esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 3 System Design
### Command and Control Center  
The main role of the command and control center in the context of the UWB-based IPS is to support its operation by storing the information related to the network's infrastructure (i.e., an updated dictionary of anchors' availability and position), so that it can be downloaded by the tag when necessary. In addition, it also receives the positions of the FRs computed by the tags, stores them in a database, and enables their visualization in real time.  
It is worth noting here that the use of cloud systems or even Internet access may not be fully available in disaster-affected areas due to power outages or unstable communication links. For that reason, when facing a real ER operation it would be highly advisable to move the command and control center as close to the disaster-affected area as possible. A dedicated emergency van is a very convenient option for this purpose, as it can quickly approach the incident area and provide FRs with the required services [63].
# Application of Artificial Neural Network to Somatotype Determination
###### Abstract  
Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath-Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), electromy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath-Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, \(\chi^{2}\) = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, \(\chi^{2}\) = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and \(\chi^{2}\) = 0.51. The bioelectrical impedance analysis and Heath-Carter method compliance was evaluated with the statistical algorithm proposed by [PERSON] and [PERSON]. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women's ratings with high accuracy and agreement with the Heath-Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future.  
s +
Footnote †: journal: Applied Sciences  
omatotype; mathematical model; artificial neural network
## 3 Experimental Set-Up and Electronic Equipment
### Experimental Set-Up  
In Figure 6 (right), the main blocks and connection of the SHM system are described. The main characteristics of the data acquisition system are listed below. Programmable parameters:  
* VGA gain from 54 to 90 dB to adjust for different sensor sensitivities
* High voltage, up to 100 V, for active mode operation
* Square wave burst (1-16 cycles), up to 500 kHz, for active mode operation Data acquisition system characteristics:
* 12-bit resolution
* 16 channels at 20 MSps
* Storage: 1 MS per channel The main components of the block diagram for the data acquisition system are the: (1) sensor layout installed on the plate-like structure, coordinate reference system, and impact point markers (#1, #2, #3, #4, #5, etc.), (2) signal-conditioning electronics, (3) multichannel VGA with ADC evaluation module (Texas Instruments AFE5851 EVM, Dallas, TX, USA), and  
Figure 5: Comparison between original signal and filtered signal with high-pass second-order filter, with cut-off frequency 270 kHz followed by a 25-dB gain amplifier (see Figure 4). The signals were normalized and the distance between the impact point and the sensor was 10 cm. The two ultrasonic-guided modes A\({}_{0}\) (marked by blue box) and S\({}_{0}\) (marked by red oval) are well discriminated with high SNR.  
(4) Spartan-6 FPGA evaluation card (Xilinx SP605).  
The experimental set-up adopted is reported in Figure 6 (left), where only 8 out of 16 channels were programmed for passive monitoring of impacts. Figure 6 (left) shows the low-energy hammer impactor in the center of the aluminum plate where the eight piezoelectric sensor arrangement was placed. The eight sensors were placed along the perimeter of the ROI (see Figure 1) and their arrangement is shown in the experimental results section below.  
The high number of sensors were also able to reveal impacts outside the ROI, up to the full size of the aluminum plate. The four edges of the aluminum plate were covered by colored play dough (see the colored edges in Figure 6) to avoid spurious reflections of the ultrasonic signal due to the edges. This simple method turned out to be necessary to manage boundary reflection due to the free edges of the plate for estimation of impact position carried out outside of the ROI. The problem concerning multiple reflections from the plate edges can also be addressed by selecting a time according to the geometry of the plate-like structure [17]. More clever algorithms exploit, also, the information retrieved by multiple reflections from boundaries to minimize the number of sensors installed on the structure [23].  
The next section reports the analysis of different errors obtained for different impact points (inside and outside the ROI) and for different numbers of channels used to find the minimum of function (1).
