esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 7 Conclusions and Future Work  
This paper has introduced a multi-level X-ray-based diagnostic system for the accurate detection of COVID-19. The proposed analysis pipeline is a deep learning system (DLS) that utilizes transfer learning. The potential of the proposed framework is documented by the presented results to accurately identify COVID-19 cases in a cohort of 7395 CXR images. Extensive evaluation of the model parameters' optimization is conducted to improve performance and reduce the losses of the proposed model. The accuracy of our system is comparable to other deep learning-based methods for COVID-19 classification on various X-ray datasets. The proposed algorithm has great potential to be used in clinical applications and can benefit front-line medical personnel for accurate and rapid COVID diagnosis. In future work, we will extend our system's ability to fuse multi-input data, including other imaging modalities (i.e., chest CT) and clinical markers, to provide more comprehensive markers (images as well as clinical) that can help physicians in providing more appropriate personalized medicine. In addition, we will try to test our model with different and more diverse datasets.  
Conceptualization: E.A., M.A.M. and F.K.; methodology, formal analysis, and visualization: A.A.A., E.A. and F.K.; project administration,: E.A., M.A. and F.K.; supervision: E.A., M.A.M.; writing--original draft: A.A.A., E.A. and F.K.; writing--review & editing: E.A., M.A.M. and F.K.; software and data curation: A.A.A. All authors have read and agreed to the submitted version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable: This study used publicly available datasets.  
**Informed Consent Statement:** Not Applicable: This study used publicly available datasets.  
**Data Availability Statement:** The datasets used in this work are publicly available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Reinforcement of Steel Beams (Plates) Using Fe-SMA  
### Joining Methods between Fe-SMA and Parent Steel Components  
When strengthening structures, a reliable connection between Fe-SMA and parent steel components is required. As shown in Figure 9, there are four joining methods between the Fe-SMA strips and steel components at present: bolt anchorage [94; 98; 101], friction clamp [102; 103], nail-anchor [104], and adhesive bonding [105; 106]. Different from installing carbon fiber reinforced polymer (CFRP) [3; 7; 107; 108], the heating process required for activating Fe-SMA needs to be considered for the connection between the Fe-SMA strips and steel components. In applications, Fe-SMA is typically activated by the electrical resistance heating [97], flame-spraying gun [35], infrared heating [71], electric heating furnace [109], and electric ceramic [110]. In particular, Fe-SMA and parent steel components must be insulated when utilizing the electrical resistance heating.  
Figure 9: Steel structures reinforced with Fe-SMA and its joining methods: (**a**) reinforcing of steel plates, (**b**) configuration details.  
The bolt anchorage system consists of clamping plates, high-strength bolts, friction foils, and GFRP laminates [94; 98; 101]. The diamond friction foils increase the friction coefficient between the Fe-SMA strips and steel components, and the GFRP laminates insulate Fe-SMA from steel components. To avoid drilling holes, [PERSON] et al. [102] introduced the friction clamps used for CFRP into the connection between the Fe-SMA strips and steel beams, and GFRP laminates are similarly arranged to insulate Fe-SMA. Compared with the bolt anchorage and friction clamp, the nail-anchor system is more convenient to operate. [PERSON] et al. [104] achieved a reliable connection using nails and the powder-actuated fastening gun. Except for the above mechanical connections, [PERSON] et al. [105; 106] studied the adhesive bonding between the Fe-SMA strips and steel components, and found that the bonding strength could exceed 70% of the tensile strength of Fe-SMA.
