esa-sceva/satcom-chunk-collection huggingface dataset:
# Bond Characteristics of a Deformed Steel Bar Embedded in Donut-Type Voided Slab
## 1 Introduction  
### Research Scope and Objectives  
This study focused on the bond characteristics of a deformed steel bar (rebar) in the donut-type voided slab. The structural performance of reinforced concrete (RC) members including voided slabs mainly depends on a sufficient bond between the rebar and the surrounding concrete [1]. Like other voided slabs [2, 3, 4, 5, 6], the donut-type void-shaper (as shown in Figure 1) makes concrete inner cover formed between rebar and void, and some part of the donut-type voided slab has smaller inner cover thickness less than 2.5 times the diameter of the bar (\(d_{b}\)). Furthermore, inner cover thickness changes along the longitudinal rebar due to the shape of void. These were expected to affect the bond behavior of rebar in the donut-type voided slab. Therefore, this study presented an experimental investigation to assess the effect of void on the bond characteristics of rebar in the donut-type voidedslab. Moreover, the calculation method of bond strength of rebar in donut-type void slab was suggested based on the test results. In this study, the slab with donut-type voids was divided into three bond regions (sufficient, transition, and insufficient regions) to identify the bond characteristics of the donut-type voided slab in each region, as indicated in Figure 2. Furthermore, to examine the influence of the inner cover between the void-shaper and the rebar in the slab on the bond performance of the deformed steel bar, an experimental study was conducted in which the inner cover thickness of the rebar was established as the variable.
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
## Materials and Methods
### Mass Spectrometric Analysis
#### 2.4.2 Liquid Chromatography Coupled Tandem Mass Spectrometry (LC-MS/MS)  
Peptide mixtures were analyzed using an LTQ Orbitrap Velos\({}^{\text{TM}}\) Hybrid Ion Trap-Orbitrap Mass Spectrometer (Thermo Scientific, Rockford, IL, USA). The peptide mixtureswere loaded onto a nanoACQUITY Ultra Performance LC (Waters, Milford, MA, USA) with a C18 BEH column (ID 75 \(\upmu\)m; length 25 cm; Waters) packed with 1.7 \(\upmu\)m particles with a pore size of 130 A. Analytes were separated using a segmented gradient within 90 min from 5% to 40% solvent B (acetonitrile with 0.1% formic acid) at a flow rate of 300 nL/min and a column temperature of 35 \({}^{\circ}\)C. Solvent A was 0.1% formic acid in water. The effluent from the UPLC column was directly electrospray onto the mass spectrometer. The mass spectrometer was operated in the positive ion mode for 120 min, and a data-directed analysis method (\(m/z\) 350-1600, 1 s/scan) was employed. In each cycle, a full-scan spectrum was acquired with resolution R = 100,000, followed by collision-induced dissociation on the three most intense ions with a target range of \(m/z\) 100-2000 (1 s/scan).
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 3 Proposed UAVs-BS Network Coverage Model
### Parameters Evaluation  
Link quality (LQ) is one of the significant factors for reliable data communication, high throughput, and other QoS operations. Due to the mobility of the UAVs-BS, the LQ is always on stack and unstable. This is the reason the proposed mobility model uses the LQ parameter by using the timer decision strategy as used in [43]. The timer decision is based on interval values 0 and 1 where the links are classified based on values. The LQ is evaluated by using the packet reception rate. There are three parameters to evaluate the LQ, including if the packet reception rate is between 10 to 90% considered connected, whereas a packet reception rate less than 10% means disconnected. Based on these parameters, the UAVs-BS defines its status with values 1 and 0. This information is stored in packet and traffic density information which is collected from the ground BS. After evaluating the LQ and traffic density, the weightage factor is applied for decision making and returns the value of 0 or 1. Figure 3 shows the packet format for the mobility model and the UAVs-BS selection. Algorithm 2 shows the UAVs-BS request and reply to messages process.  
```
1.Initializing
2.Input-source UAVs-BS IDs \(\phi\) Location Information
3.Output-LAV-BS Placement Decision
4.debroadage to call all the UAVs-BS
5.LO-Traffic Density
6.f/LQ-Traffic Density=1,then
7.Soler UAV-BS for placement else
12.Wait for a random time and repeat the process
13.If REQ-IP is received,do
14.Data Forwarding else
15.end if
16.end while
17.end process
```  
**Algorithm 2** UAVs-BS request and reply to messages process  
The five levels of the proposed model are discussed in the above sub-sections. The coverage area decision model, the UAVs-BS mobility model, the information update phase,  
Figure 3: Request and reply packets format.  
Figure 2: UAVs-BS status update process.  
and the parameters evaluation model are designed to provide coverage facility to the cellular network by using UAV nodes that are controlled and manageable for reallocation.
