esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Scenario (2)  
In this scenario, load disruptions and wind turbine disruptions have been applied to the power system according to Figure 10. In Figure 11a-e, the FR of the power system using different control methods to load disruptions and wind turbine disruptions is shown. According to Figure 11a, the MFD and ST based on the proposed controller (optimized FOPD-FOPID controller using the DOSA) are equal to 0.0007 Hz and 3.55 s, respectively. The MFD and ST based on controller 1 are equal to 0.0015 Hz and 4.46 s, respectively; the MFD and ST based on controller 2 are equal to 0.0081 Hz and 21 s, respectively; the MFD and ST based on controller 3 are equal to 0.0129 Hz and 37 s, respectively; and the MFD and ST based on controller 4 are equal to 0.0256 Hz and 45 s, respectively (Figure 11b-e). Based on these results, the proposed controller performs better in reducing the deviations of the power system than the other mentioned controllers, and it also reduces the settling time of the frequency deviations caused by disruptions in the power system.  
Figure 8: Load disruptions and wind turbine disruptions, scenario 1.  
Figure 7: Convergence of DOSA, MSA, PSO, ABC, and GTO algorithms in optimizing FOPD–FOPID controller parameters.  
Figure 10: Load disruptions and wind turbine disruptions, scenario 2.  
Figure 9: **(a)** The FR of the power system is based on the proposed controller, Scenario (1). **(b)** The FR of the power system using controller 1, Scenario (1). **(c)** The FR of the power system using controller 2, Scenario (1), **(d)** The FR of the power system using controller 3, Scenario (1). **(e)** The FR of the power system using LFC is based on controller 4, Scenario (1).
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Available Data for Impact Assessment of Selected Incidents  
The three R2-D2 stakeholder workshops helped create an overview of different data categories useful for various emergency management case studies (see Figure 3). Category 1 and 2 responders can make use of these datasets to respond, map resources, assess vulnerability and identify major risk areas. The appropriate dataset depends on the incident and end-user type (e.g., strategic, tactical or operational).  
The overview in Figure 3 highlights that, while there may be overlaps across the different case studies, some data layers will be applied for specific instances. In the case of low temperatures and heavy snow, stakeholders highlighted that an asset condition register would be beneficial to show where snow has previously covered key infrastructure. For major fires, a land use map would be beneficial to identify potential areas at risk. For electricity transmission, weather forecasts help predict future power outage events and map the temporal recovery. For all three incident types, in alignment with findings from the literature, stakeholders emphasised the importance of change detection for the following:  
* Visualising where snow is covering key infrastructure;
* Identifying potentially illegal dumping sites and legal dumping sites acting outside of their specifications;
* Identifying fallen trees and using light and dark nighttime images to show areas experiencing power outages.  
In alignment with the stakeholder requirement to synthesise different analyses into a common framework easily accessible by various actors in different phases of emergency management, the R2-D2 scoping study developed an open-source dashboard solution using React. React is an open-source web-front library developed by Meta that provides additional libraries. The dashboard further supports the Open Data Kit input form, which aims to engage the public in data collection. Using the app, users can upload files or photos and add comments, georeferenced using a web browser or an ODK-compatible mobile phone.  
Table 1 provides an overview of the different datasets used for the scoping project across the three case studies.  
Figure 3: Overview of datasets required in integrated emergency management.
# Composing as a Service: A Real-World IoT Implementation
## 4 Field Evaluation
### Composting Frame  
At the extreme edge, the physical world, the composting frame is a 20 m\({}^{3}\) tank where the composting material is processed. A set of sensors deliver indoor/outdoor environment and material metrics in real time. A set of actuators was installed to provide all necessary functions to facilitate the  
Figure 6: Frame motors.  
