esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Graph Feature Extraction Methods  
Methods for graph feature extraction can be broadly categorized into the following classes: graph learning-based methods, graph kernel-based methods, random walk-based methods, and traditional graph algorithms. The Graph Attention Network (GAT) [44] incorporates an attention mechanism to assign differentiated weights to distinct neighbor nodes, enabling the model to more flexibly concentrate on pivotal neighbor nodes. However, this approach exhibits high computational complexity and under-performs when handling dynamic and heterogeneous graphs. Additionally, the inherent complexity and instability of the model may introduce substantial noise, while its sensitivity to parameter initialization further complicates the training process. The graph wavelet neural network model [45] employs a rapid algorithm to execute graph wavelet transform, circumventing computationally intensive matrix decomposition operations and thereby enhancing algorithmic efficiency. Nevertheless, graph convolutional neural networks grounded in the spectral domain are heavily reliant on the eigendecomposition of the graph Laplace matrix, resulting in elevated computational complexity. In the realm of graph kernel-based methods, the Weisfeiler-Lehman kernel (WL kernel) [46] transforms a graph's structural information into a kernel function format through iterative node label updates and aggregations. The Graphlet kernel [47] computes graph similarities based on the subgraphs within a graph, capturing local structural characteristics. However, these methods lack flexibility when dealing with dynamically changing graphs, as they primarily focus on the static structural attributes of the graph. By decomposing the adjacency matrix or Laplacian matrix of a graph and selecting the singular vectors corresponding to the first k singular values as node feature representations, dimensionality can be reduced, and the principal structural information of the graph can be extracted. For instance, techniques such as Singular Value Decomposition (SVD) [19] and Non-negative Matrix Factorization (NMF) [25] learn graph characteristics through matrix decomposition. Statistical data-based methods directly utilize basic attributes of the graph as features, such as node degree features, centrality measures, and clustering coefficients. These methods extract graph features by computing the degrees, centrality, or clustering coefficients of nodes. However, these approaches typically only provide local or simplistic global features of the graph and are incapable of learning complex, multi-level feature representations as demonstrated by deep learning methods.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 3 Principle and Method of Polar Code Parameter Recognition
### Parameter Recognition Steps  
Since 3 GPP stipulates that the maximum mother code length of the polar code in the downlink control information is 512, that in the uplink control information is 1024 [24]. Considering the code length and code-rate range of polar codes in practical applications, the code length is selected as \(n^{\prime\prime}=[32,64,128,256,512,1024]\) and the algorithm code rate is selected as \(R^{\prime\prime}=[\frac{1}{5},\frac{1}{3},\frac{2}{5},\frac{2}{2},\frac{3}{2}, \frac{5}{6},\frac{5}{7}]\) for the polar code simulation in this paper. The range of the signal-to-noise (SNR) ratio is \(-4\leq SNR\leq 6\). Denoting the total number of program executions as \(L\), and the times of \(\text{AL}>\Lambda_{\text{opt}}\) under a certain code length, code rate, information-bit position, and signal-to-noise ratio during the execution process as Num, we then define the polar code recognition conformity \(\eta\) as follows:  
\[\eta=\frac{\text{Num}}{L}. \tag{84}\]  
According to the definition of \(\eta\), when the code length, code rate, and information-bit positions used in constructing \(\mathbf{H}_{n}\) are completely consistent with the transmitted codeword, \(\eta\) takes the maximum value. The specific algorithm is shown as Algorithm 1.  
