esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 2 Wind Turbine Parameter Description and Feature Selection
### Description of the Wind Turbine Parameters
#### 2.1.2 Identification and Handling of Data Outliers  
In data processing, it is crucial to identify and handle outliers. Linear or spline interpolations are usually used to fill in the missing values in the data. However, when the missing values are too many, deleting these values may become an option, although doing so may result in data loss. If there is a change in a data point versus an adjacent time point, this may indicate the pre-processed completed data. In this case, removing samples of those variables outside of the normal change range is necessary, and identifying the cause of the abnormality is equally important.  
The SCADA data of wind turbines contains a large amount of valuable information. Deleting all the data would result in the loss of useful content, negatively impacting the model's training and prediction. Therefore, removing only the abnormal or unexpected data helps preserve the normal operational data. Excessive data removal could lead to a sparse training set, affecting the model's learning ability. Since there is a nonlinear relationship between wind speed and power, not all of the wind speed-power values are considered anomalies. By applying specific filtering criteria, unreasonable data can be removed while retaining most of the valid data and eliminating obvious outliers, as shown in Figure 3.  
Figure 1: Raw wind speed–power diagram.  
Figure 2: Data after removing the power abnormalities.
## 3 Hepatitis
### Hepatitis B  
Hepatitis B virus (HBV) belongs to the Hepadnaviridae family and is a DNA genome virus. DNA replicates in the hepatocytes and is converted into relaxed-circular-form RC-DNA, which is then transferred to the nucleus of the hepatocytes and converted into the covalently closed circular state (cccDNA). The RNA polymerase enzymes transcribe cccDNA to multiple genomic RNAs, which are then packed into the capsid [27]. Hepatitis  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Virus** & **Classification** & **Genome** & **Envelope** & **Spread** \\ \hline HAV & _Picornaviridae_ & RNA & Non-enveloped & Fecal–oral \\ \hline HBV & _Hepadnaviridae_ & DNA & Lipid-enveloped & Parenteral \\ \hline HCV & _Flaviviridae_ & RNA & Lipid-enveloped & Parenteral \\ \hline HDV & Unclassified & RNA & Lipid-enveloped (from HBV) & Parenteral \\ \hline HEV & _Calciviridae_ & RNA & Non-enveloped & Fecal–oral \\ \hline \end{tabular}
\end{table}
Table 1: Types of hepatic viruses.  
B is remediable but a leading cause of liver cirrhosis and cancer. It is the major cause of hepatic disorders worldwide due to its easy transmission through blood and sexual contact. Clinically, it is diagnosed mainly by monitoring HBsAgAs in blood serum using immunoassay. Raman spectroscopy can add advantages, like a higher sensitivity and the detection of multiple biomarkers simultaneously, and has attracted significant attention for hepatitis B diagnosis [36].  
Ten different types of approaches have been proposed for the diagnosis of hepatitis B using Raman setups, including spectroscopic studies of blood plasma [20], blood serum [30; 31; 40; 56; 62], and extracted viral DNA [63; 64]; imaging of blood plasma [34]; SERS of PCR products [27]; SERS of serum [21; 32]; SERS of centrifugally filtered serum [24]; SERS of extracted viral DNA [35]; a SERS-based sandwich assay [38]; and a Raman biosensor for serum and plasma [36; 65]. Most of these approaches have been studied using blood serum samples for hepatitis B diagnosis. These studies are described in more detail in Section 4, although an example is detailed in the following paragraph.  
Figure 2 shows the latest study on the design of Raman-based biosensors for diagnosing hepatitis B in blood serum samples. The illustration reveals the modification of the surface of gold nanoparticles by attaching a rabbit monoclonal hepatitis B surface antibody (HBsAb) as the detection antibody and SERS tags for the Raman signals. Separately, the surface of the magnetic beads is modified and loaded with the hepatitis B surface antibody (an HBsAgAg capture antibody). When 40 \(\upmu\)L of both the modified gold NPs and magnetic beads is mixed with a serum sample from HBV-infected patients for 10 min, a sandwich immunoassay is developed through the capture of the hepatitis B surface antigen (HBsAg). This assembly is extracted using a magnetic bar, placed onto a gold substrate for 1 min, and analyzed using a Raman spectroscope.
# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 3 Methods
### The Matching Feature Points Threshold Template Updating  
Traditional Siamese network algorithms (such as SiamFC and SiamRPN, to name a few.) always set the target-tracking template to the first frame, and do not update the template during tracking. This can result in decreased tracking accuracy due to changes in the target's appearance in complex environments such as occlusion, illumination, and motion. Updating the template frequently would increase computational complexity and increase tracking errors. To balance this, this article uses thresholds to determine whether a template update is required. Firstly, the ORB feature-matching algorithm was used to match the extracted features. Then, the matched feature points threshold is set to determine whether to update the template. If the number of matched points is below the threshold (150 in this method), the target is considered lost, and the template is initialized again. If the number of matched feature points is more than the threshold, update the template.  
The ORB algorithm is utilized for feature matching in this study due to its high description and matching efficiency as well as good real-time performance compared with traditional algorithms, such as SIFT and SURF. The feature-matching process is roughly as follows:  
Firstly, the ORB algorithm was used to detect and describe key points in the extracted feature vectors, and the key points in the image and corresponding BRIEF descriptors were obtained. Secondly, the Hamming distance is used to match the key points. If the Hamming distance between feature points is less than the set threshold (set as 150 in this work), they are considered a match. In other words, by comparing the bit values of two feature descriptors, if different, the counter will be increased by one. The final counter result is the Hamming distance between the two binary numbers. As shown in Formula (10), \(ORB_{pre}\) and \(ORB_{next}\) are the feature descriptors of two adjacent feature points, respectively. Finally, the image is registered based on the homography matrix and the number of interior points. If the number of matching points is more than 150, the template is updated.  
\[Ham\big{(}ORB_{pre},ORB_{next}\big{)}=\sum_{0}^{255}ORB_{pre}\oplus ORB_{next} \tag{10}\]
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 4 Prototypes of Absorption Chillers
### Advanced Absorption Chillers
#### 4.2.1 Double-Lift Absorption Chillers
When the chiller was experimentally tested (under steady-state and dynamic operating conditions), the performance was stable over a wide range of operating conditions under each mode of operation. The schematic diagrams of the chiller cycle illustrating the single-effect and half-effect configurations are depicted in Figures 16 and 17, respectively. The chiller was designed to drive hot water inlet/outlet temperatures of 90/70 \({}^{\circ}\)C, chilled water supply/return temperatures of 6/12 \({}^{\circ}\)C, and heat rejection inlet/outlet (absorber and condenser) temperatures of 40/45 \({}^{\circ}\)C.  
The experimentally determined nominal cooling capacity of the single-lift and double-lift mode of operations is about 18.4 kW and 10.6 kW, respectively, with a corresponding energy efficiency ratio  
Figure 16: Single-lift (single-effect) ammonia/water absorption cycle of the University of Innsbruck prototype. Adapted from Reference [77]. Note: ABS = absorber; CON = condenser; DES = desorber; EVA = evaporator; RAC = refrigerant accumulator; SAC = solution accumulator; SHX = solution heat exchanger. (i.e., thermal COP) of approximately 0.57 and 0.27. These performance values (i.e., cooling capacity and thermal COP) are for the following nominal operating conditions: the heat source, cooling water, chilled water inlet temperatures, and flow rates are 85 \({}^{\circ}\)C and 4.5 m\({}^{3}\)/h, 20 \({}^{\circ}\)C and 6.5 m\({}^{3}\)/h, and 12 \({}^{\circ}\)C and 3 m\({}^{3}\)/h, respectively. The main advantages of the new concept implemented in this prototype are that dry cooling towers can be used so that maintenance and costs are reduced. Moreover, if this chiller is to be implemented effectively in solar-driven refrigeration systems, they must focus on high production and, thus, a high cooling capacity of the chiller. Finally, the experimental and numerical results obtained from this chiller prototype led to the development of an economical, scalable, flexible, and reliable ammonia/water absorption chiller.
