esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 In Silico Library Design for Medicinal Chemistry
Tautomerism represents a property of chemical compounds that affects the calculation of their physiochemical properties, such as logP, logD and pKa, and subsequently bears consequences in both QSAR and molecular docking [54]. Due to their different structural representations, tautomers are often handled as separate molecules by CADD programs [49,55]. Moreover, since proteins are known to be enantioselective toward the binding ligands, exploring chirality when designing a virtual library is an important aspect to consider. In the majority of virtual libraries, however, compounds are represented by a single stereoisomer, or the stereo information is absent altogether. Exploring chirality thoroughly will expand the database size by 2n per molecule, where n is the number of chiral centers present. With larger databases, this issue will be even more pronounced and should be considered before generating all possible stereoisomers [56,57]. In general, it is recommended to explore unspecified chirality, which should be performed on a case-by-case basis with regard to the biological context [49]. Furthermore, for compounds that have ionizable groups, multiple different structural representations should be used. Within a reasonable pH range, structures should be represented as protonated and deprotonated forms of compounds [56]. The biological context of the target protein should be used to provide an accurate representation of the environment. After the final compound 3D structure generation, energy minimization should be carried out in order to optimize the molecular geometry [19,58]. Library design should factor in the avoidance of toxic outcomes. Despite the fact that toxicity of drugs is multifactorial and that predicting the exact property responsible for toxicity is difficult, several correlations of toxicity to in vitro pharmacology profiles exist and can be translated to in silico tools which examine molecular descriptors and filter the libraries accordingly [59]. The filtering itself can also flag compounds with reactive functional groups or moieties, optically interfering components, aggregators or frequent hitters. The filtering of \"unwanted\" molecular species using computational filters represents an essential element of library preparation which should always be considered in a suitable context [60,61]. The essential filters in medicinal chemistry library design are presented in Table 1 below. The size of the chemical space used in CADD means that further entries can be calculated for compounds in molecular libraries, such as structural keys or (hashed) molecular fingerprints. These additional compound representations alleviate steps in the downstream calculations, e.g., clustering and the chemical space definition [62,63].
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 3 Proposed Approach
### Proposed Approach
#### 3.2.2 Fuzzy System-Based Route Classifier
This junction is made according to the fuzzy predicates in the normal form (conjunctive or disjunctive). Through the use of the route classification system proposed in this work, the messages carry information about expected transmission count, number of hops and energy consumed. This information is used in the fuzzy inference process of the route classifier to determine the quality of the route. Table 2 shows some fuzzy rules that represent the combination of different metrics, which characterize the input fuzzy sets, i.e., EC, ETX and NS. As the fuzzy system used has three inputs with three pertinence functions for each input, our rule base includes 27 rules, converted to the associated linguistic variables such as Low, Medium and High. For example, to elect a route with high quality, the energy consumed is low, the number of hops is low and the ETX is low. During the route selection process, fuzzy sets and rules are used according to the requirements of the application. After the fuzzy inference process, this proposed approach obtains a fuzzy set as an answer. However, because of imprecision, frequently this set is not convenient as a final response of the system, requiring a synthetic numerical representation of the fuzzy response. Thus, function of defuzzification interface is to obtain a precise (non-fuzzy) result in the output of inference system. According to [25], there are several methods of defuzzification highlighting the following: Center of area/gravity; Center of largest area; Average of the maximum; First of the highs; Mean of the highs; and Height of maximum. It was considered in this work a simple and frequently used method, which is the centroid defuzzification method [26] that locates the equilibrium point of the diffuse region of the solution, calculating the weighted average of the diffuse region. The route selection is based on the quality obtained in the last column of Table 1, which contains a fuzzy output variable, which corresponds to a route quality. From this indicator, the nodes decide locally which route to use to send data without incurring high network costs. The rule implemented in the protocol suggests that, after a route is formed, a node must always send data messages using the bigger quality route as observing information contained in your cache.
## 3 Method
### Data Processing Flow of SBAS InSAR  
The SBAS technology data processing flow is shown in Figure 3, which is divided into the following 7 steps:  
1. 75 Sentinel-1A images were converted into Single Looking Complex (SLC) images.
2. Interference pairs generation: The image of 3 April 2021 was selected as reference image, and the threshold of time baseline and spatial baseline were set to 90 days and 45%, then 290 interference pairs were derived.
