esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
To improve the security of the healthcare system most of the research is still relying on computationally heavy modular exponential operations or elliptic curve point multiplication operations. The structures of these operations are very complicated and the amount of calculations are huge. The proposed authentication and key agreement Scheme using extended Chaotic Maps has a number of advantages compared to previous health data system. The first is that it can reduce the amount of computing on wearable devices to achieve lightness to quantify the effect of the operation. The password system based on identity authentication can reduce the information that the device needs to store. It only needs to save the private key of the device, which it obtains from the registration server. After the mobile device collects the physiological data of the wearable device, it is uploaded to the database of the medical center server for storage. The second advantage is the data is calculated for the message summary and uploaded to the blockchain for storage. Using the non-tampering feature of the blockchain, the data in the medical center server can be compared with the message summary on the blockchain to ensure that the data has not been tampered with and improve data security. In terms of security, the proposed mechanism can resist a variety of attacks to reduce to possibility of privacy disclosure.  
However, the current scheme still needs to be improved. There are some limitations to the proposed scheme. The first is about the structure of the data stored in the blockchain. It is difficult to query data within a blockchain, limiting the statistical and research uses of data. A better data structure could be setup to write a large number of different health data in the blockchain for more efficiency and to implement high concurrency. The second limitation is the scalability issue for healthcare big data, due to blockchain using a decentralized architecture. If millions of users, including private clinics, healthcare centers, patients, and IoT startups etc., were to use different block chain infrastructures and blockchain technologies, a higher computation power would be required, which not every user would be able to provide.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
## 3 Experiments
### Actual Experiment  
In order to verify the ability of the remote multispectral thermal imager to integrate temperature measurements and distance measurements in flame measurement, the experiment photographed the combustion process of two sets of dynamic flames. The flame is a transient flame containing aluminum ions with a duration of about 1 s. The temperature field image obtained by the multispectral true temperature inversion method based on the verification constant constraint model is shown in Figure 5a,c. The flame distance is calculated by the model proposed above, and the change relationship between the maximum temperature and distance is shown in Figure 5b,d.  
As can be seen from the results in Figure 5, there is a certain correlation between the flame temperature change and the distance. As the flame temperature decreases, the distance between the flame and the multispectral thermal imager also decreases. When the flame distance changes by 50 m, the flame temperature decreases by 100 K.  
According to Formula (4), the calculation accuracy of distance depends on the calculation of image offset of the multi-aperture temperature field. Through the measurement results of the complex flame temperature field, it can be found that the complex optical environment will not increase the measurement error of distance. Therefore, the main problem of precision reduction is still the accuracy of image matching, and the physical characteristics of the multi-aperture spectroscopy structure limit its accuracy at a longer distance. Therefore, it is necessary to carry out high-precision calibration of the instrument before the measurement and improve the accuracy of the image-matching process.  
Figure 4: Simulation results. (**a**) Original image captured by multispectral thermal imager. (**b**) Comparison between measured value and real value.
###### Abstract  
In this study, we evaluated the antibacterial effect of floating electrode-dielectric barrier discharge (FE-DBD) plasma (1.1 kV, 43 kHz, N\({}_{2}\) 1.5 m/s, 1-60 min) against _Staphylococcus aureus_ and _Salmonella_ Typhimurium in fired fish paste. In addition, a quality evaluation (pH, VBN) of fried fish paste was conducted after the FE-DBD plasma treatment. When FE-DBD plasma was used for treatment for 1, 5, 10, 20, 30, and 60 min, _S. aureus_ decreased by 0.16-1.13 log\({}_{10}\) CFU/g, and \(S\). Typhimurium decreased by 0.25-1.13 log\({}_{10}\) CFU/g. Both decreased > log\({}_{10}\) CFU/g at 60 min. The D-value was 58.92, and R\({}^{2}\) was 0.97 for \(S\). _Aureus_ using first-order kinetics, and the D-value was 43.60, and R\({}^{2}\) was 0.97 for \(S\). Typhimurium using the Weibull model. There was no significant difference in pH after the FE-DBD plasma treatment (_p_ > 0.05). Additionally, volatile basic nitrogen (VBN) significantly decreased as the treatment time increased (_p_ < 0.05), and it was the lowest 3.46 at 60 min. Therefore, this FE-DBD plasma treatment could be considered a technology for preserving the quality of processed foods.  
