esa-sceva/satcom-chunk-collection huggingface dataset:
# Stance Detection in the Context of Fake News--A New Approach +
## 2 Literature Review
### Fake News
Their findings revealed that theBiLSTM model achieved the highest detection accuracy, reaching 94% for short English texts and 99% for longer English texts. For Chinese texts, the detection accuracy was 82%. Similarly, deep learning networks were also used to develop a new model framework to detect fake news images called Multi-domain Visual Neural Network (MVNN) [36]. The proposed framework models visual contents at semantic levels simultaneously with physical levels using a deep learning network. Their approach combines the pixel domain and the frequency of visual information to detect fake news. They utilized a CNN-based network to capture challenging images associated with different forms of fake news in the domain. To extract visual features across various semantic levels, they applied a multi-branch model combining CNN and RNN architectures. Forensic features have been employed to identify altered images generally. The Block Artifact Grids (BAG) feature is used to help identify fake news. Additionally, [PERSON] et al. [37] have introduced Multiverse, a novel feature leveraging multilingual evidence to improve the detection methods of fake news. This approach was tested in data sets that contain general news and a specific dataset focused on fake COVID-19 news, resulting in substantial performance improvements compared to baseline models. Therefore, research has shown that combining textual and visual information together can significantly improve the accuracy of the detection of fake news. [PERSON] et al. [38] proposed a multi-modal approach called SAFE (Similarity-Aware FakE news detection), which utilizes both textual content and visual features to identify fake news. Their model outperformed text-only baselines, demonstrating the importance of considering visual elements in fake news detection. Building on this multi-modal approach, [PERSON] et al. [39] introduced MCAN (Multi-modal Co-Attention Networks), which uses a co-attention mechanism to capture the intricate relationships between textual and visual content. Their model showed improved performance in identifying fake news across various datasets. Recently, the different aspects of detecting fake news on social media using advanced learning techniques have been reviewed by [40; 41; 42]. They highlighted the potential of deep learning models, particularly with attention mechanisms, for analyzing textual data and capturing complex relationships within content. Reviews emphasize broader challenges and research directions in this domain, including adapting models to various domains, ensuring explainability of decisions, and leveraging social context for better fake news detection.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 2 Theoretical Background
### Long Short-Term Memory  
RNN is a specialized model for processing time-series data by introducing the concept of \"time\", in which information from a past period of time can be remembered. LSTM is a special structure proposed to overcome the gradient explosion or vanishing problem of RNN, which possesses the ability to retain information over an extended duration and can extract information not only from a single data point but also from an entire data series [17]. LSTM is mainly divided into three gates, namely, the oblivion gate, the input gate, and the output gate, and the structure is seen in Figure 3.  
Figure 3: LSTM neural network.  
Figure 2: Schematic diagram of the residual block in TCN.  
The core concepts of LSTM lie in the cell state, which corresponds to the path of information transmission, and the gate structure, which enables the addition and removal of information, which is controlled using the _Sigmoid_ activation function. The input gate, denoted as \(i_{t}\), regulates the amount of information that should be stored for the candidate state, denoted as \(\hat{C}_{t}\), at the present time. The forgetting gate, denoted as \(f_{t}\), regulates the amount of information that should be discarded from the internal state, represented as \(C_{t-1}\), from the previous time step. The output gate, denoted as \(o_{t}\), regulates the amount of information that is transmitted from the internal state, represented as \(C_{t}\), to the exterior state, denoted as \(h_{t}\), at the present time, and the formulae for the three gates are shown below:  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{3}\]  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{4}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{5}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes\hat{C}_{t} \tag{6}\]  
\[h_{t}=o_{t}\otimes\tanh(C_{t}) \tag{7}\]  
\[\hat{C}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{C}) \tag{8}\]  
where \(x_{t}\) represents the input feature, \(C_{t}\) is the memory unit, \(C_{t-1}\) represents the memory unit of the previous moment, \(\hat{C}_{t}\) represents the memory unit of the current state of the candidate, \(h_{t}\) denotes the external state, and \(h_{t-1}\) denotes the external state of the preceding moment. \(W_{i}\), \(W_{f}\), \(W_{o}\), and \(W_{c}\) denote the input weight vectors of the input gate, the forget gate, the output gate, and the candidate unit, respectively. \(U_{i}\), \(U_{f}\), \(U_{o}\), and \(U_{c}\) denote the cyclic weight vectors of each gating unit, respectively. The activation function \(\sigma\) is the _Sigmoid_, while the activation function \(\tanh\) is the hyperbolic tangent. The symbol \"\(\otimes\)\" represents the vector product.
