esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles and Setups
### Optical NPR-Based Control for Flattening  
In Ring-1, the BBS consists of a forward pump source (@974.9 nm, Oclaro, San Hose, CA, USA), a 980/1550 wavelength division multiplexor and a section of EDF (EDFC-980-HP-C, Nufern, East Granby, CT, USA) with fixed length. By a stable driver (LDC250C, Thorlabs, Newton, NJ, USA) and cooler (TED200C, Thorlabs, Newton, NJ, USA), the pump source can output the stable power of 0\(\sim\)330 mW, and the corresponding output of BBS is about 0\(\sim\)100 mW. As shown in Figure 2a, the Lyot filter is made up two PCs, an isolator, a polarizer and a section of PMF (PM1017C, YOFC, Wuhan, China). According to [19], the NPR effect will be generated in the combination of PC-SMF-PC-Polarizer and its transmission (\(T\)) can be depicted as  
Figure 1: Scheme of multi-wavelength erbium-doped fiber laser (MWEDFL) with dual-ring configuration.  
\[\left|T\right|^{2}=\sin^{2}\alpha_{1}\sin^{2}\alpha_{2}+\cos^{2}\alpha_{1}\cos^{2} \alpha_{2}+\frac{1}{2}\sin(2\alpha_{1})\sin(2\alpha_{2})\cos(\Delta\varphi) \tag{1}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are respectively the incidence angle and emergence angle between the input light and SMF, and \(\Delta\varphi\) denotes the summary of phase difference induced by PC (\(\Delta\varphi_{\text{PC}}\)), PMF (\(\Delta\varphi_{\text{PMF}}\)) and SMF (\(\Delta\varphi_{\text{SMF}}\)). Further, \(\Delta\varphi_{\text{PMF}}=2\pi L_{p}\cdot\Delta n/\lambda\) (where \(L_{p}\) is the length of PMF, \(\Delta n\) the refractive index difference), and \(\Delta\varphi_{\text{SMF}}=-2\gamma L_{\text{s}}P\cdot\cos\!2\alpha_{1}/3\), where \(\gamma\) and \(L_{\text{s}}\) is the nonlinear factor and length of SMF, \(P\) is the pump power. We then set \(P\) = 100 mW, \(\lambda\) = 1550 nm, \(\Delta n\) = 0.00057, \(\gamma\) = 3W\({}^{-1}\)km\({}^{-1}\), \(L_{p}\) = 10 m, \(\alpha_{1}\)=\(\pi\)/3, \(\alpha_{2}\) = 5\(\pi\)/6, and the calculated transmission spectrum is shown as Figure 2b. The spectral-spacing is determined by \(\Delta\lambda=\lambda^{2}/\Delta nL_{p}\) and its value is \(\sim\)0.44 nm. Moreover, a section of SMF (SMF-28e, Corning, Corning, NY, USA) with the length of 3.3 km is added to further enhance the non-uniformity of optical ring cavity.  
Under the enough pump power, the Lyot filter can be viewed as a saturable absorber and bring the inhomogeneous wavelength- and intensity-loss to alleviate the mode competition of oscillations caused by the homogeneous gain broadening of EDF. Consequently, the intensity among the oscillations is automatically balanced, and the flat outputs of MWEDFL will be observed by an optical spectrum analyzer (81642B, Anglent, Santa Clara, CA, USA) or a power meter (1830c, Newport, Irvine, CA, USA).
