esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Studies  
In this subsection, we perform ablation studies to validate the effectiveness of different components and node-partitioning strategies of the proposed DGAN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Method** & **X-Sub (\%)** & **X-View (\%)** \\ \hline \multirow{4}{*}{RNN} & ST-LSTM [8] & 69.2 & _77.7_ \\  & VA-LSTM [34] & 79.2 & 87.7 \\  & IndRNN [35] & 81.8 & 88.0 \\  & AGC-LSTM [18] & 89.2 & 95.0 \\ \hline \multirow{4}{*}{CNN} & TCN [36] & 74.3 & 83.1 \\  & HCN [37] & 86.5 & 91.1 \\  & Ta-CNN+ [38] & 90.7 & 95.1 \\ \hline \multirow{4}{*}{GCN} & ST-GCN [21] & 81.5 & 88.3 \\  & AS-GCN [39] & 86.8 & 94.2 \\  & 2s-AGCN [40] & 88.5 & 95.2 \\  & DGNN [41] & 89.9 & 96.1 \\  & Shift-GCN [42] & 90.7 & 96.5 \\  & CTR-GCN [32] & 92.4 & 96.8 \\  & Info-GCN [14] & 92.7 & 96.9 \\  & ST-DGAT [24] & 91.1 & 96.4 \\  & H2 GCN [43] & 92.5 & 96.7 \\  & AT-Shift-GCN [31] & 91.7 & **97.1** \\ \hline \multirow{4}{*}{Transformer} & TS-SAN [44] & 87.2 & 92.7 \\  & STTR [25] & 89.9 & 96.1 \\ \cline{1-1}  & DSTA-Net [45] & 91.5 & 96.4 \\ \cline{1-1}  & HyperFormer [15] & 92.9 & 96.5 \\ \cline{1-1}  & STSA-Net [46] & 92.7 & 96.7 \\ \cline{1-1}  & DGAN (Ours) & 93.1 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with state-of-the-art methods on NTU RGB+D dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Method** & **X-Sub (\%)** & **X-Set (\%)** \\ \hline \multirow{4}{*}{CNN} & ST-LSTM [8] & 55.7 & 57.9 \\  & GCA-LSTM [12] & 61.2 & 63.3 \\  & AGC-LSTM [18] & 89.2 & 90.3 \\ \hline \multirow{4}{*}{GCN} & Ta-CNN+ [38] & 85.7 & 90.3 \\ \cline{1-1}  & ST-GCN [21] & 70.7 & 73.2 \\ \cline{1-1}  & AS-GCN [39] & 77.7 & 78.9 \\ \cline{1-1}  & 2s-AGCN [40] & 82.9 & 84.9 \\ \cline{1-1}  & Shift-GCN [42] & 85.9 & 87.6 \\ \cline{1-1}  & CTR-GCN [32] & 88.9 & 90.6 \\ \cline{1-1}  & Info-GCN [14] & 89.4 & 90.7 \\ \cline{1-1}  & ST-DGAT [24] & 86.5 & 88.8 \\ \cline{1-1}  & H2 GCN [43] & 87.4 & 89.8 \\ \cline{1-1}  & AT-Shift-GCN [31] & 88.5 & 89.0 \\ \hline \multirow{4}{*}{Transformer} & DSTA-Net [45] & 86.6 & 89.0 \\ \cline{1-1}  & STTR [25] & 82.7 & 84.8 \\ \cline{1-1}  & STSA-Net [46] & 88.5 & 90.7 \\ \cline{1-1}  & DGAN (Ours) & 89.7 & 90.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods on NTU RGB+D 120 dataset.
## 3 Methodology
### Low-Fidelity Prototype
#### 3.7.1 A/B Testing  
Split testing consists of presenting two versions of the same page, in order to understand which solution works better. This permitted a perception of the efficiency of the design solutions, comparing and validating design solutions and letting users decide, which option they felt more comfortable with. In order to avoid biased opinions in the final usability testing, these tests were conducted with another sample with similar characteristics. The variables namely concern copywriting, placement of elements, colors, and structure.  
One of the main issues that were addressed during these tests was the structure of the header, as it contained a considerable amount information to be displayed. In this case, two different approaches were presented for feedback, through an open discussion and observation of the user interacting with the prototype.  
