esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### Acquisition in High-Sensitivity Mode
#### 3.4.2 Computation of Large FFTs in the FPGA
The FPGA computes an \(N\)-point FFT by adding the results of \(P\) FFTs, each of \(\frac{N}{P}\) points, combined with different complex exponentials. This is shown in Figure 5. This method is subject to the restriction that \(\frac{N}{P}\) must be a power of two. Each FFT of \(\frac{N}{P}\) points is applied to one subset of the input samples \(x_{(Pn)}\), \(x_{(Pn+1)}\), \(x_{(Pn+2)}\),..., \(x_{(Pn+P-1)}\) according to Equation (6). In Figure 5, \(X_{k^{\prime}}^{1}\) is the output of \(FFT_{\frac{N}{P}}(x_{Pn})\), \(X_{k^{\prime}}^{2}\) is the output of \(FFT_{\frac{N}{P}}(x_{Pn+1})\), etcA state machine in the FPGA combines each set of samples produced by the \(\frac{N}{P}\)-point FFTs \(\{X_{k^{\prime}}^{1},X_{k^{\prime}}^{2},\ldots,X_{k^{\prime}}^{P}\}\), with complex exponentials, adding the results to produce \(P\) output samples of the \(N\)-point FFT. More specifically, the state machine utilizes input samples \(\{X_{k^{\prime}}^{1},X_{k^{\prime}}^{2},\ldots,X_{k^{\prime}}^{P}\}\) to compute the \(N\)-point FFT output samples \(\{X_{k^{\prime}},X_{\frac{N}{P}+k^{\prime}},\ldots,X_{(P-1)\frac{N}{P}+k^{ \prime}}\}\)). A sample register is used to hold the values \(\{X_{k^{\prime}}^{i}\}\), \(0\leq i<P\), while the FPGA is computing the long FFT output samples \(\{X_{k^{\prime}},X_{\frac{N}{P}+k^{\prime}},X_{2\frac{N}{P}+k^{\prime}},\ldots,X_{(P-1)\frac{N}{P}+k^{\prime}}\}\). Because of the method employed, the \(N\)-point FFT output samples \(X_{k}\) are computed out-of-order. The number of FFTs (\(P\)), and the length of each FFT (\(N/P\)) are determined as follows:  
* The sampling frequency is chosen so that the number of samples \(N\) representing 100 ms is a multiple, \(P\), of a power of two. \(N/P\) is constrained to be a power of two to ensure compatibility with FFT IP cores provided by FPGA manufacturers. * To minimize the computation overhead in the proposed implementation, \(N/P\) is set to its maximum feasible value. This value is determined by either the maximum transform length supported by the FFT IP cores from FPGA manufacturers or a value that ensures the sampling frequency is as close as possible to the desired value. The details regarding the selection of the sampling frequency, the number of FFTs (\(P\)), and the length of each FFT (\(N/P\)) for the receiver presented in this paper are elaborated further in Section 3.4.3.
###### Abstract  
The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.  
digital image processing; image enhancement; dynamic range adjustment and compression; guided filter +
Footnote †: journal: Applied Sciences
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 6 Case Studies and Results
### Reliability Assessment Considering Worst-Case Attack
#### 6.1.1 Impact Analysis of Worst-Case Attack  
The impact of worst-case attack depends on the attack time. The system power margin varies greatly in a day. Suppose the mean output of a wind farm is 85% of its installed capacity--the seasonal and daily characteristics of the active power margin for this scenario are presented in Figure 5. As shown in the picture, from 10 a.m. to 22 p.m., the power margin is generally less than 30% of the peak load (2850 MW). Especially in summer and winter, the lowest point of the active power margin appears multiple times within a day. As such, even if the attacker does not know the accurate supply and demand relationship of the power system, the attack during the peak load may lead to a consequence close to the worst-case attack. The corresponding distribution of impact caused by an attack is shown in Figure 6. Different penetration rates are distinguished by colors, and the seasonal results are distinguished by the marked shape, which is the same in Figure 5. As shown, the load shedding caused by an attack is consistent with the change of power margin. During the period of peak load, the probability of load shedding increases greatly with the higher penetration rate.  
