esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SSMA Broadcast Protocol
### Broadcast Approaches
#### 3.3.1 Contention-Based Transmission  
In CBT, all the nodes at the same tree level compete for broadcasting. According to the IEEE 802.15.4 physical layer standard [19], a message transmission process takes seven steps: a sender (1) takes a random delay to avoid collision (_rdelay_); (2) transfers a message from micro controller unit (MCU) to a radio chip buffer (\(t_{mr}\)); (3) turns the radio chip on (\(t_{turnn}\)); (4) performs the clear channel assessment (CCA)(\(t_{CCA}\)); (5) if the channel is idle, radio chip takes the physical layer processing delay (\(t_{ppd}\)); (6) broadcasts a message (\(t_{tx}\)); and upon receiving a message, (7) a receiver transfers a message from a radio chip to MCU (\(t_{rm}\)). Therefore, to avoid collision, two neighboring nodes have to get the  
Figure 3: Protocol structure and the schedule of broadcast sharable slots (BSSs).  
difference of _rdelay_ by at least one _delayslot_ that corresponds to the sum of \(t_{ppd}\) and \(t_{CCA}\). This implies that one of two nodes never starts broadcasting before overhearing at least one bit transmitted earlier by another. With an integer contention window (_CW_), _rdelay_ is given as follows:  
\[rdelay=random(0,\;CW)\times delayslot \tag{8}\]  
As in Figure 4, suppose that neighbors 1 and 2 generate random numbers, 2 and 3, respectively, from Equation (8). Then, node 1 takes the channel first, having node 2 delayed. The disadvantage of this approach is that since all nodes at the same level start channel competition simultaneously, some of them can generate an identical _rdelay_. This approach is similar to the way of broadcasting a message in the original SSMAb protocol [16], except that it uses the _rdelay_ function.
# Blind Image Quality Assessment with Deep Learning:
## 1 Introduction
Therefore, we consider BIQA to be of fundamental interest for lifelogging applications, as not all the captured visual lifelogs (more specifically, images) have relevant information for the task in question. Several images captured in a lifelogging process are affected by different types of degradation, mostly due to the use of egocentric camera devices. By filtering out such images using BIQA approaches, we are able to ignore images with low visually impact for the lifelogger, saving processing time and increasing the performance of the system when retrieving the desired visual lifelogs. Moreover, using this filtering process, we do not try to interpret degraded images which could lead to poor annotations, due to the fact that most of the computer vision tasks, such as image annotation, activity recognition, face recognition, and object segmentation, among others, are trained with acceptable quality images. The annotations are more accurate with a positive impact on the retrieval phase, mostly performed by text queries. The primary goal of our work is a replicability study of a BIQA deep learning methodology (KonCept512) developed and documented by [PERSON] et al. [9]. Secondly, we are interested in reproducing their work in a lifelogging application [13]. The authors of the study that we replicate in this manuscript proposed a large IQA in-the-wild dataset (KonIQ-10k) ([[http://database.mmsp-kn.de/koniq-10k-database.html](http://database.mmsp-kn.de/koniq-10k-database.html)]([http://database.mmsp-kn.de/koniq-10k-database.html](http://database.mmsp-kn.de/koniq-10k-database.html)), accessed on 25 November 2022) to evaluate and benchmark IQA algorithms with regard to the authenticity of degradation, the diversity of content, and quality-related indicators. We are interested in reproducing their results on a different, lifelogging dataset. In summary, our work advances the following contributions:* Replicability of the best model trained by [PERSON] et al. [9], KonCept512. * If the replicability is possible, we will evaluate the training of the best model (KonCept512) under different hardware setup. * Reproducibility of the best model documented by [PERSON] et al. [9] on a lifelogging dataset. * Discussion of the replicability and reproducibility journeys and lessons learned. This remainder of this paper is organized as follows: In Section 2, we provide a description of the best model proposed by [PERSON] et al. [9], KonCept512. The experimental setup and the procedures conducted in this paper are presented in Section 3. In Section 4, we present the obtained experimental results. We were able to replicate the original work up to a certain extent. We compare the performance of the model trained under different conditions, and we detail our reasoning for some of the decisions that were taken during the process. Moreover, we address the reproducibility issue through an analysis of the MOS distribution in a lifelogging dataset. Finally, we reflect on the lessons learned in Section 5.
