esa-sceva/satcom-chunk-collection huggingface dataset:
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 5 Conclusions and Further Work  
This paper tackles the challenge of lightweight algorithm design for human activity recognition (HAR) using smartwatches by proposing LIMUNet, a deep learning-based model optimized for efficiency. Through a combination of depthwise separable convolutions, residual modules, and a dual attention mechanism, LIMUNet achieves strong feature extraction capabilities while maintaining low computational complexity. The model outperforms recent HAR methods using either single or multiple sensors, achieving high recognition accuracy with minimal CPU and memory usage. Additionally, the simplified version, LIMUNet-Tiny, maintains competitive performance with the lowest parameter count and complexity among models designed for wearable devices.  
In future work, we aim to explore other modal data from the smartwatch for integration, including additional sensor inputs, to further improve recognition accuracy. We also plan to introduce heterogeneous convolutional kernels to better adapt to diverse behavior patterns and explore transfer learning approaches to enhance model adaptability for new users and unseen activities with limited data.  
Conceptualization, L.L.; methodology, J.W.; software, R.A.; validation, S.M.; formal analysis, K.Z.; investigation, H.D.; writing--original draft preparation, L.L. and S.M.; writing--review and editing, J.W. and K.Z.; supervision, H.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We leverage open source dataset PAMAP2. This dataset can be accessed at [[https://archive.ics.uci.edu/dataset/231](https://archive.ics.uci.edu/dataset/231)]([https://archive.ics.uci.edu/dataset/231](https://archive.ics.uci.edu/dataset/231)), accessed on 26 April 2024.  
Author [PERSON] was employed by the company Dongguan Water Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 2 Materials and Methods
### DNA Extraction  
Total metagenomic DNA was extracted and purified from samples with a Universal Genomic DNA Kit (CwBio Inc., Beijing, China) according to the manufacturer's instructions. The concentration of total DNA was measured using the Qubit Platform (Life Technologies, CA, USA). The V3-V4 hypervariable region of 16S ribosomal RNA gene was amplified from 30 ng of DNA in triplicate polymerase chain reactions (PCR) with Pyro best DNA polymerase (TaKaRa, Dalian, China) and the following forward and reverse primers, respectively, 5\({}^{\prime}\)-ACTCCTACGGCAGGCAGCAG-3\({}^{\prime}\) and 5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\) (Sangon Biotech, Shanghai, China), following the manufacturer's instructions. Barcode sequences were attached to the amplification primers to distinguish the different samples. PCR products were inspected on 2% agarose electrophoresis gel and the respective amplicon libraries generated. Microbial DNA was then sequenced by Honortech (Beijing, China) using the Illumina MiSeq platform. Based on the raw data, pair-end reads were spliced using the principle of 98% overlap of 19 bases using the Connecting Overlapped Pair-End software 36. Barcode and primer sequences were then filtered to obtain the clean data. Operational Taxonomic Units (OTUs) were individuated as those clustering at shared nucleotide identity equal or higher than 97%. The relative abundance of soil bacteria was calculated according to the species annotation and reads number.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Equivalent Thickness Method
#### 3.3.3 Verification of Equivalent Thickness Method
It is noteworthy that in the tested columns, the perforated opening can be considered a case resulting from severe pitting corrosion, with the pit depth standard deviation being zero. Thus, in this scenario, the equivalent thickness calculated using Equation (4) was identical to that obtained from Equation (2). Figure 16: Comparison of ultimate strength for H-section columns with openings. Figure 15: Comparison of ultimate strength for corroded columns: (a) different flange thickness; (b) different web thickness. The method based on the presented equivalent thickness was proven to be effective in predicting the ultimate strength of all the tested columns, as illustrated in Figure 16. The maximum relative error was less than 5%. For openings with diameters smaller than 7 mm, both the Chinese and European Standards displayed a tendency to underestimate the ultimate strength, with a relative error less than 2.5%. Conversely, for openings with diameters larger than 70 mm, both standards tended to slightly overestimate the ultimate strength, albeit with a relative error of no more than 4.1%.
