esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 6 Applications of AMI in Heathcare
### Case Studies for Smart Healthcare
#### 6.1.1 Smart Transdermal Drug Delivery System for Diabetic Patients  
Traditional medication delivery systems often fall short in providing real-time personalized care, especially for conditions such as diabetes where timely administration of insulin is critical. Fluctuations in drug levels can result in side-effects or reduced efficacy, posing serious risks to patients. To address this challenge, noninvasive actuators such as smart transdermal patches offer a controlled real-time solution. These patches can adjust dosage based on vital signs obtained from IoT sensors such as smartwatches and fitness bands.  
The procedure begins with real-time monitoring of a patient's vitals, including blood glucose levels, heart rate, skin temperature, and physical movements. Smartwatches continuously measure the heart rate, skin temperature, and physical activity using gyroscopic and accelerometer data, which are then uploaded to the nearest edge server via Wi-Fi connectivity. The AI analyzer in the edge server continuously monitors these vitals through preprocessing the data and analyzing patterns. If it detects abnormalities, it cross-references recent blood glucose readings from a CBGM device such as the Freestyle Libre.  
The Freestyle Libre is a noninvasive CBGM that measures vitals at predefined intervals. If abnormal AI predictions are detected, the smart transdermal patch initiates dosage adjustments as needed. With each automated dosage insertion, the AI analyzer assesses the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Objectives** & **Methods** & **Dataset** & **Performance** \\ \hline \multirow{4}{*}{Early detection of PD [155]} & Used restricted BM with multi-layer perceptron & Collected from University of Istanbul’s Cerrahpasa, Faculty of Medicine’s Department of Neurology & Acc. 95.32\% \\ \cline{2-4} \cline{3-4} \cline  
patient's vitals and determines whether additional dosage is necessary. Typically, the patient may exhibit an elevated heart rate of 90-110 bpm or higher during a hypoglycemic event. Excessive sweating may also be observed, while hypoglycemia can lead to a decrease in skin temperature due to vasoconstriction, with temperatures potentially falling below 89.6 \({}^{\circ}\)F. This integrated approach leveraging IoT sensors, edge computing, and AI algorithms offers a proactive and responsive method for managing medication delivery, enhancing patient safety, and improving treatment outcomes for conditions like diabetes. The entire framework is described pictorially in Figure 7.
## 7 Simulations  
As it has been mentioned above, the last method applied in this paper to compare the quality of meshes was to run example simulations for the different meshes of the same object and comparing the results. The three 3d \"real\" meshes were chosen: the mesh with no improvement, the one improved by TetGen and the mesh which was additionally optimized with Stellar. The parameters for all three simulations were the same: the size and shape of the surface mesh, the initial density distribution of neurotransmitter (NT), the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & **Definition** & **Ideal** & **Mean** & **SD** & **Median** & **P90** & **P99** & **Max** \\ \hline \multicolumn{8}{c}{**GEOSPHERE:**} \\ \hline \(SV\) & \(\sqrt{S}/\sqrt[3]{V}\) & \(\sqrt[4]{3}\sqrt[6]{72}\approx 2.68433\) & 2.80129 & 0.06310 & 2.79255 & 2.88640 & 2.97954 & 3.33775 \\ \(ER\) & \(E_{max}/R\) & \(2\sqrt{6}\approx 4.89898\) & 7.47835 & 1.29978 & 