## 3 Related Studies
### Hate Speech Detection in Arabic Languages
Pretrained transformer models such as the BERT family including SBERT continue to deliver remarkable performance in MNLNPTD because of their contextual understanding over sequence lengths. Some models in Arabic, such as AraBERT, are fine-tuned for task like, sentiment analysis and question answering, but these need high computational power. Most prior work focuses on structured Arabic datasets or English and have had high accuracy; however, issues have not been addressed with the informal variation between Arabic dialects and hate speech language. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**R\({}_{d}\)** & **Year** & **Techniques** & **Classifier** & **Language** & **Datasets** & **Preprocessing** & **Max-score** \\ \hline
[29] & 2018 & Machine Learning/ Deep Learning & LR, SVM, GRU & Arabic & Collection of 6000 tweets & Removing (diactics, punctuation, non-Arabic characters, emotions, and stop words). Normalizing letters & GRU = 79\% Acc. \\ \hline
[31] & 2019 & Machine Learning & SVM, NB & Arabic & Collection of 5846 tweets & Removing non-Arabic characters, emojs, URLs & NB = 88.4\% Acc. \\ \hline
[33] & 2019 & Machine Learning & SVM, NB & Arabic & Collection of 6075 comments & Removing non-Arabic characters, emojs, URLs & NB = 87.9\% Acc. \\ \hline
[22] & 2019 & Deep Learning/Machine Learning & BERT, ELMo, FLAIR, Bi-LSTM & English/Hindi & Collection of 8000 tweets, YouTube, Instagram & Removing URLs, hashtags, mentions and punctuations & FLAIR BERTMU + Bi-LSTM = 73\% Acc. \\ \hline
[22] & 2020 & Deep Learning & BGRU and CNN & Arabic & Collection of 10,000 tweets and YouTube & Removing (diactics, punctuation, non-Arabic characters, emotions, and stop words). Normalizing letters & BGRU = 91\% Acc. \\ \hline
[35] & 2020 & Machine Learning/Deep Learning & 12 machine learning models and 2 deep learning models & Arabic & Collection of 20,000 tweets and YouTube, Facebook, Instagram & Removing non-Arabic characters, emojs, URLs & RNN = 98.7\% Acc. \\ \hline
[37] & 2020 & Machine Learning & SVM & Arabic & Collection of 10,000 tweets & Data cleaning, symbol removal labeling, tokenization & SVM = 90.2\% Acc. \\ \hline
[38] & 2020 & Machine Learning/Deep Learning & LSTM, XGBosst & Arabic & Collection of 10,000 tweets & Removing punctuation marks and non-Arabic characters. & LSTM = 96\% Acc. \\ \hline
[39] & 2020 & Machine Learning & AraBERT & Arabic & Collection of 10,000 comments & Tokenization (removing user mentions, retweets, URLs, diactics emojs and hashtags with white spaces & AraBERT = 90.15\% F1 \\ \hline
[40] & 2020 & Deep Learning & RNN, CNN & Arabic & Collection of 9316 tweets & Removing non-Arabic characters, emojs, URLs & CNN = 83\% Acc. \\ \hline
[41] & 2021 & Machine Learning & SVM, NB, DT, RF & Arabic & Collection of 360\% tweets & Removing (diactics, punctuation, non-Arabic characters, emotions, and stop words). Normalizing letters & RF = 91.3\% Acc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research related to hate speech detection.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 3 Results
### Spatio-Temporal Drought Patterns
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **Data** & **P** & **SM** & **SMM** & **ET** & **TWSA** \\ \hline \multirow{8}{*}{Amazon} & \multirow{4}{*}{2010} & MAM & 24\% & 39\% & 65\% & 0\% & 0\% \\  & & JJA & 0\% & 5\% & 32\% & 2\% & 0\% \\  & & SON & 1\% & 2\% & 11\% & 29\% & 22\% \\  & 2010–2011 & DJF & 0\% & 0\% & 0\% & 13\% & 0\% \\ \cline{2-7}  & \multirow{2}{*}{2015} & JJA & 0\% & 0\% & 0\% & 0\% & 35\% \\  & & SON & 79\% & 22\% & 22\% & 0\% & 66\% \\  & 2015–2016 & DJF & 52\% & 44\% & 60\% & 1\% & 88\% \\  & \multirow{2}{*}{2016} & MAM & 22\% & 1\% & 4\% & 0\% & 69\% \\  & & JJA & 0\% & 0\% & 0\% & 1\% & 88\% \\  & & SON & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline \multirow{8}{*}{Cerrado} & \multirow{2}{*}{2013} & SON & 0\% & 0\% & 1\% & 0\% & 