esa-sceva/satcom-chunk-collection huggingface dataset:
# LieTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories
## 5 Conclusions  
This study developed an automatic tomato detection model framework that balances complexity and recognition accuracy under challenging illumination conditions. Four  
Figure 12: Comparison of detection results provided by the (**a**) Faster-RCNN, (**b**) YOLOv8m, (**c**) original RTDETR, and (**d**) LiteTom-RTDETR models.  
phases of work were undertaken: dataset construction, model architecture optimization, detection algorithm deployment, and multistage experimental validation. The key findings of this study are as follows:  
The LiteTom-RTDETR model was proposed based on an improved RTDETR architecture. This model achieved performance breakthroughs through three architectural innovations: backbone replacement with RepViT to reduce computational complexity, deployment of a CGFM to enhance multiscale feature representation under variable lighting, and integration of sliding weight mechanisms in the loss function to mitigate class imbalance. These synergistic improvements enabled real-time tomato detection at an inference speed of 52.2 FPS while maintaining a mean average precision of 88.2%.  
Ablation experiments were conducted to assess the contributions of the architectural enhancements provided in LiteTom-RTDETR by evaluating five identification models under controlled experimental conditions. The replacement of the original RTDETR backbone with RepViT improved model accuracy while reducing parameter number, computational load, and model size, thereby achieving the preliminary lightweight objectives. Furthermore, the integration of the CGFM and SVFL function improved recognition accuracy without significantly increasing the number of parameters, computations, or model size.  
Under identical experimental conditions, the LiteTom-RTDETR model demonstrated significant advantages over the Faster-RCNN, YOLOv8m, and original RTDETR models. The proposed model exhibited a 0.1-13.4% higher mean average precision while accelerating the inference speed by 15.5-84.5%; it also exhibited 33.2-53.0% fewer model parameters, 36.2-79.1% fewer GFLOPs, and a 31.6-75.6% smaller model size. These improvements collectively confirmed the successful lightweight implementation of the model while maintaining superior detection accuracy. Therefore, the proposed LiteTom-RTDETR model shows considerable promise for application in resource-constrained mobile agricultural management equipment.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 2 VLC Transmitter Light Source
### Theoretical Background of Laser for VLC Application
#### 2.2.2 Super Luminescent Diodes (SLDs)  
SLDs shown in Figure 6 are light emitters generating amplified spontaneous emission (ASE), combining optical characteristics of LEDs and LDs. An SLD combines the advantage of both an LD and LED. That is, the diffraction-limited, spatially coherent, excellent coupling to external component, and small form factor of the LD, and the wideband spectrum and time incoherence of the LED [74].  
Since developing high-power and high-speed violet-blue SLDs, VLC systems based on SLD transmitters have been demonstrated with data rates of 3.8 Gbps with a DMT modulation scheme by [PERSON], [PERSON] et al. in 2020 [75]. Studies have shown that GaN-based SLDs can achieve high power emission power, such as over 100 mW blue SLD in continuous wave operation [76].
## 3 Results and Discussion  
### EO Extraction and Yield  
The EOs from the dried Azorean _C. japonica_ aerial parts were extracted by HD with a Clevenger-type apparatus, since it is a technique included in the European Pharmacopoeia. All EOs had a density lower than water (ca. 0.90 g/mL), a strong odor, with that from cones being the most pleasant, and a yellowish color, except for MC EO, which was pale yellow. The EO yields ranged approximately from 1 to 3% (\(w/w\)), as summarised in Table 1. The highest EO yield was found in leaves, followed by foliage, MCs, and FCs, with significant differences among them (\(F\) = 249.46; \(df\) = 3; \(p\) = 0.022). Since all samples came from the same tree population, these variations could potentially be attributed to anatomical differences, particularly in the quantity of the secretory structures that accumulate the volatile components of the EO [6].
