esa-sceva/satcom-chunk-collection huggingface dataset:
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 3 Methodology
### Proposed Few-Shot Object Detection Network  
In this study, an enhanced network architecture was introduced. Featuring the cornerstone of our contribution, the serial multi-scale attention network (SMSANet) is illustrated in Figure 2. Initially, input images underwent processing via an optimized backbone network based on ResNet101 [32] within the Faster R-CNN [7] framework, resulting in the generation of feature maps C2, C3, C4, and C5. Subsequently, C5 was fed into the improved SMSANet based on a dual multi-scale attention network (DMSANet [33]), which fused channel and spatial attention weights to yield a composite feature map. This was then combined with C5 post a \(1\times 1\) convolution to produce P5. Through an up-down approach with lateral connections, a rich set of multi-scale features, namely P4, P3, and P2, were obtained. Moreover, P2 was guided through SMSANet, and upon integration with P2 post \(1\times 1\) convolution, F2 emerged. Utilizing a bottom-up methodology complemented by lateral connections, a further array of multi-scale features was extracted. These features were subsequently integrated and passed through a region proposal network (RPN) [34] and region of interest (ROI) [35] pooling, before splitting into classifier and regressor branches to produce the final output.  
Notably, the present study marked the incorporation and optimization of feature pyramid and attention mechanism modules. The network underwent a two-stage fine-tuning training process: initially, it was subjected to extensive training using numerous base class samples. Next, the model was further fine-tuned using a small number of base class samples as well as samples from new classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect Categories** & **Defect Quantity** & **Average Pixel Scale** &
\begin{tabular}{c} **Maximum/** \\ **Minimum Pixel Scale** \\ \end{tabular} \\ \hline Pits & 897 & 4361.3 & 31,303/163 \\ Cracks & 247 & 3378.4 & 14,023/316 \\ Scratches & 5150 & 25,496.0 & 368,240/115 \\ Iron oxide scales & 801 & 34,373.0 & 192,780/491 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pixel-scale distribution statistics of four defect categories.
## 3 Results
### Proteomic Analysis of MuSC-EVs  
Given the ability of MuSC-EVs to reverse oxidatively-induced mitochondrial dysfunction in myotubes, we next sought to determine the contents of the EVs in attempt to gain insight as to how they mediate these effects. UPLC MS/MS was utilized to identify the protein signature of a sample of MuSC-EVs produced during the first 24 h of MuSC culture. A total of 423 proteins were identified  
Figure 4: Mitochondrial Stress Test in C2C12 Myotubes. Myotubes were divided into one of four groups: untreated control, treatment with 3.12 \(\times\) 10\({}^{8}\) SC-EV for 24 h, treatment with H\({}_{2}\)O\({}_{2}\) for 24 h, or treatment with H\({}_{2}\)O\({}_{2}\) for 24 h followed by treatment with SC-EV for 24 h. **(A)** Oxygen consumption rate over the duration of the mitochondrial stress assay. O = oligomycin, F = 2-[-4(trifluoromethyloornethoxy)phenyl[hydrazinylidene]-propanedinitrile (FCCP), R = Rotenone and Antimycin A. **(B)** Energy map depicting reliance on aerobic vs. glycolytic energy systems and energetic state of each treatment group. **(C)** Basal respiration of myotubes in each treatment group. **(D)** Maximal respiration of myotubes in each treatment group. **(E)** Spare respiratory capacity of myotubes in each treatment group (calculated as difference between maximal respiration and basal respiration). * \(p\) < 0.05. N = 3 (technical replicates).  