# Propagation Property of an Astigmatic sin-Gaussian Beam in a Strongly Nonlocal Nonlinear Media
## 4 Conclusions  
In summary, the propagation of astigmatic sin-Gaussian beams in SNNM is investigated in detail. Based on the Snyder and Mitchell model, a closed-form propagation expression of astigmatic sin-Gaussian beams in SNNM is derived. The effect of astigmatism embedded in the input sin-Gaussian beams on the propagation properties is discussed analytically and numerically. It generally proves that the evolution of the field distribution with the propagation distance is periodic. For small values of \(\beta_{x}\) (\(\beta_{y}\)), a sin-Gaussian beam with or without astigmatism propagating in SNNM may retain a two-lobe pattern, or transform into a dark hollow pattern and even into a preferred doughnut-shaped profile depending on other parameter choices. Similarly, the phase singularity nested on the input sin-Gaussian beam can maintain its initial dislocation, or change from an edge dislocation to a vortex and even from canonical vortex to a noncanonical one periodically. In particular, theoretical analysis and the numerical simulative results demonstrate that astigmatism and the transversal orientation of the edge dislocation line of the initial sin-Gaussian beam play a key role in realizing these novel transformations during propagation in SNNM. It has been shown theoretically and experimentally that the nematic liquid crystals and the lead glasses are the SNNMs [42; 43; 44]. Therefore, the results presented here is intriguing for it reveals that the propagating astigmatic sin-Gaussian beam can transform between a two-lobe pattern and doughnut-shaped pattern in SNNM and furnishes an additional way to realize a doughnut-shaped beam with vortex using a sin-Gaussian beam with edge dislocations.  
Conceptualization, K.Z. and J.Z.; formal analysis, K.Z. and J.Z.; funding acquisition, J.Z.; software, Q.S. and H.T.; supervision, K.Z.; writing--original draft, K.Z. and Q.S.; writing--review and editing, J.Z. and H.T.  
This research is supported by the high level introduction of talent research start-up fund of Guizhou Institute of Technology, China. Many thanks to the anonymous reviewers for their good suggestions.  
The authors declare no conflict of interest.
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 2 Research Boundary and Key Parameters
### Carbon Emission Calculation of Precast Component Transportation  
Prefabricated component transportation includes two carbon emission sources, off-site transportation and on-site transportation, which can be subdivided into three parts: primary vertical transportation, horizontal transportation, and secondary vertical transportation. The carbon emission calculation considers the carbon emissions of the transportation vehicles used for transportation and the energy consumption of the mechanical equipment used for loading and unloading. The calculation process is as follows:  
\[E_{tp}=E_{tp1}+E_{tp2}+E_{tp3} \tag{7}\]  
\(E_{tp1}\): Carbon emissions from the precast component loading stage.  
\(E_{tp2}\): Carbon emissions from the transportation phase of the precast components.  
\(E_{tp3}\): Carbon emissions from the unloading stage of precast components.  
\[E_{tp1}=E_{tp3}=\sum\limits_{q}^{n}{\sum\limits_{z=1}^{n}{r_{qz}p_{qz}}} \tag{8}\]  
\(q\): The type of energy consumed by type z machinery per shift.  
\(r_{z}\): The carbon emission factor for machinery of type z.  
\(p_{z}\): The number of machine shifts of type z.  
The transportation of prefabricated components cannot reach a full load because of the different sizes of prefabricated components and the fact that too many stacked layers can affect structural safety and building life, resulting in the carbon emission factor of the transportation phase being influenced by the full-load rate [21]. In contrast to the general transportation of building materials at full load, the effect of the full-load rate must be considered when calculating the transport of prefabricated components. This means that the carbon emission factors of the means of transport at different full-load rates need to be considered.  
\[E_{\text{tp2}}=\sum\limits_{j=1}^{b2}\left(\sum\limits_{k=1}^{u}D_{\text{P}k,j}^ {2}\times Q_{\text{P}k}\right)\times f_{Tj1z} \tag{9}\]  
\(j\): Mode of transportation of prefabricated components.  
\(k\): Type of prefabricated component.  
\(Q_{\text{P}k}\): Demand for prefabricated component \(k\).  
\(D_{\text{P}k,j}^{2}\): Transportation distance of the prefabricated component \(k\) by transport mode \(j\).  
\(\sum\limits_{k=1}^{u}D_{\text{P}k,j}^{2}\times Q_{\text{P}k}\): The sum of the distances to the site for all components using transport mode \(j\).  
