esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
###### Abstract  
In this investigation, the effects of different fabrics with 0.20% carbon fiber textile (CFT), 0.21% glass fiber textile (GFT), and 0.25% basalt fiber textile (BFT) on the properties of TR-UHPC were investigated by axial tensile tests. A bending test of the BFT-UHPC pavement slab was carried out. In terms of axial tensile performance, the fiber textiles ranked in the following sequence: CFT, BFT, and GFT. Additionally, the corresponding increases in the initial cracking strength and ultimate tensile strength were 18.0% and 21.9% for the CFT, 12.0% and 16.0% for the BFT, and only 9.1% and 8.0% for the GFT, respectively. Increasing the textile reinforcement ratio of the BFT from 0.25% to 0.50% improved the cracking stress and peak stress of the specimen by 12.0% and 15.9%, respectively. Moreover, the ultimate strain of the 0.50%-BFT reinforcing case was 1.4 times that of the 0.25%-BFT reinforcing case and 2.6 times that of the unreinforced specimen in terms of ductility. The results of the stacking test on the BFT reinforced UHPC pedestrian slab indicate that the mid-span deflection of the test slab under normal use load is 0.775 mm, which is only 19.8% of the deflection limit. Additionally, the test slab remained in the elastic stage without any cracking. The BFT effectively enhanced the toughness of the UHPC thin slab after cracking. It is expected to be applied as a novel structure to bridge pedestrian slabs, bridge decks, and other thin UHPC members, thereby improving the durability and mechanical properties of bridge structures.  
Ultra-High-Performance Concrete; fiber textile; pedestrian walkway slab; tensile performance; flexural behavior
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 2 LoRaWAN Outline
### Modulation  
LoRaWAN uses chirp spread spectrum (CSS) for modulation [35]. This spread spectrum technique helps reduce interference and multipath and fading effects [23]. If the central carrier frequency is \(f_{0}\), a chirp signal changes its frequency linearly in the interval (\(f_{0}-BW/2\), \(f_{0}+BW/2\)) during a symbol time \(T_{s}\). In addition, the channel bandwidth is divided according to the SF parameter (i.e., the number of bits per symbol), which takes values from 6 to 12. Thus, the spectrum is divided into \(2^{SF}\) parts so that the symbols can start at \(2^{SF}\) at different initial frequencies. The initial frequency determines the value to be transmitted, so any symbol can carry up to \(2^{SF}\) values. Furthermore, a coding rate (CR) parameter defines the proportion of redundancy bits added to the frame for error correction and can take values of 4/5, 4/6, 4/7, and 4/8. The symbol time \(T_{s}\) can be defined in Equation (1):  
\[T_{s}=\frac{2^{SF}}{BW}. \tag{1}\]  
It can be noticed that increasing the SF by one multiplies per two the symbol time \(T_{s}\). In addition, the SF modifies the receiver sensitivity allowing negative SNR limit values, as shown in Table 2. In this way, changing SF allows broader coverage but increases \(T_{s}\). Because the consumed energy depends on the transmission power and \(T_{s}\), increasing this parameter means the energy will also increase.
## 4 Results and Discussion  
### Test Results  
The component test results of the BRBs are available elsewhere [2]; hence, further details are not given in this paper. The steel frame was tested by using the cyclic loading protocol given in Figure 7. This was performed to determine the bare frame capacity and cyclic behavior steel frame. Hence, it allowed for the determination of the contribution of the BRB member to the braced frame. The cyclic response of the steel frame and BRBF is presented in Figure 14. This figure shows the lateral force versus IDR for both the steel frame and the BRBF, and the lateral force versus CP strain for the BRB. The IDR was the ratio between lateral displacement and height of the steel frame (2265 mm). The value of the axial displacement of CP was measured from LVDTs (6-9 in Figure 6). The value of axial strain of CP was the ratio between the average displacement LVDTs (6-9 in Figure 6) and the length of the CP (1702 mm). The steel frame remained within the elastic limit during the test and its maximum tensile and compression lateral forces were measured as 170 kN and 154 kN, respectively. In addition, the maximum tensile and compression lateral forces of BRBF were measured as 677 kN and 788 kN, respectively. The out-of-plane displacement of BRBF and slip between the BRB and gusset plate are summarized in Table 4.  
Figure 13: Displacement measured in hydraulic actuator and support plates.  