3. Interference and unwrapping processing: After images were registered, interferometric processing is applied to the interferometric pairs to generate interferograms.  
Subsequently, flat earth effects are mitigated, Goldstein filtering is performed, coherence is computed, and phase unwrapping is executed using the minimum cost flow method. Following these steps, a phase map is generated.
4. Refinement and re-flatting: The area in the image that is far from the deformation area and has no residual terrain stripes was selected, and no fewer than 30 Ground Control Points (GCPs) were selected, then orbit refinement polynomials were used for refinement, and re-flatting was performed based on GCPs.
5. Linear deformation and elevation coefficient solution: An error correction model for the elevation coefficient and deformation rate of coherent points were established, then SVD was used to solve the model, and the deformation of interference pairs and elevation coefficient were obtained.
6. Elimination of atmospheric phase: To eliminate the atmospheric phase and obtain the deformation time series, high-pass filtering in the time domain and low-pass filtering in the spatial domain were employed.
7. Geocoding: The processing result in the radar coordinate system (Cartesian coordinate system) was converted to the geographic coordinate system to obtain the final results.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 2 Analytical Model of RV Reducer with Tooth Profile Modifications
### Transmission Error Calculation  
#### 2.2.1 Transmission Error Model of RV Reducer  
A TE is one of the most important methods used to measure the accuracy of an RV reducer, and is defined as the difference between the theoretical and actual rotation angles of the output shaft as the input shaft rotates at any angle. Because this study focuses on the influence of the cycloid drive in an RV reducer, the angle of the crank is expressed as the input angle. Thus, the TE may be calculated as follows:  
\[\theta_{e}^{i}=\theta_{cr}^{i}/Z_{p}-\theta_{pl}^{i} \tag{26}\]  
Here, the upper corner \(i\) indicates a certain moment, and the mean value of the TE is the following:  
\[\overline{\theta}_{e}=\frac{1}{n}\sum_{i=1}^{n}\theta_{e}^{i}, \tag{27}\]  
where \(n\) represents the number of data points. In addition, the TE amplitude is calculated as follows:  
\[\theta_{e}^{a}=max(\theta_{e}^{i})-min(\theta_{e}^{i}) \tag{28}\]  
Because the main factor generating the mean value of the TE is the backlash of the RV reducer, the mean value of the TE can be used to calculate indirectly the backlash of the RV reducer. As the mean value of the TE also includes the contact deformation caused by a tooth engagement, this part can be approximated based on the amplitude of the TE. Additionally, considering that the starting position of the cycloid gear is in the middle of the tooth gap, the backlash of the RV reducer may be approximated as follows:  
\[B\approx 2\overline{\theta}_{e}-\theta_{e}^{a} \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modification**} & \multicolumn{2}{c}{**Contact Force (N)**} & \multirow{2}{*}{**Relative**} & \multicolumn{2}{c}{**Friction (N)**} & \multirow{2}{*}{**Relative**} \\ \cline{2-2} \cline{4-7}  & **Analytical** & & & & **Difference** & & **Analytical** & **FE Method** & **Difference** \\  & **Method** & & & **(\%)** & & **Method** & **(\%)** \\ \hline
0 & 550 & 575 & 4.54 & 53 & 51 & 3.77 \\
0.02 & 582 & 603 & 3.61 & 56 & 55 & 1.78 \\
0.04 & 625 & 655 & 4.81 & 58 & 59 & 1.7
###### Abstract  
This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward.  
Industry 4.0; knowledge generation; knowledge modeling; process optimization; predictive models; data integration; system architecture; dynamic data aquisition Article
## 4 Test Results and Analysis  
### Hysteresis Curves  
The load-displacement curves of the NEDC connector and the hook bolt connector are shown in Figure 15 and reveal that each specimen exhibits stable hysteresis performance. The member exhibits stable stiffness under cyclic loading [26].  
At the beginning of loading, the horizontal displacement was small; both sets of hysteresis curves are linear, the slope is large, and residual deformation is small. As the test proceeded, the slopes of the two sets of curves continued to flatten with an increase in  
Figure 14: Diagrams depicting the wall panel cracks in specimens SF-1 (**a**) and SF-2 (**b**).  