Figure 7: Electrical panels.  
compostion procedure. Lastly, a PLC module was used to control all underlying actuators and collect all sensor data. In more detail, the compostion frame is a 20 m\({}^{3}\) stainless steel tank to hold the compost material with a transparent ceiling to take advantage of the sunlight that helps raise the temperature of the indoor environment and, thus, the temperature of the compost material. The manipulation of the indoor and material temperature is managed by a set of roof fans and a housing ventilator. Several sensors were installed to provide a detailed overview of the indoor/outdoor environment and material status. In more detail, two sensors measure the temperature of the compost material. Two sensors measure the air temperature and humidity in the frame. Two sensors measure the air temperature and sunlight levels out of the frame. Finally, a level sensor measures the height of the compost pile in the frame. As material humidity is a very critical part of the overall compostion procedure, a control mechanism is installed to manipulate it. A sprinkler is used to water the material if necessary, and excessive water is strained through the bottom of the frame and kept in a separate compartment to be reused from the sprinkler to manipulate humidity. Two level sensors are used to measure the water level in that compartment. If the water is below a certain threshold, a pump adds water from the network. To allow for an unsupervised composting procedure, several actuators were installed. A water pump is used to spray water on the material through the sprinkler in order to manipulate its humidity. An air compressor is used to periodically unclog the strain rails at the bottom of the frame by blowing in air in standard intervals. A ventilator is used to extract excessive hot air from the frame to manipulate the indoor air temperature. Two ceiling fans are used to circulate the air inside the frame and manipulate the material's temperature. Finally, two motors are used to perform regular stirring of the material, allowing oxygen to circulate through it by moving two Archimedes screws that are positioned at the bottom of the enclosure. An industrial PLC module is used to collect all sensor data through a CAN bus communication channel and control all actuators. This PLC module does not perform any automated action and it is completely controlled by the IoT gateway.
# Technology Acceptance in Healthcare: A Systematic Review
## 4 Results
### Key Factors Affecting Technology Acceptance in the Healthcare Domain  
For being the key constructs of the TAM, perceived ease of use (\(N=98\)) and perceived usefulness (\(N=105\)) have been explored and utilized in many studies to assess the acceptance of various technologies in healthcare [60; 67; 68; 69]. With evidence from 125 different studies, the analysis indicated that behavioral intention to use technology is the most used factor in evaluating the acceptance of different technologies in healthcare (see Figure 3). Although such a result is expected, it is significant to confirm the need for behavioral intention within the theory and practice of technology acceptance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sr.** & **Question** \\ \hline
1 & Does the research have clear aims and objectives? \\
2 & Are the technology acceptance model and its hypotheses well specified? \\
3 & Are the data collection methods appropriately detailed? \\
4 & Does the study explain the reliability and validity of the measures? \\
5 & Are the statistical techniques utilized to analyze the data well clarified? \\
6 & Do the findings add to the literature? \\
7 & Does the study add to the readers’ knowledge or understanding? \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality assessment checklist.  
Figure 2: Most studied technology acceptance models.  
Another aspect that needs to be considered is the user's performance and the related expected positive gain that has been investigated extensively, as per the findings in Figure 3. A similar case with the perceived ease of use factor and its equivalent effort expectancy appeared in the analysis for 98 and 24 times, respectively.  
Apart from the factors of TAM and UTAUT acceptance models, the results showed that other factors had been extensively utilized to understand the acceptance of technology in healthcare. These factors include anxiety (\(N=19\)) and computer self-efficacy (\(N=32\)) from the social cognitive theory [1,19,20], innovativeness (\(N=10\)) [70], and trust (\(N=18\)) [71] as external factors.
## 4 Principles and Solving Steps of the Multiplier Path Following Method
### Solving Steps  
Based on the preceding discussion, the steps for implementing the multiplier path following optimization algorithm to solve the problem are as follows [40]:  
1. The initialization step involves setting the initial value \(\boldsymbol{T}^{(1)}\), \(\boldsymbol{y}^{(1)}>0\), \(\boldsymbol{w}^{(1)}>0\), \(\boldsymbol{v}^{(1)}>0\), the positive parameter \(0<p<1\), \(0<\delta<1\), the permissible error \(\varepsilon>0\), and the iteration counter \(k=1\).