Figure 14: Experimental cyclic behavior of steel frame, BRBF, and BRB.  
The compression strength adjustment factor (\(\beta\)) and strain hardening factor (\(\omega\)) for BRB was determined by AISC [44]. The \(\beta\) and \(\omega\) factors are defined as follows:  
\[P_{C,\ BRB}=\beta\times\omega\times P_{y,\ BRB} \tag{6}\]  
\[P_{T,\ BRB}=\omega\times P_{y,\ BRB} \tag{7}\]  
\[P_{y,\ BRB}=F_{y,\ BRB}\times A\ BRB \tag{8}\]  
where F\({}_{y,\text{BRB}}\) is the yield stress of CP, A\({}_{\text{BRB}}\) is the cross-section area of CP, and P\({}_{\text{C,BRB}}\) and P\({}_{\text{T,BRB}}\) are the compression and tension strength of BRB, respectively. As can be seen in Figure 14, the value of \(\beta\) was determined to be 1.16. At the end of the test, the steel frame remained within the elastic limit and high mode buckling was observed on the CP (see Figure 15).
## 5 Results and Discussion
### Limitations  
While the developed ABS model has proven effective in replicating the company's logistics operations and facilitating a comparative analysis with the optimization-based PDP-TW model, certain limitations should be acknowledged. First, the dataset used in this study consisted of 24 orders, which was sufficient for the development and validation of the ABS model and its comparison with the optimization model. However, for future research, it is recommended to increase the dataset and vary the number of available drivers to better analyze the impact on model performance and scalability. Additionally, the ABS model currently operates in a deterministic framework, meaning that uncertainty factors such as traffic conditions and unforeseen delays have not yet been incorporated. Future enhancements will include stochastic elements to better reflect real-world conditions. Another limitation is the fixed average speed of 30 km/h used in the ABS model, which, while suitable for simulating urban logistics in Lisbon, could be further refined by integrating dynamic speed variations based on traffic patterns. Despite these limitations, the developed ABS model provides a strong foundation for evaluating operational efficiency and serves as a valuable tool for future improvements, where additional refinements will enhance its applicability in complex, real-world logistics environments.
## 4 Results and Discussion
### Statistical Analysis of Dataset Characteristics  
The detailed statistical analysis of the water potability dataset (Table 7) reveals considerable variability across all measured parameters. pH values range from highly acidic (0.23) to strongly alkaline (14.00), with a mean of 7.09 approximating neutral conditions. This variability reflects the diverse sources and conditions under which water samples were collected.  
Total dissolved solids (TDS) demonstrate particularly high variability (standard deviation of 8642.24 ppm), ranging from 320.94 ppm to 56,488.67 ppm, indicating that the dataset encompasses water samples from diverse sources with varying mineral content. Chloramines, identified as the most important feature by the EnhancedTabTransformer, show concentrations ranging from 1.39 ppm to 13.13 ppm (mean: 7.13 ppm), with several samples exceeding regulatory limits for drinking water in many jurisdictions. Similarly, trihalomethanes, byproducts of water chlorination associated with potential health risks, range from 8.58 ug/L to 124.00 ug/L, with approximately 25% of samples exceeding the 80 ug/L threshold established by some regulatory agencies.  
The substantial variability in water quality parameters underscores the complexity of the classification task and highlights the value of advanced machine learning approaches in capturing non-linear relationships between multiple physicochemical parameters and water potability.
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 3 Stacked DSAE Network
The input features from the stacked DSAE can be used for detecting the improved MPPSK modulation signals. It is fed by the output \(\text{a}^{(l)}=f\left(\textbf{h}^{(l)}\right)\) to a softmax classifier, and \(f(\cdot)\) is a sigmoid function. Thus, the originally generated layers can be used for feature extraction. When the dataset is sent into the stacked DSAE network, every hidden layer transforms the feature data until the last hidden layer. Then, the last set of the extracted feature is fed into the softmax layer for detection. After the construction of the whole network is complete, the cost function is finally optimized by the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Figure 3: The proposed network architecture consists of 3 stacked DSAEs and a softmax layer.