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 7 Comparative Discussion of Proposed Findings and Relevant Case Studies
### Comparison of Intersection Operations  
MUT, RCUT, and DLT intersections are designed to address the challenges caused by left-turn movements at conventional intersections, which frequently lead to traffic congestion and safety risks. However, each design employs a different approach to achieve this objective. For the MUT design, it works by rerouting left-turning vehicles through a median U-turn instead of allowing direct left turns at the intersection. This design eliminates  
Figure 13: Vehicle delay reduction for different green time percentages and signal cycle durations.  
left-turn conflicts with through traffic. Findings from this study indicate that MUT intersections significantly reduce the number of conflict points and improve operational efficiency by decreasing vehicle delays. The field data collected from a MUT intersection in Xi'an, China, confirm that the MUT design effectively addresses congestion issues and enhances the safety performance of the intersection. With regard to the RCUT design, it reduces left-turn conflicts by requiring left-turning vehicles to perform a U-turn at a specific location rather than directly at the intersection. Although the RCUT design does not eliminate all left-turn movements, it can reduce congestion at the intersection. According to [PERSON] et al., RCUT intersections are effective in reducing accidents and improving traffic flow, particularly by reducing left-turn and head-on collisions [49]. [PERSON] et al. further emphasize that RCUT intersections can reduce crash rates by up to 50% compared to conventional intersections [50]. Lastly, the DLT design redirects left-turn lanes away from the intersection to a position either before or after the main intersection. DLT allows left-turning vehicles to bypass the intersection, reducing conflicts between left-turning vehicles and through traffic. Research conducted by [PERSON] et al. and [PERSON] et al. suggests that DLT intersections can effectively reduce delays and improve safety by separating left-turn movements from through traffic [51; 52]. However, while the DLT design improves operational efficiency by reducing direct conflicts, it does not fully resolve the potential for congestion caused by the remaining left-turn traffic. In comparison, MUT intersections have the advantage of completely eliminating left-turn movements, resulting in a potentially greater reduction in conflict points and accidents.
## 3 Results and Discussion
### Linearity  
The HPLC/ELSD method was developed using an external standardization with d-fructose as standard. Because ELSD resulted in a non-linear response, the calibration curve was plotted in Log/Log scale (as shown in Figure 3). The linearity of the response was acceptable in the range between 0.2 and 0.6 mg/mL. More precisely, the measurement of goodness-of-fit (r\({}^{2}\)) was higher than 0.995 (slope of the calibration curve was found at 0.681 with intercept of \(-\)5.282).
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 4 Discussion and Recommendation
### Impact of Insufficient Temporal Coverage  
As briefly mentioned in the Introduction, both spectrum users and regulators would be interested in trend information, preferably in de form of a graph showing the increase or decrease of the radio noise over time. This information could be used to either tighten or relax the regulations on unwanted emissions. To provide that information, consistent and repeated measurements over time are needed. Figure 9 shows the distributions of the measurements over time. Unfortunately, consistently repeated measurements were only done in Germany between 2006 and 2013. They will allow (local) trend analysis over that interval only and only in one country.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Segmentation Results of Texture Image  
This part of our experiment tests the segmentation performance of texture images. Figure 5a shows the original texture image, which is derived from [4]. Moreover, the compared models are still the C-V, LBF, SBGFRLS, LIF, and LSACM models. According to Figure 5c,e,f, the object boundaries of the first image are not identified by the LBF, LIF, or LSACM model, respectively. Most of the boundaries are obtained by the SBGFRLS model. However, the internal details are not recognized; the detailed results are illustrated in Figure 5d. Figure 5e shows that the LIF model fails to segment the second image. Although the C-V, LBF, SBGFRLS, and LSACM models recognize the true boundaries of the second image, some boundaries lie in the middle of the image; the results are illustrated in Row 2 of Figure 5. Table 4 describes the segmentation results of Figure 5. Table 4 shows that the SPFLIF-IS model performs the best, the C-V model exhibits the second best performance, and the LIF model shows as poor a performance as the LSACM model. The SPFLIF-IS model can eliminate the interference of the image texture and analyze the texture image well.  
Figure 4: The segmentation results obtained for images with strong noise using the six models. (**a**) Original image, (**b**) C–V model, (**c**) LBF model, (**d**) SBGFRLS model, (**e**) LIF model, (**f**) LSACM model, and (**g**) SPFLIF-IS model.