in the sample, 129 of which were associated with DAVID IDs. Enrichment analysis revealed the significantly enriched (FDR \(<\) 0.05) GO and KEGG terms associated with the proteins identified by UPLC MS/MS. 309 GO terms were identified for biological processes, 80 of which were significantly enriched, with intermediate filament organization presenting the lowest FDR value (4.59 \(\times\) 10\({}^{-12}\)) and cytoskeleton organization and single-organism organelle organization presenting the highest count values (30 identified proteins) (Figure 5A). Overall, 62 GO terms were identified under molecular function, 25 of which were significantly enriched, with structural molecule activity presenting the lowest FDR value (1.5 \(\times\) 10\({}^{-45}\)) and carbohydrate derivative binding presenting the highest count value (39 identified proteins) (Figure 5B). Overall, 60 GO terms were identified under cell component, 35 of which were significantly enriched, with extracellular region part presenting the lowest FDR value (1.29 \(\times\) 10\({}^{-37}\)) and extracellular region presenting the highest count value (105 identified proteins) (Figure 5C). Additionally, 14 KEGG pathways were identified, 13 of which were significantly enriched, with extracellular matrix (ECM)-receptor interaction presenting the lowest FDR value (1.08 \(\times\) 10\({}^{-17}\)) and focal adhesion presenting the highest count value (21 identified proteins) (Figure 5D).
## 3 Model Definitions
### Time Model  
Each user operates with an independent, imperfect clock source. These clock sources are misaligned, so at any given instant \(n\), there is a nominal time difference \(T^{(n)}\) between any two users. These clock sources operate at the same nominal clock frequency \(f_{clock,nominal}\) but can not perfectly maintain this frequency. Thus, the time offset \(T^{(n)}\) is time-varying and its derivatives \(\dot{T}^{(n)}\) and \(\dot{T}^{(n)}\) are assumed to be nonzero [12].  
Consider two users labeled A and B, such that at time instant \(n\), clock A displays a later time than clock B. Define \(T^{(n)}\) as the time difference between clock B and clock A, such that if clock A displays a later time than clock B, \(T^{(n)}\) is positive. This configuration is depicted in Figure 3.  
User A transmits a waveform at time instant \(n\), labelled \(t_{A,Tx}^{(n)}\). This waveform takes a small amount of time to propagate to user B, labeled \(\tau^{(n)}\). User B receives the waveform at time \(t_{B,Rx}^{(n)}\). If the clocks were perfectly aligned, the receive timestamp would simply be \(t_{A,Tx}^{(n)}+\tau^{(n)}\). Because the clocks are misaligned, user B measures the event earlier with respect to clock B, thus the received timestamp, as perceived by user B, is [17]  
\[t_{B,Rx}^{(n)}=t_{A,Tx}^{(n)}+\tau^{(n)}-T^{(n)}. \tag{1}\]  
At some later time instant \(n+1\), user B transmits a waveform to user A. The received timestamp, as perceived by user A is [17]  
\[t_{A,Rx}^{(n+1)}=t_{B,Tx}^{(n+1)}+\tau^{(n+1)}+T^{(n+1)}. \tag{2}\]
## 3G Deployment Stage
### Political Sustainability  
FITEL's involvement showed the interest of the Peruvian government to support this type of initiative in the medium and long term. This interest is related to the responsibility of attending to the basic needs of the isolated populations, which cannot usually access their basic rights, and it is demonstrated that having communications is extremely useful to improve healthcare, education, and to promote the economic development of the community. For this reason, allocating public funds to subsidize deployments of 3G services or review regulatory frameworks is within the strategies of governments to improve living conditions in the most isolated communities.  
Including Telefonica in the consortium allowed the use of licensed bands to offer the 3G service to end users, although the use of unlicensed bands was maintained for the backhaul network that connected the base stations with Telefonica's core network. However, when working with a commercial operator, it was necessary to consider the obligations that the regulator had regarding service downtime and repair times, which could result in sanctions and higher maintenance costs. This was a major concern for Telefonica throughout the TUCAN3G project, threatening the sustainability of this solution in the long term and which led the consortium to maintain conversations with the Peruvian regulator OSIPTEL. OSIPTEL considered that the quality of the service should be equivalent in both rural and urban areas in order to avoid maintaining the digital divide between these areas. For this reason, it was not willing to establish specific service conditions for rural areas, although this would entail an additional cost and therefore a barrier to the deployment of 3G services in these areas.