## 5 International Standards and Current Regulations
### Recommendations for Standard Evolution
A harmonized global approach can accelerate the adoption of AW materials:* Collaborative Research: Establish international research initiatives to develop unified standards for AW-based concretes [34; 71]. * Knowledge Sharing: Create platforms for sharing best practices, pilot project results, and calibration data across regions [73; 84]. By adopting these recommendations, the construction industry can not only overcome the challenges associated with AW integration but also contribute to the development of sustainable and high-performance RC structures. In addition to addressing regulatory gaps, it is crucial to explore the market incentives and barriers that influence the adoption of AW materials in construction practices. * Incentives: The cost-effectiveness of AW materials, coupled with their environmental benefits, such as reduced carbon emissions and energy savings, presents significant opportunities for market adoption. For example, the use of CS concrete has been shown to reduce production costs by up to 20% compared to conventional aggregates [26; 75]. Additionally, alignment with circular economy goals and sustainability targets encourages the use of alternative materials. * Barriers: Despite these advantages, several challenges hinder market adoption. These include regional disparities in AW availability, high processing and transportation costs, and a lack of standardized economic evaluations. Furthermore, the absence of awareness and policies promoting the use of AWs limits their acceptance in mainstream construction practices. * Future Directions: To overcome these barriers, future research should focus on developing comprehensive cost-benefit analyses, pilot projects for validating economic and technical feasibility, and policies that incentivize the use of AW materials in construction. Addressing these aspects will facilitate broader acceptance and scalability of AW-based concrete in the market.
## 3 Results and Discussion
### Factors That Impact the Filtration Process
#### 3.2.3. Inlet Fluid Velocity  
Three inlet fluid velocities were examined (Case 10: \(u_{in}\) = 0.1 m/s, Case 2: \(u_{in}\) = 0.2 m/s, and Case 11: \(u_{in}\) = 0.3 m/s). As shown in Figure 13, the three kinds of woven fibers exhibit almost the same patterns of deposited particles (namely, the same mass load per unit area), and the front views show that slightly more particulate matter is deposited on the windward side of the orthogonal fibers with a higher inlet fluid velocity. This might be because the increased fluid velocity greatly improves the drag force of the particle-flow-fiber interactions, weakening the diffusion ability of fine particles and strengthening the particle deposition ability of fibers to different extents.  
Figure 12: Dynamic changes in \(\lambda\) with different packing densities (with error bars).  
Table 3 implies that the parameter \(k\) is positively related to the fiber perimeter, and the rate of increase in the normalized pressure drop is independent of the inlet fluid velocity. In addition, the front views in Figure 13 indicate that fibers subjected to a lower inlet fluid velocity have slightly more particulate matter deposited on their lateral side, producing significant branched dendrites, which might well explain the slight differences in the slope \(k\) (3.33-3.37) between the three cases. Figure 14b and Table 3 demonstrate that the normalized efficiency of the three cases can be written as \(\eta/\eta_{0}\) = 0.53\(M\) + 1.00, indicating that the inlet fluid velocity has no impact on the rate of increase in the normalized capture efficiency. Nevertheless, it must be noted that the clean capture efficiency is rather different and decreases as the inlet fluid velocity increases, and so the non-steady-state capture efficiency is only related to the initial capture efficiency during the particulate matter filtration process.