## 5 Mode of Action
### DAPG in Plant-Bacteria Interaction  
DAPG-producing _Pseudomonas_ spp. can trigger induced systemic resistance (ISR) in _Arabidopsis thaliana_ against _Hyaloperonospora parasitic_[38] and _Pseudomonas syringae_ pv. _tomato_[39]. In these studies, the authors demonstrated the role of DAPG in the ISR-eliciting activity by using strains impaired in DAPG production. These strains were unable to elicit ISR and to prime the plants against the incoming infection with a pathogen. By contrast, DAPG alone was sufficient to replicate the ISR-eliciting activity of the wild type strains. Inan elegant study, [PERSON] and colleagues [40] used transgenic _A. thaliana_ plants overexpressing _phlG_ and found that these plants were rendered insensitive to DAPG-mediated ISR. On the wild type, DAPG was able to induce ISR against _P. syringae_ pv. _tomato_ and _Botrytis cinerea_[40]. In these three studies, DAPG-mediated ISR relied on the ethylene/jasmonate signalling pathway.  
Besides its beneficial role in pathogen suppression, DAPG was also shown to be harmful to plants in several cases. High concentrations of DAPG were shown to inhibit plant growth and seed germination in several plant species, including wheat and tomato [13; 103]. DAPG also alters root architecture in tomato seedlings, inhibiting primary root growth and stimulating lateral root production [113]. In addition, several DAPG-producing _Pseudomonas_ strains were shown to act as minor plant pathogens when inoculated at high population densities. _P. brassicarearum_ Q8r1-96 reduced the germination of wheat seeds and caused lesions on wheat roots [103; 114]. Recently, _P. brassicearum_ strains Q8r1-96 and L5.1-96 were shown to cause necrosis when injected into immature tomato fruits and tomato stems [115]. A mutant unable to produce DAPG was consistently less virulent to tomato fruits and stems, suggesting that DAPG plays a major role in the ability of these strains to cause necrosis [115].
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
###### Abstract  
The present work investigates the technical feasibility of a condition monitoring setup aiming at the detection of gauge corner cracks (aka head checks) in pearlitric railway rails, using a wayside (i.e., stationary) setup with surface acoustic waves (SAW) as its detection principle. The experimental SAW setup consists of a pitch-catch setup using piezo transducers equipped with comb adaptors to excite and measure narrowband Rayleigh waves with a center frequency of 1 MHz. SAW experiments were performed on a rail subjected to cyclic loading in a 1:1 wheel-rail test rig yielding the specific rolling contact fatigue, i.e., head checks. Elastodynamic finite integration technique (EFIT) simulations were performed to analyze the surface and bulk wave propagation in the rail and to predict the signals at specific receiver positions. SAW transmission and reflection scenarios at cracks were analyzed numerically via modelled variations of gauge corner crack configurations according to number of cracks (0-3) and depth (0, 0.5 mm and 1 mm). The numerical and the experimental results each show a clear correlation between the appearance and intensity of head check damage and the wave attenuation in transmission mode.  
surface acoustic waves; crack detection; rails; condition monitoring; head checks
## 4 Experiments and Performance Evaluation
### Performance Evaluation  
There are many differences between the proposed model and existing models. Table 4 highlights the major differences as follows:  
1. Training data: The ARABERT4 TWC was trained on a much larger dataset than the earlier models, which included text from Twitter, Wikipedia, and other sources. This allowed the model to learn from a wider range of data and capture more of the nuances of language use on social media.
2. Model architecture: ARABERT4 TWC uses a modified version of the Transformer architecture, which includes additional layers and features designed to capture the  
Figure 8: Accuracy results and learning rate for different datasets.  
Figure 9: \(F1\)-score results and learning rate for different datasets.  
unique characteristics of Arabic text on social media. The earlier models used simpler architectures such as BERT and RoBERTa.
* Task-specific fine-tuning: ARABERT4 TWC was fine-tuned specifically for processing social media data tasks. In contrast, the earlier models were fine-tuned for general tasks such as sentiment analysis and named entity recognition. This task-specific fine-tuning allowed ARABERT4 TWC to achieve better performance on social media-related tasks.