7.26340 & 9.07162 & 11.85719 & 24.99427 \\ \(EH\) & \(E_{max}/H_{min}\) & \(\sqrt{3}/\sqrt{2}\approx 1.22474\) & 2.31219 & 0.54216 & 2.21765 & 2.98423 & 4.11482 & 9.75960 \\ \(MAXS\) & \(S_{max}/S\) & \(1/4=0.25\) & 0.30716 & 0.02864 & 0.30297 & 0.34659 & 0.38850 & 0.43438 \\ \(MINS\) & \(S_{min}/S\) & \(1/4=0.25\) & 0.19643 & 0.02555 & 0.19967 & 0.16305 1 & 0.14975 1 & 0.04684 1 \\ \hline \multicolumn{8}{c}{**GLOBE:**} \\ \hline \(SV\) & \(\sqrt{S}/\sqrt[3]{V}\) & \(\sqrt[4]{3}\sqrt[6]{72}\approx 2.68433\) & 2.81312 & 0.06964 & 2.80272 & 2.90532 & 3.01900 & 3.21246 \\ \(ER\) & \(E_{max}/R\) & \(2\sqrt{6}\approx 4.89898\) & 7.76157 & 1.49536 & 7.47481 & 9.66496 & 12.82559 & 19.98282 \\ \(EH\) & \(E_{max}/H_{min}\) & \(\sqrt{3}/\sqrt{2}\approx 1.22474\) & 2.46626 & 0.66469 & 2.33056 & 3.31735 & 4.71825 & 7.41859 \\ \(MAXS\) & \(S_{max}/S\) & \(1/4=0.25\) & 0.31461 & 0.03285 & 0.30945 & 0.36080 & 0.40590 & 0.43614 \\ \(MINS\) & \(S_{min}/S\) & \(1/4=0.25\) & 0.18995 & 0.02885 & 0.19390 & 0.15030 1 & 0.13593 1 & 0.06181 1 \\ \hline \multicolumn{8}{c}{**REAL MODEL:**} \\ \hline \(SV\) & \(\sqrt{S}/\sqrt[3]{V}\) & \(\sqrt[4]{3}\sqrt[6]{72}\approx 2.68433\) & 2.87951 & 0.14403 & 2.84965 & 3.03650 & 3.37273 & 6.39540 \\ \(ER\) & \(E_{max}/R\) & \(2\sqrt{6}\approx 4.89898\) & 9.41086 & 6.40490 & 8.34152 & 12.62199 & 24.68540 & 838.46663 \\ \(EH\) & \(E_{max}/H_{min}\) & \(\sqrt{3}/\sqrt{2}\approx 1.22474\) & 3.17968 & 2.74576 & 2.71390 & 4.63019 & 9.51595 & 383.60024 \\ \(MAXS\) & \(S_{max}/S\) & \(1/4=0.250\) & 0.32890 & 0.03993 & 0.32307 & 0.38640 & 0.43094 & 0.48228 \\ \(MINS\) & \(S_{min}/S\) & \(1/4=0.250\) & 0.17597 & 0.03685 & 0.18143 & 0.12600 1 & 0.10764 1 & 0.00286 1 \\ \hline \end{tabular}  
* In this row the values P90, P99 and max are replaced with P10, P5 and min, respectively.  
\end{table} 
Table 10: Mesh quality measures of all the models after improvement.
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## Appendix A Fitz Doppler Algorithm  
Fitz [17] gives a detailed derivation for the maximum likelihood frequency estimation. Here, we provide a simplified but adequate derivation.  
Given \(N\) complex samples, \(z_{n},n=1,\ldots,N\), the algorithm with 1-lag is:  
\[\omega_{1}=arg\sum conj(z_{n})z_{n+1} \tag{1}\]  
More generally, one can use the \(m\)'th lag [18; 19]:  
\[\omega_{m}=\frac{1}{m}arg\sum conj(z_{n})z_{n+m} \tag{2}\]  
A better estimate is a weighted average of \(J\) single-lag frequency estimations:  
\[\omega=\frac{\sum_{m=1}^{J}W_{m}\omega_{m}}{\sum_{m=1}^{J}W_{m}} \tag{3}\]  
where \(W_{m}\) is the weight assigned to the \(m\)'th lag.  
The ML weighting is \(W_{m}=\frac{1}{\sigma_{m}^{2}}\), where \(\sigma_{m}^{2}\) is the variance of the \(m\)'th lag frequency estimator. Assuming a pure complex sinusoid + Gaussian white noise (GWN):  
\[\sigma_{m}^{-2}\sim m^{2} \tag{4}\]  
Plugging Equations (2) and (4) into Equation (3) leads to exactly the [PERSON] algorithm (Equation (7) in this paper):  
\[\omega=\frac{\sum_{m=1}^{J}m\ arg\sum_{n} conj(z_{n})z_{n+m}}{\sum_{m=1}^{J}m^{2}} \tag{5}\]  
The [PERSON] paper shows that the algorithm converges on the CRLB in the first few lags. However, as stated in the last paragraph of his paper, \"in implementing this algorithm one needs to ensure that\":  
\[|\omega|\ll\pi/J\]The reason is obvious: with one lag, frequencies up to \(\pi\) can be estimated. With two lags aliasing starts at \(\pi/2\), with three lags aliasing starts at \(\pi/3\), etc. Making matters worse, successively higher lags are weighted more (with \(m^{2}\)) so the estimates that are most likely to be aliased are weighted the most.  
