esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
## 2 Antenna Design and Specification  
Figures 1 and 2 show the structure and geometries of the proposed triple-frequency stacked patch antennas. Table 1 shows the corresponding dimensions in Figure 2. The designed antenna comprises seven layers. The first layer (Layer I) has a two-by-two patch array antenna with a power divider on top of Layer II Rogers RT/duroid 5880, with a relative permittivity (\(\varepsilon_{\mathrm{r}}\)) of 2.2 and a loss tangent (tan \(\delta_{\varepsilon}\)) of 0.0009. The first and second layers cover the DSRC frequency range. A patch antenna element (Layer III) with a quarter-wave transmission line was placed on the top of a Rogers RT/duroid 5880 substrate (Layer IV) to cover the 5G frequency bands. Layer V is a common ground plane. For the 6G antenna, an eight-by-eight series-fed patch array antenna with a Wilkinson power divider (Layer VII) was placed on the top of a Rogers RT/duroid 5880 substrate (Layer VI). The port I probe feeds the two-by-two 5G patch array, while port II coaxial connector probe fed the DSRC patch antenna. The power divider connected probe III to the eight-by-eight patch array antenna. ANSYS High-frequency Structure Simulator (HFSS) 2019 R1 (v. 19.3) software is used to validate the antenna performance.  
Figure 1: Expanded view of the low-profile triple-frequency antenna structure.  
Figure 2: Dimensions and geometry of proposed low-profile triple-frequency antenna from (**a**) dimeric view, (**b**) top view, and (**c**) bottom view.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 3 Results and Analysis
### Drivers of Water Body Area Change in the Kashgar Region
Figure 13: (**a**) Temporal trend of average annual evaporation in Kashgar (2003–2023), (**b**) temporal covariation of water body area and evaporation in Kashgar (2003–2023), (**c**) correlation between water body area change and annual evaporation in Kashgar (2003–2023).
###### Abstract  
Local differential privacy algorithms combined with deep generative models can enhance secure medical image sharing among researchers in the public domain without central administrators; however, these images were limited to the generation of low-resolution images, which are very insufficient for diagnosis by medical doctors. To enhance the performance of deep generative models so that they can generate high-resolution medical images, we propose a large-scale diffusion model that can, for the first time, unconditionally generate high-resolution (\(256\times 256\times 256\)) volumetric medical images (head magnetic resonance images). This diffusion model has 19 billion parameters, but to make it easy to train it, we temporally divided the model into 200 submodels, each of which has 95 million parameters. Moreover, on the basis of this new diffusion model, we propose another formulation of image anonymization with which the processed images can satisfy provable Gaussian local differential privacy and with which we can generate images semantically different from the original image but belonging to the same class. We believe that the formulation of this new diffusion model and the implementation of local differential privacy algorithms combined with the diffusion models can contribute to the secure sharing of practical images upstream of data processing.  
deep generative models; denoising; differential privacy; diffusion models; head magnetic resonance images +
Footnote †: journal: Medical Image Generation with Provable Privacy Protection Based on Denoising Diffusion
## 3 The Proposed Routing Mechanism
### Mobility Model
The CCS-DTN protocol consists of two phases, which are the phase of cluster operation and the phase of message delivery. Since the devices in social DTNs, such as mobile phone, are smart and have time information, it can be assumed that the network is a synchronized network.
## 6 Discussion  
The results of this study indicate that the proposed algorithm has a good performance. To further evaluate the performance of the algorithm, we conducted a flume experiment using the proposed imaging algorithm in Figure 9. The flume data were obtained from the EIT system KIT 4, developed by the University of Eastern Finland. The KIT 4 system is an open EIT system that generates a two-dimensional EIT dataset. These data were not included in the training and test sets used in this article. The KIT 4 system provides a circular tank with a radius of 14 cm, equipped with 16 rectangular stainless steel electrodes evenly spaced around the tank wall. Each electrode has a width of 2.5 cm. Physiological saline was used as the filler in the tank to create the background field. The system utilizes an excitation current size of 2 mA and a frequency of 1 kHz, with a proximity drive mode. The obtained data were processed and converted into the pyEIT format.  