## 2 Theoretical Background
### Objective Functions
#### 2.1.3 Modal Flexibility  
The dynamically-measured flexibility matrix can be used to estimate the damage in the actual structure. It actually is the inverse of the stiffness matrix and replicates the applied force and structural displacement relationship. Each column represents nodal displacements when a unit force is applied at one of the DOF. The flexibility matrix may be calculated from the lower vibration modes as:  
\[\mathbf{F}=\ \frac{1}{\omega_{j}^{2}}\mathbf{\phi}_{j}\mathbf{\phi}_{j}^{\prime} \tag{4}\]  
\[\mathbf{F}^{*}=\frac{1}{\omega_{j}^{*2}}\mathbf{\phi}_{j}^{*}\mathbf{\phi}_{j}^{*\prime} \tag{5}\]  
where \(\mathbf{F}\) and \(\mathbf{F^{*}}\) are the flexibility matrices of a undamaged and a damaged structure, \(\omega_{j}\) is the frequency of a particular mode, \(\mathbf{\phi}\) is the mass normalized mode shape and asterisks show properties of the damaged structure. The diagonal values of the matrices obtained using Equations (4) and (5) has been taken, and flexibility based modal assurance criteria (FMAC) is formulated as:  
\[\text{FMAC}=\frac{|\mathbf{\varphi}_{j}\mathbf{\varphi}_{j}^{*}|^{2}}{(\mathbf{\varphi}_{ j}^{\prime}\mathbf{\varphi}_{j})(\mathbf{\varphi}_{j}^{*\prime}\mathbf{\varphi}_{j}^{*})} \tag{6}\]  
where \(\mathbf{\varphi}_{j}\) and \(\mathbf{\varphi}_{j}^{*}\) represent the diagonal values of the flexibility matrices obtained from Equations (4) and (5), respectively.  
The objective function is then formulated to calculate FMAC between \(\mathbf{F}\) and \(\mathbf{F^{*}}\) as:  
\[F_{3}=\sum_{i=1}^{n}\frac{\left(1-\sqrt{\text{FMAC}_{i}}\right)^{2}}{\text{ FMAC}_{i}} \tag{7}\]  
where FMAC represents the change in the flexibility matrix due to damage formulated in light of Equation (3).
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 2 Materials and Methods
### Sample Extraction and Analysis  
Electrical conductivity (EC) and pH analyses were carried out following official methods [34]. Both parameters were measured in a 1:2.5 soil: distilled water ratio with a pH/conducter (Metrohm AG, Herisau, Swiss) at the initial (t0) and final (tf) time of the bioassay.  
For the metal(loid)s analysis, only As, Pb, Cu, and Zn were considered since a previous study indicated those were the most dominant metal(loid)s in the contaminated soil studied [32]. A portable X-ray fluorescence analyzer (NITON XL3t-980 GOLDD+, ThermoFisher, Waltham, MA, USA) was used for directly measuring the total concentration of each metal(loid) in finely ground soil samples.  
To extract the metal(loid)s soluble fraction, soil was mixed with distilled water at a 1:2.5 ratio and agitated in an end-over-end shaker overnight. Then, samples were centrifugated at 3000 rpm for 10 min and 5 mL of the supernatant were added with 0.2 mL of nitric acid following the protocol described in [PERSON] et al. [35]. Samples were kept at 4 \({}^{\circ}\)C until analysis.  
The extraction of the metal(loid)s bioavailable fraction was carried as described by [PERSON] et al. [36]. Briefly, soil samples (5 g) were mixed with EDTA (50 mL) 0.05 M and pH = 7 and the mixture was agitated in an end-over-end shaker for 1 h. Then, samples were centrifugated at 3000 rpm for 10 min and the supernatant kept at 4 \({}^{\circ}\)C until analysis.  
The extracts containing soluble and bioavailable fractions were analyzed by inductively coupled plasma mass spectrometry (ICP-MS, PE SCIEX ELAN-5000, Perkin Elmer Inc., Waltham, MA, USA). The ICP method precision was evaluated by the six repetitive analysis of the reference material SRM 2711. The average recovery of the reference material ranged from 91 and 105% for the metal(loid)s studied [32].  
To determinate metal(loid)s concentration in shoot (SC) and root (RC) biomass the protocol used was based on EPA METHOD 3050B (successive attacks with nitric acid and hydrogen peroxide) [37], and then the extracts were analyzed by inductively coupled plasma optical emission spectrometry (ICP-OES).