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Dataset
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Spearman’s Correlation** & **Spearman’s Correlation** \\
**Variable** & **Coefficient** & **Coefficient \(p\)-Value** \\  & **with Compression Index** & **with Compression Index** \\ \hline Water content, \(w\) & 0.7717 & \(1.143\times 10^{-181}\) \\ Plastic index, PI & 0.4914 & \(8.571\times 10^{-57}\) \\ Plastic limit, PL & 0.4222 & \(7.209\times 10^{-41}\) \\ Liquid limit, LL & 0.4846 & \(4.575\times 10^{-55}\) \\ Initial void ratio, \(e_{0}\) & 0.8517 & \(1.492\times 10^{-258}\) \\ Specific gravity, \(G_{\rm S}\) & 0.0504 & 0.1273 \\ \hline \end{tabular} The water content (\(w\)) shows a robust positive correlation with \(C_{c}\), with a value of 0.7717, while the initial void ratio (\(e_{0}\)) shows an even stronger relationship with \(C_{c}\), with a correlation coefficient of 0.8517. This suggests that increases in the water content and initial void ratio may also increase \(C_{c}\). However, the \(p\)-value of \(1.143\times 10^{-181}\) obtained for water content (\(w\)) indicates that this correlation may not be statistically significant, so caution should be exercised in interpreting these results. Other parameters also show positive correlations with \(C_{c}\), with correlation coefficients of 0.4846 for LL, 0.4222 for PL, and 0.4914 for PI. The \(p\)-values for these parameters were calculated as 4.575 \(\times\)\(10^{-55}\) for LL, \(7.209\times 10^{-41}\) for PL, and 8.571 \(\times\)\(10^{-57}\) for PI, indicating that these correlations are statistically significant. On the other hand, the correlation coefficient of 0.0504 obtained for specific gravity (\(G_{\rm S}\)) indicates that there is almost no relationship with \(C_{c}\), which is supported by the \(p\)-value of 0.1273. This confirms that specific gravity (\(G_{\rm S}\)) does not have a significant impact on the compression index (\(C_{c}\)).  
\end{table} 
Table 3: [PERSON]’s correlation coefficient and its \(p\)-value with compression index. Figure 2: Matrix of [PERSON]’s correlation coefficient.
## 3 Methodology
### Assessment of the ISS Images
#### 3.1.1 Geometric Correction  
The ISS moonlight images we obtained were not geo-referenced. We first carried out geometric correction for these images, using the Landsat-7/8 images that contain accurately geo-referenced information as the reference.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods
#### 2.3.6 GraphAligner  
GraphAligner is designed to align long reads to a graph-based reference. The algorithm treats the reference genome as a graph where variations and alternative splicing events are represented as branches [50]. This allows the alignment to accommodate complex genomic structures that are not linearly represented.  
**Steps and Key Points:**  
1. Graph Construction: * The reference genome is first constructed into a graph where nodes represent sequence blocks and edges represent possible transitions between these blocks. This can include linear sequences as well as alternative paths representing variations or repeating elements;
2. Seed Finding: * The algorithm begins with identifying seed matches between the read and subsequences in the graph. Seeds are typically exact matches that are used as anchor points for more detailed alignment;
3. Seed Extension: * Using dynamic programming, seeds are extended along the paths of the graph. The extension process respects the graph's topology, exploring different paths where the read might align with segments of the reference;
4. Path Scoring and Selection: * After extension, paths are scored based on the alignment's quality, and the best-scoring path is selected as the optimal alignment. The scoring system considers mismatches, gaps, and the overall coherence of the alignment within the graph structure;
5. Handling Complexity: * GraphAligner is specifically adept at handling complex graph structures with varying paths, such as those introduced by repeats or structural variants, which are often challenging for linear alignment methods.  
GraphAligner is particularly useful in genomic studies where the reference genome includes significant alternative paths, such as in cancer genomics, where somatic mutations, insertions, deletions, and rearrangements are common, or in research involving highly polymorphic species.  
The complexity of GraphAligner depends on the size of the reference graph and the length of the read sequences. It typically has a worst-case time complexity of \(O(k\times n\times m)\), where \(k\) is the number of nodes in the graph, \(n\) is the length of the reference sequence, and \(m\) is the length of the read sequence.
###### Abstract  
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.  
Automatic Modulation Classification; attention mechanism; Convolution Neural Network; gate recurrent unit; AM-Softmax; deep learning +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### Macroscopic Fundamental Diagram  
The understanding of a network at the macroscopic level is important to propose solutions for problems such as congestions and poor speed on links. Evidence about the existence of the macroscopic fundamental diagram on neighborhood-sized sections in [40]. The traffic scenario in which in a city occurs a disturbance, inducing drives to select unfamiliar routes, in [41]; the MFD of the scenario presents a clockwise hysteresis loop, since during the congestion recovery stage the network is likely to experiment an uneven congestion distribution (it takes more time to clear the more congested areas than the less congested), therefore, for two same density values of the hysteresis loop, there is a higher flow during the loading process and a lower flow during the recovery process. For an area of San Francisco and Yokohama, in [42] was demonstrated that the MFD can be formulated for streets conformed by blocks of different widths and lengths (but no turns) and regardless that the traffic in all (or some) of the intersections are controlled by arbitrarily timed traffic signals; the regularity conditions (sufficient and not necessary) for the existence of the MFD are: (1) the demand varies slowly and is equally distributed, (2) redundant network (various routes options), (3) homogeneous network (similar links), and (4) links with a fundamental diagram not significatively affected by turning movements during steady flow. The MFD was investigated in [43] for several situations, as surface network vs. highway network and penetrating roads vs. ring roads; it was detected a hysteresis-like effect in the MFD that is observable in the surface street network but not in the centermost part (this part is usually congested and rare events does not impact on it); in the ring and penetrating roads of the highway network was also visible the hysteresis-like effect (the ring roads were not homogeneously congested during the morning, when it takes place the event that produces the congestion); a recommendation to obtain less scattered MFD is to separate signalized and unsignalized links. A MFD was developed in [44], the variables were explained with topological properties of the network; the free-flow speed was better explained with the average number of junctions per kilometer, and the network optimal density with the sum of degrees of all functions divided by the trafficable area; through sensitivity analysis it was presented evidence that the established relationships between the MFD parameters and the network topological metrics should be independent of the network size and transferable to networks of different size.
