esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurements of Antioxidant Properties
#### 2.3.5 \(\beta\)-Carotene Bleaching Assay  
Measurements of antioxidant properties using the \(\beta\)-carotene method were performed using a modified method described in [25]. An emulsion of \(\beta\)-carotene/linoleic acid in water was prepared by mixing 25 \(\mu\)L of linoleic acid, 185 \(\mu\)L of Tween 20 (200 mg) and 5 mL of \(\beta\)-carotene solution (containing 0.5 mg of \(\beta\)-carotene in 1 mL of chloroform). Chloroform was evaporated from the mixture using a vacuum evaporator, and the residue was dispersed in 100 mL of distilled water saturated with oxygen (saturation time 30 min, oxygen flow 100 mL/min). For measurement, 2900 \(\mu\)L of emulsion and 100 \(\mu\)L of extract were used, which were placed in a glass cuvette. The cuvette was tightly closed and mixed, and then (with the emulsion and antioxidant) placed in a water bath (45 \({}^{\circ}\)C). Changes in \(\beta\)-carotene absorbance were monitored at 470 nm. The first absorbance measurement (at 0 min) was performed immediately after mixing the ingredients. Subsequent readings were taken at constant time intervals (10 min) until the orange color of the control sample disappeared (approximately 180 min). A mixture containing 2900 \(\mu\)L of \(\beta\)-carotene emulsion and 100 \(\mu\)L of extraction solvent (methanol or ethyl acetate or acetone or chloroform) was used as a control sample. To zero the spectrophotometer, a mixture containing 100 \(\mu\)L of extraction solvent and 2900 \(\mu\)L of emulsion without \(\beta\)-carotene was used. The % inhibition of the sample (containing \(\beta\)-carotene and the tested extract) in relation to the control sample (containing only \(\beta\)-carotene only) was calculated according to the following formula:  
\[\text{I}(\%)=100\cdot\frac{\text{DR}_{\text{C}}-\text{DR}_{\text{S}}}{\text{ DR}_{\text{c}}}\]  
where I(%)--inhibition percent, DR\({}_{\text{C}}\)--degradation rate of \(\beta\)-carotene in the control sample = {[ln (a/b)]/t}, DR\({}_{\text{S}}\)--degradation rate of \(\beta\)-carotene in the sample with antioxidant = {[ln (a/b)]/t}, a = absorbance at time = 0, b = absorbance at defined time (for example at 10, 20 \(\ldots\). to 180 min), \(t\) = time.
## 5 Analysis of Test Results
### The Effect of Porosity on Permeability  
As can be seen from Figure 15, the porosity affects the permeability greatly; there is a strong power function between permeability and porosity. The permeability increases with increasing porosity, and when the porosity is less than 10%, the increase is slower, while the permeability increases greatly when the porosity exceeds 10%. This indicates that the porosity has a large effect on the permeability and that the permeability depends on the internal pores and microcracks, as well as their interconnectivity (pore throat size distribution).  
Figure 14: Pore size distribution. (**a**) Natural pure rock salt; (**b**) synthetic pure rock salt; (**c**) natural pure mudstone; (**d**) synthetic pure mudstone.  
Figure 15: The relationship between \(K_{\infty}\) and \(\phi\).
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 3 Preliminaries and Background
### Initialization Phase  
The initialization phase provides proper guidelines for vehicle registration in the IOT-based VANET infrastructure. Initially, the address of the infrastructure is not known to the new vehicle in the network, and it sends a broadcast message to connect with the infrastructure. In this network, let us assume that vehicles moving at an average speed of 20 km/h are only permitted to establish a communication with the infrastructure, which enhances network stability in VANET. The request-to-connect message is repeatedly broadcast by all the vehicles until it receives an acknowledgement message from the infrastructure. Thus, vehicle registration has been sequentially completed and updated in the routing table by the infrastructure services. As per [1; 24], the request message consists of GPS location, speed, RSS (received signal strength), and appended GPS location. The initialization phase is clearly demonstrated in Figure 1.  
The initialization phase helps the infrastructure to locate the vehicle precisely in the VANET. However, vehicles in IoT-based environments dynamically modify their positions. Furthermore, the addition of new vehicles and the deletion of existing vehicles requires the updating of the routing table, and this initialization process establishes proper communication between the infrastructure which in due course enhances network stability for communication.
## 3 Results and Discussion
### Batch Desorption Experiments and Reusability  
The uranium adsorbed by SiPMA-TEPA was desorbed using different concentrations of nitric acid. As shown in Figure 9a, the resin achieved complete desorption of uranium with 1 M nitric acid. Additionally, the uranyl ions were completely desorbed from the resin in less than 3 min (Figure S4). Reusability and stability evaluations of SiPMA-TEPA were conducted using 1 M nitric acid as the desorbent. As shown in Figure 9b, the adsorbent's adsorption and desorption efficiencies remained above 95% after five cycles, demonstrating good stability and reusability.