As seen in Figure 6, the first header (A) had the categories and search bar separated into different levels within the section, while on the second header (B), the search box was attached to the categories.  
It became clear during the discussion that the first version (header A) contained too much information and took too much space at the top of the page; users with less experience tended to feel overwhelmed while browsing. Although header B was the most user-friendly option, users questioned whether the top bar was necessary to display warnings, and as such, whether it could be removed.  
The second option was therefore considered for a redesign. The currency switcher in the top bar was placed in the footer, and the top bar would only be used to display occasional, relevant messages, such as event promotion, instead of shipping fees, since each seller decides their own fees.  
In the second experiment, users felt it would be harder to search with a keyword associated with a category: It would be better to use keywords independently. Additionally, the seller group agreed it would be easier to have a highlighted button in the header for seller sign-up, rather than creating an account and having to switch to a seller profile (this path involving more steps). This feature was implemented, although the seller needs to create a default account to set-up their store and may also switch to a buyer profile.  
Figure 6: First A/B testing. Self-source, 2020.  
These tests are a suitable example of how users have a significant role during the design process; with their feedback, any underlying issues were solved before moving to a more accurate visual design, by clearing unnecessary information and sticking to the basics. This minimal approach ended up influencing the overall aesthetic of the prototype.
## 4 Experimental Validation
### Damping System Effectiveness: Dynamic Behaviour of Active Boring Bar When Control Is On
#### 4.2.2 Sensors Choice and Positioning  
Control logic input signal (i.e., velocity) was obtained by integrating the acceleration signal measured using piezoelectric accelerometers (PCB JM357B11, Bruel & Kjaer 4393), by means of an external conditioner (Bruel & Kjaer 2635). Employment of such accelerometers, characterized by a resonance frequency of 50 kHz, ensures a sufficient synchronism between excitation and response. To make the actuation system able to act in both directions (x and y) two sensors were placed on the boring bar (Figure 19a). Sensor position was chosen in order to realize a \"collocated\" positioning, so that sensor and actuator act on the same point, as suggested for the application of DVF control logic.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**Standard—Natural Frequency**} & \multicolumn{2}{c}{**Active—Natural Frequency**} \\ \cline{2-5}  & \multicolumn{2}{c}{**(Hz)**} & \multicolumn{2}{c}{**(Hz)**} \\ \cline{2-5}  & **X** & **Y** & **X (Error)** & **Y (Error)** \\ \hline Free-free & 1190 & 1218 & 1134 (\(-\)4.7\%) & 1169 (\(-\)4.0\%) \\ Fixed-free (overhang 245 mm) & 329 & 276 & 317(\(-\)3.7\%) & 263 (\(-\)4.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between original boring bar and active boring bar.  
Figure 18: Setup for active boring bar experimental modal analysis. **(a)** Free-free condition. **(b)** Fixed-free condition.
## 3 Results and Discussion
### Formic Acid Oxidation Reaction on Carbon Black Embedded in Carbon Nanofibers
At a low ratio of CB/PAN, such as 0.125, the effect of the carbon black was insufficient to shift the binding energy of Pd3d(5\({}_{2}\)); however, only Pd\({}^{2+}\) and Pd\({}^{4+}\) for Pd/KECNF caused a positive shift compared to Pd/VECNF, for CB/PAN at ratios 0.125 and 0.25. The shift in the binding energy indicates a stronger interaction between the Pd particles and the carbon support, therefore, the role of particle size (as demonstrated in the XRD data) without a particular trend can be discounted [12]. The low interaction of the metal support will increase the tendency for oxidization [36]. The FAOR activity is affected by the formation of the new active sites due to interaction from the Pd and carbon [12; 37], as can be observed by the addition of carbon material to the support affecting the FAOR activity, no matter how the particle size changed. In this study, the modification of the electronic and catalytic properties is the key factor for the improvement of the FAOR activity, which is in agreement with previous research [12; 25; 38]. Figure 4: X-ray photoelectron spectroscopy (XPS) spectra for the wide scan of Pd supported CB-embedded CNF.