## 2 Materials and Methods
### Mass and Economic Allocation  
As two primary products are obtained (carotenoids and phycobiliproteins extracts), mass and economic allocation were applied to report the environmental loads of each product and determine which has the greatest impact. Mass allocation was based on the amount produced of carotenoid extracts (0.54 g L\({}^{-1}\) culture) and phycobiliproteins extracts (0.50 g L\({}^{-1}\) culture). Economic allocation was based on literature data [42]: 3.30 \(\epsilon\) mg\({}^{-1}\) for carotenoids (\(\beta\)-carotene as reference) and 14.90 \(\epsilon\) mg\({}^{-1}\) for phycobiliproteins (allophycycyanin as reference).
## 2 Related Work
### Technological Background for V2V and V2I Applications
In addition, different communication mechanisms can be supported in vehicular networks [16]: Unicast Communication shown in Figure 6a, where data is sent from one source node to one target node via multi-hop wireless communication; Multicast/Geocast Communication shown in Figure 6b,c respectively, where in both, data is sent from one source node to multiple target nodes with the difference that in geocast communication the target nodes are grouped by geographical position; and Broadcast Communication shown in Figure 6d, where the source node sends data to all his neighbors. Figure 5: Relationship between sensor networks and IoT. Reproduced with permission from [15] [PERSON] _et al._, IEEE Communications Surveys & Tutorials; published by IEEE, 2013. Figure 6: Supported mechanisms supported in vehicular networks. (**a**) Unicast communication; (**b**) Multicast communication; (**c**) Geocast communication; (**d**) Broadcast communication.
# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.2 Sentinel-1A Data Preprocessing Method  
The processing of Sentinel-1A images, based on the official open-source software SNAP7.0 developed by the European Space Agency, involves a series of preprocessing steps aimed at enhancing data quality. These steps encompass track correction, thermal noise removal, radiation calibration, speckle filtering, topographic radiation correction and geocoding, as well as geometric correction. Subsequently, SAR backscatter coefficient extraction and texture information analysis (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation, ASM, MAX, Energy) are performed to derive remote sensing feature factors associated with biomass.
## 2 The Identification of Opportunities for Innovative Design Based on Requirement Conflict
### The Identification Process of Opportunities for Innovative Design
#### 2.2.1 The Determination of Requirement Conflict  
User demands serve as the primary catalyst for driving product innovation and establishing the design trajectory. Product performance encompasses both function and quality, where quality signifies the extent to which functions are realized [16]. Building upon functional requirements, performance requirements are introduced to enhance the realization degree of functions. By aligning with user function requirements, the ultimate design objective of the product becomes clear, leading to the definition of the ideal functional outcome and meeting user expectations regarding product performance. However, existing product performance constraints within the system pose challenges in meeting necessary performance criteria, often dictated by natural laws, design principles, and formulas [30]. A lack of awareness regarding these product performance constraints can lead to user demands surpassing the boundaries of performance constraints, creating conflicting demands that obstruct the product design process [31; 32]. Identifying and addressing requirement conflicts early on in the design process is crucial to deliver a product that maximizes user satisfaction. The process of determining these conflicts is illustrated in Figure 3.  
Figure 1: The identification method of opportunities for innovative design.  