## 2 Results
### Functional Analysis of DAPs in SRPs  
We further performed gene ontology (GO) distribution analysis using the WEGO software package, analyzing the 305 DAPs (including the 213 up-regulated and 146 down-regulated proteins, which contain 265 unique proteins) obtained from the iTRAQ and DIGE analysis. The enriched outputs were 213 up-regulated and 146 down-regulated proteins (Figure 4, Table S3). At the molecular function level, nine subcategories were enriched. Among them, the largest portion, including 71 up-regulated and 34 down-regulated proteins, is involved in organic cyclic compound binding (Table S4). Fifteen components were observed at the subcellular level. Among them, most proteins are localized to being intracellular, intracellular part, intracellular organelle, or intracellular organelle part. There were also many proteins involved in the membrane-bound organelle, protein complex, endomembrane system, or the intrinsic component of membrane (Figure 4D; Table S4). For the biological process, 14 main pathways were detected. Among them, the largest portion, including 130 proteins, was involved in the organic substance metabolic process. A large part, including 71 proteins, was related to the single-organism metabolic process (Figure 4E, Table S4).  
Clustering of the 46 sharply changed proteins (those with at least a 5-fold change) upon ethylene stimulation revealed that the protein changed patterns under E24/D24 and D48/D24 were similar,  
Figure 4: Isobaric tags for relative and absolute quantification (iTRAQ) analysis of SRPs proteins upon ethylene stimulation. The Venn diagram demonstrates that 2024 unique proteins were shared in the two iTRAQ experiments (**A**). A total of 226 DAPs were determined from iTRAQ, and 49 unique proteins from 2-D DIGE, which resulted in 10 shared unique proteins (**A**). The DAPs from iTRAQ were classified into four areas based on their changed patterns in different SRPs (**B**). The statistical protein numbers of the 265 DAPs involved in molecular function (**C**), the cellular components, (**D**) and biological process (**E**) are highlighted.  
while E48/D48 and E48/E24 showed a similar change ratio for protein accumulation in the SRPs. In E24/D24, 18 proteins were sharply induced. After ethylene treatment for 48 h, only four proteins were sharply induced, and seven proteins were decreased (Figure 5A, Table S3).
## 2 Faults in a PV System
### Photovoltaic Generator Faults  
In a photovoltaic system, a fault refers to an atypical behavior that signals a potential loss of power or complete system unavailability. Given the challenging environmental conditions that photovoltaic systems operate in, defects can arise across various components, each with a unique set of issues. These faults may manifest themselves in the photovoltaic array and/or in the inverter, which can partially identify them [53]. PV generators can experience different types of faults, which are classified based on the area they affect. These categories comprise electrical, physical, and environmental faults [54]. However, faults can occur in photovoltaic systems, with the most common being on the solar panel side. These include shading, mismatch, potential-induced degradation, hotspot, open circuit, short circuit, line-to-line, line-to-ground, arc, bypass, and anti-reverse diode faults. Also, there are different types of faults that can occur in an inverter, which include open-circuit faults, short-circuit faults, insulation faults and so on [36; 55]. The following section provides a detailed explanation of the most common faults found in a photovoltaic installation. This information will help in the diagnostic process.  
Based on the analysis of Figure 2, the following subsections describe some of the most common faults that can affect a PV installation.  
Figure 2: Classification of fault types in a PV system.