The worst-case attack against WTs has an assignable influence on system reliability. In order to test the defense effect against cyber-attacks, four systems with different defense capabilities were evaluated by Monte Carlo simulation. The reliability result is shown in Figure 7. The system states only considered a single event of a worst-case attack. As seen in Figure 7, when the penetration rate  
Figure 5: The time distribution of active power margin.  
Figure 6: The time distribution of load shedding.  
was 15%, the EENS of the four systems from \"weak\" to \"perfect\" were 1266, 1110, 612 and 367 MWh/yr. The defense effects of the four systems became apparent with the increase of the penetration rate. For every 1% increase of the penetration rate, the EENS in a perfect system increases, on average, 316 MWh/yr. However, in a weak system, the growth of EENS reaches up to 1088 MWh/yr. Thus, a better defense ability limits the frequency of cyber-attacks and greatly improves system reliability.
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 2 Home Automation Systems
### Case Study: Networking for Home Automation Deployments  
The practical scenario of the multi-hop network deployment for a home automation solution represented in Figure 1 is considered in this paper. Such network could also be deployed in assisted living homes, hotels, hostels, student residences, foster homes, or any other analogous buildings.  
The network includes some wireless access nodes (ANi) and mobile nodes (MNii), as shown in Figure 1. The MNij, as the ANi, would include some sensors and actuators depending on the application of the network. The ANi in Figure 1 are deployed all through the home to provide wireless network connectivity to the MNij. Many types of sensor are included in home automation solutions (security system or alarm, smoke detector, CO2 level detector, glass break sensor, window open sensor, motion or presence detector, door open sensor, smart door lock, air conditioner, humidity sensor, temperature sensor, luminance sensor, occupancy sensor, light controller, etc.). They can be attached to both access and mobile nodes with wireless connectivity.  
The nodes would centralize the measured data into the data sink (DS). The processing of the data gathered while monitoring would generate some alarms and would require some control commands. Both commands would be generated in the DS. Furthermore, several functionalities in the computer connected to the first and the last nodes of the network would ease the monitoring process. For instance, the test commands would be generated in the network tester (NW tester) and the resultant data would be aggregated in the DS.  
Many reasons can be found to deploy a wireless network of access nodes instead of considering the wired solution. On the one hand, the deployment costs of a wireless network are lower than the costs of the wired approach. On the other hand, the installation of the network access nodes is complex, and might be risky. Moreover, the wired solution is usually inflexible. Thus, a wireless network is arranged whenever the budget restrictions, flexibility, convenience, or other issues make this approach the most suitable one.  
Generally speaking, the devices in home automation solutions are interconnected and they form a communication network. Therefore, a communication standard must be found to provide connectivity to such devices, and Bluetooth [14] is a standard that satisfies these requirements.  
Then, in the case shown in Figure 1, every \(\mathrm{MN_{ij}}\) connects to the most appropriate \(\mathrm{AN_{i}}\) periodically, once the programmed inactivity period of the node is finished. The requirements in latency depend on the specific application and the type of sensor. As an example, temperature or humidity sensors are usually included in applications with longer monitoring intervals than presence sensors. If the wireless communication standard offers many low power working modes, as it happens in Bluetooth, it is necessary to choose sensibly the low power mode for the best performance of the system.
## Abstract  
Human activity recognition (HAR) is an important research area with a wide range of application scenarios, such as smart homes, healthcare, abnormal behavior detection, etc. Wearable sensors, computer vision, radar, and other technologies are commonly used to detect human activity. However, they are severely limited by issues such as cost, lighting, context, and privacy. Therefore, this paper explores a high-performance method of using channel state information (CSI) to identify human activities, which is a deep learning-based spatial module-temporal convolutional network (SM-TCNNET) model. The model consists of a spatial feature extraction module and a temporal convolutional network (TCN) that can extract the spatiotemporal features in CSI signals well. In this paper, extensive experiments are conducted on the self-picked dataset and the public dataset (StanWiFi), and the results show that the accuracy reaches 99.93% and 99.80%, respectively. Compared with the existing methods, the recognition accuracy of the SM-TCNNET model proposed in this paper is improved by 1.8%.  