```
Input : \(n^{\prime}\); \(R^{\prime\prime}\); soft decision sequence \(r\); numbers of code length, \(N_{n}\); numbers of code rate, \(N_{r}\); execution numbers \(L\) Output : \(\hat{n}\), \(\hat{R}\), \(\hat{A}\) Initialization : \(\text{Num}=\text{zeros}(N_{n},N_{r})\), \(j=0\)
1 for\(n^{\prime}=n^{\prime\prime}\)do
2 Construct codeword matrix \(\mathbf{R}\);
3 for\(R^{\prime}=R^{\prime\prime}\)do
4\(j=j+1\);
5 construct \(A^{\prime}\) by GA algorithm with \(n^{\prime},\ R^{\prime}\);
6 construct \(\mathbf{G}_{n^{\prime}}(A^{\prime})\) with \(n^{\prime},\ R^{\prime},\text{ and }A^{\prime}\); calculate \(H_{n^{\prime}}\);
7 get statistics AL and threshold \(\Lambda_{\text{opt}}\);
8 if\(\text{AL}<\Lambda_{\text{opt}}\)then
9 \(\text{Num}(j)=\text{Num}(j)+1\);
10 \(\eta(j)=\text{Num}(j)/\text{L}\);
11 endif
12 endfor
13
14 endfor
15 Find all \(j\) that make \(\eta\) take the maximum value; number of \(j\) is lenj;
16 for\(i=1\) to \(i=lenj\)do
17\(p1=\text{mod}(j,N_{r})\), \(p2=fix(j,N_{r})\);
18 if\(p1=0\)then
19\(R^{\prime}(i)=8/9\);
20 else
21\(R^{\prime}(i)=R(p2)\);
22 endif
23\(n^{\prime}(i)=n(p1+1)\);
24 endfor
25\(\hat{n}=\min(n^{\prime}),\ \hat{R}\)= \(\min(R^{\prime})\), construct \(\hat{A}\) by GA with \(\hat{n},\ \hat{R}\);
26 return \(\hat{n},\ \hat{R},\hat{A}\)
```  
**Algorithm 1**Polar code parameter recognition
## 3 Results
### Study Characteristics
#### 3.2.2 General Characteristics  
The main characteristics of each included study are summarized in Table 2. The publication year of the included studies ranged from 2018 to 2021. In regard to study objectives, detection and classification were performed on five studies (83%) [6; 7; 8; 21; 22], and only classification was performed on one study (17%) [20]. All studies (n = 6, 100%) used private datasets for experiments [6; 7; 8; 20; 21; 22]. For CNN architecture, all studies (n = 6, 100%) used transfer learning for training and testing [6; 7; 8; 20; 21; 22]. In addition, the comparators were a radiologist (n = 1, 17%) [20], oral maxillofacial surgeons (n = 2, 33%) [21; 22], a general practitioner (n = 1, 17%) [21], and not reported (n = 3, 50%) [6; 7; 8]. To assess the performance of the CNN models, the outcomes of the included studies were used, including sensitivity (n = 5, 83%) [6; 7; 8; 20; 22], specificity (n = 4, 67%) [6; 7; 20; 22], accuracy (n = 4, 67%) [6; 20; 21; 22], area under the curve (n = 3, 50%) [6; 7; 20], F1 score (n = 1, 17%) [21], precision (n = 1, 17%) [21], recall (n = 1, 17%) [21], false positive rate (n = 1, 17%) [8], and diagnostic time (n = 1, 17%) [22].  
\begin{table}
\begin{tabular}{l c} \hline
**Items and Subcategory** & **No. (\%) of Reports** \\ \hline \hline _Journal Category_ & \\ \hline Biomedical engineering field & 2 (33\%) \\ \hline Dental or medical field & 4 (67\%) \\ \hline _Location of corresponding author_ & \\ \hline Asia & 6 (100\%) \\ \hline Europe & 0 (0\%) \\ \hline USA & 0 (0\%) \\ \hline _Job of corresponding author_\({}^{*}\) & \\ \hline Doctor or dentist & 6 (86\%) \\ \hline Engineer & 1 (14\%) \\ \hline _Type of reporting guideline_ & \\ \hline STARD & 0 (100\%) \\ \hline Other & 0 (100\%) \\ \hline None & 6 (100\%) \\ \hline _Funding source_ & \\ \hline Both private and public & 0 (0\%) \\ \hline Private & 0 (0\%) \\ \hline Public & 4 (67\%) \\ \hline None & 2 (33\%) \\ \hline Unclear & 0 (0\%) \\ \hline \end{tabular}  
* The total does not equal 100% because multiple answers were possible in each study.  
