esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### Internal Evaluation
#### 5.1.3 Statistical Test

Table 6: The \(t\)-test of ARE in different generations using \(\mathbf{BBA_{CO}}\) and \(\mathbf{BBA_{LHS}}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Reuters**} & \multicolumn{2}{c}{**WebKB**} \\ \hline
**Method** & \(\mathbf{BBA_{CO}}\) & \(\mathbf{BBA_{LHS}}\) & \(\mathbf{BBA_{CO}}\) & \(\mathbf{BBA_{LHS}}\) \\ \hline \multicolumn{5}{c}{generation 1} \\ \hline Mean & 90.85 & 86.90 & 90.72 & 86.78 \\ \(p\)-Value & 0.14 & 0.38 & 0.40 & 0.16 \\ \(t\) State & 7.45 & & 3.50 & \\ \(t\) Critical two-tail & 2.00 & & 2.02 & \\ \hline \multicolumn{5}{c}{generation 20} \\ \hline Mean & 87.91 & 86.25 & 88.00 & 85.24 \\ \(p\)-Value & 0.18 & 0.21 & 0.32 & 0.06 \\ \(t\) State & 1.88 & & 2.51 & \\ \(t\) Critical two-tail & 2.03 & & 2.00 & \\ \hline \multicolumn{5}{c}{generation 50} \\ \hline Mean & 84.73 & 85.61 & 84.00 & 81.65 \\ \(p\)-Value & 0.09 & 0.19 & 0.10 & 0.13 \\ \(t\) State & \(-0.90\) & & 1.86 & \\ \(t\) Critical two-tail & 2.00 & & 2.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The \(t\)-test of WRE in different generations using \(\mathbf{BBA_{CO}}\) and \(\mathbf{BBA_{LHS}}\). The Quality and Reduction Rate of the Selected Feature Set  
This subsection compares \(\text{BBA}_{\text{CO}}\) and \(\text{BBA}_{\text{LHS}}\) in terms of the quality of the final solution (i.e., the resultant feature set), the size of the selected feature set and the reduction rate. Tables 8 and 9 compare the two methods based on the mentioned metrics in the Reuters and WebKB datasets, respectively. Tables 8 and 9 clearly show that \(\text{BBA}_{\text{CO}}\) improves the quality and the reduction rate of the selected feature set compared with the \(\text{BBA}_{\text{LHS}}\) results. The improvement is likely attributed to the ability of \(\text{BBA}_{\text{CO}}\) to combine the advantages of dividing the solutions into smaller parts and to maintain the populations' diversity. Dividing the solution into smaller solutions allows \(\text{BBA}_{\text{CO}}\) to better optimize the solution components (i.e., parts) resulting in a better final solution. The cooperative coevolving strategy in \(\text{BBA}_{\text{CO}}\) directs the algorithm to better convergence.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Constitutive Model
#### 2.1.2 Yield Surface  
UBCSAND2 model is formulated in the framework of classical elasto-plasticity, in which yield locus comprises the two-mobilized plane: (i) maximum shear stress plane and (ii) horizontal plane in the stress space of shear stress \(\tau\) and effective normal stress \(\sigma^{\prime}\) as shown in Figure 1. The failure state line is the yield locus with the highest stress ratio that corresponds with the Mohr-Coulomb failure line (represented by a slope of \(\sin\phi_{\rm f}\)). The line with slope of \(\sin\phi_{\rm cv}\) is the yield locus with a stress ratio that corresponds to the phase transformation from failure state to residual state of sand (represented by a slope of \(\sin\phi_{\rm cv}\)). Yield loci for both planes \(f_{\rm i}\) are specified in the same way as follows:  
\[f_{i} = t_{i}-\sigma^{\prime}{}_{m}\cdot sin\phi_{mi} \tag{3}\]  
where \(i\) indicates the maximum shear plane (\(i=1\)) or the horizontal plane (\(i=2\)), \(t_{i}\) is the shear stress acting on each mobilized plane (i.e., \(t_{1}=\tau=(\sigma^{\prime}{}_{\rm x}-\sigma^{\prime}{}_{\rm y})\)/2, \(t_{2}=\tau_{\rm xy}\)), \(\sigma^{\prime}{}_{m}\) is the mean effective stress (i.e., \((\sigma^{\prime}{}_{\rm x}+\sigma^{\prime}{}_{\rm y})\)/2), and \(\phi_{mi}\) is the friction angle mobilized on each plane.  
