esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 2 Free-Space Optical Communications
### Communication Channel  
For a deeper understanding of FSO, one has to examine the channel model, which is affected by two main impairments: atmospheric turbulence and pointing error. These can be studied through statistical analyses.  
FSO links, particularly if they include terrestrial atmosphere, present the following properties [10]:  
* atmospheric attenuation of laser signals is more severe at low elevation, causing a high variation of received power;
* the link is often blocked by clouds, resulting in long-term fades;
* the amplitude scintillation patterns of received power are in the order of centimeters, which results in fast fades of optical power; and
* the beam in optical communications is very narrow, which can cause an additional source of fading from residual pointing errors of the space terminal.  
These characteristics make optical links prone to random disruptions, which add to scheduled intermittence due to orbital motion of space assets. Both disruption and link intermittence require adequate countermeasures at higher layers of the protocol stack as those envisaged by the DTN architecture.  
In order to mitigate the disruption, specific link protocols, including error control and Forward Error Correction (FEC) methods, have been designed for FSO links. Mitigation  
Figure 2: Effects of atmospheric interference on a laser.  
techniques to common challenges in FSO technologies are summarized in Reference [6; 11]. These solutions provide a quality of service to the link for a minimum data-rate, i.e., they can guarantee no losses in the receiver for certain levels of error, nor indicate when the received messages are incorrect. Besides these, higher-layer protocols are used for routing configurations and retransmission [5].
## 2 The Proposed Model
### Validation and Evaluation of the Proposed Model  
The mean absolute percentage error (_MAPE_) was used for comparing and assessing the accuracy performance of the model, as given below:  
\[\textit{MAPE}=\sum_{1}^{n}\frac{\big{|}Y_{i}-\hat{Y}_{i}\big{|}}{Y_{i}}\times 1 00. \tag{21}\]  
If the _MAPE_ of the proposed model is less than the benchmark models, then the accuracy of the proposed model is better [42]. The benchmark models that were used for the performance comparison were SWGARCH, GARCH, ARIMA-GARCH, and EWMA.  
Furthermore, regression analysis was done to verify the performance of the proposed model. The analysis also determined the relationship between the variance of FSWGARCH and its covariates, which were the fuzzy window variance, recent returns, and recent variances. The R\({}^{2}\) in the regression analysis tests on a scale of 0-100 percent shows the strength of the relationship between the model and its covariates. Hypothesis testing was also developed, as given below:  
\[H_{0}:\beta_{1}=\beta_{2}=\beta_{3}=0,\]  
\[H_{1}:\text{at least one of }\beta_{i}\
eq 0.\]  
The test statistic used in this study was \(F_{0}=\frac{MS_{R}}{MS_{E}}\), where \(MS_{R}=\frac{SSR}{k}\) and \(MS_{E}=\frac{SSE}{n-p}\), with the rejection criteria of \(F_{0}=\frac{MS_{R}}{MS_{E}}>f_{\alpha/2,k,n-p}\), where \(f_{\alpha/2,k,n-p}\) is the critical value. If \(H_{0}\) is rejected, then there is enough evidence to say that the proposed model is significantly better.