To deal with aliasing, we modify the original [PERSON] approach with the following recursive estimation process. We start with an estimate based on 1-lag. That estimate is used to heterodyne the time series closer to zero frequency, in order to avoid aliasing in the next recursion. Each successive recursion adds another lag and further heterodyning.  
\[1^{\text{st}}\text{ lag}:\ z_{n}^{\prime}=z_{n}\]  
\[\omega_{1}=arg\sum conj(z_{n}^{\prime})z_{n+1}^{\prime}\]  
\[2^{\text{nd}}\text{ lag}:\ z_{n}^{\prime}=z_{n}e^{-i\omega_{1}n}\]  
\[\omega_{2}=\omega_{1}+\frac{1}{2}arg\sum conj(z_{n}^{\prime})z_{n+2}^{\prime}\]  
\[3^{\text{nd}}\text{ lag}:\ z_{n}^{\prime}=z_{n}e^{-i\omega_{2}n}\]  
\[\omega_{3}=\frac{1}{3}arg\sum conj(z_{n}^{\prime})z_{n+3}^{\prime}\]  
and so on up to lag \(J\). The final estimate is the weighted average:  
\[\omega=\frac{\sum_{m=1}^{J}m^{2}\,\omega_{m}}{\sum_{m=1}^{J}m^{2}}\]  
The algorithm has been tested using simulations with various SNR in the range of 0.1 to 100,000 (\(-\)10 to 50 dB). The algorithm breaks down at SNR \(<\) 1 because the first lag does not provide sufficient frequency accuracy to initialize the second lag correctly, and so forth. However, for SNR \(>\) 1, there can be a dramatic improvement.
## 2 Materials and Methods  
### Monte Carlo Simulation  
MC simulation is a mathematical method based on repeated random samplings to obtain numerical results, and it is useful to solve some physical and mathematical problems when it is difficult to use other approaches. In medical biophysics, MC simulation is often used to predict radiation dosimetry [27]. Since it is difficult to visualize how radiation behaves when particles radiate to the human body without computational technology, having a simulation before the actual therapy can help determine the dose distribution of the patient in radiotherapy. The MC simulation is believed to be the benchmark of predicting dose in radiotherapy [28].  
In this study, MC simulation was used to predict the dosimetry of DNA with a gold nanoparticle irradiated by proton beams. The Geant4-DNA code was used in this nanodosimetric simulation. Geant4 (Geometry and Tracking) is a toolkit for the simulation of the passage of particles through matter, developed by CERN [29]. Geant4-DNA, an extension of the program to model biological damage induced by ionizing particles at the DNA scale using MC simulation, is written in C++ code working on the CMake platform [29; 30]. The toolkit has the capability of making the geometry of the system, materials involved, tracking of particles through materials, the response of detector components, visual of the detector and particle trajectories, and other relevant aspects. The program is from open source and can be accessed through the Geant4 Virtual Machine provided by the Center d'Etudes Nucleaires de Bordeaux-Gradignan [31]. Geant4 uses CMake3.3 or higher to run the code, C++ compiler and standard library supporting C++11 standard for programming. Geant4-DNA provides a virtual machine containing CentOS Linux, the latest version of Geant4 (version 10.7 was installed for this study), visualization tools, analysis tools, and other utilities are packaged with the Virtual Machine (VMware Workstation) [32].
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 3 System Model  
In this section, we present the LEO network scenario and backhaul model. Based on the network model, we propose the feeder link handover problem.
## 3 Results and Discussions
### Zeta Potential  
The zeta potential is a measure of the electrostatic potential difference between the dispersion medium and the surface of the particles. It is an important parameter that can affect the stability of the NPs in the dispersion medium. A higher absolute value of the zeta potential indicates greater stability of the NPs in the medium. In this investigation, we observed differences in the zeta potential values of the green-synthesized ZS-ZnO-NPs, which could be attributed to the different types of zinc salts used as precursors. The variation in the zeta potential of the ZS-ZnO-NPs synthesized using different zinc salts can be explained by the physicochemical properties of the zinc salts, which affect the formation, size, and morphology of the NPs. In this case, zinc chloride exhibited the highest zeta potential value at \(-\)46.7 \(\pm\) 0.5 mV (Figure 2 (a)), followed by Zinc nitrate at \(-\)41.5 \(\pm\) 0.1 mV (Figure 2 (d)). Zinc sulfate (ZnSO4-H2O) showed the lowest zeta potential value at \(-\)28.6 \(\pm\) 0.9 mV (Figure 2 (b)), while Zinc acetate had an intermediate value at \(-\)32.5 \(\pm\) 1.0 mV (Figure 2 (c)).  