## 3 Results and Discussion
### Antioxidant Activity  
In the current work, plant extracts were tested for their antioxidant capacity using different tests including DPPH, ABTS radical scavenging capacity, and FRAP (Table 3). The results of the DPPH test showed that the extract tested had scavenging activity in a dose-dependent manner. The extracts showed a potent antioxidant activity, especially that obtained by maceration extract with IC\({}_{50}\) = 0.180 \(\pm\) 0.020 mg/mL when compared to the one obtained by Soxhlet extraction (IC\({}_{50}\) = 0.210 \(\pm\) 0.017 mg/mL), however, no significant difference was observed between the two methods (\(p\) > 0.05).  
Regarding the FRAP bioassay, the highest reducing power was interestingly observed with the maceration extract (80.066 \(\pm\) 3.28 AAE/g extract) when compared to the one obtained by the Soxhlet method (79.4 \(\pm\) 0.45 mg AAE/g extract). In these tests, _B. hispanica_ revealed interesting antioxidant activity with insignificant differences between the two methods of extraction.  
For the antioxidant activity measured by the ABTS method, we noted that both extracts exhibited potent antioxidant activity in a dose-dependent manner in the DPPH test. The ability of the extracts to scavenge the ABTS cation is presented in Table 2. The _B. hispanica_ extract obtained by maceration showed the highest antioxidant ability (60.203 \(\pm\) 0.76 mg TE/g extract) when compared with the Soxhlet extract (56.564 \(\pm\) 1.63 mg TE/g extract).  
The obtained results showed that the plant possessed potent antioxidant activity with IC\({}_{50}\) = 0.180 \(\pm\) 0.020 mg/mL for the extract obtained by maceration, and IC\({}_{50}\) = 0.210 \(\pm\) 0.017 mg/mL for the Soxhlet extract in the DPPH assay. In the present work, we used different methods to evaluate the antioxidant potential of _B. hispanica_ methanolic extract; using the ATBS bioassay, maceration, and Soxhlet extracts showed values of 60.203 \(\pm\) 0.76 mg TE/g and 56.564 \(\pm\) 1.63 TE/g extract, respectively. The FRAP assay showed that maceration and Soxhlet extracts scored values of 80.066 \(\pm\) 3.28 mg AAE/g extract and 79.4 \(\pm\) 0.45 mg AAE/g extract, respectively. These results were in close accordance with those reported by [PERSON] et al. (2020) [12], who revealed that methanolic and aqueous extracts from Spanish species exhibited antioxidant activity with 648 and 212 mmol Trolox equivalent g\({}^{-1}\) dried extracts, respectively.  
These three methods differ from each other due to characteristics such as substrate type, reaction conditions, and data quantitation methods [18]. A complete picture of the total antioxidant capacity of the methanol extracts from _B. hispanica_ was obtained via analysis of FRAP, DPPH, and ABTS, which showed that studied plant extracts can show different antioxidant power.
# Spatiotemporal Mechanical Effects of Framework-Slope Systems Under Frost Heave Conditions
###### Abstract  
To investigate the slope instability caused by differential frost heaving mechanisms from the slope crest to the toe during frost heave processes, this study takes a typical silty clay slope in Xinjiang, China, as the research object. Through indoor triaxial consolidated undrained shear tests, eight sets of natural and frost-heaved specimens were prepared under confining pressure conditions ranging from 100 to 400 kPa. The geotechnical parameters of the soil in both natural and frost-heaved states were obtained, and a spatiotemporal thermo-hydro-mechanical coupled numerical model was established to reveal the dynamic evolution law of anchor rod axial forces and the frost heave response mechanism between the frame and slope soil. The analytical results indicate that (1) the frost heave process is influenced by slope boundaries, resulting in distinct spatial variations in the temperature field response across the slope surface--namely pronounced responses at the crest and toe but a weaker response in the mid-slope. (2) Under the coupled drive of the water potential gradient and gravitational potential gradient, the ice content in the toe area increases significantly, and the horizontal frost heave force exhibits exponential growth, reaching its peak value of 92 kPa at the toe in February. (3) During soil freezing, the reverse stress field generated by soil arching shows consistent temporal variation trends with the temperature field. Along the height of the soil arch, the intensity of the reverse frost heave force field displays a nonlinear distribution characteristic of initial strengthening followed by attenuation. (4) By analyzing the changes in anchor rod axial forces during frost heaving, it was found that axial forces during the frost heave period are approximately 1.3 times those under natural conditions, confirming the frost heave period as the most critical condition for frame anchor design. Furthermore, through comparative analysis with 12 months of on-site anchor rod axial force monitoring data, the reliability and accuracy of the numerical simulation model were validated. These research outcomes provide a theoretical basis for the design of frame anchor support systems in seasonally frozen regions.  
slope engineering; scale effects; soil arching; frame anchors; frost heave Article