esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 3 Methodology  
### Procedure of the Proposed Model  
We propose a two-stage approach for the sports prediction model. The related framework is illustrated in Figure 1.  
In the first stage, we use CNN classifier to identify the behavior of each team. The win/loss fluctuations for each team are described by the time series data for each game in sequence. These data include the pre-game odds and actual points scored by each team in the game. These data are plotted as a candlestick to represent each game, and the consecutive candlesticks of several games represent the tendency of a team's performance in sports matches. The candlestick representation of a sports competition is similar to a knowledge map that implies the behavioral patterns of victory and defeat. These candlesticks with unknown patterns are then imported into a CNN with powerful image recognition capabilities to classify winners and losers. To predict the results for the next match, the binary classification probability of win-loss is used.  
In the second stage, we use the sports match outcome judgment model based on logistic regression. The predicted win probability of each team's next outcome as obtained from the CNN in the previous stage will be used as the input for the judgment model. Given that the CNN win probabilities of both teams are estimated independently, the total win probability of these two teams is not equal to one. We need to use the judgment model to learn the CNN win probability and adjust the numerical difference of each team to determine the final result. For example, when a strong team with a 40% CNN win probability meets a weak team with an 80% CNN win probability, the result is not necessarily a loss for the strong team. Therefore, we introduce the historical matching pair and the associated CNN win probabilities into the judgment model to re-learn the behavior and determine the adjustment factors of each team, and to improve the accuracy of the outcome prediction.
## 2 Study Area and Data
### Data
#### MODIS BRDF Product  
The improved model in this paper attempts to take into account the anisotropic reflectance behavior of real land surface, and thereby needs the corresponding BRDF information. Due to the limitation of infrequent 16-day revisit cycle and 15\({}^{\circ}\) narrow field of view of TM images, Landsat imagery alone does not possess the ability to provide sufficient angular samplings of surface to invert BRDF model parameters [33]. Thus, corresponding MODIS BRDF model parameter (MCD43A1) products [34, 35]were employed to achieve the goal. MCD43A1 products record weighted coefficients of the semi-empirical kernel-driven BRDF model (Ross-Thick Li-Sparse-Reciprocal, RTLSR), and they are generated by fitting 16-day accumulation of MODIS observation data. Based on these coefficients, BRDF model can be reconstructed, from which bi-directional reflectance factor can be computed for any geometric condition [36].  
MCD43A1 products of 14 September 2007 and 14 March 2010 (closest to the corresponding TM acquisition dates) were downloaded from NASA EOSDIS website ([[http://reverb.echo.nasa.gov](http://reverb.echo.nasa.gov)]([http://reverb.echo.nasa.gov](http://reverb.echo.nasa.gov))). Then, they were re-projected using MODIS Re-projection Tool (MRT) and resampled (using nearest neighbor interpolation) and resized to match the TM images, in order to serve as BRDF shape function parameters in the following correction procedure.  
Other ancillary data includes the land use/cover map (see Figure 1b) with overall accuracy higher than 85% [37], and it will be used in the evaluation of correction results.
## 2 Finite Element Model
### Soil Constitutive Model and Mean Pressure  
The soil model was provided in Abaqus for the penetration process, incorporating the following characteristics:  
1. The clay was modelled as an undrained elastic-perfectly plastic material during the penetration process, which was described by the Tresca yield criterion with an associated flow rule;
2. The strain rate of the soil was not considered in this study;
3. The penetration process was conducted using a displacement-controlled method.  
Marine clay deposits are usually non-homogeneous and exhibited linear variation of \(S_{\text{u}}\) with depth [27]. The relationship between the undrained shear strength of the soil, \(S_{\text{u}}\) (kPa), and depth, \(z\) (m), is \(S_{\text{u}}=S_{\text{um}}+k\,z\), where \(S_{\text{um}}\) (kPa) is the soil undrained shear  
Figure 3: Illustration of the finite element mesh. The mesh type of the penetrator in Abaqus software is the “C3D8R” element (8-node Lagrangian bricks with reduced integration), and the mesh type of the soil is the “EC3D8R” element (8-node Eulerian bricks with reduced integration). The soil is meshed with 2012500 elements. The RP point is set directly above the penetrator and coupled with the penetrator to simplify subsequent operations.  
strength at the ground surface and \(k\) (kPa/m) represents the rate of increase in shear resistance with depth. In general, the linear gradient \(k\) of \(S_{\text{u}}\) with depth ranges between 0 and 3 [28]. [PERSON] reported that the undrained shear strength of seafloor sediments in the South China Sea ranges from 1 to 3 kPa [29]. \(S_{\text{um}}=2.0\) and \(k=1\) were considered in this analysis to ensure the value of \(S_{\text{u}}\) was the same as that of seafloor sediments in the South China Sea. The other parameters of the soil were also under consideration. The effective soil weight was adopted as \(\gamma=8.7\) kN/m\({}^{3}\). The elastic modulus of the soil, \(E\), was 500 times that of \(S_{\text{u}}\), and [PERSON]'s ratio, \(v\), was taken as 0.499 [30]. The penetrator structure was penetrated using a displacement-controlled method with a soil-structure interface friction coefficient of 0.25. In the numerical simulation, it was modelled as a rigid body to simplify computational analysis.  