The differences in the zeta potential values could be attributed to the different surface charges of the synthesized nanoparticles, which are influenced by several factors, including size, shape, and surface functionalization. The type of precursor used may have affected the size, structure, and shape of these nanoparticles, which could explain the observed differences in the zeta potential values [52]. The results suggest that the type of zinc salt precursor used can significantly influence the zeta potential of the synthesized ZS-ZnO-NPs, which may be attributed to the solubility and surface charge of the zinc salts. Further investigation using TEM and SEM techniques was conducted to understand the specific characteristics underlying these differences.
# Spatial Performance Indicators for Traffic Flow Prediction
## 2 Preliminaries
### Traffic Flow Prediction Problems  
Suppose that the traffic sensor at each node \(n\) measures the \(t\)-th traffic flow. The traffic prediction problem is used to predict the traffic flow data in the future. Given a traffic flow series \(X_{t-T+1:t}\) in the previous \(T\) time invervals, the traffic prediction method predicts \(T^{\prime}\) intervals in the future, which can be summarized as \([X_{t-T+1:t},\ldots X_{t}]\rightarrow[X_{t+1},\ldots X_{t+T}]\), where each vector \(X_{i}\in\mathbb{R}^{N}\) represents the traffic flow data of all nodes for each time interval in the traffic network. Traffic flow prediction problems are illustrated in Figure 1.  
Figure 1: Illustration of traffic flow prediction problems. The area highlighted in blue represents past states \([X_{t-T+1:t},\ldots X_{t}]\), which are utilized to predict future states \([X_{t+1},\ldots X_{t+T}]\), represented by the area highlighted in red.
## 1 Introduction  
The power supply system is an integral part of the plasma generation system, determining the possibility of using the plasma process on an industrial scale and requiring special design and construction methods. The correct operation of a non-thermal plasma arc reactor depends on the characteristics of the power supply system [1]. On the other hand, the power supply system reacts to such an unusual receiver as a plasma reactor. A big design and operational challenge is the generation by the plasma reactor of significant electromagnetic interferences, both conductive and radiated, as well as surges [2]. The regulation of output power and its parameters within wide limits, good operating properties and high energy efficiency and resistance to interference are the most desirable features of the plasma reactor power supply system.  
Generation of non-thermal plasma by means of arc discharge requires careful selection of the structure of the power supply system, i.e., topology of the system, selection of components and optimal selection of output parameters, such as values of current, voltage, their instantaneous course and frequency. Therefore, a plasma reactor power supply system is usually designed and constructed together with a plasma reactor and the required plasma process geometry, at a specific composition of process gas, its flow velocity and pressure.  
Transformer power supplies work well as power supplies for plasma reactors [3]. Their disadvantage is the inability to smoothly regulate the voltage, current and frequency of the reactor power supply to compensate unwanted fluctuations in the parameters of the plasma process. A good power supply system should be able to control the parameters of the generated plasma depending on the type of process gas, its flow velocity, pressure, humidity, chemical purity and requirements of the plasma process. Such conditions can be provided by specialised power electronic AC/DC/AC converters designed to work with an arc plasma reactor.
## 5 Modeling for Prediction and Optimization
### Parameter Configuration of the Models Used for Comparison  
In order to validate the predictive performance of the proposed joint model (PSO-LSSVM), other hybrid models (GA-BP) and single models (LSSVM and BP) were used for comparison purposes. BP neural networks were proposed by [PERSON] et al. [50]. Their structure consists of an input layer, a hidden layer, and an output layer. In this study, a three-layer neural network was used. BP has the disadvantage of high workload and reliance on empirical values when assigning the connection weights and thresholds between neurons [51]. GA belongs to the metaheuristic algorithm [33]. Using the algorithm function of GA (e.g., selection, crossover, and mutation) to optimize BP is considered adequate [52].  
According to the method in [53], the LSSVM was trained three times for the training datasets with three different sets of \(\gamma\) and \(\sigma^{2}\), i.e., (50, 1.9), (10, 0.6), and (4, 0.04). The one with the smallest RMSE was taken to participate in the comparison. The research experience in [52] was introduced to show that the BP and GA-BP methods were applied in the prediction of PPV. The accuracy of the training model was set to 0.0001; the learning rate was 0.1; the maximum number of iterations was 1000. The weight threshold was set in the range of \([-2,\,2]\) for the BP neural network without optimization. The multiple predictions (10 times) of BP were performed, and the group with the smallest RMSE was taken to participate in the discussion. All the RMSEs of LSSVM and BP in the modeling process are shown in Figure 8.
