esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Functional Analysis of _Lithocarpus polystachyus_ (wall.) Rehd Black Tea
###### Abstract  
This study examined the development conditions and functional properties of a novel compound tea _Lithocarpus polystachyus_ (wall.) Rehd (_L. polystachyus_, LPR) black tea (LPRBT). The compound tea was developed by fermentation using fresh leaves (_Camellia sinensis_ cv. Qianmei 601) as the main raw material with LPR powder as an additive. Based on the single factor and orthogonal tests with sensory scores as indicators, a withered leaves-LPR powder mass ratio of 9:1 with a 6 h fermentation time was determined to be the production condition of LPRBT with a sensory score of 89.09. In addition, phlorizin content, anti-oxidation function, hypoglycemic function, and tumor suppressor effect of LPRBT were measured. The results demonstrated that LPRBT phlorizin content was significantly higher than apple. It also showed that the equivalent 2,2\({}^{\prime}\)-azinobis(3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) radical clearance rate with Vitamin C (Vc) and the 2,2-diphenyl-1-picrylhydrazyl (DPPPH) radical clearance rate was 81% of Vc. Both hydroxyl and superoxide anion radical clearance increased with the increase in LPRBT amount. LPRBT also showed a good inhibitory effect on \(\alpha\)-glucosidase and \(\alpha\)-amylase, indicating certain hypoglycemic activity. Moreover, it inhibited the growth of HeLa and A549 cancer cells showing tumor suppressor activity. This study provides a reference for the development and application of LPR food products.  
Lithocarpus polystachyus (wall.) Rehd; fermentation; anti-oxidation; hypoglycemic effect; tumor suppressor
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 5 Discussion
### Comparison with Recent Methods  
The proposed method was compared to state-of-the-art methods that used the three benchmark sequences: Highway (Video I), M-30 (Video II), and M-30-HD (Video III). As shown in Tables 16 and 17, the proposed method achieved the highest accuracy, with no error in the Highway sequence.  
For M-30-HD, the accuracy of the proposed method is lower than that in [37]. However, the experimental vehicles in the study are larger, which increases the probability of errors, as shown in Table 18.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sequence** & _TP_ & _FN_ & _FP_ & _True_ & _Counted_ & _Recall_ & _Precision_ & _F-Measure_ & _Accuracy_ \\ \hline Video I & 254 & 0 & 2 & 254 & 256 & 100\% & 99.22\% & 99.61\% & 99.21\% \\ Video II & 233 & 1 & 2 & 234 & 235 & 99.57\% & 99.15\% & 99.36\% & 99.57\% \\ Video III & 27 & 0 & 0 & 27 & 27 & 100\% & 100\% & 100\% & 100\% \\ Video IV & 103 & 2 & 3 & 105 & 106 & 98.10\% & 97.17\% & 97.63\% & 99.05\% \\ Video V & 281 & 2 & 5 & 283 & 286 & 99.29\% & 98.25\% & 98.77\% & 98.94\% \\ Video VI & 84 & 1 & 2 & 85 & 86 & 98.82\% & 97.67\% & 98.25\% & 98.82\% \\ \hline
**Total** & **982** & **6** & **14** & **988** & **996** & **99.39\%** & **98.59\%** & **98.99\%** & **99.19\%** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Experimental results of each sequence.  
Figure 19: Example of the experimental results in Video V.  
Figure 20: Example of the experimental results in Video VI.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
###### Abstract  
The complexity of modern power systems is increasing because of the development of various intermittent generators. In practical reliability evaluations, it is essential to include both the failure of conventional generators and the output characteristics of renewable energy; the use of the latter has increased rapidly. The weather-dependent nature of renewable energy output, which is inexplicable in the load duration curve method, highlights the need for further study of the methods of a reliability evaluation that can consider temporal characteristics. This paper proposes a deterministic reliability evaluation method based on the Booth-Baleriaux method, chronologically extended to address the preventative maintenance schedule of a generator and the characteristics of renewable energy. The proposed method was applied to an IEEE reliability test system for performance verification, and a reliability evaluation was performed considering various chronological patterns. The proposed method was also applied to determine the adequate capacity reserve that should be installed in a Korean power system. The proposed method is stable, and it produced robust results.  
power system reliability; reliability evaluation; convolution method; chronological reliability; loss of load probability; deterministic method; reliability test system Article
## 3 Materials and Methods
### Requirement of Recovery State after Interference  
In most of studies, recovery time is involved to evaluate the recovery state of the navigation receiver after each interference. However, the experiment on the navigation receiver shows that the positioning altitude instantly recovers from 0 (loss of positioning) to 66 (positioning) when the interference signal generator is turned off, while the C/N0 needs some time to get back to the normal positioning state. When the working power is \(-90\) dBm, the process of losing positioning under interference and the C/N0 recovery is shown in Figures 1 and 2. Where the variation of C/N0 is shown in Figure 1 when the navigation receiver is interfered with at the working frequency point, Figure 2 illustrates a similar process when the interference frequency offset is 9 MHz. In the experiment, 9 satellites are turned on in the navigation signal simulator. In the Figures, 9 different satellites are denoted as S1 to S9. Each expression of S1 to S9 is corresponding to each satellite number of 1 to 9 in the navigation signal simulator.  