## 2 Materials and Methods
### Methodology
#### 2.2.5 Logistic Regression  
Logistic regression is a common model in landslide susceptibility assessment [27], which is a multivariate data analysis model similar to multiple linear regression analysis. The dependent variables of LR can be bi-categorized or multi-categorized. In this study, the occurrences of landslides were taken as dependent variables of the model, which could be expressed as 0 for non-landslide and 1 for landslide. The factors of landslide susceptibility, such as altitude, slope, and aspect, were selected as independent variables of the model. The application of LR model in landslide susceptibility assessment was to find the optimal fitting function, which can quantitatively describe the relationship between the occurrence of landslide and causal factors. The advantage of the LR model is that the independent variables can be either continuous, discrete, or any combination of both types. They do not necessarily have normal distributions. The formula can be expressed as:  
\[y=\frac{1}{1+e^{-(\alpha+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n})}} \tag{11}\]  
where \(\alpha\) is a constant, n is the number of independent variables, \(x_{i}\)(\(i\) = 1, 2..., \(n\)) is the predictor variables, and \(\beta_{i}\)(\(i\) = 1, 2..., \(n\)) is the coefficient of the LR model.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 5 Simulations and Experimental Results
### Simulation Results
#### 5.1.1 Case (1): Step Change in the DC Link Voltage  
Figure 5 depicts the DC link voltage during system startup, a step increase in the DC link voltage from 400 to 600 V at t = 0.6 s, and a step decrease in the DC link voltage from 600 V to 500 V at t = 1 s. The proposed DC link voltage controller was compared to the conventional and Fuzzy type 1 controllers. The proposed DC link voltage controller outperformed the Fuzzy type 1 and conventional controllers regarding the system performance parameters. Figure 6 shows the inverter (load) voltage, current, and source voltage and current for the proposed system when the three control techniques were implemented during the step-up in the DC link voltage: Figure 6 for the conventional controller, Figure 6 for Fuzzy type 1, and Figure 6 for the proposed controller. Figure 7 shows the inverter (load) voltage, current, and source voltage and current for the proposed system when the three control techniques were implemented during the step down in the DC link voltage: Figure 7a for the conventional controller, Figure 7b for Fuzzy type 1, and Figure 7c for the proposed controller. The source current and its spectrum for the three control methods are given in Figure 8, where Figure 8a,d,g shows the conventional controller, Figure 8b,e,h for Fuzzy type 1, and Figure 8c,f,i for the proposed controller. The total harmonic distortion (THD), displacement factor, power factor, and whether it agrees or disagrees with IEEE 1547 standards are illustrated in Table 3. From Table 3, the proposed controller had the nearest unity power factor and agreed with the IEEE 1547 standards.  
Figure 5: Start up, step increase, and step decrease for the DC link voltage using the three control techniques.  
Figure 6: Inverter (Load) voltage and current, source voltage, and current for the three control techniques during the step increase of the DC link voltage: **(a)** automated (conventional) PI, **(b)** Fuzzy type 1 controller, and **(c)** Fuzzy type 2 controller. The black line is the voltage, the red line is the current. The red line represents the boarder line in IEEE Standard 1547 and it wititten just above the red line.  
Figure 7: Inverter (Load) voltage and current, source voltage and current for the three control techniques during step decrease of the DC link voltage: **(a)** autotuned (conventional) PI, **(b)** Fuzzy type 1 controller, and **(c)** Fuzzy type 2 controller. The black line is the voltage, the red line is the current. The red line represents the boarder line in IEEE Standard 1547 and it wititten just above the red line.
# Protection of Digital Elevation Model--One Approach
## 2 Materials and Methods  
### Creation DEMs Based on LiDAR Data  
The cloud of points used for the continuation of work was obtained by LiDAR technology, and as the beginning of the work noted, the entire process of creating, analyzing, and visualizing digital elevation models was possible via the use of open-source software and tools. Regarding digital elevation models of radar records that are compared with models created by LiDAR technology, as noted, they can be downloaded from the Internet completely free of change. The software and tools used are:  
- Quantum GIS (QGIS), version 2.18.20;  
- Software package LasTools [39].  
Quantum GIS is an open-source software, a computer application that allows you to view, edit, and analyze geo-data. Also, Quantum GIS supports a variety of raster and vector data formats. As an open-source program, there are countless upgrades and adding so that the program can be used in different areas of application.  
In addition to its many features, LasTools is capable of working in a multi-purpose series. It contains tools for classification, transformation, filtering, rasterization, contouring, cutting, and polygonizing LiDAR data. All tools can be run through the Command Prompt operating environment and are also available as LiDAR data processing tools for ArcGIS, QGIS, and ERDAS IMAGINE.  
