esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 1 Introduction
### Automated Tools for Productive Research with Diatoms  
Currently, digital microscopy provides several automation methods with many advantages over traditional ones, particularly in applied studies where the identification, counting, and classification--in the sense of pattern recognition--of diatoms are required. In this context, the tasks subject to being automated are related to: (1-DIG) image capture and digitalization; (2-PRE)image preprocessing to improve image quality by eliminating artifacts [14]; (3-SAV) the storage, recovery, and display of digital images; and (4-ADV) advanced image processing algorithms. Next, we enumerate the tasks for each mentioned category:  
* (1-DIG) Programmable illumination: To obtain better image contrast, it is possible to choose different illumination parameters such as intensity, modality (e.g., brightfield, darkfield, oblique, etc.), wavelength (e.g., white, UV, etc.), and so on [24; 25; 26].
* (1-DIG) Scanning automation: When the sample under observation is not covered by the field of view (FOV), the slide should be shifted below the objective--by stage motion--to obtain information from the whole slide [27].
* (1-DIG) Stitching: If a slide requires several image captures (i.e., patches) to cover the whole preparation, the stitching process produces a new image generated by joining the individual patches [28; 29].
* (1-DIG) Multifocus and multiexposure fusion: When several images are captured at different focal planes, the inherent three-dimensional morphology of diatom valves is recorded and could be fused in a new complete in focus image. Similarly, multiexposure fusion is used to obtain the right exposure over all regions in the fused image from individual images with underexposed and overexposed regions [9; 10; 13; 30; 31; 32; 33; 34; 35; 36].  
* (2-PRE) Noise reduction and artifact cleaning: Digital images captured by any microscope are affected by several sources of noise and artifacts--derived from imperfect illumination, optical systems, debris on the slide, etc.--the elimination of which is desired [14; 37; 38; 39].
* (2-PRE) Contrast enhancement: Contrast is related to the different levels of intensity present in the image. The more levels are in the image, the easier it is to distinguish the details [14; 38].
* (3-SAV) Adding scale bars and metadata: Useful information should be recorded and linked to each image for enrichment with additional information--not visible in the raw image--for easy recovery as needed.
* (4-ADV) Detection and identification: This task consists of determining the presence of diatoms in an image and the information about their location and quantity [8; 16; 40; 41; 42; 43; 44].
* (4-ADV) Classification: If diatoms are present in the image, classification answers the question about their taxa [45; 46; 47; 48; 49; 50; 51; 52; 53].
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods
### Methods
#### 2.2.2 Time-Frequency Sequence Modeling Block  
In both the speech and noise branches, the recurrent modules are deployed to receive the outputs of their respective encoders and model the sequential dependencies within the encoded features. Different from the traditional CRN architecture, which only utilizes multiple RNN layers to capture temporal correlations in encoded features, we additionally integrate sequence modeling along the frequency dimension. Similar to the works in [16,38], we adopt a recurrent module comprising multiple time-frequency sequence modeling (TFSM) blocks [22] to simultaneously modeling the local and global context along the time and frequency dimensions.  
Figure 3 illustrates the diagram of the TFSM block. The input representation \(I\) is processed by two consecutive sequence modeling sub-blocks, which are designed to apply sequence modeling in the frequency and time dimensions, respectively. To ensure causal inference, we adopt the causal TFSM block in the speech branch. Specifically, the TFSM block in the speech branch utilizes a gated recurrent unit (GRU) layer to capture sequential correlations in the time dimension. In contrast, the TFSM block in the noise branch employs a bidirectional GRU (BiGRU) layer for temporal sequence modeling. Since the sequence modeling in the frequency dimension does not affect causal inference, the BiGRU layer is used in both branches' TFSM blocks. After the GRU or BiGRU layer, a layer normalization and a PReLU function are utilized for better performance. Additionally, the reshape operations serve the purpose of realizing sequence modeling in two different dimensions and recovering the sequence modeling result to the same shape as the input \(I\). Eventually, a residual connection is employed to sum the original input \(I\) and the result after sequence modeling, yielding the TFSM block's output \(O\).  
In both the speech and noise branches, we stack 3 TFSM blocks, whose hidden BiGRU&GRU units are {128, 64, 32}.  
Figure 2: **(a)** The detail of the two-dimensional convolutional (Conv2d) block. **(b)** The detail of the two-dimensional deconvolutional (DeConv2d) block.
## 3 Experimental Procedure
### Results and Analysis
#### 3.3.3 Fatigue Damage Evolution Experiment of Test Pieces Based on Ultrasonic Scanning  
The ultrasound scan results of the test pieces E1, E2, E3, and E4 are similar. In this paper, taking experimental piece E1 as an example, ultrasonic scanning analysis was conducted on the damage of each scanning point when N = 0 (before fatigue loading), N = 72,000, N= 216,000, N = 396,000, N = 432,000, and N = 440,160 (fatigue failure). The results are shown in Figure 19.  