## 6 Synergy with Hybrid Communication Systems
### Hybrid Optical-RF Systems  
Optical radio frequency (RF) combines optical and RF links to enable a smooth transfer of all kinds of communication despite the environment. For example, optical link transmissions offer high-capacity communication in good weather. In contrast, an RF link assures connectivity in situations where there is fogginess or rainy conditions, which can affect the transmission of optical signals. This complementary approach improves the system's reliability and excludes communication interruptions [25; 102].  
A typical example of this integration synergy is the use of RF-optical combined systems adopted for 5G and beyond networks that are enhanced by beam-steering in even more dynamic conditions. Interference and the throughput are minimized with regard to the positioning of the beam in relation to other beams [25; 103].
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
###### Abstract
The network is divided into two branches, the trunk branch for extracting the intermediate features from the LR image, as shown in the upper part of this figure, and the gradient branch for extracting the gradient map of the LR image, as shown in the lower part of this figure. Notably, the intermediate features extracted on the trunk branch are propagated to the gradient branch to reduce the number of parameters; at the same time, the gradient features extracted on the gradient branch are propagated to the trunk branch through a feature fusion block to guide the super-resolution process. scale convolution kernels to extract intermediate features at various scales. It is worth noting that the proposed multi-scale convolution module is able to simultaneously extract coarse and fine features from the input LR image without increasing the network depth, so as to facilitate more effective learning of the complex mapping relationship between LR and HR counterparts [17; 18; 19]. Then, to fully utilize the extracted features, we propose an adaptive weighted residual feature fusion block (RFFB) to fuse features extracted from different intermediate layers of the residual block, which can reduce the network training difficulty, and additionally generate more beneficial features simultaneously. Existing studies [14; 15] show that there exist some important inter-dependencies between channel features, and the feature representation ability of the model can be viably enhanced by fully exploring and exploiting such interdependencies. To this end, an adaptive channel attention block (ACAB) [20] is incorporated into our GFSR model to highlight the informative features in different channels, so as to further promote the feature utilization. In general, the primary contributions of this paper are as follows:  
* We propose a gradient-guided and multi-scale feature network for image super-resolution (GFSR), including a trunk branch and a gradient branch, and extensive experiments demonstrate that our GFSR outperforms state-of-the-art methods for comparison in terms of both visual quality and quantitative metrics. * The gradient feature map is extracted from the input image and used as structural prior to guide the image reconstruction process. * Two effective multi-scale feature extraction modules with parallel structure (i.e., RRIB and RRRFB) are proposed to extract more abundant features at different scales. * An adaptive weighted residual feature fusion block (RFFB) is proposed to exploit the dependency of image contextual feature information to generate more discriminative representations. The organization of this paper is as follows. In Section 2, we summarized the related works. In Section 3, we described the proposed GFSR in detail. In Section 4, we analyzed the experimental results and compared the results with the recent state-of-the-art SR methods. Finally, we concluded this paper in Section 5.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 5 Conclusions  
Due to the uncertain and rapidly changing behavior of the sensor nodes, energy-efficient and reliable routing remained a great matter of concern in resource-constrained-fog-based IoT networks. Therefore, this paper proposes Energy Efficient and Secure Fog Based Routing Mechanism (ESFRM) to defend against various internal as well as external attacks. The main purpose of ESFRM is to identify the malicious and faulty internal nodes as soon as possible to prevent them from disrupting the normal transmission of data from source to destination node. For this purpose, comprehensive trust value is computed which encompasses the volatilization factor in addition to direct trust, indirect trust and energy trust values. The comprehensive trust value of every node is compared with the threshold comprehensive trust value to decide whether it is a rogue node or an appropriate normal node. Moreover, an enhanced RSA algorithm has been utilized to prevent unauthorized access when data is traveled from CH to fog node, fog node to cloud server and directly from CH to cloud server in addition to clustering technique for the sake of efficient CH selection. The simulation results show that the malicious nodes detection rate of the proposed ESFRM is 16.44% higher than TSRP and it utilizes, on average, 13.67% less energy than SEFR. In the future, the proposed methodology can be implemented and extended within the IDS to secure the IoT network from internal as well as external attacks.  
