esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Lue
#### 3.2.2 Parameterization Approach with CI  
Considering the effect of different radiation modes, we added the CI in the parameterization approach and gained a second global LUE distribution (Figure 6). In general, the LUE distributions produced by this approach were similar to those produced by the parameterization approach without the CI; the differences between the two approaches ranged from \(-0.1\) to \(0.4\) gC/MJ. For example, the differences included the LUE calculated by the second approach (V2) being even higher in high-value zones, which agrees with a previous study that found that diffuse light enhanced LUE within similar environmental conditions [82]. Looking at LUE in every single season, we found (1) from 2014049 to 2014136, the LUE calculated by V2 in South America, middle Africa, and the Southeast Asia reached \(1.8\) gC/MJ, which was approximately \(0.3\) gC/MJ more than the LUE calculated by V1. In Europe, the LUE calculated by V2 slightly exceeded that calculated by V1, by \(0.1\) gC/MJ. On the other hand, the LUE calculated by V2 was lower than the LUE calculated by V1 in the southwestern USA, the north of Africa, and the southwest of Asia; (2) from 2014137 to 2014224, the LUE calculated by V2 in Europe, Canada and northern Asia reached approximately \(1.25\) gC/MJ and slightly exceeded the LUE calculated by V1 by \(0.15\) gC/MJ. However, the LUE calculated by V2 was lower than that calculated by V1 in the southwestern USA, the north and south of Africa, the southwest of Asia and the north of Australia; (3) from 2014225 to 2014320, the differences between the two approaches narrowed. Great increases occurred in the north of South America, middle of Africa, and the southeast of Asia. (4) In the last season, variations continuously decreased in the Northern Hemisphere but gradually increased in the Southern Hemisphere, with more areas showing the LUE calculated by V2 being higher than the LUE calculated by V1. In the four seasons, the equatorial regions all showed an increase in LUE as calculated by V2. The main reason is that these areas had abundant rain for the whole year, which reduced the radiation reaching the surface of the earth, consequently leading to a low CI. Therefore, the addition of the CI increased the value of LUE. Other increases in LUE in the region during the growing season can be explained by there being more rain and lower CI.  
Figure 5: The distributions of LUE by parameterization approach without the CI (V1).
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 3 Background and Motivations
### NDN Forwarding in VANETs  
#### 3.2.1 Single-Face Communications  
NDN communication over IEEE 802.11 is generally based on controlled flooding [10; 11; 26]: the Interest is broadcast by the consumer and re-broadcast by some available intermediate nodes until a content provider is found or the Interest Time-To-Live (TTL) expires.  
Broadcasting is generally useful because: (i) it allows one to reach multiple nodes, potentially storing the content; and (ii) it may facilitate content sharing, since broadcast packets can be overheard over the wireless channel. However, broadcast may unavoidably result in high channel access contention, collisions and redundancy, especially in dense multi-hop environments like IoV.  
To limit such adverse effects, solutions in the literature typically assume that the transmission instant of both Interest and Data packets is delayed in each node of a given defer time, and packet suppression techniques based on overhearing are applied. According to them, during the waiting time, each transmitting node listens to the channel: if it overhears the same packet, then the imminent transmission is canceled. The additional delay can be randomly selected in a pre-defined temporal window [11; 27] or it can be related to the distance from the previous sender [8; 9]. Such schemes are totally distributed and performed autonomously by vehicles and RSUs; therefore, we refer to such mechanisms as \"Blind Flooding\" (BF).  
To restrict the use of broadcasting, in some works, Interests are flooded only initially, when the content location is unknown; then, once a provider is discovered, an eligible forward/provider is addressed hop-by-hop. For instance, the Listen First Broadcast Later (LFBL) protocol [28], frequently used as the reference NDN forwarding strategy in wireless networks, piggybacks additional information (e.g., the hop distance from the content source and its unique identifier) in Interest and Data packets, which are tracked by traversed nodes. In particular, each intermediate node receiving the Interest checks if it is closer to the provider than the previous sender. If this is the case, it computes the defer time to rebroadcast the Interest. In so doing, Interests are more quickly and efficiently driven towards the content source.  
