esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 3 Methods and Result  
This study aimed to evaluate the likelihood of a product being a normal or defective item by analyzing similarities between images. Precise image comparison analysis is essential for automated quality inspection in manufacturing processes, especially in detecting subtle differences. Higher SSIM and PSNR values indicate greater similarity between images, while a lower MSE value suggests a smaller difference, thus representing greater similarity. However, high SSIM and PSNR values and a low MSE value do not guarantee that two images will appear visually identical. These metrics reflect only specific aspects of image similarity and may not be sensitive to subtle differences or variations in object position and scale. For instance, identical objects may yield high SSIM and PSNR values even if the image is rotated or shifted, but they may still look visually different. Since SSIM reflects structural similarity and PSNR and MSE focus on pixel differences, their reliability may decrease when structural and detailed differences are mixed. In particular, if a product is rotated or shifted, high SSIM, PSNR, and low MSE values may still exhibit significant visual discrepancies. To address this issue, this study applied the SIFT algorithm to detect the product's orientation first. SIFT identifies keypoints within an image and extracts features that are robust to rotation and translation, enabling the product to be restored to its original orientation even if positioned at various angles. This alignment allows the more reliable use of SSIM, PSNR, and MSE.  
Additionally, the \(\mathrm{S_{combined}}\) formula combines the values of SSIM, PSNR, and MSE to produce a final evaluation score, determining whether a product should be inspected. The \(\mathrm{S_{combined}}\) formula appropriately adjusts the weights of each metric, integrating diverse quality information that cannot be assessed with a single metric alone. This approach allows for clearer identification of potential defects when SSIM, PSNR, or MSE values exceed a threshold. Furthermore, if the \(\mathrm{S_{combined}}\) result deviates from a specific standard, the difference image technique is applied to visually confirm changes in the product. The difference image technique calculates pixel-level differences between two images, effectively highlighting surface defects or color changes. This method allows for accurate identification of defect locations, providing a basis for operators to address issues immediately if necessary. In conclusion, by using the SIFT algorithm to align the product's position and orientation, applying the \(\mathrm{S_{combined}}\) formula to assess quality, and employing the difference image technique to visually detect minor defects, this study contributes to enhancing overall quality management.
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
###### Abstract  
Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses--a ranked list of symptom-disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert-patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts.  
knowledge graph; semantic reasoning; medical diagnostic; decision-support systems; semantic technology +
Footnote †: journal: Applied sciences  
1
# Axial Bearing Mechanism of Post-Grouted Piles in Calcareous Sand
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pile No.**} & **Total Shaft Resistance, \(\mathbf{Q_{su}}\) (N)** & **Mobilized Tip Load, \(\mathbf{Q_{bu}}\) (N)** & **Ultimate Bearing Capacity, \(\mathbf{Q_{u}}\) (N)** & **Total Shaft Resistance Rate (N)** & **Tip Resistance Increase Ratio (\%)** \\ \hline MP1 & 665 & 70 (9.50) & 735 & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) \\ \hline MP2 & 3943 (93.87) & 257 (6.13) & 4200 & 492.93 & 267.14 \\ \hline MP3 & 2919 (94.76) & 161 (5.24) & 3080 & 338.95 & 130.00 \\ \hline MP4 & 1043 (86.91) & 157 (13.09) & 1200 & \(\mathrm{N/A}\) & \(\mathrm{N/A}\) \\ \hline MP5 & 2871 (93.22) & 209 (6.78) & 3080 & 175.26 & 33.12 \\ \hline MP6 & 4186 (93.43) & 294 (6.57) & 4480 & 301.34 & 87.26 \\ \hline \hline \end{tabular} Note: The values in parentheses in the table are the percentage (%) of the total shaft resistance and tip resistance to the ultimate bearing capacity.  
\end{table} 
Table 3: Measured values of the ultimate bearing capacity, total shaft resistances, tip resistances of each single pile, and their corresponding increased range. effect of post grouting. Some scholars [30; 31; 32; 33] developed a device for forming an ideal geometric shape of prefabricated piles and bored piles after grouting to solve the problem of the uncertainty of the grout flow direction and the difficulty of forming ideal geometric shapes, which can effectively control the grouting amount and grouting pressure to form an ideal geometric shape. The increased range of the shaft resistance and tip resistance can be predicted according to the ideal geometric size of the grout bubble, so the post-grouting technique is more reasonable. Therefore, a directional grouting device should be adopted in future engineering practice to reduce the uncertainties in the post-grouting process and ensure the expected effect of post-grouting. Given that this study is limited to small-scale model tests, further validation is required from field full-scale load tests, especially field full-scale group tests. In addition, the test results and related analysis in this paper are limited to calcareous sand in the South China Sea, which is difficult to directly extend to any area because of its regional characteristics, so future studies will focus on performing relevant quantitative research. The main purpose of this paper is to elucidate the axial bearing mechanism of post-grouted piles, as well as provide a theoretical basis and technical support for the popularization and application of post-grouted piles in calcareous sand, and it is also instructive for improving the safety of island and reef engineering and reducing the economic cost.
