esa-sceva/satcom-chunk-collection huggingface dataset:
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 2 Background
### Blockchain Layers
#### 2.2.3 Data Layer  
The data layer of the blockchain is typically used to describe the physical layer of the blockchain or DLTS. Included in this layer is practically the entire underlying technology of the blockchain. This includes data block and chain structure, hash function, Merkle tree, asymmetric public key data encryption, and time stamp technology [15]. Despite all of this technology included within the data layer, the most important aspect of this layer is storing data. The data layer is the blockchains database and safely stores all information in the form of data blocks. These data blocks, which are formed into chains, can be accessed by any full node.  
In regard to data security, the blockchain system uses the previously mentioned Merkle tree structure to record transactions. Hashes of transactions are computed using the Merkle tree data structure and are stored as Merkle root. The Merkle root, previous hash, timestamps, and the decentralized nature of blockchain make it incredibly difficult to tamper with the system. Along with the security nature of the Merkle tree structure, it also allows transactions to be carried out safely between nodes in the case of decentralization. A drawback is that it can be very energy demanding and have slow processing [11].
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 2 Power Quality Issues of WPP Integration
### Harmonic Issues of WPP Integration
#### 2.1.3 PQ Indices under Harmonic Distortion  
Harmonics can cause line losses and excessive heating of equipment, thereby decreasing their lifespan [19]. Subharmonics could cause flickers that lead to an uncomfortable visual effect on the eyes and thermal aging of IGs. There are many parameters developed to evaluate the PQ of a system concerning the harmonic distortion level. The IEEE-519 standard follows a set allowable limit of harmonics (IEEE Standard 519-1991, 1992). The two most commonly used indices of harmonic analysis are THD and TDD. The allowable limit set for system distortion is 5% for THD and 3% for any individual harmonics as stipulated by IEEE 519-1992 [23].
###### Abstract
The conditions and regulations for rail traffic also limit the possibility of the temporary exclusion of selected routes from standard use to conduct survey campaigns using the mobile platform. Hence, the assessment and verification of the adjustment procedures used to process the satellite data are gaining importance. This study aimed to gather a significantly large dataset of synchronous stationary observations from six receivers to test three adjustment methods: OLS, robust estimation, and adjustment with conditions binding the parameters. The site of the measurements was chosen in the vicinity of the POLREF network point for assessing the compliance of adjustment estimates with coordinates in the national coordinate system PL-2000 determined using the tachymetric method. The configuration of receivers in the field was adapted to the same geometrical conditions that are used on the railway platform. The stationary measurements with a frequency of 20 Hz ensured the gathering of a large data set for the analysis. The paper is divided into four sections. After the introduction, the Materials and Methods section describes the field test, methods for collecting and processing observation data from GNSS receivers, and the procedures for adjusting observations. The third section presents the results of the conducted analyses. The paper ends with a summary section containing the main conclusions of the study.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 3 Data Acquisition
### Data acquisition of Ground 3D Laser Scanner  
Ground three-dimensional laser scanning technology can record the three-dimensional geometric information of a measured target surface through joint calculation of the distance between the laser emission point and the target as well as the position and attitude information of the laser transmitter, so as to obtain the three-dimensional point cloud of the measured target [21]. Compared with traditional measurement methods, this technology has realized the transformation from traditional single-point measurement to area measurement. It is characterized by its high precision, automation, and strong environmental adaptability. Recently, it has been widely used in engineering construction, mine engineering, digital protection of cultural digital heritage, etc. [22].  
In this paper, the Maptek I-site 820 ER 3D laser scanner of MAPTEK was used to collect 3D point cloud data. The flow chart of point cloud data acquisition is shown in Figure 4. Without the interference of metal minerals and the magnetic field, the maximum range of observation is 750 m, the minimum range is 1 m, the scanning range is 125\({}^{\circ}\) vertically and 360\({}^{\circ}\) horizontally, and the repeated ranging accuracy is \(\pm\)6 mm. A high-resolution panoramic camera is also available to obtain point cloud texture information. Its powerful and comprehensive performance enables complex surveying and mapping tasks, such as underground mining engineering.  
