esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology  
Our methodology follows the pattern shown in Figure 1. The first step is to acquire raw data, which will be in the form of a dataset. The dataset contains both of VOCs features and the patient's diagnosis, either malignant or benign. The dataset will then be reduced using dimensionality reduction techniques. In our methodology, a correlation coefficient and maximum relevance-minimum redundancy (mRMR) are used for dimensionality reduction. An algorithm known as a classifier is then used to implement classification. We used 9 different classifiers in our methodology. The dataset is split into training and testing datasets. These datasets are locally acquired for classification. The training dataset is used to train the classifier, while the testing dataset is then used to evaluate the classifier's performance on new data that are not seen in the training phase.  
The data acquisition process is as follows. The exhaled breaths collected in 1 L Tedlar bags were drawn through a microreactor chip by applying a vacuum, as shown in Figure 2. Then, 2-(Aminooxy)-_N,N,N_-trimethylethanolammonium (ATM) iodide is used to coat the micropillars of the microreactor chip. ATM uses oximation processes to chemoselectively capture the carbonyl molecules in the exhaled breath. ATM adducts in the microreactor chip were eluted with 100 micro-liters of methanol from a gently pressurized tiny vial after the breath sample was entirely evacuated from the Tedlar bag. Fourier-transform ion cyclotron resonance mass  
\begin{table}
\begin{tabular}{l l} \hline Age (years) & 30–96 \\ \hline Malignant & 252 \\ \hline Height (cm) & 126–193 \\ \hline Weight (Kg) & 33–183 \\ \hline Active smoker & 169 \\ \hline Previous smoker & 232 \\ \hline Lifelong non-smoker & 100 \\ \hline Personal history of lung cancer & 61 \\ \hline Family history of lung cancer & 125 \\ \hline \end{tabular}
\end{table}
Table 1: Patients clinical characteristics.  
spectrometry (FT-ICR-MS) was used to examine the eluted solution directly. The FT-ICR-MS is a hybrid linear ion trap MS (Finnigan LTQ FT, Thermo Electron, Bremen, Germany) with a TriVersaNanoMate ion source (AdvionBioSciences, Ithaca, NY, USA), and an electrospray chip (nozzle inner diameter 5.5 micro-liter) was used to analyze all breath samples using the eluted solution. As an internal reference for measurement of ATM adducts, a known quantity of deuterated acetone fully reacted with ATM (ATM-acetone-d6) in methanol was added to the eluted solution. The relative abundance of all 27 carbonyl VOCs identified in the exhaled breath was compared to that of the added ATM-acetone-d6 to determine their concentrations. Please see [26] for more details.  
The only reprocessing that occurred to the features is the normalization for each feature to be in the range from zero to one. This normalization helps in solving the machine learning challenges by making the learning process faster. In the following subsections, the feature selection and classification steps were described in detail.
# On the Performance of Underlay Device-to-Device Communications
## 5 Simulation Results
We observe that the behavior of the average sum rate with respect to the network radius \(O\) is  
Figure 2.0 Outage Probability of the first D2D receiver vs. \(R\) under various values of network radius \(O\). Solid lines are plotted by employing (12). The location of all devices (both D2D transmitters, receivers, and cellular users) are randomly distributed under the coverage area of the BS. Figure 2.1 Amount of Fading vs. \(P_{\text{tot}}\) under three distinct schemes. Solid lines are plotted by employing (17) and markers are Monte Carlo simulation. similar to the outage probability that increasing \(O\) will improve the metric. Moreover, the path-loss-based scheme, again, is the best scheme, although the gap is minor, especially compared to the equal power allocation.