## 6 Simulation Experiments and Performance Analysis
### Simulation Experimental Data Analysis  
Table 5 is a comparison table that shows a summary of the numerical analyses conducted for the results provided in Figures 14-19. The table result is an average of 10 simulation runs performed for each of the routing protocols using the various evaluation metrics. In general, the results show that the IEEAB outperformed the other protocols in terms of the considered evaluation metrics, showing the efficacy of swarm intelligence (SI)-based approaches.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & RMASE \\ Network size & 1000 \(\times\) 1000 m\({}^{2}\) \\ Node deployment & Random \\ Number of sensor nodes & 100–1000 \\ Transmission range (\(r_{k}\)) & 35 m \\ Data packet size & 512 bytes \\ Time deadline (\(T_{c}\)) & 400 s \\ Data transmission rate & 250 kbps \\ Collection rate of DCV & 40k bps \\ DCV speed & 10 m/s \\ Initial energy (\(E_{so}\)) & 2.0 J \\ MAC & 802.11 \\ Radio propagation model & Two-Ray Ground \\ Channel model & Wireless channel \\ Antenna & Omni-Antenna \\ \(E_{elc}\) & 50 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ Simulation time & 1500 s \\ Initial position of DCV & (0,0) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 14: Comparison based on the packet delivery ratio.  
Figure 15: Comparison in terms of throughput.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Routing Algorithms**} & \\ \hline  & **IEEABR** & **EBAR** &
\begin{tabular}{c} **Bee-Sensor-** \\ **C** \\ \end{tabular} & **FF** \\ \hline Packet delivery ratio (\%) & 91.2714 & 85.85 & 84.5911 & 77.778 \\ Throughput (bits/s) & 93.4888 & 87.5245 & 86.188 & 77.8831 \\ Network lifetime (sec) & 78.4888 & 74.188 & 75.5245 & 76.8831 \\ Energy consumption (J) & 1389.6 & 2950.0 & 1468.2 & 6271.2 \\ Energy efficiency (bits/J) & 1981.87 & 1187.5 & 1476.67 & 1163.55 \\ Latency (ms) & 3.2637 & 10.161 & 7.375 & 10.663 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of simulation results of routing protocols using different metrics.  
Figure 16: Comparison in terms of network lifetime.  
Figure 17: Comparison based average energy consumption.  
Figure 18: Comparison in terms of energy efficiency.
## 2 Land-Streamer Design  
The name of the land-streamer is originally borrowed from marine streamers, however, rather than a streamer of hydrophones, a land-streamer is consisting of a group of geophones designed to be towed along the surface of the ground. Early land-streamers were designed for different innovative towing mechanisms.  
The main components of land-streamers (Figure 1a) are (a) the geophone, which could be a P-wave or S-wave receiver, (b) the plate, which is a heavy metal that enhances the coupling between the receiver and the ground. In our design, we have two plates, the lower is a steel plate and the upper is aluminum. We also added two side wings made of iron to increase stability during the acquisition (Figure 1b,c) the streamer, which is any tough material that can connect all plates together, (d) the cables that connects the receivers to the recording system, and (e) the recording system. Components (a-d) are connected to each other (Figure 1b) and moved to/from the field as one unit, which saves a lot of installing time at the field.  
Figure 1: (**a**) The design of the land-streamer used in this work. It shows the receiver, the plates, the streamer, and the cables connected to the recording system. (**b**) An actual photo of the used land-streamer. The two screws shown at the sides of the plate are used to increase the stability of the land-streamer on irregular terrains.  
Generally, geophones planted in the ground set the standard for data quality, but land-streamers record excellent data in many applications while making the survey economically feasible. A \"good\" affordable survey often will be a better choice than a \"great\" survey that can't be done due to its cost.  
To reach deeper depths without increasing the length of the land-streamer, [PERSON] showed in [42] how the length of a land-streamer can be effectively doubled or tripled if two or three seismic sources are used at offsets equal to the length of the land-streamer. Hence, the depth of penetration will be increased without increasing the logistics requirements.