## 3 Results and Discussion  
### Effect of Operating Parameters on Responses  
The predicted experimental design and actual results for both the responses (MC and L*) are given in Table 2. The experimental findings revealed that MC and L* were found in a range of 0.63 to 2.97% and 31.81 to 62.03, respectively. The final equations in terms of coded factors for MC and L* are as under:  
\[MC=\ +2.25+0.17A+0.25B+0.58C+0.10 AB-0.086 AC-0.13 BC-0.12A^{2}-0.22B^{2}-0.080C^{2} \tag{8}\]  
\[L^{\star}=\ +55.17+2.79A+3.66B+7.25C+1.70 AB-1.28 AC-2.04 BC-1.78A^{2}-2.5B^{2}-1.60 C^{2} \tag{9}\]  
where \(A\), \(B\), and \(C\) are coded values of feeding capacity, conveyor speed, and thermal oil flow rate, respectively, whereas the statistical significance of Equations (8) and (9) are given in ANOVA Tables 3 and 4 respectively:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Design Points**} & \multicolumn{3}{c}{**Actual Level of Factor**} & \multicolumn{3}{c}{**Predicted Responses**} & \multicolumn{1}{c}{**Observed Responses**} \\ \cline{2-7}  & **Feeding capacity** & **Conveyor Speed** & **Oil Flow Rate** & **MC** & **L*** & **MC** & **L*** \\  & **(kg)** & **(rpm)** & **(Ls*)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 5.00 & 0.50 & 0.01 & 0.71 & 33.88 & 0.63 & 33.81 \\
2 & 15.00 & 0.50 & 0.  
In Tables 3 and 4, the F-values of the models infers that the models are statistically significant and there is only a 0.01% chance that a \"Model F-Value\" this large could occur due to noise. A _p_-value less than 0.05 shows the statistical significance of the model terms (A, B, C, AB, AC, BC, A2, B2, and C2) in both the models. Whilst, the F-values for lack of fit in Tables 3 and 4 imply that the lack of fit is not significant relative to the pure error which indicates that the models are well fitted with the experimental data. The coefficient of variation for both the models shows the reliability of the model results with the experimental data because generally, a model with less than 10% CV is considered reasonably reproducible. It is also observed that the R\({}
## 2 Materials and Methods  
### Experimental Setup of a Detection System for Anomalous Moisture Region in Grain Piles  
Multiple grain samples with moisture content exceeding 15.0% were prepared for this experiment. These anomalous samples were placed in acrylic cubic containers of \(7\:\mathrm{cm}\times 7\:\mathrm{cm}\times 7\:\mathrm{cm}\) volume. Dry grain was placed in a 0.4 m \(\times\) 0.4 m \(\times\) 0.4 m cubic experimental bin, with the moisture-anomalous samples positioned at arbitrary locations within the bin. Patch antennas were uniformly distributed on all four side walls, with the most effective arrangement of this paper shown in Figure 1 (the impact of different transmitter and receiver layouts on detection area resolution is discussed in Section 3.1). These antennas serve as both electromagnetic wave transmitters and receivers. For precise localization, antennas on two side walls were designated as transmitters, while those on the remaining walls functioned as receivers. This study employed ultra-wideband electromagnetic waves with a frequency range of 2-3 GHz. The measurement speed was approximately 2 min. Although computational speed decreased as sampling data increased, improvements in both sampling and calculation speeds could be achieved through hardware optimization.  
The data acquisition and processing unit comprised the following components and functionalities: radio frequency (RF) switches were employed to selectively connect electromagnetic wave transmission channels; a micro-controller served as the control circuit, working in conjunction with a host computer to coordinate the channel switching of the RF switches; a vector network analyzer (VNA) (LiteVNA, v1.3), operating through the RF switches, was utilized to collect electromagnetic field data; a master control computer managed the vector network analyzer to acquire data for subsequent processing. This system operated by controlling the emission and reception of electromagnetic waves, collecting electromagnetic field data that contained information about moisture anomalies. The computer then processed and analyzed this data.
## 4 Discussion
### Limitations and Future Work  
Uncertainties in the footprint calculation of observed GPP can result from the footprint models or errors in the input parameters [60]. Specifically, the key parameters to run FSAM, such as surface roughness length (\(z_{0}\)), are related to the accuracy of footprint model. In this paper, the key parameters were obtained from the experimental parameter, which could bring some uncertainties in the estimation of the source area of the observed carbon flux data. Besides, footprints from nine flux tower sites covered most of the land cover types in the Heihe River Basin, but they are still insufficient to reflect all the land cover types in this  
Figure 10: Changes in field GPP footprint with the changes in atmospheric stability.  
region. In the future, we may conduct further research to assess the representativeness of the locations of the flux tower sites in this area. Furthermore, uncertainties related to the FSAM could also bring some errors. In the future, we may compare the difference between several footprint models, such as the flux footprint prediction (FFP) model [46] and the footprint symmetry index [4].