Keywords:human activity recognition (HAR); channel state information (CSI); deep learning; temporal convolutional networks (TCN) +
Footnote †: journal: Applied Sciences
# Oxidation in Low Moisture Foods as a Function of Surface Lipids and Fat Content
## 1 Introduction
Rapidly extractible lipids have been used to show that surface lipids are more susceptible to lipid oxidation in spray-dried powders compared to the lipids encased by wall materials in the powder interior during microencapsulation [13; 14; 15; 16; 17]. [PERSON] and [PERSON] (2008) found that the extractible oil on the surface of dry powders will oxidize before internal fat and that the surface fat can be used to predict the shelf life of a product [17]. Another factor that has not been studied in low moisture foods is how the level of fat impacts lipid oxidation. In meat products, fat concentration has little impact on oxidation rates. This is because lipid oxidation in meat initially occurs in the cell membranes, and increasing fat concentration by the addition of adipose lipids (triacylglycerionels) does not increase membrane lipid concentrations and, thus, has little impact on oxidation rates [18; 19; 20]. In oil-in-water emulsions, fat content can increase lipid oxidation rates if the fat concentration is increased while the particle size remains constant, as this will increase the surface area of the lipids [21]. Since oxidation of lipids in oil-in-water emulsions initially occurs at the emulsion droplet interface [22; 23; 24; 25; 26], this can increase oxidation rates. The aim of this study is to determine the rate of lipid oxidation of surface versus total lipids to evaluate how lipid location impacts the oxidative stability of chemically leavened crackers. In addition, the impact of fat content on oxidation rates of low-moisture foods is investigated.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 2 Literature Review
### Range of MANET Applications
#### 2.3.1 Military Application  
Battlefields are unpredictable areas in which to operate, with little, if any, infrastructure in place, rapidly changing tactical situations, and highly mobile personnel and equipment, accompanied by hostile and friendly fire. Equipping each unit, such as soldiers, tanks, planes, and unmanned aerial vehicles (UAV), with MANET nodes would allow communication of vital information across battlefield areas without fixed infrastructures. The nodes could vary from small basic devices carried by foot soldiers to full computer systems in command headquarters at some distance away from the actual combat zone. Nodes can join in as additional resources when deployed, and nodes may leave when they are destroyed or pulled out of the field of operation. Depending on the location of the operation, it may be possible to provide edge connection from the MANET to fixed infrastructure military networks via field headquarters. A survey on MANET application in battlefield operations has been conducted by [PERSON] and [PERSON] (2011) [PERSON] and [PERSON] (2011), and MANETs have actually been deployed by the United States (US) marines ([PERSON], 2013, ([PERSON], 2013)). According to [PERSON] (nd) [PERSON] (1964), Commercial-off-the-shelf (COTS) open software has been used to support the deployment of MANETS in real battlefields. [PERSON], et. al (2008) [PERSON] et al (2008) has developed a mobility model from a heterogeneous military MANET trace.
## 5 Deep Learning Algorithm Model Employment
### DNN Algorithm Model Set-Up  
Figure 5 shows the framework of the DNN algorithm model. To develop an optimal deep neural network (DNN) algorithm model, it is first necessary to find the ideal network structure scenarios and tune hyperparameters. The DNN algorithm model employs the backpropagation algorithm, which uses the weights of nodes that vary depending on input and output variables [58]. Different hyperparameters--such as the activation function, batch size, dropout, optimizer, and epoch--are combined to find the optimal combination. The method used to find the minimum cost function is determined by the activation function, while the data-learning unit of the model for effectual learning is determined by the batch size. To prevent overfitting, which can hinder the performance of deep learning algorithms, a regularization penalty called Dropout is used. The optimizer wheels the constancy of learning by controlling the learning rate of the model, and the epoch specifies the quantity of times the model learns the data [59]. In the present work, due to the limited volume of data, the network structure scenario was established to three layers, with dropouts of 0 and 0.2 selected. The Adaptive Moment Estimation (Adam) was selected as the optimizer because it is widely used and easy to calculate [60]. The batch unit was established at 5, and the epoch was set to 1000. The Rectified Linear Unit (ReLu) function was nominated as the activation function, which is commonly used [60]. The optimal combination was found through a simulation process focused on the network structure scenario and hyperparameters. Figure 6 shows the architecture of the DNN algorithm model.  