Other approaches use regular beacon exchange to carry information helpful to select the best forwarder, at the expense of additional signaling, e.g., [9; 29].  
Finally, in [24], the concept of \"geo-faces\" is introduced: content names are bound to the providers' geographic area(s), and Interests are forwarded towards such area(s) by using a shortest path algorithm over the road topology. When a geo-face is not present for content, flooding is performed. If a provider is found, it attaches its geo-area name to the returned Data packet, which follows the breadcrumb trace of the Interest and allows nodes to learn the binding between the data name prefix and the corresponding geo-area.
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## 3 Results and Discussion
### Optimization of the Undoped Region Thickness (\(H_{2}\))  
Based on the thickness and doping concentration of the upper layer region determined above, the thickness of the undoped region is optimized to obtain better power added efficiency and DC AC parameters. The effect of the thickness of the undoped region on the DC, AC parameters and PAE of the device is shown in Figure 5. In Figure 5a. We can see that as \(H_{2}\) increases, the absolute value of the threshold voltage gradually decreases. At the same time, when \(H_{2}\) is less than 0.05 \(\upmu\)m, the breakdown voltage increases as \(H_{2}\) increases, when \(H_{2}\) is greater than 0.05 \(\upmu\)m, the breakdown voltage shows a basically constant trend with the increase of \(H_{2}\). In Figure 5b. As \(H_{2}\) increases, the transconductance (\(g_{\text{m}}\)) shows a decreasing trend. When \(H_{2}\) is less than 0.15 \(\upmu\)m, the transconductance decline trend is more gradual. When \(H_{2}\) is greater than 0.15 \(\upmu\)m, the transconductance decreases sharply as \(H_{2}\) increases. When \(H_{2}\) increases, \(C_{\text{gs}}\) shows an upward trend. In Figure 5c. As \(H_{2}\) increases, the saturation current shows a linear decline. When \(H_{2}\) is less than 0.05 \(\upmu\)m, the PAE increases as \(H_{2}\) increases, and when \(H_{2}\) is greater than 0.05 \(\upmu\)m, the PAE decreases as \(H_{2}\) increases. The PAE reaches the maximum at \(H_{2}\) = 0.05 \(\upmu\)m.  
Figure 4: The effects of \(N_{\text{d}}\) and \(H_{1}\) on the PAE.
# PAM-\(N\)--Fundamental Limits in Chromatic Dispersive-Uncompensated Channels
## 5 Limitations on Channels' Capacity  
To quantify the trade-off in this system, it is necessary to define the channel information capacity. For a given PAM-\(N\) constellation, the maximum channel capacity is (provided the SNR >> \(N\)):  
\[S=Bb=B\log_{2}N \tag{24}\]  
where \(b=\log_{2}(N)\) is the number of information bits in a single symbol. Clearly, in case of the SNR \(<N\) then Equation (24) reduces to the Shannon formula \(S\cong B\log_{2}(1+SNR)\). Therefore, using Equations (24) and (22), the tradeoff between the channel capacity \(S\) and the maximum distance can be written as a function of a single parameter, \(b\):  
\[S^{2}L_{\max}\beta_{2}=\frac{2}{\pi^{2}}\text{acos}\Big{(}1-2^{1-b}\Big{)}b^{2} \cong\frac{4b^{2}}{\pi^{2}2^{b/2}} \tag{25}\]  
The last approximation is valid in the large \(b\) regime. As can be seen from Figure 6, Equation (25) has a maximum of \(b=6\), in which case:  
\[S^{2}L_{\max}\beta_{2}\cong\frac{18}{\pi^{2}}\cong 1.83 \tag{26}\]  
Obviously, these values apply only when the channel is noiseless. Therefore, they are important to evaluate the maximum errorless channel's capacity. No better performances can be achieved without dispersion compensation (DC), regardless of the channel's noise levels. In the presence of noise, the maximum result in Equation (26) is still valid provided  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N\) & **Bits per Symbol** & \(\xi_{\max}\) & \(z_{\max}\) **for C-Band at 53 GBaud (km)** \\ \hline
2 & 1 & 0.318 & 5.66 \\
4 & 2 & 0.212 & 3.77 \\
8 & 3 & 0.147 & 2.62 \\
16 & 4 & 0.102 & 1.82 \\
32 & 5 & 0.072 & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of Equation (22) for different Q-values.  
