esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Activation of Biochar
#### 2.2.2 Chemical Activation  
The goal of chemical activation is two-fold: to enhance the porosity and simultaneously enrich the surface with the desired functional groups that can impart the biochar with special properties [14]. Typically, the raw biochar is impregnated with a chemical agent for a predetermined time. Subsequently, the biochar is re-pyrolyzed to allow for the reaction between carbon and the chemical agent during which the pore structure is further expanded while the surface carbon is functionalized [54]. Several types of chemical agents have been used to activate the biochars.  
Some of the common chemicals include acids (sulfuric acid, sulfonic acid, nitric acid, and phosphoric acid), bases (sodium hydroxide, potassium hydroxide, urea, and melamine), metal salts (zinc chloride, potassium carbonate), oxidants (hydrogen peroxide, permanganate, and ammonium persulfate), and gases (ozone and NH\({}_{3}\)) [55; 56; 57; 58; 16; 52]. Depending on the type of chemical activation, the activated biochars are usually equipped with sulfonic, carboxylic, phenolic, lactonic, pyridynic, pyrrol, and quaternary nitrogen and phosphorus-carbon functionalities.
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach
### Security Policies of Information Flow in Architecture
#### 3.3.1 Security Policies of Information Flow under the Goals of Confidentiality  
As mentioned above, the confidentiality policy of information flow is intended to prevent information from flowing to subjects that are not authorized to access the information, that is, information is only allowed to flow inside a security class or from a class with low security level to a class with higher security level. We propose security polices as follows:  
**Policy 1.**_Policy for reading operation in service invocation under the goal of confidentiality._  
If there is an invoking service A and an invoked service B, A can perform the reading operation on B if and only if \(\text{C}_{\text{a}}\geq\text{C}_{\text{b}}\). The information flow is \(\text{B}\rightarrow\text{A}\). \(\text{C}_{x}\) stands for the confidentiality level of service \(x\).  
**Policy 2.**_Policy for writing operation in service invocation under the goals of confidentiality._  
If there is an invoking service A and an invoked service B, A can perform the writing operation on B if and only if \(\text{C}_{\text{a}}\leq\text{C}_{\text{b}}\). The information flow is \(\text{A}\rightarrow\text{B}\).  
**Policy 3.**_Policy for reading and writing operation in service invocation under the goals of confidentiality._  
If there is an invoking service A and an invoked service B, A can perform both reading and writing operation on B if and only if \(\text{C}_{\text{a}}=\text{C}_{\text{b}}\). The information flow is \(\text{A}\leftrightarrows\text{B}\).
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Multimodal Knowledge Graph Construction Experiments
#### 4.2.2 Entity and Relationship Extraction Results  
Through deep learning and distant supervision methods, this study successfully extracted a large number of entities and relationships from extensive course resources. Due to the vast size of the original database, which includes course resources across multiple disciplines, computer science courses were selected as examples to visually demonstrate the experimental results. This choice not only showcases the method's effectiveness but also reflects current trends in popular technology fields and education.  
Figure 6: Comparison diagram of modal fusion effect.  
For entity extraction, based on multimodal features and ranked by importance, three main entity types were identified: course names, knowledge points, and skills. This section does not include the following course entities in the visualizations: \"course content\", \"difficulty level\", \"duration\", \"rating\", \"interactivity\", \"multimedia type\", \"teaching style\", \"course structure\", \"learning objectives\", \"prerequisites\", \"update frequency\", \"language\", \"certification type\", \"course category\", \"subject area\", \"practical opportunities\", \"assignment difficulty\", and \"exam format\". The resulting entity relationship graph is shown in Figure 7.  
Based on the extracted entities and relationships, a knowledge graph of computer science course resources was constructed, as shown in Figure 7. This graph illustrates the complex associations between courses, knowledge points, and skills, highlighting the interdisciplinary nature of the subject and the integration of theory and practice. Analysis reveals that the degree distribution of nodes follows a power law, with a few core concepts (such as \"recommendation system\" and \"machine learning\") exhibiting high connectivity. The network displays a community structure, with subgraphs forming around topics such as machine learning and data structures. Centrality analysis indicates that nodes like \"recommendation system\" and \"machine learning\" play a critical role in knowledge transmission.  
The extracted relationships and their interconnections within the recommendation system are visualized in Figure 8, demonstrating the complex network of educational concepts and their associations.  
Figure 7: Entity relationship extraction results for computer courses (partially visualized).
## 4 Application to WWTP
### Control problem  
The basic control strategy proposed in the \(BSM1\) is the feedback control of the dissolved oxygen \(S_{O2}\) level in the reactor by manipulation of the oxygen transfer coefficient \(K_{La}\) and the control of the nitrites and nitrates concentration in the last anoxic compartment by manipulation of the internal recycle flow rate \(Q_{a}\).  