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unit** & **Reliability** & \begin{tabular}{c} **Probability of** \\ **Failure** \\ \end{tabular} & **Unit** & **Reliability** & \begin{tabular}{c} **Probability of** \\ **Failure** \\ \end{tabular} \\ \hline Communication module & 0.985335 & 0.014665 & \begin{tabular}{c} Network module \\ application software \\ \end{tabular} & 0.973514 & 0.026486 \\ \hline Communication module & 0.982986 & 0.017014 & \begin{tabular}{c} Information exchange \\ module software \\ \end{tabular} & 0.970757 & 0.029243 \\ \hline Network module & 0.980189 & 0.019811 & Power supply & 0.999201 & 0.000799 \\ \hline Server & 0.98021 & 0.01979 & Signal receiver & 0.976337 & 0.023663 \\ \hline Server application software & 0.977734 & 0.022266 & \begin{tabular}{c} Information \\ Exchange Module \\ \end{tabular} & 0.980207 & 0.019793 \\ \hline RAID controller & 0.986777 & 0.013223 & Hard disk & 0.992037 & 0.007963 \\ \hline Data storage module & 0.992029 & 0.007971 & KVM switch & 0.984137 & 0.015863 \\ \hline Data load module & 0.984164 & 0.015836 & EDID reader & 0.980207 & 0.019793 \\ \hline Data storage module & 0.981386 & 0.018614 & \begin{tabular}{c} Data loading module \\ application software \\ \end{tabular} & 0.978161 & 0.021839 \\ \hline \begin{tabular}{c} Integrated Information \\ Management Module \\ \end{tabular} & 0.976393 & 0.023607 & \begin{tabular}{c} Comprehensive information \\ module software \\ \end{tabular} & 0.973141 & 0.026859 \\ \hline EDID reader & 0.976703 & 0.023297 & \begin{tabular}{c} CPEX main \\ processor module \\ \end{tabular} & 0.980204 & 0.019796 \\ \hline Motherboard module & 0.986735 & 0.013265 & Control exterior parts & 0.984137 & 0.015863 \\ \hline Comprehensive & 0.98417 & 0.01583 & \begin{tabular}{c} Comprehensive \\ decompression \\ module software \\ \end{tabular} & 0.978624 & 0.021376 \\ \hline CPEX main & & \begin{tabular}{c} USB interface \\ extension module \\ application software \\ \end{tabular} & 0.979436 & 0.020564 & \begin{tabular}{c} USB structure \\ extension module \\ application software \\ \end{tabular} & 0.979821 & 0.020179 \\ \hline USB interface & 0.986761 & 0.013239 & Display processing module & 0.986735 & 0.013265 \\ \hline Display processing module & 0.984111 & 0.015889 & \
\begin{tabular}{c} Control keyboard, \\ Remote control unit, \\ Communication telephone \\ \end{tabular} & 0.99734 & 0.00266 \\ \hline Multifunctional information & &
\begin{tabular}{c} Multifunctional information \\ demodulation \\ module software \\ \end{tabular} & 0.982437 & 0.017563 \\ \hline Touch display & 0.993354 & 0.006646 & Monitor & 0.993354 & 0.006646 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: C\({}^{4}\)ISR System unit Reliability (100 h working time).
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 5 Conclusions  
This article presented the analysis and comparison of turbo code performance for diverse interleavers with short block lengths. A new deterministic interleaver with behavior based on the Duffing map model is presented. The interleaver uses the chaotic qualities of the Duffing map, CMSC criteria, and circular displacement to ensure distance and decorrelation. In a short-length communication of 64 bits, the proposed interleaver is advantageous as its BER is better than the performance obtained by other interleavers. In addition, the application of the criteria ensures a minimum dispersion and decorrelation that favors iterative decoding and TC performance. The chaotic interleaver proposed is deterministic, which allows for a simple implementation. The algorithm can be adapted to other lengths. The Duffing map requires two parameters \(a\) and \(b\), as well as two initial conditions \(x_{1}\) and \(y_{1}\), which could influence the construction of the interleaver. After comparing this interleaver in short bit blocks, performance is similar to that of existing interleavers, with small differences between them. In the case of 64 bits, the BER obtained during the test that assesses the proposed chaotic interleaver is lower than that of other interleavers for SNRs of 3 dB. Regarding the 512-bit length, the BER results of the test with the chaotic interleaver are inferior to those of the random interleaver for SNRs of 0 dB, 0.5 dB, 1.5 dB, and 2 dB. Regarding the 128-, 256- and 512-bit lengths, despite performing similarly to the already existing interleavers, the proposed interleaver always performs better than the chaotic Lozi map interleaver. The proposed chaotic interleaver, based on a chaotic Duffing map, achieves a performance similar to that of the existing interleavers, reaching excellent levels in 64-bit lengths. In future work, the performance of the proposed chaotic interleaver could be improved by changing the initial conditions of the Duffing map.  
Conceptualization, C.U., J.K. and R.L.-E.; methodology, C.U., J.K. and R.L.-E.; software, C.U., J.K. and R.L.-E.; validation, C.U., J.K. and R.L.-E.; formal analysis, C.U., J.K. and R.L.-E.; investigation, C.U., J.K. and R.L.-E.; resources, C.U., J.K. and R.L.-E.; data curation, C.U., J.K. and R.L.-E.; writing--original draft preparation, C.U., J.K. and R.L.-E.; writing--review and editing, C.U.; visualization, C.U., J.K. and R.L.-E.; supervision, C.U.; project administration, C.U.; funding acquisition, C.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Faculty of Engineering of the University of Santiago of Chile, Chile. [PERSON] completed his doctoral studies in Engineering Sciences, specializing in Automation, funded by CONICYT: CONICYT-PFCHA/Doctorado Nacional/2019.  
