esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Literature Review
#### 2.1.2.  
Usability was first introduced in the 1970s, with the concept varying among researchers and target groups [31].  
IEEE Std.610.12 (1990) defines usability as an attribute that facilitates system input and output and makes it easy to learn how to operate the system [32]. [PERSON] (1993) defines usability as how easily users can utilize system functionalities, setting five criteria for usability evaluation: Learnability, Efficiency, Memorability, Errors, and Satisfaction [33]. [PERSON] and [PERSON] (1993) define usability as the degree to which a user of a product can quickly and easily complete tasks, considering good usability as user-centered, effective, efficient, and above all easy to use [34]. ISO 9241-11 (1998) [35] describes it as the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use. [PERSON] et al. (2003) view usability as the ability of users to instinctively and effectively use a product, interface, or system [36]. [PERSON] (2006) [37] considers usability as the degree to which specific users can efficiently, effectively, and satisfactorily implement a system, product, or service in a specific environment.  
Numerous studies measure product usability based on these attributes, which serve as benchmarks in the evaluation process, such as those by [PERSON] and [PERSON] on the usability of smart home apps [38] and [PERSON] and [PERSON] on the factors influencing user experience of smartphone travel apps [39]. Thus, in this paper, we also use these standards as benchmarks for measuring the usability indicators of medical websites.  
Moreover, the literature indicates that the concept of usability has evolved with time. Where it was initially mainly considered to be a product or system's ease of use and learning, usability has now expanded to include personal evaluations and users' subjective feelings. In particular, modern usability indicators not only focus on technical operability but also emphasize user experience, making the concept more comprehensive [40]. Therefore, in this paper, we also consider the characteristics of user experience in constructing usability indicators for medical websites.
## 3 The Proposed Method
### Backbone Object Simplification and Feature Vertices Computation  
The backbone object is a set of vertices, in which each vertex is the average point between two continuous vertices in that object. Thus, the backbone object of \(\mathbf{P}_{i}\) is \(\mathbf{B}_{i}=\left\{b_{i,j}\middle|j\in[1,|\mathbf{B}_{i}|]\right\}\) where \(|\mathbf{P}_{i}|=|\mathbf{B}_{i}|\) and the value of \(b_{i,j}\) is computed as shown in Equation (1). Figure 3a shows a backbone object of a polyline object.  
\[b_{i,j}=\left\{\begin{array}{ll}v_{i,j}\text{ with }j=1\text{ or }j=|\mathbf{P}_{i}|\\ \frac{v_{i,j}+v_{i,j-1}}{2}\text{ \ \ for otherwise }\end{array}\right. \tag{1}\]  
Figure 3: (**a**) Backbone object of a polyline, and (**b**) feature vertices of a backbone object.  
Our purpose is to simplify the backbone object with the scale factor \(s\) in order to compute the feature vertices of the backbone object. Thus, we must check the condition before simplifying. The condition of a backbone object \(\mathbf{B}_{i}\) can be simplified as:  
\[|\mathbf{B}_{i}|>\frac{1}{s} \tag{2}\]  
If the value of \(|\mathbf{B}_{i}|\leq\frac{1}{s}\), that backbone object is encrypted by the cipher function \(C_{P}(.)\) as shown in Equation (3).  
