esa-sceva/satcom-chunk-collection huggingface dataset:
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 4 Experimental Results
### Entity Type Extraction
#### 4.2.2 NMF Topic Modeling  
After preprocessing the data, this paper uses the TF-IDF method to extract the features of online reviews of scenic spots and converted the online review text into vector representation for NMF model training and inference. When selecting the number of model topics, this paper uses the calculation of topic consistency and reconstruction error to calculate the optimal number of topics. Topic coherence measures the semantic relevance of words in the topic, that is, whether the feature words in the topic often co-occur in the actual text. High topic consistency means that the words within the topic have a strong semantic relevance, indicating that the topic has better semantic interpretation. On the contrary, it means that the feature words within the topic do not have an obvious co-occurrence relationship [28]. The calculation formula is shown in Formula (17); the reconstruction error is used to evaluate whether the model can accurately reconstruct the original data in low dimension. A lower reconstruction error means that the model captures the global structure and main features of the document, and the model fits better. A higher reconstruction error means that the model does not fit the data well. The calculation formula is shown in Formula (2). Combining topic consistency and reconstruction error helps to more comprehensively measure the effect of topic clustering and find a balance between the interpretability of the model and the accurate representation of the original data.  
\[C_{k}=\sum_{m=2}^{M}\sum_{l=1}^{m}\log\frac{N\left(v_{m}^{k},v_{1}^{k}\right)+1 }{N\left(v_{1}^{k}\right)} \tag{17}\]  
As shown in Figure 6, when the number of topics k is set to 3, 5 and 7, the consistency is the highest, which is 0.54. Taking the reconstruction error into consideration, 7, with a lower reconstruction error, is finally selected as the final number of topics, and its reconstruction error is 67.26.
## 4 Performance Analysis in the Absence of Non-Idealities
### Iterative Joint Channel Decoding at the AP
In both cases, the decoding complexity is linearly dependent on the number \(N\) of coded bits, i.e., one can write \(\mathcal{C}_{\rm dec}=N\mathcal{C}_{\rm dec-bit}\), where \(\mathcal{C}_{\rm dec-bit}\) is the decoding complexity \"per coded bit.\" Finally, at the input of each decoder one needs to consider a proper combination of the LLRs on the information bits output by the other \(n-1\) decoders. This combination has a complexity which depends linearly on the number \(L\) of information bits per sequence. Indicating as \(\mathcal{C}_{\rm LLR}\) the complexity required to combine 2 LLRs, one can assume that the complexity required to combine \(n-1\) LLRs, relative to corresponding \(n-1\) bits at the same epoch, is on the order of \(n\mathcal{C}_{\rm LLR}\). Therefore, recalling that the number of external iterations is \(n_{\rm it}^{\rm ext}\), the overall complexity, denoted as \(\mathcal{C}\), can be written as  
\[\mathcal{C}\sim n_{\rm it}^{\rm ext}n(N\mathcal{C}_{\rm dec-bit}+n\,L\, \mathcal{C}_{\rm LLR})\]  
where we use the symbol \(\sim\) to loosely indicate \"on the order of.\" Since \(L=N\,r\), where \(r\) is the transmitting rate, the complexity can finally be expressed as follows:  
\[\mathcal{C}\sim n_{\rm it}^{\rm ext}N(n\mathcal{C}_{\rm dec-bit}+n^{2}\,r\, \mathcal{C}_{\rm LLR})\]  
As one can see, the complexity depends linearly on the number of external decoding iterations at the AP and on the sequence length, but it has a quadratic dependence on the number \(n\) of sources. We remark that the above complexity level is realistic in the presence of digitization and decoding. In the case of real sources, one should perform more complicated (and computationally heavier) marginalization process to convert bit probabilities into symbol probabilities, and vice versa (see, for example, [22]). In this scenario, one could consider alternative schemes where real-valued phenomena are quantized and transmitted without resorting to channel coding/decoding and where detection and signal reconstruction are performed jointly on a single factor graph, as proposed in [31].