The authors declare no conflict of interest.
## 3 Proposed Solution
### Trust Calculation in Proposed Methodology
The characteristics to detect a blackhole node are as follows:  
* Deviation in Gini index: The Gini index deviation for node \(m\) can be defined as shown in Equation (2): \[Gini\_Deviation_{m}=|G_{m}-\bar{G}|\] (2)
* Packet loss: The condition to detect potential blackhole nodes based on packet loss can be expressed as described in Equation (3): \[P_{m}>\theta_{P}\] (3)
* Latency: The condition to detect potential blackhole nodes based on latency can be expressed as described in Equation (4): \[L_{m}>\theta_{L}\] (4)
* Throughput: The condition to detect potential blackhole nodes based on throughput can be expressed as described in Equation (5): \[EC_{m}<\theta_{EC}\] (5)
* Traffic distribution: By analyzing the traffic distribution patterns and identifying nodes with abnormal or inconsistent traffic distribution, blackhole nodes in a CPS network are detected and subsequently eliminated. The pseudo-code for the detection of blackhole nodes using the Gini index is described in Algorithm 2. The time complexity of the algorithm is O(n), while the space complexity is also O(nlogn). * Indirect Trust Calculation for GreyholeTo identify a greyhole node in a CPS, several factors that distinguish anomalous behavior displayed by nodes and indicate the presence of a greyhole node can be taken into account. The following is a list of greyhole node characteristics:  
1. Gini index deviation: Each node's Gini index in the CPS network is determined based on the flow characteristics of packet loss, delay, and throughput. Nodes with disproportionately high Gini index values might be greyhole nodes. 2. Selective packet dropping: By keeping an eye on a node's packet-forwarding activity, greyhole nodes can be identified. 3. Latency: Greyhole behavior can be detected by tracking the node latency and finding nodes with a higher latency than anticipated. 4. Energy consumption: Greyhole nodes can be identified by tracking the energy consumption and spotting those with noticeably low residual energy values. 5. Throughput: In comparison to other nodes, greyhole nodes may manipulate or restrict the flow of data, resulting in reduced throughput. Greyhole nodes in the CPS can be found by taking into account the criteria mentioned above and the Gini index analysis. The mathematical model below shows how the Gini index can be used to find greyhole nodes in a CPS. Let the characteristics of node \(m\) at time \(T\) describe several parameters. The Gini index value is denoted by \(G_{m}(T)\), representing the data distribution. The packet loss rate is indicated by \(P_{m}(T)\), reflecting the rate of lost packets. The latency is represented by \(L_{m}(T)\), capturing the data transmission delay. The energy consumption is denoted by \(EC_{m}(T)\), signifying the power usage. The throughput is indicated by \(Th_{m}(T)\), representing the data transfer rate. \(\theta\) represents the threshold value for a particular metric and \(\bar{G}\) represents the average value of the Gini index. These parameter values are essential for evaluating and managing the performance of node \(m\) within the CPS network.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### Filling Technique  
Cold lateral compaction has demonstrated robust adhesion values in certain studies [37; 42]. However, the thermoplastic injectable technique, specifically using the Calamus Flow Delivery System, has shown a weakening effect on the bond strength of EndoSequence BC [23]. This might be attributed to heightened sealer polymerization, as heat can expedite hydration reactions and the formation of hydroxyapatite. Calcium silicate-based sealers exhibit accelerated setting times with a subsequent decrease in flow rate. Consequently, this rapid setting results in increased stiffness, leading to a diminished bond strength [23].  
Interestingly, the warm vertical compaction technique did not impact the bond strength when evaluating CereSeal and BC HiFlow. However, it did have a negative effect on the bond strength of BioRoot [26]. Thermoplastic gutta-percha tends to shrink upon cooling, and this shrinkage can concentrate stress in the sealer, affecting its adhesion to the root dentin surface [26].  
