esa-sceva/satcom-chunk-collection huggingface dataset:
# Circularly Polarized Broadband Printed Antenna for Wireless Applications
## 2 Antenna Design Structure  
### Antenna Schematic Layout  
The geometric layout of the realized sickle-shaped antenna is displayed in Figure 1, which is designed and fabricated on the low-cost epoxy resin (FR4) substrate with the relative permittivity of 4.5 and a dielectric loss of 0.02. The physical dimensions of the anticipated antenna are \(39\:\mathrm{mm}\times 39\:\mathrm{mm}\times 1.6\:\mathrm{mm}\), which is \(0.29\lambda\:\times 0.29\lambda\:\times 0.012\lambda\) for the lowest \(-10\) dB frequency. The anticipated antenna is comprised of a semicircular slotted patch with a tapered microstrip feed line on the upper side of the substrate and a circular slotted square ground plane on the other side of the substrate. This semicircular patch radiator is fed by a 50 \(\Omega\) tapered microstrip line to create a sickle-shaped with a width of \(W\!f_{1}\) and a length of \(L\!f_{1}\) for signal transmission. A circle with center \(O\) and radius \(R1\) is imprinted out from the rectangular ground plane, which is responsible for the created wideband impedance bandwidth with linear polarization (LP) in the desired operating band. On the other hand, the semicircle patch is created by subtracting the circle (\(B\), \(R3\)) from the circle (\(O\), \(R2\)), where \(A\) and \(B\) define the center of the two circles and R is the radius of the circle. These three points (\(O\), \(A\), \(B\)) make an angle \(<\!\!AOB\), which is defined by \(\alpha\). By tuning the angle \(\alpha=96^{\circ}\), the maximum impedance and AR bandwidth are acquired. The semicircular slotted patch and feed line make the shape appear as a sickle. The comprehensive dimensions of the optimum antenna configuration as exposed in Figure 1a are \(L=39\:\mathrm{mm}\), \(L\!f_{1}=4.15\:\mathrm{mm}\), \(L\!f_{2}=5.14\:\mathrm{mm}\), \(W\!f=2.24\:\mathrm{mm}\), \(R1=17\:\mathrm{mm}\), \(R2=11.2\:\mathrm{mm}\), \(R3=7.7\:\mathrm{mm}\), \(h=1.6\:\mathrm{mm}\), \(\alpha=96^{\circ}\), and \(L1=25.42\:\mathrm{mm}\).
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 1 Introduction
Our motivation is to explore using the singing speech as an input to ASV system, because we want to see whether the singing speech is more effective for voiceprint information protection in real-world ASV applications, such as personalized accessing of WeChat, QQ accounts, etc. Firstly, we designed a new corpus for short-time text-dependent ASV experiments. We released it on the Zenodo website ([[https://zenodo.org/record/3241566](https://zenodo.org/record/3241566)]([https://zenodo.org/record/3241566](https://zenodo.org/record/3241566))) and put our implementation code in the Github repository ([[https://github.com/Moonmore/Speaker-Verification](https://github.com/Moonmore/Speaker-Verification)]([https://github.com/Moonmore/Speaker-Verification](https://github.com/Moonmore/Speaker-Verification))) for public research. Based on this corpus, we performed the text-dependent (TD) ASV comparison experiments using either the natural speech or the singing speech, or both of them. Then, we focused on the TD ASV experiments to exploit the effectiveness of natural and singing speech for speaker verification. We first examined the speaker discrimination between natural and singing speech in both the Normalized Cross Correlation Function (NCCF) coefficients [22] and 2-D Mel Frequency Cepstral Coefficients (MFCC) feature spaces. Then, the conventional Gaussian Mixture Model (GMM), Dynamic Time Warping (DTW) and the state-of-the-art deep neural network were investigated for speaker modeling. Preliminary results show that the voiceprint information in the singing speech was more distinguishable than the one in the natural reading speech for the short-time gender-dependent as well as independent ASV tasks.
## 2 Evolution of NMR Methodologies
### Solid-State NMR for Polysaccharides  
Solid-state NMR (ssNMR) has become an important method for studying polysaccharides due to it providing detailed information about how rigid and semi-rigid molecules are arranged. Scientists can use advanced technologies like CP/MAS and DNP with ssNMR. These technologies allow them to detect the molecular structure, crystallinity, and dynamic behavior of polysaccharides in different systems. These methods have been shown to work very well when studying plant cell walls, biofilms, and hydrogels, where polysaccharides have different levels of crystallinity and amorphous regions [2].  