Based on the data presented in Table 2, it is evident that our algorithm outperforms the other methods in terms of the \(SSIM\) and \(MSE\) evaluation indicators while ranking second in terms of \(PSNR\). Although artifacts are present in the reconstructed images produced by all three methods, the artifacts are most effectively reduced in the reconstructed images generated by the proposed algorithm. It is important to note that actual measurements are subject to measurement errors, model errors, and environmental noise interference, which result in a reduced signal-to-noise ratio of the measured voltage. Consequently, the quality of the actual reconstructed image is lower than that of the simulated calculation. This further supports the conclusion that our algorithm offers superior imaging quality.  
Figure 8: The \(SSIM\), \(MSE\), and \(PSNR\) values of reconstruction methods under different \(SNR\).  
Figure 9: Physical model imaging of a circular sink with acrylic cylinders.  
We contribute to the research on lung EIT imaging algorithms by conducting simulation experiments and experiments with ACT 3 and KIT 4 datasets to verify the effectiveness of the proposed improved GNN algorithm. However, further optimization of the algorithm is still necessary due to experimental conditions. There is potential for additional research and improvement. To facilitate observation and verification of imaging results, a circular field is used instead of a chest model to construct the EIT simulation model. However, since there are notable differences in shape and size between the human chest contour and the circular field, distortion of the reconstructed image may occur when applying the algorithm in practical lung imaging.
## 4 Discussion
### Load-Induced Strain
This indicates that the increased creep strain in these mixes can only be attributed to the presence of recycled aggregate. Replacing cement with 50% fly ash increased the creep strain in the mix SCC30 but not as much as the decrease in compressive strength would suggest. On the other hand, replacing the fine aggregate with RCA doubled the creep strain compared to the mix SCC30 despite the lower cement paste content. When considering whether the influence of the fine RCA is greater than the influence of coarse RCA, it should be borne in mind that the fine RCA in the SCC mix accounted for 60% of the total aggregate. Therefore, it is not obvious that fine RCA makes a greater contribution to creep than coarse RCA. Figure 10 also shows the average density of the concrete samples for the compressive strength test. With the exception of the mix SCC50, the decrease in concrete density corresponds well with the increase in creep strain. The linear correlation between density and 28-day, 90-day and 180-day creep compliance has coefficients of determination of 0.81, 0.87 and 0.90, respectively. In comparison, the linear correlation between compressive strength and creep compliance has coefficients of determination of 0.64, 0.62 and 0.56 for 28, 90 and 180 days under load, respectively. Most models for predicting the creep of concrete use the 28-day compressive strength as input [74]. ACI 209R-92 requires the concrete density in addition to the compressive strength to evaluate [PERSON]'s modulus [74]. It is therefore known that both compressive strength and density are strongly correlated with creep and both are governed by porosity. What concretes with recycled aggregates have in common is their increased porosity, which is caused by the porosity of the recycled aggregate. For the concrete mixtures analysedin this work, the volume of the pores contained in the aggregate can be calculated from density and absorption properties (Table 1) and the concrete mix composition (Table 3). This calculation shows that the volume of pores contained in the aggregate alone, excluding air voids and cement paste porosity, ranges from 0.69% for the SCC0 mix to 9.45% for the SCC60 mix. The creep strain measured in this work is the total creep, which is a sum of basic creep and drying creep which is strongly influenced by moisture movement [57]. This implies that the incorporation of RCA in concrete affects the moisture movement, which also affects the creep deformation, and that this process could be the origin of differences in the measured creep deformation between concrete with natural aggregate and concrete made with RCA.
## 6 Discussion
### Assessment of Risk to Human Safety  
There are risks in any scientific and engineering task that utilises physical equipment. These risks are mitigated as part of the health and safety procedures of an organisation's working practices. However, there were two risks of particular concern for the AirQKD project:  
1. The FSO-QKD system uses lasers for the optical link. This required enhanced health and safety checks and considerations for the handling and installation of the FSO-QKD optical system.