From Figures 1 and 2, we can get that the C/N0 of each satellite varies in a consistent way after interference. The initial C/N0 of them is relatively stable. It confirms that all of the satellites could recover to the initial C/N0 after a period of recovery. After an interference, the recovery time at the working frequency point is obviously longer than that at the out-of-band frequency point. The recovery time of the out-of-band interference frequency point is less than 30 s, and the closer it is to the working frequency, the longer the recovery time is. Although a fixed interference recovery time cannot be used as the sensitivity criterion, it is feasible to take the initial C/N0 as the recovery state criterion of the navigation receiver.  
Figure 1: The process of C/N0 recovery when the interference is at working frequency point.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 4 Experiments
### Baseline Models  
#### 4.2.1 ATE Models  
We compare recent models in the ABSA task to demonstrate the effectiveness of the proposed ATE architecture and the ASC architecture. Since the performance of the multitasking model is lower than that of the single-tasking model, the model comparison takes place between independent single-tasking models.  
The group of models compares the performance of aspect term extraction models and trains single-task models independently. In order to compare the effectiveness of local information extraction based on an equal-width convolutional neural network, a model that integrates local contextual information (in a way involving attention mechanisms and other convolutions) is chosen to verify the effectiveness of the method.  
BiLSTM and MNA models perform aspect term extraction with the help of recurrent neural networks. DTBCSNN, DE-CNN, and STC models apply convolutional neural networks for the aspect term extraction. BERT-AE and CSAE perform aspect term extraction by using BERT as a word embedding layer for word vectorization. The following is a description of each ATE model.  
BiLSTM [13] is a commonly used named entity recognition model using bidirectional LSTM for word embedding representation.  
DTBCSNN [6] is a dependency tree-based convolutional stacking neural network to extract aspect terms without any artificial feature engineering.  
DE-CNN [7] is based on domain embedding and generic embedding using a multilayer convolutional neural network model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Setting** \\ \hline batch size & 16 \\ learning rate & \(3\times 10^{-5}\) \\ max length & 120 \\ rate & 0.4 \\ dropout & 0.1 \\ optimizer & AdamW \\ \hline \hline \end{tabular} Note: the rate parameter determines the magnitude of the semantic relative distance.  
\end{table}
Table 2: Some important hyperparameter settings for ATE and ASC models.  
Figure 5: Processing of data flow.  
BERT-AE [10] uses the BERT model and softmax for the aspect term extraction task.  
CSAE [5] is an aspect term extraction model that combines contextual features, dependent syntactic relations, and lexical properties.  
STC [24] is an aspect term extraction model that aggregates local information using graph convolutional neural networks.  
MNA [25] is based on an improved multi-head attention mechanism for aspect term extraction.
## 3 Background Work
### Attacks on the Zigbee Network  
Here is a summary of the most common attacks in Zigbee networks:  
1. **DoS Attack This attack may put a victim device on an unending retransmission cycle.**
2. **Zigbee Network Key Sniffing Attack** Vulnerability in the key transport of the Zigbee network while using the minimum security level.
3. **Network Discovery and Device Identification Attack** Zigbee devices send beacon request frames on a specific channel during the network discovery process. ZR and the coordinators will react by exchanging some important information.
4. **Interception of Packets Attack** Possibility of an attacker listening in on Zigbee network traffic and using the information they obtain
5. **Command Injection Attack** An attacker can take control of target devices by impersonally dating simple machines after obtaining the network key.
6. **Hijacking via Network Key Update Attack** The target node can be forced to change its network key to a value of its choosing by an attacker posing as a Zigbee coordinator.
7. **Worm Attack** This novel attack can disable the smart lighting system for an entire city and set off a chain reaction that renders bright lights useless.
8. **IoT Worm Hack on Philips Hue Light Bulbs** In November 2016, researchers published an attack against Philips Hue Light Bulbs, demonstrating the use of Zigbee standards. Attackers utilized a Philips hue light drone and infected the light bulbs with a worm/virus to enable and disable attackers. Funny enough, the assailants were checking the lights and flashing an \"SOS\" Morse code. This technique drives them through the Zigbee network using hardcoded symmetric keys from the light bulbs. The worm may attack a light bulb from a distance of up to 400 m and spread to neighboring amps because Zigbee utilizes hard-coded skeleton keys. The worm led Philips to provide an automated firmware upgrade for the lights and to circumvent built-in security protections to prevent unwanted remote access. Then, the attackers could quickly decipher the AES-CCM key used in all hue light bulbs. Zigbee can then extend the worm to nearby bulbs.