## 5 Conclusions  
This paper presents an assessment of the Galileo E6B demodulation performance at high latitudes. The work exploits data collected on a vessel traversing the Norwegian Sea and reaching the Arctic Circle. The test environment represents a challenging scenario, considering the high latitudes covered by the vessel trajectory and that the path is at the border of the Galileo HAS service area.  
The large dataset, corresponding to about three months, allowed the extraction of significant statistics of different Galileo E6B demodulation performance indicators, such as the BER, PER, and TTRD. For the entire period under analysis, the empirical BER curves closely follow the theoretical curves, and the daily PER is always below one percent. Moreover, the TTRD analysis shows that the average time to retrieve the HAS corrections is always below eight seconds if the number of connected satellites is equal to or above three. These results, which are consistent over the observation period, showcase the effectiveness of the HAS dissemination scheme at high latitudes. As future work, it will be of interest to perform a comparable analysis at moderate latitudes considering similar visibility conditions.  
All authors contributed to the preparation of this paper. [PERSON], [PERSON] and [PERSON] designed the methodology adopted for the analysis and contributed to the data processing and analysis. [PERSON], [PERSON] and [PERSON] prepared the experimental setup and collected the data used for the analysis. [PERSON] contributed to the data analysis and provided valuable insights on the results. [PERSON], [PERSON] and [PERSON] drafted the paper that was reviewed and edited by the other co-authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Basic Structure of FBMC Systems
In wavelet transform, a particular signal of interest is decomposed into a set of waveforms known as wavelets [39]. When DWT is employed, two sets of function associated with low-pass and high-pass filtering are incorporated [39]. In this system, the time domain signal is decomposed into different frequency bands using high-pass and low-pass filters. The incoming signal is passed through a half-band high-pass filter and a half-band low-pass filter, where the former removes all frequencies below half of the highest frequency of incoming signal and the latter remove frequencies that are below half of the highest frequency of the incoming signal [39]. As shown in Figure 6, OFDM-QAM is an FBMC system based on DFT, while DWMT is an FBMC system based on DWT [40]. Their respective transceiver structures are very similar except that DFT is used for transformation in the receiver of the former while DWT is adopted for transformation in the latter. Inverse DFT (IDFT) and inverse DWT (IDWT) are implemented in their respective transmitters. The low-pass FIR prototype synthesis and analysis filters of the FBMC schemes mentioned in Figure 6 (for both DFT and DWT based FBMC systems) can be implemented using various FBMC structures such as exponentially modulated filter banks (EMFB) [41], cosine modulated filter banks (CMFB) [42; 43], modified discrete Fourier transform (MDFT) [44] and extended lapped transforms (ELT) [45].
## 3 Results
### XANES, EXAFS  
Figure 6 shows (a) XANES and (b) Fourier transformed (FT) extended X-ray absorption fine structure (EXAFS) spectra for the Pt/Ti-PLA and Pt/Ti-P25 samples. According to the XANES analysis, both samples have Pt\({}^{4+}\) in an environment similar to PtO\({}_{2}\), i.e., fully oxidized Pt species with oxygen atoms as nearest neighbors. In the FT EXAFS spectra, much lower contributions in the second shell than in Pt foil or PtO\({}_{2}\) are found. To further evaluate the structure and quantify the average oxidation state of Pt sites, EXAFS spectra were fitted to several possible structural models. Statistically significant fits with realistic parameters were obtained when using Ti (Table 2) or Pt neighbors (Table A2) in the second shell; see Appendix A (Figure A1, Figure A2) for comparison of the fitted graphs in the figures. Of them, fits with Pt nearest neighbors have significant misfit at high distances and significantly different Debye-Waller factors for Pt and O shells which would be very difficult to explain for a single stable oxide compound such as PtO\({}_{2}\). Therefore, the fits involving O and Ti shells are strongly preferred, and the resulting structural models (Table 2) suggest single oxidized Pt atoms strongly bonded to the TiO\({}_{2}\) support.
## 3 Results
### Validation of Satellite-Derived Soil Moisture
For 2021, the sensor L-S5-30 at a depth of 30 cm stands out as the best-performing sensor, with a correlation coefficient of 0.8237, further reinforcing the finding that sensors positioned at this depth are optimal for capturing reliable soil moisture data that are comparable to satellite observations. Other sensors at 30 cm, such as F-S8-30 and I-S8-30, also demonstrate strong correlations, confirming the significance of this depth. For the 2022 season, the A-S7-12 sensor exhibits the highest correlation coefficient, albeit lower than the top values observed in 2021. Despite this, sensors at 30 cm, such as A-S2-30 and A-S1-30, continue to perform among the best, supporting the hypothesis that this depth is consistently adequate across different seasons. Figure 6: _Cont._  
The bottom 10 sensors for both seasons show significantly lower or negative correlation coefficients, indicating discrepancies between field measurements and satellite estimates. These results highlight the variability in sensor performance based on placement and environmental conditions. This visualization provides a comprehensive overview of the most and least effective sensors, aligning with the conclusion that a 30 cm depth consistently yields the most reliable data for satellite validation.