###### Abstract  
In this paper, we propose an electrically small antenna consisting of a composite right/left-handed (CRLH) transmission line (TL) and a non-Foster matching circuit. An interdigital capacitor (IDC) and spiral inductor are used to fabricate the very high frequency (VHF) band antenna based on CRLH TL. The size of the proposed antenna is as small as \(0.025\times 0.014\times 0.0008\)\(\lambda\) at 145.5 MHz using the zeroth-order resonant generated by the CRLH TL. The antenna operation bandwidth is extended by the non-Foster circuit (NFC) consisting of a pair of transistors in a cross-coupled manner. An antenna prototype is fabricated and the input impedance, the received power, and gain of the proposed antenna are measured. The results show that the broadband characteristic is maintained while the form factor is extremely small compared to the wavelength. The average received power enhancement and increased bandwidth of antenna are 17.3 dB and 335.5 MHz (from 249.2-268.2 to 145.5-500 MHz), respectively. The calculated gain of the proposed antenna with the non-Foster is about -45 dBi at 155 MHz. The proposed antenna can be considered as a potential candidate of a low-profile antenna for military ground communications at the VHF band.  
composite right/left-handed transmission lines; military antenna; non-foster circuit; VHF band antenna +
Footnote †: journal: Applied Sciences  
10 August 2020; Accepted: 10 September 2020; Published: 12 September 2020
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 4 Implementation  
### Practical Issues  
The general mathematical framework described in the previous section is valid for band-limited filters and more classical FIR filters. Although the impulse responses of band-limited filters are theoretically infinite, their decay can be controlled by design such that they can be truncated with a minor loss of precision. In our implementation, we instead choose an alternative approach similar to \"fast Fourier transform (FFT) filter banks\" proposed by [PERSON] [60]. We start by considering the input signal as a finite-length vector in \(\mathbb{R}^{L},\ L\in\mathbb{N}\). In an overlap-add block-processing scheme like the one proposed in [46; 60], such a sequence would be a single windowed block possibly zero-padded on both ends. In the offline setting assumed in this paper, the sequence represents the entire input signal. We discretize the continuous frequency \(\xi\) by assuming the sequence is one period of an \(L\)-periodic signal. This introduces circular boundary effects that can be diminished by zero padding (increasing \(L\)), provided the filters' impulse responses decay rapidly. Increasing \(L\) preserves the perfect reconstruction property. Such assumptions allow implementing the filtering, downsampling, and upsampling directly in the frequency domain using sampled frequency responses of analysis and synthesis filters \(H_{k}\) and \(G_{k}\), respectively. The filtering with an analysis filter followed by downsampling is done using the standard point-wise product of the \(L\)-point FFT of the signal with a sampled frequency response, while the downsampling is achieved by folding the result to a sequence of length \(L/d_{k}\) (manual aliasing) and performing \(L/d_{k}\)-point inverse FFT (IFFT). Performing downsampling this way is exactly equivalent to time-domain downsampling by a factor of \(d_{k}\). Upsampling and filtering is achieved by taking a \(L/d_{k}\)-point FFT of the sub-band, periodizing the result to length \(L\) followed by a point-wise product with the sampled frequency response of a synthesis filter. A final \(L\)-point IFFT brings the result back to the time domain. In this framework, working with strictly band-limited filters is even advantageous for two reasons. First, the frequency domain point-wise product can be restricted to the filter bandwidth and second, for band-limited filters, the parameters can be chosen such that the system is painless [53](no aliasing is introduced by downsampling), for which the approximate dual filters from (20) are _exact_ and thus achieve perfect reconstruction.
# Study on the Minimum Operation Width of Human-Powered Bicycles for Safe and Comfortable Cycling
## 3 Materials and Methods
### Field Cycling Experiment Settings
#### 3.2.3 Test Road Setting Scenarios Arrangement for Cycling Workload  
Field cycling tests were conducted on a straight and flat segment of a cycle path, flanked by markings indicating the designated test width. This setup avoids curves and gradients, which would require additional width beyond the basic minimum operational width being studied. In addition, the test road was carefully chosen to ensure test safety, devoid of interference from motor vehicles, E-bikes, and pedestrians. As a result, a recently repaired asphalt roadway with high-quality concrete pavement situated within a university campus was selected. The testing took place during the winter holiday when campus activity was minimal, ensuring the limited use of the roadway. Furthermore, access to the testing site was strictly limited to human-powered bicycles to realize the basic width.  
Figure 4: ECG Sensor ((**a**) whole set; (**b**) on a cyclist).  
Figure 5: Speedometer fixed to handlebars.  
Figure 6: Test scenarios and real field mid-section.  
The test road spanned 220 m, with two 70 m pretest road sections and one 80 m middle section. The pretest road sections are designed to provide direct access and facilitate the preparation for the two-way cycling starts. The length of the middle section is chosen to ensure that cyclists can maintain the test speed for a minimum of 10 s, allowing for the collection of stable and consistent cycling data.  