When it comes to filling without a gutta-percha cone, it has been observed that this approach yields a higher bond strength for cement sealers (CSCs) compared to samples filled with gutta-percha and cement. This difference is likely because sealers form more robust physical and chemical bonds with dentin than with gutta-percha. Additionally, the plastic deformation of gutta-percha can negatively impact resistance to dislocation [3].
## 2 2-D Phased Array Design
### Antenna Element  
The antenna element chosen to assess the 2-D phased array configurations is illustrated in Figure 12. This patch element was selected because it is easy to set and fabricate. Furthermore, it provides a wide bandwidth and good radiation characteristics, as explained in [31]. The dimension values for the design (Figure 12) are as follows: \(l_{1}=11.56\) mm, \(l_{2}=11.33\) mm, \(w_{1}=5.4\) mm, \(w_{2}=4.97\) mm, \(g=0.73\) mm, \(s=1.  
Figure 14 illustrates the behavior of the reflection coefficient of the antenna element using the experimental measurements and full-wave simulations (CST solver). The chosen element achieves a bandwidth of almost 600 MHz (from 5.5 GHz to 6.1 GHz). Furthermore, the radiation pattern of the chosen element was measured in an anechoic chamber to study its behavior. Figure 15 shows the 3-D radiation pattern of the chosen antenna element using frequency cuts at 5.5 GHz, 5.75 GHz and 6.0 GHz. This antenna element provides good radiation characteristics, considering a bandwidth adequate to be applied to the 2-D phased array architectures.  
Figure 14: Behavior of the reflection coefficient of the chosen antenna element.  
Figure 13: Current distribution of the chosen element for (**a**) 5.75 GHz and (**b**) 6.0 GHz.
### RPL Security  
Security concerns in the RPL pose challenges to its growth, especially in IoT and WSN applications, where data exchange between nodes follows a vulnerable route. Different attacks like Wormhole, Sybil, Fragmentation, Black hole, Rank, Clone ID, Version number, and Denial of Service attacks have made IoT nodes and network topologies vulnerable. Resource-constrained IoT nodes make it difficult to mitigate attacks, and different tech  
Figure 1: Topology of the IoT.  
niques such as IDS, trust-based models, machine learning, and cryptography have been proposed to secure the RPL [9]. Trust-based models are easily adoptable and effective in detecting and mitigating attack nodes [10].  
The proposed methodology in this study is trust-based to secure the RPL network. As described, RPL-based networks are vulnerable to multiple attacks. Figure 2 contains all the possible occurring attacks that are identified in the different studies.
## 4 Method
### Data Collection Instrument  
Data collection was carried out using the validated self-assessment tool [6] and was applied digitally from the project \"Pla Mobils.edu\" [27] Virtual Learning Environment (Moodle). Teachers who were part of the project had automatic access to the virtual classroom. The questionnaire was designed with platform tools that allowed us to control a secure environment for both data collection and data processing (Figure 2).  
Participants received specific training on digital technologies and mobile devices in education prior to the questionnaire through their enrolment in the educational program \"Pla Mobil.edu\", which is based on digital resources, activities, and online seminars.  
The questionnaire was divided into two parts: dimension 1 on biodata and dimension 2 on core aspects. Dimension two presented a self-assessment questionnaire consisting of 7 elements (cores aspects): (1) The Content, (2) Methodological Strategies, (3) Activities, (4) Evaluation, (5) Technology Resources, (6) Technology Learning Spaces, and (7) Teachers, with 67 items evaluated using a Likert-type scale of 4 points, 1 being \"strongly disagree\" and 4 being \"strongly agree\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Educational Stage** & **Teachers (_n_)** & **\%** \\ \hline Childhood Education (3–6 years) & 20 & 6.1\% \\ Initial cycle of Primary Education (6–8 years) & 33 & 10.1\% \\ Middle cycle of Primary Education (8–10 years) & 37 & 11.3\% \\ Upper cycle of Primary Education (10–12 years) & 68 & 20.8\% \\ Compulsory Secondary Education (12–16 years) & 169 & 51.7\% \\ \hline
**Total** & 327 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Educational stage of the participating teachers.  
At the end of the questionnaire, teachers were given a score based on their knowledge on how mobile devices can help them plan, carry out, and evaluate teaching and learning processes (Table 2), as well as feedback based on their scores. This feedback provided them with specific educational resources to improve their pedagogical knowledge.  