\end{table}
Table 1: Descriptive characteristics of six reports using deep learning neural network for automated detection of cyst and tumor of the jaw.
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 3 Performance of the Eight-Element Antenna System  
### Eight-Element MIMO Antenna System Prototype  
The fabricated eight-element antenna system prototype is shown in Figure 12; Figure 12a shows the side frames where the two pairs of antennas are placed; Figure 12b shows the interior of the prototype as well as the L-shaped coupled feed branch; Figure 12c shows the defective ground structure of the prototype's metal common ground plane; and eight SMA connectors were soldered at the back ports of the prototype for testing. Similar results are no longer shown due to the symmetrical arrangement of the MIMO system.
## 4 Analysis and Results
### Data Analysis of Teachers' STEM Education Intention
#### 4.2.1 Reliability and Validity Tests  
This research applied AMOS 26.0 to test the original model and found that the SMC values of CO2 and CR5 were less than 0.5, indicating a weaker corresponding relation between the two measurement items and their dimensions. Therefore, we removed them. We also corrected the model according to the MI (modification indices) standard and found some unreasonable measurement items, including PU1, PEU2, SUII, SUI3, CI3, CI4, STI4, STI5, PA4, PBC1, SE5, SE6, FC1, and BR5; therefore, they were also deleted. In order to make sure the corrected model could effectively assess teachers' behavioral intention to implement STEM education, this research first conducted the analysis at the measurement level to weigh the measurement effect of the observed variables on the latent variables, and then conducted structural model analysis to test the structural relationships among the latent variables. 1. Measurement-Level Analysis  
Through our test, both parts of the research, i.e., the experiment and corrected model, had a Cronbach alpha value greater than 0.8 (Tables 1 and 4), which proves their good internal consistency. Usually, the standardized factor loading, average variance extracted (AVE), SMC, and its composite reliability estimation are used to test the convergent validity of a model [53]. With regards to the AVE, the suggested value is 0.5 or above [54]. The suggested value for the standardized factor loading is 0.50 or above; in an ideal situation, it would be 0.70 or above [54]. The suggested value for the SMC is greater than or equal to 0.5. As shown in the results in Table 4, each value reached the stipulated threshold, showing that the model had good convergent validity. In order to test the discriminant validity of the model, we compared the index of the square root of the AVE and the absolute value of correlation coefficients among the potential variables. As shown in Table 5, the square root of the AVE of each potential variable was higher than the correlation among the latent variables, which indicates that the discriminant validity of the model was good. \begin{table}
\begin{tabular}{c c c} \hline
**Items** & **Mean** & **SD** \\ \hline BI1 & 3.6 & 0.852 \\ \hline BI2 & 3.65 & 0.84 \\ \hline BI3 & 3.72 & 0.804 \\ \hline BI4 & 3.77 & 0.796 \\ \hline BI5 & 3.88 & 0.792 \\ \hline OA & 3.724 & \\ \hline \end{tabular} Note. BI: behavioral intention.  
\end{table} 
Table 3: Descriptive statistics of each item of behavioral intention.
# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
###### Abstract  
The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit (\(R^{2}\) = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0-800 mgL\({}^{-1}\), and 0-306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others.  