\[\mathbf{B}\prime_{i}=C_{P}(\mathbf{K},\mathbf{B}_{i})=\left\{b^{\prime}{}_{i, j}\big{|}j\in[1,|\mathbf{B}_{i}|]\right. \tag{3}\]  
The backbone object \(\mathbf{B}_{i}\) is simplified by the multi-scale simplification algorithm in order to obtain the simplified backbone object \(\mathbf{B}_{i}^{s}\) as shown in Equation (4):  
\[\mathbf{B}_{i}^{s}=\left\{b_{i,\ \frac{1}{s}}\bigg{|}j\in[1,|\mathbf{B}_{i}| \times s]\right. \tag{4}\]  
\[\mathbf{F}_{i}=\ \mathbf{B}_{i}^{1/2}\cap\ \ \mathbf{B}_{i}^{1/3}\ \cap\ \mathbf{B}_{i}^{1/4} \tag{5}\]  
Here, we obtain three simplified objects \(\mathbf{B}_{i}^{1/2},\ \mathbf{B}_{i}^{1/3},\ \mathbf{B}_{i}^{1/4}\) with differential scales \(s=1/2\), \(s=1/3\), and \(s=1/4\) (see Figure 3b). We then calculate the feature vertices \(\mathbf{F}_{i}\), which is a set of the common vertices of three simplified backbone \(\mathbf{B}_{i}^{1/2},\mathbf{B}_{i}^{1/3}\) and \(\mathbf{B}_{i}^{1/4}\) as shown in Equation (5). From Equations (4) and (5), we can see that \(\mathbf{F}_{i}\) will be determined if \(|\mathbf{B}_{i}|>4\). Figure 3b shows three simplified backbone objects of a polyline object with differential scales 1/2, 1/3, and \(\frac{1}{4}\), and the feature vertices of the backbone object.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance  
Abrasive resistance is the mechanical characteristic of concrete that is related to durability. It is mostly determined by how well the coarse aggregates adhere to the paste, their surface hardness, and how resistant they are to abrasion. Mass loss and depth of wear are abrasion characteristics that are assessed in standard tests as statistics of abrasion durability [26]. Abrasion is greatly influenced by the concrete's age and the kind of loading to which its surface is subjected [13]. Durability and soundness are sometimes evaluated concurrently with aggregate toughness and abrasion resistance [27].  
Figure 3: Inorganic abrasion test equipment according to ABNT NBR 12042 [22].  
Several techniques are used to increase the resistance of concrete against abrasion, including surface finishing optimization, improving the properties of coarse aggregate, and/or adding supplementary cementitious materials (SCMs) and fibers to the concrete [28]. In recent times, various studies have been carried out exploring a feasible approach to resolving the issues associated with the durability of concrete pavement.  
According to ACI, there are four ways to cause abrasion on a concrete surface: high water velocities that cause cavitation at the concrete surface; vehicle traffic wear from studded tires and snow tire chains; human traffic wear on concrete floors; and abrasive materials in water that affect hydraulic structures like dam spillways [20].  
Friction and abrasion are influenced by texture type, with different textures offering varying levels of skid resistance. Traffic volume, particularly from heavy vehicles like trucks, accelerates wear and reduces skid resistance. Additionally, concrete's compressive strength affects its frictional performance, with higher strength providing better resistance to abrasion and tined surfaces being more sensitive to strength due to the risk of groove edge breakage [29].  
Permeable concrete is known to be more susceptible to abrasion than impervious concrete. A previous study found that permeable concrete had an abrasion depth of 28.9 mm, which is higher than the requirement for normal concrete (20 mm) [30].  
A study on the characteristics of tire-road wear particles (TRWPs) and road pavement wear particles (RPWPs), conducted using a novel tire abrasion simulator designed to replicate real road pavement conditions, found that the abrasion rate for the concrete pavement was 2.8 times greater than the asphalt pavement [31].  