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 2 Related Work
### Skeleton Detection  
The skeleton is a structure-based object descriptor that reveals local symmetry as well as connectivity between object parts [38; 39]. Skeleton detection has been used in many applications, including object recognition and retrieval, pose estimation, hand-gesture recognition, shape matching, scene text detection, and road detection in aerial scenes [22]. Among them, the most common and popular use is detecting and locating key points of the human body to recognise different body movements effectively.  
A pioneer work of skeleton-detection methods, the edge-detection method HED [40] turns pixel-wise edge classification into image-to-image prediction. The side-output residual network (SRN) [41] leverages the side-output residual units to build short connections between adjacent side-output branches for matching object symmetry at different scales. For the problem that the scales of object skeletons may dramatically vary among objects and object parts, Hi-Fi [42] introduces a novel hierarchical feature-integration mechanism to capture high-level features from deeper layers and low-level details from shallower layers, which essentially establishes dense side-output branches. To cope with object parts of large widths, Ref. [22] proposes a \"skeleton context flux\" representation, which encodes the relative position of skeletal pixels to semantically meaningful entities.  
Our previous work [43] proposes to formulate the pixel-wise binary classification tasks as linear reconstruction problems within a linear span network architecture (LSN) consistingof three components: feature linear span, resolution alignment, and subspace linear span. Each component contains several linear-span units implemented by a concatenation layer, a convolutional layer and a slice layer to minimize the reconstruction error. Building on top of an LSN, our improved research, adaptive linear-span network (AdaLSN) [24], defines a mixed unit-pyramid search space. A genetic architecture search is applied to jointly optimize unit-level operations and pyramid-level connections for adaptive feature-space expansion.  
Compared to the state of the art, AdaLSN with sufficient feature-space expansion achieves significantly higher accuracy by utilizing complementary feature extraction and architecture optimization. Considering the serious imbalance between positive and negative samples in railway-skeleton images, Dice loss function [28] is more suitable for our research. Therefore, in this paper, we propose using our previous work, AdaLSN, with a Dice loss function as the base architecture for the skeleton-detection branch.
## 2 Methodology: PartsNet
### Encoder-Net
#### 2.2.1. Gated Recurrent Unit (GRU) Layers  
An RNN encounters two drawbacks when dealing with sequential data. Firstly, the limited retention capacity leads to the issues of vanishing or exploding gradients. Secondly, the model's dependence on sequential data results in low parallelism. The GRU proposed by [PERSON] et al. (2014) in 2014 demonstrated good performance in sequential data prediction ([PERSON] et al., 2014).  
The GRU handles sequence data and learns the underlying structures through the reset gate \(R_{i}\) and the update gate \(Z_{i}\), as shown in Figure 4. \(X_{i}\) represents the input data with sequence value of \(i\), while \(H_{i-1}\) denotes the hidden state vector output from the previous timestep of the GRU. Upon receiving inputs \(X_{i}\) and \(H_{i-1}\), the current GRU computes the gate control states \(R_{i}\) and \(Z_{i}\) through a calculation process.  
\[R_{i}=\sigma\Big{(}X_{i}W_{X_{i}^{R}}+H_{i-1}W_{h_{R}}+b_{R}\Big{)} \tag{7}\]  
\[Z_{i}=\sigma\Big{(}X_{i}W_{X_{i}^{Z}}+H_{i-1}W_{h_{Z}}+b_{Z}\Big{)} \tag{8}\]  
\(R_{i}\) is responsible for controlling to what extent the previous feature information can be remembered and how it should be incorporated into the candidate hidden state \(\widetilde{H_{i}}\). \(Z_{i}\) filters out certain information from \(H_{i-1}\), retaining only the relevant and useful portions, enabling the neural network to exhibit retention capability.  
\[\widetilde{H_{i}}=\tanh\Big{(}X_{i}W_{X_{i}^{H}}+(R_{i}\odot H_{i-1})W_{HH}+b_ {H}\Big{)} \tag{9}\]  
Figure 3: The architecture diagram of Encoder-Net in PartsNet.  