Keywords:computer vision for measurements; convolutional neural network (CNN); transfer learning; total suspended solids (TSS); turbidity +
Footnote †: journal: _sciences_
# Resazurin-Based Assay for Quantifying Living Cells during Alkaline Phosphatase (ALP) Release
## 3 Results and Discussion  
### Determining the Optimal Concentration of Resazurin  
Metabolic activity may differ between cells, which makes optimizing the concentration of resazurin important. Cells were plated in 48-well plates and incubated for 24 h to allow for attachment. Different concentrations of resazurin were added, starting with the highest possible concentration that would not be toxic to cells (\(\sim\)200 \(\mu\)M). This was then serially diluted to 1:2 to produce a range of \(\mu\)M/well. All measurements were done with 24-h incubation times. Figure 2A-D shows that the resazurin reduction rate was positively proportional to the resazurin concentration. However, this gradual increase was limited by the highest concentration, where, after the optimal concentration, the rate of resazurin reduction decreased. Figure 2A-C illustrates the highest reduction rate at 50 \(\mu\)M and then shows an inverse relationship. Figure 2D shows the highest reduction rate at 100 \(\mu\)M, while 200 \(\mu\)M exhibited a high deviation. This data indicate that the optimal concentration of Balb/c3T3, A549, and MCF-7 was 50 \(\mu\)M, and that of the Ht-29 cell was 100 \(\mu\)M. Figure 2E shows the coefficient variance of the analysis data at various resazurin concentrations. At the lowest recorded concentrations of resazurin, the relative variability exceeded 20%. The CV% then fell and reached its lowest level at 100 \(\mu\)M.  
However, at higher concentrations, this trend reversed and CV% started to increase. These findings indicate that resazurin at a high concentration may be destructive to cells [31].
# LEO-Based Satellite Constellation for Moving Target Detection
## 3 Results  
To illustrate the effectiveness of the proposed method with LEO-based satellite constellation, it is necessary to consider the impact of terrain and sea clutter on moving target detection under down looking mode, that is, the signal-to-clutter ratio (SCR) problem. Furthermore, the influence of fusion energy dispersion on moving target detection caused by the high-speed motion of LEO satellites should also be taken into account, that is, the SNR problem. Thus, the performance of the presented method is evaluated comprehensively from three aspects, i.e., pre-compensation, clutter suppression and multi-source fusion detection. Considering that there is no published real measured data for LEO satellite scenario, without loss of generality, the simulation results of multi-source echoes are generated to evaluate the performance of the proposed algorithm.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### Enabling Technologies  
Most popular communication technologies include CAN bus, Common Industrial Protocol (CIP), Ethernet, UPB, X10, Insteon, Z-wave, EnOcean, nanoNET, IEEE 802.15.4 (6 LowPAN, Zigbee), IEEE 802.11 (Wi-Fi [126]), Bluetooth (Bluetooth Low Energy). The work in [127] investigates IEEE 802.15.4 against IEEE 802.11 ah. The latter achieves better throughput than IEEE 802.15.4 in both idle and non-idle channels, although the energy consumption of IEEE 802.15.4 outperforms the values of IEEE 802.11 ah, especially in dense networks. Furthermore, cellular networks include WiMAX [128; 129] and 4G/5G LTE [130]. In particular, 4G LTE uses carrier aggregation up to 100 MHz, downlink and uplink spatial multiplexing, multipath propagation and heterogeneous networks to provide higher throughput, greater bandwidth and efficiency, and facilitate simultaneous connection. Highly integrated chipsets exist for most of these protocols, allowing for easy hardware integration. The protocols mentioned have supporting development environments and in some cases manufacturers offer open source APIs.  
The protocols presented offer at least some form of rudimentary congestion control, error recovery and some ad-hoc capability. None of the communication protocols are designed for an actively hostile environment. Another specific technologies in use are RFID, Near Field Communication (NFC) and UWB.
## 2 Theoretical Foundations  
### Unified Theory of Acceptance and Use of Technology (UTAUT)  
The UTAUT model, proposed by [PERSON] et al. in 2003, is one of the seminal models for understanding information technology acceptance. It effectively explains individuals' behavioral intentions to adopt emerging technologies [7]. The UTAUT model has been demonstrated to explain up to 70% of user acceptance intentions, highlighting its significant influence in information technology applications. The core components of the model include four key variables: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) [8]. Of these, PE, EE, and SI are vital factors influencing behavioral intentions to use technology. In addition, behavioral intentions, in combination with FC, determine the actual use of the technology. [PERSON] [9] also found that different combinations of the four moderators--age, gender, experience, and voluntariness of use--moderate these relationships, influencing how each factor affects the intentions and behaviors associated with technology adoption. Nowadays, UTAUT models are widely used in the fields of information technology [10; 11; 12], e-commerce [13; 14; 15], socio-cultural studies [16; 17; 18], and educational technology [19; 20; 21] for evaluating and predicting users' acceptance and usage behaviors toward new technologies.