0\% \\  & 2013–2014 & DJF & 26\% & 21\% & 27\% & 6\% & 0\% \\  & & MAM & 0\% & 28\% & 40\% & 10\% & 88\% \\  & 2014 & JJA & 0\% & 3\% & 7\% & 29\% & 88\% \\  & & SON & 0\% & 0\% & 1\% & 22\% & 90\% \\  & 2014–2015 & DJF & 0\% & 1\% & 2\% & 2\% & 100\% \\  & & MAM & 0\% & 0\% & 1\% & 1\% & 62\% \\  & 2015 & JJA & 0\% & 2\% & 2\% & 0\% & 62\% \\  & & SON & 0\% & 0\% & 0\% & 0\% & 53\% \\  & 2015–2016 & DJF & 0\% & 0\% & 0\% & 0\% & 2\% \\ \hline \multirow{8}{*}{Pampa} & \multirow{2}{*}{2011} & JJA & 0\% & 0\% & 0\% & 1\% & 0\% \\  & & SON & 20\% & 1\% & 7\% & 1\% & 3\% \\ \cline{1-1}  & 2011–2012 & DJF & 11\% & 62\% & 76\% & 51\% & 56\% \\ \cline{1-1}  & \multirow{2}{*}{2012} & MAM & 78\% & 62\% & 47\% & 4\% & 25\% \\ \cline{1-1}  & & JJA & 3\% & 20\% & 28\% & 0\% & 73\% \\ \cline{1-1}  & & SON & 2\% & 0\% & 0\% & 11\% & 78\% \\ \cline{1-1}  & 2012–2013 & DJF & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage (%) of the polygon area affected by monthly negative anomalous values (z-score \(\leq-1\)) for precipitation (\(P\)), surface soil moisture (\(SM\)), subsurface soil moisture (\(SSM\)), evapotranspiration (\(ET\)), and terrestrial water storage anomalies (\(TWSA\)), for the Amazon, Cerrado, and Pampa. compared to the other regions of this study. The 2014-2015 drought in the Cerrado clearly represented a long-lasting hydrological drought episode, with a slow recovery time, and large impact in _TWSA_. In the Pampa, the first sign of major \(P\) anomalies (z-score \(\leq-1\)) were observed in SON 2011 (20% of the area), followed by anomalies in all other variables, especially from December 2011 to May 2012. It was noticed that _TWSA_ continued to show anomalies in a large part of the area from June to November 2012 (73% in JJA and 78% in SON). The Pampa's 2012 drought appeared as both a meteorological and agricultural drought, followed by a hydrological drought. The duration of this drought was shorter than the one in the Cerrado, approaching the duration of the Amazon droughts.
## Materials and Methods
### Feature Wavelengths Selection  
Applying appropriate methods to select feature wavelengths for modeling would obtain the same performance as the full-range wavelength models in seed quality detection [23; 35]. In this study, the genetic algorithm (GA) was utilized to identify the feature wavelengths. The GA method randomly generated sufficient subsets (each subset was called an individual), including various combinations of variables (where the variables were equivalent to the concept of genes). Since the adjacent variables contained correlated information, every nine adjacent variables were merged into one gene to improve the signal-to-noise and the speed of variable selections while performing a GA procedure [36]. Namely, 1557 variables in a spectrum were assigned to 173 genes on average.  
The variables that frequently appeared in high-performance models were considered useful for cultivar distinguishing after numbers of generations of iteration. The multiple linear regression (MLR) was used as the fitness function. The root mean square error calculated from cross-validation (RMSECV) was used to evaluate the model performance. Meanwhile, to guide the GA towards solutions only using about 10% of the total variables, a penalty mechanism was added. The final RMSECV was calculated by the following equation:  
\[RMSECV=RMSECV_{0}*(1+\rho*n) \tag{3}\]  
where \(\rho\) was the penalty factor and \(n\) was the number of variables included in the individual above or below the target range. In each generation, 25% of the subsets that produced poor-quality models were eliminated. After each generation, cross-over and mutation were allowed to occur within the remaining subsets, which simulated natural evolutionary processes [37]. In this way, variables that contributed to improving the performance of the models were retained, and these variables were commonly regarded as feature wavelengths.
