esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 4 Discussion and Related Work
### Related Work for CVE Detection in Source Code  
The detection of CVEs is important when applications use existing software components known as being vulnerable, being them free or open source, included as external libraries or just partially copied as functions or code snippets inside the developed code.  
Previous studies for source code vulnerabilities detection use code clone [2; 10] comparison between versions of the same software and are not developed as to identify technical CWE patterns. The goal of the research initiatives is to identify modules with public and known CVE vulnerabilities.  
Code clones' detection hackathons [3] have demonstrated that software components with security weaknesses fixed years ago, are still widely present in active projects publicized in software repositories, which makes security code review a mandatory exercise of the supply chain beneficiaries.  
Discovering critical taint-style vulnerabilities such as the \"Heartbleed\" bug in OpenSSL is an important step in securing applications. [PERSON] et al. [8] demonstrated that using code property graphs as a C/C++ source code representation can reduce the amount of code to inspect by security analysts for finding known vulnerabilities by 94.9%.  
Despite the importance of detecting CVEs in all applications made operational, the software development process of new products and solutions must also identify and fix the technical flaws (CWE) as earliest as possible during the development stage, in addition to ensuring that publicly known vulnerable code (CVEs) is avoided.  
Figure 4: Feature importance for the Intermediate Representation.
## 2 Materials and Methods
### Data Availability
#### 2.2.3 Meteorological Data, Soil Moisture, and SPEI  
The MOD11C3 dataset provides monthly land surface temperature (LST) data at a resolution of 0.05\({}^{\circ}\) ([[http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)]([http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)), accessed on 25 January 2021). Rainfall is sourced from the CHIRPS satellite precipitation (PPT) product. CHIRPS is a precipitation climate database limited to land areas, which combines satellite precipitation with ground-based meteorological station data to provide daily and monthly precipitation products with a resolution of 0.05\({}^{\circ}\) ([[https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)]([https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)), accessed on 5 July 2020). Soil moisture (SM) data are obtained from the GLDAS product whose resolution is 0.25\({}^{\circ}\) and provided on a daily basis ([[https://disc.sci.gsfc.nasa.gov](https://disc.sci.gsfc.nasa.gov)]([https://disc.sci.gsfc.nasa.gov](https://disc.sci.gsfc.nasa.gov)), accessed on 16 October 2021). This study utilizes mean synthesis to accumulate daily meteorological and hydrological data into monthly scales.  
SPEI is a multiscalar drought index used to explain the influence of temperature on drought and has been utilized in many studies [48; 49]. The SPEI data were obtained from the SPEI Global Drought Monitor ([[http://spei.csic.es/](http://spei.csic.es/)]([http://spei.csic.es/](http://spei.csic.es/)), accessed on 22 May 2022) with a spatial and temporal resolution of 0.05\({}^{\circ}\) and monthly. SPEI has various time scales (1, 3, 6, 12 months, etc.), and different scales of SPEI can realize the monitoring and assessment of different types of droughs. In this research, the SPEI dataset at a 3-month scale was used to identify typical drought events. For simplicity, SPEI, SM, PPT, and LST are collectively referred to as drought indicators.  
To directly analyze the relationship of SIF, SIF\({}_{\text{yield}}\), and NDVI with GPP under drought conditions, we divided the period from 2007 to 2015 into drought and no-drought months based on the magnitude of SPEI. As shown in Table 1, SPEI \(>-0.5\) indicates no-drought conditions, while SPEI \(\leq-0.5\) defines drought conditions [50].
## 4 Experimental Section and Results Analysis
### Experiments and Result Analysis
#### 4.3.1 Comparative Experiments between OASVR, SVR and AONSVR  
We compared OASVR with offline SVR, with the experiment set on the same dataset (K3 dataset). Initially, we pre-trained both models with 500 data points using the same hyperparameters. We then compared the prediction performance of OASVR and offline SVR, and the results are shown in the following Figure 3.  
OASVR has an RMSE of 0.126, while offline SVR has an RMSE of 0.36. It is evident that dynamic model updates can lead to better model prediction performance.  
We conducted a comparative experiment on the performance of AONSVR and OASVR on the same dataset (K3 dataset), with the same hyperparameter set. We compared the training time and model accuracy of the two models, and the results are shown in Figure 4 below.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **K1** & **K2** & **K3** & **K4** \\ \hline Hoeffding Adaptive Tree & 0.654 & 0.632 & 0.664 & 0.795 \\ Adaptive Random Forest & 0.941 & 0.911 & 0.874 & 0.865 \\ SAM-kNN & 0.938 & 0.908 & 0.865 & 0.911 \\ SVR & 0.920 & 0.894 & 0.887 & 0.912 \\ AONSVR & 0.941 & 0.881 & 0.851 & 0.862 \\ \hline OASVR & **0.957** & **0.924** & **0.898** & **0.922** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction accuracy (\(R^{2}\)) between OASVR and other algorithms.  