The interaction data from the questionnaire were first analysed with Moodle statistical analysis, and then they were put through the SPPS programme (Statistical Package for the Social Sciences).
# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 3 Methodology  
### Aspect Sentiment Quad Prediction Based on the Generative Paradigm  
Aspect sentiment quadruplet prediction aims to predict all aspect terms (AT), aspect categories (AC), opinion terms (OT), and sentiment polarities (SP) within a given sentence \(x\). Aspect terms and opinion terms are generally words present in the sentence, but sometimes aspect terms and opinion terms may be implicitly represented in the sentence, denoted as \"NULL\" in such cases. Aspect categories belong to a predefined set \(V_{c}\). A sentence may contain multiple quadruplets.  
Currently, aspect sentiment quadruplet prediction based on the generative paradigm involves arranging the quadruplets in the dataset into a template format to create targets. The text and targets are then fed into a Sequence-to-Sequence model for fine-tuning training. Finally, the trained model generates targets, which are split into quadruplets based on the template format. The key component in achieving this task is the learning process of the Sequence-to-Sequence model. This involves learning parameters \(\theta\), maximizing the probability \(p_{\theta}(y|x)\), where \(x\) is the original sentence, and \(y\) is the target sentence to be obtained. Since the target sentence is generated token by token, the \(i-th\) token of \(y\) is determined by \(x\) and the preceding \(i-1\) tokens of \(y\).  
\[p_{\theta}(y_{i}|x,y_{1},\dots,y_{i-1})=softmax(W^{T}y_{i-1}) \tag{1}\]  
In the process, \(W\) maps \(y_{i-1}\) to a vector of vocabulary size and subsequently utilizes the softmax function to determine which word from the vocabulary the model should choose as the next token.  
During training, we chose the T5 model [18] to initialize the parameters. The T5 model, proposed by Google in 2020, is a pre-trained model designed to handle various text tasks through a unified framework. It converts all tasks into text-to-text problems and completes different tasks by adding different prefix prompts to the text, such as translation and summarization. T5 follows the standard Transformer encoder-decoder structure. We initialized the model parameters with T5-base and input the ABSA text data into the model. The data are converted into a sequence of word vectors through the word embedding layer and then passed into the Transformer encoder. The encoder transforms it into high-dimensional hidden representations. The decoder combines the encoder's output and the previously generated text to autoregressively generate new words step by step. As shown in Figure 2.  
During training, T5 uses cross-entropy loss to measure the difference between the generated text and our Self-Inference Template target, updating the model parameters accordingly.  
\[L(x,y)=-\sum_{i=1}^{n}logp_{\theta}(y_{i}|x,y_{1},\dots,y_{i-1}) \tag{2}\]  
where \(n\) represents the length of the target sequence \(y\).
## 3 Results and Discussion
### Influence of Fe Application on the Diazotrophic Communities in Soil
_Bacteroides_ were only detected in Fe plot soil, which suggests their nitrogen fixation was enhanced by iron application, i.e., they are iron-reducing bacteria. _Bacteoides_ strains harboring iron-reducing abilities were reported [36]. _Cyanobacteria_ (days 2, 4, and 6) and _Anaerolinea_ (days 2, 4, and 6) were also frequently detected in the Fe-applied soil as well as in the Con plot soil. Since the iron-reducing ability of these bacteria has not been reported thus far, they are likely to be active nitrogen fixers in both plot soils, irrespective of iron application. Candidatus _Solibacter_ was detected on all the days examined, as well as on day 2 in the Con plot soil. Although there are no _nif_ genes registered in the NCBI database, it is strongly suggested that they are active nitrogen fixers in soils. Overall, 165 rRNA gene amplicon sequence analysis revealed that iron-reducing bacteria belonging to the genera _Anaeromyxobacter_, _Geomonas_, and _Geobacter_ are the most active diazotrophs in native and iron-applied paddy soils. In addition to these bacteria, _Bacteroides_, most likely consisting of iron-reducing bacteria, are active diazotrophs in iron-applied paddy soil. Iron application to the soil might stimulate the nitrogen-fixing activity of these iron-reducing diazotrophs and lead to increased nitrogen incorporation into the soil. _Anaerolinea_, _Cyanobacteria_, and Candidatus _Solibacter_ are active nitrogen fixers in paddy soils, irrespective of iron application.