2. Subsequently, these formulas \(\boldsymbol{\rho}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{T}^{(\mathrm{k})} +\boldsymbol{w}^{(\mathrm{k})}\), \(\boldsymbol{\sigma}=\boldsymbol{C}+\boldsymbol{Q}\boldsymbol{T}^{(\mathrm{k})} -\boldsymbol{A}^{\mathrm{T}}\boldsymbol{y}^{(\mathrm{k})}\), \(\gamma=\left(\boldsymbol{y}^{(\mathrm{k})}\right)^{\mathrm{T}}\boldsymbol{w}^ {(\mathrm{k})}\), and \(\mu=\delta\frac{\gamma}{m}\) are solved. If the resulting solution satisfies condition \(\max\Bigl{(}\|\boldsymbol{\rho}\|,\|\boldsymbol{\sigma}\|,\gamma,\left\| \boldsymbol{C}_{\mathrm{e}}\boldsymbol{T}\boldsymbol{w}^{(\mathrm{k})}- \boldsymbol{f}\right\|\Bigr{)}\leq\varepsilon\), we can determine \(\boldsymbol{T}^{*}=\boldsymbol{T}^{(\mathrm{k})}\), and the iterative process is terminated. Otherwise, the Lagrange multiplier vector \(\boldsymbol{v}\) is updated based on the convergence criterion, and the algorithm proceeds to the next iteration.
3. The search direction \(\left(\Delta\boldsymbol{T}^{(\mathrm{k})},\Delta\boldsymbol{y}^{(\mathrm{k})},\Delta\boldsymbol{w}^{(\mathrm{k})}\right)\) is calculated from Formulas (23) and (24).
4. The step size parameter \(\lambda=\min\biggl{\{}p[\max_{i}(-\Delta y_{i}^{(\mathrm{k})}/y_{i}^{( \mathrm{k})},-\Delta w_{i}^{(\mathrm{k})}/w_{i}^{(\mathrm{k})})]^{-1},1\biggr{\}}\) is obtained for the search along the direction \(\left(\Delta\boldsymbol{T}^{(\mathrm{k})},\Delta\boldsymbol{y}^{(\mathrm{k})},\Delta\boldsymbol{w}^{(\mathrm{k})}\right)\).
5. Following the determination of the step size parameter \(\lambda\), these formulas \(\boldsymbol{T}^{(\mathrm{k}+1)}=\boldsymbol{T}^{(\mathrm{k})}+\lambda\Delta \boldsymbol{T}^{(\mathrm{k})}\), \(\boldsymbol{y}^{(\mathrm{k}+1)}=\boldsymbol{y}^{(\mathrm{k})}+\lambda\Delta \boldsymbol{y}^{(\mathrm{k})}\), and \(\boldsymbol{w}^{(\mathrm{k}+1)}=\boldsymbol{w}^{(\mathrm{k})}+\lambda\Delta \boldsymbol{In summary, the iterative application of the multiplier path following optimization algorithm to the stay cable force \(\mathbf{T}^{(k)}\) aims to obtain the optimal solution \(\mathbf{T}^{*}\). The iterative process is illustrated in Figure 1 below.
## 3 System Implementation in MATLAB  
The transmission-reception system of the PDCCH channel is constructed based on the fundamental principles outlined in 3 GPP Release 15 [4] and Release 16 [5]. This study comprises two distinct scenarios. Firstly, a transmission-reception system for the PDCCH is implemented to assess the bit error rate (BER) against the signal-to-noise ratio (SNR). This scenario operates within an environment devoid of losses generated by the wireless channel, referred to as the \"Lossless System\" henceforth in this section. Secondly, for the evaluation of BER against the transmit power, the study transitions to an environment marked by losses in the wireless channel. This setting is situated within a rural macrocell (RMa) scenario, this case is referred to as the \"Lossy System\".  
The implementation and coding of the transmission-reception system for both scenarios are carried out using MATLAB 2021b software [8], with the utilization of functions provided by the 5G Toolbox [8]. In a general sense, the two scenarios consist of the following blocks (Table 1) [4, 5]:
## 4 Conclusions  
In this work, a new framework was applied, which used a combined ANN and PCA model as an alternative for the simultaneous concentration determination of the three dyes in aqueous mixtures. As the main contribution, the combination of PCA and ANN models allowed us to overcome the high dimensionality, low sample size of the input data, and high UV-Vis spectra overlapping, leading to obtaining satisfactory results for quantifying the three dyes in aqueous mixtures.  
With the use of the PCA model, it was discovered that more than ninety-nine percent (99%) of the total variation was explained by three principal components instead of 350 original variables. The optimal ANN model had 175 training epochs, one hidden layer with 500 neurons, a performance prediction with r\({}^{2}\) values greater than 0.99, and MAPE values of less than 1% for the three dyes. This study proved that the combination of the PCA-ANN model predicts the concentration of B1, AR, and TZ dyes in aqueous mixtures as a rapid and accurate alternative.  