Figure 19: _Cont._  
In Figure 19, take the ultrasound scan image of scan point 1 at N = 0 as an example. The left side of the image is the A-scan result, which is used to monitor the defect reflection wave of the CFRP shaft tube in the thickness direction (that is, before the bottom wave). The right side of the image is the B-scan result, the red horizontal bar is the bottom wave, and the image above the bottom wave is the damage of the CFRP shaft tube in the thickness direction. The darker the color indicates that the more serious the damage inside the CFRP shaft tube. If a large area of damage occurs inside, the bottom wave will become blurred or even disappear.  
When N = 0, the bottom wave in the B-scan images of each scanning point is very clear, with only a few green spots and no obvious damage, indicating that there are some small original defects in the CFRP shaft tube before fatigue loading. At N = 72,000, no obvious damage was observed. As N = 216,000, the bottom wave in the B-scan image of scan point 4 begins to become fuzzy, and more green patches can be seen, indicating that the fatigue damage at scan point 4 has begun to appear to a relatively low degree, while no obvious damage has been seen in the other three scan points. While N = 396,000, the bottom wave has completely disappeared in the B-scan image of scanning point 4, and the red spot increases, indicating that the fatigue damage of scanning point 4 is further expanded. Fatigue damage has begun to appear at scanning points 3, and no obvious damage has been seen at scanning points 1 and 2. When N = 432,000, a large area of red patches appeared in the image of Scan point B of scan point 4, and relatively serious fatigue damage appeared at scan point 4, while the fatigue damage at scan point 3 was constantly expanding. No obvious damage was found at scan point 1 and scan point 2. At N = 440,160, the CFRP shaft tube had fatigue failure, and the fatigue damage of scanning point 4 was very serious, and the fatigue damage of scanning point 3 was also very serious. Only minor damage appeared at scanning points 1 and 2, and no obvious fatigue damage was observed.  
To sum up, the fatigue damage of the CFRP shaft tube initially appeared at scanning point 4 nearest to the loading head. With the progress of fatigue loading, the fatigue damage became more and more serious and gradually expanded to scanning point 3. No obvious fatigue damage was observed at scanning points 1 and 2 in the process of fatigue loading.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 2 Related Work  
In this section, recent studies on the TSIN routing method are reviewed, which can be classified into two types: the satellite routing schemes and the hybrid terrestrial-satellite routing schemes.
# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
###### Abstract
* Cabling and Piping: The robot must minimize the number of cables and reduce drag forces that could impede mobility. This paper presents a robot prototype designed for the inspection of hydraulic steel gates. The prototype is based on a wheeled mobile platform equipped with six permanent magnet wheels for reliable adhesion to magnetic surfaces. An image sensor captures real-time images of the weld areas to correct the robot's heading. Additionally, the visual system detects the presence of weld defects and classifies their types. By connecting the drive modules via the EtherCAT bus, the design significantly reduces the number of cables, thereby minimizing the risk of cable entanglement. In the weld inspection task, YOLOv5 and Hough transform are combined for defect location and trajectory extraction. We built a multi-axis control platform based on EtherCAT that supports rapid deployment and module expansion. The robot completes visual recognition and weld tracking closed-loop control on the actual surface of the hydropower station gate, which has good practicality. The remainder of this paper is organized as follows:  
* Section 2 details the overall structure of the mobile robot platform. * Section 3 presents the visual sensing system and trajectory tracking strategy used to correct the robot's heading. * Section 4 describes the design of the weld defect detection system and conducts simulation and experimental tracking tests to validate the control system prototype. * Section 5 concludes the paper.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G 5 th Generation  
AWGN  Additive White Gaussian Noise  
CFR  Channel Frequency Response  
CIR  Channel Impulse Response  
CNN  Convolutional Neural Network  
CP  Cylic-Prefix  
DMRS  DeModulation Reference Signal  
DNN  Deep NN  
FFT  Fast Fourier Transform  
IFFT  Inverse FFT  
i.i.d.  Independent and Identically Distributed  
LSTM  Long Short-Term Memory  
ML  Machine Learning  
MNN  Multi-Layer NN
## 6 Experimental Set-Up
### Energy Requirements  
The first step has been to estimate the real energy requirements of the leaf node for EDETA. For EDETA, the equivalent functional configuration used in the prototype is:  
* Three transmissions per day;
* One second per transmission, 10 dBm transmission power;
* Two second mean time for CH polling (RX), maximum receiver sensitivity.  
Table 4 summarizes maximum energy requirements for EDETA in this configuration.  
Table 5 shows battery life for the EDETA configuration.  