# Compact and Accurate Scene Text Detector
## 5 Conclusions  
Previous studies for scene text detection mainly focused on more accurate models, but these models were computationally heavy and inefficient. We improved the efficiency in terms of the inference time, FLOPS, and the number of parameters, without heavily degrading much effectiveness. We design a new model, namely Compact and Accurate Scene Text detector (CAST), which has MobileNetV2 as a backbone and a balanced decoder. The balanced decoder is a stack of IRB and standard convolutional layers, but has an IRB at only two stages to balance the complexity between stages. According to the experimental results, we demonstrated that the balanced decoder outperforms other existing decoders and some unbalanced decoders using IRB. Using three datasets (e.g., ICDAR2013, ICDAR2015, and ICDAR2017 MLT), the CAST has demonstrated its superior efficiency (i.e., FLOPS) and comparable effectiveness (i.e., F1 score). Especially, when CAST is compared with the other models using only a CPU, there was a huge gap (e.g., 30\(\sim\)115 times) in the FLOPS. We believe that CAST has great potential to be extended to mobile environments. For future work, we will conduct further experiments with mobile environments such as Android phones or a Raspberry pi. We also plan to apply the Balanced Decoder to other recent models (e.g., MobileNetV3 [35]) and keep developing better design of decoder  
**Author Contributions:** Investigation, Methodology and Writing--original draft: M.J.; Conceptualization, Supervision and Writing--review & editing: Y.-S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Soonchunhyang University Research Fund. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP; Ministry of Science, ICT & Future Planning) (No. 2019021348).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sustainability as a Challenge and Driver for Novel Ecosystem 6G Business Scenarios
## 3 Results
### Communities Scenario 1D  
In the customized experience and corporate-centric-driven Communities scenario, 6G enables humans to participate and directly collaborate and act in society in an unprecedented way. In addition to open access to technologies, the scenario presumes competences and skills needed in applying new applications, services, and new forms of organization and innovation such as sharing economy and crowdsourcing. In the community society, well-being and working life build on social networks and the trust and reciprocity they foster.  
Telecommunication network funding and support for deployment programs that have conventionally been engaged with extending rural area coverage and non-served areas have extended to public-private partnership (PPP) covering several segments of smart communities, such as logistics, transportation, health, public safety, and utilities. Novel PPP funding models emphasize sustainable development and the use of wireless telecommunication infrastructure as a general-purpose digital platform. Availability of real-time trusted personal health data will empower personalized and preventive care and medical consultation by healthcare professionals in need. Genome editing will enable the development of new medicines and treatments.  
In this corporate-centric scenario, less restrictive regulation on platforms and data can lead to a country acting as resource pools of cheap labors, resources, and human data. Platforms can assist in developing frugal innovations serving the growing population in low-income nations, as well as powering education with the Internet of skills applications and services. Access to a global pool of virtual courses and degrees and global educational networking will support wide range of international career opportunities.  
The nature of physical and virtual communities has fragmentation and there is growing tension between individuals and communities and between communities with morphing values and identity. Leveraging 6G technologies, online communities have moved toward a more accelerated and hyperreal set of interactions. Cyberterrorism has affected every networked system with emergent potential for a global crisis and a shocking impact on the economy. Holistic media experience and interaction have intensified public opinion-shaping activities with a stronger impact than ever on mobilizing people. Enabled \"wisdom of crowds\" allows for more egalitarian and informed decision making and empowerment. At the same time, the hyperreal experiential hate \"wisdom\" may be perverted without bounds, resulting in a bleak communal life with crime and terrorism.