\(\widetilde{H_{i}}\) reflects the retention capacity of the GRU, while \(Z_{i}\) determines the strength of the retention. The GRU ultimately outputs the current hidden state vector, which is a weighted sum of \(H_{i-1}\) and the current candidate hidden state \(\widetilde{H_{i}}\).  
\[H_{i}=Z_{i}\odot H_{i-1}+(1-Z_{i})\odot\widetilde{H_{i}} \tag{10}\]  
Here, \(\sigma(\cdot)\) and \(\tanh(\cdot)\) are activation functions, and their output ranges are \([0,\ 1]\) and \([-1,\ 1]\), respectively. \(W_{X_{i}^{R}}\), \(W_{X_{i}^{L^{\prime}}}\), \(W_{X_{i}^{H}}\), \(W_{h_{R}}\), \(W_{h_{Z}}\), and \(W_{HH}\) represent weight matrices, while \(b_{R}\), \(b_{Z}\), and \(b_{H}\) represent bias terms.
# Current-Feedback Instrumentation Amplifier Using Dual-Chopper Fill-In Technique
## 2 Proposed Chopper Fill-In Instrumentation Amplifier
### Top Architecture and Fill-In Technique of the Proposed CFIA  
The top architecture of the proposed CFIA is illustrated in Figure 5a.  
Figure 4: Diagram of the noise voltage summation of the amplifiers in the dual path.  
An on-chip I/V reference, clock generator, and SPI are included in the two-channel CFIA. The clock generator generates an 18 kHz chopper clock. Figure 5b illustrates the channel-enabled logic and timing. Each CFIA uses a different chopper clock (\(f_{1}\) and \(f_{2}\);\(f_{3}\) and \(f_{4}\)). The chopper clock of channel 2 is delayed by 1/4 period relative to that of channel 1, as shown in Figure 5b. The glitch is generated at the end of each chopper clock, distorting the original signal. Enabling the two channels to send signals to the output alternately eliminates the glitch section and reduces distortion. Consequently, the final output signal does not contain a glitch.
# Vulnerability of Buildings to Meteorological Hazards:
## 2 Materials and Methods
### The Impact of Meteorological Hazards on Buildings
#### 2.1.2 The Impact of Heavy Rain on Buildings  
In the literature, definitions of heavy rainfall vary and depend on the geographical location. In Austria for example, the 98 th percentile of the precipitation distribution has been chosen as a criterion to define heavy precipitation [22]. The impact of heavy rain on buildings has not been described in detail in the literature so far. Research focuses mainly on the impact of rainfall-induced hazards on buildings including flooding, landslides, and debris flows. Nevertheless, [PERSON] et al. [23] describe the damage pattern of buildings in Germany following extensive rainfall events. They claim that the damages are mainly due to water or moisture loads on the exterior of the building. In more detail, they outline some typical impacts, including damage of the roof terraces and green flat roofs, damage to the connection points between balconies or the pitched roofs of the main buildings, and damage to the basement and exterior basement walls, as well as in underground garages. Finally, damage has also been recorded to waterproofing and its connection to the main building. Deficiencies in drainage systems and gutters have also been observed.
## 2 Sector-Specific Applications  
LCDPs are redefining industrial digitalization by enabling domain experts to rapidly prototype, deploy, and iterate applications tailored to sector-specific challenges [23; 24; 25; 26; 27]. This section explores how low-code technologies address critical needs in automotive, equipment manufacturing, aerospace, electronics, and energy industries, emphasizing their transformative role in bridging operational gaps and accelerating innovation. To better illustrate the LCDPs and their suitability across various industrial scenarios, Table 1 presents a comparative analysis of several widely adopted platforms. This comparison includes key features, target domains, underlying technical architectures, strengths, and notable limitations. The selected platforms--ranging from enterprise-grade solutions like OutSystems and Mendix to specialized tools such as Huawei AppCube and UI Bastery--reflect the diversity of LCDP offerings in both functionality and industrial focus. By providing a side-by-side overview, this table helps contextualize the sector-specific applications discussed in subsequent sections and highlights the trade-offs organizations face when selecting a low-code solution.
