esa-sceva/satcom-chunk-collection huggingface dataset:
# Intent to Adopt Location Sharing for Logging Safety Applications
## 4 Discussion
Specifically, we showed how attitudes, descriptive norms, and perceived behavioral control correlated with logger intent to adopt safety practices involving an emerging technology: LS. Our results are consistent with other studies using the TPB to explain occupational safety behaviors [36; 37; 38], as well as technology adoption [59; 60; 61]. The greatest factor in logger intent (i.e., highest Chi-Squared (X\({}^{2}\)) values) was instrumental attitude. This result suggests that perceived safety improvements strongly influence loggers' decisions about safe logging practices involving LS. A potential limitation to our study was the measurement of descriptive (i.e., _what others do_) rather than injunctive (i.e., _what others think I should do_) norms. TPB studies sometimes exclude normative pressure, the weakest indicator of intent [31; 53], though [PERSON] and [PERSON] argue that norms are important for understanding technology adoption among older individuals, who may initially feel less confident in their ability to make judgements about technology [63]. Over half of our participants were fifty years or older, but intent did not vary by age in our study, indicating that older loggers are just as likely as younger ones to use LS technology. Another caveat to our results is that perceived behavioral control was measured using only a 3-point scale and accounted for only one aspect of control (i.e., perceived influence), whereas control may also be measured as perceived difficulty, which could be a factor if LS devices are not regarded as affordable or user-friendly. These factors may help explain why control correlated with intent less than the other TPB attributes (i.e., much lower X\({}^{2}\) values), even though it is a typically strong intent variable [59; 53; 60].
## 4 Discussion
### Hybridisation of Internal Positioning Technology  
Recent developments in indoor positioning research offer promising prospects for creating effective on-site systems, with notable trends such as combining different positioning technologies, merging positioning principles, linking with indoor navigation, facilitating infrastructure-free and collaborative positioning, applying game-theoretic strategies, adopting device-free positioning, and incorporating BIM models [76].  
The combination of RFID and Wi-Fi is a common method to hybridise indoor positioning technologies and offers a promising solution for IoT and context-aware systems with indoor positioning capabilities [77]. RFID provides higher positioning accuracy, while Wi-Fi is more scalable, so their combination improves the performance of the system. There are two approaches: one involves estimating positions separately with RFID and Wi-Fi systems and then combining them for improved accuracy, known as fusion-based positioning. The other approach integrates RFID tags with a Wi-Fi system, using the tags as accelerometers for position monitoring [76].  
A common approach to hybridising indoor positioning technologies is to combine vision with Wi-Fi systems for fusion-based positioning, which can improve accuracy and precision [78].  
The studies reviewed did not explore the combination of different technologies to monitor workers in real time, which could provide more accurate data and improve workplace safety.
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
###### Abstract  
Although good field emission from graphene has been demonstrated from a wide variety of different microfabricated structures, very few of them can be used to improve the design of cold field emitters for electron microscopy applications. Most of them consist of densely packed nano-emitters, which produce a large array of defocused overlapping electron beams, and therefore cannot be subsequently focused down to a single nanometer electron probe. This paper reviews the kind of single-tip cathode structures suitable in cold field emission guns for instruments such as scanning electron microscopy, transmission electron microscope or scanning transmission electron microscopy, and reviews progress in fabricating them from graphene-based materials.  
graphene; cold field emission; single-tip cathode; electron microscopy 1
# Anatomical Characteristics of the Lateral Pterygoid Muscle in Mandibular Programishism
## 4 Discussion  
In our study, CT was applied to detect the relationship between the skeletal components of the lateral pterygoid and jawbone. Previous study has investigated the efficacy of digitally produced and CAD/CAM-fabricated mock-ups of masticatory muscle activity in patients [17]. However, they were impossible to use to survey the pterygoid muscle activity, which could reveal the efficacy of appliances on the position of the condyle. Although electromyography and ultrasonography were also used for analysis, the development of 3D CT enabled the accurate measurement and organization of prognathic muscle/skeletal volumes and orientations.  