## 3 Aim, Materials, and Methods
### Rescue Team Scheduling Mode of Multi-Dimensional Resource Integration
#### 4.5.1 Medical Rescue Team  
In the event of a natural disaster, relevant agencies in China will immediately start the emergency plan according to the actual situation, casualties, and injuries. The system will intelligently assign a certain number of medical personnel to the affected area for rescue. These medical personnel usually come from cross-regional medical institutions across the country. They will gather and form a rescue team in the initial time after the disaster, carrying necessary medical equipment and medicine to the affected area. In the rescue process, medical personnel not only need to provide on-site first aid to the injured, but also need to classify the condition and transfer the seriously injured to nearby hospitals for further treatment. In order to ensure the smooth development of rescue work, China will also mobilize various resources to provide necessary living materials and infrastructure support to the affected area in the initial time after the disaster. For example, the rapid establishment of temporary hospitals, provision of clean drinking water and food, and guarantee of the normal operation of power and communication facilities will be paid attention to and implemented.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 5 Results and Discussion
### Model Comparison  
The data in Table 5 show significant differences among the models in terms of parameter count and prediction time. The RF model has the shortest prediction time, while the BP neural network model has the fewest parameters. In contrast, the CLMFD model has the highest number of parameters, but its prediction time is second only to the RF model, outperforming the other models.  
The results in Table 6 demonstrate that the CLMFD model excels across various evaluation metrics (e.g., MAE, RMSE), showcasing superior predictive performance and highlighting its exceptional modeling capability and generalization ability. Moreover, to ensure the reliability and stability of the experimental results, all experiments were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Combination** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline CLMFD(1) & 0.1055 & 0.3248 & 0.2102 & 0.6988 \\ CLMFD(2) & 0.0977 & 0.3125 & 0.2068 & 0.7211 \\ CLMFD(3) & 0.1043 & 0.3231 & 0.2134 & 0.7020 \\ CLMFD(12) & 0.1048 & 0.3237 & 0.2212 & 0.7008 \\ CLMFD(13) & 0.1027 & 0.3205 & 0.2071 & 0.7067 \\ CLMFD(23) & 0.0962 & 0.3102 & 0.2078 & 0.6716 \\ CLMFD(123) & 0.1053 & 0.3245 & 0.2129 & 0.6993 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of Different Hidden Layers as Intermediate Features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Parameters (Million)** & **Prediction Time (Seconds)** \\ \hline BP neural network & 0.02 & 0.18 \\ RF & 1.40 & 0.06 \\ RNN & 0.07 & 0.32 \\ Transformer & 0.15 & 4.06 \\ PatchTST & 0.41 & 7.18 \\ CLMFD & 13.05 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The running time and number of parameters for each model.  
conducted multiple times independently, and the average values were taken to minimize the impact of random factors on performance evaluation.  
The two subgraphs in Figure 6, respectively, show the absolute errors between the predicted values and the true values of each prediction model in 40 samples. In order to clearly compare the performance of different models, the first sub-graph shows the first 20 samples, and the second sub-graph shows the last 20 samples. As a whole, it can be seen that the error of the CLMFD model is the smallest, and the predicted value is the closest to the real value.  
Figures 7 and 8, respectively, present the comparison between the predicted and actual fuel consumption values of the CLMFD model on the Xi'an-Shanyang and Xi'an-Baomao routes. From the figures, it can be observed that the CLMFD model maintains a high level of prediction accuracy on both routes.
# Mobility Prediction of Mobile Wireless Nodes
## 5 Methodology  
The current study was concerned with mobility models and how ML algorithms are affected by these models. We used four mobility models, i.e., GM, RD, RW, and RSSGM, to perform mobility node movement, generating a dataset of user locations, as well as current and previously connected access points. Then, different classifiers were applied, i.e., DT, LR, KNN, LDA, GNB, and SVM, using the previously produced dataset, to predict user activity. Finally, we evaluated each classifier's accuracy to determine which one could best predict mobility behavior. Figure 2 briefly illustrates the steps taken in this study.  
In the first phase of this work, mobility models were implemented using Python in sixteen scenarios using a variable number of access points (5, 10, 15, and 20) alternately  
Figure 2: The method used in our study.  
with a different number (5, 10, 15, and 20) of nodes that have recurrent visits--referred to as centroids. All of the nodes (i.e., access points and centroids) had fixed locations in a predefined domain with an area of [\(-\)1000, 1000] meters. Table 1 shows the locations of chosen nodes. The positions of the nodes were randomly distributed to cover the entire region. Figure 3 shows an example of how nodes extended into a certain area, using fifteen access points and ten centroids as an example. We applied the mobility models using 5000 steps, which represents the average amount an adult walks in a day [37]. The execution of the mobility model provides a dataset of user positions that the node will visit before reaching its target location. The connected access point is determined using the shortest-path method to identify the nearest access point (AP). Table 2 shows some of the data generated using the RSSGM model.  