###### Abstract  
In this study, copper films were deposited by magnetron sputtering on poly(ethylene terephthalate) knitted textile to fabricate multi-functional, antimicrobial composite material. The modified knitted textile composites were subjected to microbial activity tests against colonies of Gram-positive (_Staphylococcus aureus_) and Gram-negative (_Escherichia coli_) bacteria and antifungal tests against _Chaetomium globosum_ fungal molds species. The prepared samples were characterized by UV/VIS transmittance, scanning electron microscopy (SEM), tensile and filtration parameters and the ability to block UV radiation. The performed works proved the possibility of manufacturing a new generation of antimicrobial textile composites with barrier properties against UV radiation, produced by a simple, zero-waste method. The specific advantages of using new poly(ethylene terephthalate)-copper composites are in biomedical applications areas.  
Keywords:knitwear textile; poly(ethylene terephthalate); polymer functionalization; copper; composites; magnetron sputtering; antibacterial activity; antifungal activity +
Footnote †: journal: Applied Sciences
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 6 Performance Evaluation
### Scenario Configuration  
We consider a 2000 m-stretch of a highway, composed of four lanes and traversed by platoons only. Cellular connectivity is provided by one eNodeB, whose V2X-reserved bandwidth is 10 MHz.  
Unless otherwise stated, we simulate \(N\) PLs and \(10\times N\) PMs. The size of each of the \(N\) platoons is randomly chosen with the constraint that the overall number of vehicles is constant. \(N\) is varied over the simulations.  
Intra-platoon gap is set to \(d=10\) m. CAM cycle duration is \(T=100\) ms and each CAM is 300 bytes at the application layer (hence, excluding extra headers introduced by lower layers). This size matches the needs to accommodate data conveying cooperative manoeuvres and cooperative perception data, according to [22]. As mentioned before, VUEs request resources according to Mode 3, whereas the eNodeB schedules per-TTI resources according to the Max C/I policy. H-ARQ retransmissions are disabled, in order to better understand the interference dynamics and how they are addressed by the eNodeB with proper CQI/MCS settings. For each configuration, we performed ten repetitions of 20 s. The main simulation settings are summarized in Table 1.
## 3 Methods
### Multi-Layer Neural Networks
#### 3.1.1 Neural Network Setup  
We used a network with five layers: one input layer, one output layer, and three hidden layers with 50, 30, and 20 neurons, respectively. The input layer uses three geometry angles and TOA radiances as inputs. The output layer contains a total of five outputs: snow grain size, snow impurity concentration, VIS albedo, NIR albedo, and VSWIR albedo. Three MLNNs were configured to employ TOA radiance channels similar to those used by the SBG, MODIS, and SGLI sensors. The continuous hyperspectral SBG MLNN used all 213 TOA radiance bands from 380 nm to 2500 nm, in 10 nm increments. To enable comparisons with existing multispectral sensors, such as MODIS and SGLI, a MODIS MLNN was constructed with seven TOA radiance bands (470 nm, 560 nm, 650 nm, 860 nm, 1240 nm, 1640 nm, and 2130 nm). Similarly, an SGLI MLNN was constructed with thirteen TOA radiance bands (380 nm, 410 nm, 440 nm, 490 nm, 530 nm, 570 nm, 670 nm, 760 nm, 870 nm, 1050 nm, 1380 nm, 1630 nm, and 2210 nm). A fourth Max-Min MLNN was formed using local maxima and minima of the radiance vs. wavelength and the albedo vs. wavelength (Figures 2 and 3). This approach led to an MLNN with forty-nine unique TOA radiance bands, with the majority of bands located between 600 nm and 1200 nm. Because we are using supervised learning, predictor inputs and desired output values were used to train the MLNN. Hence, we expect that the selection of predictor inputs for wavelengths producing the largest contrast in desired outputs (the local maxima and minima) should lead to an MLNN that easily learns the training data [39; 40]. All MLNNs produced the same five parameters as outputs.  
A hyperbolic tangent function was used as the neuron transfer (activation) function. A stochastic gradient descent method was used to minimize the cost function to obtain optimized weights and biases [41]. An adaptive learning rate, initially set to 0.001, was used for weight updates. A tolerance for optimization was set to \(10^{-8}\). When the loss function did not improve by at least this tolerance amount after ten consecutive iterations, convergence was considered to be reached and the training was terminated.
