esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
## 5 Results and Discussion
### Effectiveness of the Large-Scale Monolingual Corpus of the Target Language  
As mentioned, in the scenario of low parallel corpus but high monolingual target language corpus, a byproduct of BERT supervision is that we can further improve the translation performance by training the decoder with the large-scale monolingual corpus of the target language. To validate this point, we conducted experiments on the De\(\rightarrow\)En task, in which we added the extra large-scale monolingual English corpus TED and WMT to the original training dataset IWSLT'14 in decoder training. The TED includes 500,000 English sentences from www.kaggle.com (accessed on 8 January 2020), and the WMT includes  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strategy** & **De\(\rightarrow\)En** & **Ro\(\rightarrow\)En** & **Vi\(\rightarrow\)En** & **De\(\rightarrow\)Zh** & **Ko\(\rightarrow\)Zh** & **Ru\(\rightarrow\)Zh** \\ \hline BERT frozen & 35.86 & 32.46 & 29.08 & 28.76 & 30.81 & 26.29 \\ \hline BERT fine-tuning & **36.38** & **32.86** & **29.65** & **29.17** & **31.24** & **26.57** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The effectiveness of the BERT Fine-tuning.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & **De\(\rightarrow\)En** & **Ro\(\rightarrow\)En** & **Vi\(\rightarrow\)En** & **De\(\rightarrow\)Zh** & **Ko\(\rightarrow\)Zh** & **Ru\(\rightarrow\)Zh** \\ \hline \multirow{3}{*}{Ours} & Transformer (base) & 34.84 & 32.06 & 27.87 & 27.88 & 30.24 & 25.43 \\ \cline{2-7}  & EncT + DecT & 30.88 & 26.51 & 22.11 & 22.82 & 25.33 & 18.95 \\ \cline{2-7}  & EncT + DeT + JO (40 epoch) & 33.77 & 31.19 & 28.12 & 27.58 & 28.71 & 23.23 \\ \cline{2-7}  & EncT + DeT + JO (80 epoch) & 35.28 & 31.67 & 28.38 & 28.03 & 29.59 & 25.87 \\ \cline{2-7}  & EncT + DeT + JO (full training) & **36.38** & **32.86** & **29.65** & **29.17** & **31.24** & **26.57** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effectiveness of joint optimization. EncT, DecT, JO represent encoder training, decoder training, and joint optimization, respectively.  
4,000,000 English sentences from www.statmt.org (accessed on 8 January 2020). The results are listed in Table 7. \"EncT + DecT\" represents the encoder training and decoder training using the IWSLT'14; \"+TED\" and \"+WMT\" mean that we add the TED and WMT to train the dataset in decoder training.  
Comparing the \"EncT + DecT(TED)\" with the \"EncT + DecT\", we found that using an additional monolingual corpus TED in the decoder training improves the performance of the Transformer. This finding also holds when we used an additional corpus WMT. The results in the fifth and the sixth rows show that joint optimization can further improve the performance of the \"EncT + DecT(TED)\" and the \"EncT + DecT(WMT)\".
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
### Applications of Blockchain in 6G Network
#### 1.1.4 Smart Vehicles  
To establish V2V communication, vehicles must quickly send the vehicular information, comprising number of pedestrians, road conditions, number of vehicles, and their conditions, to reduce the number of road accidents and enhance road safety. However, the scalability of V2V communication increases the security, safety, reliability, and trust issues of vehicular network users. Therefore, there is a requirement for a prominent solution to overcome these issues. Blockchain is used to manage the ground truth of vehicle information and offers data authentication among vehicles. Many researchers recently focused on blockchain technology for V2V communication. [PERSON] et al. [21] integrated blockchain with IoT-driven smart devices that improve customer safety, ease congestion, and pollution reduction. For that, they proposed a certifiateless key agreement protocol for Internet of vehicles in a smart transportation context (Block-CLAP). The proposed scheme outperforms the precedent authentication schemes' security, communication, and computational overheads.  