User demand is usually the requirement for product function. The function goal is the final design goal of the product. The IFR method can be used to express the product function goal as an idealized result, and the user-expected demand can be obtained. The user-expected demand determined by the IFR method does not consider any constraints, but the actual design process is affected by various product performance constraints. Therefore, the realization of the final design goal is limited when the function goal is taken in the idealized process. And the requirement conflict is produced between the idealized result and product performance constraint. A 4-tuple representation model is adopted to represent the requirement conflict \(C_{UD}\) in this study as follows:  
\[C_{UD}=\{DO_{Final},DC_{IFR},LF,CR\} \tag{1}\]  
where \(DO_{Final}\) represents the final design goal, \(DC_{IFR}\) represents the idealized result of the product function goal, \(LF\) represents product performance constraint, and \(CR\) represents the contradictory relationship between the idealized result and product performance constraint. Because of the hierarchy of product function, the conflict between the subfunction and the idealized result can be expressed similarly.  
Figure 2: The identification process of opportunities for product innovative design.
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 2 Related Work
The scheme ensures the quality and accuracy of sensed data based on the amount of data sensed during each task. Delay-tolerant sensing networks are other possible types of MCS systems where the sensed data are beneficial even after some delay. The authors in [35] propose a comprehensive framework that uses delay-tolerant routing protocols to report data acquisition. Security and privacy are the biggest concerns for the users; hence, the location awareness feature of mobile phones can be disabled at the time of selection. An alternate solution that effectively performs while addressing these concerns is spatial cloaking [36]. In [37], the authors propose to allocate the opportunistic MCS tasks in a context-aware manner with the ultimate goal of recruiting the most favorable users. To this end, a set of indicators keeps track of the participant-task matching similarity. Moreover, energy consumption is also considered by the same study in conjunction with user privacy, e.g., users are not enforced by the platform to grant access to their location. [PERSON] et al. [38] use social relations to find out the most efficient and trustworthy route from the requester of the service to the provider and vice versa. Normally, a requesting entity aims to obtain information through acquired data about a certain phenomenon, which is preferably provided by the service provider who co-exists with the requester in the same community. If a provider in the same community cannot offer the service, the task is extended to users of other overlapping communities. Collaboration among community members ensures successful service provisioning by exploiting the social ties between the users. In [39], the authors propose a reverse auction procedure, namely MSensing, that intends to maximize both platform and user utility. MSensing was proposed as an improvement to the Local Search-Based (LSB) auction that recruits users with higher marginal contribution as the winners to maximize the platform utility. As an extension, in [40], the authors proposed a new auction-based mechanism called the IMCUauction, which follows the same rules and steps deployed in MSensing, but in the new research, two different sets of data are used for evaluating the performance of user-centric MCS platforms. While the proposed research proved is pretty comprehensive and competent in terms of computational efficiency, truthfulness, profitability and individual rationality, we believe there is still room for research that considers the energy efficiency of incentive methods, as well as the characteristics of each participant such as social links and user reputation, for those who contribute to the sensing of the surrounding phenomena.
## 1 Introduction
Inspired by the effective conditions, we propose a computationally reasonable DPMM algorithm which shows superior performance in mitigating short delay multipath over the RHCP-based algorithm. The rest of this paper is organized as follows: Section 2 introduces the received signal model of the dual-polarization antenna concerning both the reflection and the polarization mismatch loss on reception. Based on the analysis of the power of the received signal components in the LHCP antenna, we generate four typical cases for multipath mitigation evaluation; in Section 3, we compare the proposed maximum likelihood estimator (ML estimator), which is suitable for the dual-polarization antenna, with other signal-polarization-intended ones and find the effective condition and its capability of mitigating short delay multipath; a DPMM algorithm concerning the effective conditions with reasonable complexity is proposed in Section 4; the simulations in Section 5 test the performance of the proposed algorithm in both normal and critical scenarios; conclusions are given in Section 6. Received Signal Model of a Dual-Polarization Antenna in the One LOS and One Reflected Path Environment  
A dual-polarization antenna is a single antenna whose internal elements are combined in two different ways to produce RHCP-sensitive and LHCP-sensitive outputs [32]. As Figure 1 shows, when the NLOS signal is reflected, it turns to an elliptical polarized (EP) wave; the dual-polarization antenna receives the RHCP LOS signal and both the RHCP and LHCP components of the reflected EP signal.