2. The V2I communication testing used a research vehicle on a public road within the University of Warwick campus. This required additional health and safety scrutiny for the operation of the test. All aspects of the V2I test operation were approved by a university safety committee.
3. Transmission of fake CPM messages in a public location during the experiment could have been a concern if vehicles were able to process them. However, the capability to process CPM messages, especially within our testbed region, has not been integrated into mass-production vehicles; therefore, this risk was seen as very low.
# Implementation of an Obstacle Recognition System for the Blind
## 3 Obstacle Recognition Model and System Architecture
### System Architecture  
This section describes the system architecture (Figure 1) in which the system works. First, the Raspberry Pi was capable of streaming videos in real time. Streamed videos were imported to the server using a network module. These images were divided into 30 frames per second in the data processing. The obstacle recognition module was the main module in the system. It recognized obstacles in the images using an obstacle recognition module based on the retraining, and then it executed either the crosswalk discrimination module or location calculation module. Each of the divided images from the Raspberry Pi were input into the obstacle recognition model (Step 3, Figure 1) to recognize crosswalk lights and bollards. Images that were recognized as crosswalk lights were then input into the crosswalk discrimination module (Step 4).  
The crosswalk discrimination module extracted only the position of the incoming crosswalk light images where the bounding box was drawn. We used the identification function [6] to identify red and green in the images. This function identified a value for each color, based on the threshold of whether it was red or green. The result of the color calculation was input into the crosswalk safety guidance algorithm. The algorithm analyzed 30 frames per second and finally produced results for none (as in no light was detected), and red, green, and flashing lights. The guidance results were passed to the Raspberry Pi via the network module. A voice guidance determination module in the Raspberry Pi used the guidance results to determine which voice was played. The module could play different voices corresponding to none, green light, red light, or flashing light. For example, when the system determined a green light, Raspberry Pi would voice, \"Green light went on. You can cross the crosswalk.\" If the object recognized was a bollard (Step 7), the image was input into the location finding module. This module calculated the correct location for a bollard using the coordinates of the bounding box where the bollard was present. The location result was passed to the Android app by the network module, which monitored the location of the obstacle. For instance, when a bollard was located to the left of a client, the Raspberry Pi would voice, \"A bollard is on your left.\"
## 3 Materials and Experiment
### Experiment to Test the Performance of the Proposed Smart Stethoscope  
To test the performance of the hybrid smart stethoscope prototype, two goals were set in the experiments: (1) testing the accuracy of heart-sound and PPG-signal measurement; and (2) testing the accuracy of automatic S1 and S2 heart-sound detection using the developed algorithm. Twenty healthy adult subjects (gender: male; age: 24-27 years old) with no history of cardiovascular disease were invited to join this study to record the heart sounds and PPG signals. The subjects were informed of the experiment's procedure and this study's purpose to evaluate the smart stethoscope, and signed informed-consent forms prior to the experiments.  
In this study, to test the performance of heart-sound and PPG-signal measurements, an MP160 multichannel physiological data-acquisition system (BIOPAC System, Inc., USA) was used as a reference for comparison with the prototype device's data acquisition. During the experiment, the subject was asked to sit comfortably in a chair and relax for about two minutes. A contact acoustic transducer (SS17 LA) and a PPG sensor (TSD200C), also provided by the MP160 system, were attached to the surface of the subject's chest and to the middle finger of the left hand, respectively. At the same time, the subject was required to wear the prototype device on the middle finger of the right hand and to place it on the chest near the contact acoustic transducer to measure the heart sounds together. Figure 9 shows the photo of the experiment's setup and procedure. The experiment of heart-sound and PPG-signal-data acquisition was conducted by two devices simultaneously in one minute. In addition, for data storage, a high-performance PC was used to receive and store the data from the MP160 and the prototype device by USB cable and Bluetooth, respectively.  