Here, Table 3 presents the MAE and RMSE values calculated for each scenario based on different network structure scenarios and dropout values. The scenario with the lowest MAE and RMSE was ultimately selected as the final model. The results show that the scenario with a dropout value of 0.2 has a higher loss function than the scenario with a dropout value of 0.0, and with an increasing number of hidden layer nodes, the loss function value decreases. Among the scenarios tested, the 500\(-\)500\(-\)500 scenario showed the lowest MAE and RMSE values, as represented in Figure 7. Therefore, the final model was developed with a network structure scenario of 500\(-\)500\(-\)500 and zero dropout.  
Figure 5: Framework of the model.  
Figure 6: Architecture of the model.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 4 Typical Experimental Results  
Based on this prototype device, the verification experiment was carried out in Kunshan (120\({}^{\circ}\)57\({}^{\prime}\) E, 31\({}^{\circ}\)30\({}^{\prime}\) N), Jiangsu Province, China, from December 2017 to January 2018. The whole experiment was conducted respectively for two parts: one to carry out the MF sounding to verify the mesospheric sounding capability of MAI system, and the other to sound the ionosphere in the form of vertical sounding to verify the ionospheric sounding performance of MAI system. They will be described separately below.
## 3 Results and Discussion
The low numbers for Finland result in a jumping curve with less reliability, and jumps between 1 and zero cannot be presented on a log scale. Figure 3: The log–lin epidemic curve for new deaths in USA, Portugal, Belgium, the Netherlands and Finland [1]. The low numbers for Finland are noisier, and zero cannot be shown on a logarithmic scale. The peaks in the middle are not observable in a lin–lin plot because it is about a factor of 30 lower than the highest peak. The slopes are indications for the analysis in \(\tau_{grow}\), \(\tau_{recov}\) and \(n=\tau_{recov}/\tau_{grow}\). Figure 4: Partitioning in isolated groups without parameter change during the outbreak and its effect on \(\tau N\equiv\tau N^{\prime}\). (**a**) A large number of equal islands all starting at \(t=0\) results in a sum with high initial values (blue arrow) for \(N\), \(\tau N\) and one peak in \(\dot{N}\) at \(t/\tau=5.8\). In (**b**,**c**), four isolated outbreaks are modelled at equidistant time lags. The outbreak intervals in (**b**) are \(t/\tau=3\) and in (**c**) are \(t/\tau=4\), and the result in a plateau, and (**d**) is the epidemic curves in a lin–lin plot as simulated in (**b**,**c**) by four patchy outbreaks at fixed time intervals. (**e**) A fast group isolated from a slow with equal size but with characteristic time \(\tau\) and \(2\tau\) results in a first and second peak. (**f**) Special case of partitioning (\(m=1\)) for comparing \(0\%\) with \(75\%\) of vaccination. Statistical heterogeneity was also used to explain deviations from a pure exponential decay in cancer survival curves [6]. Our approach can explain bumpy pandemic curves with a broad plateau, a steep start and even with \(n>2\), which means that we can explain deviations from \(V_{n2}\) because the recovery of the fast group is hidden by the recovery of a slower group.