Figure 15: Hysteresis curves of SF-1 (**a**) and SF-2 (**b**).  
the number of load cycles; consequently, the hysteresis loop area of the envelope gradually increased, stiffness of the structure degraded, and residual deformation increased. The main reasons behind structural stiffness degradation are the loss of mortar between the wall panels, severe damage to the corners of the panels, and the fracture of the welds at the beam-column nodes.  
Stiffness and strength degradation were observed in both sets of specimens during loading. However, the stiffness and strength degradation of the SF-1 specimens were more noticeable because during the loading process, the steel frame gradually changed from the elastic phase to the elastoplastic stage and plastic phase. Consequently, the AAC wall panels were crushed and began cracking, which led to stress concentrations in the wall plate at the bolt holes. Specim SF-1 used a rigidly connected hook bolt as the connector, which is more likely to lead to cracks at the bolt holes. Meanwhile, specimen SF-2 used NEDCs, in which bolts can slip during the crushing of the AAC wall panel, thus offsetting this adverse effect and reducing the degree of wall panel damage. This slip releases more energy and facilitates structural seismic resistance. Therefore, the hysteresis loop of specimen SF-2 is more extensive and fuller than the envelope area of specimen SF-1.
## 6 Conclusions  
This study addresses the core challenges of small, densely arranged, and arbitrarily rotated objects in aerial images by proposing an anchor-free oriented object detection method based on architecture search. The method leverages the Feature Alignment Search (FAS) module to automatically optimize feature fusion paths, the decoupled detection head (DDH) to separate classification and regression tasks, and an improved ellipse center sampling (ECS) to mitigate sampling bias for targets with extreme aspect ratios. Experiments on the DOTA and HRSC2016 datasets achieve 75.2% and 89.1% mAP, respectively, with 21.7 FPS, significantly outperforming mainstream models. The proposed method offers advantages in lightweight design and real-time performance but is limited by complex Gaussian gradient computations and suboptimal small-object detection. In future work, we plan to design a differentiable IoU loss to simplify gradient computation and integrate Neural Architecture Search (NAS) to compress the model for improved inference speed. Additionally, FP16/INT8 quantization will be explored to enhance operational efficiency for edge deployment. Furthermore, the incorporation of Transformer-based feature extraction will be investigated to enhance the model's rotation invariance.  
Conceptualization: Y.K. and W.S.; investigation: Y.K. and W.S.; data curation: Y.K. and W.S.; methodology: Y.K. and W.S.; validation: Y.K. and W.S.; writing--original draft preparation: B.Z. and Y.K.; writing--review and editing: W.S.; visualization: Y.K. and W.S.; supervision: Y.K. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data supporting the reported results in this study are openly available. The DOTA1.0 dataset is accessible at [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)) (accessed on 1 June 2025), the DOTA1.5 dataset is available at [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)) (accessed on 1 June 2025), and the HRSC2016 dataset can be found at [[https://ieee-dataport.org/documents/hrsc2016-0](https://ieee-dataport.org/documents/hrsc2016-0)]([https://ieee-dataport.org/documents/hrsc2016-0](https://ieee-dataport.org/documents/hrsc2016-0)) (accessed on 1 June 2025).  
Authors [PERSON] were employed by the company Hangzhou Neptune Technology. 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.  
Figure 13: The experimental effect that demonstrates the method proposed in this paper can detect densely distributed targets and objects with large aspect ratios.
# The Effects of Stress on Second Harmonics in Plate-Like Structures
## 3 Analytical Method of Second Harmonics Under Stress Change  
The amplitude coefficient, \(\overline{A}_{n}(x)\), under the different stress can be acquired by replacing the related mode shapes \(u_{n}\) and \(u_{n}{}^{(1)}\) into Equation (18). The mode shapes of ultrasonic guided waves can be solved through the semi-analytical finite element (SAFE) method. By comparing \(\overline{A}_{n}(x)\) under the different stress, the response law of the amplitude of second harmonic mode with the stress in plate is obtained.