\(f_{T1z}\): Carbon emission factor for transport mode \(j\) when considering the full-load factor.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
###### Abstract  
So far, existing sub-GHz wireless communication technologies focused on low-bandwidth, long-range communication with large numbers of constrained devices. Although these characteristics are fine for many Internet of Things (IoT) applications, more demanding application requirements could not be met and legacy Internet technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP) could not be used. This has changed with the advent of the new IEEE 802.11 ah Wi-Fi standard, which is much more suitable for reliable bidirectional communication and high-throughput applications over a wide area (up to 1 km). The standard offers great possibilities for network performance optimization through a number of physical- and link-layer configurable features. However, given that the optimal configuration parameters depend on traffic patterns, the standard does not dictate how to determine them. Such a large number of configuration options can lead to sub-optimal or even incorrect configurations. Therefore, we investigated how two key mechanisms, Restricted Access Window (RAW) grouping and Traffic Indication Map (TIM) segmentation, influence scalability, throughput, latency and energy efficiency in the presence of bidirectional TCP/IP traffic. We considered both high-throughput video streaming traffic and large-scale reliable sensing traffic and investigated TCP behavior in both scenarios when the link layer introduces long delays. This article presents the relations between attainable throughput per station and attainable number of stations, as well as the influence of RAW, TIM and TCP parameters on both. We found that up to 20 continuously streaming IP-cameras can be reliably connected via IEEE 802.11 ah with a maximum average data rate of 160 kbps, whereas 10 IP-cameras can achieve average data rates of up to 255 kbps over 200 m. Up to 6960 stations transmitting every 60 s can be connected over 1 km with no lost packets. The presented results enable the fine tuning of RAW and TIM parameters for throughput-demanding reliable applications (i.e., video streaming, firmware updates) on one hand, and very dense low-throughput reliable networks with bidirectional traffic on the other hand.  
IEEE 802.11 ah; Internet of Things; TCP/IP; bidirectional traffic; scalability; streaming; Traffic Indication Map (TIM); Restricted Access Window (RAW) +
Footnote †: journal: Sensors
## 3 Experiments and Results  
In order to verify the position and direction angle estimation system for driving assistance in a mapless environment proposed in this study, experiments for the before and after applying the UWB sensor data in the pre-processing, the direction angle estimation using a complementary filter, and the sensor fusion position estimation system were conducted.
###### Abstract
The measurement of droplet size and distribution using a high-speed camera and validation through CFD simulations are effective techniques for understanding and optimizing gas-liquid systems. In the present study, the assessment and characterization of water droplet size distribution utilized the less resource-intensive Eulerian-Lagrangian ([PERSON] et al., 2019) approach. This method divides the gas-droplet flow into two distinct models: the continuous phase, described by the Eulerian model, and the discrete phase, represented by the Lagrangian model. Our primary objective in this research was to ascertain the size distribution of droplets produced by two nozzle plates. To achieve this, we employed a high-speed camera equipped with a zoom lens to capture images of droplet formation at the time of inception and at two different heights: 200 mm and 400 mm. Subsequently, we analyzed these droplet images using the open-source Fiji software (version 1.53t) ([PERSON] et al., 2019) to determine their respective sizes. The resulting droplet size distribution data were then graphed, and an attempt was made to fit them to established distribution models such as the log-normal, Gaussian, or normal distributions. At the injection point, the distribution model that best aligned with our empirical data was subsequently used as the input for our CFD multiphase flow simulation to predict droplet diameter distribution along the square tube. The CFD simulations were performed to validate their results against experimental measurements of droplet distribution. The literature mainly combines experimental methods with CFD to study single-nozzle sprays. The present study, however, used a nozzle plate with multiple nozzles. CFD simulations were used to predict the droplet diameter distribution from this nozzle plate, and these predictions were then validated with experimental results. The primary aim of this research was to identify an appropriate CFD model capable of accurately predicting droplet size and distribution, with the intention of validating its performance against experimental data. Once validated, this CFD model holds potential for applicability in predicting the fluid dynamics and droplet size distribution of alternative geometric configurations within the system.