Our previous study reported the relationship between the masseter muscle and prognathic mandibular skeletal units [10]. The study showed significantly larger masseter volume/length ratios for participants in the normal group compared to the prognathic group. Considering that the difference in masseter thickness and contraction force contributes to differences in the mandible, it is natural to reason that the same logic could be applied to the lateral pterygoid. Volume/length ratio data of the lateral pterygoid are relevant to this theory. However, the difference was not as clear as in the case of the masseter muscle. It is possible to assume that, since the masseter muscle has a greater contact area with the mandible, the lateral pterygoid muscle would have less of an influence.  
We also evaluated the orientation of the lateral pterygoid and calculated the values using three reference planes. The primary plane was the mandibular plane because it is the only plane that moves parallel to the mandibular movement. Among all the muscles involved in mastication, the lateral pterygoid is the only muscle with fibers running horizontally [18]. Angle data revealed that the normal group had a smaller average lateral pterygoid muscle angle with the palatal and mandibular planes, suggesting a greater horizontal tendency of the lateral pterygoid muscle. This indicates that participants in the normal group had physical predominance over those in the prognathic group, considering that lateral pterygoid is operated horizontally along the line of mandibular movement [19]. The value with the occlusal plane was less significant according to the \(p\)-value.  
We have stressed that although a certain etiology between mandibular prognathism and muscle interaction has not yet been specified, it is clear that weak lateral pterygoids with ineffective orientations are strongly associated with mandibular prognathism. Masticatory muscles change the shape of the mandible and change continuously. Evaluation of the lateral pterygoid would help to understand the characteristics of mandibular prognathism, especially in the condylar area. However, the correlation between masticatory muscles was not considered. Further studies on the two remaining masticatory muscles, the medial pterygoid and temporalis muscles, are required for more information.
## 1 Introduction
### Novelty  
This work presents a novel intelligent methodology for day-ahead regional load forecasting. It utilizes a hybrid approach that integrates LSTM neural networks with K-means clustering and a similar-day strategy. The methodology aims to harness the strengths of these techniques: K-means clustering assists in pre-processing and grouping similar load patterns, while similar days enhance the classification of load patterns. In conjunction with representative regional climate conditions, they improve the training of the LSTM network, making it better equipped for modeling sequential dependencies in time-series forecasting.  
This work is organized as follows. Section 2 presents the innovative methodology, illustrated by a flow diagram in Figure 1. Section 2.1 details the data preparation, while Section 2.2 thoroughly describes the process of identifying load demand seasonality and briefly outlines the fundamentals of cluster analysis. To refine the data for training the LSTM neural network, the similar-day methodology is discussed in Section 2.3. Section 2.4 outlines the calculation of representative weather conditions. Next, Section 2.5 describes the day-ahead load forecasting process, including the basic concepts underlying the operation of an LSTM neural network. Finally, Section 2.6 details the metric used to quantify the accuracy of the predictions. The proposed methodology is applied to a case study in Section 3, where the process is explicitly demonstrated step by step, along with its results in each subsection, following the order established in the previous section. Section 4 presents a discussion of the novel methodology and the results obtained. Lastly, Section 5 presents the conclusions of this work.  
Figure 1: Methodology for day-ahead load forecasting.
# Collaborative Filtering Based on a Variational Gaussian Mixture Model
## 2 Background and Related Work  
At present, deep learning is achieving great success in many fields, with many researchers paying attention to this area. Because of the powerful ability of the neural network to discover the non-linear relationships in a complex dataset of recommendation systems, researchers have begun to utilize the neural network to address the problem of CF. MF is the most successful technique among the model-based methods [12; 13; 17]. [PERSON] et al. [18] used singular value decomposition (SVD) to conduct CF, which is the earliest CF model based on the MF algorithm. [PERSON] et al. [13] proposed a probabilistic linear model with Gaussian noise, called the PMF. [PERSON] et al. [19] introduced a standard way to assign an inverse gamma prior to the singular values of a certain SVD of the matrix of interest. The experimental results showed that the performance of the PMF is better than that of SVD. [PERSON] et al. [20] tried to combine the advantages of some previous methods, such as soft-clustering on user's social networks. However, the above models are linear models, and they cannot capture the complex relationships between users and items. [PERSON] et al. [21] proved that adding non-linear features to the hidden linear factor model can effectively improve the performance of the recommendation systems.  
