esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
## 4 Results and Discussion
### Theoretical and Practical Implications
Medium-intensity level sustainability parameters are the implementation of measures for improving efficiency in water use and the use of technology, which resulted in a significantly more efficient use of materials when first implemented. This means that they are easily implemented when the high-intensity level parameters are already used in firms. When it comes to resilience high-intensity level parameters, they are the use of standardized and detailed work instructions, the implementation of activities raising employees' awareness on data security, and the use of software specifically. They are the most common among firms in the manufacturing sector in developing countries, but they are not key for carrying out resilience. The indicators that are crucial for establishing and maintaining resilience belong to the low-intensity level, and they are the use of mobile industrial robots, the use of collaborating robots (co-bots), and the use of organizational measures specifically. Although they are not common, they are an important factor for achieving resilience. Medium-intensity level parameters of this approach are the use of internet/network connection in real time for automated data exchange, the use of hardware solutions specifically, and product revamping or modernization. These activities contribute to resilience, but they are not relatively easy to implement. According to the analysis performed by the authors, in order to strengthen Industry 5.0, firms should make investments that will have long-term benefits for the firm itself, but for society as well. These investments include: (1) new learning programmes and opportunities for employees; (2) data centralization, (3) task integration for human-centricity; (4) developing energy management systems; (5) designing and implementing take-back services as a part of the firm's offer in the market; and (6) implementing technologies for recycling and water re-use in the production systems for sustainability. For resilience, it is necessary to: (1) include industrial robots as a part of production systems; (2) include co-bots in workplaces and develop processes that require human-robot collaboration; and (3) centralize data and enable its real time use.
## 1 Introduction
### Physical Properties  
#### 2.1.1 Hydrophobicity  
Hydrophobicity is an important indicator in evaluating the aging effects of composite insulators under different aging times. The most commonly accepted methods for measuring hydrophobicity of silicone rubber are the HC and CA tests. In this paper, both were employed on samples A\({}_{1}\)-A\({}_{6}\). Three samples of the same aging time were applied in the HC and CA tests, and test methods were as follows.  
In this research, the HC test criterion was in accordance with IEC/TS 62073--2003 [19], where HC test results are classified to 7 levels. If the HC test result is 1-4, insulators are regarded as being  
Figure 1: Aging environments of composite insulators in a high altitude area experimental station.  
in good operating condition; if the HC result is above 5, insulators are regarded as being in poor operating condition.  
In this research, a Drop Meter A-100P was used to evaluate the hydrophobicity of silicone rubber. The CA test criterion was in accordance to IEC/TS 62073--2003 [19], where CA test results are divided into two parts. In this standard, if the CA test result is more than 90\({}^{\circ}\), insulators are regarded as hydrophobic, otherwise, insulators are regarded as hydrophilic. Three samples were selected under the same conditions for six repeated tests, and the average CA, denoted \(\theta_{av}\), was obtained for comparing the hydrophobicity between samples aged for different years.  
The results of HC and CA tests are listed in Table 1.  
Table 1 shows that when aging time increases, it leads to an increase of HC along with a decrease of \(\theta_{av}\). For example, the HC and \(\theta_{av}\) of sample A\({}_{1}\) aged for 1 year are level 1 and 130.2\({}^{\circ}\), respectively. When aging time reaches 10 years, \(\theta_{av}\) of sample A\({}_{5}\) declines to 94.3\({}^{\circ}\), and HC increased to 4. Moreover, according to DL/T 864-2004 [20], when \(\theta_{av}\) of sample A\({}_{5}\) is above 90\({}^{\circ}\) and HC is below 5, the conclusion that composite insulators of Factory A maintain good hydrophobicity for a period of up to ten years can be obtained.