In the UBSCAN2 model, both maximum shear stress and horizontal plane work at the same time for loading when only the horizontal plane is triggered upon unloading. Therefore, the modification of shear stress ratio is essential to account for the contribution of each plane to the plastic deformation as shown in Figure 2. First, the initial yield locus is located at the stress reversal point C as shown Figure 2 and the yield locus enters into unloading phase until the shear stress changes signs, or reversal occurs. At this time, the mobilized shear stress and shear stress at failure are modified to calculate the plastic shear modulus. (i.e., \(\tau\) *\({}_{xy}=\tau_{r}-\tau_{xy}\) for unloading and \(\tau\) *\({}_{xy}=\tau_{r}+\tau_{xy}\) for reloading and \(\tau\) *\({}_{f}=\tau_{r}+\tau_{f}\), where \(\tau_{r}\) is the shear stress at failure).  
The stress ratio on each mobilized plane \(\eta_{i}\) is expressed as follows:  
\[\eta_{i}\ =\ \frac{t_{i}}{\sigma^{\prime}m}\ =\ sin\phi_{mi} \tag{4}\]  
Figure 1: Yield locus of the UBSCAN2 model.  
Figure 2: (**a**) Schematic of shear stress–strain relationship, and (**b**) modified shear stress during unloading and reloading.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Comparison with Other Flooding Techniques  
The section above shows that SFLS scaled well with the total number of nodes in the network since the mean number of retransmissions was of form \(1+a\times M\), where \(M\) is the mean number of neighbors of a node (M is constant since the network is homogeneous.). However, if a proactive routing protocol is already implemented in the network, one may consider using information that is already present to help the flooding of location information. The MPR flooding of the optimized-link-state -routing (OLSR) protocol, which exploits the information included in the Hello packets, was shown to be very efficient and, in particular, more efficient than the dominating set flooding (see [8]). We propose to disseminate location information with the multipoint relays (MPRs) of OLSR. The main idea is to ensure the dissemination of a packet using special nodes called multipoint relays (MPRs). The multipoint-relay set of a node is a minimal set of its neighbors that can reach all of its two-hop neighbors (The two-hop neighbors of a node are the neighbors of the neighbors that are not one-hop neighbors. Using MPRs recursively is enough to broadcast a packet to all network nodes. The MPR set is minimal in terms of number of nodes.).  
We now consider a 2D homogeneous network. Using MPR flooding, an upper bound on the total number of retransmissions required to complete full flooding is \(N/M^{\frac{3}{3}}\), where \(M\) denotes the mean number of neighbors of a node. Thus, the upper bound of the total number of retransmissions for MPR flooding is  
\[\frac{N}{M^{\frac{3}{3}}}=\frac{\lambda\pi R^{2}l_{i}^{2}}{M^{\frac{3}{3}}}=M ^{\frac{1}{3}}l_{i}^{2}.\]  
For SFLS, where flooding is based on the retransmission of 1 in \(q\) messages, the upper bound on the total number of retransmissions per update is  
\[1+\lambda\pi R^{2}\Big{(}\frac{q+1}{(q-1)^{2}}+\frac{(1-2l_{i})q+2l_{i}-3}{(q -1)^{l_{i}-1}(q-1)^{2}}\Big{)},\]  
which can be written as  
Figure 8: Mean number of messages sent sent per location update with respect to number of hops in 3D homogeneous network.  