Figure 8: Picture of assembled finger-ring-shaped smart stethoscope prototype: (**a**) side view in user mode; (**b**) front view in user mode; (**c**) bottom view in user mode; (**d**) side view in collapsed mode.  
Figure 9: Pictures of the experimental setup for heart-sound and PPG-signal-data acquisition: (**a**) photo of a subject and measurement with the prototype device and MP160; (**b**) diagram of the experimental setup for data acquisition.
###### Abstract  
Recently, interest in developing functional foods that promote health has grown significantly. This study aimed to evaluate the feasibility of microencapsulating guinea pig blood erythrocytes by vacuum drying and incorporating them into gummies fortified with tumbo juice. Physicochemical analysis (proximate analysis, iron content, color, pH, soluble solids, and particle size) and functional group analysis by Fourier transform infrared spectrophotometry were performed on three formulations of gummy candy with added encapsulated erythrocytes from guinea pig blood (EEGPB): F1 (4% EEGPB), F2 (5% EEGPB), and F3 (6% EEGPB). The results showed a significant decrease in the moisture content (52.02% in F1 to 43.27% in F3) and increases in protein (11.44% in F3) and iron (2.63 mg Fe/g in F3) contents when higher EEGPB levels were used. Sensory evaluation revealed that F3 was the most acceptable formulation in terms of taste, aroma, and texture, with no significant differences in color. FTIR analysis confirmed physical incorporation with no chemical interactions between ingredients. These results demonstrate that the encapsulation of erythrocytes by vacuum drying not only preserves the bioactive compounds but also improves the organoleptic properties of the gummies, making them an attractive product for consumers. In conclusion, this technique is effective for fortifying functional foods and has potential application in other food products. This approach represents a significant advance in the development of innovative functional foods.  
microcapsulated erythrocytes; iron fortification; sensory analysis; vacuum drying; gummy candy; functional confectiony Article
# PID Control Assessment Using L-Moment Ratio Diagrams
## 2 Applied methods
### L-Moments  
[PERSON] [24] proposed the notion of L-moments as a linear combination of ordered statistics. This theory incorporates a new description of the distribution function shape, supports the task of the estimation of coefficients for an assumed PDF and enables testing hypotheses about eventual theoretical distributions. We define the L-moments for any random variable, for which the moments exist. The L-moments allow obtaining an almost unbiased estimation, even for a very small observation sample. Additionally, the L-moment estimators are robust against outliers and the distribution function tails [34]. The above properties are highly appreciated in the life sciences. However, the same observation might be made in the engineering sciences, and control is not an exception.  
We evaluate L-moments according to the following algorithm. At first, the data \(\{x_{1},\ldots,x_{N}\}\), \(N\)--number of samples, are ranked in ascending order from \(1\) to \(N\). In the consecutive step, the sample L-moments (\(l_{1},\ldots,l_{4}\)) are evaluated, followed by the sample L-skewness \(\tau_{3}\) and L-kurtosis \(\tau_{4}\). The algorithm equations are sketched below:  
\[l_{1} =\beta_{0},\ l_{2}=2\beta_{1}-\beta_{0},\] \[l_{3} =6\beta_{2}-6\beta_{1}+\beta_{0},\] \[l_{4} =20\beta_{3}-30\beta_{2}+12\beta_{1}-\beta_{0},\] \[\tau_{2} =\frac{l_{2}}{l_{1}},\ \tau_{3}=\frac{l_{3}}{l_{2}},\ \tau_{4}=\frac{l_{4}}{l_{2}}, \tag{5}\]  
where  
\[\beta_{j}=\frac{1}{N}\sum_{i=j+1}^{N}x_{i}\frac{(i-1)(i-2)\cdots(i-j)}{(N-1)(N- 2)\cdots(N-j)}. \tag{6}\]  
These formulation of moments allow obtaining the shift estimator, i.e., the L-shift \(l_{1}\), two scaling factors in the form of the L-scale \(l_{2}\in[0,1)\), L-covariance denoted as L-Cv \(\tau_{2}>0\), and two higher moments: L-skewness \(\tau_{3}\in(-1,1)\) and L-kurtosis \(\tau_{4}\in\left(-\frac{1}{4},1\right)\).  