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
## 2 Statement of the Problem and Calculation Technique
### Equations of Motion  
The evolution of the system in the interaction picture is described by the Schrodinger equation \(i\hbar\partial_{t}|\Psi\rangle=\hat{V}|\Psi\rangle\) with the interaction Hamiltonian given by:  
\[\hat{V}=\exp\left(\frac{i}{\hbar}\hat{H}_{ph}t\right)\Big{(}\hat{H}_{0}+\hat{H }_{L,ph}+\hat{H}_{I,dc}+\hat{H}_{T}\Big{)}\exp\left(-\frac{i}{\hbar}\hat{H}_{ ph}t\right). \tag{5}\]  
The state vector of the \"atomic chain + light\" system is:  
\[|\psi(t)\rangle=\sum_{n}\sum_{p}\bigl{\{}a_{p,n}(t)\bigl{|}a_{p},n\bigr{\rangle} +b_{p,n}(t)\bigl{|}b_{p},n\bigr{\rangle}\bigr{\}}. \tag{6}\]  
Here, \(\bigl{|}b_{p},n\bigr{\rangle}=\bigl{|}b_{p}\bigr{\rangle}\otimes\bigl{|}n \bigr{\rangle}\), \(\bigl{|}a_{p},n\bigr{\rangle}=\bigl{|}a_{p}\bigr{\rangle}\otimes\bigl{|}n\bigr{\rangle}\), are Fock states with \(n\) photons and Wannier states centered around \(p\)-th atom in ground and excited state, respectively, \(a_{p,n}\) and \(b_{p,n}\) are the unknown probability amplitudes. From the Schrodinger equation we get following equations for the probability amplitudes:  
\[i\hbar\frac{\partial a_{p,n}}{\partial t}=\bigl{(}\delta\varepsilon-\mathbf{E} _{dc}\cdot e\mathbf{R}_{p}\bigr{)}a_{p,n}+t_{a}a_{p+1,n}+t_{a}^{*}a_{p-1,n}-g \sqrt{n+1}b_{p,n+1}, \tag{7}\]  
\[i\hbar\frac{\partial b_{p,n+1}}{\partial t}=-\bigl{(}\delta\varepsilon+\mathbf{ E}_{dc}\cdot e\mathbf{R}_{p}\bigr{)}b_{p,n+1}+t_{b}b_{p+1,n+1}+t_{b}^{*}b_{p-1,n+1}-g \sqrt{n+1}a_{p,n}, \tag{8}\]  
where \(\delta\varepsilon=(\varepsilon_{a}(0)-\varepsilon_{b}(0)-2\omega)/2\). In the limit \(E_{dc}=0\) the System (7) and (8) goes to the corresponding equations obtained in [41] for Rabi-waves in QD chains.
## 2 System Architecture  
Figure 2 shows the proposed architecture of the 28 GHz SDR system, using a digital beamforming phased array structure. This architecture has two complementary domains, the digital and the analog, which are described below.
## 2 Methodology
### One-Class Support Vector Machine  
The One-Class Support Vector Machine (OC-SVM) is an unsupervised machine learning algorithm widely used in the fields of anomaly detection and fault diagnosis. The OC-SVM is designed to detect abnormal or faulty states by learning only from data representing normal operating conditions [19; 20].  
Unlike conventional Support Vector Machines, which learn from both normal and faulty classes, the OC-SVM constructs a hyperplane based solely on normal data to separate the region of normality from potential anomalies. This approach is particularly effective in industrial environments where fault data is rare or difficult to obtain [21].  
As illustrated in Figure 2, OC-SVM maps input data points nonlinearly into a high-dimensional feature space and learn a hypersphere or hyperplane that maximizes the separation from the origin through the following optimization process [19].  
The OC-SVM has proven to be highly effective in detecting abnormal states in machine fault diagnosis based on vibration data, as it can identify subtle differences between signal features [22]. In particular, it demonstrates high accuracy in detecting early faults and anomalies by effectively learning the decision boundary using only normal data [20; 21].
# Variable Guide Vane Scheduling Method Based on the Kinematic Model and Dual Schedule Curves
###### Abstract  
The variable guide vanes (VGV) of gas turbine engines are commonly utilized to expand operating range and to improve efficiency of the compressor. Guiding air flow using the VGVs in the compressor prevents aerodynamic instability by making proper incidence angle to the blades. In this study, we dealt with rig-type three-stages VGVs for developed engine tests. The three link mechanism of VGVs are linked to each other with two hydraulic actuators, and inevitably, induced hysteresis exists between vane rotations and actuators strokes, due to links with non-fully constrained degree of freedoms for easy installation and instrumentation, as well. Therefore, the adjustment of each VGVs link mechanism is required to satisfy vane angle demands. To adjust coupled three-stages VGVs link mechanism, an analytical VGV-link kinematic model was derived, and effects of two adjusting parameters (lengths of bell cranks and vertical links) were discovered. Lastly, we obtained two vane angle schedule curves from the experiments according to link moving directions, and applied them to the engine controller to minimize hysteresis of the variable inlet guide vane (VIGV). The proposed VGV adjusting and controlling method can be simply applied to the pre-designed or pre-manufactured VGVs system without mechanical compensation or additional cost.  
gas turbine engine; compressor; variable guide vanes; mechanical hysteresis; link mechanism model; VGV scheduling +
Footnote †: journal: Applied Sciences
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 4 Methodology
### User Traffic Prediction Module  
For the user traffic prediction module, we use two different architectures: an RNN, namely, an LSTM model, and a hybrid model consisting of a Transformer and a Temporal Convolutional Network (TCN) model. These two architectures produce results that are different in nature but similar in performance.  