## 1 Introduction
Based on the premise that the FPAR data of MODIS (\(\rm FPAR_{\rm MCD15A2}\)) is more reliable, the ANN was developed to correct \(\rm GIMMS3g\) to be consistent with the \(\rm FPAR_{\rm MCD15A2}\) in the overlapping period from 2002 to 2015. The estimations were evaluated with independent on-the-ground GPP observations from the eddy covariance-based carbon flux towers in China (ChinaFLUX). Then the FPAR data were estimated through the ANN using the \(\rm GIMMS3g\) from 1982 to 2002. The final FPAR data were compiled for the period from 1982 to 2018 with the two parts of the ANN algorithm-based estimations for 1980-2003 and the MODIS-based product in 2004-2018 for terrestrial ecosystems in China. The new dataset, \(\rm FPAR_{\rm ANN}\)), has a spatial resolution of 1 km and a temporal step of 8-days for a long-term time series, from 1980 to 2018. Therefore, the aims of this paper include (1) evaluation of the new \(\rm FPAR_{\rm ANN}\) dataset through eddy covariance-based observation on the ground; (2) application of the new dataset to explore the spatiotemporal changes in the terrestrial ecosystems in China in the past nearly four decades; and (3) quantifying the possible impact from climate change on ecosystems. Through this study, however, we try to provide a methodological framework for fusing data from different sources to provide a dataset that can be used as model input for carbon cycle research, or directly used for the assessments of degradation or restoration of ecosystems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Products** & **Sensors** & **Spatiotemporal Resolution** & **Temporal Span** & **Advantages** & **Disadvantages** & **References** \\ \hline Fraction of & & & & & & \\ Photosynthetically & & & & & & \\ Active Radiation & & & & & & \\ Derived from & Advanced Very & & & & & \\ Global Inventory & High Resolution & \(1/12^{\circ}\) & & & & \\ Modeling and & Radiometer & 15 days & 1981–2011 & Long time series & & \\ Mapping Studies & (AVHRR) & & & & & \\ Normalized & & & & & \\ Difference & & & & & \\ Vegetation Index & & & & & \\ (FPAR3g) & & & & & \\ \hline Climate Data & & & & & Containing many \\ Record (CDR) & AVHRR & 0.05\({}^{\circ}\) & Long time series; & & missing pixels; \\  & & & High temporal & & Low spatial \\  & & Daily & & resolution & & \\  & &
# Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide
## 2 Study Area  
In the northeastern Italian Alps, in the upper valley of the Tagliamento River, a narrow gorge called Passo della Morte (46\({}^{\circ}\)23\({}^{\prime}\)49.7\({}^{\prime\prime}\) N, 12\({}^{\circ}\)42\({}^{\prime}\)45.7\({}^{\prime\prime}\) E) is affected by several slope instabilities. The main valley road, a national road following the Italian classification, has to cross the area since no other route is possible due to the constricted geomorphological setting.  
In Passo della Morte, four landslides have been investigated through monitoring and modelling for more than a decade since their movements are interfering with the road network [13; 14]. Also the damming of the Tagliamento River due to a secondary landslide may occur [15]. The largest landslide is a 24 \(\times\) 10\({}^{6}\) m\({}^{3}\) block slide [16] that accounts for a large portion of Mount Tinisa's (2120 m a.s.l.) southern slope, covering an area of about 400,000 m\({}^{2}\).
## 3 Results and Discussion  
### Physicochemical Stability of Vitamin-Loaded Nanoemulsions  
#### 3.1.1 MPS, ZP, and PDI of BC- and VE-Loaded Nanoemulsions  
In this study, three bioactive compounds, i.e., BC, CIN, and VE, were loaded within the emulsion-based matrices by using two kinds of carrier oils, which were emulsified by PGU and TW-80. In the primary section, BC alone and with diverse levels of VE were loaded into TFO and MCT-based lipid phases, stabilized by PGU. Nanoemulsions comprising 0.1% \(w/w\) BC-loaded TSO (NE1) and MCT (NE2) exhibited a droplet size \(<\) 250 nm, as illustrated in Table 1. On the other side, mixtures of BC with diverse levels (2% and 3% \(w/w\)) of VE were equipped in both kinds of carrier oils, which were loaded into nanoemulsions emulsified by PGU and TW-80. An increment in MPS was observed for NE3 and NE5, both encompassing 2% VE, and nanoemulsions (NE4 and NE5) enriched with 4% VE showed NPS above 250 nm [40].  
It has been concluded in previous studies that, incorporation of the maximum quantity of VE in any nano-emulsified system reduces the droplet size owing to the interfacial tension lowering effect of CIN [39, 41]. PDI, a significant factor, has known to evaluate the distribution of particle size of any nano-emulsified system; thereby, smaller PDI results elaborate fine and smooth distribution of fabricated particles [9]. Table 1 clearly depicts that VE loading could not exhibit any significant influence on PDI as well as there was no direct relationship observed among the kinds of carrier oils and PDI variations. ZP is a key parameter that is used for evaluating the stability of emulsion-based matrices; thus, greater ZP values (\(>\)\(\pm\)30) are well thought-out to offer superior stability to the emulsified systems [42]. Nanoemulsions coded by NE1, NE3, and NE5 holding TFO displayed greater ZP rather than MCT-loaded nanoemulsions (NE2, NE4, and NE6). In addition, the VE loading resulted in a decrease in the ZP values except for NE4. A negative charge on the exterior layers of OSA-MS is accredited to the succinylation used for modification of native starches. A very minor disparity in ZP findings of all nanoemulsions might be owing to the existence of diverse nature of oil combinations as well as entrapped lipophilic bioactive compounds [43].