L-moments can be applied to many tasks; for instance, in the modified method-of-moments for theoretical distribution fitting to empirical data. L-skewness and L-kurtosis are used as a proper goodness-of-fit measure. We can evaluate L-moments for theoretical distribution functions as well [35]. As an example, the L-moments for a normal distribution are as follows: \(l_{1}=\mu\), \(l_{2}=\frac{\sigma}{\pi}\), \(\tau_{3}=\frac{l_{3}}{l_{2}}=0\) and \(\tau_{4}=\frac{l_{4}}{l_{2}}=0.1226\).  
It should be noted that L-moments have allowed the introduction of several other ordered-type estimators such as trimmed TL-moments [36], LL- [37], LH- [38], LQ- [39], and PL- moments [40].
## 2 Materials and Methods
### Estimation of Multi-Scale Emissions from Building Operations
#### 2.3.2 Mapping BCEs at the Grid Scale by Integrating Remote Sensing Data and Statistical Results  
As previous studies have proved that the economic output, population, and urban expansion have positive effects on buildings' carbon emissions because of increasing demand for energy consumption [40; 41; 42], we selected nighttime light data, grid-scale population data, and built-up land data to represent the socioeconomic factors influencing BCEs. Summer and winter temperatures affect the building energy consumption required to maintain comfortable conditions [35; 36; 37]. Vegetation loss indirectly affects BCEs by intensifying the urban heat island effect and influencing the temperature around buildings [38; 39]. Therefore, the summer mean temperature, winter mean temperature, and vegetation index were selected as natural factors in regression models. The remote sensing data were adjusted to a resolution of 1 km and Lambert conformal conic projection after preprocessing, normalized using Equation (5).  
\[NX_{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{5}\]  
The BCEs of public buildings (\(BCEP\)) included emissions from the commercial and retail sectors, other sectors, and the transport sector. The BCEs of residential buildings (\(BCE\)) include emissions from the residential sector. The emissions from central heating were divided into public heating and residential heating using the proportion of heat in the BTH's energy balance sheet. Equations (6) and (7) show the multiple linear regression (MLR) models for public and residential BCEs, respectively. \(NNTL_{i}\), \(NPOP_{i}\), \(NPBL_{i}\), \(NRBL_{i}\), \(NEV1_{i}\), \(NST_{i}\), and \(NWT_{i}\) represent the nighttime light data, population, public built-up land, residential built-up land, enhanced vegetation index, summer temperature, and winter temperature after normalization, respectively.  
\[BCEP_{i}=\beta_{0}+\beta_{1}NNTL_{i}+\beta_{2}NPOP_{i}+\beta_{3}NPBL_{i}+\beta _{4}NEVI_{i}+\beta_{5}NST_{i}+\beta_{6}NWT_{i}+\varepsilon_{i} \tag{6}\]  
\[BCER_{i}=\beta_{0}+\beta_{1}NNTL_{i}+\beta_{2}NPOP_{i}+\beta_{3}NRBL_{i}+\beta _{4}NEVI_{i}+\beta_{5}NST_{i}+\beta_{6}NWT_{i}+\varepsilon_{i} \tag{7}\]  
Based on the results of Equations (6) and (7), the models were applied to a 1 km grid. To obtain the final results of grid-scale emissions (\(BCE_{g}\)), prefecture-level emissions were used to revise the estimation results.  
\[BCE_{g}=\frac{BCEP_{g}^{\prime}}{\sum_{g=1}BCEP_{g}^{\prime}}\times BCEP+\frac {BCER_{g}^{\prime}}{\sum_{g=1}BCER_{g}^{\prime}}\times BCER \tag{8}\]