In this work the NECLGPC algorithm is applied for controlling the oxygen \(S_{O2}\) in the aerobic reactor and nitrate levels \(S_{NO1}\) in the anoxic reactor. A multivariable control strategy is used where the controlled variables are \(S_{NO1}\) and \(S_{O2}\), the manipulated variables are oxygen transfer coefficient \(K_{La}\) and the internal recycle flow rate \(Q_{a}\). The considered measurable disturbances are the influent flow \((Q_{in})\) (Figure 2), the organic matter concentration \((S_{sin})\) (Figure 3) and the ammonium concentration \((S_{NHin})\) (Figure 4) in the influent.
###### Abstract
established an elasto-plastic model of UGMs using a simplified model which employed the linear kinematic hardening assumption and a non-associated flow rule [3; 4]. Taking the short-term and long-term performance into account, this model was constructed by classical elasto-plastic theory, which can be used for the monotonic and repeated loads. The model was verified using multi-stage tests with satisfactory results, and the effects of stress can be captured accurately. However, the fixed yield surface in this model has some differences with the actual situation under repeated loads. The Mohr-Coulomb model is one of the most classical elasto-plastic constitutive models, which has been used widely and conveniently for friction materials with three plastic parameters [16; 17; 18]. The Drucker-Prager model also has close relationship with the Mohr-Coulomb model. The parameters of the two models can be interconverted under plane strain condition [3; 4; 6; 15; 19]. To some extent, the two models have the same characteristics but different formations. [PERSON] et al. proposed the spatially mobilized plane (SMP) criterion which was regarded as an extension (the square means) of the Mohr-Coulomb criterion in three dimensions [20; 21]. Using the SMP and Mohr-Coulomb criteria, it was found that the limiting stress calculated by the SMP criterion was greater than that obtained from using the Mohr-Coulomb criterion, which verified the importance of intermediate principal stress [20]. The SMP criterion included the first, second, and third effective stress invariants, which could conveniently reflect the shear and hydrostatic pressure effects. With these considerations, the SMP criterion can be preferable for the plastic yield criterion of UGMs. Moreover, the yield surface of SMP was smooth and without sharp angles, which can avoid the problem of convergence [22]. Based on the achievements of the model proposed by [PERSON] et al., the authors constructed an elasto-plastic model which was called the revised SMP model [5]. The revised SMP model was constructed using the SMP yield surface, a simplified hardening law and the non-associated flow rule [5]. With the plastic strain rate and resilient strain, a relationship was established to describe the variation of yield surface along with the increase of loading cycles. The hardening law was separated into two parts, in which the kinematic hardening law was used for the process of loading-reloading in the repeated loads and the isotropic hardening law for the loading-unloading in the single load. This simplified hardening model contributed to a better understanding and clear description of the development of plasticity. With a subroutine, the revised SMP model was validated by a laboratory repeated loads test using the precision unbound material analyzer (PUMA). However, the revised SMP model has not been used for the numerical simulation of pave ment structure, and the characteristics of calculation results are still unknown. Considering the relationship among the SMP, Mohr-Coulomb and Druck-Prager criteria, these three models were used in this work for the computation of pavement analyses.
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Analysis of Experimental Results
In addition, the introduction of the energy loss and regularization strategy provides additional constraints for the channel weighting and optimization process of the model, which effectively prevents overfitting and enhances the generalization ability of the model. For example, in the experiments on all datasets, each model of AdaptiveConv2d improved in segmentation performance, especially in the COVID-19 dataset and the Kvasir dataset, where the regularization term and the energy loss effectively improved the consistency of the feature maps, allowing the model to better focus on the target region in complex backgrounds rather than being distracted by background noise. Although traditional methods are also capable of handling these datasets, AdaptiveConv2d demonstrates its unparalleled advantages in the recognition of complex details and irregular structures. AdaptiveConv2d has also made significant progress in reducing computational complexity. While the model enhances feature extraction by introducing adaptive channel weighting and a global context-aware mechanism, it effectively simplifies the computational process by combining global average pooling and 1 \(\times\) 1 convolution. In the table, we can see that AdaptiveConv2d is able to maintain good performance despite the optimized computational complexity of the model. Especially in resource-constrained environments, the efficiency of AdaptiveConv2d makes it ideal for handling real-time medical image segmentation tasks. This feature is particularly important in clinical applications, especially in medical diagnostic systems that require real-time feedback to keep latency low and ensure high accuracy. By analyzing the results from multiple datasets, we can conclude that AdaptiveConv2d not only excels in improving accuracy, but also demonstrates its unique advantages in detail extraction and complex background processing. The introduction of global context information and energy loss enables the model to effectively capture complex patterns and weak changes in images, especially when processing medical images containing complex structures or blurred boundaries, demonstrating significant improvements. In addition, the inclusion of residual connectivity avoids information loss and improves the stability of the network, while the energy optimization strategy further enhances the generalization ability of the model. Overall, AdaptiveConv2d demonstrates its strong adaptability and superiority on multiple datasets and tasks, fully proving the potential of the module in medical image segmentation tasks.