# A Review of the T-Stub Components for the Analysis of Bolted Moment Joints
## 2 Analytical Model
### Proposed Analytical Model for T-Stub under Large Deformation  
The proposed analytical model for T-stub under large deformation is based on EC3 and was developed by [PERSON] et al. [12] to estimate the ultimate resistance of T-stub connections under large deformation due to gap openings. Thus, it is suitable for connections with \(\beta\) (see Equation (20)) parameter not greater than 1.2. Besides, for connections designed for Mode 1 and 2. Moreover, the proposed model is characterized for combine the primary effects due to gap opening in small deformations (see EC3) with the secondary effects due to the development of the membrane action and its corresponding pattern of plastic deformations. It is important to mention that the analytical model took into account three secondary resistance mechanisms: the tensile yielding of the flange with its resistance R1, the bearing of the bolt to edge with its resistance R2 and the failure of the bolts under shear force, axial force and bending moment with its resistance R3. The equations that estimate the resistance under large deformation are given as follows:  
\[\beta=\frac{4M_{pl,Rd}}{(m\sum F_{t,Rd})}, \tag{20}\]  
\[F_{T}=F_{T,1}+F_{T,LD}formode1, \tag{21}\]  
\[F_{T}=F_{T,2}+F_{T,LD}formode2, \tag{22}\]  
Figure 10: AASC T-stub model.  
where \(F_{T,1}\) and \(F_{T,2}\) are the resistance for failure Mode 1 and 2 (according to EC3), while \(F_{T,LD}\) is the additional contribution due to the development of large deformations, and it is determined using Equation (23):  
\[F_{T,LD}=2\times\min(R_{1},R_{2},R_{3})=2\times\min(\frac{b_{eff}}{2}t_{ff}f_{u} \sin(\alpha);F_{B,R}\tan(\alpha);F_{t,R}\times\psi), \tag{23}\]  
where \(b_{eff}\), \(t_{f}\), \(f_{u}\) and \(F_{B,R}\) are the effective length, the flange thickness, the steel ultimate strength of the flange and the bolt to edge bearing resistance, respectively. The angle \(\alpha\) is the ratio between the imposed gap opening and the distance \(m;\psi\) is the reduction factor to decrease the tensile resistance of the bolts to account for the interaction with shear force and bending moment, ranging within 0.3-0.4 for mode 1 and 0.4-0.6 for Mode 2.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 5 Discussion
This opens the possibility of using the model to forecast seasonal GPP within the season, running the inversion using observed NDVI and weather estimates available at a given time in the growing period, and using weather forecast for the remaining of the season. Currently, the estimation of model parameters during the assimilation of NDVI is time-demanding. This is a disadvantage against data-driven GPP models, where parameter values are known a priori. Processing time currently amounts to about 20 s to optimize the model parameters over a single season on a standard desktop computer. Therefore, an operational wall-to-wall application of the model will require to optimize the code in order to run on parallel computing infrastructures. Further analysis will focus on evaluating the potential improvements of considering different maximum light use efficiency for C3 and C4 crops and a direct impact of temperature on GPP through a stress factor in addition to that of water stress.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
###### Abstract  
A \(2\times 2\) circularly polarized (CP) sequential rotation microstrip patch antenna array with high gain for long-range ultra-high frequency (UHF) radio frequency identification (RFID) communication is proposed in this paper. In order to meet the operational frequency band requirement of 840-960 MHz and, at the same time, achieve enhanced broadside gain, a two-level sequential rotation structure is developed. Series power divider is used as the basic element of the feed network that is implemented with the substrate-integrated coaxial line technology for minimizing radiation losses. The manufactured prototype exhibits a peak gain of 12.5 dBic at 900 MHz and an axial ratio (AR) bandwidth (AR \(\leq\) 3 dB) of 18.2% from 828 to 994 MHz. In comparison with the state-of-the-art, the proposed antenna shows an excellent gain/size trade-off.  
antenna arrays; high gain; circular polarization; sequential phase +
Footnote †: journal: Electronics Letters
## 2 Methodology
### Two-Stage Feature Selection and Ensemble of Classifiers
#### 2.6.1 Ensemble Classification  
The final step of the method focused on evaluating each ensemble of descriptors derived from the preceding step. This assessment was conducted using a strong ensemble of classifiers, exploring five algorithms from distinct categories (based on the function, probability, decision tree, and instance-based K-nearest neighbors). In this case, the ensemble of classifiers was built upon the following algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Naive Bayes. The approach allowed combining results from individual classifiers to avoid overfitting [29; 59; 60]. The classifiers were individually trained, and the ensemble was applied taking into account the sum rule [29; 56; 60]. Figure 3 illustrates the evaluation process with the ensemble approach. The blocks indicated by dashed lines indicate that only one of the internal blocks was used in the composition of the model.  
