esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Parameters and Performance  
**Filter size:** Small filter sizes such as \(3\times 3\) are a popular choice in current CNN-based image super-resolution. Due to limited computational resources, CNN-based SR prefers a deeper network with small filter size rather than a wider network with large size [18; 21]. Following the above studies, we set the filter size to \(3\times 3\).  
**Network size:** One of the issues for training the deeper network is overfitting. We investigated the performance and network size based on the baseline of DGGRN (the SR subnet with eight gradient-guided Resblocks). The number of blocks was increased from 2 to 12 to evaluate the performance vs. the number of gradient-guided Resblocks. Table 2 demonstrates the PSNRs of ResNet and the proposed network with various network sizes. For DGGRN, the PSNR values are improved progressively as the number of gradient-guided Resblocks rises from 2 to 12. On the other hand, a sharp descent is observed when we try to stack more Resblocks in ResNet; the same observation was reported by [PERSON] _et al._[13]. This might be because the gradient modeling subnet performs similar to a selector, which drops out selected inputs of the next layer to prevent overfitting.  
**The number of filters:** The SR performance can benefit from a reasonable number of filters within networks. Thus, an appropriate number of filters \(K\) of each convolutional layer must be selected. We trained DGGRN with different \(K\) to find the appropriate value of \(K\). It took 33, 45 and 70 s for training one epoch when \(K=32\), 64 and 128, respectively. Table 3 presents how the number of filters affects the performance. DGGRN\({}_{K}\) denotes DGGRN with \(K\) filters. When \(K\) increases from 32 to 64, the average PSNRs of the three datasets improve about 0.56 (dB). Compared with DGGRN\({}_{64}\), DGGRN\({}_{128}\) achieves little improvement (0.06 dB) in PSNR. However, both the number of parameters and training time are double those in DGGRN with 64 filters. Thus, we set \(K=64\) as a trade-off between reconstructed image quality and the computational complexity.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Block Number** & **2** & **4** & **6** & **8** & **10** & **12** \\ \hline ResNet & 36.83 & 36.96 & 37.06 & 37.00 & 14.20 & 14.20 \\ Ours & 37.00 & 37.10 & 37.18 & 37.21 & 37.16 & 37.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The compared PSNR values of ResNet and our method with various network sizes.  
Figure 6: Convergence curve of DGGRN and ResNet with upscaling factor 2 on BrainWeb, NAMIC and IXI test sets. DGGRN achieves higher PSNR values than ResNet for all epochs.
## 5 Discussions  
\(V_{DD}\) **and temperature tracking:** Any biasing technique to achieve a particular switching probability (pulse width/duration) is susceptible to \(V_{DD}\)/temperature. Therefore, \(V_{DD}\)/temperature tracking is required to select appropriate biasing condition which can be designed based on statistical data.  
**Considerations to other magnetic memory architecture:** Consecutive writes of 1 s with less than \(T_{1\to 1}\) write time gives 75%, 50% and 35% of number of 1 s in the data (observation (c)) for the toggle MRAM chip. However, three different write times (\(T_{75\%}\)/\(T_{50\%}\)/\(T_{35\%}\)) can be implemented for other memory (that does not show this behavior).  
**Novelty of this work:** Prior works consider bitwise normal distribution of switching probability. However, there are several practical challenges with the real memory implementation. First, it offers a narrow distribution with some columns/rows stuck at 0/1. Besides, the real memory chip does not provide granular access to each individual bits and for that biasing of the bits cannot be bitwise tailored. Even custom biasing for each row is impractical. To the best of our knowledge, we make the first attempt to systematically understand and address these practical challenges in this paper.  
Note that we have selected MRAM since it is very promising due to its low static and read power consumption. However, the proposed post-processing techniques to improve the inter-HD of PUF and entropy of TRNG are applicable to other memory technologies.  