Although the Explicit module of ABAQUS cannot explicitly account for pore water pressure, the soil mean pressure is presented as it effectively represents pore water pressure behavior [31]. Figure 4 illustrates the mean soil pressure before penetration. The results demonstrate that the mean pressure increases linearly with depth, attaining a maximum mean pressure (\(\sigma_{\text{m}}\)) of 315.3 Pa.
## 5 Conclusions  
In this study, a novel WPT system with self-limitation was proposed. In contrast to the conventional design method, which resonates with the self-inductance of a series on the primary and secondary sides, the proposed design method resonates with a leakage inductance on the primary side. Owing to the partial primary resonance, the proposed topology reached ZVS in all load ranges. The output characteristics of the load were analyzed. Under a short-circuit load, the output current converged to the limit, and under an open-circuit load, the output voltage converged to the limit. A prototype was constructed, and the experimental results validated the theoretical analysis. The system delivered approximately 400 W. Under the short-circuit condition, the output current was limited to 24.1 A. Under the open-circuit condition, the output voltage was limited to 35.1 V. Experiment results confirmed that the proposed design method showed reasonable performance in safety and efficiency. Output characteristics of the proposed design method can be calculated and designed for short-circuit current and open-circuit voltage by considering pad inductance, input voltage, and coupling coefficient. The proposed design method can be applied to low-cost WPT charging systems while ensuring safety and efficiency.  
Figure 14: Experimental results on the output characteristics from short to open loads of the proposed design method.  
Figure 13: 400 W loss graph of the SS topology with conventional design and proposed design.  
**Author Contributions:** Conceptualization, S.J., C.-S.S. and D.-H.K.; data curation, S.J.; formal analysis, S.J.; funding acquisition, D.-H.K.; investigation, S.J. and D.-H.K.; project administration, S.J. and D.-H.K.; writing--original draft, S.J.; writing--review and editing, D.-H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-Automobile industry technology development) (20018829, Vehicle Underbody Charging Type Automatic Charging and Management/Control System Development) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Republic of Korea).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 5 Multiple Reconfigurable Features in Antennas  
In multi-reconfigurable antennas, the two features of the antenna can be switched independently. These antennas are listed in a couple of categories:  
Figure 7: The prototype of the bifilar segmented helical antennas at (**a**) left-handed and (**b**) right-handed angles [103].  
* Frequency and polarization;
* Frequency and pattern.  
The advantages of reconfigurability of these two features compared to the one feature of the antenna is having more flexibility and diversity which leads to a new domain of research.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 2 Materials and Methods
### Model Updating  
Model updating is the process by which the model parameters are updated using the measured data to make the numerical model behave as close as possible to the actual structure. The mode shape vector used during this process is the mode shape vector related to the node DOF. This is in contrast with the mode shape vector obtained by modal identification in relation to the physical quantities measured at the location and direction of the sensors. These two mode shape vectors thus have different forms. Accordingly, a method to make them fit with each other is necessary. Among them, model updating using the sensitivity method [5] is explained.  
The basic formulation of model updating is as follows.  
\[\min\bigl{|}z_{mean}-z(\theta)\bigr{|} \tag{31}\]  
where \(z_{mean},\ z(\theta)\in\mathbb{R}^{n_{z}}\) (\(n_{z}\) = the number of compared variables) are the values obtained, respectively, by measurement and by analysis; and, \(\theta\in\mathbb{R}^{n_{\theta}}\) (\(n_{\theta}\) = the number of model parameters) is the model parameter to be updated. This equation can be reformulated as follows for sensitivity analysis.  
\[\min\bigl{|}r^{k}-G^{k}\Delta\theta^{k}\bigr{|} \tag{32}\]  
with  
\[r^{k}=z_{mean}-z\bigl{(}\theta^{k}\bigr{)} \tag{33}\]  
\[G^{k}=\left[\frac{\partial z_{i}}{\partial\theta_{j}}\right]_{\theta=\theta^{ k}} \tag{34}\]  
where \(k\) is the number of iterations; \(r^{k}\in\mathbb{R}^{n_{z}}\) is the residual; and, \(G^{k}\in\mathbb{R}^{n_{z}\times n_{\theta}}\) is the sensitivity matrix. The iteration proceeds by solving Equation (32) for \(\Delta\theta^{k}\) to obtain \(\theta^{k\ +\ 1}=\theta^{k}+\Delta\theta^{k}\) of the next step until convergence.  