We conducted a feature importance analysis to justify the input features of the two user traffic prediction architectures. The results of the analysis are illustrated in Table 1. As discussed in Section 3.1, the first and second derivatives refer to the first- and second-order differences, while the original values refer to the raw values of the 5G Traffic dataset. The least impactful input features are the rolling mean and rolling standard deviation. The reason for this is that the dataset has low local volatility, so those two values stay similar to the original values.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 3 Experiments
### Experiment Analysis
Pair D corresponds to rural areas which consist of roads and fields. Although the intensity difference is significant, the structural features are rich and the radiometric difference is small. The three compared algorithms can overcome the intensity difference and achieve ideal matching results. The RMSE of the proposed algorithm is above two pixels. However, it is superior to OS-SIFT and RIFT both in CMN and RMSE. Note that there are obvious geometric distortions between optical and SAR images, and the affine transformation model used cannot effectively solve these distortions. Therefore, the RMSEs in Table 3 are relatively large. In sum, the experiments show that the proposed algorithm performs best among the three state-of-the-art algorithms.
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
###### Abstract  
Array gain (AG) is significant in evaluating the detection performance of the vertical line array, which is directly determined by the correlation of signal and noise, respectively. In this paper, we analyze the vertical correlation for a 16-element vertical line array experimented in the deep ocean in 2016. The ray interference theory is utilized to interpret the mechanism of the vertical correlation of the sound field in different zones. In the direct-arrival zone, the direct rays and once-surface-reflected rays are two dominated components, whose arrival time difference for each element are nearly the same, and the vertical correlation is high. In the shadow zone, the sound field is mainly dominated by bottom-reflected rays and the vertical correlation decreases due to different grazing angles and arrival times of each ray. Different from the previous assumption of noise independence, the effect of noise correlation on the AG is analyzed through the measured marine environmental noise. Results indicate that the noise correlation coefficients in two zones are low but not 0. In the direct-arrival zone, AG is about 10 dB, very close to the ideal value of \(10\log M\). AG even exceeds it when NG is negative. Moreover, AG in the direct-arrival zone is higher than the one in the shadow zone.  
array gain (AG); signal correlation; noise correlation; direct-arrival zone; shadow zone; vertical line array (VLA); deep ocean +
Footnote †: journal: Applied Sciences  
Article
# Design Considerations of Multi-Phase Buck DC-DC Converter
###### Abstract
In Section 3, design considerations of multi-phase buck DC-DC converters are presented. A discussion of the optimum phase numbers for some common input and output voltages is also presented. In Section 4, an analytical and simulation study of power losses in 8-phase synchronous buck converters is performed.
## 2 Materials and Methods
### Analysis of Visible and Thermal Imaging Data
#### 2.4.3 Thermal Data Extraction and Analysis  
The sets of the 68 facial landmarks detected in the visible images were identified in the corresponding frames of IR imaging, applying the geometrical transformation obtained from the optical co-registration process. Figure 4a,b show an example of the 68 feature landmarks detected on a visible frame and the set of the 68 points identified on the corresponding thermal image.  
A fundamental aspect for obtaining an accurate co-registration was the need of temporal synchronization between visible and IR videos. Since the acquisition frame rate of thermal videos was lower than that of the visible camera, the corresponding frames within the visible domain were determined according to the specific timestamps of IR frames. Specifically, among the visible frames acquired around the IR frame timestamp, the one that minimized the temporal difference with IR imaging was chosen. The timestamps of the frames were considered reliable as the videos were acquired on the same PC.  
For each thermal video, four ROIs were considered and positioned on facial areas of physiological importance (nose tip, right and left nostrils, and glabella) [44]. The ROIs' coordinates were automatically determined from the location of the 68 landmarks. In this way, the initialization of the position of the ROIs was automatically determined (Figure 4c).  
With reference to the topographical distribution of the points as represented in Figure 3, the coordinates of the four ROIs were determined, as described in Table 3:  
Figure 4: (**a**) Facial landmark identification in the visible image by OpenFace; (**b**) facial landmark identification in the corresponding thermal image applying the geometrical transformation obtained from the optical co-registration process; (**c**) Regions Of Interest (ROIs) identification (nose tip, right and left nostrils, glabella); (**d**) average thermal signals extracted from the ROIs in an exemplificative time window of 50 s. Notice the breathing signal is clearly appreciable from the right and left nostrils’ average thermal signals plots.  
For each ROI, the average value of the pixels was extracted over time (Figure 4d). Relatively to the nostrils' ROIs, the average value between ROI 2 and ROI 3 was considered for further statistical analysis, them being related to the same physiological process (i.e., breathing function).  