fried fish paste; FE-DBD plasma; first-order kinetics; _Staphylococcus aureus; Salmonella Typhimurium; Weibull model Article  
The Efficacy of Floating Electro-Dielectric Barrier Discharge Plasma against _Staphylococcus aureus_ and _Salmonella_ Typhimurium on Fried Fish Paste. _Appl. Sci._**2024**, 14, 1875. [[https://doi.org/10.3390/appl4051875](https://doi.org/10.3390/appl4051875)]([https://doi.org/10.3390/appl4051875](https://doi.org/10.3390/appl4051875))
# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 4 Configurations of the System under Study
### Electric Vehicle Load Model  
EVs were modeled as a dynamic PQ capturing EV charging loads in every minute. Daily profiles of EV charging loads were synthesized based on real datasets. A real diurnal mean profile was selected as a reference from the CLNR project \"TC6 Dataset\" workbook (_i.e._, the daily mean profile of charging power for 93 EVs in January 2014) [31]. EV charging profiles were individually generated in MATLAB using the \"_randi_\" function to produce the diversity across EV charging profiles. This function generates integer random numbers. Matrices of zeros and ones were generated using the \"_randi_\" function (one for charging state and zero for idle state). Departure and arrival times were assigned per EV based on the National Travel Surveys (NTS) of the UK from the Department for Transport [32] during working days.  
At transformer T8, all residential customers were assumed to have a single phase charger of 3.3 kW per hour at slow charging mode. Therefore, a 100% EV uptake was considered across residential customers at transformer T8 (see Figure 2). EV load profiles were synthesized as follows:  
* Generate the requested minutes for charging each EV (less than or equal 480 min a day) using the \"_randi_\" function.
* Synthesize the daily load profile of each EV by assigning zero for idle state and one for charging state.
* Concatenate minute-by-minute the charging profiles of 384 EVs in one matrix (384 \(\times\) 1440).
* Shift charging loads of the produced matrix according to arrival and departure times to match the pattern of the real daily mean profile from [31].  
Figure 4 represents the comparison between the daily mean of the synthesized EV charging loads and the daily mean of the real EV charging loads. It can be seen that the synthesized daily mean of 384 EVs is greater than the daily mean of 93 EVs obtained from CLNR datasets. However, their daily patterns are approximately similar. Figure 5 illustrates charging durations of 384 EVs during a day with one-minute time resolution.  
Figure 3: Profiles of, (**a**) the aggregated load of 384 customers for each low voltage (LV) transformer, (**b**) the daily mean of real and generated loads.
## 4 Empirical Assessment Using Real-World Data
### Results
#### 4.3.2 Regression Stage  
In the regression stage of our analysis, we specifically sought models that are adept at handling positive count data, as our dataset does not contain zero-crash frequencies at this stage. For this task, we employed two distinct types of models: zero-truncated models developed in R using the PSCL package, which are particularly designed for datasets without zero counts, and the CatBoost models developed in a Python 3.8 environment using the Scikit-learn library. As before, the data were split at an 80:20 ratio to ensure consistent training and testing datasets across the analysis. The results showcased in Table 5 provide a comprehensive look at each model's performance.  
The zero-truncated Poisson model typically assumes that the mean and variance of the distribution are equal, which may not be a suitable assumption for all datasets, as indicated by its higher RMSE (4.873) and MAE (1.92). These metrics suggest a less accurate fit for our crash frequency prediction. The zero-truncated negative binomial model extends the Poisson model by allowing for variance that exceeds the mean, often a more realistic assumption for real-world count data. This is reflected in its improved performance, with lower RMSE (2.321) and MAE (1.282) values. The CatBoost model with the default loss function demonstrated further improvements in predictive accuracy. Notably, the CatBoost model with the Poisson loss function significantly enhanced the model's predictive accuracy, with the lowest RMSE (1.855) and MAE (0.916). These metrics illustrate the model's precision and its effective handling of the count nature of our crash frequency data. In essence, the CatBoost model with the Poisson loss function not only demonstrates superior performance in predictive accuracy but also exemplifies stability and reliability in its learning process, as evidenced by the behavior of its loss curves over numerous iterations.  