Figure 6: Product \(S^{2}L_{\max}\beta_{2}\) as function of number of bits per symbol \(b\).  
\(\log_{2}(\text{SNR})>6\) (i.e., \(\text{SNR}>64\)). Otherwise, the maximum capacity will be evaluated by substituting \(b=\log_{2}(\text{SNR})\) in Equation (25).  
Furthermore, from the definition of \(G(\pi)\), it is even possible to derive an approximated evaluation of the relation between \(\delta\) and \(\Delta\). From \(G(\pi)=\pi/4\) and Equation (11), the following can be derived:  
\[\frac{\pi}{4}\cong\frac{1}{2}\bigg{[}\text{erfc}\bigg{(}\frac{\pi(1-\Delta)}{ \delta}\bigg{)}\bigg{]} \tag{27}\]  
and then:  
\[\delta\cong\frac{\pi}{\text{erfc}^{-1}(\pi/2)}(1-\Delta)\cong 5.6(\Delta-1) \tag{28}\]  
In practice, this expression ignores the fact that relatively large values of \(\Delta\) have a severe distortion effect on the signal, which therefore cannot be used for shorter distances, in which case a larger attenuation parameter is required, i.e., something like:  
\[\delta\cong 10(\Delta-1) \tag{29}\]  
However, Equation (28) is a very good approximation despite the many approximations and assumptions made in the derivation process.
## 3 Results and Discussion
### High-Temperature Property Analysis  
#### 3.2.1 Viscosity  
Aiming at investigating the effect of the layered silicate type on the flowability and high-temperature properties of virgin bitumen, the viscosity of all bitumen specimens at four high temperatures (120 \({}^{\circ}\)C, 135 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 165 \({}^{\circ}\)C) is provided (Figure 4). As expected, viscosity increased with the incorporation of the modifiers, which can be attributed to the restriction of the modifiers on the motion of the bitumen molecules [29; 33; 36]. With respect to the modifier type, regardless of the test temperature, OREC endowed the virgin bitumen with the largest viscosity, contrary to MMT, and there was a similar viscosity for the REC-modified bitumen and the OMMT-modified bitumen, which is probably related to the specific surface area of the layered silicates in the bitumen matrix [17; 36]. In detail, OREC is characterized by the largest interlayer space (obtained from Figure 4) and aspect ratio amongst these four silicates. Therefore, OREC has the largest specific surface area to hinder the motion of the bitumen molecules (or the flowability of bitumen) most effectively. However, although organophilic OMMT has a smaller aspect ratio and interlayer space than hydrophilic REC, it has a better compatibility with bitumen due to the organic treatment, which helps to enhance the specific surface area of OMMT particles in the bitumen matrix [17; 18]. Additionally, it can also be noted that, in Figure 4, the temperature dependency of the viscosity was obvious, as the viscosity reduced nearly linearly with the temperature. The viscosity at other temperatures can be predicted based on the linear regression functions listed in this figure.  
To ensure uniform mixing and effective compaction of the bitumen mixture, as well as to prevent the aging caused by too high temperatures, the bitumen pavement construction needs to control the construction temperature (including the mixing and compaction temperatures) of the bitumen mixture within an appropriate range. According to the Superpave specification [38], the mixing and compaction temperatures of these bitumens are obtained, as displayed in Figure 5. It can be seen that the modification promoted an increase in the mixing and compaction temperatures. Among these  
Figure 3: The XRD test result for untreated and treated layered silicates: (**a**) Diffraction angle; (**b**) interlayer space (\(d_{001}\)).  
layered silicates, OREC endowed bitumen with the highest mixing temperature, followed by OMMT, REC, and MMT. Similar trends could be found for compaction temperature results. Significantly, the results in Figure 5 provide guidance for practical construction.