Due to the powerful ability of generative models to fit non-linear and uncertain data, they have become a very popular choice for CF algorithms. [PERSON] et al. [22] proposed an enhanced VAE to improve recommendation performance through adding auxiliary information input. [PERSON] et al. [14] proposed a model-based CF algorithm called Mult-VAE, which takes user-item binary score data as the input and learns a compressed latent representations. The latent factors are then used to reconstruct the input vector and to calculate the missing score. Different from the standard VAE, Mult-VAE adopts multinomial likelihood instead of Gaussian likelihood. They authors claim that the polynomial distribution is more suitable for the recommendation systems. [PERSON] et al. [16] modified the distribution of the hidden factors in the standard VAE and adopted a compound prior method combining a standard Gaussian distribution and a potential code distribution. [PERSON] et al. [23] utilized two VAEs to form a compound VAE, which can learn the relationships between users and items at the same time.  
Compared to the previous methods, in this paper, we overcome the shortcoming of VAEs using very simple posterior distribution to learn latent codes between users and items. In the current CF, the Gaussian distribution is used, which allows our model to fit some extremely distorted multi-peak distributions.
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 2 Materials and Methods  
### Antenna and Ground Modeling  
Two types of antenna were considered: (a) a vertical, linear antenna named, hereafter, monopole, and (b) a two-element array consisting of two vertical monopoles, simply termed array.  
The monopole was assumed to operate at 1494 kHz; its length was \(h=84\) m, i.e., \(h=0.418\lambda\), with \(\lambda\) standing for the wavelength. The antenna was fed at its end point and the transmitted power was assumed to be \(P=50\) kW. Since the diameter of the (cylindrical) wire was \(D=1.94\) m, \(D/h=0.023\) and \(D/\lambda=0.0097\). Thus, the monopole may be considered to have a negligible diameter [25].  
The two monopoles that constitute the array were assumed to operate at 1008 kHz; their length was 149 m (\(h=0.501\lambda\)) and their distance was \(d=75\) m. They were fed at their end point and the transmitted power by each element was \(P_{1}=33\) kW and \(P_{2}=17\) kW, respectively. The diameter of each (cylindrical) element was assumed to be \(D=1.94\) m.  
Therefore, the monopoles may be considered of negligible diameter, since \(D/h=0.013\) and \(D/\lambda=0.0065\).  
The antennas were assumed to be mounted on the ground, which, generally, is not a perfect electric conductor (PEC) since its usual conductivity does not exceed \(10^{-1}\) mho/m. However, since the radiation characteristics of an antenna depend on the ground conductivity, it is convenient to \"create\" an artificial PEC ground. The latter may be formed by mounting the antenna above a metal surface much greater than the antenna dimensions, which is prohibitive for MF antennas. Alternatively, the PEC surface may be replaced by metal strips or wires arranged on the ground, radially, around the antenna. As regards the monopole, 180 metal wires, 50 m long with a 2-mm diameter, were placed radially (every 2 deg) on the ground. A similar artificial PEC ground was assumed for the array; 120 metal wires, 75 m long, were arranged radially (every 3 deg) on the ground, around the antenna.  
It is well known that a monopole of length \(h\) sitting on a PEC ground plane is equivalent to a center-fed, linear dipole of length \(2h\), in free space [26]. Thus, the monopole on the PEC ground plane, as described above, may be modeled as a dipole of length \(2h=0.836\lambda\) (Figure 1a), whereas the monopoles that constitute the aforementioned two-element array are equivalent to dipoles of length \(2h=1.002\lambda\) (Figure 1b) in free space.