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
## 2 Materials and Methods
### The Optimization Algorithm
To create \(Ind_{test}\), three random individuals from \(Pop_{1}\)  
Figure 2: Differential evolution algorithm flowchart. \((Ind_{x},Ind_{y},Ind_{z})\), different from \(Ind_{target}\), are selected, and the crossover occurs following (9), \(F\in[0,2]\) being known as the mutation factor [23]. \[Ind_{test}=Ind_{x}+F\big{(}Ind_{y}-Ind_{z}\big{)} \tag{9}\]  
After the crossover and mutation step, it is necessary to assess whether the new individual meets the restrictions presented in (1) to (7). If \(Ind_{test}\) does not pass the existing restrictions, it is no longer evaluated and \(Ind_{target}\) keeps its value and its position in the new population (\(Pop_{new}\)). However, if \(Ind_{test}\) passes the constraints, it can be evaluated by the cost function (8), resulting in the value \(fc_{test}\). If \(fc_{test}\) is lower than the cost function of the target individual (\(fc_{target}\)), \(Ind_{test}\) replaces \(Ind_{target}\) into the new population. If \(fc_{test}\) is greater than \(fc_{target}\), it means that the new individual does not fit; thus, \(Ind_{target}\) keeps its value and position in (\(Pop_{new}\)). Finally, after the first generation, when all individuals of \(Pop_{1}\) have become targets and have been evaluated, \(Pop_{new}\) is composed of the combination of the individuals initially generated and those best adapted, generated by the DE approach. For the next generation, \(Pop_{new}\) assumes the position of \(Pop_{1}\) and the process restarts. Since the process is iterative, always restarting for a new generation, the number of generations or the convergence of the results can be used as stopping criteria. For the prediction of the photovoltaic generation and costs, at the end of the generations, the resulting individuals correspond to the optimal value of \(I_{b}\) for the first minute, taking into account the predicted curve for the next 119 min, which means an interval of 120 min or 2 h.  
The initial photovoltaic production and cost curves are predictions of behavior. It is important to mention that in a real application, these curves are dynamic, and their profiles are updated throughout the day. Therefore, the effectiveness of the algorithm is based on the actual measurement of each minute of the day, based on the forecast for the next 119 min. That is, for each one-minute step, the input curves are updated, and the optimal current is calculated having \(I_{PV}\) and \(\beta\) as predicted inputs for the next two hours. In this way, the optimal operating profiles, including \(I_{b}\), \(I_{g}\) and \(\theta\), are obtained, minute by minute, for a 24 h time interval.
## 3 Review Results
### Sample Size and Discipline in ChatGPT Adoption Studies  
#### 3.10.1 Sample Sizes  
The sample sizes used in studies on adopting ChatGPT in higher education vary widely, reflecting the diversity of the research contexts and objectives. Here are the sample sizes reported across different studies:  
* **Small Sample Sizes (up to 150):** Several studies, particularly those with a qualitative focus or pilot studies, feature small sample sizes. For example, [35] has a sample size of 70, [36] analyzed 138 responses, and [19] collected data from 114 respondents. Qualitative interviews often have even smaller sample sizes, such as [46], which interviewed 34 educators.
* **Medium Sample Sizes (151 to 400):** Studies with medium sample sizes appear to be more common, capturing a broader yet focused group of respondents. For instance, [14] involved 400 respondents, [43] had 383 participants, and [47] gathered 222 responses.
* **Large Sample Sizes (401 to 700):** Large-scale surveys are particularly common in quantitative research, offering a more robust analysis of adoption patterns. Studies like [4] involved 458 responses, while [27] received 503 responses.  
* **Very Large Sample Sizes (above 700):** A few studies, primarily those conducted at a broader scale, report very large sample sizes. For example, [33] had a sample size of 1389, and [34] gathered responses from 1004 participants.  
Overall, these studies' most commonly used sample sizes tend to fall within the medium to large range (151 to 700 respondents). This suggests a preference for balancing depth and breadth in the analysis. However, smaller sample sizes are still prevalent in more exploratory or qualitative studies, while very large ones are typically employed in extensive quantitative surveys. These varying sample sizes reflect the complexity and scale of research into ChatGPT adoption, from small-scale pilots to expansive cross-sectional surveys, helping to form a comprehensive understanding of the factors influencing its adoption in higher education.