# How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?
## 4 Kfa-Peks
### Syntax  
Public key encryption with keyword search supporting keyword frequency analysis is a protocol shared among a KGC, an MS, a KFS, a TS, multiple DPs, and multiple DUs, as follows:  
* \(Setup(x)\rightarrow(PP,r,r_{2})\): Given the security parameter \(\kappa\), the KGC generates the public parameter \(PP\), a value shared by the KFS and DUs, and a key \(r_{2}\), shared by the TS and DUs.
* \(KeyGen_{Sor}(PP)\rightarrow(MSK_{MS},MSK_{KFS})\): Given the public parameter \(PP\), the KGC generates two keys for the MS and KFS, \(MSK_{MS}\) and \(MSK_{KFS}\).  
* \(KeyGen_{DO}(PP)\rightarrow(pk_{DO}\),\(sk_{DO})\): Given the public parameter \(PP\), the KGC generates the public-private key pair \((pk_{DO}\),\(sk_{DO})\) for the DOs.
* \(KeyGen_{DU}(PP)\rightarrow(pk_{DU}\),\(sk_{DU})\): Given the public parameter \(PP\), the KGC generates the public-private key pair \((pk_{DU}\),\(sk_{DU})\) for the DUs.
* \(KeyGen_{TS}(PP)\rightarrow(pk_{TS}\), \(sk_{TS})\): Given the public parameter \(PP\), the KGC generates the public-private key pair \((pk_{TS}\),\(sk_{TS})\) for the TS.
* \(PERS(W,W_{T},pk_{DO})\rightarrow[T]_{pk_{DO}}\): Given the universal keyword set \(W\), a keyword subset \(W_{T}\), and the DO's public key, the DO computes the searchable ciphertext \([T]_{pk_{DO}}\).
* \(Trapdoor(W,\,r,\,r_{2},\,sk_{DU},\,pk_{TS},\,TSO)\rightarrow[t]_{PK}\): Given the universal keyword set \(W\), the value \(r\), the key \(r_{2}\), the DU's secret key \(sk_{DU}\), the TS's public key \(pk_{TS}\), and a timestamp, the DU computes an encryption key \(PK\) and the trapdoor \([t]_{PK}\).
* \(Test([T]_{pk_{DO}},[t]_{PK},\,MSK_{MS},\,MSK_{KS},\,pk_{DO},\,PK)\rightarrow[1]_{PK}\) or \([0]_{PK}\): Given the searchable ciphertext \([T]_{pk_{DO}}\), trapdoor \([t]_{PK}\), the MS's secret key \(MSK_{KS}\), the KFS's secret key \(MSK_{KS}\), the DO's public key \(pk_{DO}\), and the encryption key \(PK\), the MS and the KFS compute a test result. The DU can compute the test result and outputs \(1\) if \(W_{t}\subseteq W_{T}\), or otherwise, \(0\).
* \(TimeTokenGen(sk_{TS},\,pk_{DU},\,r_{2},\,TSN)\to TT\): Given the TS's secret key \(sk_{TS}\), the DU's public key \(pk_{DU}\), the key \(r_{2}\), and a timestamp, the TS generates a time token \(TT\).
* \(OpenKeyword([t]_{PK},\,r,\,TT)\to t\): Given the encryption key \(PK\), the value \(r\), and a time token \(TT\), the KFS can recover to obtain \(t\).
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Applications of MFCs
#### 3.4.1 Electricity Generation
[213] reported powering both a piezo-resistive pressure sensor as well as an ultra-wide-band transmitter with a 0.34 L MFC energy supply system. [PERSON] et al. [120] reported their works on use of marine benthic MFC that powers up sensors and acoustic modems for data transmission over a range of 0.5 km. Even though an MFC is a promising green technology, its low voltage and power output is considered as its biggest limitation. Sediment MFCs are criticized for their rather impractical architecture for generating a reasonable amount of required energy [214].