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 2 Previous Research
### Machine Learning Technique for Posture Recognition  
Many fields are actively researching posture recognition for worker safety, musculoskeletal disorders, and posture correction. In industries, these studies include the development of a production plan and the enhancement of productivity. A wide range of fields, from the human posture classification field considering simple postures such as sitting, standing, and lying down [45,46] to the medical [47,48], industrial [49], and sports [50] fields, have focused on the definition and recognition of human postures. As hardware for collecting posture recognition data, three-dimensional depth cameras [51], smartphones [52,53], and inertial measurement unit sensors [17] are used. In addition, machine learning algorithms such as support vector machine (SVM) [54], CNN [19], and  
Figure 4: Depth hole detection from general objects: (**a**) RGB image, and (**b**) its depth map.  
deep belief network [55] are widely used to learn posture recognition data in terms of software.  
CNN is a well-known machine learning technique that has been widely applied to image classification and recognition. This technique is currently used for image classification [56] and object detection [57] in the shipbuilding industry for safety management and productivity improvement. There is no need to manually extract features because CNN learns the features of an object through data. It also has good reusability because it can learn new data based on the existing network. This study proposes a CNN model as a data learning algorithm for the reasons of convenience, reusability, and efficiency.
## 2 Materials and Methods
### Parameter Self-Calibrating Algorithm for the Application Rate Control  
The flow chart of the parameter self-calibrating algorithm for application rate control is shown in Figure 3. The cumulative increment signal of the weighing sensor \(D(t)\) was obtained by the application rate measuring system, the vehicle speed \(V(t)\) was obtained by the speed measuring sensor, and  
Figure 1: Block diagram of an open-looped control strategy for the application rate control.  
Figure 2: Block diagram of a closed-looped control strategy for the application rate control based on the parameter self-calibrating system.  
the rotation speed \(R(t)\) of the fluted rollers was obtained by a rotary encoder. The control parameter self-calibrating system, combining the sensor signals from multiple sources, obtained the latest relationship model between the mass flow rate and rotation speed during the actual operation of the drill machine. The specific algorithm is described as follows.  
1. Acquisition of dynamic cumulative application rate Q(t)  
The dynamic cumulative increment of the weighing sensor \(D(t)\) was obtained by the application rate measuring system and was used to determine the cumulative application rate. However, the low stability of the \(D(t)\) signal, due to the complexity of field operations, required a more complicated processing method. To obtain more accurate dynamic cumulative application rate signals, a least squares method [28; 29] was used to perform a zero-crossing linear fitting of the dynamic cumulative increment \(D(t)\) and dynamic cumulative operation area \(S(t)\). The linear fitting results gave the dynamic cumulative application rate \(Q(t)\). \(S(t)\) was calculated by accumulating the product of vehicle speed and operation width of the seed-fertilized drill machine.
2. Acquisition of the relationship model between mass flow rate and rotation speed  
Linear fitting of the dynamic cumulative application rate data \(Q(t)\) and dynamic cumulative rotation cycles \(N(t)\) over a certain observation time \(T\) was used to obtain the relationship model between the mass flow rate and the rotation speed during the operation of the seed-fertilized drill machine. The parameters of the linear fitting equation included the calibration parameters \(k\) and \(b\), and \(T\) were determined according to the actual operation condition. The dynamic cumulative rotation cycles \(N(t)\) were calculated by accumulating the rotation speed of the fluted rollers over the observation time \(T\).
## 2 Materials and Methods
### DLSS Data Construction
#### 2.2.4 DLSS Data Construction Based on TSUI  
To ensure the accuracy of the learning data for PWDT, a spatiotemporal analysis was performed on TSUI of identical regions to extract the areas of confirmed PWDT and construct the learning data. The pine trees present in the primary TSUI images but not present in the secondary TSI images may be conjectured to have been felled in the field due to PWDT. Hence, as shown in Figure 2, the artificially felled trees were regarded as the ground truth for PWDT on TSUI in accordance with the pest control plan.  
Figure 2: Ground truth verification based on TSUI.  
The respective concepts are summarized in Figure 3, where the TSUI are given from the primary to the quaternary in line with the processed UAV imagery in this study. Despite the variation in time, pest control is ultimately performed on PWDT and the pest-controlled trees are confirmed as PWDT on the secondary to quaternary TSUI. The trees found in identical locations through the feedback on the primary to tertiary images were registered as the ground truth for PWDT.  