## 2 Species Selection and Exploitation  
Microalgae present a rich biodiversity comprising 40,000-50,000 described species with an estimation of nearly 800,000 existing species [37]. Chlorophyta comprises 6952 algal species [38], and Bacillariophyta is the most diversified group within microalgae, with more than 10,000 diatom species being described [39]. Despite this great diversity, only a few microalgal species have been exploited for biotechnological applications, with only 18 species of Chlorophyta and 10 species of Baccilariophyta phyla being produced by European algae farmers (Figure 2b). A smaller number of species is recorded for the other phyla: Cyanobacteria, Ocrophyta, Haptopbyta, Rhodophyta, Euglenozoa, Chaorphyta, Miozoa, and Prasinodermatophyta.  
The industrial production of microalgae heavily depends on biomass productivity, which is the most significant factor that can reduce the production cost levels [40]. To increase microalgae productivity, several strategies have been developed, namely the exploitation of the cultivation conditions to direct the metabolism towards desired product accumulation, selection and breeding of strains with increased biomass productivity, and genetic modification [40].  
The selection of the right microalga considering specific culture conditions and desired product content could be performed through an exhaustive screen of scientific data through databases and literature, followed by physiological data collection (Figure 1). With respect to the product synthesized by microalgae, they can be applied in several fields from bioenergy (low-value market) to cosmetics and pharmaceutics (high-value market).  
When exploiting the great biodiversity of microalgae as new natural sources of high-value phytochemicals, a screening pipeline should be developed including the following key aspects: _(i) growth_--for the successful improvement and progression of microalgae-based industries, the discovery and improvement of new fast-growing strains is essential [41]. Thus, the study of the maximum growth rate, maximum cell density, and dry cell biomass at different growth conditions, along with their amenability for heterotrophic/mixotrophic growth, are critical features to predict the feasibility of microalgae for large-scale production [42]; _(ii) robustness_--the selected microalgae should withstand variable local climatic conditions and be resistant to possible infections (e.g., other algae strains, grazers, bacteria, or viruses) in order to prevent large-scale crashes [43]. Nevertheless, strategies have been developed to combat and prevent contamination in microalgae cultivation such as the use of extreme conditions to create an unfavorable environment for the competitive organisms of the microalgae [44; 45], and the use of allebotathic approaches to control microalgae cultivation [46]; _(iii) metabolite production_--in the production of microalgae for food and health purposes, the potential toxicity (phycotoxins) of some species as well as desired product (high-value products) accumulation should be considered.
## 3 Test Results and Analysis  
### Disintegration Rate  
The disintegration of granite residual soil is caused by water infiltration through soil fissures, which dissolve free oxides crucial for cementation between soil particles [30]. The test soil samples were dried to a constant weight at 60 \({}^{\circ}\)C and then soaked in water. The reference group used the uncured soil samples, which generated bubbles and rapidly  
Figure 5: Sampling location for determination of calcium carbonate content: (**a**) Grouting; (**b**) Soaking.  
disintegrated after encountering water for 30 s and disintegrated entirely after 3 min. The soil samples strengthened with five different methods illustrate different anti-disintegration properties. After four cycles of disintegration tests, the residual soil solidified with the soaking method had the lowest amount of disintegration at only 3.21%. This is mainly due to the homogeneity and thickness of the surface consolidated layer that was formed. In addition, the disintegration rates for multiple and single grouting using a peristaltic pump were 9.7% and 13.77%, respectively. In comparison, the disintegration rates for primary grouting using an air compressor and multiple grouting machine were 9.26% and 16.28%, respectively. The results showed that the strengthening effect of multiple grouting is significantly better than that of primary grouting, and the grouting effect of the peristaltic pump is slightly better than that of air compressor grouting. Additionally, the highest disintegration rate for all five grouting modes occurred during the second soaking, as shown \(\epsilon\)n Figures 6 and 7. The surrounding consolidated soil protects the unconsolidated soil in the first disintegration test. However, in the second test, the uncured soil's cohesion and internal friction angle decrease, leading to rapid soil disintegration. The third and fourth disintegration tests showed that the disintegration rate was almost zero and tended to be stable. This indicated that a stabilized soil structure was gradually formed due to the deposition of calcium carbonate.  