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles
### Vehicle-to-Vehicle (V2V)  
Modern transportation systems depend on V2V communication, which enables vehicles to communicate directly with one another. V2V communication is an important factor in the growth and development of smart and interconnected transportation networks that allow vehicles to communicate. With fast mobility and short communication windows being the unique characteristics of such networks, designing a robust communication protocol is crucial to the maintenance of efficient data dissemination and network performance. It is a technology that is the key to the development of safer and more efficient transportation systems and is a fundamental part of intelligent and interconnected vehicles. In [74], a  
Figure 6: Internet of Vehicle communication model.  
novel framework was proposed using V2V for vehicular traffic information monitoring and aggregation. The wireless radio relationships between vehicles and roadside units are based on DSRC and are appropriate to vehicular environments [75]. Automated systems like intersection collision warning systems send DSRC warnings to other vehicles and pedestrians if a vehicle is approaching a red light at high speed. The considerations for designing new V2V communication protocols are crucial for congestion control to improve the efficiency and effectiveness of traffic management strategies in a connected vehicle environment. These protocols help to reduce the congestion of traffic and enhance the performance of the entire network of roads [76]. Likewise, ref. [77] demonstrates that the validating and optimizing the performance of V2V systems for congestion control leads to safer and more efficient transportation management solutions.
## 2 Materials and Methods
### Study Sites
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Plot** & **Lat** & **Lon** & **Stand Age** & **Elevation** & **Inclination** & **Orientation** & **LAI** \\  & & & **(y)** & **(m asl)** & **(\%)** & **Orientation** & **(m\({}^{2}\) m\({}^{-2}\))** \\ \hline AMAL & 39.02 & 26.59 & 13 & 316 & 10 & SE & 0.67 \\ PEV & 39.16 & 26.37 & 40 & 213 & 5 & SW & 1.66 \\ LML & 39.16 & 26.38 & 72 & 166 & 10 & SW & 2.13 \\ ACHL & 39.13 & 26.30 & 92 & 261 & 0 & - & 1.67 \\ \hline OLY1 & 39.09 & 26.33 & 82 & 350 & 3 & NE & 4.26 \\ OLY2 & 39.09 & 26.33 & 95 & 450 & 7 & N & 2.08 \\ OLY3 & 39.09 & 26.33 & 96 & 550 & 5 & N & 1.23 \\ OLY4 & 39.08 & 26.33 & 64 & 650 & 7 & SE & 1.23 \\ OLY5 & 39.08 & 26.34 & 90 & 750 & 0 & - & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic characteristics and stand age of the study plots. Latitude (Lat) and Longitude (Lon) are in decimal degrees. Stand age was estimated by counting the number of annual tree-rings in trees adjacent to the study plots [45]. Plots AMAL, PEV, LML and ACHL were located across a post-fire chronosequence, while OLY plots were located along an elevation gradient. Leaf area index (LAI) was estimated using a ceptometer. Figure 2: Distribution of the five plots located across the elevation gradient (from 350 to 750 m asl) on the island of Lesvos. The dominant tree species in all plots was _P. brutia_. See Table 1 for additional plot information. The coordinate system is WGS 84.
## 4 Experiments
### Experimental Setup
#### 4.1.3 GLUE Benchmark Datasets and Downstream Setup  
The GLUE (General Language Understanding Evaluation) benchmark [32] is a collection of standardized datasets for evaluating natural language understanding (NLU). It comprises various natural language processing (NLP) tasks to assess a model's overall language understanding capability. The GLUE benchmark includes several subtasks:  
* Multi-Genre Natural Language Inference (MNLI): Classifying the relationship between two sentences. Given two sentences, it determines whether the second sentence is in an entailment, neutral, or contradiction relationship with the first one.
* Quora Question Pairs (QQP): This binary classification task involves determining whether pairs of questions collected from Quora are semantically identical.
* Stanford Sentiment Treebank (SST): This binary classification task categorizes the sentiment of sentences extracted from movie reviews as positive or negative.
* Corpus of Linguistic Acceptability (CoLA): This binary classification task involves judging whether a sentence is grammatically correct.
* Microsoft Research Paraphrase Corpus (MRPC): This binary classification task involves determining whether two sentences are paraphrases of each other.
* Recognizing Textual Entailment (RTE): This binary classification task involves determining whether the second sentence can be inferred from the first when two sentences are given. This task is similar to the MNLI task but uses a smaller dataset.