Cycling Speed: There is an inherent uncertainty in the experimental design stemming from Chinese regulations governing speed limits on non-motorized vehicles and the capacity of shared dock-less bicycles. Female participants in the study rarely reached speeds of 25 km/h on flat terrain, with most reaching a maximum of approximately 24.5 km/h, despite the utilization of new products from the shared dock-less bicycle service. This indicated average cycling speeds ranging from 10 to 24.5 km/h on flat roads. The experiment also considered extreme conditions by including both minimum and maximum speed levels. Speed intervals of 5 km/h were incorporated into the experimental protocol. So, the target cycling speeds were set at 10, 15, and 20 km/h, along with participants' minimum and maximum speed choices.  
The test widths were established following the minimum width guidelines outlined in international bikeway manuals, as summarized in Table 1. To account for extreme conditions, considering the standard bicycle width of 0.62 m, we incorporated test widths of 0.65 m and 0.70 m. The predetermined test width configurations are presented in Table 5 for reference.
## 3 Methodology
### Complexity Analysis  
In DESTformer, MVI-Attention is used to capture the periodic fluctuations of the seasonal term, while MSC-Attention is used to capture the long-term changes of the trend term. For a sequence of length \(L\), in MVI-Attention, FFT is used to effectively reduce the time complexity to \(O(LogL)\)[2]. On this basis, we also leverage a sampling strategy to effectively reduce the time and memory complexity to \(O(L)\)[3]. In MSC-Attention, we use one-dimensional convolution to extract \(J\) different sub-trends. Since the time complexity of one-dimensional convolution when encoding time series is \(O(L)\) and \(J\) is a constant, the final time complexity of MSC-Attention is \(O(J^{2}L)=O(L)\), and the memory complexity of MSC-Attention combined with the sampling strategy is also \(O(L)\). In summary, DESTformer achieves an \(O(L)\) time and memory complexity. In Table 1, we summarize the comparison of time complexity and memory usage for training and inference steps.  
```
0: Input time series \(X\) in the conditioning window with length \(T_{x}\), prediction window length \(T_{y}\), data dimension \(K\), hidden state embedding length \(D\), the number of encoder layers \(N\), the number of decoder layers \(M\).
0: Predicted results \(\hat{Y}\)
1: Use the uniform distribution to initialize model parameters \(\theta\sim U(-1,1)\).
2:\(X_{en}^{0}=X\)
3:\(X_{ens},X_{ent}=FreDecomp(X_{en}\frac{T_{x}}{2};T_{x})\)
4:\(X_{des}=Concat(X_{ens},X_{0})\);
5:\(X_{det}=Concat(X_{ent},X_{Mean})\)
6:/* Encoder */
7:for\(l=1,2,\cdots,N\)do
8:\(S_{en}^{l,1}=MVI-Attention(X_{en}^{l-1})\)
9:\(T_{en}^{l,1}=MSC-Attention(X_{en}^{l-1}-S_{en}^{l,1})\)
10:\(S_{en}^{l,2}=S_{en}^{l,1}+FeedForward(S_{en}^{l,1})\)
11:\(T_{en}^{l,2}=T_{en}^{l,1}+FeedForward(T_{en}^{l,1})\)
12:\(X_{en}^{l}=S_{en}^{l,2}+T_{en}^{l,2}\)
13:endfor
14:\(S_{de}^{0,2},T_{de}^{l,0}=X_{des},X_{det}\)
15:/* Decoder */
16:for\(l=1,2,\cdots,M\)do
17:\(S_{des}^{l,1},T_{de}^{l,1}=FreDecomp(MVI-Attention(S_{de}^{l-1,2},S_{en}^{N2}))\)
18:\(S_{de}^{l,1}T_{de}^{l,1}=FreDecomp(MSC-Attention(T_{de}^{l-1,2},T_{en}^{N2}))\)
19:\(S_{de}^{l,1}=S_{des}^{l,1}+S_{det}^{l,1}\)
20:\(T_{de}^{l,1}=T_{des}^{l,1}+T_{det}^{l,1}\)
21:\(S_{de}^{l,2}=S_{de}^{l,1}+FeedForward(S_{de}^{l,1})\)
22:\(T_{de}^{l,2}=T_{de}^{l,1}+FeedForward(T_{de}^{l,1})\)
23:endfor
24:\(X_{de}^{M}=S_{de}^{M,2}+T_{de}^{M,2}\)
25:\(\hat{Y}=MLP(X_{de}^{M})\)
```  
**Algorithm 1** Overall DESTformer procedure.
###### Abstract  
Image segmentation is a fundamental but critical step for achieving automated high-throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.  
feature extraction; imaging; machine learning; phenotyping; segmentation +
Footnote †: journal: plants  
0023