Figure 6: Disintegration test: (**a**) Side view; (**b**) Top view; (**c**) First test (**bottom**); (**d**) Second test (**bottom**); (**e**) Third test (**upper**); (**f**) Fourth test (**bottom**).
# Study on Vertical Uplift Resistance Characteristics of Pre-Drilled Planted Piles in Boulder Strata
## 5 Calculation Results and Analysis  
### Q-S Curve Analysis  
Figure 6 displays the load-displacement (Q-S) curve of the pile under vertical loading, derived from experimental data and numerical simulations. The numerical results are consistent with the experimental results, thereby validating the high accuracy of the numerical simulation. The Q-S curves for both conditions exhibit similar trends, with distinct inflection points (marked in red in the figure). Before the inflection point, the pile head displacement increases gradually with the applied load; after the inflection point, the displacement increases abruptly, and the pile undergoes rapid uplift.  
The red-marked inflection points in Figure 6 represent the ultimate uplift resistance of the pile. The Q-S curves of the piles generally exhibit a consistent abrupt pattern, with their characteristics closely related to the presence of boulders. The Q-S curve for Condition 2 is more gradual. Specifically, for Condition 1, the load at the inflection point is 12.8 N, with a corresponding pile head displacement of 2 mm; for Condition 2, the load at inflection point is 25.6 N, with a corresponding displacement of 2.7 mm. Clearly, the pile in Condition 2 can withstand a greater uplift load. Under the same applied load (12.8 N), the uplift displacement of the pile in Condition 2 is only 1.2 mm, whereas the pile in Condition 1 reaches its ultimate uplift displacement of 2 mm. Under the same applied load (12.8 N), the uplift displacement of the pile in Condition 2 is only 1.2 mm, whereas the pile in Condition 1 reaches its ultimate uplift displacement of 2 mm. This phenomenon can be attributed to the increased cross-sectional area due to the presence of a boulder, which enhances the vertical support provided by the overlying soil, enabling the pile with the boulder to withstand greater uplift loads.  
The ultimate uplift resistance of the pile in Condition 2 (25.6 N) is approximately twice that of the pile in Condition 1 (12.8 N), while the corresponding pile head displacement (2.7 mm) is about 1.35 times that of the pile in Condition 1 (2 mm). This indicates that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Constitutive Model**} & \multicolumn{2}{c}{**Weight Density**} & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion** & **Friction Angle** \\  & & \(\gamma\) & E & - & c & \(\varphi\) \\  & & **kN/m\({}^{3}\)** & **GPa** & **-** & **kPa** & \({}^{\circ}\) \\ \hline The core pile & Elasticity & 27 & 70 & 0.33 & - & - \\ The outer concrete & Elasticity & 12 & 2 & 0.38 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: FLAC3D model parameters.  
Figure 6: Load–displacement curve.  
boulders significantly enhance the uplift resistance of the pile and reduce the uplift displacement. The boulders act as load-sharing components, becoming an integral part of the pile-bearing system, not only providing greater load-bearing capacity but also significantly increasing the side friction resistance.