* Question Natural Language Inference (QNLI): This binary classification task requires determining if a given sentence answers a specific question. It tests the model's ability to infer information within a question-answering context.  
By synthesizing the evaluation results of each subtask, we evaluate the model's overall natural language understanding capability. Most recent NLP models are evaluated through the GLUE benchmark, and their performance continually improves.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 2 Materials and Methods  
### Materials  
A pure ZnO sputtering circular target (diameter 10 cm, thickness 6 mm, purity 99.999%) was purchased from GoodFellow Ltd. (Thetford, UK). Poly-(tetrafluoroethylene) (PTFE) cylindrical bars (diameter 10 cm) were purchased from Sigma-Aldrich (Milan, Italy) and cut into slices of suitable dimensions (2 cm thick) to obtain a sputtering target. Target materials were used as received, without further cleaning procedures. They were both stuck onto two circular stainless-steel holders using a silver-based epoxy glue from Gambetti Kenologia srl (Milan, Italy). Composite films were deposited onto Si/SiO\({}_{2}\) slides unless otherwise stated. The Si/SiO\({}_{2}\) slides underwent chemical cleaning before the deposition via sequential treatment at 80 \({}^{\circ}\)C in trichloroethylene (Fluka, 97%), acetone (Sigma-Aldrich, Italy, 99%), and isopropanol (Sigma-Aldrich, Italy, 99%).
## 2 Encoding of Categorical Variables
### Embedding  
The embedding encoding \(f_{E}\) maps each category to a point in a vector space, and vectors with similar categories are closer together in the vector space.  
Setting embedding converts the categorical variable \(CV\) into a numerical variable \(NV\) with a specified embedding dimension \(d_{E}\). The embedding dimension \(d_{E}\) denotes the dimension of the mapped \(n_{i}\), i.e., \(c_{i}\overset{f_{E}}{\rightarrow}\boldsymbol{n_{i}}=\begin{bmatrix}n_{i}^{1}&n_ {i}^{2}&\cdots&n_{i}^{d_{E}}\end{bmatrix}\).  
Embedding is roughly divided into four steps.  
(1) Predefine the vector space, determine the number \(N\) of categories \(c_{i}\) contained within the categorical variables, specify the embedding dimension \(d_{E}\), and construct a weight matrix \(\boldsymbol{W}\) of \(N\) rows and \(d_{E}\) columns. The weight matrix is randomly generated by default from a standard normal distribution with mean 0 and standard deviation 1. That is, \(\boldsymbol{W}\sim N(0,1)\).  
(2) Assign an integer index starting from 0 to each category in the categorical variables.  
(3) Map the integer indexes into a predefined vector space by means of a lookup table (this table is the weight matrix \(\boldsymbol{W}\)), where each index is associated with a vector corresponding to the number of the weight matrix.  
(4) Return the mapped numeric variables for the categorical variables.  
\[CV:\begin{bmatrix}c_{1}\\ c_{2}\\ \cdots\\ c_{i}\\ \cdots\\ c_{N}\end{bmatrix}_{N\times 1}\overset{f_{E}}{\rightarrow}NV:\begin{bmatrix} \boldsymbol{n_{1}}\\ \boldsymbol{n_{2}}\\ \cdots\\ \cdots\\ \cdots\\ \cdots\\ \boldsymbol{n_{N}}\end{bmatrix}_{N\times d_{E}}\to \boldsymbol{W}=\begin{bmatrix}n_{1}^{1}&n_{1}^{2}&\cdots&n_{1}^{d_{E}}\\ n_{2}^{1}&n_{2}^{2}&\cdots&n_{2}^{d_{E}}\\ \cdots\\ n_{i}^{1}&n_{i}^{2}&\cdots&n_{i}^{d_{E}}\\ \cdots\\ n_{N}^{1}&n_{N}^{2}&\cdots&n_{N}^{d_{E}}\end{bmatrix} \tag{7}\]
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 3 Stealth Communication  
The hybrid system in Section 2 demonstrated interference management with under-sampled signals. To recover the hidden signal in a stealth communication system is similar to interference management in a way that both of them are to separate SOI from the wideband interference/noise. This means the separation method developed in Section 2 creates both potential threats to the existing stealth system and new skillsets that, if deployed by the authorized users, the communication privacy can be better protected with innovative ways to hide and recover the stealth signals. This review section discusses related work (Section 3.1), methods to address the threats (Section 3.2), and the new skill sets for stealth communication (Section 3.3).