The broader application of ssNMR in polysaccharide research has helped us understand the molecular system and functional properties better. For example, ssNMR is very crucial for telling the difference between crystalline and amorphous regions, allowing us to analyze the mechanical and functional properties in detail. In addition, when we combine ssNMR with other technologies, it can be expanded in more areas, offering a comprehensive way to deal with the problems of polysaccharide systems.
# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 1 Introduction
Though many of the applied algorithmic foundations (such as backpropagation) were conceived long ago, modern innovations (such as batch normalization [30] and dropout [31]) were crucial in facilitating recent developments [32]. Supplementary factors, such as the increased use of Cloud Computing (and thus easier access to powerful computing), software libraries which make it easier to execute ML projects despite the limited number of available skilled ML engineers, and the Internet of Things which provides more data-generating sensors [33], support the trends outlined above [34]. The increasing digital transformation in companies and supply chains and the associated greater availability of evaluable real-time data open up new potential for a proactive SCRM [21; 35; 36]. ML approaches can leverage a vast amount of supply chain data and generate solutions that represent improvements over traditional methods [37]. Although the integration of ML into the SCRM can be useful, this topic has rarely been considered scientifically. For this reason, in this paper, special attention is paid to ML which is intended to improve SCRM. The aim of the paper is, therefore, to investigate how ML can be incorporated into SCRM. By showing which areas ML have already been taken into account for the implementation in SCRM, we would like to offer decision-makers an easier start for implementation. We will answer the following research questions:  
* In which supply chain areas has ML already been considered for implementation in SCRM--both in literature and practice? * Which are primary risks considered in the use-cases? * How might ML shape and improve SCRM? Furthermore, we will develop a set of four propositions based on the literature analysis which can be used as an impetus for further research. The paper is organized as follows: In Section 2, we explain the methodological procedure using a systematic literature analysis, which was subsequently extended by a multi-vocal literature review. We then evaluate the results and describe the identified use cases. In Section 3, we discuss the findings from our analysis regarding ML added value in SCRM. We also derive propositions from the results of the literature analysis, which could be used in future research. The article closes in Section 4 with summary and limitations.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Taxol
\begin{table}
\begin{tabular}{c c c c c} \hline
**Secondary** & **Producing** & **Host Plant 1** & **Molecular Approach Used** & **Discovered Genes or** & **Reference** \\
**Metabolite** & **Species/Strain 1** & **for Gene Discovery** & **Putative Homologues 2** & **Reference** \\ \hline Polyphenols, flavonoids, terpenoids, isoquinoline alkaloids & _Aspergillus_ spp. Gbtc\_1, Gbtc\_2 & _Ginkgo biloba_ & WGS (BGISEQ-500 and PacBio Sequed System) & _DFR_, _F3M_, _ANR_, _CH_, _C3\({}^{\prime}\)_H_, _CAD_, _PLR_, _PMVK_, _TAT_ & [83] \\ \hline Polyphenols, flavonoids & _Penicillium citrium_ & _Penicillium citrium_ & _Penicillium citrium_ & _WGS (genome mining of publicly available genomes)_ & ND & [84] \\ \hline \end{tabular}  
* Species names are the latest accepted in taxonomy, and not necessarily the same as those used in the corresponding references. 2 Abbreviations in the table stand for genes; full gene names are specified in the text. ND: not determined.  