After the field survey of the underground mine, the control points were arranged along the roadway by means of Total Station Branch traverse, and the 3D coordinates of six target points were collected (the coordinate system is CGCS2000 coordinate system), which was convenient for the coordinate conversion and accuracy comparison of the 3D point cloud data. The data acquisition is shown in Figure 5 (in Figure 5a, 3D laser scanner could not be completely erected due to the narrow shaft). Although there were 39 stations that we had collect data by 3D laser scanner, we only intercepted the missing part of the point cloud data of the underground access shaft roadway for 3D point cloud repair experiment.  
Figure 3: Overview of the study area. (The model is constructed by tilt photogrammetry). (**a**) Overall view of the mine; (**b**) Front view of the mining part of the mine.
## 5 Results and Discussion
To further examine the accuracy of the proposed technique in the detection of trace gas levels, we measured the gas concentration using the developed DAQ system. Figure 8 shows the mean square error (MSE) of the retrieved concentration for various known mole fractions of water molecules (1% to 6%). As shown, the proposed system has a lower MSE, especially at lower gas concentration levels.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## Materials and Methods
### Bayesian Hyper-Parameter Optimization Algorithm  
The basic idea of the Bayesian hyper-parameter optimization algorithm is to establish a substitute function based on the evaluation result of the past objective to find the minimum value of the objective function. The substitute function established in this process is easier to optimize than the original objective function, and the input value to be evaluated is selected by applying a certain standard to the proxy function [25]. Although the genetic algorithm is currently widely used in the parameter optimization of machine learning [26; 27; 28; 29], its disadvantages are also obvious, such as a poor ability of local search, many control variables, a complicated process, and no determined termination rules. The random searching algorithm is a traditional parameter optimization algorithm in machine learning [30]. It uses random sampling within the search range for parameter optimization, but the effect is poor when the dataset is small. In contrast, Bayesian hyper-parameter optimization takes the result of the previous evaluation into account when trying another set of hyper-parameters, and has a simpler process of optimizing model parameters than the genetic algorithm, which can save a lot of time.  
The HY_LightGBM model proposed in this paper uses one of the Bayesian optimization libraries in Python, Hyperopt, which uses Tree Parzen Estimation (TPE) as the optimization algorithm.  
The optimization process of the Bayesian optimization algorithm is shown in Figure 4.  
Figure 4: Flow chart of parameter optimization.  
By defining the objective function, domain space, and constructing the substitution function, the optimal parameters were finally obtained, with mean square error (MSE) as the evaluation indicator. The obtained optimal parameters were input into the LightGBM model to further improve the prediction ability of the model.
# Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam
## 6 Future Development  
This study has demonstrated the effectiveness of the RNN model in predicting chaotic vibrations of the laminated composite cantilever beam. Nevertheless, several promising directions remain for further investigation, including the following:  
Firstly, future research should focus on extending the RNN model to more complex structural problems involving material nonlinearity, boundary nonlinearity, and other types of nonlinear behavior, in order to assess its applicability and generalization capability across a broader range of nonlinear dynamic systems.  
Secondly, constructing confidence intervals or prediction intervals will allow for a more comprehensive evaluation of both the RNN model's response to input uncertainty and its overall robustness.  
Thirdly, by introducing interpretability techniques such as attention mechanisms and feature attribution, future studies can analyze the temporal features learned during training and gain deeper insight into their physical significance, thereby enhancing the transparency and interpretability of the RNN model.  
X.L. ([PERSON]): methodology, investigation, writing--original draft. L.S.: investigation, data curation, resources, funding acquisition, supervision. X.L. ([PERSON]): conceptualization, methodology, writing--review and editing, supervision. Y.D.: resources, funding acquisition, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NNSFC) through the grant No. 11602234, the talent scientific research fund of Liaoning Petrochemical University (No. 2017 XJJ-058), and Liaoning Provincial Education Department Basic Scientific Research Project for Youth (No. LJ212410148060).  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
## 3 Experimental Results and Analyses  
The experiment involved the deployment of a mid-sized AUV and a small-sized AUV, as depicted in Figure 11. These AUVs operate within complex underwater terrain with depths ranging from 20 to 500 m, as well as a flat seafloor area at a depth of 40 m. Notably, these two AUVs displayed differing maximum cruising speeds, with one achieving a top speed of 4 kt while the other boasted an impressive speed of 20 kt. Each AUV was equipped with distinct side-scan sonar models: the Lcocean-developed Shark-S450D and the ESI000 from Hydro-Tech Marine. Both vehicles exhibited the capacity to operate at 1000 kHz and 500 kHz frequencies, offering a maximum operational range of 100 m for high-frequency mode and 200 m for low-frequency mode.  