The \(i\)th eigenvalue \(\lambda_{i}\in\mathbb{R}\) and eigenvector \(\phi_{i}\in\ \mathbb{R}^{n_{d}}\) of the analytic model are obtained from the stiffness matrix \(K(\theta)\) and the mass matrix \(M(\theta)\).  
\[K(\theta)\phi_{i}=\lambda_{i}M(\theta)\phi_{i} \tag{35}\]
# The Effects of 3D and 2D Imaging on Brain Wave Activity in Laparoscopic Training
## 1 Introduction
Half of these trials reported a significantly reduced workload when using 3D vision [9; 18; 24; 25], and the other half could not find any significant differences between the two vision systems [6; 26; 27; 28]. Therefore, additional studies investigating the effects of 3D vision on mental workload need to be performed before any conclusions can be reached. Moreover, 3D vision was thought primarily to give surgeons an advantage during more complex laparoscopic operations or simulation tasks [2; 9; 28; 29; 30]. Nevertheless, studies have reported that 3D laparoscopy is also advantageous for non-complex tasks [6; 22; 31; 32]. There is still a lack of systematic research comparing mental workloads between different depth-perception tasks. As noted above, the evaluation of newly-developed laparoscopic tools and displays, and that of the training procedures focused on them, should incorporate measures of mental workload. However, few researchers have engaged in systematic efforts to develop standardized measures of mental workload that can be used to judge the effectiveness of new surgical displays and similar technological innovations. If equipment and training procedures are to be evaluated, then multiple mental workload measures should be selected to ensure that the evaluation is both accurate and comprehensive [33]. For this reason, this study attempted to integrate physiological and subjective measures to develop standardized assessment measures for evaluating the effects of 2D and 3D vision systems on mental workload during the performance of different depth-perception laparoscopic tasks.
###### Abstract  
A major limitation of near-infrared spectroscopy (NIRS) is its high sensitivity to the scalp and low sensitivity to the brain of adult humans. In the present work we used multi-distance hyperspectral NIRS (hNIRS) to investigate the optimal source-detector distances, wavelength ranges, and analysis techniques to separate cerebral responses to 30 s breath-holds (BHs) from the responses in the superficial tissue layer in healthy adult humans. We observed significant responses to BHs in the scalp hemodynamics. Cerebral responses to BHs were detected in the cytochrome C oxidase redox (rCCO) at 4 cm without using data from the short-distance channel. Using the data from the 1 cm channel in the two-layer regression algorithm showed that cerebral hemodynamic and rCCO responses also occurred at 3 cm. We found that the waveband 700-900 nm was optimal for the detection of cerebral responses to BHs in adults.  
near-infrared spectroscopy; brain; BOLD signal; breath-holding; cytochrome C oxidase +
Footnote †: journal: Medical  
the much lower concentration of CCO than of hemoglobin. Therefore, the development and validation of the methodology for rCCO measurements remains a hot topic of NIRS research. Reference [18] provide comprehensive reviews of the physiological significance, and of the history and various aspects of measurements of cerebral rCCO. A review of multiple possible factors that can change rCCO in the brain (changes in oxygen tension, ATP, NADH, etc.) is available in [19].  
In this work we used hNIRS to study the possibility to measure specific cerebral autoregulation changes concomitant with systemic changes at source-detector distances of 1 cm, 3 cm, and 4 cm in healthy adults during breath-holding (BH) respiratory challenges. Since BH was proposed as a clinical paradigm to assess cerebral status in various clinical conditions such as stroke, concussion, etc. [20; 21; 22], the dynamics of cerebral responses to BH have been studied both by NIRS [23; 24] and functional magnetic resonance imaging (fMRI) [25; 26], which allows for a direct comparison of our results with other studies.
## 3 Methods  
In a previous study on this research topic, the walking phase was classified into four states: swing flexion (SWF), swing extension (SWE), stance flexion (STF), and stance extension (STE). The user's gait intention was recognized by utilizing the GRF sensor values from both sides and motion information acquired from the IMU [24].  
The flexion torque of the hip joint during walking naturally induces rotation in the knee joint [23]. However, in hemiplegic patients with brain damage, spasticity in the knee muscles makes it hard for the joint to move naturally [1]. Accordingly, this study defined an additional pre-swing state to apply appropriate torque in advance, enabling natural flexion of the knee joint during the swing phase. Particularly, the time difference from the pre-swing phase to the beginning of knee flexion in the SWF state was used as a performance indicator to evaluate how quickly the proposed gait control strategy recognizes the user's gait intention.  