Furthermore, [PERSON] et al. [22] integrated blockchain with the Internet of vehicles (BIoV) and proposed a novel V2V electricity trading approach using Bayesian game pricing. They utilized a Bayesian game that predicted the price in the distributed BIoV with incomplete information-sharing. In the pricing game approach, they applied smart contracts that provide security, trust, and reliability for electricity transactions. The results reveal that the proposed scheme reduces communication overhead and time complexity in the decentralized IoVs compared to the static game. Then, in [23], the authors incorporated a blockchain network with the vehicles that stores real-time information about vehicles, such as relaying, transmitting, and receiving data. As a result, the adopted scheme is able to block malicious or unauthorized vehicles from participation. Finally, they considered network parameters, such as throughput and error rate analysis (i.e., bit error rate) to evaluate their proposed scheme. They proved the proposed scheme's efficiency and effectiveness by enhancing throughput by 3 bps and decreasing BER by 2 dB.
## 3 Proposed Scheme
### Maintenance of Trustworthy and QoS-Assured Paths  
Path maintenance is a mechanism to decide how to use or search alternative paths if network conditions change (e.g., the occurrence of link bottlenecks due to attacks by malicious nodes or large amounts of data). If the mechanism discovers a node that is unable to act normally, it sends a route error message (RERR) with a blacklist to the gateway node. When the gateway node receives information that the path has been compromised, it can try to use another known path to the sensor node or can perform a path discovery routine again to search for a new path.  
Path maintenance verifies the path validity at specific time intervals. When the path cache entry exceeds the maximum validity time, a new path discovery procedure is also initiated. In addition, control messages are sent and received periodically for the selection and maintenance of trustworthy paths with satisfactory QoS. The gateway node calculates the \(PQTV\) based on the values received from nodes on valid paths and sends it to each sensor node. The sensor node updates the \(PTV\), E2E delay, \(ETX\), and \(PQTV\) received from the gateway. As a result, the gateways and sensor nodes  
Figure 2: Example of path selection based on mission-critical data priority.  
can periodically check the trustworthiness and QoS values for each path. Consequently, a new optimal path is established after the completion of this procedure.
## 5 Discussion
### IVCI Model Verification and Evaluation
Figure 13: The SBAS-InSAR results within Y-1. (**a**) Velocity map of the Y-1 SBAS-InSAR results; (**b**) map of P-1 cumulative settlement. Figure 16: The SBAS-InSAR results within Y-2. (**a**) Velocity map of the Y-2 SBAS-InSAR results; (**b**) map of P-2 cumulative settlement. Figure 17: Comparison of landslide susceptibility for Y-3 under the (**a**) IV-CatBoost model and (**b**) IVCI model. Figure 15: Y-2 optical remote sensing interpretation. (**1–8**), delineated by the red lines, are all classified as shallow surface landslides. (**a**) image from Maxar Technologies, (**b**) image from CNES/Airbus. The deformations in the Y-3 area are mostly between 15 mm/y and 20 mm/y (Figure 19). Based on the settlement measurements at point P-3 (E 119.280389\({}^{\circ}\), N 27.604171\({}^{\circ}\)) (Figure 19), the area experienced a maximum settlement exceeding 45 mm, despite noticeable rebound phenomena in the later period. Overall, there is a clear indication of descent. Based on the above findings, it is clear that the three distinct abnormal areas exhibit a pattern of subsidence deformation, and historical occurrences of landslide disasters are evident in each of these areas. This suggests that these three zones are in an unstable condition, making them susceptible to landslides. However, the IV-CatBoost model primarily classified these areas as having low to very low susceptibility, which does not align well with the current slope conditions. Upon adjustment using the IVCI model, the majority of grid cells within these zones were reclassified into the high and very high susceptibility categories, more accurately reflecting the real slope conditions. This underscores the superiority of the IVCI model over traditional approaches. To comprehensively validate the precision of the IVCI model, an ROC curve was generated for the new model, and the outcomes are depicted in Figure 20. The AUC value for the IVCI model stands at 0.90, surpassing the IV-CatBoost model's value of 0.88. This signifies that the IVCI model maintains a higher level of accuracy and precision on a substantial dataset compared to the IV-CatBoost model. Figure 19: The SBAS-InSAR results within Y-3. (**a**) Velocity map of the Y-3 SBAS-InSAR results; (**b**) map of P-3 cumulative settlement. Figure 18: Y-3 optical remote sensing interpretation. (**1–4**), delineated by the red lines, are all classified as shallow surface landslides. Both (**5**) and (**6**) are characterized as bare rock. Images (**a**,**b**) are both derived from CNES/Airbus satellite data.
