esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Evaluation  
### Testing Power Consumption  
In this section, we conducted a measurement of the current consumption of the design at different operating modes in the main components. As shown in Section 2.1.1, operating voltages for PIC16 LF19168, RN4020, ADS1293 (digital module) is 1.9 V and ADS1293 (analog module) is 4.2 V. We used Agilent U3606A power supply to create 1.9 V and RIGOL DP832 power devices to generate another voltage supply of 4.2 V. Both devices have an internal built-in current measuring device. The ECG device board has one button which can change the operation mode of the main components. The details of the current consumption of the devices in operation are tested as described in Table 8.  
When the buffer in MCU is 810 bytes, MCU will transmit this data via the Bluetooth module at the rate of 115,200 bps, to transfer 810 bytes about 60 ms. During this time, MCU and Bluetooth modules consume a current of 1.5 mA and 3.4 mA, respectively [18; 19], while the ECG device operating at 3.3 V current consumption of Bluetooth module will be much higher at about 14 mA.  
According to the test results, we can see that the ECG device operating at the voltage of 1.9 V, current consumption without the data transmission process via Bluetooth module is 2.1 mA lower than the device operating at 3.3 V at about 8.2 mA, which is mainly consumed by PIC16 LF19186 (operating voltage at 3.3 V and operating frequency at 32 MHz) and Bluetooth module RN4020. Normally, the device operates in Mode 4 to save energy. When the device is preparing to connect to another Bluetooth device for data transfer, it will operate in Mode 3, and after connecting to the Bluetooth device, it works in Mode 2, and while reading data from ADS1293, it works in Mode 1.  
For the evaluation of the energy consumption, we conducted ECG testing with many different energy sources. Table 9 describes the operating time of the ECG device with different energy storage devices with a sampling frequency of 100 Hz. This design only supports low power mode at sampling frequency Fs = 100 Hz. Higher sampling frequencies require an external power supply.
# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
###### Abstract  
Micro-electromechanical systems (MEMS) technology-based sensors have found diverse fields of application due to the advancement in semiconductor manufacturing technology, which produces sensitive, low-cost, and powerful sensors. Due to the fabrication of different electrical and mechanical components on a single chip and complex process steps, MEMS sensors are prone to deterministic and random errors. Thus, testing, calibration, and quality control have become obligatory to maintain the quality and reliability of the sensors. This is where Artificial Intelligence (AI) can provide significant benefits, such as handling complex data, performing root cause analysis, efficient feature estimation, process optimization, product improvement, time-saving, automation, fault diagnosis and detection, drift compensation, signal de-noising, etc. Despite several benefits, the embodiment of AI poses multiple challenges. This review paper provides a systematic, in-depth analysis of AI applications in the MEMS-based sensors field for both the product and the system level adaptability by analyzing more than 100 articles. This paper summarizes the state-of-the-art, current trends of AI applications in MEMS sensors and outlines the challenges of AI incorporation in an industrial setting to improve manufacturing processes. Finally, we reflect upon all the findings based on the three proposed research questions to discover the future research scope.  
artificial intelligence; machine learning; micro-electromechanical systems; manufacturing process; sensors +
Footnote †: journal:  
1 Faculty of Informatics, Institute of Academia-Industry Innovation, Eotvos Lorand University, Egyetem ter 1-3, 1053 Budapest, Hungary 1
# A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
## 4 The Intrinsic Limit Determination Algorithm
### Effect of One Single Flip  
Let us consider what happens if one single component, say the \(j\)th component, of \(\mathbf{p}_{S}\) is changed from 1 to 0. The \(TPR\) and \(FPR\) values clearly change. By denoting with \(\mathbf{p}_{1}\) the prediction vector in which the \(j\)th component was changed, the following equations hold:  
\[\left\{\begin{aligned} TPR|_{\mathbf{p}_{1}}=& 1-\frac{m_{1}^{[j]}}{ \sum_{i=1}^{N_{B}}m_{1}^{[i]}}\\ FPR|_{\mathbf{p}_{1}}=& 1-\frac{m_{0}^{[j]}}{ \sum_{i=1}^{N_{B}}m_{0}^{[i]}}.\end{aligned}\right. \tag{23}\]  
Therefore, \(TPR\) and \(FPR\) will be reduced by an amount equal to the ratio \(m_{1}^{[j]}/\sum_{i=1}^{N_{B}}m_{1}^{[i]}\) and \(m_{0}^{[j]}/\sum_{i=1}^{N_{B}}m_{0}^{[i]}\), respectively. As an example, the effect of multiple single flips on \(TPR\) and \(FPR\) is illustrated in Figure 3. Here are shown the ROC curves resulting from a random flipping starting from \(\mathbf{p}_{S}\) for a real-life dataset, namely, the Framingham dataset [23] (see Section 5). As expected, flipping components randomly results in a curve that lies close to the diagonal. As the diagonal corresponds to randomly assigning classes to the observations, randomly flipping does not bring to the best prediction possible with the given dataset.  