## 5 Conclusions  
Limit cycle oscillations conditions due to quantization-induced perturbation in a digital two-loop current controlled converter are presented and analyzed in this paper. LCO conditions includes both loops ADCs quantization, DPWM quantization and gains of the to control laws to show the undesired quantization effects in a two-loop digital voltage regulator of a dc-dc converter with an integrator at its output. Simulation and experimental results, obtained after developing different tests on a 400 V 1.6 kW coupled-inductor buck-boost purpose-built prototype, validate that the current waveforms present perturbations when these conditions are not fulfilled. These tests also demonstrate that fulfilling the condition for the external loop is enough to reduce the quantization induced perturbations. Nevertheless, the comparison of test results suggests that fulfilling conditions for both loops is the best option to avoid LCO and QIP in the system variables. The work presents a useful guidance for the design of the PI digital controllers in DC-DC converters, in order to improve significantly the dynamic responses, increasing the voltage loop bandwidth without ADC quantization effects.  
Conceptualization, J.C. and E.V.-I; methodology, C.G.-C.; software, C.R.; validation, C.G.-C., C.R., and R.G.; formal analysis, J.C.; investigation, C.G.-C. resources, C.R.; data curation, C.R.; writing--original draft preparation, C.G.-C. and R.G.; writing--review and editing, C.R., R.G., and E.V.-I.; visualization, C.R. and C.G.-C.; supervision, J.C.; project administration, E.V.-I.; funding acquisition, E.V.-I. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) under research projects DPI2016-80491-R (AEI/FEDER, UE) and DPI2017-84572-C2-1-R (AEI/FEDER, UE). The work was also supported by the the Chilean Government under Project CONICYT/FONDECYT 1191680 and by SERC Chile (CONICYT/FONDAP/15110019).  
The authors declare no conflicts of interest.  
Figure 8: Time domain waveforms of \(i_{g}\) and \(i_{L}\): (**a**) Test 1, (**b**) Test 2, (**c**) Test 3, (**d**) Test 4. CH3: \(i_{g}\) (2 A/div), CH4: \(i_{L}\) (2 A/div), and time base of 200 \(\mu\)s.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 5 Results  
In this study, we designed and executed four different experiments using combinations of two datasets and two model settings: (1) the original dataset trained on a sequential baseline CNN model, (2) the original dataset trained on an optimized and fine-tuned model, (3) the augmented dataset trained on the baseline CNN model, and (4) the augmented dataset trained on the optimized, fine-tuned model. Table 3 shows the comparative results (training, validation, and testing) of the four experiments. In the first experiment, the validation and testing accuracy gave very low results on the original dataset. The reason is that 6229 audio files are too few for 84 classes, which are on average 74 instances per class. Therefore, when performing validation and testing, the sample size becomes very small. However, after data augmentation, model optimization, and fine-tuning, the validation and testing accuracy increased to 95.87% and 95.77%, respectively. This implies that data augmentation and fine-tuning proved useful in model improvement.  
Figure 4a,b show the accuracy and loss of the optimized and fine-tuned CNN model. We can observe that the training and validation losses improve continuously. The optimized and fine-tuned CNN model has achieved the best training accuracy of 99.857% and loss of 0.0073, and the validation accuracy is 95.87% and loss of 0.2329.  
Figure 5 shows the details of the misclassification by the classifier. Eleven of the 84 classes have 0% error rate. Only 20 classes have four or more mispredictions. The Figure shows that class 10 (phoneme (\(\hat{\underline{\cdot}}\), \(\theta_{a}\))) and class 15 (phoneme (\(\hat{\underline{\cdot}}\), dgu)) have been misidentified a maximum of eight times. From the analysis of the confusion matrix, we observe that the topmost misclassified classes are those not spoken in CA pronunciation by the native local people. For example, class 10 (phoneme (\(\hat{\underline{\cdot}}\), \(\theta_{a}\))) is often pronounced as 7 (phoneme (\(\hat{\underline{\cdot}}\), ta)) in local Arabic dialect.