The assessment process for each classification algorithm involved the use of the cross-validation k-fold method, with \(k=3\)[62]. In this approach, the instances were divided into \(k\) independent groups, with each group further divided into a training set and a test set. In each iteration of the training process for \(k\), one fold was used for evaluation, while the remaining \(k-1\) folds served as training data. Instances within each group were classified, resulting in \(k\) performance measures, one for each fold. The ultimate performance was determined by calculating the arithmetic average of these partial performances. The metrics used to evaluate each solution were the area under the ROC curve (AUC) and accuracy [98].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Architecture** & **Number of Descriptors** & **Extracted Layer** \\ \hline ResNet-50 & 2048 & avgpool \\ VGGNet-19 & 4096 & FC2 \\ Inception v3 & 2048 & last avgpool \\ DenseNet-121 & 1024 & global avgpool \\ EfficientNet-B2 & 1408 & FC \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of extracted descriptors and their respective layer in each architecture.
## 8 Limitations and Future Work  
The fuzzy expert system shows promising results in diagnosing chemical deterioration in reinforced concrete structures, but there are certain limitations that need acknowledgment to drive further improvements and developments in this field. Firstly, the accuracy and reliability of the system heavily rely on the quality of data collected from inspections and other sources. Inaccuracies in data recording or insufficient data availability can impact the system's performance. Future work should focus on enhancing data collection methods and ensuring the consistency and completeness of the input data.  
Secondly, the effectiveness of the fuzzy expert system depends on the rules and knowledge base it operates upon. Fine-tuning and expanding the rule base with additional concrete technology expertise could lead to more precise and comprehensive diagnoses. Collaboration with domain experts to refine the rules can be a valuable step in this direction.  
While the fuzzy logic approach helps manage uncertainties to some extent, the system may still face challenges in dealing with extreme or rare cases. Future research should explore advanced fuzzy logic techniques or consider incorporating other machine learning approaches to further improve the system's ability to handle uncertainty. Additionally, the developed fuzzy expert system requires rigorous validation and calibration to ensure its accuracy and reliability across various scenarios and environments. Conducting extensive case studies and comparing the system's outputs with real-world data will be crucial to validate its performance.  
Future efforts should focus on optimizing the system's performance to provide real-time or near-real-time results. A user-friendly interface that allows easy input of data and interpretation of results is vital for the practical implementation of the fuzzy expert system. Ensuring accessibility and usability for engineers and practitioners with varying  
Figure 11: Deteriorations types diagnosis of a bridge in Portugal by a fuzzy expert system: case study.  
levels of expertise is crucial. Extending the fuzzy expert system to support multi-attribute decision-making can enhance its capabilities. By considering multiple factors, such as cost, feasibility, and urgency, the system can aid in prioritizing maintenance actions more effectively. To gauge the long-term effectiveness of the fuzzy expert system in practical applications, continuous monitoring and assessment of repaired structures over time are necessary. This feedback loop will help refine and improve the system's performance based on real-world outcomes.
## 1 Introduction
As a result, the proposed methodology can precisely determine the type of the received signal, either SoI or a combination of SoI with any other jammers, and also the modulation type of SoI. We have investigated four different types of modulation due to their more applicable in DVB-S2 standard, namely, quadrature phase-shift keying (QPSK), 8-array asymmetric phase-shift keying (8-APSK), 16-array APSK (16-APSK), and 32-array APSK (32-APSK). The rest of this paper presents the related works in Section 2, the proposed methodology in Section 3, and the simulation results are provided in Section 4. Finally, the paper is concluded in Section 5.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Structure  
There exist three types of structures, namely fully connected, hybrid connected, and partially connected.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
###### Abstract  
A headphone-based virtual sound image can not be perceived as perfectly externalized if the acoustic of the synthesized room does not match that of the real listening environment. This effect has been well explored and is known as the room divergence effect (RDE). The RDE is important for perceived externalization of virtual sounds if listeners are aware of the room-related auditory information provided by the listening environment. In the case of virtual reality (VR) applications, users get a visual impression of the virtual room, but may not be aware of the auditory information of this room. It is unknown whether the acoustic congruence between the synthesized (binaurally rendered) room and the visual-only virtual listening environment is important for externalization. VR-based psychoacoustic experiments were performed and the results reveal that perceived externalization of virtual sounds depends on listeners' expectations of the acoustic of the visual-only virtual room. The virtual sound images can be perceived as externalized, although there is an acoustic divergence between the binaurally synthesized room and the visual-only virtual listening environment. However, the \"correct\" room information in binaural sounds may lead to degraded externalization if the acoustic properties of the room do not match listeners' expectations.  