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 4 Conclusions and Discussion  
In this article, we extend the optimization model of the disintegration strategy in the multiplex network to layer node-based attack mode. The tabu search algorithm is introduced to solve the optimization model. Experiments in the model and real multiplex networks prove that the approximate optimal disintegration strategies obtained from the TS algorithm are superior to other strategies in different attack length. The optimal strategies divide the aggregated network of the multiplex networks into several independent clusters by removing some layer nodes from different layers. The advantages of them are especially obvious in the denser networks and shorter attack length. Meanwhile, we found that the effect of the optimal disintegration strategy is close to that of some layer node-based strategies when we need to make the multiplex networks complete collapse.  
This article focuses on the general multiplex network where different layers are independent and share a set of nodes. Actually, there are many other coupling ways between networks in reality, such as interdependence or collaboration. The same problem in these networks would be the future research direction.  
Figure 6: Optimal disintegration strategies in the three-layer top Noordin extremist network under layer node-based attack. For the TS algorithm, use the maximum value of \(\Phi\) in 20 independent realizations. The left side of the figure shows the layer nodes (the orange nodes in the first three layers) included in the optimal disintegration strategy when the percentage of node removed is about 0.32 (where \(\Phi=0.886\)). The size of the nodes is determined by their layer degree. At the bottom, we give the aggregated network of the multiplex network after nodes removed in the fourth layer, where four giant components with size 8 are in different colors. Similarly, we present a visualization of the HLDA strategy on the right side of the figure (where \(\Phi=0.316\) and the size of the giant component in the aggregated network is 51).  
**Author Contributions:** Conceptualization, M.Q. and Y.B.; methodology, M.Q., Y.B. and X.L.; software, M.Q., Y.B. and X.L.; validation, H.D. and T.W.; formal analysis, M.Q. and Y.B.; investigation, M.Q. and Y.B.; resources, H.D.; data curation, M.Q.; visualization, M.Q. and Y.B.; supervision, H.D. and T.W.  
**Funding:** This work was supported by the National Key R&D Program of China [2018 YFB1003903] and the National Natural Science Foundation of China (NSFC) [grant number 71771214, 71690233, 71971213].  
**Acknowledgments:** The authors are grateful to [PERSON] and [PERSON] for their help on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Assessment of Fluoride-Releasing Materials in Remineralization of Adjacent Demineralized Enamel
## 2 Materials and Methods
### Formation of Artificially Demineralized Proximal Enamel Lesion  
Fifty-four extracted premolar teeth were prepared as adjacent demineralized enamel teeth. The proximal surfaces were grounded flat and polished by water-cooled abrasive paper no. 400, 800, and 1000 grit, respectively, to remove extraneous matter and expose fresh enamel under running water. Then, all teeth were coated with acid-resistant nail varnish (Revlon(r), New York, NY, USA) on enamel surfaces, except for the 2 \(\times\) 3 mm enamel windows at the contact area adjacent to the restoration tooth. The window of each tooth was separately exposed to a demineralizing solution containing 2.2 mM CaCl\({}_{2}\), 2.2 mM KH\({}_{2}\)PO\({}_{4}\), and 50 mM acetic acid adjusted to pH 4.4 with 1 M KOH at 37 \({}^{\circ}\)C for 96 h to create an artificially demineralized enamel lesion approximately 100-200 \(\upmu\)m in depth, with constant circulation was until lesions were induced. The demineralizing solutions were changed daily. Lesion development was assessed using the International Caries Detection and Assessment System (ICDAS) criteria for stage 2 and confirmed by Bitewing radiography. Lesions that were not deeper than the outer 1/2 of enamel were included in this study. After this, each tooth was cut vertically in a buccolingual direction into two pieces to produce a crown of 4 mm in thickness, which is the same size as the phantom. The cutting surface of the dentine slice was polished manually by microfine 4000-grit sandpaper. The polished specimens were examined for cracks and other defects under a stereomicroscope. Finally, all samples were given the numbers 1-54, respectively, and examined for the baseline LD and MD by using Micro-CT SkyScan 1275 (Bruker micro-CT, Kontich, Belgium).
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?  