This life reduction for the EDETA configuration is perfectly acceptable in our case because it provides a higher level of flexibility, scalability and functionality.  
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline
**Description** & \begin{tabular}{c} **Current** \\ **(\(\mu\)A)** \\ \end{tabular} & \
\begin{tabular}{c} **Working time** \\ **per day (s)** \\ \end{tabular} &
\begin{tabular}{c} **Annual requirements** \\ **(mAh)** \\ \end{tabular} \\ \hline Microcontroller sleep + RTC & 1.0 & 86,293.60 & 8.74 \\ Microcontroller active & 3,000.0 & 106.40 & 32.36 \\ Sensirion SHT1x active & 900.0 & 5.00 & 0.45 \\ Sensirion SHT1x unpowered & 0.0 & & \\ Sensirion SHT1x sleep & 0.3 & & \\ LED active & 1,000.0 & 43.20 & 4.38 \\ TAOS light sensor active & 780.0 & 43.20 & 3.41 \\ TAOS light sensor unpowered & 0.0 & & \\ RF modem CC1101 transmitting + idle & 35,000.0 & 3.00 & 10.64 \\ RF modem CC1101 sleep & 1.0 & 86,397.00 & 8.75 \\ RF modem CC1101 receiving + idle & 19,600.0 & 6.00 & 11.92 \\ \hline  & \multicolumn{1}{c}{**Energy required**} & \multicolumn{1}{c}{80.69} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{**(mAh)**} & \\ \hline \end{tabular}
\end{table}
Table 4: Energy requirements for each part. EDETA configuration. V\({}_{\rm DD}\) = 3.3 V.  
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline
**Battery model** & \begin{tabular}{c} **Capacity** \\ **(mAh)** \\ \end{tabular} & \
\begin{tabular}{c} **Usable** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Estimated** \\ **lifetime (years)** \\ \end{tabular} \\ \hline EMB er14250 3.6 V (selected) & 1,100 & 75 & 10.22 \\ Lithium-thinonyl 2/3 AA 3.6 V & 1,700 & 75 & 15.80 \\ Duracell 34 mm \(\times\) 16.9 diam. 3 volts. DL12 AB1 Ultra M3 & 1,500 & 75 & 13.94 \\ Duracell Ultra 3 v. 27 \(\times\) 15.6 diam DCLR2 & 950 & 75 & 8.83 \\ \hline \end{tabular}
\end{table}
Table 5: Estimated battery life. EDETA configuration.
## 4 Results and Discussion
### Performance Comparison with Other Models  
To further validate the performance of our proposed model (YOLOv8-small), we conducted a comparative study with two other deep learning models: RT-DETR (Real-Time Detection Transformer) and YOLOv11-small. RT-DETR represents a transformer-based detection framework, while YOLOv11 is the latest evolution of the YOLO family. All models were evaluated on the same training and test datasets under identical experimental conditions to ensure fair benchmarking.  
As shown in Table 7, YOLOv8s attained a strong accuracy of 99.70%, outperforming RT-DETR (98.27%) and nearly matching the performance of YOLOv11s (+0.08%). Notably, YOLOv8s achieved a higher F1-score than RT-DETR, with a delta of 3.85%, reflecting a well-balanced trade-off between precision and recall. While YOLOv11s marginally exceeded YOLOv8s in raw accuracy and recall, the differences were minimal and may be negligible in practical applications, particularly in resource-constrained environments. In terms of efficiency, YOLOv8s performed better than RT-DETR, as it has a smaller model size (21.51 MB) and faster inference times (974.50 ms). RT-DETR, although competitive in recall, suffered from a large model footprint (63.10 MB) and significantly slower inference (2391.70 ms), making it less suitable for real-time deployment.  
Figure 8: Model performance and efficiency trade-off.  
In comparison, RT-DETR illustrates the promise of transformer-based detection but currently lacks the computational efficiency needed for edge-level applications. Meanwhile, YOLOv8s provides a better trade-off between accuracy, speed, and model size for deployment in our mHealth application, especially considering the resource-constrained environment of mobile devices.