[PERSON], [PERSON] and [PERSON] designed the study. [PERSON] collected the data. [PERSON] and [PERSON] analyzed the data, [PERSON], [PERSON] and [PERSON] contributed equally to write the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Consejo Nacional de Humanidades, Ciencias y Tecnologias (CONAHCYT, Mexico), grant number 794324.  
Figure 9: The scatter plot of target versus predicted dye concentration in mixtures and Regression coefficient (r\({}^{2}\)) (a) TZ, (b) AR and (c) B1 by the PCA–ANN model in test samples.
## 3 Proposed QCA Circuits
### Power Dissipation Analysis  
QCAPro can handle a large number of cells through a fast approximation-based technique and can calculate switching energy dissipation due to polarization in the QCA circuit [45; 46; 47]. Figures 13-15 classify the energy loss of the proposed circuits by energy amount using QCAPro. Average leakage energy dissipation, average switching energy dissipation, and total switching energy dissipation were measured and a temperature value of 2 \(k\) was specified as a parameter in QCAPro. The levels of applied energy were designated as 0.5 \(Ek\), 1.0 \(Ek\), and 1.5 \(Ek\), respectively, and comparative analysis was performed.  
Figure 13: Avg. leakage energy dissipation based on the amount of energy (meV): (a) [16]; (b) [17]; (c) [18]; (d) [19]; (e) [20]; (f) [21]; (g) [22]; (h) [23]; (i) [24].  
The results of energy loss analysis showed that both proposed circuits showed lower total energy dissipation compared with conventional RAM cells. The circuit with the best performance was MXCR, which had about 35.04% lower energy dissipation at 0.5 \(Ek\), 28.58% at 1.0 \(Ek\), and 24.73% lower energy efficiency at 1.5 \(Ek\) compared with the results of [20], which had the highest energy efficiency among the previously proposed circuits.  
MXLR also confirmed that total energy dissipation is far superior to existing circuits. In addition, MXLR has more energy loss when low energy is applied compared with MXCR, but the higher the applied energy, the lower the increase in the average leakage energy and the smaller the switching energy loss, such that the total energy dissipation is relatively low. Therefore, the performance of MXLR outperforms that of MXCR when an energy of 1.5 Ek or more is applied.  
Figure 16 shows dissipated power maps that occur when 0.5 Ek of energy is applied to RAM cells. The rectangles represent QCA cells; the darker the color, the greater the amount of energy leakage from the cell.  
Figure 14: Avg. switching energy dissipation based on the amount of energy (meV): (a) [16]; (b) [17]; (c) [18]; (d) [19]; (e) [20]; (f) [21]; (g) [22]; (h) [23]; (i) [24].  
Figure 15: Total switching energy dissipation based on the amount of energy (meV): (a) [16]; (b) [17]; (c) [18]; (d) [19]; (e) [20]; (f) [21]; (g) [22]; (h) [23]; (i) [24].
## 3 Results
### Teledermatology as Emerging Assistence Paradigm in Moderate to Severe Patients  
The outbreak of the SARS-CoV-2 pandemic has promoted the use of telemedicine, so dermatologists have been encouraged to use it in an outpatient setting.  
Among dermatological patients, those with chronic, plaque type, and stable disease on systemic/topical maintenance therapy may be considered as ideal candidates for telemedicine, and several authors have demonstrated both the effectiveness and feasibility of this care approach [100; 101; 102].  
Preference for telemedicine among psoriasis patients has been investigated by [PERSON] et al. who investigated the preference for telemedicine over in-person visit among psoriasis patients receiving biologics and the reasons behind their preferences.  
A positive attitude toward telemedicine was found in almost half of the patients affected by psoriasis in maintenance therapy with biological drugs [103].  
Main reasons for preferring telemedicine were saving time, feasibility, and safety issues related to the COVID-19 pandemic.  
The preference for telemedicine was found to correlate directly with the age of the patients, with disease benignity, and grade of severity [104].  