# A Novel Scheme for MIMO-SAR Systems Using Rotational Orbital Angular Momentum
## 4 Results
### SISO Mode  
The schematic configuration of the SISO mode is shown in Figure 6a, and the experimental scenario is displayed in Figure 6b. In the SISO mode, the rotational OAM beams were generated by the transmitting antenna platform connected to the signal generator. At the receiving end, another horn antenna with 20 dB gain was used to receive the backscattered signal from the target, and a spectrum analyzer was used to observe the frequency spectrum. The polarizations of both Tx and Rx were the same. The target distance was 3 m, which satisfied the far-field condition. The signal generator generated a continuous wave (CW) signal at 14.5 GHz.  
The rotational Doppler frequency shifts of different OAM echoes versus the rotation speed are shown in Figure 7. For conventional plane EM waves (\(l=0\)), there existed no frequency shift, whether the incident wave rotated or not. For vortex EM waves, the frequency shifts of the two OAM modes, i.e., \(l=+1\) and \(l=-1\), were different. The frequency shift also increased with the rotation speed. The experimental results showed that with the rotational vortex EM waves, a different OAM mode brought a different frequency shift at the receiving end, which made it possible to distinguish different OAM modes in the frequency domain.  
Figure 5: Prototypes of the transmitting antenna platform.  
Figure 6: (**a**) Schematic configuration of the single-in–single-out (SISO) mode; (**b**) experimental scenario of the SISO mode.  
Figure 7: The rotational Doppler frequency shifts of different OAM echoes versus the rotation speed.
## 4 Results and Discussion  
### Characterization of the Nano Biochar Nanoparticles  
#### 4.1.1 FTIR Analysis of Biochar Nanoparticles  
Infrared Spectroscopy (FTIR) was used for polymer identification of nano-biochar particles. The FTIR spectrum of nano-biochar contains multiple different bands. (Figure 2). The band at approximately 3230-3550 cm\({}^{-1}\) is attributed to the O-H stretching vibrations, while the peak at the 2850-3300 measure is assigned to C-H vibrations. The band at 1680 cm\({}^{-1}\) is related to carbonyl group stretching. The band at 1750 cm\({}^{-1}\) is assigned to the stretching mode of C=C groups. Furthermore, the bands at 1448, 1400, and 1350 cm\({}^{-1}\) are attributed to O-CH\({}_{3}\) and the vibration of the hydroxyl group.
## 2 Relevant Theory
### Res2 Block  
The ResBlock effectively mitigates the problems of vanishing and exploding gradients that often occur in deep networks due to stacked convolutional layers during backpropagation. As illustrated in Figure 5, the residual block comprises three convolutional layers: a \(1\times 1\) convolution is used to adjust the number of input feature map channels; a \(3\times 3\) convolution is responsible for feature extraction; and a residual connection is employed at the output to merge the input with the processed features. This configuration enables the network to extract deeper features while simultaneously preserving shallow features from earlier layers.  
The Res2 Block modifies the traditional residual block by replacing the standard \(3\times 3\) convolutional layer with a structure capable of multi-scale feature extraction, as illustrated in Figure 6. It introduces a new dimension, referred to as scale, in which the input feature map is split along the channel dimension, and each subset is treated as an independent scale. Convolution is applied to all scale groups except the first. Additionally, each group is connected to its preceding group through a residual connection after convolution, allowing for progressive feature aggregation across scales. The computation process is defined by the following formula:  
\[S_{i}^{*}=\begin{cases}S_{i},&i=1\\ C_{i}(S_{i}),&i=2\\ C_{i}(S_{i}+S_{i-1}^{*}),&2<i\leq s\end{cases} \tag{2}\]  
Figure 5: Res Block module.  
Here, \(S_{i}\) denotes the feature map obtained after channel splitting, while \(S_{i}^{*}\) refers to the corresponding output following the convolution operation. The notation \(C(\cdot)\) represents a \(3\times 3\) convolutional layer.  
In the Res2 Block, each scale performs one additional convolution operation relative to the output of the preceding scale. The resulting feature maps, each having undergone a different number of convolutions, are then concatenated to form the final output. This design facilitates the extraction of multi-scale semantic information, enabling the encoder to capture coal-rock features at various levels of abstraction. Moreover, it significantly expands the network's receptive field, thereby enhancing its ability to detect and represent features across different spatial scales.