## 1 Introduction
Another possible use is for early warning where, for example, in the case of earthquakes, it may detect and undertake preliminary processing of recorded ground motion. This may include predicting damage to structures, as well as the activation of an appropriate alarm system (e.g., audio-visual), or the sending of messages to designated stake holders (e.g., civil protection, infrastructure operators). While the focus of the system's development has so far has been on seismic hazard and risk, the long-term goal of the work is on a multi-hazard system. Another important criteria considered during its development is its ease of use/installation. This is to enable its exploitation (and that of the accompanying processing software) by Civil Protection (CP) authorities with the minimal amount of training and without the need for specialized expertise. The next section provides a general overview of the MPwise, including a detailed description of its central hardware, communications protocols, current processing capacity and possible applications. This is followed by a description of possible applications, including -site and regional earthquake early warning (EEW) systems (Section 3), structural monitoring characterization (Section 4) and site amplification assessments (Section 5). The paper concludes (Section 6) with a summary of the ongoing development and the outlook for additional ways the system may be exploited. Note that much of the work in this paper was undertaken within the context of the SelSmic monitoring and vulneraBilitY framework for civil protection project (SIBYL; www.sibyl-project.eu). Hence, the figures showing output from the developed system will have the SIBYL logo attached to it.
## 3 Results and Discussion  
### Rheological Properties of Geopolymer Concrete  
Figure 4 presents the rheological properties such as yield stress and plastic viscosity of fresh geopolymer concrete with FCC residue content ranging from 0-100%. In general, increasing content of FCC residue as replacement of fly ash led to increasing yield stress (1597-1948 Pa) and plastic viscosity (210-476 Pa-s). Fly ash-based geopolymer specimens (G1, G7, and G13) comprising only fly ash as source material were highly workable concrete with the lowest yield stress and plastic viscosity ranges of 1597-1671 (Pa) and 210-380 (Pa-s), respectively.  
Figure 4: Relationship between yield stress and plastic viscosity of fresh geopolymer concrete and FCC residue content (% by binder mass). (**a**) 0% FCC residue. (**b**) 20% FCC residue. (**c**) 40% FCC residue. (**d**) 60% FCC residue. (**e**) 80% FCC residue. (**f**) 100% FCC residue.  
For instance, in the case of ALGS = 0.65 mixtures, yield stress and plastic viscosity values of G4-6 with high FCC residue content (\(\geq\)60%) were 4-12% and 27-39% higher than G1 specimen without FCC residue, respectively. This can possibly be explained by the fact that fresh geopolymer concrete with both spherical-shaped fly ash (as shown in Figure 1a) and angular-shaped FCC residue (as shown in Figure 1b) possessed higher frictional resistance compared to geopolymer with only fly ash. A combination of particles with spherical and angular shapes made the mixture less workable than that with only spherical fly ash particles. In addition, the large particle size of FCC residue was one of the main causes of reduction in fresh geopolymer workability. This was in agreement with previous findings reported by [PERSON] and [PERSON] [36].
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 2 Methodology
### Gcn
Specifically, we first calculate the difference between the model's predicted values and the actual photovoltaic power generation data, using Mean Absolute Error (MAE) and Mean Squared Error (MSE) as loss functions. Then, we utilize the backpropagation algorithm to compute the gradients of the loss function with respect to the model's parameters. Finally, we use the gradient descent optimizer to update the model's parameters based on the calculated gradients. By repeatedly performing these steps (forward propagation, loss calculation, backpropagation, and parameter update), the model gradually learns effective ways to extract hidden features from photovoltaic power generation data and optimizes its performance during the training process. This module solves the problem that the output of the encoder layer in the Informer model cannot be well extracted to the characteristics between adjacent contacts. After using this GCN module, the information output of the encoder layer can be more accurate, and the output after combining with the decoder layer can better fit the curve of the real data.