## 5 MARL-Based Scheduling Solution  
Once the routing agent identifies the next link for traffic scheduling, the scheduling agent takes responsibility for assigning data frames to time slots, aiming to minimize buffering time at intermediate TSN switches. The primary objective is to select positions that allow data frames arriving at an intermediate node to be forwarded immediately, avoiding queuing delays. This process is based on the following assumption: if node \(n\) schedules a data flow at position \(i\), the frames will arrive at node \(n+1\) at a time slot corresponding to position \(i\). Ideally, the optimal policy is to maintain consistency by scheduling data frames in the same position as the previous link whenever possible. If resource constraints make this infeasible, the next available position in the temporal domain should be selected, following the minimum residence time criterion. Conversely, selecting a position immediately before the optimal one in the timing domain is highly inefficient, as it forces data frames to wait nearly an entire period before being forwarded, thereby significantly increasing end-to-end latency. To mitigate this, the model ensures that each episode lasts only a single time step, with one action taken per episode. As a result, scheduling frames along the entire path between end devices requires initiating a separate episode for each hop. Building on these principles, a scheduling Markov decision process (MDP) has been formulated, as detailed below.
## 2 Materials and Methods
### Experimental Section: Workflow, Hyperparameters, Metrics, and Input Data Pre-Processing
#### 2.3.4 Input Data Pre-Processing for Training  
During model training, each backbone receives a different version of the image data (Figure 5). The following types of modifications (augmentations) on the basis of the PIL Python Library were used to modify the input data for the additional channels: autocontrast, brightness, equalize, invert, solarize, hue adjustment, saturation adjustment, and posterize [37]. This paper focuses primarily on analyzing color transformations that simulate and address real-world scenarios under low-light conditions, as they are particularly relevant for improving generalization under low-light conditions. While spatial transformations like rotation and scaling were not the focus of this study, we acknowledge their potential value in enhancing model performance for specific scenarios. These transformations will be explored in future research to evaluate their effectiveness in different contexts and use cases. Finally, this Synchronized Multi-Augmentation (SMA) technique allowed us to input the original image and add synchronously varying image modifications (augmentations) of the same modification type to the NN, train the NN, and investigate the effect of each modification (augmentation) type on the NN's performance.  
The techniques that showed the best results are as follows (Figure 5):  
* \"Original\": all three channels receive the original image.
* \"Contrast\": one channel receives the original data, while the other two receive images with changed contrast. This function calculates a histogram of the input image, removes (according to the predefined cutoff value) a percentage of the lightest and darkest pixels from the histogram, and remaps the image so that the darkest pixel becomes black (0) and the lightest becomes white (255). On the channels, the cutoff values (0, 40) and (40, 0) were used, which removed 40% of the darkest and brightest pixels.
* \"Move\": one channel receives the original image, and the other channels receive a translated image with padding at some constant step. A step of 6 pixels was used.
* \"Equalize\": one channel receives the original image, while the other channels receive an equalized and an equalized+inverted image. The equalize function applies a non linear mapping to the input image in order to create a uniform distribution of grayscale values in the output image. The inverse function inverses the pixel values.
* \"btarget\": one channel receives the original image, and the other channels receive images adjusted to specific brightness levels, resulting in darker and brighter images.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 6 Conclusions  
1. A small amount of deflection data can be used to identify the location and degree of cable damage. The wavelet coefficient distribution curve of the first derivative of the girder deflection difference before and after the cable damage monotonically changes from negative to positive from left to right on either side of the anchoring point where the damaged cable passes through the 0 point.
2. The deflection difference distribution curve of the girder obtained using a small amount of measurement points almost coincides with that obtained with the original number of measurement points. The first derivative of the deflection difference of the girder before and after the reduction in the measurement points of the measurement point is transformed by wavelet transform, and the position of the damage cable (single-cable damage or double-cable damage) can be accurately identified.
3. Before and after the reduction in the measurement points, instead of the finite element model, the Kriging surrogate model is computed based on the deflection difference corresponding to the cable damage. Combined with the theory of the particle swarm algorithm, it is possible to accurately identify the degree of damage to the cables.
4. The proposed cable damage identification method based on monitoring data from a small number of measurement points is robust against interference and has promising applications.  
Methodology, Y.Y.; Investigation, Y.Y.; Writing--original draft preparation, Y.Y.; Software, Y.Y.; Conceptualization, M.S.; Validation, M.S.; Funding acquisition, M.S.; Writing--review and editing, M.S., Y.Y. Experiment, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundations of China (Grant No. 51278315).  
Not applicable.  
No new data were created or analyzed in this study.  
Figure 25: Cable damage identification results (after adding noise). (a) Single-cable damage; (b) Double-cable damage.