By ordering the points in \(\mathcal{P}\) in ascending order based on the ratio \(TPR|_{\mathbf{p}_{j}}/FPR|_{\mathbf{p}_{j}}\), a new set of points \(\hat{\mathcal{P}}\) is constructed. It can happen that in a given dataset, multiple points have \(FPR|_{\mathbf{p}_{j}}=0\). In this case, this ratio cannot be calculated. If this happens, all the points with \(FPR|_{\mathbf{p}_{j}}=0\) can be placed at the end of the list of points. The order between those points is irrelevant. It is interesting to note that a flip for perfect buckets for which \(m_{0}^{[j]}=0\) will have \(TPR|_{\mathbf{p}_{j}}=0\) and for all perfect buckets for which \(m_{1}^{[j]}=0\) will have \(FPR|_{\mathbf{p}_{j}}=0\).  
With the set of ordered points \(\hat{\mathcal{P}}\), a curve \(\mathcal{C}\) can be constructed by joining the points in \(\hat{\mathcal{P}}\) as described in the previous paragraph. Note that the relative order of all points with \(TPR|_{\mathbf{p}_{j}}=0\) is also irrelevant, in the sense that this order does not affect the AUC of \(\mathcal{C}\).  
Figure 3: Two examples of ROC curves obtained from random flipping using the dataset as described in the text.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 4 Optical Transmission: Key Challenges and Proposed Directions
### Capacity Increase Employing a Greater Number of Channels  
The second way to increase the transported capacity per fiber is to employ a higher number of channels. For this purpose, two options can be envisaged: (a) Populate the low-loss attenuation spectrum of the single mode fiber (around 365 nm) with channels, in this way extending the transmission beyond the C-band, realizing Ultra-Wideband (UWB) transmission (also named Multi-Band transmission; however, throughout the text, we use the term UWB) and/or (b) exploit the space dimension, e.g., populating a bundle of SMFs, or considering transmission within a Multi Core/Multi Mode/Few Mode Fiber, in this way performing SDM. Both UWB and SDM are candidate approaches for F6G and have their own merits, which will be presented in the rest of this sub-section.
## 3 Empirical Results
### Efficiency Evaluation over the Period 2013-2017
The highest score during 2013-2017 was 8.926 of DMU10 in 2016, which leads to the highest average score for the whole observed period was 4.77. According to data in Table 2, it is clear to note that the distance from the lowest average score (0.714) to the highest one (4.77) is notable, which indicates an extreme difference in controlling the inputs and outputs of these companies. Additionally, this also implies the high competitiveness in the ICT industry in Vietnam. By looking at the average score of each year, it is noted that the average scores slightly fluctuated over period 2013-2017. Particularly, the average score decreased from 1.204 in 2013 to 1.129 in 2015 the rapidly recovered in 2016 with a score of 1.335, and then slightly went down in 2017. During 2016, the average score reached the highest thanks to the extremely high score of DMU 10 caused by the rapid growth of net sale revenue.
###### Abstract  
This paper presents an analytical method for the dimensioning and selection of the four glass tube collector types: single-glazed with an air layer, single-glazed with a vacuum layer, double-glazed with an air layer, and double-glazed with a vacuum layer. In the first part of the paper (dimensioning phase), the iterative thermal resistance calculation algorithms were developed for all glass tube collector types, whereby the iterative thermal resistance calculation algorithm of the single-glazed tube collector with an air layer was experimentally tested and validated. The second part of the paper (selection phase) uses a multi-criteria decision-making method to determine the optimal glass tube collector design. Unlike other papers, three indicator groups are taken into account in this case: geometric (mass, surface occupation, total surface occupation, volume occupation), economic (manufacturing and exploitation costs), and ecological (embodied energy and greenhouse gas emission). The proposed method is characterized by simple and fast calculations with satisfactory accuracy, which avoids high investment costs (experimental research), approximation and discretization of physical models (numerical research), and a large number of input parameters with boundary conditions (theoretical research). It should be noted that, with certain additions and changes, it can also be applied to other solar thermal collectors, so the authors believe such tools are handy for the global scientific public.  