To make the analysis of the results more thorough and clearer, it is necessary to collect a sample of values. For comparative analysis of digital elevation models, the number of points for sampling that was used in this paper was 100 randomly distributed points in one area of interest. Of course, this number can be increased or decreased depending on the type of analysis being performed.
## 4 Discussion
### Algorithm Performance in Male and Female  
Now, we analyze if the algorithm's performance has any relationship with whether the subjects are men or women. The routines of older adults when they live alone can vary if we consider that a man lives alone or a woman lives alone. This can help us tailor the network and customize the sensors even more.  
The tests run for 14 days, with the network running all the time. For seven days, the network works without the energy optimization algorithm implemented in the sensors. For the remaining seven days, we run the algorithm on the nodes. With the above, we can compare the batteries' energy consumption and energy savings (ES). In Figure 18, we plot a close relationship represented in the energy saving for the consumption of the sensor network in each person of the chosen sample. We note that there is an additional 12% energy saving with the algorithm in houses of single women compared with houses of single men. This is interesting from the point of view of the person's activity in the different areas of her house. Because the sensors represent a higher ranking for the active mode while the person uses some areas more than others, this generates greater consumption in the energy of the sensors. Interestingly, the lowest consumption is in the homes of older men, and this phenomenon may be because women are more active and move to a more significant number of areas in the house. This may lead to better adapting the algorithm for gender-based usage strategies, and could further help energy consumption.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variables** & **\(p\)-Value for Shapiro–Wilk Normality Test** &
\begin{tabular}{l} **H0 = Samples Have Normal Distribution** \\ **(with 95\% Confidence)** \\ \end{tabular} & **Paired Test to Use** \\ \hline Breathing frequency & 0.05744 & H0 non-rejected & \(t\)-test \\ Heart rate & 0.20114 & H0 non-rejected & \(t\)-test \\ Sleep rhythm & 0.36112 & H0 non-rejected & \(t\)-test \\ Body temperature & 0.55846 & H0 non-rejected & \(t\)-test \\ \hline \hline \end{tabular}
\end{table}
Table 13: Normality test for male subjects and the corresponding paired test used.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variables** & **Test** & \(p\)**-Value for “Paired \(t\)-Test”** & **H0 = Samples Means Are the Same (with 95\% Confidence)** \\ \hline Breathing frequency & Paired \(t\)-test & 0.005909565 & H0 rejected \\ Heart rate & Paired \(t\)-test & \(2.3\times 10^{-9}\) & H0 rejected \\ Sleep rhythm & \(t\)-test & \(1.25\times 10^{-17}\) & H0 rejected \\ Body temperature & Paired \(t\)-test & 0.139209399 & H0 non-rejected \\ \hline \hline \end{tabular}
\end{table}
Table 14: Result of difference of means test for female subjects.
## 3 Results and Discussion
### Odorants in Dried Shititake Mushrooms
#### 3.2.2 Odorants Identified by DSE-SAFE  
In the dried shiitake mushrooms, 31 odorants were detected by DSE-SAFE, of which 24 were positively identified, seven were tentatively identified, and two were unknown. Among these 31 odorants, 25 odorants showed their relatively high FD factors (FD \(\geq\) 3) (Table 2). And dimethyl tetra-sulfide (mushroom), 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel), 1,2,4,5-tetrathiane (sulfury, burnt) and lenthionine (sulfury, burnt) showed the highest FD factors of 729, followed by 3-methyl-butanal (chocolate) with FD factor of 243. Additionally, 2-methyl-propanal (chocolate), 1-hexanol (sweet, oily), 2-ethyl-1-hexanol (fresh, fatty), (E)-2-octen-1-ol, phenylacetaldehyde (floral), 3-(methyl-thio)-1-propanol (cabbage), phenylethyl alcohol (honey) and benzoic acid (balsam-like) were identified for the first time as predominant odorants in the dried shiitake mushrooms. Concentrations and Odor-Activity Values (OAVs) of Positively Identified Predominant Odorants in Shititake Mushrooms  
Concentrations and OAVs of 26 positively identified predominant odorants determined in fresh and dried shitake mushrooms are given in (Table 3). Among the 18 predominant odorants quantitated in the fresh shititake mushrooms, 1-octen-3-ol was the most abundant with 157.65 \(\pm\) 1.17 \(\upmu\)g/g, which accounted for approximately 94% of total odorants, followed by (E)-2-octenal (1.70 \(\pm\) 0.09 \(\upmu\)g/g), 3-methyl-butanal (1.39 \(\pm\) 0.07 \(\upmu\)g/g) and 3-octanone (1.34 \(\pm\) 0.03 \(\upmu\)g/g). [PERSON] et al. [38] reported that 1-octen-3-ol had the highest concentration and (E)-2-octenal also had higher content in the American mastutake. 1-Octen-3-ol had the highest OAV of 105100, followed by 1-octen-3-one (36547) and lenthionine (4048), which made major contributions to the aroma of fresh shiitake mushrooms. 1-Octen-3-ol, 3-methyl-butanal and lenthionine had higher concentrations, as well as higher OAVs in the fresh samples. However, 1-octen-3-one and _trans_-4,5-epoxy-(E)-2-decenal had lower contents, but relatively higher OAVs. This might be due to the fact that there was a strong correlation between odor thresholds of aroma compounds and their aroma contribution except concentrations, and the OAV was defined as the ratio of the aroma concentration to its odor threshold [31; 39].