## 3 Data and Methods
### U-Net  
The U-net model is a CNN-based algorithm for image segmentation, which features an encoder-decoder architecture. The encoder extracts features using convolutional and max-pooling layers and the decoder upsamples the image through transposed convolutions and restores spatial resolution [83]. We used four convolution and pooling layers for both the encoder and decoder, together with four upsampling and merging layers, as illustrated in Figure 5. The input sample dimensions are 128 \(\times\) 128 pixels (Section 3.1.2). To search for the optimal hyperparameters, the model was trained for up to 30 epochs with an early stopping procedure (patience = 15), selecting the epoch with the highest validation accuracy. A learning rate of 0.001 was used, along with a batch size of 16 to ensure training stability. Cross-entropy was used as the loss function, and the Adam optimizer was employed. All input data were batch-normalized to a range of 0-1 before being fed into the model. The entire training process was implemented in Jupyter Notebook using TensorFlow 2.0.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class Name** & **Description** \\ \hline Amazon Forest & Includes evergreen forests, semi-deciduous seasonal \\  & forests, gallery forests, and riparian forests. \\  & Represents a transitional ecological woodland between the \\  & Amazon dry forest and the Brazilian savanna, \\  & characterized by dense vegetation. \\  & Encompasses typical Cerrado, sparse Cerrado, and rocky \\  & Cerrado, with typical Cerrado being the predominant type \\  & in the study area. \\  & Covers all farmland and pastures. \\  & Interludes rivers, streams, and lakes. \\  & Includes isolated buildings, urban areas, and other \\  & human-made surfaces such as paved roads. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Name and description of land-use and cover classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Date Type** & **Number of Bands** \\ \hline \multirow{3}{*}{Optical + SAR} & Optical Fusion + Sentinel-1 & 12 \\  & Sentinel-2 + Sentinel-1 & 12 \\  & PlanetScope + Sentinel-1 & 6 \\ \hline \multirow{3}{*}{Optical} & Optical Fusion & 10 \\  & Sentinel-2 & 10 \\  & PlanetScope & 4 \\ \hline SAR & Sentinel-1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: All datasets compared in the study.
## 3 Method and Results  
The approach here is to apply the multi-attribute analysis [1] to predict a relationship between porosity and seismic data. Based on the relationship, a porosity volume is generated with training data (21 porosity logs), filtered to a maximum cutoff frequency of 100 Hz, and a series of seismic attributes obtained by applying transforms to a spectrally broadened seismic volume. The logs are filtered to 100 Hz because this is sufficient to show the most significant reservoir layers in the Daqing field. Applying higher frequencies from the logs than necessary will require a porosity prediction algorithm, resulting in degraded predictive accuracy and stability. Then, stepwise regression [15] is applied to selected groups of attributes separately to determine the attributes from each group used to predict porosity. The attributes are added one at a time in the order of the stepwise regression until the validation error starts increasing. This methodology is implemented to determine the number of attributes that will be used to perform the rock property prediction throughout the seismic volume [2]. It will not necessarily select the best possible attribute combination but rather a useful one. In addition, this procedure is further validated with three out-of-sample blind wells that are not utilized in the training. Porosity is predicted for each group and the results are compared to determine the accuracy, resolution, and statistical significance of the predictions. In this study, the spectrally broadened seismic volume is considered a seismic attribute, without any requirement or conclusion regarding the accuracy or completeness of the inferred high-frequency content (Figure 2). The spectrally broadened data has a high-frequency cutoff of about 150 Hz that is more than twice the original seismic bandwidth. The comparison of the original and spectrally broadened data is presented in [2]. The rock properties prediction is an empirical statistical approach that can leverage geological correlations that are not comprehended by a rock properties inversion based entirely on inverting the physics of wave propagation. For this reason, it is not required that the spectral broadening be physically correct such that it will reproduce the seismic data acquired at those frequencies. However, it is required that the spectral broadening be useful for the research purpose, determined by the well-to-seismic tie and the validation results of the blind out-of-sample tests [2].  
Figure 2 presents the amplitude spectrum of spectrally broadened data.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 3 Results and Analysis
### Visualization of Surface Crack Healing  
Although incorporating calcium oxide in the bio-AAS mixture led to a decrease in the mechanical properties, the treated Ca-5 specimens presented a good potential for crack width reduction and pore healing. The surface improvement included pore and crack healing, as illustrated in Figure 9. The results of the XRD confirmed the formation of calcium carbonate, which in turn reduced the water absorption and porosity when compared to the reference mixture. These findings are consistent with a previous study [4] on adding calcium carbonate to the AAS composite incorporating Bacillus pasteurii. The results highlighted the formation of calcium carbonate, which enhanced healing performance. However, a decrease in mechanical properties was mentioned due to the aforementioned reasons. However, the influence of bacteria on healing the cracks was much more substantial in the microbial AAS mix without calcium oxide.  