Figure 3(a) represents the training time of the model. From the graph, we can see that, with the same training samples, the training time of the model is reduced by half, while, comparing with Figure 3(b), the prediction performance of the model is not significantly reduced. With a training data size of 500, the RMSE for OASVR is 0.09, while the RMSE for AONSVR is 0.07. This indicates that the OASVR algorithm has similar performance to AONSVR but is more efficient.
# Scaling of Rain Attenuation Models: A Survey
###### Abstract  
The scaling of rain attenuation methods is promising to quickly estimate power degradation in radio links due to rain with known findings from previous measurements. Although the frequency scaling of rain attenuation technique was introduced ages ago, it has not been addressed adequately. Furthermore, some emerging scaling techniques have recently been proposed in the literature through polarization, elevation angle, and pathlength parameters. A survey paper might play a vital role in order to comprehend all these study areas systematically. However, a survey paper on this research field is currently unavailable in the literature. This review categorizes all the research works using the inherent properties of scaling techniques. Furthermore, this study presents a comparative investigation of parameter-based scaling techniques by considering their working procedure, applicable frequency ranges, and innovative ideas incorporated with all of these models.  
attenuation measurement; frequency scaling; terrestrial link; slant link; received signal level +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON].  
Scaling of Rain Attenuation Models: A Survey. _Appl. Sci._**2021**, _11_, 8860.  
[[https://doi.org/10.3390/app1118860](https://doi.org/10.3390/app1118860)]([https://doi.org/10.3390/app1118860](https://doi.org/10.3390/app1118860))  
Academic Editor: [PERSON]  
Received: 12 August 2021  
Accepted: 6 September 2021  
Published: 9 September 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Cognition:** 0221 by the authors. Licence MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
Compared with the traditional fixed-flow RAS, the variable-flow RAS can increase the total water circulation to accelerate the water treatment process when organic particles increase, and the ammonia and nitrite then can be eliminated from the source [16]. In addition, the variable-flow RAS consumes a low amount of electricity when the water is relatively clean. However, manual operation is often used to adjust the circulation pump frequency to determine the appropriate total water circulation in the variable-flow RAS. The manual operation experience may cause the water treatment efficiency to not match the actual situation, resulting in insufficient water processing efficiency or waste of electricity. Hence, an intelligent variable-flow RAS for culturing _Litopenaeus wannamei_ was developed in the present study. Machine learning, which has emerged with Big Data technologies and created new opportunities in multidisciplinary aquaculture, was used to develop the intelligent variable-flow model. Currently, machine learning is applied in related fields, including environmental assessment, water management, animal welfare, disease detection, feeding control, and species recognition [17; 18; 19; 20; 21; 22; 23]. More data-intensive machine learning approaches have been reported, but model- and technology-intensive approaches have been infrequent [24; 25]. For industrial control in recirculating aquaculture, in particular, there is an urgent need to apply machine learning models to improve instrument efficiency and promote the development of intelligent equipment applications. The primary purpose of the present study was to develop the circulating pump-drum filter linkage working technique using machine learning methods. Water quality indicators and the backwash frequency of the drum filter were used as primary indicatorsin developing a variable-flow model. An intelligent variable-flow RAS can rapidly remove suspended solids and reduce ammonia and nitrite generation from the source.
## 5 Experiment
### Experimental Settings
#### 5.3.1 Experimental Results  
The experimental results of probing tasks are shown in Table 2. We use accuracy to express the performance since \(P=R=F\) in classification. \(P\), \(R\), and \(F\) represent precision, recall, and \(F\)-measure: \(P=\frac{true\ positives}{predicted\ as\ positives}\), \(R=\frac{true\ positives}{actual\ positives}\), \(F=\frac{2 PR}{P+R}\). We use the mean and standard deviation (number in brackets) to express the performance of models. \(BERT_{cls}\) means that the encoding of the token [CLS] is treated as the representation of the input sentence. \(BERT_{max}\) means that the max-pooling of the encodings in BERT's last layer is treated as the representation of the input sentence, and \(BERT_{mean}\) means that the mean-pooling of the encodings in BERT's last layer is treated as the representation of the input sentence. \(fasttext_{max}\) and \(fasttext_{mean}\) are the same as \(BERT_{max}\) and \(BERT_{mean}\)-\(fasttext_{cls}\) represents the native sentence representation model of fasttext. We input the embeddings of sentences into the classifier described in Figure 7 to evaluate each sentence representation model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Probing Task** & **Class** & **Train Set** & **Development Set** & **Test Set** \\ \hline \multirow{2}{*}{Sentence\_Len} & Long Sentence & 1571 & 209 & 195 \\  & Short Sentence & 2419 & 288 & 305 \\ \hline \multirow{2}{*}{Voice} & Passive Voice & 311 & 35 & 37 \\  & Active Voice & 3689 & 461 & 462 \\ \hline \multirow{2}{*}{Bshift} & Shift & 3993 & 498 & 499 \\  & Non-shift & 3993 & 498 & 499 \\ \hline \multirow{2}{*}{SubjNum} & Plur & 115 & 21 & 20 \\  & Sing & 3796 & 466 & 471 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detail of the Chinese probing dataset derived from CoNLL2017.  