* Model size: ARABERT4 TWC is larger than the earlier models, with over 300-million parameters compared to the 140-million parameters of the largest version of AraBERTv3. This larger size allows ARABERT4 TWC to capture more complex patterns in the data.  
Table 5 explores the performance comparison of the BERT model using five datasets with existing models in terms of accuracy. Our model ARABERT4 TWC surpasses the previous recent models AraBERT, CAMoLBERT, and hULMona on three datasets, except ASTD and ArsenTD-LEV, which yield inferior results for ASTD and ArsenTD-LEV. When comparing AraBERT and ARABERT4 TWC to hULMona, we find that the former improves accuracy by 14.9% and the latter by 9%, respectively, on ASTD. As mentioned, the model outperformed models that used the SANAD, HARD, and AJGT datasets.
# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 4 Conclusions  
In this paper, we proposed a multiscale feature fusion network called MCIFFN for source camera identification. To suppress the image content, the MCIFFN uses two sizes of filters to extract camera attribute noise and fuses the two sizes of filter noise through identity mapping. The fused noise can retain more types of camera attribute-related noise. To extract different types of features as much as possible, we used multiple CNNs to extract image features and fused the features extracted from each branch network. Finally, the network selected the useful features by itself. Experimental results showed that the proposed MCIFFN can effectively suppress image content and extract multiscale source camera-related features. Compared with the original SE-SCINet, the classification accuracy improved by more than 2%. In addition, traditional networks such as AlexNet and ResNet can be easily embedded in the MCIFFN so as to achieve great performance improvements. Although our algorithm has been greatly improved in speed and accuracy, it still cannot meet the requirements of an engineering application for model size and running speed. Therefore, our next work will be to further simplify the network structure and realize the engineering application of camera source detection.  
Conceptualization, C.Y. and H.Z.; methodology, C.Y.; software, C.Y.; validation, Z.G.; formal analysis, Z.G.; investigation, T.W.; resources, Z.G.; data curation, T.W.; writing, C.Y. and H.Z.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Confusion matrix for identification with 23 different camera models.  
**Funding:** This work is supported by Supported by the National Key Research and Development Program of China under Grant No. 2020 YFF0304902 and the National Natural Science Foundation of China under Grant No. 61771352.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data used in this research can be provided upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Numerical Model and Simulation Setup
#### 2.4.2 MIKE 3 FM
The eddy viscosity is determined using [PERSON]'s formulation in the horizontal direction and the standard \(k\)-\(\varepsilon\) model in the vertical direction. The upstream and lateral boundaries of the domain are set as the land boundary so that no water flows into the reservoir; the reservoir length is held constant, the downstream boundaries are set as free outlets, and a downstream far-field boundary condition [42] is adopted in the dry-bed case. The weir in the downstream boundary is set for the wet bed. The initial condition is defined as specified constant level \(h_{0}\) in the upstream reservoir and tailwater water depth \(h_{1}\) in the downstream area. Manning coefficient \(n\) used in the numerical computation is given the constant value of 0.012, which corresponds to the tested Perspex flume. Simulation Setup of Experiments by [PERSON] et al. [39]  
For the MIKE 3 FM model, simulations were conducted with mesh resolutions of 0.2, 0.1, 0.05, and 0.01 m, with mesh independence occurring at 0.05 m. The results presented here are derived from computations using the 0.05 m mesh, which had approximately 3132 nodes and 2860 quadrilateral elements. A fixed time step of 0.001 s was applied over 30,000 times. In the numerical computations, the eddy viscosity and upstream boundary had the same settings as those for previous experiments for the dry-bed case, except for the use of a constant water level \(h_{1}=0.08\) m at downstream boundaries for the wet-bed case. The value of Manning coefficient \(n\) was set to 0.011, which corresponds to the tested Perspex flume configuration.