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
### Motivation and Problem Background  
As shown by Figure 1, AML attacks include falsifying data samples to achieve ML model inaccuracy in classifying new data inputs [3]. A typical ML process splits the dataset between two phases, training and testing. A poisoning attack affects the training dataset, while an evasion attack injects carefully crafted samples into the testing dataset. AML research is literature designed to measure AML's impact on ML models to find a way to increase their robustness against such attacks [4]. For instance, works of [5; 6; 7] evaluated Neural Networks (NN)-based systems against different types of evasion attacks. The work of [5] performed an evasion attack on a Multi-Layer Perceptron (MLP) Intrusion Detection System (IDS), where they succeeded in dropping the model's accuracy from 99.8% to 29.87%. Moreover, [PERSON]. [6] injected adversarial samples to fool Conventional Neural Networks (CNN) malware detection. Meanwhile, [PERSON]. [7]'s work was successful in tricking a DNN visual recognition model into classifying adversarial inputs as benign.  
Additionally, [PERSON]. [8]'s work is an example of a poisoning attack that tested the susceptibility of a Support Vector Machine (SVM) spam filtering system by inserting a well-crafted label-flipped malicious sample into the training dataset.  
In e-Health applications, the authors of [9] applied a poisoning attack on a LASSO regression ML model trained on a dataset that contained records of 5700 patients that predicted the dosage of Warfarin, an anticoagulant drug. Applying a 20% poisoning attack caused patients' dosages to change by an average of 139.31%. An increased dosage of Warfarin can cause severe bleeding, while a decreased dosage could cause blood clots, leading to heart attacks if the patient has a history of blood clotting [10]. This notable impact of AML attacks on people's health can severely affect other similar e-health systems [4]. A more comprehensive survey of similar studies on AML effects on other ML models, and domains can be found in [3].  
The AML field of research can easily state that most types of ML are prone to adversarial attacks since it is impossible to assume that the system's environment is entirely benign. Security researchers are always on the work to deploy robust methods against AML. For example, Blockchain is an emerging technology that uses means of cryptography and decentralization to provide stable, secure, and immutable blocks of records. Multiple blocks are connected together through a hash-based procedure. This hashing procedure and the utilization of other cryptography methods have given Blockchain technology its property of protecting block content integrity [11]. More security researchers incorporate  
Figure 1: An illustration of poisoning and evasion attacks workflow.  
Blockchain with ML to protect against AML. However, we believe that a more sophisticated kind of integration of the ML decision process is still needed.
# Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms
## 3 Proposed Methods
### Definition 2.11 [48]  
Euclidean distance, though used many times in clustering-based anomaly detection algorithms, has limitations. Euclidean distance measures the shortest distance between two points. Euclidean distance does not take into consideration the correlation between the attribute values, so Euclidean distance assigns equal weight to such variables which essentially measure the same feature. Therefore, this single feature gains extra weight. Consequently, correlated variables gain excess weight by Euclidean distance which affects accuracy. Since the IoT data are highly correlated, it is preferable to use Mahalanobis distance instead, as it takes into account the correlation between the variables. It is a scale-invariant metric which gives distance between a point \(x\in R^{n}\) generated from a given \(p\)-variant probability distribution \(P_{X}\left(.\right)\) and the distribution's mean \(\mu=E\left(X\right)\). Suppose \(P_{X}\left(.\right)\) has finite second-order moments and \(\sum=E\left(X-\mu\right)\), the covariance matrix, then the Mahalanobis distance [43; 44; 47] is given by  
\[d(X,\mu)=\sqrt{\left(X-\mu\right)\sum^{-1}\left(X-\mu\right)} \tag{13}\]  
If the covariance matrix is an identity matrix, the Mahalanobis distance reduces to the Euclidean distance.
## 5 Application in Predicting Water Inrush
The thermal conductivity and heat capacity of the rock and fluid properties parameters are listed in Table 2.  
Because the scale parameter is constant, when the shape parameters are 2, 8, and 50, the average porosity at 619 points in an effective aquifuge are all 0.2, and the average fluid velocity at these points are all \(6\times 10^{-4}\) m/s. Figure 14 shows the fluid velocity distribution of homogeneity rock and different heterogeneity rock, in which, Figure 14b,d,f show the fluid velocity over average value of \(6\times 10^{-4}\) m/s. In this figure, the fluid velocity heterogeneity and the maximum fluid velocity increases with decreasing the shape parameters. Meanwhile, the distribution area of fluid velocity over the average value gradually extends to the disturbed area by decreasing the shape parameter, which will increase the possibility of water inrush. In the results section, the rock heterogeneity increases with increasing temperature. Therefore, the danger of water inrush will increase with the increase of rock heterogeneity due to the combined effect of high temperature and high water pressure in deep mines. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rock and Coal** & **Elastic Modulus (MPa)** & **Poisson Ratio** & **Density (m3/kg)** \\ \hline Overlying strata & 9.988 & 0.27 & 2657 \\ Coal seam & 3.609 & 0.39 & 1490 \\ Disturbed area & 8.055 & 0.28 & 2635 \\ Aquifuge & 13.434 & 0.20 & 2687 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The model parameters of rock and coal.