Figure 7: The architecture of MLP for probing tasks.  
From Table 2, we can see that \(BERT_{cls}\) performs best on Bshift and subjNum but worst on Sentence_Len. SEMTS-BERT and _sent2 vec_ perform best on Sentence_Len and Voice. In the Voice sub-task, all models achieve good performance. In Bshift, all models except \(BERT_{cls}\) do not perform well. Among all the models, the standard deviation of \(BERT\) is the largest, which shows that its training results are unstable and tend to fall into the local extremum easily during the optimization process. We try to reduce the number of neurons in the hidden layer to reduce \(BERT\)'s standard deviation, but doing so will reduce its overall performance. Compared with the native sentence representation model, max and mean pooling operations sometimes achieve better performance.
## 3 Results
### Trends in Vegetation Phenology (SOS and EOS)  
We further examined the SOS and EOS trends of the Zoige wetland vegetation from 2000 to 2011 (Figure 4). The SOS results delineated by the EVI, LSWI, and GPP showed that there was a significant advancement of SOS at an average rate of 1.61 days\(\cdot\)year\({}^{-1}\) (\(R^{2}\geq\) 0.37, \(p\leq\) 0.035, \(n=12\)). The earlier start of the growing season corresponded with similar increasing trends in the spring (\(R^{2}\) = 0.46, \(p\) = 0.015) and winter temperatures (\(R^{2}\) = 0.35, \(p\) = 0.044) during 2000-2011 (Figure 4a). We also ran a statistical analysis for the spring temperature, winter temperature, and SOS results delineated by the GPP, EVI, and LSWI. The results showed that there were negative relationships between the spring temperature and SOS, although these relationships were not significant (\(p>\) 0.05).  
The EOS results delineated by the EVI, LSWI, and GPP showed that there was a clear delay for EOS at an average rate of 1.57 days\(\cdot\)year\({}^{-1}\) (\(R^{2}\geq\) 0.41, \(p\leq\) 0.03) (Figure 4b). This autumn phenology trend corresponded with a significant increase in autumn and growing season temperatures (\(p<\) 0.05). The mean temperature in autumn and growing season increased by about 0.11 (\(p\) = 0.017) and 0.12 \({}^{\circ}\)C\(\cdot\)year\({}^{-1}\) (\(p\) = 0.022) during 2000-2011, respectively. Overall, the advancement in SOS and delay in EOS extended the length of the growing season (LOS) by an average of 3.1 days\(\cdot\)year\({}^{-1}\) (\(p<\) 0.05).  
Figure 4: Interannual variation in (**a**) the start date of the growing season (SOS) and spring and winter temperatures; and (**b**) the end date of the growing season (EOS) and autumn temperature during 2000–2011 for the Zoige alpine wetland. The SOS and EOS were delineated by MODIS-based eight-day vegetation indices (EVI and LSWI) and simulated GPP by the VPM model. Dashed lines are the 12-year linear trend of SOS, EOS, and temperature. Spring: March to May; autumn: September to November; winter: December to February; growing season: May to September.
# Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide
## 3 Health Benefits of Digital Access and Use  
Given the benefits of social media for public health, the digital divide has been found to contribute to disparate health outcomes. This apparent influence was seen during the COVID-19 pandemic in the U.S., as counties with the highest percentages of people without internet access had the highest rates of infection and death but the lowest rates of vaccination [16]. This finding was echoed by a study that showed that a lack of internet access was consistently associated with higher COVID-19 mortality rates [17].  