Conceptualization, T.S.M. and J.T.; Data curation, M.R.M.; Formal analysis, S.A. and M.R.M.; Funding acquisition, A.K.; Investigation, S.A.; Methodology, T.S.M., J.T. and H.A.; Supervision, A.K.; Writing--original draft, T.S.M., S.A. and H.A.; Writing--review & editing, M.R.M. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 3 Non-Thermal Processing
### Ultrasound (Us)  
The use of ultrasonic waves in meat processing is aimed at tendering the raw material and improving its textural parameters. Considerable tenderness of meat is achieved through the release of myofibrillar proteins from the muscle cells. In addition to the tendering effect, US has an influence on improving the water-binding capacity and cohesiveness of meat [42]. Research by [PERSON] et al. [43] has shown that ultrasonic treatment of beef increases the content of volatile flavors, especially aldehydes, alcohols, and ketones. Similar results were obtained by [PERSON] et al. [44], who analyzed the effect of US on the physicochemical properties of yak meat. Apart from changes in physical properties, US also significantly increased the content aldehydes and ketones.  
US is also used in the heat treatment process. Such studies were carried out by [PERSON] et al. [45], who assessed the effect of cooking with US on the quality and profile of VOCs of mortadella. On the first day of the experiment, significant differences in the amount of compounds such as hexanoic acid, pentane, \(\beta\)-pinene, caryophyllene, copaene, dimethyl sulphide, diallyl disulphide, nitric acid, ethyl ester, benzene, and 1-propene-1-methylthio-E were observed. These differences intensified during storage, which resulted in the recording of 19 new VOCs.
## 5 The Proposed Approach
### Communication Phase  
For each topic \(T_{j}\) on which \(P\) publishes, both \(P\) and \(B\) construct a _hashchain_ as follows. We denote by \(H_{t-1}^{T_{j}}\) the hashchain built so far. When \(P\) publishes a message \(m_{t}\) on topic \(T_{j}\), the broker concatenates \(m_{t}\) with the current hashchain \(H_{t-1}^{T_{j}}\) (the hash value of the previous chain) and computes the hash:  
\[H_{t}^{T_{j}}=\mathrm{Hash}(m_{t}\|H_{t-1}^{T_{j}})\]  
Here, \(\|\) denotes the concatenation operation, and \(H_{t}^{T_{j}}\) represents the current hash value after including \(m_{t}\).  
At the end of each temporal round \(R_{i}\), both \(P\) and \(B\) proceed with the construction of a Merkle hash tree (MHT) \(M_{i}\) using the hashchains of all topics on which \(P\) has published during \(R_{i}\). We explain this process in the following.  
For each topic \(T_{j}\), the final hash value of the hashchain at the end of the round, say \(H_{\text{end}}^{T_{j}}\), is used as a leaf node in the MHT.  
Therefore, given the set of the topics \(\{T_{1},T_{2},\ldots,T_{n}\}\) on which \(P\) publishes, the hash values \(H_{\text{end}}^{T_{1}},H_{\text{end}}^{T_{2}},\ldots,H_{\text{end}}^{T_{n}}\) are the leaves of the MHT.  
To build the MHT, consecutive pairs of hash values are hashed together to form parent nodes. For example, two consecutive leaves \(H_{\text{end}}^{T_{i}}\) and \(H_{\text{end}}^{T_{i+1}}\) are concatenated and hashed to form their parent node, say \(PN_{i}\):  
\[PN_{i}=\text{Hash}(H_{\text{end}}^{T_{i}}\|H_{\text{end}}^{T_{i+1}})\]  
This process continues iteratively, building up the tree until a single root hash \(\text{Root}_{M_{i}}\) is obtained at the top of the tree.  
In the meantime, all the subscribers proceed as follows. Consider a subscriber \(S\) interested in a subset of topics published by \(P\), denoted as \(\{T_{j},T_{j+1},\ldots,T_{j+k}\}\subseteq\{T_{1},T_{2},\ldots,T_{n}\}\). During the round \(R_{i}\), \(S\) receives messages on these topics from the broker \(B\) and constructs the corresponding hashchain. At the end of the round, \(S\) receives the root of the MHT from \(B\).  