**TRNG robustness to Machine Learning Attack:** Random Number Generator (RNG) can be vulnerable to machine learning attack [30]. However, an RNG can be robust against such attack if the non-linearity is very high (i.e., no repetitive patterns in outcomes of RNG). Since the response of the proposed TRNG is non-linearly dependent on numerous parameters, e.g., write pulse width, write voltage and temperature due to non-linear magnetization dynamics of MRAM free layer, the proposed MRAM TRNG is expected to be robust against machine learning attack.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 4 The Proposed Protocol
### Traffic Condition Detection and Representation
If speed is the only considered parameter, then:  
\[\left(CF_{ij}\right)_{t_{x}}=\frac{1}{t_{x}-t_{0}}\sum_{l=t_{0}}^{t_{x}}\frac{ \overline{V_{t}}}{V_{f}} \tag{3}\]  
where \(\overline{V_{t}}\) is the mean speed computed during time interval [\(t_{0}\), \(t_{x}\)] and \(V_{f}\) is the free-flow speed, which is utilized to weight the speed parameter. In [39], we show that the participation of low percentage of vehicles can be sufficient to indicate traffic condition. Individual vehicles are expected to record their own speed data and share them with the nearest edge. We assume that edges are integrated into a subset of RSUs. The edge computes a resultant speed graph for each link in its transmission range from the collected data, by computing the average speed at each time interval. In Figure 2, we show a sample speed data of a road link collected from the traversing vehicles by the nearest edge. This sample is extracted from the results generated by simulating the traffic scenario described in Section 5. This sample data illustrates how speed parameter is effectively representative, because it enables not only traffic condition estimation, but also defining congestion points. As the figure shows, vehicles start to accelerate until reaching the maximum allowed speed, where a free-flow condition can be estimated. After around 700 s, vehicles start to travel at low speeds, where traffic congestion can be indicated. Around 100 s later, the congestion is dissolved, before starting to form again. Network edges are responsible for calculating the weights of the links they supervise, and then store them in their local databases. Edge nodes have to communicate with the remote cloud server to upload data or answer decision making queries based on comprehensive data analysis.
# Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?
## 3 Materials and Methods
These tags provide the location of each person in a 3D coordinate space. Therefore, it is possible to track the location of each individual during the procedures. Finally, the Core runs on a dedicated server connected to all three computers. We deployed a prototype of HealthStack in a clinical partner of the project, the Cardiology Institute--University Foundation of Cardiology (Porto Alegre, Rio Grande do Sul, Brazil). They provided us with access to an actual hybrid operating room, which primarily accommodates cardiac procedures. Figure 5 demonstrates the hardware employed and how we deployed them in the surgical suite. We installed three Microsoft Kinect v2 devices on three different walls to provide different points of view. Kinects are camera devices that provide different types of image data. However, due to privacy restrictions related to surgical procedures, we only collect depth images from them. Depth images are data matrices in which each pixel represents the distance between that point and the camera sensor [33]. Therefore, it is impossible to see a color image and identify people as traditional camera images. Instead, this type of data allows 3D data visualization of the procedures. It permits artificial intelligence techniques to reconstruct the scenes and recognize human bodies, equipment and actions. Besides the image devices, we also installed an Ultra-wideband (UWB) Real-Time Location System (RTLS) solution from Sewio [34]. We installed five UWB anchors in the ceiling that read several UWB tags. Each person involved in the procedures carries one tag with previous identification. A server software combines the readings of all anchors to locate the tags in a 3D coordinate space. It provides an API that delivers the 3D positions in real time. Therefore, it is possible to track each person's position and the movement patterns of the medical staff and the patient. We installed individual mini-computers to acquire data from the camera devices that run a Data Gateway instance. The Gateway instances extract information from the device attached to its computer. In the RTLS server  
Figure 4: Deployment of the architecture in a real operating room. Three camera devices and several tags track the surgery workflow. case, a virtual machine runs at the computer processing the anchors' information. A Data Gateway connects the virtual machine through its API and extracts tag positions in the same computer. Each node connects to a Gigabit Ethernet switch; therefore, communication between the Gateways and the Core occurs in a private network. Additionally, Sewio requires the UWB anchors to communicate with each other through a wired network. Therefore, the RTLS server node also connects to the anchors' network, which means that this node connects both the anchor network and the main network to reach the Core. Finally, the Core server node has two network cards: (_i_) one to connect the private network and reach the Gateway nodes; and (_ii_) a second to connect a Gigabit Ethernet switch to serve the client applications.