## 4 Discussion
In addition, an increase in skeletal muscle mass and muscular strength with resistance exercise may not affect peripheral arterial stiffness in young adults. For example, a study reported that lean body mass and muscular strength significantly increased after 11 weeks of high-intensity resistance exercise, but femoral-ankle PWV reflecting peripheral arterial stiffness did not ameliorate after the exercise [43]. The current study measured grip strength, an index reflecting muscular strength, and analyzed its correlation with baPWV. BaPWV accurately reflects arterial stiffness in the lower body when compared with that inthe upper body because it reflects peripheral arterial stiffness [44]. However, grip strength is better correlated with muscle mass and muscular strength of the upper extremities and is rather limited in assessing the ability of the lower extremities [45]. This fact may explain the lack of correlation between grip strength and baPWV in this study. Several limits of this study should be mentioned to benefit future research. First, in the present study, we conducted in a small number of healthy young male adults as a preliminary research. Therefore, the results of our study cannot be generalized to female subjects and individuals of different age ranges. Second, causality between anaerobic power and arterial stiffness was not confirmed in the present study; therefore, future studies investigating the physiological relationship between these factors are warranted. Although the present study did not suggest the exact mechanism of how skeletal muscle mass, muscular strength, and anaerobic power mutually affect the arterial stiffness, it showed that higher skeletal muscle mass and muscular strength might have a positive effect on Alx. These findings support the fact that maintaining or improving skeletal muscle mass, muscular strength, and anaerobic power to a higher level can produce a positive effect on Alx even in the young male adults.
# Stability Analysis of Mixed Convection Flow towards a Moving Thin Needle in Nanofluid
## 4 Conclusions  
In this paper, we investigate the effect of the velocity ratio parameter, mixed convection parameter, nanoparticle volume fraction parameter and the needle size on the fluid flow and heat transfer analysis of the mixed convection boundary layer flow past a moving vertical thin needle in nanofluid. The stability analysis is considered to identify which of the dual solutions obtained is stable by applying the bvp4c package in MATLAB software. The following are some conclusions that can be made through this study:  
* The magnitude of the skin friction coefficient and the local Nusselt number increases as the size of the needle decreases. The thinner surface of the needle causes the heat to be easily diffused, hence reducing the drag force between the needle and the free stream.
* The existence of the dual solutions occurs when the needle and the fluid move in the opposite way (\(\epsilon<0\)), while the solution is unique when they move in the same way (\(\epsilon>0\)). Nevertheless, the range of the dual solutions exists only in between \(\varepsilon_{c}<\epsilon\leq 0\).
* The presence of the nanoparticles volume fraction in the flow causes the skin friction coefficient, as well as the heat transfer rate on the needle surface to increase especially for the thinner surface.
* The dual solutions are more pronounced when the flow is opposing (\(\lambda<0\)) and the range is between \(\lambda_{c}<\lambda\leq 0.3\). When \(\lambda<\lambda_{c}\), no solutions are obtained.
* The stability analysis has confirmed that the upper branch solution is stable, while the lower branch solution is unstable by observing the positive and negative sign of the eigenvalues obtained.  
All authors participated in the analysis of the results for the final version of the paper. They have read and approved this manuscript.  
The authors wish to express their thanks to the anonymous Reviewers for their valuable comments and suggestions. This work was supported by the Putra Grant of Universiti Putra Malaysia (Project code: GP-IPS/2016/9513000) and acknowledges the financial support received from Universiti Putra Malaysia.  
The authors have declared no conflict of interest.
## 3 Experimental Tests
### Test Results
#### 3.2.4 Moment-FRP Strain Relationship  
The applied moment versus the FRP strain is presented in Figure 11. The FRP bars assumed little deformation before the cracking moment was reached, indicating that the load was mainly sustained by concrete in the precracking stage. After cracking, the moment-FRP strain curves of FRP bars increased with reduced slope until failure.  
Figure 11: Moment–deflection relationship: (**a**) Series I; (**b**) Series II and III.  
Figure 10: Deflections at service load.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 1 Introduction
Experimentally, SMH ensures precise power allocation, enabling seamless video streaming across different resolutions for VR glasses, computers, and smartphones. Additionally, it achieves over 100% target power coverage with a significant 11.6 dB improvement at the coverage edge compared with the multiple focal (MF) method from wavefront shaping, and it secures communication through accurate OAM key matching, facilitating error-free 1080P video playback. This study positions SMH as a pioneering approach to designing VLC systems optimized for multi-user environments, laying the foundation for next-generation communications in the 6G era.