###### Abstract  
Previous EEG studies using graph analysis have revealed altered functional brain networks in children with developmental dyslexia (DD). The influence of sex on these networks within this childhood disorder remains unclear. The study emphasizes the importance of considering sex and individual differences by investigating brain connectivity in 8-year-old children (42 controls and 72 children with DD, half girls) during a task involving low- and high-contrast discrimination of low-spatial frequency illusion (LSFI). Understanding these variations is crucial for elucidating the neurobiological underpinnings of developmental disabilities. Control children showed sex differences in association networks, while children with DD exhibited them in sensorimotor networks. The control boys' \(\alpha\), \(\beta\)2-frequency functional networks were more integrated than control girls in low-contrast LSFI and in \(\beta\) and \(\gamma\)2-networks in high-contrast LSFI. Boys exhibited stronger anterior connectivity (language, visual motion), while girls showed stronger posterior connectivity (visuospatial, visuomotor attention). There was a notable overlap in association networks between boys and girls. Sex-related differences were pronounced in the \(\gamma\)2 frequency sensorimotor, and association cortical networks exhibited dispersion in both hemispheres for boys and in the left hemisphere for girls (both contrast LSFIs). Boys with DD exhibited hubs in \(\alpha\)-sensorimotor networks (low-contrast LSFI) and \(\beta\)1-networks (high-contrast LSFI) in the right brain hemisphere, while girls' hubs with DD were in the left hemisphere. The differing rates of cortical network maturation between sexes with DD during childhood contribute to variations linked to disruptions in brain network development, even within sensorimotor networks. The study showed that this task enhanced even minor individual differences in functional connectivity characteristics and revealed subtle differences in brain connectivity, especially in children with DD.  
Keywords:functional connectivity; small-world propensity model; sex differences; individual differences; developmental dyslexia +
Footnote †: journal: Applied sciences
# Sea Buckthorn Leaves as a Potential Source of Antioxidant Substances
## 2 Materials and Methods
### Quantification of Antioxidant Compounds  
#### 2.4.1 Total Polyphenol (TP) Determination  
The TP content was determined by UV-VIS spectroscopy (Analytik Jena, SPECORD(r) PLUS, Jena, Germany) using the Folin-Ciocalteu reagent [42] and using gallic acid as a standard. Absorbance was measured at \(\lambda\) = 765 nm against a blank sample. The results were expressed in mg gallic acid equivalents (GAE) per 1 g of SBT leaf DM. Gallic acid solutions of various concentrations were prepared to determine a standard curve (y = 0.0115x + 0.0332; R\({}^{2}\) = 0.9997).
## 4 Application Scenarios and Platform Extensions
### The Importance and Extended Application Scenarios of Co-Simulation Platform  
A CPPS increases the complexity and difficulty of the power system modeling process. Before the co-simulation method was proposed, researchers usually simplified the information process to analyze the performance of various services and applications. Through implanting the information module into power system software such as by adding a time-delay module in the measure loop and control loop, researchers could conduct CPPS analysis in a simple way. However, it is an approximate method without enough accuracy and reasonability for a particular CPPS service. Actually, the real-time delay is influenced by several factors such as packet formats, transmission rate, channel properties, and exterior environments, which cannot be reflected by the simplified simulation method. Moreover, real delay is influenced by the physical environment of a communication system, which presents a probability distribution instead of a fixed value. These are not reflected in the delay process of the traditional simulation. Instead, the co-simulation method provides a more intuitive and accurate way to reconstruct a CPPS environment. The time-delay attribute was the focus of this paper as a way to analyze how the communication environment influences the power system operation results. Actually, the influencing factor was not only in delay, but also included various attributes such as bit error and channel rotation, which are further research fields proposed by our team.  
Besides the load control case analyzed in this paper, the proposed platform could also be applied to analyze other CPPS services such as the coordination control of distributed generation, the impact of communication failure on the security stability control, and cyber-attacks on the power system. The increasing integration of power service and information features pushes researchers to focus on the influence of information characteristics on power system control, analysis, and application.  
Figure 10: Network delay comparison at different sampling frequencies.
# Implant Model Generation Method for Mandibular Defect Based on Improved 3D Unet
## 1 Introduction
In the repair of defective mandibles, the reconstruction of the defect area needs to meet three design criteria: safety, functionality, and shape consistency. This paper aims at the shape consistency criteria and studies the application of deep learning methods in the automatic repair of mandibular defects and the generation of implant models. The main contributions of this paper are as follows:  
1. In order to solve the problem of the insufficient data of the mandible 3D model, an algorithm that can generate defects randomly in the mandible was proposed, namely GRD (generate random defect). Then, a dataset of the mandible 3D model was constructed using GRD. 2. In order to solve the problems of deep learning in the repair of mandibular defects, 3D Unet is used for the automatic repair of mandibular defects. Aiming to solve the problem that the classic traditional 3D Unet has in terms of the difficulty of effectively extracting the structural features of the mandibular defect, an improved 3D Unet (3D RDUnet) was proposed by fusing the residual structure and the dilated convolution layer to obtain the mandibular repair model. Using a reconstruction-subtraction strategy, a 3D model of the defect implant was obtained. 3. The effects of mandibular restoration and defect implant generation were compared on different networks, and the results were statistically analyzed. The results show that the method proposed in this paper has the best effect and is significantly better than other methods in a statistically significant fashion. The implants generated in this paper can meet the medical requirements for the shape consistency of defect repair.