TSUI and GPS points of the PWDT were then used to identify the affected trees, and a MATLAB S/W (ver. R2022b)-based vectorization program was used to derive the exact tree canopy of the affected trees. The images were saved as PNG files with a size of 256 \(\times\) 256 pixels. A total of 2350 PWDT were vectorized at 9 sites, and 795 images of 2000 damaged trees at 8 sites were used as training data. In addition, 200 images from Angang-eup, Gyeongju city, Gyeongbuk province, where 350 damaged trees were found, were used as the test dataset. In some cases, one image contained more than one damaged tree, so the number of datasets was smaller than the number of damaged trees found (Table 3).
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Defect Detection Evaluation Metrics  
IoU of the detected region with the matched GT is used to evaluate detection performance as shown in Figure 8. The IoU threshold is set to 0.5 to distinguish whether the detected bounding box is a defect.  
The detection results of each category defect are divided into true positive (TP), false positive (FP), true negative (TN) and false negative (FN) as shown in Figure 9. The upper of the outer rectangle is true defects including TP and FN, the lower of the outer rectangle is non-defects including FP and TN. The inner rectangle is the detected defects, which includes TP and FP detected by mistake.  
We use Accuracy (ACC), mean Average Precision (mAP), Average Recall (AR), Average IoU of the detected defects, ground truths (IoUs) and Frame Per Second (FPS) as the evaluation criteria.  
Figure 8: Intersection over union (IoU) of the detected defect and the ground truth. (**a**) the detected defect and the ground truth, and (**b**) the schematic of IoU.  
Figure 7: Defects of the denim fabric. (**a**) knot, (**b**) coarse warp, (**c**) three wire, and (**d**) flower jump.  
Precision \(p\) is the ratio of the detected true defects to all detected defects. Recall \(r\) is the ratio of the detected true defects to all true defects. ACC, \(p\) and \(r\) are defined, respectively, as follows:  
\[ACC=\frac{TP+TN}{TP+FP+FN+TN} \tag{7}\]  
\[p=\frac{TP}{TP+FP} \tag{8}\]  
\[r=\frac{TP}{TP+FN} \tag{9}\]  
For each class defect, the \(p\) and \(r\) decrease with increasing of the threshold of IoU.  
AR is the ratio of the detected true defects of all classes to the true defects of all classes, which is calculated as follows:  
\[AR=\frac{\sum\limits_{i=1}^{N_{cls}}TP_{i}}{\sum\limits_{i=1}^{N_{cls}}\left( TP_{i}+FN_{i}\right)} \tag{10}\]  
where \(i=1,2,\ldots,N_{cls}\), \(N_{cls}\) is the number of the defect categories.  
Average Precision (AP) is the average of the precision values corresponding to the different recall. Normally, it takes \(r=[0,0.1,0.2,0.3,\ldots,1]\) respectively to calculate the precisions. For each recall, the corresponding maximum precision \(p(\overline{r})\) among its right adjacent interval is taken to calculate the AP by Formula (11). mAP is the average of AP for all classes, which is calculated by Formula (12):  
\[AP=\frac{1}{11}\sum\limits_{r\in(0.0,1,\ldots,1)}p(\overline{r}) \tag{11}\]  
\[mAP=\frac{\sum\limits_{i=1}^{N_{cls}}AP}{N_{cls}} \tag{12}\]
## 3 Results and Discussion
### Figures of Merit
Although uniformizing the input noise clearly increases the diversity of primes for the classical scheme, the uniformized QS still outperforms it by an order of magnitude. Indeed, we observe an additional gain of 585.0% that cannot be solely attributed to the biased distribution of the unprocessed randomness sources. Nevertheless, at least for the 32-bit case, the diversity of primes remains low even for the uniformized QS, confirming the underlying bias revealed in Section 3.1 for both sources considered. Given that, in reality, the QS is a mixture of quantum noise and classical contributions, these can also be attributed to the CS. Despite this, it would be important to assess this figure of merit after implementing an adequate RE layer. For completeness, in Table 1, we also present the variation in the diversity of prime numbers taken for the entire 1 GB total number of primes generated across different noise sources, which are also reflected in the values calculated. Here, the behavior previously described can still be observed, and, notably, the diversity variation for the 64-bit case is much more pronounced than in the first 10 M values. Finally, it should be noted that, in total, the raw CS yielded only 0.056% of the entire set of 32-bit prime numbers. On the other hand, both the QS and the uniformized CS were much more successful, generating approximately 0.53% of all possible outcomes. Meanwhile, the uniformized QS showed an even greater performance increase, outputting nearly 5% of all 32-bit prime numbers, which clearly illustrates its superiority. Figure 5: Number of unique primes observed for each of the randomness sources considered. Values were evaluated for 10 M primes with lengths of (**a**) 32 bits and (**b**) 64 bits. Additionally, all the values obtained are represented for each case.