The remaining half part of the patients preferred the in-person visit owing to the need of directly interacting with the clinicians without any \"technological barriers\". However, providing patients with training related to \"telecommunication\" technologies might increase patients' adherence to telemedicine.  
The most limiting barriers associated with telemedicine have been identified as follows: recruitment of technically skilled staff; resistance to change; costs for technical equipment; implementation of simple procedures to obtain reimbursement of visits; attitude of patients to use technology daily depending on age, educational level, and personal predisposition; written and verbal communication impairment [105; 106].  
A strength point connected to the use of teledermatology for the assistance of psoriasis patients is the reduction of indirect costs related to health care and the opportunity of offering assistance to patients unable to reach the care centers for fragility, geographical reasons, difficulty in moving.  
Considering all the opportunity and limitations, data from literature highlight teledermatology as an effective tool to counter limitations during COVID-19 pandemic, they also recommend the need to continuously examine our operative models to understand patient preferences, overcome practice-driven barriers, and ensure the sound allocation of limited health care resources [107].
## 6 Influence of Boundary Condition Parameters on the Temperature Field Distribution
### Influence of Short-Wave Absorptivity in Concrete  
Short-wave absorptance of concrete refers to the proportion of short-wave radiation (mainly solar radiation) absorbed by concrete material. In this study, the influence of the short-wave absorptance of concrete on the temperature field distribution of a box girder structure was explored by setting the short-wave absorptance of concrete to four values of 0.8, 0.6, 0.4, and 0.2. Figures 14-16 show the temperature diagrams of key points of the box girder at different short-wave absorptivity.  
Figure 14: Critical point temperatures of the top plate for different short-wave absorptivity.  
As can be seen in Figures 14 and 15, the short-wave absorptivity of concrete has a more significant effect on the temperatures of the top and web plate. When the short-wave absorptivity decreased from 0.6 to 0.4, the temperatures of the top and web plate decreased significantly. The maximum decrease in the temperature of the top plate reached 41.8%, and the maximum decrease in the temperature of the web plate reached 57.1%.  
As can be seen in Figure 16, the bottom plate temperature is less affected by the short-wave absorptivity. Although there is a small decrease in the bottom plate temperature as the short-wave absorptivity decreases, the regularity is not obvious.  
In summary, the short-wave absorptivity of concrete can significantly affect the temperature distribution of concrete box girders, and the temperatures of the top plate and web plate decrease with the decrease in the short-wave absorptivity of concrete. When designing box girders, the structural temperature can be reduced covering the structural surface with materials with low short-wave absorptivity.
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
## 2 Related Work
### Security Challenges  
Fog computing has been introduced as an extension to the cloud and therefore the fog inherits many of the cloud security and privacy challenges [10; 11]. The security solutions designed for the cloud could address some of the security issues related to the fog, but these solutions might have some limitations due to the fog's distinct characteristics [12]. On the other hand, fog computing provides a platform to address issues related to IoT privacy and security, since the IoT devices have very limited computational resources, and those devices might not be able to perform the required computational tasks [13]. Hence we can think of fog computing as an additional security tool for IoT networks which helps mitigate some of the IoT security concerns [14; 15]. The main security issue facing fog computing can be summarized in the following main categories:  
* _Access Control_: Issues in access control can result from a normal user being allowed to access sensitive data or having administrative privileges to change the system configurations. Thisissue could occur due to poor management and the distributive nature of the fog makes this task more challenging.
* _Authentication_: Authentication is the task of allowing only authorized users to access the fog system and it is more critical since the fog services are offered to a large number of end users. For instance, using biometric authentication, such as fingerprint, face, and touch-based authentications could prove to be very useful in the fog systems authentication. The resource-constrained IoT devices can delegate the authentication process to a fog device which in turn performs the cryptographic operations needed by the authentication protocol.
* _Privacy_: Users typically are concerned about their data privacy, usage pattern, and location privacy. Since fog nodes are located in the vicinity with the end users there is a significant risk of breaching the privacy using the information collected regarding identity, usage, and location.
* _Intrusion Detection_: Fog systems are also vulnerable to classical network attacks such as man-in-the-middle attack, flooding attack, and port scanning. In fog computing, it is essential to deploy IDS in the nodes to facilitate the intrusion detection on both the client's side and the centralized cloud's side.