A study was conducted to investigate abrasion in CRCP across various regions of Texas. The abrasion resistance assessed using the ASTM C944 test revealed that the average weight loss of cores taken from the center lane was significantly greater than that from the wheel path. Figure 4 shows the average weight loss for different test sections for the center lane and wheel path. The abrasion test was conducted using the ASTM C 944 method, and the top surface of the sample showed higher average weight loss compared to the middle and bottom surfaces.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 5 Experiments
### Better Baseline  
To further prove the importance of the knowledge graph structure to the entity linking, we used the _KGEmbs_ module for a better baseline. _FGS2 EE_[8] is an improvement of _Ment-Norm_[7], which introduces fine-grained semantic information into the original entity embedding to improve the model performance. _KGEL-FGS2 EE_ adds the _KGEmbs_ module on the basis of _FGS2 EE_. The experimental results are shown in Figure 5. We can find that for the average F1 score, _KGEL-FGS2 EE_ can further improve the performance based on  
Figure 4: F1 scores for different \(\alpha\), where F1 is the average result on five out-domain datasets.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **MSNBC** & **AQUAINT** & **ACE2004** & **CWEB** & **WIKI** & **Avg** \\ \hline Ment–Norm & 93.9 & 88.3 & 89.9 & 77.5 & 78.0 & 85.5 \\ _KGEL_ & 94.26 & 88.11 & 90.54 & 77.21 & 78.40 & 85.7 \\ _Related-Fixed_ & 94.26 & 88.39 & 89.74 & 77.41 & 78.06 & 85.57 \\ _Related-Vari_ & 93.65 & 88.25 & 88.13 & 77.07 & 77.82 & 84.98 \\ _Related-Vari-diff_ & 93.8 & 87.41 & 87.73 & 77.01 & 77.39 & 84.67 \\ _Related-nn_ & 92.58 & 86.43 & 88.13 & 74.87 & 71.67 & 82.74 \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1 scores of different variants on out-domain datasets.  
FGS2 EE. This shows that the _KGEmbs_ module we proposed is effective. Similarly, the _KGEmbs_ module can also be used in other methods. In other words, it should be useful to introduce knowledge graph structure based on other methods.
## 2 Related Work
### Related Work of Artificial Neural Networks  
The artificial neural network (ANN) is a massively parallel distributed information processing system that has certain performance characteristics resembling biological neural networks of the human brain. The neural network is characterized by its architecture that represents the pattern of connection between nodes, its method of determining the connection weights, and the activation function [36]. In the past few decades, ANN methods have been extensively used in a wide range of applications, including hydrology, satellite telemetry, satellite images, etc. [PERSON] et al. used an artificial neural network to forecast the river flow in the semiarid mountain region and the northwestern flow in the semiarid mountain region of Northwestern China [36]. This study suggests that ANN methods can be successfully applied to establish river flow with complicated topography forecasting models in semiarid mountain regions. [PERSON] et al. estimated signal fading on telecommunication satellite telemetry signals with hybrid numerical weather prediction and an ANN approach under presence of an aerosol effect [37]. In this research, an implementation of artificial deep neural networks over outputs of 24-h multi-domain high-resolution nested real-case Weather Research and Forecasting (WRF) model runs was carried out over two high-resolution simulation domains, which are tested and compared for rainfall generation in order to assess the signal-fading event observed on geostationarytelecommunication spacecraft in orbit for a real multiscale storm case. In [38], [PERSON] et al. used satellite images to establish artificial neural network models to predict precipitation characteristics in the Western Himalayas region. In this research, the satellite images acquired in IR (10.5-12.5 \(\upmu\)m) and WV (5.7-7.1 \(\upmu\)m) bands were used for to develop an ANN model for both qualitative and quantitative precipitation forecasting. The model results were validated with ground observations, and skill scores were computed to check the potential of the model for operational purposes.  
From the above research report, it can be seen that ANNs are more suitable for the related application fields compared with the traditional methods. Therefore, in this study, to reduce the service time and complexity, we introduce the ANN model.
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 1 Introduction
### Motivations and Contributions  
With the rapid development of IIoT, a reliable and low-complexity authentication scheme guarantees that URLLCs can be widely used and deployed. The transmission efficiency of critical mission applications is considered to be an urgent problem. However, traditional higher-protocol access schemes require additional overload, which reduces transmission efficiency. Furthermore, there is a lack of systematic analysis for channel correlation impact in URLLC massive MIMO. Therefore, establishing a highly reliable and lightweight access method is a critical problem in URLLCs with MIMO, and radio frequency identification can be a remarkable solution for physical-layer access via leveraging unique hardware-level impairments. However, traditional supervised learning requires large-scale labeled samples and cannot efficiently learn distributed features. Semisupervised learning (SSL) can achieve remarkable identification accuracy by utilizing meta pseudo labels with few labeled samples and many unlabeled samples. To address this challenge of RFI-aided URLLCs, we utilized RFI and massive MIMO to improve transmission efficiency with low error probability and high availability. The contributions of this paper are summarized as follows.  