## 3 A Systematic Review of Blockchain Research in Thailand
### Focus of Blockchain Research in Thailand
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{113.8 pt}} \hline \hline
**Domains** & **Sub-Domains** & **Article Titles** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Government \\ services \\ \end{tabular} } & & - & A practical national digital ID framework on blockchain (NIDBC) (2018) \\  & Government & - & Design of blockchain lottery for Thai government (2019) \\  & services & - & Proof-of-Concept (PoC) of Land Mortgaging Process in Blockchain-based Land Registration System of Thailand (2020) \\  & & - & The prototype of Thai blockchain-based voting system (2020) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Supply chain \& \\ traceability \\ \end{tabular} } & & - & The acceptance of the application of blockchain technology in the supply chain process of the Thai automotive industry (2017) \\  & & - & Supply network design to address United Nations Sustainable Development Goals: A case study of blockchain implementation in Thai fish industry (2020) \\ \cline{2-4}  & Traceability & - & Blockchain Eco-System for Thai Insect Industry: A Smart Contract Conceptual Framework (2019) \\  & & - & Thai agriculture products traceability system using blockchain and Internet of Things (2019) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Finance \\ \end{tabular} } & \multirow{2}{*}{Accounting} & - & The development of accounting verification process affecting digital currency transactions using blockchain technology for small and medium business in Thailand (2018) \\  & & - & Could Bitcoin enhance the portfolio performance? (2018) \\  & & - & Asymmetric correlation and hedging effectiveness of gold and cryptocurrencies: From pre-industrial to the 4 th industrial revolution (2020) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Other (Energy, Construction) \\ \end{tabular} } & & - & A Business Model Guideline of Electricity Utility Systems Based on Blockchain Technology in Thailand: A Case Study of Consumers, Prosumers and SMEs (2020) \\  & & - & Understanding Stakeholders Needs for Using Blockchain Based Smart Contracts in Construction Industry of Thailand: Extended TAM Framework (2020) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Articles categorized by domains and sub-domains. uation section, such as speed and security. Most of the articles only presented a framework with a prototype test. 3. Adoption of blockchain  
This was an indirect approach of blockchain research. Examples of blockchain research papers in the adoption group are the following. An article studied about behavioral factors of stakeholders in a successful blockchain implementation in collaborative projects. Another article studied applications of products with blockchain as an infrastructure, such as, evaluating Bitcoin's performance in investment. Because this group of presentation did not deliver blockchain research directly, the focuses of these articles were mostly business application or other objectives apart from blockchain development. This group of presentation had the least number of research.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Other Possible Future Research Directions
#### 10.4.1 Zero-Day Attacks  
To counter zero-day attacks, smart grid security requires a proactive strategy to quickly find and fix vulnerabilities not currently covered by available security solutions. Quickly identifying and addressing zero-day exploits entails putting advanced threat detection technologies, real-time monitoring systems, and automated reaction mechanisms into place. Additionally, encouraging cooperation between cybersecurity researchers and industry professionals is essential for the prompt disclosure of vulnerabilities and the creation of efficient updates and patches that lessen the impact of zero-day attacks on smart grid systems.
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 2 Materials and Methods
### Varying Thickness of Hollow Electrodes
#### 2.2.2. Machining Parameters for EDM of Silicon  
After preparing the silicon workpiece by cutting it using wire EDM and the tool electrode by machining graphite electrodes using the milling machine, die-sinking EDM was used for machining silicon. Figure 2 shows the experimental setup before and during the EDM of silicon. The EDM performance was assessed via the output parameters, such as the material removal rate, tool wear rate, quality of the machined parts, and surface roughness. From the literature review, it has been observed that the peak current is the most important parameter that affects the output parameters, followed by the pulse on time and pulse off time. That is why in this experiment, we were mainly concerned about the peak current, pulse on time, and pulse off time. The peak current values varied for 4 A, 6 A, and 8 A. The pulse on time and pulse off time parameters were varied for 200 \(\upmu\)s, 150 \(\upmu\)s, and 100 \(\upmu\)s (T\({}_{\text{on}}\)) and 50 \(\upmu\)s, 75 \(\upmu\)s, and 100 \(\upmu\)s (T\({}_{\text{off}}\)), respectively. Other parameters were kept unchanged for all the experiments. The machining time and depth of machining were recorded. Each experiment was repeated three times. The depth of machining for different parameters is listed in Table 4. It can be seen that the depth of machining obtained by the Level 1 parameters is higher than those obtained by Levels 2 and 3 parameters by approximately 1.9 times and 5.63 times. Therefore, the optimized process parameters for die-sinking EDM for slotting silicon using graphite electrodes are shown in Table 5.