Emerging data support the notion that increased broadband access is associated with a measurable improvement in health behaviors and, in some cases, health outcomes. For example, the prevalence of diabetes drops 9.6 percent with each higher quintile of broadband access in U.S. counties [18]. One longitudinal study on Medicare beneficiaries found that increased broadband access over the study period accounted for 16% of the improvements in outcomes [19]. Other studies have found that internet access correlates strongly with health outcomes, even when controlling for education, income, and rurality [20]. Evidence from Bangladesh indicated that social media users were three times more likely than non-users to follow established health guidelines during the pandemic [21]. Other studies on COVID-19 prevention suggest that social media and internet usage led to improved health outcomes [22]. Weekly internet usage has also been linked to greater knowledge of fertility cycles among females and increased antenatal care in Nigeria and Rwanda, as did ownership of a mobile phone, which also has been associated with greater use of iron supplementation during pregnancy in these countries and Senegal [23]. The inability to access or use social media--a consequence of the digital divide--can be a marker for health outcomes.  
We can consider social media access--and digital access at large--to be a social determinant of health. Suppose social media has emerged as a significant source of health information, and yet many people do not have access to or cannot make use of such information. In that case, it seems to follow that such individuals will suffer worse health, as was apparent during the COVID-19 pandemic. Social media is a \"non-medical factor that influences health outcomes\" [24], thus fitting the WHO's definition of a social determinant of health.
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform  
### Monitoring System Composition  
The purpose of SHM for offshore platform is to monitor the static and dynamic performance of a structure by collecting, processing, and analyzing data comprehensively and in real time. Such monitoring systems generally contain the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem, as shown in Figure 1. In this system, the function of the sensor subsystem is to obtain physical information from the offshore platform and convert it into electrical signals. The function of the data reading and transferring subsystem is to collect sensor signals and transfer them to the data administration subsystem. The data administration subsystem can collect, organize, and store the monitoring data, and the assessment subsystem evaluates and analyzes the collected data and gives feedback on structural safety information in time.  
Figure 1: Schematic design of the offshore platform structural health monitoring system.
## 3 Results and Discussion
### Contact Pressure and Gap Distance under Axial Compression
The broadest detachment region was from \(-16.2\) to \(10.1\) mm at TR = 0.25, with the largest gap of 0.7 mm at an arc length of 7.3 mm. At TR = 0.5, the detachment region decreased slightly from \(-15.8\) to 8.2 mm, with a reduced gap of 0.46 mm. In the case of type 3, as shown in Figure 6c, the detachment region was the broadest of the three types. At TR = 0.25, the symmetrical detachment region was the broadest from \(-15.7\) to \(15.7\) mm, with a maximum gap of 0.6 mm at the arc length of 7.3 mm. The detachment region and gap decreased gradually at TR = 0.5. Type 1 showed the best contact with the retainer ring at TR = 0.25, and the detachment was small and narrow. However, types 2 and 3 showed a significant detachment region. As the TR increased, the detachment region tended to decrease. At TR = 0.50, the detachment of type 1 disappeared almost completely, but the detachment regions of types 2 and 3 were still observed broadly. In particular, the detachment region of type 2 was unstable because of its asymmetrical geometry. For all types 1, 2, and 3, the detachment disappeared completely at TR = 1.0 or higher, which implied that full sealing was occurring. It appeared that unstable contact at the retainer was observed at TR = 0.25 as deformed wrinkles that were generated during axial compression because of the thinner UMS and its lower stiffness. This phenomenon caused a large deformation in the UMS with no open groove or asymmetric grooves during compression.
## 4 Proposed Model
### Candidate Sentences Selection  
The selected tag in the CBS is obtained by the detection method but does not always need to be described. The CBS contains multiple cases, each case contains various selected tags and generates beam size candidate sentences. Traditional CBS only outputs sentences that contain all the selected tags with the highest loss. However, the never-seen word with a fixed loss is harmful for sentence selection, and the sentences containing all selected tags may not always be optimal.  
In order to reduce the impact of never-seen words and the selected tags for the captioning process. This manuscript designs a novel candidate sentence selection to replace the original selection method. The candidate sentence selection evaluates the semantic similarity between the given image and candidate sentences using external knowledge through Equation (2).  
\[s_{i}=cos(v_{i},w_{all}^{s,i}) \tag{6}\]  
where \(v_{i}\) is the CLIP feature of the given image \(i\), \(w_{all}^{s,i}=(w_{1}^{s,i},...,w_{n}^{s,i})\) is the CLIP feature of all candidate sentences, \(n\) is the number of candidate sentences. The final output is the similarity of each sentence with the image \(i\).  
Figure 4: The structure of the Semi-Fixed Word Embedding.  
In this way, this manuscript avoids the condition that output sentences must contain all selected tags, further improving the accuracy of the generated sentences.