###### Abstract
Numerous comfort models exist and can predict thermal sensation. However, these comfort indices need to be validated in hot zones and quantify the neutral range considering people's thermal resilience. The present study investigates the outdoor thermal comfort of people who live in hot areas and are accustomed to this and quantifies this effect. For that, predictions provided by the COMFA thermal comfort model were compared with the occupants' perceptions given in the field campaigns' questionnaires. The field campaigns were associated with on-site monitoring of local climate variables. It was observed that during the survey period, the entire space was predicted to be uncomfortable by the COMFA model. On the contrary, the results of the questionnaires showed that the most frequently encountered thermal sensations were distributed between the comfort zone and the hot zone. The proposed methodology has been designed to be used by other researchers, and it is adaptable to other outdoor thermal comforts such as PET or ITS. The comparison between the model's predictions and the users' responses to space highlighted the tendency of the COMFA to overestimate the thermal sensations. This work's results allow extending the neutral comfort band from 50 W/m\({}^{2}\) (value of literature) to 80 W/m\({}^{2}\). So, the paper quantifies that the effect of the thermal resilience of the people increases the thermal band of comfort by around 60%. These results will allow an accurate assessment of the effectiveness of future mitigation solutions implemented to improve outdoor thermal comfort in other world areas. It is due to the propose of a higher neutrality range researchers or designers could achieve outdoor thermal comfort in effective and reliable ways, even in hot climates. Keywords: Outdoor thermal comfort; urban open spaces; thermal sensation; monitoring; survey +
Footnote †: journal: Applied Sciences
###### Abstract  
To mitigate inter-symbol interference (ISI) caused by Faster-than-Nyquist (FTN) technology in a multiple input multiple output (MIMO) optical wireless communication (OWC) system, we propose an ISI cancellation algorithm that combines multi-head self-attention (MHSA), a one-dimensional convolutional neural network (1D CNN), and bi-directional long short-term memory (Bi-LSTM). This hybrid network extracts data features using 1D CNN and captures sequential information with Bi-LSTM, while incorporating MHSA to comprehensively reduce ISI. We analyze the impact of antenna numbers, acceleration factors, wavelength, and turbulence intensity on the system's bit error rate (BER) performance. Additionally, we compare the waveform graphs and amplitude-frequency characteristics of FTN signals before and after processing, specifically comparing sampled values of four-pulse-amplitude modulation (4 PAM) signals with those obtained after ISI cancellation. The simulation results demonstrate that within the Mazo limit for selecting acceleration factors, our proposal achieves a 7 dB improvement in BER compared to the conventional systems without deep learning (DL)-based ISI cancellation algorithms. Furthermore, compared to systems employing a point-by-point elimination adaptive pre-equalization algorithm, our proposal exhibits comparable BER performance to orthogonal transmission systems while reducing computational complexity by 31.15%.  
Faster-than-Nyquist; Bi-LSTM; deep learning; interference cancellation; optical wireless communication 202411982[[https://doi.org/10.3390/photonics11100982](https://doi.org/10.3390/photonics11100982)]([https://doi.org/10.3390/photonics11100982](https://doi.org/10.3390/photonics11100982))
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## Appendix A The Mesh Sensitivity Analysis  
To ensure that the solution was independent of the mesh, two different mesh dependency studies were performed. The first mesh study was conducted to analyze the overall flow behavior in the nozzle and external domain to ensure that flow features such as shocks were accurately captured. The three following different mesh resolutions were analyzed: coarse (730,000 cells), fine (1,420,000 cells), and very fine (2,980,000 cells). The mesh at the nozzle, mask, and substrate walls was kept consistent for all three resolutions. The gas pressure and velocity profiles were compared along the centerline of the geometry, as shown in Figures 13 and 13, respectively. As can be seen, all three mesh resolutions were able to adequately model the flow in the nozzle, as well as capture the shocks at the nozzle exit, as indicated by the sharp increase in pressure and decrease in velocity downstream ofthe nozzle exit. Therefore, to save computational costs, the flow could be modelled with the coarse mesh resolution.  