## 5 Results and Discussion
### Evaluation Method 2: SUS Evaluation Results
This aligns with previous studies [69], which also found the systems easy to learn. The Watch 5 was found to be particularly effective for everyday use due to its long battery life, customization options, and wearability, as also reported by the authors of [70]. Overall, the usability results were positive for the effectiveness of the watch applications. However, issues were reported after updates, which confirmed the findings of earlier studies by the authors of [69; 71] that the applications were efficient for users. Additionally, the participants reported that they would easily remember how to use the watches in the future, a finding supported by the authors of [72; 73]. The users also encountered fewer errors while using the watch applications, a result consistent with earlier studies by [71; 72; 74]. The users were also satisfied with the features, functionalities, design, information, and display quality of the Watch 5 among all four watches, as reported in studies by [69; 74; 75]. The Galaxy watches were also found to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Item** & **GW4** & **GW5** & **FB Charge 5** & **FB Versa 2** \\ \hline
1 & I think that I would like to use this system frequently. & 4.45 & 4 & 4 & 3.95 \\
2 & I found the system unnecessarily complex. & 1.55 & 1.6 & 1.95 & 1.9 \\
3 & I thought the system was easy to use. & 4.05 & 4.15 & 4.15 & 4.15 \\
4 & I think that I would need the support of a technical person to be able to & 1.25 & 1.25 & 1.7 & 2 \\  & use this system. & 4.75 & 4.8 & 4.35 & 4.2 \\
5 & I found the various functions in this system were well integrated. & 1.35 & 1.3 & 1.35 & 1.45 \\
6 & I thought there was too much inconsistency in this system. & 4.45 & 4.65 & 4.4 & 4.3 \\
7 & I would imagine that most people would learn to use this system & 1.4 & 1.4 & 1.6 & 1.6 \\  & very quickly. & 4.15 & 4.2 & 3.8 & 3.75 \\
8 & I found the system very cumbersome to use. & 1.3 & 1.3 & 1.35 & 1.3 \\
9 & I felt very confident using the system. & 86.875 & 87.375 & 81.875 & 82.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: System usability score for each watch. be easy to use, leading to user satisfaction, as reported in studies by [75; 76]. The new haptic bezel feature of the Galaxy watch was highly rated for its aesthetic appeal, as also observed in [76]. The Fitbit Charge 5 provided advanced fitness tracking features, as reported in [75], which found the application useful in achieving fitness-related goals.
## 3 Information Rate
### QAM Modulation  
Firstly, we consider a popular modulation scheme allowing both amplitude and phase modulation for each sub-carrier as a baseline. This scheme is also known as QAM-OFDM (quadrature amplitude modulation-orthogonal frequency-division multiplexing). In this scheme, the source symbol \(x_{n}\) follows the complex normal distribution as \(x_{n}\sim\mathcal{CN}\left(0,\frac{P_{i}}{N_{t}}\right)\).  
Knowing the SNR of each frequency bin (8), the channel capacity implementing this scheme is then  
\[C_{QAM}=\sum_{n=1}^{N_{t}}\int\frac{B}{N_{t}}\log_{2}\left(1+\gamma_{n}\right) Pr(\gamma_{n})d\gamma_{n}. \tag{9}\]  
\(Pr(\gamma_{n})\) represents the probability that the SNR reaches the value \(\gamma_{n}\).
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 2 Related Work
### Thermal Radiation Analysis  
The long-wave infrared data in the dataset were acquired in April when the outdoor temperature was about 15 \({}^{\circ}\)C, the surface temperature of a moving car was roughly 30 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, and the temperature of a boat in a river was about 20 \({}^{\circ}\)C to 30 \({}^{\circ}\)C.  
\[M(T,\lambda)=\frac{2\pi hc^{2}}{\lambda^{5}(\exp(\frac{c\lambda t}{\lambda t} )-1)} \tag{1}\]  
where \(M(T,\lambda)\) is the spectral radiance of the blackbody (W\(\cdot\)m\({}^{-2}\cdot\)um\({}^{-1}\)), \(\lambda\) is the wavelength of radiation (um), T is the absolute blackbody temperature (k, T = t + 273 k), C is the speed of light (2.998 \(\times\) 10\({}^{8}\) ms\({}^{-1}\)), h is [PERSON]'s constant (3.626 \(\times\) 10\({}^{-34}\) JS), and k is the Boltzmann constant.  