* We propose a general framework of RFI-aided URLLCs with massive MIMO to further improve uplink transmission efficiency with low error probability and high availability. The key idea is using the physical-layer access scheme instead of the conventional high-layer protocol scheme by RFI. In the proposed framework, we utilize RFISSL to achieve reliable physical access and reduce overload. To the best of our knowledge, this is the first attempt to involve RFI into URLLCs.
* For the proposed RFI, in a typical scenario, we constructed an SSL model on the basis of meta pseudo labels to improve identification accuracy and robustness. We first constructed a novel one-dimensional ResNet model to achieve identification by raw radio frequency (RF) time signals. Due to feedback learning, only a small proportion of the labeled data was needed to achieve excellent performance.
* For a typical short packet transmission scenario, we established a rigorous model with massive MIMO. Saddle-point approximation was applied to characterize system reliability. Then, the error probability and network achievability of uplink transmission are investigated with finite block length. Furthermore, we evaluate the impact of channel correlation on network availability. Experimental and numerical results demonstrate that RFI-aided URLLCs can achieve high-efficiency transmission and robustness. Lastly, thanks to the generality and robustness of radio frequency identification, the proposed RFI framework can be applied to, for example, physical-layer security, sensing, and localization.
## 4 Explosive Capability Assessments
### Drop Jump Test  
The drop jump test, which can be conducted as a horizontal drop jump (HDJ) or as a vertical drop jump (VDJ) test, is used for measuring and developing athletes' stretch-shortening cycle ability [92]. The athletes are instructed to stand on a pre-set box (at a height of 0.30-0.40 m). The athletes then drop down to the ground, quickly bend their knees, and immediately perform a rebound jump as quickly as possible (<0.25 s), minimizing their contact time with the ground. For the HDJ, they must jump as far forward as possible, while for the VDJ, they must jump up as high as possible. The test ends with their controlled landing on the ground [93; 94].  
The drop jump test focuses on evaluating the athletes' stretch-shortening cycle ability, which is crucial for the rapid and powerful movements required in ball games, including basketball. The stretch-shortening cycle involves the rapid stretching (eccentric phase) and subsequent shortening (concentric phase) of muscles, resulting in increased power production during explosive movements. By measuring and assessing the athletes' performance in the drop jump test, coaches and trainers gain insights into their ability to efficiently utilize the stretch-shortening cycle [92; 94; 95].  
Moreover, the drop jump test serves as a valuable tool for developing athletes' stretch-shortening cycle ability. By incorporating specific training protocols, such as pyometric exercises, coaches can enhance the athletes' neuromuscular coordination and muscular power, improving their ability to generate explosive movements. This can directly translate into improved performance in ball games where quick and powerful actions are essential [57; 93; 94; 95].  
Additionally, the drop jump test provides valuable information for individualized training programs. By evaluating athletes' performance in both the HDJ and VDJ variations,coaches can identify specific areas of strength and weakness. For example, athletes who excel in the HDJ may possess exceptional horizontal power and could be suitable for roles requiring quick bursts of speed and agility. On the other hand, athletes who demonstrate superior performance in the VDJ may possess remarkable vertical power and could be well-suited for roles that involve jumping, rebounding, and shot blocking [92; 93; 94; 95].  
In summary, the drop jump test is a valuable assessment tool for evaluating and developing athletes' stretch-shortening cycle ability in ball games, including basketball. It provides valuable data for coaches and trainers to assess performance, design targeted training programs, and improve overall athletic performance. By focusing on the stretch-shortening cycle, coaches can enhance athletes' power production and explosiveness, leading to improved performance on the basketball court [92; 95; 57].
## 5 Results and Discussion
### Results and Discussion of Base Classifier Models
#### 5.3.3 Comparison of Results with Some Machine Learning and Deep Learning Algorithms  
This section compares the proposed CNN-LSTM-autoencoder model with the LSTM model and three other ML algorithms includes K-nearest neighbors algorithm (KNN), SVM, and XGBoost. We use the same datasets and number of epochs to determine the difference in performance between the proposed model and these two ML models. The results appear in Table 5.  