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 2 Background
### Ps/2  
The current implementation of HyperIO supports only PS/2 keyboards. We selected this type of keyboard for the simplicity of its communication protocol. Previous research [17] was motivated by this simplicity, too.  
The communication with PS/2 keyboards is managed by a PS/2 controller [14]. The PS/2 controller uses two IO ports for its operation, 0x60 as a data port and 0x64 as a command and status port. Port 0x64 communicates with the controller, such as reading and writing its internal RAM and selecting the destination device. Port 0x60 sends and receives data from the previously selected destination device.  
Assuming that the selected destination device is a PS/2 keyboard, reading from port 0x60 reads the keyboard's last scan code. A scan code represents a keyboard event, such as pressing or releasing a key. The scan codes can then be translated according to a conversion table. Some scan codes, e.g., presses and releases of shift keys, affect other scan codes' translation.  
The act of writing to port 0x60 sends a command to the keyboard. The commands configure various aspects of a keyboard's behaviour. Some are single-byte; an argument follows others. For example, the 0 xFF command is a single-byte command which resets the keyboard. The 0 xF3 command defines, through its argument, the rate at which a pressed key produces events. Another example is the 0 xED command, which configures the LEDs' state. The lower three bits in the byte that follows the 0 xED command define the state of scroll lock (bit 0), number lock (bit 1), and caps lock (bit 2) LEDs.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 3 Max-SINR Joint Precoder-Decoder Design  
IA is an emerging DoF-based interference management technique in wireless communications that aligns the interference caused by other users in an interference signal subspace that is orthogonal to the user-desired signal subspace [62]. This technique can be applied in the time, frequency or spatial domain. Furthermore, in a high-SNR regime, this tech nique can achieve high-interference elimination and offer a \(\frac{K}{2}\) achievable total DoF upper bound in the \(K\)-interference-communication-user scenario without frequency/time/spatial symbol extension [21; 50]. Symbol extension means additional diversity, for example, by expanding bandwidth or adding antennas in this paper. At the same time, the traditional orthogonal spectrum allocation, which allocates a nonoverlapping signal to each user, could only obtain \(\frac{1}{K}\) interference-free DoFs.  
In this section, we propose a joint precoder-decoder design using the max-SINR criterion and IA approach to solve the radar-communication coexistence problem. Communication receivers are interfered with by radar via a direct path. As the radar signal reflected from the target may also have a high power, we could treat the scattered radar signal as noise for the communication receiver and consequently a medium SNR scenario. IA in a high-SNR regime typically focuses on minimizing the leakage interference while ignoring noise. However, in this radar-communication coexistence problem in a medium-SNR regime, it is necessary to take the noise into account. Hence, we will employ the max-SINR criterion for our design.
## 2 Related Work
### Detection Model  
As mentioned before, there are three forms of data annotation for cracks, namely, image-level, object-level, and pixel-level annotation, so there are three corresponding data-processing models. The popularity of classification networks started with AlexNet [34], after which more powerful and efficient ANNs were proposed, such as GoogLeNet [35], ResNet [36], and MobileNet [37]. Their structure usually comprises a series of convolutional blocks used to extract image information and a fully connected layer that follows the convolutional layer for classification. Image classification is a fundamental task in the field of computer vision, and image classification networks are also used as the backbones of object detection and segmentation models, which means that the backbone network affects the results of detection or segmentation. Object detection models are often classified into two categories: one-stage and two-stage. R-CNN [38], Fast R-CNN [39], and Faster R-CNN [40] are two-stage object detection networks. In the first stage, the candidate regions need to be obtained, and then the candidate regions are detected and classified in the second stage. YOLO [41], SSD [42], RetinaNet [43], and FCOS [44] are single-stage target detection models that use a single CNN to detect objects and classify them, so the advantage of these networks is fast processing. The main segmentation models are FCN [45], DeepLab [46], and U-Net [47]. Although these types of networks can generate pixel-level predictions for cracks, we did not use them in this study because pixel-level annotation is too resource- and time-consuming to be applied in realistic scenarios.  
In this study, 13 popular object detection models were divided into four groups according to model complexity, and extensive experiments were conducted on the dataset presented here, as shown in Section 4.