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 2 Proposed Methodology
### Second Registration  
After the initial registration is completed, the coordinates of the thermal visible image pairs are roughly aligned. In order to register the two images more accurately, this paper proposes adaptive-downsampling-unit PIIFD to perform a two-stage registration of the processed image pairs. The process of this method is shown in Figure 3. The thermally visible image pair, which has undergone an initial coordinate transformation, is utilized as the input for this process. After simple filtering and denoising, the Harris Corner Detector was chosen to extract the feature points from the single-band grayscale image. Then, the Gaussian pyramid of the image was constructed, and the PIIFD descriptor was created for the Harris corner point in each layer. The detected feature points are matched by bilateral best-bin-first (BBF) [45]. After screening out the mismatched key points, the calculation of the transformation model parameters is completed. After that, the model parameters need to be scored periodically to optimize the hyperparameters of the scale space of the constructed image.
###### Abstract  
Finite control set model predictive control (FCS-MPC) methods in different power electronic applications are gaining considerable attention due to their simplicity and fast dynamics. This paper introduces an assessment of the two-level three-phase voltage source converter (2L-VSC) utilizing different MPC schemes with and without a modulation stage. In order to perform such a comparative evaluation, 2L-VSC efficiency and total harmonics distortion of the voltage (THDv) have been investigated, when considering a linear load. The results demonstrate the performance of different MPC algorithms through an experimental verification on a Danfoss converter, and a set of analyses have been studied using the PLECS and MATLAB/SIMULINK together. It can be concluded that a comparable performance is achieved by using conventional MPC (CMPC), improved MPC (IMPC), periodic MPC (PMPC), and MPC scheme with modulator (M\({}^{2}\)PC) controllers. The assessment is critical to classify the strategies as mentioned earlier according to their efficiency. Furthermore, it gives a thorough point of view on which algorithm is suitable for the grid-forming applications.  
finite control set (FCS); grid-forming converters; model predictive control (MPC); cost function (CF); power quality; power losses; efficiency +
Footnote †: journal: Applied Sciences
# Multi-Directional Shape Change Analysis of Biotensegrity Model Mimicking Human Spine Curvature
## 1 Introduction
Although many studies of biotensegrity robots have been conducted through numerical and experimental works, the static and kinematic problems are hardly solved due to highly constrained coupling systems, particularly when subjected to an external load [17]. Inspired by the flexible and versatile multi-directional movement ability of the human spine, the shape change process of three Class-1 spine biotensegrity models is the focus of this study. The proposed models preserve the slenderness and natural curvature of the human spine in the geometry, particularly emphasizing the stabilizing network of the spinal column and muscle through the biotensegrity principle. The aims of the study are two-fold: (i) evaluation of the effectiveness of the shape change control strategy proposed by the authors [18] when applied on the spine biotensegrity models, and (ii) investigation of the potential deformation characteristics of the spine biotensegrity models in uni-, bi- and tri-directional modes. The outcome of this study can be possibly extended to the shape change control of bio-tensegrity models where sensors and Internet of Things (IoT) technology are made use of to perform automated tasks. This paper is structured as follows: Section 2 explains the anatomical parameters and topologies of the spine biotensegrity models. Section 3 presents the shape change strategies--particularly the formulation of the incremental equilibrium equation, the optimization method via sequential quadratic programming and the shape change analysis algorithm. Section 4 discusses the effectiveness of the proposed shape change method through numerical examples of spine biotensegrity models. Lastly, conclusions are presented in Section 5.