In the complex underwater terrain, experiments were conducted employing the constant-altitude, fixed-direction approach and the constant-de  
Figure 11: Small-sized and mid-sized AUVs.  
The goal was to evaluate the suitability of survey line deployment methods in complex seabed topography. To conduct feature detection tests in the flat underwater terrain, a combination of constant-altitude and fixed-point modes, selected for their focus on enhancing positioning precision, and a fusion of constant-depth and fixed-point modes were employed. These efforts were designed to evaluate the suitability of survey line deployment approaches in flat-seafloor underwater settings and to acquire empirical data to analyze side-scan sonar image features during AUV feature detection operations.
## 2 Proposed Time-Based Sensor
### Measurement Error in Direct Measurement  
The discrete discharge process described in the preceding section results in quantization error that is pivotal to consider because it can influence the effectiveness of the authentication process. An ideal discharge cycle can be obtained by rewriting Equation (4) and taking the quantization error into consideration:  
\[N_{DC}-q=\frac{R_{metal}C_{metal}\ln 2}{T_{CLK}} \tag{5}\]  
where \(q\) (\(q\in(0,1]\)) denotes the quantization error. It is important to note that as the discharge process progresses and the \(V_{c}\) node voltage drops below the threshold voltage, the quantization error assumes a positive value and is subtracted in the ideal discharge cycle.  
The measurement error, expressed as a percentage of the discharge cycle, can then be calculated as:  
\[Error(\%) =\frac{(Meas.- Ideal)}{ Ideal}\times 100\%\] \[=\frac{(N_{DC})-(N_{DC}-q)}{N_{DC}-q}\times 100\%\] \[=\frac{q}{N_{DC}-q}\times 100\%\] \[\approx\frac{q}{N_{DC}}\times 100\% \tag{6}\]  
Given that the quantization error ranges from zero to one, maximizing the discharge cycle corresponds to minimizing measurement error. However, the routing time-constant  
Figure 3: Time-based sensor operation flowchart.  
may be in the same order as the clock period, leading to a small discharge cycle and a large measurement error. For example, a long connecting metal trace in a 130 nm CMOS process could have a point-to-point resistance of 6 k\(\Omega\) (\(R_{metal}\)) and a total capacitance to ground of 1.5 pF (\(C_{metal}\)). Assuming a system clock of 500 MHz, this could result in a measurement error of up to 22%. Such a large error significantly diminishes the precision of the measured time-constants, limiting the effectiveness of the RC sensor in authenticating die origins.