air layer; glass tube; multi-criteria decision-making method; absorber plate; solar thermal collector; thermal resistance; vacuum layer +
Footnote †: journal: Applied sciences  
Article  
Multi-Criteria Decision-Making Method for Simple and Fast Dimensioning and Selection of Glass Tube Collector Type Based on the Iterative Thermal Resistance Calculation Algorithm
## 5 Evaluations
### Performance Evaluation
#### 5.2.4 Performance versus Network Type  
In this experiment, we compare the schedule results and the sum of end-to-end delays of the four techniques for different network types, which affects network connectivities and the options for the route. The \(x\)-axis represents the network type and its below corresponds to the algorithm shown in Figure 14. We noticed that higher network connectivities can improve the traffic schedulability, similar to the partial analysis in the previous subsection. The consequence of this is that longer routes are applied to the scheduling algorithm, resulting in increased end-to-end delays. In general, APCRS-FC obtained a higher success rate compared with other techniques in a ring network, i.e., the network topo is relatively simple and there are no two entirely nonoverlapping link routes between any two end systems.  
Figure 13: Percentage of solved, timeout, and infeasible schedules and execution time of the SPRF-EtoED, LBF-EtoED, MPFRS-FC, and APCRS-FC for 100 test cases with 40 flows chosen from flow group 1, tested in MM network. (**a**) Technique 1: SPRF-EtoED. (**b**) Technique 2: LBF-EtoED. (**c**) Technique 3: MPFRS-FC. (**d**) Technique 4: APCRS-FC.
## 3 VRDeepSafety Simulation Environment
### Sensor Fusion Implementation  
After acquiring data from each sensor (LiDAR, camera, radar), sensor fusion techniques, such as the Kalman filter or extended Kalman filter (EKF), are applied to merge the measurements into a unified state estimate. The Kalman filter involves a prediction step and an update step.  
The prediction step computes the predicted state based on the vehicle's motion model:  
\[\hat{\mathbf{x}}(t\mid t-1)=f(\hat{x}(t-1),\mathbf{u}(t-1)) \tag{17}\]  
where \(f(\hat{x}(t-1),\mathbf{u}(t-1))\) is the motion model, and \(\hat{\mathbf{x}}(t\mid t-1)\) is the predicted state at time \(t\) based on the previous state and control input.  
The update step incorporates new sensor measurements \(\mathbf{z}(t)\) from LiDAR, camera, and radar to correct the state estimate. The corrected state is given by the following:  
\[\hat{\mathbf{x}}(t)=\hat{\mathbf{x}}(t\mid t-1)+\mathbf{K}(t)(\mathbf{z}(t)- h(\hat{\mathbf{x}}(t\mid t-1))) \tag{18}\]  
where \(h(\hat{\mathbf{x}}(t\mid t-1))\) is the predicted measurement based on the predicted state, and \(K(t)\) is the Kalman gain, which determines the weight given to the measurement update.  
Finally, the error covariance matrix, \(\mathbf{P}(t)\), is updated to reflect the uncertainty in the state estimate:  
\[\mathbf{P}(t)=(I-\mathbf{K}(t)\mathbf{H}(t))\mathbf{P}(t\mid t-1) \tag{19}\]  
where \(\mathbf{P}(t\mid t-1)\) is the predicted error covariance, \(\mathbf{H}(t)\) is the measurement matrix, and \(\mathbf{K}(t)\) is the Kalman gain.  
In Unity, these steps are implemented using C# scripts that handle the sensor data, apply the measurement models, and run the Kalman filter to continuously update the state estimate in real-time.  
By combining these sensor simulation techniques and sensor fusion processes, VRDeepSafety creates a comprehensive environment simulation for AVs. This system accounts for vehicle dynamics, environmental conditions, and sensor data fusion, providing a robust simulation for AV development.