room divergence effect; virtual reality; perceived externalization; binaural room impulse response; head-related transfer function; headphone-reproduced virtual sound images +
Footnote †: journal: Applied Sciences
# An Energy-Efficient Strategy for Microcontrollers
## 4 Experiments and Results
### Measuring Methodology  
In our previous papers [36; 37], a design of the energy consumption measuring platform for embedded systems was proposed. In this platform, a KEITHLY 22805-32-6 type high precision digital power supply was used to provide power consumption data, which is a product of Tektronix. The sampling rate of the KEITHLY 22805-32-6 type high precision digital power supply can reach 25 kHz when the measuring resolution is set to 31/2 digits, and the sampling rate is 5 kHz when the resolution is 41/2 digits [38]. The digital power supply was equipped with General Purpose Interface Bus (GPIB or IEEE 488 bus), Ethernet, and Universal Serial Bus (USB) interfaces, and it supports queries using commands such as Standard Commands for Programmable Instruments (SCPI); as a result, the real-time power consumption data of the core board can be easily obtained. Its output voltage is set to 5.000 V in this study.  
STM32F407 is a widely used microcontroller based on ARM(r) Cortex(r)-M4, and its operating frequency can reach up to 168 MHz. Its working current is approximately 238 \(\mu\)A/MHz, according to STMicroelectronics's website [39]. The ESP32 module is a low-power, feature-rich MCU with integrated Wi-Fi and Bluetooth connectivity for a wide range of applications developed by Espressif-System and manufactured by Taiwan Semiconductor Manufacturing Company, Limited (TSMC). It is equipped with an Xtensa(r) 32-bit LX6 processor. In this study, an STM32F407 core board and an ESP32 module were used as the target devices. The core board was equipped with the STM32F407 processor, the lead-out pins, and essential peripheral components that support the basic functionalities of the board. Figures 3 and 4 show the setup and connection of our measuring platform.  
Figure 3: The measuring platform.
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 3 Traffic Subdivision Methods
### Evaluation of Optimization Effect  
To select the optimal TAZ division scheme from a series of optimization schemes with different TAZ numbers, three macro-indicators are used to evaluate the overall characteristics of each scheme.  
(1) Traffic cell equivalent radius  
The traffic cell radius RE is the radius of the equal-area circle \(TAZ_{AREA}\) for each traffic cell, which is used to describe the geographic accuracy of the divided traffic cell (see Equation (19)).  
\[R_{E}=\sqrt{\frac{TAZ_{AREA}}{\pi}} \tag{19}\]  
(2) Traffic cell travel density The traffic cell travel density \(AVETRI_{DENSITY}\) is used to calculate the objective function each time the traffic cell cycles through the aggregated grid, and its value is constantly updated (see Equation (20), where \(OD_{i}\) and \(AREA_{i}\) represent the traffic trips and the grid area of the \(i\) grid, respectively).  
\[AVER_{TRIP\_DENSITY}=\frac{\sum_{i=1}^{N}\frac{OD_{i}}{AREA_{i}}}{N} \tag{20}\]  
(3) Standard deviation of travel density in traffic neighborhoods  
The standard deviation of traffic cell travel density is used to describe the traffic cell aggregation objective function. As far as possible, the traffic cell in the aggregation process is a constant iterative calculation of the value, to ensure the homogeneity of travel within the traffic cell (see Equation (21)).  
\[STDEV_{TRIP\_DENSITY}=\frac{\sum_{i=1}^{N}(AVER_{TRIP\_DENSITY})-(\frac{OD_{i }}{AREA_{i}})^{2}}{N} \tag{21}\]  
(4) Coefficient of variation of travel density  
This indicator is used to evaluate the homogeneity of the zoning system traffic cell travel density. The larger the value of the indicator, the weaker the homogeneity of the travel density of the traffic cell, reflecting the strengths and weaknesses of the traffic cell program, calculated as shown in Equation (22).  