\end{table} 
Table 1: \(Cont.\)Another transcriptome sequencing and analysis of two taxol-producing strains of _Aspergillus aculeatinus_ was performed in 2020, but the results were inconclusive as the exerted biosynthesis pathway was not clear [66]. Genes from the mevalonate (MVA) and nonmevalonate (MEP) pathways, involved in the biosynthesis of terpenoids, were expressed, including those encoding 1-deoxyxylulose 5-phosphate reductoisomerase (DXR), 3-hydroxy-3-methylglutaryl-coenzyme A synthase (HMGS), 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR), and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HMDR). In addition, genes encoding for delta(2)-isopentenyl pyrophosphate transferase (IPPS), geranyl pyrophosphate synthase (GPPS), and GGPPS were also found. Moreover, a unique sequence was found to correspond to taxane 10-\(\beta\)-hydroxylase (T10\(\beta\)H) involved in the synthesis of taxol by _Ozonium_ sp. BT2. A comparison between the transcriptomes of the original strain _A. aculeatinus_ Tax-6 and its mutant strain BT-2 (producing higher taxol yield) revealed that most genes involved in the MVA pathway in BT-2 were upregulated. Surprisingly, transcripts related to the conversion of GGPP to taxa-4(5)-11(12)-diene, representing the key step in taxol production, were absent in both strains. Figure 1: Chemical structures of some plant bioactive products from endophytic fungi reviewed in this paper.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 3 Datasets used in State-of-the-Art Techniques
### Presence of Nonverbal Cues
#### 3.4.1 Context Variability  
Context information is also available in most samples of the datasets selected for evaluation in this survey. Even for datasets such as AfeW and AfeW-VA, which are commonly used for FER, background and scene information is available and can be used to leverage context. However, when proposing the CAER dataset, the authors argue that the list of datasets available until the date of publication (including EMOTIC) did not contain a dataset with multiple context information. When exploring CAER-S, we note that, even though the source of the samples is 79 television shows, a significant number of frames contain repeated background information, which does not occur in EMOTIC at the same significance due to the source of the data.  
To validate the variability in background information (context) in EMOTIC and CAER-S, we propose an experiment using open-source code for scene recognition. For this, we use the work by [80], which extracts features from images for classification. Given an image \(I\), we feed it to the pipeline of the proposed network to extract the scene label \(s\), which describes at some level the context from the input image \(I_{c}\). Please note that, although this technique is compatible with the state of the art with a sufficient Top@1 score, our intent is not to quantify the different context information to disclose how many images have a specific background, but rather to understand if these images are different enough to have different classifications among the dataset.  
First, we modified the data loaders proposed by the authors, to load images from the CAER-S and EMOTIC datasets separately. After this, we employed the same transforms used in their evaluation to extract comparable information. We decided empirically to use the model pre-trained on the Places 365 dataset [81] given its high data variability. After this, we sampled each image and extracted its Top@1 classification, storing it in a file for further processing. We show the results of this experiment in Figure 6. This graph shows that the classifications given by [80] are more grouped in CAER-S than in EMOTIC, implying that the images in this dataset are less diverse. Please note that, although the datasets contain a different number of samples, our investigation is regarding data distribution. A dataset with images containing diverse backgrounds would be more distributed among this graph, as happens with EMOTIC, independent of the number of samples present in it. Finally, this experiment complements individual observations on the dataset, indicating that CAER and, subsequently, CAER-S do not have higher context variability than EMOTIC.
# Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu
## 2 Related Work
An aspect-level sentiment analysis approach was introduced, titled as M-IAN, to capture the interactions between target and context using a deep interactive memory network [42]. Word representation plays a vital role in the text classification task. In a series of experiments on benchmark datasets using CNN with word2 vec, [PERSON] achieved a remarkable accuracy [16]. It was identified that, with a little fine-tuning of the CNN hyperparameters, a remarkable performance can be achieved. [PERSON] was of the view that pre-trained word vectors could better help deep networks improve their performance. A recent study on aspect-based sentiment analysis has proposed a deep learning model NA-DLSTM. They deliberated that sentiment analysis and aspect-based sentiment analysis can be differentiated in a way that the former considers the whole document whereas the later analyses and categorises the text into various aspects and assigns the polarity to each aspect. The study was aimed at developing a context-aware aspect-based SA model, experimenting on the Rest14, Rest15, and Rest16 dataset [43]. An important task while performing the sentiment analysis is feature selection, which suffers in the presence of uneven class distribution of the datasets [44; 45]. In text classification, usually two classes are involved: the positive class (the class of interest) and the negative class, which is mostly over-represented. This over-representation is known as the class imbalance problem [46]. This imbalanced class distribution allows for the redundant, noisy and erroneous features to gain more weight. An earlier study of imbalanced data by [42; 47; 48] also identified that imbalanced class distribution poses a significant challenge to the classifier's performance. To overcome this problem, they proposed the Adaboost algorithm. However, this uneven distribution was still a challenge while selecting the features. Both of our Roman Urdu datasets also face the same issue of imbalanced class distribution, which triggers a performance downgrade. There exists no pre-trained word vectors for Roman Urdu. Assessing the need to enrich this resource-starved language, we introduced for the very first time three different word embedding schemes: word2 vec, Glove, and FastText.