## 3 Major Bioactive Compounds and Potential Applications
### Sterols (Phytosterols)  
Phytosterols are bioactive isoprenoid compounds structurally similar to cholesterol, found in marine microalgae such as _Pavlova lutheri_, _Tetraselmis_ sp., _Nannochloropsis_ sp., and _Limnospira maxima_. The primary sterols include \(\beta\)-sitosterol, stigmasterol, campesterol, and fucosterol, with concentrations reaching up to 5.1% of dry biomass in _Pavlova lutheri_ under optimised cultivation conditions [115]. Microalgae-derived phytosterols are gaining attention due to their broad health-promoting effects and sustainable production potential.  
These compounds exhibit potent cholesterol-lowering effects by inhibiting dietary cholesterol absorption in the intestine through competition at the Niemann-Pick C1-like 1 (NPC1L1) transporter. Clinical studies support that consumption of 2 g per day of phytosterols can reduce low-density lipoprotein cholesterol by 8-10%, a significant impact in cardiovascular disease prevention [116; 117].  
Beyond lipid regulation, sterols exert anti-inflammatory, antioxidant, anticancer, neuroprotective, and hepatocyte effects. _Limnospira maxima_, for instance, produces sterols that have been shown to suppress oxidative stress and inhibit proliferation in cancer cell lines such as MCF-7 and HepG2 [118]. Fucosterol, found in brown algae and microalgae, is particularly notable for its anti-diabetic, anti-Alzheimer's, and anti-obesity properties, acting through modulation of inflammatory cytokines and oxidative stress pathways [119].  
In cosmetics, phytosterols support skin hydration, elasticity, and barrier function. They are incorporated into anti-ageing products where they stimulate collagen production, reduce UV-induced damage, and soothe inflammation [70]. Encapsulation technologies are also improving the bioavailability and stability of phytosterols in topical and oral formulations.  
In the functional food industry, phytosterol-enriched microalgal products such as spreads, yoghurt drinks, and dietary supplements are already available or in development. These compounds align with vegan and clean-label trends, providing natural and plant-derived alternatives to synthetic or animal-based ingredients [120].  
To enhance production yields, cultivation techniques involving nutrient stress, salinity adjustments, and UV-C exposure have been used successfully. For instance, _Pavlova lutheri_ cultured under salinity stress exhibited a 2-fold increase in sterol content, reaching commercial viability [115]. Novel screening pipelines and biorefinery models are further enhancing microalgae's competitiveness in the phytosterol market [119].  
In summary, marine microalgae represent a sustainable and multifunctional source of phytosterols with documented applications in cardiovascular health, metabolic modulation, cancer prevention, and skin care, underlining their growing relevance in pharmaceutical, cosmetic, and functional food sectors.
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 4 Fault Data Analysis  
Comprehensive experiments are performed to verify the effectiveness of the proposed fault diagnosis scheme. The electrical signals are collected from point machines that are installed and currently being used in operation lines. We have termed this current dataset Cu-3300, which contains three kinds of normal current classes and one fault class. The numbers of the signals in these normal classes are 474, 1043, and 1474, respectively, and the number of the signals in the fault class is 309. The exemplary comparison of normal signals and fault signals are shown in Figure 4. The number of the data points in the current signals differs from 92 to 300. The proposed locally connected autoencoder is trained using the Keras framework, where the CUDA acceleration technology is employed to accelerate the training process. The computational system environment is an Intel Core i9-9820X 3.30 GHz processor running Ubuntu 18.04 LTS. The integrated development environment is Anaconda 5.3.1.  
In traditional approaches, the features are manually designed and the PCA method is used for dimension reduction. In this paper, the fault diagnosis scheme termed \"Featurebased\" employing handcrafted features together with the kernel PCA method and the LOF algorithm is considered to be the baseline method. One can refer to our open source code for the detailed feature extraction process. Further, our proposed locally connected autoencoder scheme is also compared with other autoencoder-based methods:  
1. A stacked denoising autoencoder with L2 regularization (STDAE-150-50) [27], which adopts a two layer structure, with the first and the second layer containing, respectively, 150 and 50 units.
2. A sparse denoising autoencoder with L1 and L2 regularizations (SPDAE-80) [28], in which the number of units is set to 80.  
Figure 4: The exemplary comparison of normal signals and fault signals.  
Two gated recurrent unit-based sequence to sequence autoencoders [29]. In the network structure, 100 units are employed in the encoder as well as the decoder. The autoencoders with 40 hidden units and 70 hidden units are named as GRU-40 and GRU-70, respectively.
4. The proposed fault diagnosis scheme without the weighting strategy.