normalization. The ground FAPAR measurements were scaled to the coarse-resolution satellite product following the proposed scaling methodology. The derived coarse-resolution FAPAR reference data were used to validate the MODIS product. Finally the performances of the scaling methodology were evaluated quantitatively. This study provides valuable reference for understanding how spatial and temporal mismatches occur and how to address these issues in scaling FAPAR, a variable with high spatial and temporal variations, from the field to the satellite.
# Study on Spray Characteristics and Breakup Mechanism of an SCR Injector
## 2 Numerical Models and Methods
### Model Description and Methods of Primary Breakup
#### 3.1.2 Breakup Length of Liquid Jet  
The average breakup length (\(L_{c}\)) of a liquid jet is one of its important characteristic parameters. [PERSON] et al. [33] summarized the empirical equation for the average breakup length of common liquid jets (such as water and ethanol) based on previous studies:  
\[\frac{L_{c}}{d_{0}}=5 We^{1/2},\text{ for }We<400 \tag{19}\]  
Figure 7 compares the correlation between the average breakup length of the simulations and the empirical curve. The average breakup length is the average of the transient breakup lengths over a given period. It can be seen from Figure 7 that the three cases are basically consistent with the empirical curve. Therefore, the empirical equation is also applicable to the average breakup length of the liquid jet in this study when \(We<510\).  
Figure 5: Initial radial velocity of the jet versus time.  
For the injector in this study, the jet velocity increases gradually with the increase in injection pressure, while the characteristic diameter of the liquid jet hardly changes. Thus, the Reynolds number increases monotonously with the increase in pressure, and the breakup length of the liquid jet also increases.
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Commercial Biosensors
#### 4.1.3 Gas Sensors
The most common oxygen sensors are photoluminescence-based (fluorescence or phosphorescence) that rely on the quenching principle of oxygen sensitive dyes. Photoluminescent dyes are usually embedded in oxygen permeable polymers or sol-gel matrices in the form of labels or dots. Oxygen is considered a very common fluorescence quencher of the electronically excited oxygen sensitive dye. The interaction of light with oxygen molecules will deactivate the phosphors of the active dye, so the emission intensity and the lifetime of the dye will decrease, reflecting the change in level of oxygen inside the package [105; 131]. The concentration of oxygen is related to quenching fluorescence through the Stern-Volmer equation, which can be used for quantitative measurements [132]. This type of sensor is considered superior over conventional oxygen sensors such as electrochemical-based oxygen sensor for food packaging applications for the following reasons: non-destructive, clean, reversible, fast response, and does not consume oxygen in the headspace of the package. However, degradation in the dye may cause disturbance in the light path and is a limitation together with its susceptibility to drift [19]. There are several common dyes, that were used for oxygen sensing of packaged vegetables such as [Ru(dpp)\({}_{3}\)]\({}^{2}\)[124], PtTFPP [133], PtOEPK [134; 135], and PtTPTPBPF [136]. Table 3 provides a brief comparison of photophysical properties for four common sensing dyes. Figure 13: Structure of CO\({}_{2}\) sensor in a Wheatstone bridge configuration. The first commercial photoluminescence-based sensor for oxygen detection was developed by OxySense, also known as O2 xydotsTM which is suitable for packaged vegetables [13]. The dots have an oxygen sensitive dye that is illuminated with blue light. A photodetector is used to detect red color emissions and measure the fluorescence lifetime [13]. The fluorescence will depend on the oxygen concentration which will quench the emissions as its level increases. Another popular oxygen sensor that can be used for freshness monitoring is the galvanic cell type. This electrochemical sensor has three main parts: cathode, anode, and oxygen permeable membrane that allow oxygen diffusion from the surrounding to the electrodes (cathode and anode) where oxidation and reduction reactions takes place [141; 142; 143]. The electrolyte soluble anode will free electrons that will reach the cathode where diffused oxygen will absorb these free electrons. This will allow for current to flow between electrodes depending on the concentration of oxygen diffused. The self-powering property, low cost, and the absence of cross-sensitivity to carbon dioxide are important advantages of these sensors [141; 142; 143]. A comparison between some of the commercial oxygen and carbon dioxide sensors used in packaged vegetables with respect to principle of operation, typical specifications, and cost is summarized in Table 4.
## 5 Experiments and Results
### Auditive Transparency
#### 5.2.3 Key-Based Multiplicative Strategy  
In the same fashion as the additive strategy, the multiplicative strategy was evaluated for \(\alpha\) values \(\in[\frac{1}{4}...2]\), in steps of \(\frac{1}{4}\) in descending fashion, until the highest \(\alpha\) value was achieved that approximated the distortion results for the unsecured data-hiding version. The found \(\alpha\) value was 1; Figure 9 shows the PSNR results and the PSNR distribution for that \(\alpha\) value.  
Figure 6: Objective Difference Grade (ODG) results for watermarking without key-based security. (**a**) ODG results. (**b**) ODG distribution.  
Figure 7: PSNR results for watermarking using the additive strategy. (**a**) PSNR results. (**b**) PSNR distribution.  
The PSNR mean and median were calculated as 44.34 and 43.29, respectively.  
The ODG evaluation achieved similar values as the unsecured data-hiding version; Figure 10 shows the ODG results and the ODG distribution. The ODG mean and median were calculated as \(-0.9387\) and \(-0.6222\), respectively.  
Figure 8: Objective Difference Grade (ODG) results for watermarking using the additive strategy.  
Figure 10: ODG results for watermarking using the multiplicative strategy. (**a**) ODG results. (**b**) ODG distribution.  
Figure 9: PSNR results for watermarking using the multiplicative strategy. (**a**) PSNR results. (**b**) PSNR distribution.  
For the multiplicative strategy, the statistical tests showed no statistical differences in terms of PNSR and ODG. Similar to the unsecured data-hiding version, the multiplicative PSNR and ODG results are very good for practical applications.