For SFLS, the developed upper bound in the analytical model was used. Our protocol always outperformed OLSR when the number of hops was large. However, OLSR outperformed our scheme for a small number of hops or when the network was very dense.  
As a result, unlike other flooding-based location services, the semifioding location service is scalable in terms of the number of messages that must be sent to keep all the nodes updated, the number of updates that are performed in the network, and the time required to answer a location request.
## 2 Airborne Air-to-Air Channel Laser Communication Link Analysis and Modeling
### Simulation of Laser Transmission Characteristics under Different Visibility Conditions
The simulation results of laser multipath transmission show that the percentage of laser pulse time-domain broadening caused by the airborne multipath channel tends to decrease with the increase in the initial pulse width of the transmitting laser. Simulation of Laser Transmission Characteristics after Multipath Channel Transmission over Different Distances  
The channel used for the simulation process features clear weather conditions and the laser time-domain pulse characteristics after air-to-air transmission over different distances (i.e., 150 km/200 km/250 km) are shown in Figure 9. The mean value and variance in laser pulse time-domain broadening over different transmission distances are shown in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmission Distance (km)** & **Average (ps)** & **Variance (ps\({}^{2}\))** \\ \hline
150 & 61 & 4.67 \\
200 & 72 & 2.67 \\
250 & 86 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean value and variance in laser pulse time-domain broadening over different transmission distances. Figure 8: Simulation and analysis of time–response characteristics after transmission of laser pulse signals with different initial widths. (**a**) Time–response characteristics and (**b**) pulse spread to pulse half-width ratio. Figure 9: Simulation results and analysis of time-domain waveform of laser pulses under different transmission distances. (**a**) Simulation of laser pulse time-domain waveform and (**b**) ratio of pulse spread to pulse half-width. The laser pulse time-domain spread (pulse half-width) (in ps) after airborne transmission over different distances of 150 km, 200 km, and 250 km are 63 (463), 74 (474), and 85 (485), respectively. The laser pulse time-domain spread to pulse half-width ratios are 14%, 15.8%, and 18%, respectively. An analysis of these results is presented below:  
1. With the increase in transmission distance, the laser pulse transmitted by the airborne multipath channel will experience greater scattering and turbulence effects and within a certain range of the transmission distance, the time-domain broadening of the laser pulse effectively received by the antenna tends to increase;
2. With the increase in the transmission distance, the changing trend in the growth of the time-domain spread to pulse half-width ratio of the received laser pulse gradually stabilizes;
3. Considering the variation in the laser beam's power density with the transmission distance, the number of pupil photons meeting the reception requirements will be reduced under the long transmission distances and the pulse broadening of the effectively received optical signal shows a gradual smoothing characteristic.
###### Abstract  
Solar tracking is an efficient strategy to increase the radiative capture of photovoltaic collectors. Within the multiple efforts made in recent decades to improve the production of these facilities, various works have studied solutions to optimize the number of rotation axes (single or dual rotation axes), the degree of collector coverage, the distances between trackers, the geometric arrangement of trackers or the minimization of shading between collectors. However, although in this type of installation it is common to find collectors with geometric shapes other than rectangles, no studies on the influence of the shape of the collectors on the radiative incidence are found in the literature. In this connection, the present work systematically addresses the study of incident solar radiation in photovoltaic installations with dual-axis trackers with collectors of different geometric shapes. By means of the exhaustive study, the conclusion is drawn that, for dual-axis photovoltaic installations with an optimal tracking strategy, the main variables that influence the annual radiative incidence are the spacing between collectors, the coverage ratio (GCR), and the collector surface, while the type of arrangement of collectors and the shape of these do not show predictive values.  