## 7 Processing Architecture
### Physical Layer Operations  
The CHP2 physical layer also includes the following operations:  
* Carrier Frequency Correction: CHP2 performs two carrier frequency offset (CFO) corrections on the received data. The first correction coarsely estimates the CFO using the pre- and mid-ambles and applies the correction to the data being passed to the communications decoding processing chain. The second correction finely estimates the CFO using the pre- and post-ambles and applies the correction to the data being passed to the ToA correlation engine.
* Pulse Shaping Filter: We apply a raised cosine FIR filter in the Tx and Rx engines to limit the spectral leakage. This filter uses 65 taps, a 0.25 roll-off factor, a 16 symbol span, and 4 samples per symbol.
* Channel Equalizer: We apply a 5-tap adaptive Wiener filter to the received data in the communications processing chain to mitigate multi-path effects. This filter is evaluated on each reception using the pre-amble.  
* Automatic Gain Control: We employ automatic gain control (AGC) to maintain a received instantaneous SNR between 20 and 25 dB. This prevents power saturation and starvation as the platforms move closer or farther apart, respectively.
## 4 Schematic Design  
### Power Source Circuit  
In the given circuit with an input of 220 V 50 Hz AC, the first component encountered is a bridge rectifier, constructed using four diodes. This rectifier is pivotal, transforming the 220 V alternating current into a pulsating direct current, making the current flow in one direction but with variations in voltage levels. Post rectification, the pulsating DC is processed by the WS157 monolithic switching regulator. The role of this component is paramount. It efficiently converts the unstable, high-voltage pulsating DC into a regulated, stable DC voltage by swiftly switching the input voltage on and off and controlling the duty cycle, minimizing energy loss, and ensuring consistency in output. It acts to mitigate any fluctuations and ripples in the voltage, providing a smoother and more reliable power supply. After the stabilization and regulation by WS157, the transformed current is then passed to a transformer, where it is stepped down to the requisite 12 V DC. This lower, stable, and regulated voltage is now suitable and safe for utilization in the connected devices or components, ensuring optimal performance and reliability in the overall system's operation. This systematic arrangement of components ensures the efficient conversion of high-voltage AC to low-voltage DC, with precise regulation and stability, making the circuit adaptable and reliable for its intended applications. The schematic model of the AC to DC converter is shown in Figure 8.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 1 Introduction
To realize drive mode decisions with high driver acceptance, the concept of decision sight distance can be considered for continuous risk assessment [10]. In this paper, the main contribution is the merging between V2V communication and a vehicle-based sensor as radar in order to get most valuable data for safety in longitudinal collision risk. The idea to incorporate such integration with a user-centered algorithm for predictive drivers' behaviors, in the intention to include a human-like behavior in the automation, is the other contribution. Section 2 demonstrates the information fusion and fundamental descriptions for a radar, camera and the V2V communication are discussed. In Section 3, the threat assessment method describing a safe triangle analysis is demonstrated. The risk assessment algorithm with the V2V communication is investigated through simulation studies in Section 4. Finally, conclusions are provided in Section 5.
###### Abstract  
Spectrum auctions in international mobile telecommunications (IMT) are a representative method for selling the right to transmit signals within a specific band of electromagnetic waves to communication service providers (CSPs); it is important to design a fair spectrum auction that can benefit both government and auction bidders. The government should reduce the burden of maintenance costs by setting a reasonable initial price and selling it to bidders at the highest price they can afford. However, due to the complex auction rules and decision-making process, not many studies has been conducted on how to select an appropriate initial price for the auction. This study aims at introducing a novel simulation modeling approach to develop a spectrum auction policy for international mobile telecommunications (IMT) using agent-based simulation (ABS), which involves three telecommunications service provider types (i.e., the Aggressive bidder, the Moderate bidder, and the Conservative bidder) and the auction environment of IMT in South Korea. In particular, the proposed approach adopts the exponential utility theory to model the behavior of auction bidders and identify the optimal initial bid price. The devised ABS model is calibrated to the IMT spectrum auction conducted in 2018 in South Korea, and the best initial pricing policy identified (i.e., $85.24 million per spectrum block) regarding a sustainable market environment for existing service providers (i.e., 10 blocks for the Aggressive bidder, 10 blocks for the Moderate bidder, and 8 blocks for the Conservative bidder). The proposed approach will be beneficial to both government agencies and auction bidders under fair competition in the IMT market.  
agent-based simulation; spectrum auction; international mobile telecommunications; clock auction; exponential utility theory +
Footnote †: journal: Applied sciences  
1