The plot of training and validation losses for the CatBoost model with the Poisson loss function, as shown in Figure 6, confirms these findings. The plot reveals a significant initial decrease in training loss, indicating a rapid capture of the underlying distribution in the data. The alignment and stabilization of training and validation losses suggest a model  
Figure 5: Training and validation losses for custom classification model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **RMSE** & **MAE** \\ \hline Zero-Truncated Poisson & 4.873 & 1.92 \\ Zero-Truncated Negative Binomial & 2.321 & 1.282 \\ CatBoost (Default Loss Function) & 1.997 & 1.101 \\ CatBoost (Poisson Loss Function) & 1.855 & 0.916 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix.  
that is well tuned and generalizes effectively to unseen data, as shown by the narrow gap between the two curves.
###### Abstract  
This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input-multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.  
m 2025, 12, 496
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 3 Considerations on Filter Designing
### Cutoff Frequency Shift  
After applying the steps of design for one filter, the plot of scattering parameters reveals that the effective cutoff frequency does not lie in the proposed \(f_{c}\) value. The cutoff frequency of the obtained filter could be shifted even up to 0.5 GHz from the desired value.  
As an example of the shift compensation for \(f_{c}\), a Chebyshev filter \(L_{A_{r}}=0.01\) of seventh order was designed for a cutoff frequency of 2 GHz. Following the design steps, without shift compensation, the effective cutoff frequency lies at 2.486 GHz, as can be seen in the plot of Figure 5, red line. After applying the \(f_{c}\) shift compensation, the cutoff frequency lies at 2 GHz, as shown in the same figure. However, in order to obtain the filter with shift compensation, in this case, the filter should be designed by following the steps for a filter of \(f_{c}=1.6179\) GHz.
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### Neighborhood Sink Discovery  
In MSCWSNs-PC, only one packet frame format need to be defined, which is denoted as the _HELLO_ frame. As shown in Figure 3 the _HELLO_ packet carries the _ID_ and location information of the sender. After the network initialization, each sink first pre-detects its coarse position in the work area, such as topside, underside, leftside or rightside. Let all sink nodes belong to the corresponding set of _Topside_, _Underside_, _Lefside_ or _Rightside_. Each sink knows its neighborhood sink by the process of sink discovery communication between them.
###### Abstract  
Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN's lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HB), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 10% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.  
Keywords:IDS; machine learning; DoS; WSN security; feature selection; LEACH +
Footnote †: journal: Sensors
# A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review
## 2 Software
### Numerical-Based Approaches
#### 2.1.2. FRANC3D
* Crack growth direction: FRANC3D assumes that the crack growth occurs along the predefined crack front, and it does not account for changes in crack growth direction during the simulation. This can be a limitation when dealing with complex crack growth paths or when considering crack branching [41]. * Load redistribution: FRANC3D may not consider load redistribution effects as the crack propagates. This can lead to inaccuracies in stress intensity factor calculations and crack growth predictions, particularly in cases where load redistribution significantly impacts the crack growth behavior [42]. * FRANC3D is designed to handle arbitrarily complex component geometries and local loading conditions. However, when dealing with non-planar crack growth, especially under plane strain conditions, FRANC3D may face challenges. Local crack front elements can become highly distorted.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening
### Temperature Increase  
PD results in the decrease of laser power, and the lost energy is eventually converted into the heat inside the fiber, which causes the temperature of the fiber to rise. In 2010, [PERSON] et al. [38] measured the temperature of Yb-doped LMA fiber during a PD experiment using a thermal camera. In these experiments, the fiber temperature was measured while PD experiments were performed using infrared radiation (i.e., 915 nm). When the pump power was 5.7 W and 10.5 W, the attenuation of the core absorption coefficient and the change of the fiber temperature during the pumping process of the probe signal with the wavelength of 600 nm were detected simultaneously. They observed that the temperature increased as the loss increased (Figure 7).