## 4 Discussion
From early inflammation and hepatocellular proliferation to fibrogenesis and neoplastic transformation, the EGFR signaling mechanisms have been recognized as a central player in all stages of the liver response to injury [39; 40]. [PERSON] et al. [54] reported Gefitinib, a potent EGFR tyrosine kinase inhibitor to curtail HCC development in rats exposed to diethylnitrosamine. Further, [PERSON] et al. [55] reported that Tropomodulin 3 levels rise in HCC conditions, especially when it spreads outside the liver. Tmod3 expression was also linked to aggressive malignancy and poor patient survival in HCC patients [56]. Tmod3 enhances matrix metalloproteinase-2, -7, and -9 transcription, which is dependent on PI3K-AKT. The interactions between Tmod3 and EGFR, which promote EGFR phosphorylation, are required for signaling activation of PI3K-Akt. Similarly, VersicanV1 was also found to enhance the EGFR-PI3K-Akt signaling pathway in HCC cells [56]. A recent study by [PERSON] et al. [57] demonstrated that in HCC cells, the EGFR-P38 MAPK axis may upregulate PD-L1 via miR-675-5p and downregulate HLA-ABC via HK2, and this might be responsible for the immune suppression in HCC, and they suggest that EGFR signaling might be targeted for HCC immunotherapy. Hence, based on the potential role of EGFR in HCC and key network hub status in the current study, the phytocompounds were screened against EGFR. Table S9 provides information on phytocompounds targeting protein target and modulated pathways (edge count was mentioned for w.r.t. protein target, i.e., number of compounds and number of pathways connecting to respective protein targets). First, the WT-EGFR X-ray crystallographic protein structure was obtained from the PDB, and the missing residues were fixed by homology modeling. The structural stability of the best model of the EGFR protein was confirmed by molecular dynamics for a 50 ns production run. The least potential energy conformation from MD simulations was retrieved for utilization in docking studies. The molecular docking of 11 compounds from _A. paniculta_ and 3 from _T. populnea_ showed significant intermolecular interactions with active site residues. Further, based on the BE and highest interactions with active site residues, 5-Hydroxy-7,8,2\({}^{\prime}\)-Trimethoxyflavone (Andrographin) from _A. paniculta_ and Gossypetin (_T. populnea_) were identified as lead hits against WT-EGFR. All shortlisted compounds were predicted to interact with active site residues. On post docking studies, the MD method was used to delineate the effects of protein structure stability on ligand binding. To explain the structural modification of the protein upon ligand binding, we ran three replicates of 100 ns MD simulations for Andrographin and Gossypetin with WT-EGFR to ensure the plausibility of complex formation. The protein-ligand interactions for RMSD were found to be stable and formed stable interactions throughout 100 ns production runs in all three replicates.
# Integration of Cost and Work Breakdown Structures in the Management of Construction Projects
## 4 Results
### Structural Equation Model (SEM)
#### 4.3.1 Reliability  
Cronbach's alpha (C\(\alpha\)) value is used to test the consistency of the hypothesized constructs based on the data [154]. C\(\alpha\) is inflated by a large number of variables, and there is no exact interpretation as to what constitutes an acceptable limit. However, a rule of thumb applies to most situations with the following ranges [155; 156]:  
* C\(\alpha>0.9\) as excellent.
* 0.9 \(>\) C\(\alpha>0.8\) as good.
* 0.8 \(>\) C\(\alpha>0.7\) as acceptable.
* 0.7 \(>\) C\(\alpha>0.6\) as questionable.
* 0.6 \(>\) C\(\alpha>0.5\) as poor.
* 0.5 \(>\) C\(\alpha\) as unacceptable.  
Due to almost all C\(\alpha\) coefficients (including the six constructs and the overall model) were above 0.7, the measurement of this study can be considered at least as acceptable, in terms of consistency of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Questions**} & \multicolumn{2}{c}{**Variance Explained**} & \multicolumn{2}{c}{**KMO Test**} & \multicolumn{2}{c}{**Bartlett Test**} \\ \cline{2-7}  & **CIT** & **CIM** & **CIT** & **CIM** & **CIT** & **CIM** \\ \hline Q01–Q06 & 82.51\% & 87.37\% & 0.771 & 0.868 & 283.81 (15,*) & 557.51 (15,*) \\ Q07–Q12 & 87.64\% & 87.12\% & 0.766 & 0.793 & 448.07 (15,*) & 464.29 (15,*) \\ \hline \end{tabular} Note: * 15 degrees of freedom with a significance (\(p\)-value) below 0.001.  
\end{table}
Table 15: Validity of the model confirmatory factor analysis (CFA) results.  
the measurement scale. However, although C\(\alpha\) coefficient is the most widely used estimator of the reliability of tests and scales, the composite reliability (CR) is also proposed as an alternative [157]. The CR represents the ratio of the true score variance of a construct divided by its observed score variance. Values of CR above 0.9 are considered very good and above 0.7 as acceptable. As shown in Table 16, the items measured in the six constructs (and the overall model) were sufficiently consistent and reliable.
## 2 Materials and Methods  
The basis of Logic Tensor Networks (LTN) is called real logic, which is described by a first-order language that contains constants, variables, functions, predicates, and logical connectives and quantifiers [24]. The way LTN uses real logic is by transforming the provided formulas into TensorFlow computational graphs. Those formulas can then be used for querying, learning, and reasoning over the data.  