In the next section, we describe how the publisher and the subscribers can detect any integrity violation in the published flow of messages.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.2 Content-Based Image Retrieval (CBIR)  
Generally speaking, CBIR is a search method in which the query is formulated using a sample image instead of keywords. For this group, implemented solutions were based only on 2D image processing. A sample picture was provided as input data. The solution searched for other floor plans, classified as similar, and returned a set of possibly closely matched results. An example that returns the TOP3 most similar results is presented in Figure 6. There were two types of input images found. In the works of [PERSON] and [PERSON] [97] and [PERSON] et al. [98], the query was formulated with the use of a sample floor plan. [PERSON] et al. [114] proposed a solution that works with a user-providedfreehand sketch. All these systems analyzed the input image, processed it, and utilized a generated graph representation for similarity search across the database of other floor plans.
# Effect of Parameters on Lumbar Compressive Force during Patient Transfer
## 4 Discussion
### Benefits of This Study  
The existing biomechanical simulators require bulky devices to obtain a large amount of motion data and dynamic data [20; 21; 22]. Some analytical models cannot appropriately calculate the results of the dynamic effect on peak CF [8; 9]. Thus, we proposed a model to simplify the calculations and present the dynamic effect using the trunk flexion angle, angular velocity, angular acceleration, GRF, and horizontal distance between lumbar and wrist. These factors can be measured or estimated using three inertial measurement unit (IMU) sensors (i.e., MTi 1-seires, Xsens Inc., Enschede, The Netherlands), two for the left and right wrist, and the other one for the lumbar instead of using a motion capture system.  
Meanwhile, the change of these factors is easily felt and controlled by caregivers in real-life scenarios. In addition, since the external load in this study was controlled and did not exceed 15 kgf, the external load only caused a small effect on peak CF in Equation (3). In the actual case, the GRF can be simplified as GRF = mg + 15 \(\times\) g, where m represents body mass (kg), g represents gravitational constant 9.8 m/s\({}^{2}\), and 15 (kg) is the maximum of external load controlled in this study. Thus, in the actual case, the GRF need not necessarily be measured. The reason to control the external load at 15 kgf is to protect the caregivers' lumbar safety.  
The suggestion of reducing H in this study is similar to that adopted by [PERSON] et al., who asked the patient to lean forward at the beginning of lifting. The results showed that the caregiver's peak CF could be reduced by approximately 1400 N or 34.5% of that obtained in the conventional transfer method [24]. However, the authors did not show the quantitative relationship between the distance and peak CF. In this study, we determined that changing the H, trunk angle, and angular velocity was more significant than the other factors in reducing the peak CF, as shown in Equation (3). In addition, Equation (3) can estimate the acceptable values of each factor (of course including H) without exceeding the CF limit (3.4 kN). Thus, Equation (3) also provides insight into the safety assessment for caregivers.  
The proposed model also provides insight into the effectiveness of the assistive product (i.e., lifting slings). By comparing the peak CF obtained with and without the assistive device, the effectiveness of the assistive device can be evaluated. In addition, the factor by which the assistive device must be optimized so as to reduce the peak CF can be intuitively presented owing to the model's simplicity.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach
#### 3.2.3 K-Nearest Neighbors (KNN)  
KNN is a non-parametric approach that extracts patterns and relationships between independent and dependent variables [42; 43]. The algorithm proceeds by applying a distance metric to identify the \(k\)-nearest neighbors from the training dataset based on the input feature similarity [44]. In this work, the number of neighbors was set to \(k\) = 5 based on preliminary simulations. Euclidean distance was employed as a distance metric. Subsequently, the weights of the \(k\) neighbors were calculated for each output based on their proximity to the new data point. The distance-weighting scheme was applied where closer neighbors had a higher influence. The numerical value for the new data point was predicted by calculating the weighted average of the target values of the KNN using Equation (1) [45].  
\[\hat{y}_{j}=\sum_{i=1}^{K}\frac{w_{i}}{\sum_{i=1}^{K}w_{i}}\cdot y_{i,j} \tag{1}\]  
where \(\hat{y}_{j}\) is the predicted value of the \(j\)-th output variable, \(y_{i,j}\) is the target value of the \(j\)-th output variable for the \(i\)-th neighbor, and \(w_{i}\) denotes the weight assigned to the \(i\)-th neighbor based on its proximity to the new data point.