## 1 Introduction
Since the 1970s, authors have predicted the flow characteristics in the tank by means of residence time distribution (RTD) or tracer tests, which are based on the temporal evolution of a measurement that is a consequence of the flow pattern (concentration of a tracer), but none of them have provided a quantitative description of the flow pattern [4,24, 25, 26, 27, 28]. These research works are useful for assessing the performance of the tank, but they cannot provide useful information for the tank's structural optimization (types of tank, geometrical configuration, structure size, etc.). It is important to directly measure the velocity distribution in the tank. To our knowledge, [PERSON] (1977) may be the first to directly measure the velocity distribution in the tank. He used an ultrasonic current meter to measure the one-dimensional velocity (the longitudinal velocity) in the tank, with a measurement error up to 10 cm [20]. [PERSON] et al. [29] measured the velocity distribution along the centerline using a one-dimensional laser Doppler anemometer (1D LDA). [PERSON] et al. [30] used a two-dimensional laser Doppler velocimeter (2D LDV) to conduct flow field measurement in the rectangular settling tanks. The above research works were limited by the instrument function, meaning that there were a few measurement locations, and the three-dimensional flow characteristics could not be obtained to provide sufficient information for the tank's structural optimization. In recent years, the acoustic Doppler velocimeter (ADV) has made it convenient to obtain complex flow information. [PERSON] et al. [31] carried out the experiment tests using ADV to validate the numerical model which was used to simulate the flow field in a primary settling tank, and the optimum position of the baffle in the tank was obtained by numerical simulation. In this paper, an inclined-tube settling tank used in the Yellow River Irrigation Region was introduced, and the research was conducted as follows:1. ADV was used to measure the flow field in the inclined-tube settling tank, and the distribution of the dimensionless turbulence intensity and dimensionless Reynolds shear stress in the tank was further analyzed. On this basis, the influence of the tank structure on the flow movement was analyzed, and the reason for the high settling efficiency of the tank was clarified. 2. Through detailed investigation of the flow field in the tank, the structures which were bad for the sediment settling in the tank were determined, and some suggestions for the tank's structural optimization were proposed.
## 5 In-Lab System Test
### Variable Power Reference Operation  
To evaluate the system's responsiveness to dynamic control inputs, a second test was conducted under constant wind speed conditions. The wind turbine was subjected to a fixed airflow of approximately 17.5 m/s using the laboratory fan. During the test, the supervisory app was used to command a step change in the power reference sent to the Arduino(r)-controlled boost converter.  
Initially, the power setpoint was maintained at approximately 11 W. At time t = 5.0841 s, a command was issued through the user interface to reduce the power reference to approximately 5 W. The Arduino(r) processed this command and began modifying the converter duty cycle at t = 5.2441 s, as recorded by the onboard sensors and illustrated in the voltage and current trace. Figure 13a presents the wind speed and electrical power output during the transition. Despite minor turbulence inherent to the fan-driven wind, the average wind speed remained close to 17.5 m/s throughout the test. Following the reference change, the power output gradually decreased, reflecting the system's tracking of the new setpoint. Figure 13b shows the corresponding voltage and current measurements at the rectifier output. A zoomed-in view highlights the timing of the command and system response. After receiving the new reference, the Arduino(r) adjusted the duty cycle of the boost converter, resulting in a drop in current while voltage remained regulated. This behavior demonstrates the closed-loop response of the system to changes in the power reference.
## 3 Results
### Temporal and Spatial Patterns of Drought Characteristics in 1982-1998 and 1999-2015
Figure 6: The spatial distribution of total drought duration (TDD) of each pixel in Northern Hemisphere during 1982–1998 and 1999–2015. (**a,b**) exhibited the spatial distribution of TDD during 1982–1998 and 1999–2015, respectively; (**c**) showed the spatial patterns of the TDD different between 1982–1998 and 1999–2015. (**d**) represented the frequency distribution of TDD in 1982–1998 and 1999–2015. (**e**) represented the frequency distribution of the TDD different between 1982–1998 and 1999–2015. High drought frequency (>11 events) areas accounted for 16.1% of the study area in 1982-1998, and these areas were located mainly in northwestern Canada, northern Kazakhstan, and the central and eastern parts of Russia (Figure 8a). After 1998, the proportion of high drought frequency areas decreased significantly and accounted for only 9.6% of the total pixels. These high drought frequency areas in 1999-2015 were located mainly in northern Mongolia and northeastern China (Figure 8b). When comparing the drought frequency areas between the 1982-1998 and 1999-2015 periods, we found that 54.1% of the pixels showed a decrease in drought frequency and that 4.9% of them had decreased by more than 6 events (Figure 8c,e). Overall, the mean values for drought frequency decreased from 8.9 events in 1982-1998 to 8.0 events in 1999-2015 (Figure 8d). However, in a small proportion of areas, the drought frequency also showed a pronounced increase (>6 events), such as in Mongolia and northern China (Figure 8c).