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
###### Abstract
In our filtering method, we mainly focus on the similarity of HTML forms and judge them based on the feature information of the form and the page on which it is located. It reduces the time spent by the scanner in visiting and fuzzing similar states. In our ranking method, we propose that a higher priority should be assigned to forms with more injection points in the context of fuzzing. To evaluate the effectiveness of our methods, we present our black-box scanner SSBW by incrementally implementing the filtering method and ranking method on Black Widow. Experimental evaluation illustrates that the code coverage on eight applications of the scanner with our filtering method increased by 17% on average, with the highest increase being 57%. The scanner with the ranking method can detect 53 more vulnerabilities in three out of eight applications compared to the scanner without the ranking method. The combination of the filtering and ranking methods helps to get an average increase of about 19% in code coverage and detect 81 more XSS vulnerabilities. In summary, our contributions are the following:  
* We have designed a stricter filtering approach that excludes similar states without the need to actually visit these states. This enhancement makes the scanner more attuned to different states, resulting in fewer repetitive visits to similar states. This improvement, in turn, boosts the code coverage of the black-box web scanner within a limited scanning time. * In order to fuzz a greater number of injection points within a specific time frame, we have devised a heuristic ranking technique for HTML forms. This approach prioritizes the forms with a higher count of injection points, thereby enhancing the efficiency of the black-box web scanner in identifying XSS vulnerabilities. * We implement a filtering method and a ranking method in our black-box web scanner SSBW. These methods were experimentally evaluated on eight real-world applications. Our evaluation results show that SSBW can attain an average increase of about 19% in code coverage and detect 81 more XSS vulnerabilities compared to Black Widow within a limited scanning time.
## 1 Introduction  
The requirement for attitude (orientation) information of a spacecraft has been the mother of invention of many devices and algorithms, notably the process of autonomously identifying stars (Star-ID). Though there is much history of devices used to identify stars and compute an attitude that do not use a star camera, this paper primarily analyzes algorithms that use a star camera with an imaging array and an algorithm to match observed (body) directions of stars with catalog (inertial) directions of stars without requiring reorienting the camera or the spacecraft. These algorithms fall into two basic categories, _lost-in-space_ algorithms, in which no information regarding the attitude of the spacecraft is available, and _recursive_ algorithms, in which some information regarding the attitude is available. These techniques typically use inter-star angles (the angle between the line-of-sight of two stars from the perspective of a camera), the brightness of the stars, and some computations of these values to distinguish stars. A further subcategory of both categories is _non-dimensional_ Star-ID, in which the exact angular separations are not required but the values are normalized to be insensitive to poor camera calibration, or time-varying camera calibration parameters [1; 2; 3].  
Star-ID is just one step in the process of determining the attitude of a spacecraft, as depicted in Figure 1. The first algorithms in the sequence measure body-frame vectors for the locations to observed stars. The Star-ID process has as its primary purpose determining the inertial-frame vectors for some or all of the body-frame vectors given to it. Subsequent algorithms determine a suitable transformation that correctly maps the body vectors to the inertial vectors, thereby calculating the spacecraft's attitude. When sensing the stars with a CCD imaging array, the information available for the identification process is the brightness of the star and the angular separation between stars.  
Star-ID task has three basic pieces, with an optional fourth, as illustrated in Figure 2. First, an algorithm must extract features from a set of body vectors and associated brightness. Second, a database search matches a subset of the observations with entries in the database, and third makes some estimate as to the probability that they are correct. Optionally, the remaining body vectors are identified once an estimate of the attitude is available in a method called \"recursive\" Star-ID. This fourth step typically implements a variation of the \"direct match\" technique in which stars are identified by their close proximity to their predicted location. The recursive mode is typically much faster than the first two, and can usually be repeated successively for additional observations with an a priori estimate of the attitude.