## 2 Experimental Setup
The results are summarized in Figure 4. The SHG pulses exhibited a 260-fs pulse duration. Furthermore, the spectral profile was much cleaner, which made it more suitable to be used as the reference for the XFROG. The obtained parameters of the reference pulse from the SHG-FROG were used in the XFROG algorithm. The generated SHG and SC were focused into a 0.1-mm lithium niobate (LiNBO\({}_{3}\)) crystal, and the sum-frequency generation (SFG) was achieved. XFROG traces were obtained by measuring the spectra of the SFG beam while changing the delay between the SHG and SC pulses. Figure 4: (**a**) Measured trace, (**b**) retrieved trace, (**c**) retrieved temporal profile, and (**d**) retrieved spectrum from the SHG-FROG with the best error value of 0.004. Figure 3: Recorded spectrum of the generated SC in 3-mm YAG.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 4 Regulation of Virulence and Antibiotic Resistance
### Biofilm Formation through Quorum Sensing  
This link between virulence and antibiotic resistance often resides within the formation of a biofilm community structure. Biofilm formation is an essential part of pathogenicity (or a combination of virulence and antibiotic resistance); thus, the formation is highly regulated by a complex network of transcriptional regulation. Formation of biofilms occur in several steps with each step typically associated with a specific cell surface organelle [146; 173]. Reversible attachment, where the bacterium may loosely attach to a surface and detach again is aided by flagella, fimbiae, and pili. Once the cells begin to produce adhesions and some extracellular polymer matrix the cells are considered irreversibly attached to the surface. During the maturation phase, the attached cells begin to proliferate and produce an extracellular polysaccharide matrix, which helps define the three-dimensional structure of the biofilm [146; 149]. The initiation of biofilm formation not only begins with quorum sensing, which may be considered a virulence regulator, but the resistance of the biofilm structure itself may also be considered a regulator of both virulence and antibiotic resistance. Once a biofilm is established, it can activate additional virulence genes within the biofilm again through quorum sensing or two-component systems. In _L. monocytogenes_, the VirR/S controls expression of 17 genes, including resistance to a variety of antibiotics and food preservatives [147]. Eliciting a response that upregulates genes involved in competence, conjugation, motility, sporulation, biofilm formation, and many other virulence factors [159]. Furthermore, in addition to biofilms being inherently more resistant to antibiotics, they also facilitate the transfer and spread of antibiotic resistance and virulence due to the close proximity of the cells. This process is accelerated when put under environmental stress, such as in the presence of an antimicrobial. Finally, the conversion of certain cells to persisters and regulation of virulence is associated with a significant enrichment of genes regulated by MyfR [174].
applied sciences Article  
Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems  
[PERSON] 1, [PERSON] 2, [PERSON] 1,* and [PERSON] 1  
Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, Japan Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-Cho, Toyonaka City 560-8531, Osaka, Japan Correspondence: [EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.  
multi-objective scheduling; estimation; weighting factors; machine learning; simulated annealing; inverse optimization
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.1 Dataset Considerations  
CNNs can be susceptible to biases present in training datasets, such as biased sampling or label noise, which can affect the validity of training results. They typically operate on fixed input spatial resolutions, which may not be optimal for images of varying size or aspect ratio, resulting in information loss or distortion. While pre-trained models for CNNs are well-established, pre-trained models for ViT are (still) less common for some datasets, which may affect the ease of use in some situations.
## 2 Techniques for Bessel and Perfect Vortex Beam Formation
### Transformation of Quasi-Bessel Beams into \"Perfect Vortex Beams\"  
As follows from expression (1), the diameters of the rings of Bessel beams grow with the topological charge \(\ell\). For some applications, for example, when radiation is input into dielectric fibers, this property is a disadvantage. In the case of Bessel beams, the problem can be solved by transforming them using a lens. From (1)-(4), it follows that, regardless of the magnitude and sign of the topological charge, if the beams have the same transverse wave number \(\kappa\), they form an annular beam of the radius  
\[R_{f}\approx f\frac{\kappa}{k_{z}}\approx\frac{\kappa}{k}\bigg{(}1+\frac{ \kappa^{2}}{2k^{2}}\bigg{)}f\approx\frac{\kappa}{k}f \tag{12}\]  
in the focal plane of the lens, the beam topological charge being equal to the charge of the initial beam. Such beams are usually called \"perfect vortex beams\" [14]. The amplitude-phase distribution in the real \"perfect vortex beams\", however, depends on the method used to create the \"parent\" quasi-Bessel beam. In [27], the authors showed that the radius and width of the ring of ideal beams obtained by focusing of Bessel-Gaussian beams increased with the topological charge and demonstrated that in experiments at a wavelength of 623 nm.