Keywords:photovoltaics; dual-axis solar trackers; shading in PV plants; solar tracking; backtracking +
Footnote †: journal: Applied Sciences
Returning to mobile internet access, purchasing the computer necessary for cheaper internet access is prohibitively expensive for households with lower incomes. Opting for the initially cheaper mobile access, the poor ultimately pay more for the internet, as mobile internet subscriptions can be more expensive in the long run. Thus, the equipment divide is not narrowed. To this end, [PERSON] and [PERSON] assert the importance of policies aimed at reducing entry costs to mobile telephony for the poor, given the limited services they could afford on their cheaper handsets [18]. [PERSON] (2009) similarly proposes subsidized rates for the poor to access communication. Focusing on Mexico, Uruguay, Brazil, Costa Rica, [PERSON] characterizes those citizens from these nations who cannot pay 10 USD per month fall into \"digital poverty\" [19]. At one level, this raises the question as to whether these services are a luxury or a necessity. If impoverished people are willing to commit a large portion of their income to these services, they are, in some sense, seen as a necessity. This is the idea behind the Engel Curve. If there is a large degree of elasticity in the consumption of a product as income increases, this indicates that the product is a necessity. There is the suggestion here that this may be a characteristic of mobile internet, though there are some serious caveats. The cost in the US, our setting, is even higher. Here, we propose this broader trend applies to internet access. Specifically, we focus on mobile phones as an alternative means of connectivity that allows teens otherwise unable to access the internet to go online. We interpret 2009 statistics as a snapshot of mobile internet use that point to the future through present trends. Taking into account innovations in mobile internet, this (arguably) lesser internet is evolving, bringing with it the possibility of improved handset usability and data flow for everyone, including teens with no other means of access. Yet PC and tablet infrastructures are also evolving, while income structures remain stagnant. Thus, in some respects the digital divide is narrowing with mobile devices, but we do not see this gap completely closing in the coming decade.
## 4 Results  
### Data Collection and Preprocessing  
As we said in Section 3.1, we used a Python crawler to crawl the review data from the Chinese University MOOC ([[https://www.icourse163.org/](https://www.icourse163.org/)]([https://www.icourse163.org/](https://www.icourse163.org/)) (accessed on 1 February 2024)). As the largest online course platform in China, the Chinese University MOOC boasts a significant volume of reviews. We gathered reviews for two courses: C language and Java language, spanning from January 2018 to November 2022. The numbers of reviews for the two courses are 12,560 and 3980, respectively.  
We then segmented the reviews by splitting the reviews with multiple sentences into a single sentence and filtering out those with sentences less than five characters in length. It should be noted that we added some turning words for sentence segmentation, such as \"but\" and \"however\", because the text before and after these words often have different sentiments. Although some sentences were removed because they were less than five characters, some long reviews were split into multiple sentences, resulting in 11,327 and 3865 sentences, respectively.  
Subsequently, we conducted part-of-speech (POS) tagging on both datasets, focusing on extracting nouns and their respective document frequencies. Document frequency refers to the count of review sentences in which a particular term appears. We then filter out words with too few document frequencies. Since the number of reviews in the two courses is different, it is not fair to filter words with the same criterion. Hence, for the C language course, we eliminated nouns with a document frequency of less than five, while for the Java language course, we excluded those with a document frequency of less than two. We manually weeded out nouns or noun phrases unrelated to the course content, such as \"thank you\", \"not interesting\", and \"a waste of time\". Consequently, we arrived at 340 alternative attribute words for the C language course and 350 for the Java language course.
## 5 Evaluation Results and Discussion  
The experimental evaluation has two main goals. First, it aims to evaluate the accuracy of the contF0 using adaptive refinement methods. The second goal is to evaluate the proposed vocoder in the context of statistical parametric speech synthesis and to compare its performance with baseline and STRAIGHT vocoders.