## 2 Related Work
### Primary Challenges in 5G-Enabled IoTs  
In the IoT, dealing with noise, outliers, and missing values is a critical area of research. These challenges can have detrimental effects on the accuracy and reliability of collected data. Therefore, it is crucial to investigate and develop solutions to these challenges thoroughly before proceeding with further data exploration. Among the techniques employed to address these challenges, the RPCA can be used often, even though it offers an unsupervised learning approach which is well suited to IoTs. This is primarily because the RPCA does not require labeled data for normal and abnormal classes, a condition that is often challenging to meet in the IoT. However, few recent studies have focused on unsupervised learning, particularly using the RPCA [38].  
In addition, research has addressed multivariate data reconstruction, a crucial aspect given the multisensing nature of modern IoT devices [11]. The inclusion of correlations among attributes in multivariate data enhances the accuracy of data reconstruction and anomaly detection, highlighting the need for intensive research in this area [38].  
Recent studies have suggested different shrinkages of singular values based on their importance, which is a potential approach for improving data imputation processes [13]. However, a limited number of studies in the literature have exploited this approach for IoTs.  
Furthermore, IoTs typically encounter numerous types of noise such as Gaussian and impulsive noise [39; 40]. Previous research focused only on a single type of corruption, such as Gaussian noise, outliers, and missing values, without considering other types of corrupted data during data reconstruction. This points to a significant gap in the current knowledge, indicating a strong need for an analytical model to effectively address these complexities.  
These challenges highlight the importance of our research on IoTs, which aims to exploit the potential of the RPCA. Our focus includes exploring multivariate data reconstruction, applying different weights to shrink singular values, and addressing a mixed-noise environment encompassing Gaussian noise, impulsive noise, and other corruption types, such as missing values and outliers. This comprehensive approach can significantly boost the robustness and precision of data reconstruction in IoTs.  
A summary of research exploring RPCAs, TRPCAs, and our work within the context of noise, outliers, and missing value reconstruction in IoTs is outlined in Table 1. The X in Table 1 indicates that the paper considers the problem.
## 3 Result and Discussion
### Curing Time Effect  
The effect of curing time on the compressive strength of the prepared geopolymers samples (using 10 M NaOH) has been studied.  
The compressive strength for geopolymer samples that were cured at room temperature for 3, 7, and 28 days are depicted in Figure 1. As can be seen, increasing the curing time from 3 to 28 days increased the compressive strength. The results agree with [22; 28]. It is believed that the increment in compressive strength alongside curing time is related to the improvement of the geopolymerization process.  
Samples 17 and 25 reported the highest compres  
Figure 1: The comparison of compressive strength for prepared samples using 10 M NaOH in 3, 7, and 28 days curing time.  
The compressive strengths of geopolymer are dependent upon the source of the aluminosilicate and reaction conditions (the type and concentration of the alkali used, curing time, and temperature). Rice husk (RH), Rice husk ash (RHA), and Palm oil fuel ash (POFA) contain a high percentage of silicon dioxide or alumino-silica in amorphous form and can be used as pozzolanic material [29] to prepare geopolymers. The compressive strength could be tuned by controlling the strength of the gel phase, the ratio of the gel phase to undissolved Al-Si particles, the nature of the amorphous phase, the degree of crystallinity, and surface reactions between the gel phase and undissolved particles [30; 31]. The OH\({}^{-}\) in the activators plays a catalytic role for reactivity, while the metal cation helps form a structural element, and balance the negative framework of tetrahedral aluminum [32] to accelerate the precipitation and crystallization of the siliceous and aluminous species. In order to develop a more enhanced version of geopolymer, we took into account the influence of the aforementioned parameters on geopolymer's compressive strength, while the organic materials utilized as precursors should be studied in greater detail.  
The sample contains RHA 20%, POFA 10%, MK 35%, while slag 35% without raw rice husk was investigated for comparison purpose, which reported a compressive strength of 5.48 MPa (28 days curing) and water absorption of 30.8% (24 h soaking). It was confirmed that although the addition of raw rice husk decreases compressive strength, it increases the percentage of water absorption percentage.
# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 3 Results
### Gains and Improvements  
The most common benefits identified are related to efficiency, measured by Length of Stay (LOS) and waiting time, improved patient safety, and cost reduction [39; 40; 41; 42; 43; 44; 45]. Among the evaluated studies, several indicated that the technologies used were accurate and potentially viable for tracking resources, patients, and staff [39; 41; 44; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. Notably, one study using BLE demonstrated precision above 99% in location accuracy at zone level, significantly increasing patient experience, safety, and operational efficiency [39]. While this was the only study providing such specific precision data, it exemplifies the potential performance levels achievable with these technologies. Additionally, most of these studies emphasised the benefits of improving workflows and patient care, which could provide valuable data for assessing service quality in the future [39; 41; 44; 45; 50; 51; 54; 56; 58; 59; 57; 54; 58].  
Several studies reported time savings in hospital activities, including shorter waiting times [43; 45; 58; 60; 61], reduced delays in medical care and locating equipment [48; 26; 42], and faster test completion [45]. One study used RFID to track laboratory samples, reducing sample identification time and contamination risk since [33].  
Figure 3: Main technologies per year.  
Additionally, studies highlighted the benefits of indoor location technologies for patient or staff safety [9, 13, 30, 38, 39, 40, 41, 61, 62, 63, 64], and some mentioned cost reduction advantages in hospital activities [42, 65, 66, 67]. However, safety is typically not a direct outcome but an assumed improvement resulting from enhanced workflows and operational efficiency.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 3 Results  
In this section, we present and discuss the experimental results of the LTE model and compare them with those of th baseline models. In Section 3.1, we explain the prediction performance of the LTE model in comparison with a total of 14 baseline models, and in Section 3.2 we compare the execution time and the number of parameters between Transformer-based models, including our LTE model. In Section 3.3, we analyze the efficiency of the presence or absence PE and LN.
## 2 Dispersion Curve Calculation  
The methodology for obtaining the dispersion curves of 2D periodic structures is presented in this section. The WFE method is used to obtain wave propagation characteristics, wavenumbers, and wave modes of the periodic waveguide. This is followed by the necessary energy and power flow considerations for calculating the group and phase velocities of guided wave modes. Finally, the algorithm for calculating the 2D group velocity profile is provided.
## 4 Analysis and Evaluations
### Comparison between SDN and Legacy Network
#### 4.1.2 Packet Transmission Rate (PTR)  
We compared both networks based on the PTR obtained by executing the _PING_ tool in Mininet. PTR for both topologies is shown in Figure 10 for source and destination hosts that use AS65000 or the SDN network as a transit network for a varying number of packets.  
The results obtained for the PTR indicate that the total time taken by both networks for a given number of packets is almost equal. It is noted that both networks are active for the same number of time intervals to execute the command.
## 1 Introduction
Since most 3D periodic nanostructures created through optical 3D nanofabrication techniques are inevitably composed of photoreactive polymer-based dielectrics, a material conversion step is required to convert them into functional inorganics or composites that can be used for electronic or electrochemical devices. Among various infiltration systems, electroplating, which can grow an inorganic thin film on a conductive substrate through a reduction reaction using electrical energy, has already been widely used for industry because of its advantages in terms of process simplicity, chemical diversity, low cost, and high throughput [47]. In particular, a bottom-up growth behavior from a seed layer makes electroplating very well suited for densely filling 3D nanostructured porous templates with functional inorganics. In addition, unlike other infiltration techniques that generally require a high-temperature environment [48], electroplating, which is based on a solution process near room temperature, can transform the constituent materials of 3D nanostructures without causing heat-induced structural collapse to the polymer template, with a relatively low melting point generally below 200 \({}^{\circ}\)C.  
Through this strategy, several research groups have successfully reported a new class of functional materials produced by the combination of optical 3D nanofabrication and electroplating. The form of the proposed materials can be divided into two types: (1) 3D bicontinuous organic-inorganic composites and (2) porous inorganics. The former is generated when the porous network of the polymer template is filled with a heterogeneous material, and in this state, the latter may be generated when the polymer template is completely removed through a chemical or physical process [49]. These two types of 3D nanostructured materials are contributing to the innovation of existing electrochemical or energy-related device architectures, including batteries, catalysts, and thermoelectric devices, and fundamentally improve their performance limits imposed by planar architectures. As such, research on the development of functional materials with 3D periodic nanostructures through the combination of optical 3D nanofabrication and electroplating is gradually maturing, but the latest research cases have not been organized yet. In this mini review, we would like to introduce the recent achievements related to 3D functional nanostructures generated by combining advanced optical 3D lithography and electroplating and discuss in-depth important technical issues that should be considered critical in the fabrication process [50; 51; 52].