## 3 Test Results and Analysis
### Test Phenomenon and Failure Modes
#### 3.1.2 Contrast Beam B0  
The contrast beam B0 was in the elastic stage during the initial loading. When the load increased to 19.70 kN (23% \(P_{\text{m}}\)), the first vertical crack appeared in the tensile area  
Figure 10: Test beam crack diagram: (**a**) contrast beam A0; (**b**) strengthened beam A1; (**c**) contrast beam B0; and (**d**) strengthened beam B1.  
Figure 9: Failure mode of beams: (**a**) contrast beam A0; (**b**) strengthened beam A1; (**c**) contrast beam B0; and (**d**) strengthened beam B1.  
near the middle. When loaded to 64.22 kN (74% \(P_{\rm m}\)), the longitudinal tensile steel bar yielded. When the loading continued, the crack width and specimen deflection increased rapidly. When loaded to 86.77 kN, the peak load was reached and the concrete in the pressure area was crushed. The load dropped sharply to 74.25 kN (85% \(P_{\rm m}\)), and the test was stopped. The crack distribution is shown in Figure 10b. The longitudinal reinforcement ratio of specimen B0 is smaller, and its crack spacing and crack width are larger than that of specimen A0.
## 3 Results
### Cellobiolyrolase Integration and Activity  
To evaluate the effect of the reporter protein on activities observed for integration at various loci, we investigated whether integration of a different cellulase gene, _T.e.cbh11_, would result in different outcomes. We repeated the integration experiments, targeting the same chromosomal sites in the M1744 haploid strain background. Transformants were confirmed via PCR (Figure S1) and cultivated on YPD to determine CBH activity. Delta integrated _T.e.cbh1_ strains displayed significantly higher CBH1 activities than the single locus transformantsures where the gene was targeted to chromosome X,-XI, or-XII. At 72 h of cultivation, the highest CBH activity observed among the single locus targeted yeast strains was 110 mU/gDCW for the Ch12 targeted strain, while the lowest activity observed was 19 mU/gDCW for the Ch10 targeted strain. Our CRISPR system again yielded high integration efficiencies for targeting different sites of the yeast genome. In addition, all of the M1744 strains with _T.e.cbh1_ targeted to the delta sites for multi-copy integration had higher activity than the single locus targeted M1744 CBH strains. The highest CBH activity observed at 72 h among the selected delta-integrated M1744 strains was 248 mU/gDCW, while the lowest was 210 mU/gDCW. Interestingly, there were no statisti  
Figure 3: Endoglucanase activity profiles of recombinant yeast strains after 48 and 72 h cultivation. (**a**) The activity of EG2-producing haploid strains and (**b**) diploid strains on CMC. Numbers after the strain names refer to different transformants that were tested. Values obtained were normalized with the dry cell weight of each specific yeast strain. The values given are the mean values of enzyme assays conducted in triplicate. Error bars indicate the standard deviation from the mean value.  
cally significant differences between the single locus targeted transformantss tested, but a significant difference between those strains and delta-targeted transformants was confirmed (\(p\leq 0.05\)). The activity of the delta-targeted transformants testsed did not differ significantly.  
We again determined the number of _T.e.cbh1_ copies integrated into these transformants (Table 3). Varying integrated copy numbers were detected in these strains. Strains M1744-Ch10-CBH1 and M1744-Ch11-CBH1, targeted for single locus integration of _T.e.cbh1_, had one copy of the gene each, as expected, and displayed comparatively low CBH activity. However, the M1744-Ch12-CBH1 strain, which was also designed for single locus _T.e.cbh1_ integration, contained 4 copies of the gene. As with the integration of the _T.r.eg2_, the Ch12 site presented a challenge for obtaining the desired integration, and we suspect off-target integration, as the integration locus for these transformants could not be confirmed via PCR, as opposed to those of the Ch10 and Ch11 targeted transformants.