From Table 5, the results show the lower performance of the LSTM model with the DDoS attack SDN dataset when it gains 92.6% in training accuracy and 79.433% in validation accuracy. It also achieves very low results in recall, 6% in normal and 62% in attack, and the precision is the same in both normal and attack (about 80%). So, the model  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Accuracy (\%)** & **Standard Deviation (\%)** & **Validation Accuracy (\%)** & **Standard Deviation (\%)** \\ \hline Mendeley & 99.99 & 0.0118198 & 99.923 & 0.000954 \\ UNSW 2018 & 100 & 0.9250918 & 100 & 0.023263 \\ \hline \end{tabular}
\end{table}
Table 4: Results of tests using the UNSW 2018 dataset of the proposed model.  
Figure 12: The standard deviation metrics for proposed model using (**a**) Mendeley dataset and (**b**) UNSW 2018 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Val. Accuracy (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} \\ \cline{3-8}  & & & **Normal** & **Attack** & **Normal** & **Attack** & **Normal** & **Attack** \\ \hline LSTM & 92.6 & 79.433 & 80 & 80 & 6 & 62 & 11 & 70 \\ KNN & - & - & 97 & 95 & 97 & 95 & 97 & 95 \\ SVM & - & 95 & 98 & 91 & 94 & 96 & 96 & 94 \\ XGBoost & 99.55 & 99.54 & - & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 5: Results of tests using the Mendeley dataset for ML models.  
gains poor results in the F1-score, 11% for normal, and 70% in attack. Table 5 shows good results for the KNN model with the DDoS attack SDN dataset when it gains 97% for normal and 95% for attack for precision, and for recall, it gains 97% in normal and 95% in attack. The F1-score is then good at 97% for normal and 95% in attack. However, these results are less than the proposed CNN-LSTM-autoencoder model. The SVM also gains good results which are 98% for normal and 91% for attack for precision, and for recall, it gains 94% in normal and 96% in attack. The F1-score is then good at 96% for normal and 94% in attack. The XGBoost achieves higher accuracy and reaches up to 99.54%.  
By comparing the deep learning model (LSTM) with the proposed CNN-LSTM-autoencoder model, the proposed model is more accurate and stable than the LSTM model and achieves higher accuracy in lower epochs. Compared with the machine learning model (KNN), the proposed CNN-LSTM-autoencoder model is also more accurate than all ML for the DDoS attack SDN dataset.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 3 Power Quality Functionality Implementation  
The designed IoT PQ sensor prototype can measure stationary PQ data as current and voltage RMS, as well as events as voltage sags. Active and apparent energy is also measured, as it can help identify not only the presence of inductive loads from appliance compressors and motors but the increasingly abundant nonlinear loads, which can introduce phase imbalances and increase the disturbing harmonic content on the power line. PQ information is meaningful for knowing the origin and to determine the responsibilities of the network operator, appliance manufacturer, or final customer. PQ analytics also support the forensic investigation of how to chain the events that have occurred. All those data are then uploaded to the Cloud, and accessible through an electronic device with an Internet connection, as it was previously stated in Section 2. System Architecture, and will be further described in the Results section.  
In resource-constrained devices it is important to apply aggregation data techniques to reduce the amount of transmitted data which implies, on the one hand, the reduction of energy consumption of the device and, on the other, the upload to the Cloud of only the relevant information. The measurement frequency interval is software configurable and depends on the data upload ratio given by the Cloud platform--in this case is 30 s. The stored RMS value calculated by the IoT PQ sensor is the average of the input values between two consecutive uploads to the cloud platform, e.g., if 30 s elapses between two consecutive sendings, the RMS voltage and current measurement will be a cycle-to-cycle average in this period, then aggregated within the considered interval. When an upload to the cloud platform is done, the saved voltage and current RMS value is reset to 0 V or 0 A, but managed by a different routine to the one responsible for handling the received data.