## 3 Results and Discussion
### BET Analysis  
The adsorbent materials biochar, biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol showed lower surface areas and pore volumes than biochar/rGO (Table 2). The decrease in surface area and pore volume following modification can be attributed to the introduction of DES, resulting in surface modification and blocking of the pores [21]. Nonetheless, biochar/rGO/DES-glycerol still showed higher adsorption of ZDV and phenol from wastewater.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
** Adsorbent** & **C** & **O** & **Si** & **Al** & **CA** & **Cl** & **O/C** \\ \hline Biochar & 85.53 & 13.05 & 0.81 & 0.61 & 0.73 & - & 0.15 \\ Biochar/rGO & 89.37 & 9.87 & 0.48 & 0.28 & 0.28 & - & 0.11 \\ Biochar/rGO/DES-CTAB & 80.9 & 13.01 & - & 2.52 & - & 3.58 & 0.16 \\ Biochar/rGO/DES-glycerol & 84.92 & 13.95 & - & - & 0.31 & 0.82 & 0.16 \\ \hline \end{tabular}
\end{table}
Table 1: EDS elemental content (%) of biochar; biochar/rGO, biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol.  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Biochar** & **Biochar/rGO** & **Biochar/rGO/DES-CTAB** & **Biochar/rGO/DES-Glycerol** \\ \hline BET Surface Area (m\({}^{2}\)/g) & 2.806 & 84.969 & 1.059 & 2.506 \\ Pore volume (cm\({}^{3}\)/g) & 0.007 & 0.031 & 0.004 & 0.004 \\ Pore size (nm) & 10.16 & 1.44 & 13.04 & 6.87 \\ \hline \end{tabular}
\end{table}
Table 2: BET surface analyses for biochar; biochar/rGO, biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol.  
Figure 4: SEM image (**A,B**) biochar; (**C,D**) biochar/rGO; (**E,F**) biochar/rGO/DES-CTAB and (**G,H**) biochar/rGO/DES-glycerol.
# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 3 Problem Statement and Proposed Solution
### Problem Statement and Model Framework  
Nowadays, once rumors appear on social media, posts related to rumor events can be easily searched according to keywords. The content of these posts describes the central content of rumor events better. However, the forwarding and comments of relevant posts cause the rapid spread of rumors. The research object of this paper is rumor events. The purpose of the experiment is to detect whether the event on a microblog is a rumor, rather than whether the post related to the event is a rumor. Once an event on the microblog is detected as a rumor, the post related to the event is also regarded as a rumor. For example, \"# Qiqihar flood # Heilongjiang Qiqihar has also had a serious flood. Watching the videos  
The goal of the Doc2 Vec model is to maximize the average log probability, the calculation equation is shown in Equation (1).  
\[\underset{D,W}{argmax}\frac{1}{N}\sum_{n=k}^{N-k}logp(w_{n}|w_{n-k,\ldots,}w_{n+k}) \tag{1}\]  
In which \(N\) is the number of words in the paragraph text, the word vector represented by a word is \(w_{n}\), the set of word vectors is \(W\), and \(D\) is the set of all paragraph vectors. \(p\) (\(w_{n}\,|\,w_{n-k},\,\ldots,\,w_{n+k}\)) is used to calculate the maximum likelihood probability of \(w_{n}\) based on the characteristics of \(w_{n-k},\,\ldots,\,w_{n+k}\).  
Equations (2) and (3) use the Softmax function to predict the probability of the words in the paragraph.  
\[p(w_{n}|w_{n-k},\ldots,w_{n+k})=\frac{exp(\theta^{T}x_{n})}{\sum_{i}exp(\theta ^{T}x_{i})} \tag{2}\]  
\[x_{n}=h(g_{j},w_{n-k},\ldots,w_{n+k};D,W) \tag{3}\]  
In which \(\theta\) is the parameter of the Softmax function, \(g_{j}\) is a paragraph vector. Additionally, \(h\) represents the connection function or the average function. \(X_{n}\) represents the vector composed of \(k\) word vectors \(w_{n}\) and paragraph vector \(g_{j}\). \(\theta^{T}X_{n}\) is the un-normalized log-probability for each output word n.