## 1 Introduction
Through an integrated computing and bandwidth scheduling framework, iSAC achieves unified scheduling of the computing network, enhancing the rational allocation of resources. The main contributions of this paper are as follows:  
(1) Computing resource allocation for connected cars involves decisions in discrete action spaces, while the SAC algorithm is designed for continuous action spaces. To adapt the SAC algorithm for discrete action spaces, it was modified to iSAC. Additionally, the prioritized experience replay (PER) method [12] was introduced to accelerate the learning process while maintaining stability, improving the efficiency and accuracy of computation offloading and enhancing the resource utilization rate of the CPN for intelligent vehicles. (2) A simulation environment has been designed and implemented to encompass the random variations of vehicles and tasks, communication links, and edge servers, achieving unified management of global information variables. This environment can simulate the task transmission process, including the transmission medium, path, and distance, as well as the time that tasks reside on servers, such as queuing and computation time. To validate the system's reliability, simulations of server energy consumption and loads have also been conducted. (3) Within the simulation experimental environment of this study, the performance of the iSAC, PER-iSAC, the original SAC, and several other offloading strategies based on common deep reinforcement learning algorithms was tested and compared in terms of task offloading. An in-depth analysis was conducted and insights were provided regarding the results.
# Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials
###### Abstract
The peer of the network will be able to keep track of the data as it is stored in a distributed ledger. Finally, DTLs data sure as a proof-of-delivery (POD) chain that will make it complex and difficult to counterfeit the vaccine. With the help of blockchain technology the medical units and vaccine stakeholders can check the details related to the vaccine. In this article, the authors have proposed a blockchain-enabled framework for transparent supply chain management of COVID-19 vaccine. The framework provides a transparent track of registration, delivery and self-reporting. Leveraging blockchain will introduce the following features of the proposed framework: \begin{table}
\begin{tabular}{c c c} \hline \hline
**COVID-19 Vaccine** & **Frozen Viel** & **Thawed, Unpunched Viel** & **Thawed, Punched Viel** \\ \hline \multirow{4}{*}{Pfizer} & Frozen Temperature: \(-60\)\({}^{\circ}\)C to & \multirow{4}{*}{Temperature: 2 \({}^{\circ}\)C to 8 \({}^{\circ}\)C and for up to \multirow{4}{*}{}} & Storage temperature: 2 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. \\  & Protection from sunlight. & 120 h or up to +25 \({}^{\circ}\)C for no more than 2 h. \\  & Preferred storage temperature: & \(-70\)\({}^{\circ}\)C. \\  & Temperature: \(-15\)\({}^{\circ}\)C to \(-25\)\({}^{\circ}\)C & \\  & Protection from light. & \\ Moderna & Preferred storage temperature: & 12 h at 8 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. \\  & \(-20\)\({}^{\circ}\)C. & Avoid exposure to sunlight during storage. \\  & Shelf life: 6 months. \\ \multirow{4}{*}{Covisheild} & Temperature: 2 \({}^{\circ}\)C to 8 \({}^{\circ}\)C. \\  & Do not freeze. & Storage temperature: 2 to 8 \({}^{\circ}\)C. \\ \multirow{4}{*}{Covaxin} & Temperature: +2 \({}^{\circ}\)C to +8 \({}^{\circ}\)C. \\  & Do not freeze. \\ \multirow{4}{*}{Oxford AstraZeneca} & NA & Storage temperature: 2 \({}^{\circ}\)C to 8 \({}^{\circ}\)C. \\ \cline{2-2}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Immunization COVID-19 vaccine and parameters ranges [13; 14; 15]. * DLTs-based framework for immutable data, transparency and efficiency of registration process for COVID-19 vaccination in order to avoid counterfeit and identification theft. * Smart contract enabled framework for self-administerying the vaccine distribution constraints in the cold chain about the fulfilment of COVID-19 vaccine. The framework for vaccine supply chain management will enable the features of tamper-proof, person identification and avoidance of counterfeiting. * The proposed solution helps vaccination centers to monitor the process of vaccination; it will also help patients register in a secure manner. The organization of remaining paper is as follows: In Section 2 the related literature reviews in the area of blockchain, immunization vaccines and supply chain management will be discussed. Section 3 includes the method and procedure of the proposed framework. The environmental setup and results have been discussed in Section 4. Finally, Section 5 presents the conclusions and future scope.