We have included a total number of 42 research articles that explain the methods and techniques used in short-text semantic similarity.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 1 Introduction
Our proposal focuses on reducing the computational cost of EtEQ prediction in order to allow direct execution in the routers, contrary to other works that use Machine Learning and Deep Learning to predict routing information and routing tables [21; 22]. To the best of our knowledge, all previous works have a high computational cost and require dedicated hardware. Routers, particularly those used in community networks, have very limited hardware. Additionally, many of the proposed computation techniques (Machine Learning and Deep Learning based) are offline, outside the network, and there is an important cost of moving the information from the network to the computing resources. Some works [23] allow them to execute online Machine Learning algorithms inside a router with low computational cost, but we believe that these algorithms are devoted to compete in CPU with the routing algorithms. Conversely, we prioritize routing decision over machine learning training for faster response times. Our samples show small topology changes, so we can arrange the machine learning training over a longer period of time, resulting in low effective computational cost. The main contributions of this work are the following:  
1. A detailed analysis of path properties behavior in Wireless Mesh Community Networks (WMCNs) shows that Path Quality prediction is possible and meaningful. 2. Time-Series analysis is used to estimate EtEQ in the routing layer of real-world WMCNs. Clear evidence that EtEQ values computed through Time-Series algorithms can make accurate predictions in WMCNs is provided. * A detailed analysis of the prediction accuracy for the next step considering also the hour of the day, and for some steps ahead in the future is presented. * An analysis of the computational cost of the prediction in terms of CPU utilization, energy consumption and temperature to confirm that the prediction can be executed directly in the routers is presented. * A detailed analysis of a use case shows the behavior of two orthogonal routing strategies, in terms of Packet Delivery Ratio, ETX of links, and End-to-End Delay. This paper is structured as follows. Section 2 overviews prediction in computer networks focused on link/path quality prediction in WMCN. Section 3 describes and analyses the dataset assumed in this work and introduces our proposal by showing the EtEQ behavior of a WMCN. Section 4 describes the experiments made to show the value of EtEQ prediction and it also analyzes the computational cost of the prediction. Finally, Section 6 provides some conclusions and future work.
## 4 Parametric Analysis  
As previously mentioned, the case study represents the typical characteristics of a Portuguese masonry arch bridge in a railroad network [33]. Nevertheless, there may be key differences that may influence the bridge response to flood-related effects. Thus, a parametric study was performed by changing the geotechnical properties of the bridge's soil materials, such as the stiffness of the soil in which the foundation is settled and the backfill. Even though the influence of the mechanical properties of the material was partially evaluated in the previous section, the influence of the quality of the material will be further analyzed. The different values for the mechanical characteristics of the three types of soils were obtained from the JCSS model code [45], and the selected soil types are assumed as non-cohesive sand with uniform grain size and medium plastic inorganic cohesive soil for the soil beneath the foundation and the backfill, respectively (see Table 3). The difference between all three densities can be observed in the difference in both saturated and dry densities and the friction angle and cohesion regarding the backfill mechanical properties [45]. It is worth mentioning that the case study previously discussed is considered the base scenario for both parametric analyses, namely medium (see Figure 11).  
Figure 10: Fragility surface: (**a**) 3D plot; (**b**) Contours.  
As expected in both scenarios, the loose soil presents higher failure probabilities when compared with more dense soils, especially when variations on the backfill are being assessed. This was expected due to the strong influence of the backfill on the overall in-plane bridge loading capacity [52]. Finally, a large increase in the failure probability can be seen for values higher than 100 m\({}^{3}\)/s, in which the water is found above the bridge, and the scour depth forms mainly below the foundation [8]. It is possible to conclude that soil density affects the overall vulnerability of the bridge. Moreover, for extremely low and high values of discharge, the influence of the soil density is not as notorious as for the other values.
# Parallel-Coupled-Line Bandpass Filter with Notch for Ultra-Wideband (UWB) Applications
## 1 Introduction
Alternatively, two notched bands can be generated using an L-shaped defected microstrip structure and a T-shaped resonator [17]. To this end, designing wideband bandpass filters with excellent in-band performance, wide rejection band, sharp selectivity, and narrow notched bands is still a challenge. In this article, a microstrip parallel-coupled-line bandpass filter with an ultra-wideband (UWB) passband and a narrow notched band is designed and developed. The proposed microstrip UWB filter was designed with three parallel-coupled-line sections which have electrical lengths of a quarter-wavelength at a mid-band frequency of 6.9 GHz. A transmission zero near each side of the wanted UWB passband can be generated by attaching a short-circuited stub at one end of the central line of the coupled-line structure as shown in Figure 1. The two transmission zeros effectively increased the sharpness of the passband skirts. In addition, a narrow notch was generated within the passband to reject the unwanted WLAN signal by using a stepped-impedance resonator. The proposed microstrip wideband filter was realized on a RT/Duriod 6010 microstrip substrate that has a relative dielectric constant of 10.8 and a thickness of 1.27 mm. The design and analysis of this wideband filter were demonstrated and verified by electromagnetic simulation and experimentation for validation. Figure 1: Structure of the presented microstrip wideband bandpass filter with a notch.