# Role of Functional Monomers upon the Properties of Bisphenol A Molecularly Imprinted Silica Films
## 3 Results and Discussion
### Reusability and Stability of Films  
The reusability and stability of the prepared BPA-MIP and NIP films were also evaluated. Reusability and, implicitly, stability are some of the most important features of MIPs due to the fact that are decisive factors for establishing the whole cost-effectiveness of the process. Thereby, in order to evaluate the performance at repeated use, the films were subjected to 5 reutilization cycles. As shown in Figure 9, all the films can be effectively regenerated for further usage with only around 35% loss of the initial rebinding capacity after three cycles, demonstrating high stability and reusability in BPA recognition. After the 4 th cycle the binding capacity drops dramatically, up to 65% in the final cycle. Therefore, the BPA-MIP films proved to maintain their structure after several reuse. Yet, it is recommended to use the films only for 3 cycles as the specificity is also altered after the 4 th and 5 th cycles; the binding capacities of the NIP presented a much lower binding capacity loss. The relative standard deviation (RSD) value of 2.1% ascribed to the good repeatability of the films. It is relevant to mention that all the films were able to withstand the conditions for reconditioning and rebinding for at least five cycles without detaching from the glass support. In terms of long-term stability, the films were kept in dry conditions for several months at room temperature when not in use.  
Figure 8: Variation of (**a**) binding capacity, Q, in mg of BPA per g of BPA-MIP/NIP films on BPA-MIP films and their control NIP, and (**b**) Imprinting factor (F) obtained by batch experiments after 5 min with various BPA feed solutions, C\(i\) (0.08–0.2 g/L\({}^{-}\)).
## 1 Introduction
### Current Stress of Switches and Diodes for HG-SIQBC  
The current of the switch is given by the following equation [18]:  
\[I_{sw}=\big{(}I_{in}+I_{\text{C}_{2}}\big{)}D. \tag{33}\]  
Figure 8: HG-SIQBC—equivalent circuit to find the voltage stress across the switch. (The current flow is given in dotted lines).  
Figure 9: HG-SIQBC—equivalent circuit to find the voltage stress across diode \(D_{1}\).  
Similarly, the current stress of the diodes can be calculated by applying KCL to Figures 5 and 6. For example, the current stress of the diode \(D_{1}\) is given as  
\[I_{D_{1}}=I_{in}=D\Bigg{(}\frac{\left(D-2\right)^{2}}{2\left(1-D\right)^{2}} \Bigg{)}\frac{V_{0}}{R}. \tag{34}\]  
Similarly,  
\[I_{D_{2}}=I_{in}+I_{C_{3}} \tag{35}\]  
\[=\Bigg{(}\frac{\left(D-2\right)^{2}}{2\left(1-D\right)^{2}}\Bigg{)}\frac{V_{0} }{R}+\bigg{(}\frac{D}{1-D}\bigg{)}\frac{V_{0}}{R}. \tag{36}\]  
Therefore,  
\[I_{D_{2}}=\Bigg{(}\frac{4-D(D+2)}{2\left(D-1\right)^{2}}\Bigg{)}\bigg{(}\frac{ V_{0}}{R}\bigg{)}. \tag{37}\]  
In the same manner, the current stress on other diodes is determined as given below.  
\[\begin{cases}I_{D_{11}}=\frac{\left(D-2\right)^{2}}{4\left(1-D\right)^{2}} \left(\frac{V_{0}}{R}\right)\\ I_{D_{12}}=\frac{\left(D-2\right)^{2}}{4\left(1-D\right)^{2}}\left(\frac{V_{0 }}{R}\right)\\ I_{D_{3}}=\left(\frac{D\left(-7D^{2}+8D-4\right)}{4D^{2}-6D+2}\right)\frac{V_ {0}}{R}\end{cases},\ \left\{\begin{array}{l}I_{D_{4}}=\left(\frac{3\left(D-2\right)D+4}{2-4D} \right)\frac{V_{0}}{R}\\ I_{D_{5}}=\left(\frac{D^{2}}{1-D}\right)\frac{V_{0}}{R}\\ I_{D_{6}}=D\frac{V_{0}}{R}\end{array}\right.. \tag{38}\]