# Streaming-Based Anomaly Detection in ITS Messages
## 2 Materials and Methods
### Data Generation, Pre-Processing and Transformation
#### 2.1.1 Data Generation  
The simulation depicted the movement of C-ITS equipped vehicles in the French city of Reims. The simulation settings were configured as shown in Table 1. The initial stage was to configure the SUMO simulator, after which the map of Reims, France, was retrieved from Openstreetmap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 12 February 2023) and loaded into the simulator. The second stage was the generation of random trips for a hundred vehicles. The SUMO simulator periodically transmitted the locations and details of each vehicle to the OMNeT++ simulator. The Veins framework then collected the sent data from the OMNeT++ simulator, and the Artery framework generated the CAM messages. The frequency of CAM message generation varied from 10 Hertz to 1 Hertz (100 milliseconds to 1000 milliseconds).  
The secure message structure was used to construct the security headers and certificates for the CAM messages in the Vanetza framework public key infrastructure (PKI) system as stated in the European Telecommunications Standards Institute Technical Specifications, ETSI TS 103 097 [38]. The CAM signature and validation mechanisms were constructed using the PKI system's pseudonyms. Each generated CAM was signed with a pseudonym by appending the signature at the end of the message. This signature is used by the receiving vehicle's validation system to authenticate the received message. The mobility and communications between the vehicles were designed to simulate actual movement in the C-ITS environment. This was done to ensure that the outcomes were as realistic as possible. The simulation ran for two hours, with each vehicle taking a random route. The CAM messages for each vehicle for the entire simulation duration were saved in separate files.
# Surface Gloss, Radiopacity and Shear Bond Strength of Contemporary Universal Composite Resins
## 4 Discussion
As a matter of fact, according to the results of this study, GBU used with CD (pH: 1.8) showed statistically lower bond strength with PBU used with NS (pH: 2.5). Although there were no significant differences among CRs in terms of failure types, observation of solely adhesive failures also supported the notion of lower bonding values of CD and NS. The smaller crystals and plaque-like structure of dentin hydroxyapatite compared with enamel is considered more accessible for chemical bonding [47]. [PERSON] et al. [48] have suggested that 10-MDP has a relatively constant interaction with collagen due to hydrophobic interactions between the collagen surface. Four of the adhesives used in the present study contain MDP, however, the purity and amount of each ingredient in each adhesive may differ. In a recent study examining dentin bond interfaces with SEM analysis [49], the self-etch modes of GPB, SBU, and PBU were also evaluated. The adhesive layer thickness of PBU (2-3 \(\upmu\)m) was found to be less than that of GPB (8-10 \(\upmu\)m) and SBU (8-10 \(\upmu\)m). In the present study, the tested CR, NS showed statistically lower bond strength for both enamel and dentin compared with other CRs. Although there is still no consensus on the ideal adhesive layer thickness, some studies [50; 51] have suggested that a thicker adhesive layer may be better in terms of durability. The findings regarding CR and NS may be related to the adhesive used. Studies evaluating the bond strength of adhesives generally test a number of adhesives with a single type of CR. However, in this in vitro study, adhesives and universal CRs of the same brand were matched. This may have influenced the findings of this study. Additionally, long-term water storage or thermal cycle is also one of the limitations of this study in order to evaluate the bonding stability. Although the SBS test set up has been the most commonly employed laboratory technique [52], there are also tests where the bonding is evaluated more realistically, such as microtensile bond strength. The present report evaluated surface gloss, radiopacity, SBS, and failures of contemporary universal composite resins. Future research with the same materials is needed in order to test other mechanical properties such as flexural strength and hardness in order to complete overview of the materials tested [53; 54]. Moreover, long-term clinical observations of the tested either the newly introduced or available commercial CRs in service would provide information on the in vivo outcome.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Distribution Network Flexibility Resources
#### 2.2.2 Nodal Flexibility Resource  
ESS, as a typical nodal flexibility resource, can realize fast two-way conversion of flexibility through charging and discharging strategies with certain time-scale regulation capabilities. The objective is to achieve a better matching of source and load in ADN, as well  
Figure 3: Schematic diagram of SOP operation boundary.  