Figure 9: Visual observation of cracks before and after 28-day treatment with various biomixtures for C5 and Ca-5.  
Figure 10 represents the crack width measured before and after implying the bacteria. The urealytic reaction of incorporating microbes into the AAS environment succeeded in precipitating calcite crystals within the crack's mouth. The formation of calcite could result in reducing the existing crack width from 1 mm to 0.3 mm in C5 specimens. Despite the severe Ca-5 matrix environment, _Bacillus subtilis_ could proceed with the self-healing mechanism and fill in the crack width to achieve a maximum crack healing of about 80%.  
Figure 10: Healing of crack width observed before and after 28-day treatment of various biomixtures for C5 and Ca-5.
## 4 Simulations
It is worth noting that the TSP itself is crucial in reducing the overall cost. Figure 5 shows the maximum, median and minimum cost of all the possible combinations of products, for various values of \(N_{p}\). For simplicity, the number of components is kept constant (\(N_{c}=10\)), and the simulations are run 20 times for statistical relevance. The results show how the TSP (blue line) greatly reduces the overall cost, even considering the median value (black line).
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 3 Materials and Methods
### Algorithms and Evaluation Metrics
#### 3.3.2. CNN-Based Algorithm  
In this section, the CNN-based Detection Algorithm is introduced. For the evaluation phase of the proposed method, an embedded subroutine was developed within Algorithm 2. This subroutine identified victims using three types of images through convolutional neural networks. Specifically, the architecture of YOLOv8 was employed due to its notable features: its high inference speed and high precision rate in detection. The version of YOLOv8 utilized was'm', chosen for its balance between accuracy and inference time compared to the 'n' and's' versions. While the 'x' and 'l' versions marginally increase accuracy, the processing time hinders real-time inference.  
As a preliminary step to detection, a preprocessing phase was conducted on the multispectral images. Though the multispectral camera outputs RGB channels as well, for the purposes of this study, channels relevant to the victim detection index (Red, Green, Near Infrared) from Equation (2), as previously proposed by the authors in [22], were utilized. Although both the thermal camera and the multispectral camera produce an output image as a matrix with normalized intensities ranging from 0 to 255, unlike RGB, which combines three channels, when sending this data as inputs to the CNN, as the network requires three channels for processing, for the first two cases, the channel was replicated three times.  
\[InVD=\frac{\rho GRE-\rho RED-\sqrt{\rho NIR}}{\rho GRE+\rho RED} \tag{2}\]
## 2 Experimental Equipment and Signal Processing  
### Geomagnetic Flaw Detection Equipment  
The model of marine rigging flaw detection equipment used in the experiment is CM-801. It is a non-destructive testing system designed based on weak magnetic detection technology, mainly composed of a high-precision sensor and a high-precision weak magnetic detection system, which is specially used to detect cracks or broken wires and other defects on the surface of pressed rigging. The equipment mainly includes scanning probes, computers, etc., as shown in Figure 1. The equipment is mainly used in the inspection and detection of marine rigging, especially the steel wire rope that is widely used in ship hoisting, lifting, traction, and other scenes, as this compression rigging often withstands greater tension and stress changes. With the increase in use time, internal fracture or fatigue damage may occur. When the pressed rigging to be detected is defective, it will cause stress changes in the material itself, resulting in changes in the atomic structure of the material crystal, which will spontaneously generate an abnormal magnetic field, which will lead to deterioration in the performance of the wire rope and increase the risk of accidents. There are eight detection chips on the scanning probe, which act as eight-channel detection and can detect broken wire defects at a depth of \(\geq\)0.3 mm. The sweep probe has a sampling frequency of 25 Hz and a resolution of 0.1 nT.