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 6 Discussion
### Considerations of Hardware and Software Security  
In this reference architecture, we chose not to include the physical viewpoint and have only included a virtual implementation viewpoint as full representations of both viewpoints increase the difficulty of performing a high-level security analysis of a CAV. However, it is important to consider the security of the hardware and software of these systems. An issue is that for vehicular systems the software is typically only available as a black box, as manufactures are in general unwilling to supply the source code used for implementation. The same is typically true for the hardware in a CAV. This means that it can be useful to consider the system in terms of its functional components and their interactions. The high-level reference architecture presented in this work will be useful to initially describe the system but a more detailed modelling language (such as SysML [74]) may be preferable when more details need to be specified. However, a reference architecture will be useful to highlight the attacker's path to achieve its goal and select in which component or interaction to implement mitigations for the attack. Additionally, it can also be referred in security verification when upgrading software or hardware for the system, which can happen frequently in a CAV's life cycle.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 3 Multicarrier Data Frames of the DSRC and LTE-V2X Standards
### General Overview of IEEE 802.11p and LTE-V2X Specifications
The subcarrier spacing is set to 15 kHz (i.e., 180 kHz per RB). Additionally, the frame duration is fixed to 1 ms, which allows the transmission of 14 symbols including GIs. The duration of each symbol and GI is 66.31 \(\upmu\)s and 4.69 \(\upmu\)s, respectively. The frame structure is shown in Figure 2, where the symbols are enumerated from 0 to 13. The LTE-V2X frame includes nine data symbols, four demodulation reference signal (DMRS) symbols and one empty symbol for \(T_{X}-R_{X}\) switch and timing adjustment [9; 14]. The last symbol allows tracking the channel for the next transmission. These parameters are likely to be employed by emerging C-V2X technologies since 5G specifications are also considering the use of the PC5 interface for direct D2D applications in vehicular environments [15].
## 3 Cd-Mediated Genes Upregulation
### Cellular Detoxification  
#### 3.6.1 Metallothioneein  
Metallothioneein (MT) is a low-molecular weight, cysteine-rich, metal-binding protein that regulates metal homeostasis and detoxification by binding essential and non-essential metal ions [68; 69]; it is also involved in the immune response and in free radical scavenging [70; 71; 72]. MT gene expression is readily upregulated by exposure to heavy metals [73; 74; 75; 76], providing a mechanism by which the metal can be sequestered in a relatively inert, non-toxic state [77].  
MT is expressed during the embryonic development of many vertebrates and performs its detoxifying function by protecting embryos from toxic metals such as cadmium [78; 79; 80; 7].  
Under normal conditions, in _P.iculus_ MT transcripts accumulate during oogenesis [81; 82]; in embryos, in situ hybridizations elucidated the precise tissue distribution of MT transcripts during the development [82], but failed to discriminate between maternal and embryonic transcripts, making it difficult to determine when expression of the embryoonic MT gene begins. However, the same analyses show that at 40 days from oviposition the embryonic gene is active in the brain and lungs; before hatching, at about 60 days of development, the gene is also expressed in hepatic monocytes and in Kupffer cells [82].  
Northern blot analysis demonstrates an appreciable increase in MT transcripts in _P. siculus_ embryos developed for 20 days in Cd-contaminated soil when compared with control embryos, thus demonstrating the induction of embryonic MT gene expression by heavy metals. In situ hybridization shows markedly increased localization of MT mRNA in liver sinusoids and in the intestine [7]. However, this de novo expression of the MT gene is not able to completely protect the embryo from damage induced by Cd; hence, as described above, lizard embryos incubated in Cd contaminated soil show serious morphological alterations [7].