# The Synergy of Renewable Energy and Desalination:
## 3 Application of Renewable Energy Sources in Desalination  
Renewable energy sources have a steadily increasing role in the desalination industry, primarily due to the increasing global demand for freshwater and the need for sustainable  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **RO** & **NF** & **FO** & **ED** \\ \hline Cost & High & Moderate & Moderate & Moderate \\ \hline Energy consumption & High & Moderate & Low & Moderate \\ \hline Technology Maturity Level & Mature & Mature & Emerging & Moderate \\ \hline Fouling Potential & High & Moderate & Low & Moderate \\ \hline Water Recovery Rate & High & Moderate to High & High & Moderate to High \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of membrane-based desalination technologies.  
Figure 2: Comparison of energy consumption rates (kWh per m\({}^{3}\)) for different desalination methods, including MSF, MED, RO, and NF.  
energy solutions. Integrating RES into desalination can significantly mitigate negative environmental impacts caused by the intense use of fossil fuels. The main renewable energy sources are shown in Figure 3. Some of the RES can provide both thermal and electrical energy to supply thermal and membrane-based desalination processes.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Local Optimization  
The keyframe [21] was a representative one, and it needed to be selected with enough frames from the previous keyframe while being far enough away from the nearest keyframe. At the same time, it needed to have enough co-visual feature points in the local optimization. If the current frame was identified as a keyframe, a new 3D map point was created using that frame. In this phase, the camera pose and 3D map points were adjusted by constraining the minimization of the reprojection error, i.e., by using local optimization.  
Using the itch frame camera poses, \(\mathbf{R}_{C_{i}}^{C_{1}}\) and \(t_{C_{i}}^{C_{1}}\), corresponding to the camera matrix \(\mathbf{P}_{i}\), there were a total of \(m\) frames of images in the local map. The \(n\) map points corresponding to frame \(i\) were \(X_{1}^{W},\cdots,X_{n}^{W}\). The reprojection error was as follows (14):  
\[\begin{split} e&=x-\mathbf{P}X\text{ and }\\ \frac{1}{2}\sum_{i=1}^{m}\sum_{j=1}^{n}\left\|e_{ij}\right\|^{2} =\frac{1}{2}\sum_{i=1}^{m}\sum_{j=1}^{n}\left\|x_{ij}-\mathbf{P}_{1}X_{j}^{W} \right\|^{2}.\end{split} \tag{14}\]
## 4 Quantitative Analysis of the Effect of Various Factors  
### Stability Analysis in Extreme Operating Conditions  
To further guide practical operations, it is necessary to perform a stability analysis of the skid-and-mud-return line in extreme operating conditions. Based on the individual factor analysis mentioned above, the most critical scenario for each factor is selected to form the current set of extreme operating conditions. The selected conditions for the extreme operating conditions are as follows: a wind condition with a recurrence period of 25 years and a wind direction angle of 90\({}^{\circ}\), a sea condition with a recurrence period of 25 years, a water flow angle of 90\({}^{\circ}\), an electric pump voltage at 110% of the rated voltage (110 Un), and an electric pump speed of 1200 rpm. The analysis results are shown in Figure 10.  
The analysis results indicate that in extreme operating conditions, as the depth of water at the location of the skid-and-mud return line decreases, the stress initially decreases and then increases. The minimum stress value is about 570 MPa at a depth of about 50 m, as shown in Figure 10a. The displacement increases as the depth decreases, and it starts to rapidly increase at a depth of 50 m, reaching a maximum displacement of about 32 m, as shown in Figure 10b. The angular rotation of the pipelines approximates a linear increase at a depth between 2000 m and 700 m. It is stabilized at a depth between 700 m and 50 m and  
Figure 10: Stability analysis of the skid-and-mud-return line in extreme operating conditions: (**a**) equivalent stress, (**b**) displacement, (**c**) rotation angle, and (**d**) bending moment.  
then rapidly increases to a maximum rotation of about 1.8 rad, as shown in Figure 10c. The bending moment of the pipelines is relatively small at a depth between 2000 m and 50 m, but it rapidly increases to about 80 kN-m from 25 m, as shown in Figure 10d. Obviously, the stability of the skid-and-mud-return line is significantly affected in extreme operating conditions. The occurrence of such conditions needs to be avoided.