# Experimental Research on the Effects of Suction Ports on Twin Screw Expander Performance
## 4 Results and Discussion  
### Effects of Rotation Speed  
With a suction pressure of 442 kPa and a discharge pressure of 200 kPa, \(\Delta p\) and \(\alpha\) variations according to rotation speed are shown in Figure 5. With the rotation speed increased from 1400 rpm to 1600 rpm, \(\Delta p\) through the suction port increased from 47.1 kPa to 62.8 kPa. A higher rotation speed meant a shorter suction time and, consequently, a higher pressure loss. \(\alpha\) increased from 0.106 to 0.142. \(\eta_{s}\) variations with rotation speed are shown in Figure 6. With the rotation speed increased from 1400 rpm to 1600 rpm, \(\eta_{s}\) decreased from 0.965 to 0.912. The performance of the twin screw expander with different rotation speeds indicated that, under certain working conditions, a lower rotation speed decreases the \(\Delta p\) and increases \(\eta_{s}\).
## 1 Introduction
### Advantages of Using CR in WSNs
#### Energy Efficiency  
In WSNs, there is a large amount of power waste for packet retransmission due to packet losses. CR wireless sensors may be able to change their operating parameters to adapt to channel conditions. Therefore, energy consumption due to a packet collision and retransmission can be mitigated.
## 4 Compositions Analysis of PM\({}_{10}\) from Drilling Operations  
To assess the danger level of PM\({}_{10}\) from drilling operations to community health and surroundings, the compositions of PM\({}_{10}\) from drilling operations at the Coc Sau open-pit coal mine were analyzed by energy dispersive X-ray spectroscopy (SEM-EDS) (Quanta 450--FEI Company, Hillsboro, OR, USA). Accordingly, three dust samples of different boreholes were collected at the Coc Sau open-pit coal mine for analysis of their size and composition. The results from the scanning electron microscope coupled with SEM-EDS showed that the dust particles from the drilling process in the study area were relatively small with a size of less than ten \(\upmu\)m. The composition of the dust consisted of many different elements, such as Al, Si, K, Fe (Figure 6), which are very dangerous for human health when breathed in. Therefore, accurate predictions of PM\({}_{10}\) concentration from drilling operations in open-pit mines are necessary to minimize impact on public health and the surrounding environment.  
Figure 6: The composition of dust particles in drilling operations at the Coc Sau open-pit coal mine. The \(y\)-axis depicts the number of counts, and the \(x\)-axis represents the energy of the X-rays.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests  
In order to evaluate a prototype of the system, we conducted extensive experiments in an office facility with different types of areas. The location map is depicted in Figure 9 and has a size of 431.8 m\({}^{2}\). This scenario represents a typical environment with furniture, people working, opening/closing doors and commercial electronic devices (computers, routers, WiFi networks, _etc._). The map has been divided into five rooms of interest: two offices, one store, one garage and a backyard. The system has been designed with a total number of seventeen SRs positioned around the building at different reference points: five SRs with omnidirectional antennas (OA1-OA5) and twelve SRs with directional antennas (DA1-DA12). The defined rooms and the location/orientation of the SRs deployed in the scenario can be seen. Note that before the deployment of the system, the planning phase was previously performed, simulating the indoor environment and selecting the SRs' initial positions. A connectivity of _PRP_ > 90% to at least one SR from any location of the scenario was ensured.  
Figure 8: Probability of received packet (PRP) _vs._ received signal strength.  
Figure 9: Experimental site and the sensor readers (SRs) deployed. Red arrows indicate the orientation of directional antennas.  
To analyze the effect of integrating directional antennas on the system performance, different configurations of SRs with different numbers and types of antennas (directional/omnidirectional antennas) have been tested. For each configuration, the overall success probability \(P_{S}\) and specific \(PM\) were calculated. Next, we describe the deployment of the prototype system on the real scenario. Later, a discussion is also given about the experimental results regarding the effect of different configurations of omnidirectional/directional antennas.