According to the Wien's law formula:  
\[\lambda_{\rm m}\cdot T=2.898\times 10^{3}(\mu\rm m\cdot K) \tag{2}\]  
where \(\lambda_{\rm m}\) is the wavelength at the maximum blackbody spectral radiation brightness (um) and T is the absolute temperature of the blackbody (K).  
According to Wien's law, when the surface temperature of a driving car is 30 \({}^{\circ}\)C, the wavelength range can be found between 9.25 and 9.56 um by heat radiation calculation. When the surface temperature of a boat in the river is 20 \({}^{\circ}\)C, the wavelength range is between 9.56 and 9.89 um. The wavelength of the thermal radiation generated by the car and the boat in the river is between 8 and 14 um. A long-wave infrared detector is used to obtain long-wave infrared image information of the target.
## Methodology
### Development of the Support Vector Machine Model
#### 3.1.2 Model Training and Testing  
For the proposed SVM model, a total dataset N includes a training-sample dataset \(N_{tr}\) and a testing dataset \(N_{te}\). The former is used to construct the SVM prediction model and the latter to be tested to evaluate the accuracy of the prediction model. The effectiveness of the SVM model depends not only on its creator's chosen learning algorithm but also on the required size of \(N_{tr}\). Traditionally, N is divided into \(N_{tr}\) and \(N_{te}\) randomly, due to a lack of rigorous standards for such division. However, random division has some drawbacks: for example, \(N_{tr}\) and \(N_{te}\) must be resampled until limitations on model-prediction accuracy are satisfied, in case the model-prediction error obtained by testing \(N_{te}\) is excessive. This process of random resampling can be time-consuming. Accordingly, several cross-validation approaches to model-error evaluation have been introduced to help determine the optimal division of N into \(N_{tr}\) and \(N_{te}\) datasets. These include re-substitution validation, hold-out validation, leave-one-out cross-validation, and K-fold cross-validation (KFCV) [30]. The present study adopted KFCV, which divides N equally into K subsets (K < N), and each K subset into one K-1 training dataset and one validation dataset, as shown in Figure 2. K experiments are then conducted, and the accuracy \(A\) of any one such experiment is calculated using Equation (8):  
\[A=1-\frac{1}{n}\sum\
olimits_{i=1}^{n}\left|\frac{y_{p}-y_{i}}{y_{i}}\right| \tag{8}\]  
where \(y_{p}\) is the predicted value; \(y_{i}\) is the target value; \(n\) is the number of data in a validation subset, which is equal to \(\frac{N}{R}\), as shown in Figure 2; and \(\frac{1}{n}\sum\
olimits_{i=1}^{n}\left|\frac{y_{p}-y_{i}}{y_{i}}\right|\) is the mean absolute percent error (MAPE). Proposed by [PERSON] [31], MAPE has been widely used in the field of DM as a criterion to assess the performance of a model's prediction-capability [32; 33; 34]. The predictions corresponding to different values of \(A\) are listed in Table 1. In the case of A \(\geq\) 0.9, 0.8 \(\leq\) A \(<\) 0.9, 0.5 \(\leq\) A \(<\) 0.8, and A \(<\) 0.5, the prediction-capability of the model is defined as precise, good, reasonable and poor prediction, respectively. The experiment with the highest \(A\) is set as the optimal model for further testing of new data.  
\begin{table}
\begin{tabular}{c c} \hline
**Accuracy** & **Meaning** \\ \hline
1.0–0.9 & Precise prediction \\
0.8–0.9 & Good prediction \\
0.5–0.8 & Reasonable prediction \\
0.0–0.5 & Poor prediction \\ \hline
**Experiment 1** & \\
**Experiment 2** & \\
**Experiment 3** & \\
**Experiment K** & \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy assessment criteria.  
Figure 2: K-fold cross validation.