In the second phase, we used six ML classifiers (i.e., DT, LR, KNN, LDA, GNB, and SVM) based on the dataset of locations generated by different mobility models, current and previous APs. The ML classifier was also used to predict the future connected AP, that is to say the AP that the node will connect with. Finally, the classifier accuracy metrics' performance was evaluated using the Scikit-learn library. Scikit-learn is a well-known library dealing with issues associated with data mining and data analysis [38]. Subsequently, we used this information to determine the best classifier algorithm with multiple mobility models to provide better movement prediction based on classifier accuracy results.  
Figure 3: Example of how nodes extend over the predefined area using 15 access points and 10 centroids.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 4 Diversity Performance Parameters  
### Envelope Correlation Coefficient (ECC)  
To evaluate the proposed MIMO antenna's diversity performance, ECC is analyzed and presented in Figure 14. The far-field characteristics are used to determine the ECC as [27,28]:  
\[ECC=\left|\frac{\iint_{4\pi}S_{i}(\theta,\phi).S_{j}^{*}(\theta,\phi)d\Omega}{ \sqrt{\iint_{4\pi}S_{i}(\theta,\phi).S_{i}^{*}(\theta,\phi)d\Omega\iint_{4\pi}S _{j}(\theta,\phi).S_{j}^{*}(\theta,\phi)d\Omega}}\right|^{2} \tag{1}\]  
where \(S_{i}\) and \(S_{j}\) represents far-field radiation characteristics of port \(i\) and port \(j\).  
According to [29], the value of ECC should be set at \(<\)0.5 for practical applications. As shown in Figure 14, the proposed MIMO antenna exhibits ECC \(<\) 0.1.
# Skin Absorbed Dose Coefficients for Human Legs from Beta Radiation as a Function of Height
###### Abstract
In this manuscript, we limited the energy range for beta particles to 4 MeV and used the word electron to refer to beta particles unless it is mentioned otherwise. The outer layer of the skin can act as a shield against very short-range radiation, such as alpha particles or beta particles with energy less than 60 keV, as beta particles below this energy range cannot penetrate the outer layer of the skin [23]. Beta emissions with higher energy particles than that threshold can damage the skin tissue; thus, several models have been developed to measure the energy deposited in human skin tissue due to beta emissions in health physics and biomedical studies. [PERSON] et al. used a Monte Carlo simulation to calculate the absorbed dose deposited into human skin from different radionuclides that emitted beta particles [24]. VARSKIN 5 is a computer code that is widely used to calculate the absorbed dose to the skin [25]. [PERSON] et al. compared and verified this program with the results from MCNP 5 calculations [26]. [PERSON] et al. measured the accuracy of VARSKIN 5 with GAMOS, a Geant4-based health physics and medical physics simulator for beta sources. They calculated the absorbed dose at 70 um depth of skin from the beta decay of 20 different radionuclides [27]. Similar Monte Carlo simulations were performed to estimate the absorbed dose from beta particles to health personnel in medical radiology centers [28]. Geant4 is a computer program based on C++ that simulates the interaction between different radioactive particles through matter, and it is widely used to estimate the absorbed dose estimations in Monte Carlo studies [29; 30; 31; 32]. Recently, [PERSON] et al. used several simulation packages, including Geant4, to estimate the skin absorbed dose coefficient (SADC) [33]. Skin-absorbed dose coefficients are important values in radiation. In the event of a nuclear disaster, knowing these values for different radioactive emissions, including beta decay, would be essential for the safety of personnel and surrounding people. [PERSON] implemented [PERSON]'s electron point kernels to calculate the absorbed dose from different beta emission sources [34]. He also included contributions from bremsstrahlung emissions based on the National Institute of Standards and Technology(NIST) dataset. This dataset was used in Federal Guidance Report 12 to calculate the skin absorbed dose coefficient for human skin tissue located at 1 m above the soil contaminated with beta decay [35]. The U.S. Environmental Protection Agency has published several versions of these federal guidance reports over the past few decades to ensure the consistency of assessments of ionizing radiation [35; 36]. This technical information provided in the most recent Federal Guidance Report, 12, is widely used for radiation protection in the U.S. to ensure the population's safety during unexpected incidents. This published data will be used in the current manuscript to ensure the accuracy of the results.