\[GLOBAL_{C_{V_{TRIP\_DENSITY}}}=\frac{\sum_{i=1}^{N}[(\frac{\sum_{i=1}^{N}AVER _{TRIP\_DENSITY_{i}}}{N})-AVER_{TRIP\_DENSITY_{i}}]^{2}}{\frac{\sum_{i=1}^{N} AVER_{TRIP\_DENSITY_{i}}}{N}} \tag{22}\]  
where \(AVER_{TRIP\_DENSITY_{i}}\) denotes the average trip intensity of transportation neighborhood \(i\) in each scenario.  
(5) Average proportion of trips in scheme  
According to the travel proportion of each TAZ in the scheme, the arithmetic mean is calculated to obtain the average proportion of trips in the scheme.  
Based on the above evaluation indicators, this study adopts the TOPSIS comprehensive evaluation method to analyze all schemes and to obtain the most ideal TAZ division scheme. This method finds the worst plan and the best plan (represented by the worst vector and the best vector, respectively) by normalizing the original data matrix. The distance between the optimal scheme and the worst scheme and all of the evaluation objects is calculated to obtain the proximity between the optimal scheme and all of the evaluation objects. In this way, the merits and disadvantages of all of the evaluation objects are evaluated.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems
### Gamma Imaging
#### 4.3.3 Pinhole Gamma Camera  
[PERSON] et al. [198] used a Pinhole gamma camera together with a 3D-LiDAR (to adjust the gamma camera focal length) housed inside a truck to obtain the radiation image of the buildings and debris in the vicinity of FDNPP. Since radiation fields may achieve values of  
Figure 13: Examples of coded-aperture cameras: (**a**) Three-dimensional Stand-Off Radiation Detection System (SORDS-3D). Reproduced, with permission, from Reference [179]; copyright Elsevier (2011). (**b**) Stand-Off Radiation Imaging System (SORIS). Reproduced, with permission, from Reference [196]; copyright Elsevier (2011). (**c**) GAMPIX detector to be coupled in a multi-rotor UAV (MOBISIC project). ©2011 IEEE. Reprinted, with permission, from Reference [197].  
tens of \(\rm{mSv/h}\) it was necessary to develop a heavily shielded operation box so that two operators can work safely. In order see the targets, a thermal camera and a pan-tilt-zoom camera are used. A gamma-ray sensor is also coupled to a remote-controlled robot (wireless communication) to measure the radiation field around the truck. A gamma dose (rate) meter with a telescopic probe (Teletector 6112D [199]) is also available when the workers need to leave the truck.
## 4 Discussion
### Methods of Increasing Accuracy  
There are some methods that could be taken out in the experiment to increase the accuracy of the system:  
(1) The accuracy of the rotating paraboloid array required calibration before application, which contained two translations and two orientations. Calibration experiments with the help of a laser measurement system (Renishaw, Gloucestershire, UK) and coordinate-measuring machines (CMM) (Hexagon, Stockholm, Sweden) were carried out several times in the laboratory. After calibration, some deficiencies caused by installation errors could be reduced, although these errors were extremely small. The experimental accuracy could be improved by taking experiments repeatedly and selecting high-precision calibration devices.  
(2) Reducing the processing error of the rotating paraboloid is the basic requirement to ensure the accuracy of the system. At the same time, it is necessary to keep the surface of the rotating paraboloid  
Figure 17: Approximate error caused by Equation (6).  
clean. The surface profile of the rotating paraboloid in the experiment was measured by an optical 3D surface profiler and the testing result is shown in Figure 18. From Figure 18, we can see that the root mean square (RMS) of the surface was 0.021 \(\upmu\)m and the value of peak value (PV) was 0.148 \(\upmu\)m, which confirmed the manufacturing accuracy of the rotating paraboloid. In particular, a laser with low power (about 5 mW) should be selected and a laboratory environment with controlled temperature and humidity to protect the rotating paraboloid from damage should be used.  
(3) Improving the manufacturing and installation accuracy of the rotating paraboloid array is also significant. To prevent loosening of the rotating paraboloid array, strict fixation of the array and caution of the experimenters are needed.