A secondary mesh refinement study was performed, which focused on the near-wall region, specifically near the mask walls and near the substrate surface. As discussed, the SST k-\(\omega\) turbulence model was used for the analysis, which performs well with a fully resolved near-wall mesh. Therefore, to ensure that the near-wall mesh was sufficiently resolved, three different meshes with varying average y+ values were analyzed. The y+ is a non-dimensional value that corresponds to the height of the first cell center adjacent to the wall. The mesh characteristics for the three different cases are shown in Table 10. To analyze the flow, the gas pressure and velocity profiles were examined along a line through the mask rods, from the nozzle exit to the substrate. The respective profiles are shown below in Figures 11 and 12. As can be seen, the profiles are similar for all three meshes. However, as will be discussed in Appendix B, the fine and very fine meshes are better able to resolve the flow near and downstream of the mask. Therefore, the near-wall mesh at the mask is refined inline with the very fine resolution, with an average y+ of less than 1.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Experimental Feasibility of \(\text{Sys}_{8}\)  
The proposed \(\text{Sys}_{8}\) configuration, incorporating a multilayer SPR structure with silver, silicon nitride, graphene, and a thiol-tethered ssDNA layer, is designed with both theoretical optimization and practical feasibility in mind. Experimental implementation of such a configuration is feasible using well-established fabrication techniques. For instance, the deposition of silver and silicon nitride layers can be achieved using physical vapor deposition (PVD) [37] or sputtering methods [38], which provide precise control over layer thickness and uniformity. Graphene can be transferred using established chemical vapor deposition (CVD) techniques [39], which yield a monolayer or few-layer graphene with excellent consistency across the sensor surface.  
The thiol-tethered ssDNA layer, serving as a binding site for SARS-CoV-2 RNA, can be introduced through a straightforward self-assembly process [40]. Thiol-modified ssDNA strands bind covalently to the silver surface, forming a stable monolayer that enhances the biosensor's specificity. The thickness of the ssDNA layer can be controlled by adjusting the ssDNA concentration and incubation time during this process. Optimizing these parameters helps achieve a uniform layer thickness, which is critical for reproducible sensor performance. Further, rinsing with PBS solution post-immobilization removes excess ssDNA, stabilizing the layer.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
###### Abstract  
Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2 hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network.  
underwater wireless sensor networks; area towards destination; pipeline radius; virtual vector; potential neighbor number; vector-based forwarding; linear programming; composite priority function; qualified forwarding +
Footnote †: journal: Sensors
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Analysis of Imperfect Synchronization on Symbol Estimation
#### 2.2.2 Impact of the Doppler Rate on Symbol Estimation  
When only the DR is present (i.e., \(\{\Delta\tau,\Delta f\}=0\)), we observe an uncompensated frequency offset that varies linearly with time at a slope \(c_{d}\). For the sake of simplicity and to qualitatively understand the effect of this DR, let us approximate this linear variation as constant over a symbol time and changing from symbol to symbol (i.e., \(f_{d}(nT_{s})=f_{d}(pMT_{s}),\ \forall n\in[pM,(p+1)M-1]\)). Under this assumption, the frequency corresponding to the maximum amplitude of the FFT, when performed on consecutive symbols, will increase or decrease linearly (depending on the sign of \(c_{d}\)). Hence, (13) can be written as:  
\[\begin{split} z(n,p)&=\sqrt{p}e^{2\pi n\left(\frac {S_{p}+\frac{c_{d}p}{M}T}{M}\right)}+w(n)\\ &=\sqrt{p}e^{2\pi n\left(\frac{S_{p}+\frac{c_{d}p}{M}T^{2}}{M} \right)}+w(n).\end{split} \tag{16}\]  
Then the \(M\)-point FFT of \(z(n,p)\) gives:  
\[Y[k,p]=\sqrt{\frac{P}{M}}\Gamma_{M}(k,S_{p}+\frac{c_{d}p}{2}T^{2})+W[k] \tag{17}\]  
As depicted in (17), the \(argmax\) of the FFT is shifted from symbol to other. We notice that the signals with the highest symbol times are more sensitive to the effect of the Doppler rate.