For each of the extracted signals, six representative features were computed over consecutive temporal window of 30 s:  
1. Absolute value of the difference between the average of the signal in the first 5 s and in the last 5 s (\(\Delta\));
2. Standard deviation of the raw thermal signals (STD);
3. The 90 th percentile of the raw thermal signals (90 th P);
4. Kurtosis of the raw thermal signals (K);
5. Skewness of the raw thermal signals (S);
6. Ratio of the power spectral density of the raw thermal signals evaluated in the low-frequency band (LF = (0.04-0.15) Hz) and in the high-frequency band (HF = (0.15-0.4) Hz) (LF/HF).
## 3 Results
### Types of EEG Features  
A feature is a unique or distinguishing measurement, transformation, or component that is taken from a pattern's segment. During the feature extraction process, a feature vector is created from a regular vector [13]. An efficient feature extraction pipeline needs to be followed during emotion recognition from EEG due to the weak, nonlinear, and time-varying nature of the signals [14]. Features from the EEG signals are classified generally into three categories: frequency domain (FD), time domain (TD), and time-frequency domain (TFD). The FD represents the amplitudes of EEG signals in their relevant frequency domain. This provides valuable insights into the underlying characteristics such as dominant frequencies, harmonics, and other spectral features of the signal. However, transforming a signal into its FD results in a loss of time information. The drawback is that this domain assumes the signal is stationary, meaning that the statistical properties of the signal remain constant over time. Real-world signals are mostly non-stationary. This leads to misinterpretations about the signal [15].  
The time domain describes how EEG signals vary with time. TD analysis has the capacity to simulate all nonlinearities; however, the computation can be time-consuming [16].  
Features in the time-frequency domain (TFD) have now widely been used in EEG signal processing as TFD accurately analyzes non-stationary waves. TFD combines information from the time and frequency domains and simultaneously allows for localized analysis in the time-frequency domain. This way, the signal's temporal information is not lost [17].  
The feature categories commonly used from each domain are summarized in Table 6.  
It was observed that the majority of the researchers utilized PSD values in the frequency domain for implementing the models. Time domain features were rarely used, and time-frequency domain features have never yet been used for confusion emotion recognition. A detailed summary of the features utilized by each researcher is illustrated in Table 7.  
[PERSON] et al.'s [4] dataset, which many of the researchers used to conduct their own research, consists of EEG data in the form of PSD values. They stated that when training models use features in the time series domain, the models tend to overfit. They assumed that this was caused by there not being enough EEG data samples in the dataset.  
[PERSON] et al. [8] and [PERSON] et al. [19] used time domain features for model training. [PERSON]. [8] introduced a new algorithm model that analyzes time series features together with data from audio and video features of lectures to detect learners' confusion states. [PERSON]. [19] trained existing ML algorithms.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
###### Abstract  
Smart cities like Neon require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neon city. Our channel model incorporates log-normal (LN) and gamma-gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the sliding loop model, providing valuable insights into the system in real-world scenarios. To assess the system's performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system's bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.  
Weom smart city; high-speed trains; free space optical communication; sliding loop; sustainable mobility +
Footnote †: journal: _Photonics_  
0
Footnote †: journal: _Photonics_  
0
Footnote 0: 0
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
###### Abstract  
The response mechanisms of vegetation dynamics to climate change in arid regions, particularly under extreme low-altitude and high-temperature environments, remain unclear. Focusing on China's lowest and hottest Turpan-Hami Basin, this study investigates the spatiotemporal evolution of vegetation cover (using MODIS NDVI) and its response to temperature, precipitation, and potential evapotranspiration (PET) based on data from 2001 to 2020. Theil-Sen trend analysis, the Mann-Kendall test, and Pearson correlation were employed. Key findings include the following: (1) NDVI exhibited a significant increasing trend, with the largest rise in winter and peak values in summer. Spatially, high NDVI was concentrated in oasis and mountainous forest-grassland zones, while low values prevailed in desert Gobi regions; 34.2% of the area showed significant improvement, though localized degradation occurred. (2) Temperature showed no significant overall correlation with NDVI, except for strong positive correlations in limited high-altitude cold zones (2.9%). Precipitation had minimal influence (no correlation in 75.4% of the area), with localized positive responses in northwestern foothills linked to runoff. PET exhibited positive correlations (weak or strong) with NDVI across nearly half of the region (46.8%), predominantly in oasis-desert and piedmont transition zones. (3) Human activities, notably irrigation and shelterbelt projects, are key drivers of oasis vegetation restoration. Critically, the positive PET-NDVI correlation challenges the conventional paradigm viewing evapotranspiration solely as water stress. This study elucidates the compound responses of vegetation dynamics to climatic and anthropogenic factors in a low-altitude arid region, providing a scientific basis for ecological restoration and water resource management optimization.  
Turan-Hami Basin; hyper-arid; Normalized Difference Vegetation Index (NDVI); climate change; potential evapotranspiration (PET) +
Footnote †: journal:  
_Article_