###### Abstract  
Industry requires defect detection to ensure the quality and safety of products. In resource-constrained devices, real-time speed, accuracy, and computational efficiency are the most critical requirements for defect detection. This paper presents a novel approach for real-time detection of surface defects on LPG cylinders, utilising an enhanced YOLOv5 architecture referred to as GLDD-YOLOv5. The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network's backbone. It also modifies the P3-P4 head structure to increase detection speed. These changes enable the model to focus more effectively on small and medium-sized defects. Based on comparative analysis with other YOLO models, the proposed method demonstrates superior performance. Compared to the base YOLOv5s model, the proposed method achieved a 4.6% increase in average accuracy, a 44% reduction in computational cost, a 45% decrease in parameter counts, and a 26% reduction in file size. In experimental evaluations on the RTX2080 Ti, the model achieved an inference rate of 163.9 FPS with a total carbon footprint of 0.549 \(\times\) 10\({}^{-3}\) gCO2e. The proposed technique offers an efficient and robust defect detection model with an eco-friendly solution compatible with edge computing devices.  
Keywords:green deep learning; real-time; defect detection; YOLO; lightweight network; LPG cylinder +
Footnote †: journal: Applied Sciences
# Design of a Compact Ultra-Wideband Microstrip Bandpass Filter
## 3 Parameter Analysis
As can be seen from Figures 6 and 7, the limit of passband bandwidth tunability mainly comes from the coupling area of multilayer design patterns and the diameter of blind holes. Special attention should be paid to the changes in return loss and stopband suppression caused by their adjustment and adjusting the bandwidth while maintaining the overall performance of the filter. In Figure 8, effects of width \(W_{9}\) of wider and longer rectangular slots on the resonance points have been presented. Different from those DGS structures composed of multiple identical dumbbell-shaped patterns or some other combination of patterns, the method adopted here is simpler to design and adjust, since the design only consists of three rectangular slots. Two wider and longer rectangular slots of the same size are etched, and then a narrow and short rectangular slot is etched on their symmetry axis. Adjusting the size of three rectangular slots will change the current distribution on the grounded metal surface, which will lead to a change in the equivalent capacitance, and then cause the frequency shift of two resonance points. Of course, the distance between the slots will also have an impact on this. However, the DGS structure corresponds to the SIR on the second floor vertically, and the change in the distance will affect the return loss in the passband, so its parameter adjustment is limited to some extent. Finally, the size of the rectangular slot itself is selected for parameter analysis. In addition, the corresponding resonance frequency will gradually decrease due to the influence of the slow-wave characteristic [28] caused by the blind holes. Therefore, we can find that with the width changing from 1.4 mm to 2 mm, the low-frequency resonance point moves from 11.385 GHz to 11.225 GHz, and the corresponding return loss decreases from \(-\)33.44 dB to \(-\)38.51 dB. Considering that RL is less than 20 dB, \(W_{9}=1.8\) mm is finally selected as the optimal value. Figure 7: Simulation of S parameters when diameter \(d_{0}\) of the blind hole is varied from 0.2 mm to 0.5 mm, with a step of 0.1 mm increment.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Red Noise
#### 4.3.1 Sum of Two Sinusoids  
In this section, we will examine an example with the signal defined in (13). We recall that the rank of the signal in this case is 4. As with white noise, an increase in the variance results in the second sinusoid becoming mixed with noise. As the variance continues to increase, after a period of uncertainty, the optimal rank becomes equal to 2 and then to 0.  
As in the preceding analysis, 20 values of \(\sigma\) will be considered, ranging from 0.01 to 100 in equal logarithmic steps.  
Figure 11 depicts the mean estimated ranks and the optimal ranks for the smallest root of average MSEs over 1000 realizations. As demonstrated in Figure 12, the methods exhibit a high degree of similarity in their performance. At plateaus, the criteria accurately determine the rank of the signal, thereby confirming their capacity to estimate the rank correctly. However, at the transition from model order 2 to 0, both methods demonstrate a notable decline in performance, with a pronounced tendency to underestimate the rank.  
Figure 11: Example ’sine’: Dependence of rank estimation on red noise level, TRMAT and MDL.  
Figure 12: Example ’sine’: Dependence of relative RMSE on red noise level, TRMAT and MDL.