## 3 Results and Discussion  
### Threshold Determination and Quality Characteristics of High-CL and Low-CL broiler Breast Fillets  
In a previous report of WHC values statistics of broiler breast fillets, average CL values for high-WHC and low-WHC groups were of 18.4-19.6% and 21.6-22.3%, respectively [45]. Referring to these results, in our study, fillets were classified as having high-CL when CL was \(\geq\)20% and as having low-CL when CL was \(<\)20%. The sample distribution of CL values of 74 fillets (one of the 75 was excluded due to image damage) is illustrated in Figure 3, and the number of samples in two groups accounted for half (\(n=37\)) of the total based on this threshold standard (20%) fittingly.  
Besides CL values, descriptive statistics such as mean, range, and standard deviation (SD) of the other six quality attributes of the broiler breast fillets with high-CL and low-CL are also shown in Table 1. The CL values of all fillets ranged from 15.17% to 36.18%, and mean values for high-CL and low-CL were 22.05% and 18.15%, respectively. No differences at the significance level of 0.1 were observed for the color attributes. However, numerical differences of overall average values could be observed. For example, for high-CL samples the overall a* and b* values were 0.20 and 12.61, respectively, compared with \(-\)0.11 and 12.17 for low-CL samples, which demonstrated that high-CL fillets exhibited a little more red and yellow than low-CL fillets. Because poor WHC is closely associated with pale color and low muscle pH in chicken meat [46], as expected, high-CL fillets exhibited higher overall average L* values and lower pH values than low-CL fillets. Also, a significant difference is shown in the \(t\)-test (\(p<0.1\)) for the average WBSF values between high-CL and low-CL groups, which indicated that less juice fillets also generally presented lower tenderness.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Trait**} & \multicolumn{3}{c}{**High-CL Filllets (\(n=37\))**} & \multicolumn{3}{c}{**Low-CL Filllets (\(n=37\))**} \\ \cline{2-7}  & **Min.** & **Max.** & **Mean \(\pm\) SD** & **Min.** & **Max.** & **Mean \(\pm\) SD** \\ \hline L* & 47.29 & 63.76 & 57.35 \(\pm\) 3.26 a & 48.39 & 61.60 & 56.45 \(\pm\) 2.79 a  \\ a* & \(-\)1.51 & 1.71 & 0.20 \(\pm\) 0.84 a  & \(-\)1.39 & 1.96 & \(-\)0.11 \(\pm\) 0.84 a  \\ b* & 8.88 & 16.32 & 12.61 \(\pm\) 1.78 a  & 8.55 & 17.48 & 12.17 \(\pm\) 1.90 a  \\ pH & 5.75 & 6.24 & 6.01 \(\pm\) 0.11 b  & 5.84 & 6.27 & 6.06 \(\pm\) 0.12 a  \\ WBSF (kgf) & 1.66 & 12.08 & 5.06 \(\pm\) 2.43 a  & 2.64 & 8.24 & 4.27 \(\pm\) 1.26 b  \\ CL (\%) & 20.02 & 36.18 & 22.05 \(\pm\) 2.96 a  & 15.17 & 19.76 & 18.15 \(\pm\) 1.25 b  \\ \hline \end{tabular}
\end{table}
Table 1: Quality attributes of broiler breast fillets with high-CL and low-CL.  
Figure 3: Sample distribution of 74 fillets based on mean CL values.
# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 4 Experiments and Evaluation
### Experimental Setup and Evaluation Metrics
The recall metric is computed as follows:  
\[\textit{Recall}=\frac{N_{rs}}{N_{r}}. \tag{13}\]  
F1 is a combination of precision and recall expressed as follows:  
\[F1=\frac{2*\textit{Precision}*\textit{Recall}}{\textit{Precision}+\textit{ Recall}} \tag{14}\]  
Note that neighborhood size has a significant impact on recommendation quality [23; 24; 25]. According to previous research [16], 30 is an optimal neighborhood size; thus, in our experiments, we set \(\text{n}=30\) for each item in the target item's neighborhood, which means that we utilized an item's top 30 most similar items to represent the given item's characteristics. However, to indicate changes in the evaluation metrics as the size of the target item's neighborhood increases, we also used different neighborhood sizes {10, 20, 30, 40, 50}. Furthermore, to calculate the precision, recall and F1 values, we treated items rated no less than 3 as relevant items and the number of recommendations was set to 2, 4, 6, 8, 10 and 12.