**Grounding in LTN [24]**. Constants are depicted as tensors that can be of any rank. Tensors can be represented as n-dimensional arrays, as shown below. Let the following expression (1):  
\[\bigcup_{i_{1},i_{2},\ldots,i_{k}\in\mathbb{N}^{*}}\mathbb{R}^{i_{1}\times i_{2} \times\ldots\times i_{k}} \tag{1}\]  
denote the set of tensors of any rank. The tensor of rank 0 represents a scalar value, a rank 1 tensor represents a one-dimensional vector, a rank 2 one represents a matrix, and so forth.  
For the constant \(\alpha\), let us refer to the associated tensor by \(\mathbb{G}(\alpha)\). As an example shown in (2), consider the constants \(x,y,z\) and the associated tensors \(\mathbb{G}(x),\mathbb{G}(y),\mathbb{G}(z)\) of different ranks.  
\[\mathbb{G}(x)=\begin{pmatrix}1&2\end{pmatrix},\mathbb{G}(y)=\begin{pmatrix}1& 2\\ 6&7\end{pmatrix},\mathbb{G}(z)=\begin{pmatrix}1&2&3\\ 4&5&6\\ 7&8&9\end{pmatrix} \tag{2}\]  
Predicates in LTN are mapped to functions or tensor operations and return a value in [0, 1] that denotes a satisfaction level. It can be interpreted as a truth degree. A predicate can be a simple \(\lambda\) function or a neural network of any shape and activation function (the only exception would be on the last layer, where a sigmoid function should be used to return a value of partial truth). Connectives implemented in LTN are modeled to the semantics of first-order fuzzy logic [25]. Some of these are negation, implication, conjunction, and disjunction. Quantifiers supported in LTN are \(\forall\) (universal) and \(\exists\) (existential) and are defined as aggregation functions. In the following experiments, only the \(\forall\) p-mean error aggregator is used. A parameter \(p\) used in (3)  
\[X(a_{1},a_{2},a_{3},\ldots,a_{k-1},a_{k})=1-\left(\frac{1}{k}\sum_{i=1}^{k}(1-a _{i})^{p}\right)^{\frac{1}{p}} \tag{3}\]  
adjusts the focus on the deviation, and it is especially useful in querying where the focus is on the formulas, contrary to training, where a lower value is used to prevent overfitting and make it possible to generalize. For example, a \(p=2\) would result in an aggregation function that outputs the standard deviation of the inputs and a \(p=\infty\) would output the minimum value.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 5 Experiment and Result
### Measurement Metrics  
We leverage accuracy, precision, recall, and F1-score to evaluate the effectiveness of BLSTM neural network in detecting malicious JavaScript. In general, the effectiveness of a model is evaluated by the accuracy rate, i.e.,  
\[accuracy=\frac{N_{true}}{N_{telle}} \tag{8}\]  
where \(N_{true}\) represents the number of samples that predict correctly, and \(N_{telle}\) represents the total number of samples in the dataset. However, it is not enough to evaluate the performance of the model based on accuracy alone, so we choose precision and recall as supplements. The precision refers to the ratio of true positive malicious JavaScript to the total number of samples detected as malicious, i.e.,  
\[precision=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) is the number of samples with malware JavaScript detected correctly, and \(FP\) is the number of samples with false malicious JavaScript detected. The recall measures the ratio of true positive malware JavaScript to the entire population of malicious JavaScript samples, i.e.,  
\[recall=\frac{TP}{TP+FN} \tag{10}\]  
where \(FN\) is the number of samples with true malicious JavaScript undetected. F1-score takes into account both precision and recall, that is:  
\[F1-score=\frac{2\times precision\times recall}{precision+recall} \tag{11}\]  
Ideally, our detector should not miss any malicious code and not trigger false alarms, that is, it both has high precision and recall at the same time, but these two indicators are mutually exclusive in some scenarios. For example, if only one sample is taken, and the sample is also a positive sample, then the precision = 1.0, and recall may be lower (because there may be multiple samples in the dataset). As a comprehensive index, the F1-score is to evaluate a classifier comprehensively in order to balance the influence of precision and recall.