Figure 2: Basic structure of SOP.  
as to improve the tidal flow, by low charging and high discharging [19]. The constraints of ESS are as follows:  
\[\delta_{dis,t}+\delta_{ch,t}\leq 1 \tag{5}\]  
\[0\leq P_{t,t,\varepsilon}^{ESS}\leq\delta_{ch,t}P_{t,\max}^{ESS} \tag{6}\]  
\[0\leq P_{t,i,\Delta}^{ESS}\leq\delta_{dis,t}P_{t,\max}^{ESS} \tag{7}\]  
\[E_{t+1,i}^{ESS}=E_{t,i}^{ESS}-\eta_{c}P_{t,t,\varepsilon}^{ESS}\Delta t- \frac{P_{t,i,\Delta}^{ESS}\Delta t}{\eta_{d}} \tag{8}\]  
\[E_{t,0}^{ESS}=E_{t,T}^{ESS} \tag{9}\]  
\[E_{i,\min}^{ESS}\leq E_{t,i}^{ESS}\leq E_{i,\max}^{ESS} \tag{10}\]  
where \(i\) represents the node number of the ESS installed in ADN; \(T\) and \(\Delta t\) are the number of time periods and the duration of each time period of the operation scenario, respectively, and \(T\) = 24 and \(\Delta t\) = 1 h are chosen in this paper; \(\delta_{ch,t}\) and \(\delta_{dis,t}\) are state variables in binary numbers, respectively; \(\delta_{ch,t}/\delta_{dis,t}\) = 1 is the charging state, and \(\delta_{ch,t}/\delta_{dis,t}\) = 0 is the discharging state; \(P_{t,i,\varepsilon}^{ESS}\), \(P_{t,i,\varepsilon}^{ESS}\), and \(P_{t,\max}^{ESS}\) are charging and discharging power and the maximum value; \(E_{t,i}^{ESS}\) is the charge capacity of ESS; \(\eta_{c}\) and \(\eta_{d}\) are charging and discharging efficiencies; \(E_{t,0}^{ESS}\) and \(E_{t,T}^{ESS}\) are the storage capacities of the ESS before and after optimization; \(E_{t,\max}^{ESS}\) and \(E_{i,\min}^{ESS}\) are the upper and lower charge limits.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 4 Channel Models and Simulator  
The method which is employed for the modeling of the wireless signal propagation environment has quite a crucial impact on the protocol performance or the application being evaluated [6]. The simulator, which is the most popular among the ones used in the survey sheets, is \(NS-2\)[47]. If the power received for a packet goes beyond a specified threshold, a packet is regarded as having been received successfully, while the noise floor will be constant based on the channel model chosen. The current stable \(NS-2\) release is the 2.35 version, involving support for 5 channels: two-ray ground, Friis free-space path loss, Nakagami-M, shadowing based on log-normal, and shadowing based on obstacle. \(NS-3\), which is new version of \(NS-2\), provides similar channel model sets. The VANET research had most attention on shadowing based on the obstacle to distinguish static objects between source and destination on the direct line-of-sight path. Whether or not such direct paths exist, one of the two alternative shadowing models based on log-normal is configured. A similar approach is followed by this model to the CORNER channel model [48; 49], with attempts to make a model of the channel based on specific physical environments in the given node pair's vicinity. Most research is done by using \(NS-2\) and choosing channel models such as Nakagami-M, two-ray, or free-space. The simulator distribution used in the validation process of the surveyed algorithms is illustrated in Figure 3[50].  
Figure 3: Simulators used in validation of the surveyed algorithms.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### ABE Scheme Based on SM9 IBBE
#### 4.2.4. Decrypt (M\({}_{pk}\), U, SK\({}_{U}\), C)  
Given the master public key \(M_{pk}\), a set of attributes \(U\), the private key \(SK_{U}\), and the set of ciphertexts \(C\), this algorithm converts the set of attributes \(U\) into an identity \(ID_{U}\in\{0,1\}^{|u|}\) by running the algorithm \(\varphi\) and then gets the plaintext \(M\) by running the IBEDecrypt algorithm.  
\[M\leftarrow\textbf{IBBEDecrypt}\big{(}M_{pk},\varphi(U,u),SK_{U},C\big{)} \tag{8}\]