# Anthropometric Characteristics of Road Cyclists of Different Performance Levels
## 1 Introduction  
Road cycling is an endurance sport discipline characterized by a broad range of performance factors, which can be categorized as physiological, psychological and anthropometric parameters [1]. Road cyclists need to tolerate extremely high training volumes in order to develop a wide range of physiological adaptations: either maximal (maximal oxygen consumption-VO2 max) or submaximal (both ventilatory thresholds and gross efficiency) [2]. At the same time, cyclists present several well-developed psychological capabilities, such as the ability to endure suffering, to avoid negative self-talk or to pace correctly [3]. Regarding the anthropometric factors, previous studies have shown that road cyclists are generally characterized by low body mass and body mass index (BMI), low percentage of body fat and reduced waist to hip ratio [4]. However, previous research has highlighted that there is significant variation in these parameters across cyclists of different specialties, such as sprimters, time trialists, general classification riders or climbers [5, 6].  
Anthropometric assessment is a widely implemented methodology in the practical field for the following reasons: reduced costs, lack of complex equipment and easy implementation in the field setting [7]. However, anthropometric assessment also presents several setbacks. In order to guarantee a minimal degree of inter- and intra-observer variability, the anthropometric must be highly skilled and qualified, and even when this is the case, the variability in measurements may be too large to allow comparisons between athletes assessed in different facilities [7]. One of the most interesting characteristics of whole-body anthropometry is the possibility to measure skinkolds and perimeters in different bodyparts, which may be of interest in sports disciplines, in which specific muscle groups are being predominantly used, as in the case of road cycling.  
Although the main body composition parameters, such as body mass, fat mass or muscle mass, have already been well studied in road cyclists, there is a lack of knowledge regarding the trends in skinkolds and perimeters in cyclists of different performance levels. Given that higher performing cyclists are characterized by a lower body mass, body fat percentage and increased power to weight ratio, differences in anthropometric measurements could be expected across performance groups, at least theoretically. Accordingly, the objectives of the current study were the following: (1) Describe the anthropometric differences across cyclists of different performance levels and (2) Assess whether anthropometric values could relate to physiological markers that are commonly used to classify road cyclists according to their performance levels.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 2 Proposed Architecture
### Word Embeddings and Semantic Clustering  
We identify semantical similarities between tokens in the control flow and group them in clusters accordingly. We obtain an abstract form which is able to preserve the CWE patterns, and at the same time is less dependent on the precise lexical structure of the program.  
Semantical grouping considers the Word Embeddings [13] concept widely used in Natural Language Processing in recent years. For the semantic representation of the control flow tokens, we have used the Gensim ([[https://pypi.org/project/gensim/](https://pypi.org/project/gensim/)]([https://pypi.org/project/gensim/](https://pypi.org/project/gensim/)), last accessed on 24 October 2022) platform to compute Word2 Vec embeddings starting from source code, combined with NLTK ([[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/)), last accessed on 24 October 2022) (Natural Language Toolkit) for tokenization, which produced similarity values for each of the 309 tokens. The list of 309 tokens represents the vocabulary for tokenization and semantical transformation of the source code. To identify the optimal Intermediate Representation of the source code in conjunction with the machine learning ability to detect security weaknesses patterns, we have used the NIST dataset tokenized with these 309 tokens and the K-Means clustering algorithm for the resulting word embedding vectors (from Word2 Vec), configured as in Equation (1). The Hierarchical Agglomerative Clustering (HAC) algorithm has been tested as an alternative, with similar results.  
\[\begin{split} KMeansClusterer(no\_of\_clusters,\ distance=nltk.cluster.util.cosine\_distance,\ repeats\\ =25,\ avoid\_empty\_clusters=True)\end{split} \tag{1}\]  
In Figure 2, we present a bi-dimensional representation for 2 (out of 50) K-Means clusters of control flow tokens. Tokens are represented by their corresponding word embeddings vectors, resulting from the Word2 Vec transformation. Clusters contain between 2 and 22 tokens. One of the illustrated clusters includes the following tokens: {addition, array, bounds, decrement, if, increment, index, size, src, this}.