Figure 3 shows the finite state machine developed in this study for determining the control state. The initial state is stance extension (STE), a stance phase where the orthosis's knee is fully extended. In this state, the knee joint maintains a high impedance state to prevent the patient from falling due to sudden flexion [24]. When gait intention is detected, the system transitions to the pre-swing state and generates a predefined minimum flexion torque according to the individual patient's pathological condition, supporting natural bending of the knee joint during the swing phase due to hip movement.
## 3 Cell Base Structure and Frequency Response Analysis  
Because the structure is anisotropic and there is no complete band gap in the horizontal direction, so only the vertical vibration isolation base is designed in the paper. Based on the energy band analysis of the cell structure of re-entrant hexagon and traditional hexagon, the vibration isolation effect of re-entrant hexagon is verified. Based on re-entrant hexagonal cell structure and traditional honeycomb cell structure, respectively, different honeycomb bases are constructed, and the frequency response analysis of the bases is done to further verify the vibration isolation effect of the bases.
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 1 Introduction  
The National Institution of Standards and Technology (NIST) ([[https://www.nist.gov/accessed](https://www.nist.gov/accessed)]([https://www.nist.gov/accessed](https://www.nist.gov/accessed)) on 2 August 2021) defines a cyber attack as a cyberspace attack targeting a business cyber system with varying degrees of malicious consequences, such as disrupting infrastructure functionality or data destruction [1]. With increasing numbers of advances being made every day towards the sector of Information Technology (IT), attackers must adapt to stay on top. To do so, they must evolve their existing attack methodologies, thus, creating newer and improved attacks to fulfil their objectives.  
Coincidentally, cyber security specialists must also stay on their toes to be able to secure these new IT systems from an attack, whilst also taking into account new threats that will inevitably be developed. This vicious circle represents the ongoing battle in cyber security to protect and secure before an attack can take place. Unfortunately, even with these proactive methods, it is not always possible to fully secure against all threats, which, in many cases, can cause devastating consequences depending on the system compromised. Furthermore, these technological advancements also create new entry points for attackers to exploit, thus, adding to the already significant task of system protection.  
In this paper, we focus on the different methods of categorisation employed in the literature. Furthermore, we explore these methods from within the context of the Internet of Things (IoT), in particular IoT-based wireless devices in the area of Critical Infrastructures (CI), undertaken as part of the CyberSANE [[https://www.cybersane-project.eu/](https://www.cybersane-project.eu/)]([https://www.cybersane-project.eu/](https://www.cybersane-project.eu/)) accessed on 2 August 2021 H2020 project.  
We provide a novel insight and understanding of attack categorisation through the analysis of the different methodologies used throughout the scientific community. Indeed,due to the vast quantity and diversity of cyber attacks in the cyberspace, specialists must be capable of organising threats to their systems in an easy and understandable way. Such categorisation techniques, therefore, provide the capacity to structure and organise the various attacks, based upon the specifications of the underlying system.  
We undertake this study from the initial standing point of a bystander, basing our analysis upon the categories themselves as explained in the literature. We then conclude our study by shifting our point of view to that of CI IoT wireless applications. From the stand point of CI systems and their IoT applications, we gain an insight into the different characteristics and security challenges encountered with these systems. Critical Infrastructures bring new constraints, mainly in terms of time responses, recovering delays, and data protection, especially when focusing on wireless systems, which are more prone to attacks due to their pervasive nature.
# REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks
## 4 Results and Discussions
### Performance Analysis and Evaluation
It is evident that the novel system has long-term solidity in comparison to the current method. The graph supports the findings shown in Figure 4 and demonstrates that the suggested algorithm records a minimal mortality rate in general rounds of nodes until all nodes have died. The comparison between the packets transmitted to the BS and the packets collected by the BS for each routing algorithm is illustrated in Figures 6 and 7. It was discovered that the proposed method transmitted additional packets to the BS, while the BS also collected more packets from the unique strategy compared to the previous schemes. This is because the new protocol incorporates a multi-hop communication strategy. Up until something reaches the sink, the nodes need much less energy to send their data to the following region. The information is also aggregated by the gateway node rather than the CHs. Figure 4: Alive nodes. Figure 5: Dead nodes. In Figure 8, the network lifespan contrasts with existing protocols in which the proposed protocol acquires the maximum service lifetime of the network. The consumption of energy as shown in Figure 9 achieves a minimum consumption of energy compared to the existing proposed protocols in which the total energy consumed will be analyzed by the network per round. The observation will lead to a maximized network lifetime and a minimized consumption of energy in which the gateway node will be placed in the middle of the network, which will help in lessening the higher cost of energy for communicating the data between SN and BS placed out of the regions. Figure 8: Lifetime of the network. Figure 6: Packets sent to the base station. Figure 7: Packets acquired by the base station.