esa-sceva/satcom-chunk-collection huggingface dataset:
# Dilated Multi-Temporal Modeling for Action Recognition
## 4 Results
### Main Results
#### 4.3.2 Results on Something-Something V1  
As Shown in Table 2, our method achieves competitive accuracy compared with the other models on SthV1. For fair comparison, we show the results by taking a single clip with a center crop as input. In order to gain further insight into the capabilities of DMTNet, we conducted additional evaluations by sampling two temporal clips with three spatial crops. These two strategies adopted in the evaluation can both demonstrate the efficiency and accuracy of DMTNet. DMTNet achieved a 1% improvement in performance with 8 frames and a 1.8% improvement with 16 frames compared to TANet, which is our baseline that also uses the same attention strategy and adoptive kernel generating for depth-wise convolution. TEFE [34] showed competitiveness at the 16-frame sampling, but DMTNet exhibited significant results for real-time applications such as for 8-frame rapid inference. The results were obtained by testing the validation set.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **Backbone** & **Pretrained** & **Modality** & **Mean** & **Top-1** \\ \hline ActionVLAD [31] & VGG-16 & ImageNet & RGB & 50.1 & 69.5 \\ TSN [23] & BNInception & ImageNet & RGB & 61.4 & 74.8 \\ TRN [32] & BNInception & ImageNet & RGB & 68.7 & 79.9 \\ TRNms [32] & BNInception & ImageNet & RGB & 68.8 & 79.5 \\ TSM [25] & ResNet-50 & ImageNet & RGB & 70.6 & 80.4 \\ I3D [15] & ResNet-50 & ImageNet & RGB & 63.2 & 74.8 \\ I3D * [15] & ResNet-50 & Kinetics-400 & RGB & 64.4 & 75.6 \\ NL I3D [29] & ResNet-50 & ImageNet & RGB & 62.1 & 73.0 \\ NL I3D * [29] & ResNet-50 & Kinetics-400 & RGB & 64.3 & 75.3 \\ TPN [10] & ResNet-50 & ImageNet & RGB & 53.3 & 75.0 \\ TANet [27] & ResNet-50 & ImageNet & RGB & 80.6 & 85.8 \\ STPG-Net (TSN) [33] & ResNet-50 & ImageNet & RGB & 83.4 & 88.6 \\ STPG-Net (I3D) [33] & ResNet-50 & ImageNet & RGB & 82.6 & 87.9 \\ \hline TSN [23] & BNInception & ImageNet & Flow & 75.6 & 84.7 \\ TRN [32] & BNInception & ImageNet & Flow & 77.2 & 85.0 \\ TRNms [32] & BNInception & ImageNet & Flow & 77.6 & 85.5 \\ TSM [25] & ResNet-50 & ImageNet & Flow & 80.3 & 87.1 \\ \hline TSN [23] & BNInception & ImageNet & 2 Stream & 76.4 & 86.0 \\ TRN [32] & BNInception & ImageNet & 2 Stream & 79.8 & 87.4 \\ TRNms [32] & BNInception & ImageNet & 2 Stream & 80.2 & 87.8 \\ TSM [25] & ResNet-50 & ImageNet & 2 Stream & 81.2 & 88.4 \\ \hline DMTNet & ResNet-50 & ImageNet & RGB & 83.0 & 87.5 \\ DMTNet 1 & ResNet-50 & ImageNet & RGB & 84.0 & 89.3 \\ \hline \hline \end{tabular}  
* * I3D model without the incorporation of temporal downsampling. 1 The double sampling which use two clips and three crops, each with a resolution of 256 \(\times\) 256, can improve accuracy.  
\end{table}
Table 1: FineGym99 result with other methods. These results that were not reported in the original articles have surfaced in a subsequent study FineGym99 [13], and the results of TPN [10] and TAM [27] are reproduced by us in MMactions2 [28], with the same evaluation metrics in eight frames.
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 2 Methods
### Evaluating Indicator  
In deep learning object detection, to verify the superiority and effectiveness of algorithms, detection accuracy, inference speed, model size, and frames per second (frame rate) are usually selected as the leading indicators for evaluating model performance. Therefore, this paper adopts the following commonly used evaluation indicators in object detection: (1) precision (P); (2) recall (R); (3) average precision (AP) of a single category; (4) mean average precision (MAP); (5) frames per second (FPS) is used to measure the model comprehensively.  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
Among them, \(TP\) (True Positive) is the number of targets correctly detected by the model, \(FP\) (False Positive) is the number of targets for model error detection, \(FN\) (False Negative) is the correct number of targets missed by the model.  
The mean average precision (MAP) for a set of queries is the mean of the average precision scores for each query, as shown in Equation (7).  
\[MAP=\frac{1}{c}\sum_{i=1}^{c}AP_{i}=\frac{1}{c}\sum_{i=1}^{c}\int_{0}^{1}P(R)dR \tag{7}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]  
Among them, \(AP\) represents the accuracy of each category, and \(c\) represents the number of categories in the dataset.  
The number of frames detected per second refers to the average value of the model's detection speed for foreign object images in the test set, as shown in Equation (9).  
\[FPS=\frac{F_{T}}{T_{C}} \tag{9}\]  
Among them, \(F_{T}\) represents the total number of frames, and \(T_{C}\) represents the total time for model detection.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 2 Laser Crosslink Mission  
### Mission Statement  
The VISION mission is aimed at demonstrating novel laser crosslink systems using two nanosatellites, achieving a data rate of 1 Gbps at 1000 km apart. To establish the crosslink, the optical axes of each nanosatellite are aligned precisely, reducing the residual jitter to a LOS smaller than 1 \(\upmu\)rad to ensure the optical link performance. Table 1 lists the top-level mission requirements (TMR) and constraints for the system design. The mission lifetime should be longer than one year, and the systems are enveloped in the standard 6U nanosatellite.
## 6 Conclusions  
Examining river bar and vegetation dynamics in response to upstream damming is crucial for riverine flood management and ecological assessment. In this study, we evaluate the hypothesis that intensified erosion and attenuated discharge pulses downstream of dams contribute to the expansion of river bars and vegetation. Our investigation employs a case study from the Middle Yangtze River (MYR) in China to substantiate this hypothesis. We find that the bar area diminished by \(\sim\)10% due to minor bar erosion, whereas the vegetation area expanded by \(\sim\)66% in a from-tail-to-head and from-edge-to-center trend, which can be attributed to the reduced frequency of bar submergence. Further analysis revealed that the diminished bar submergence frequency is resulting from the flatter discharge pulses and larger bar-channel elevation gap, which is caused by the approximately tenfold discrepancy in erosion volume between the channel and bar. In addition, we have improved the machine learning-based leaf area index (LAI) inversion model, identifying a \(\sim\)24% increase in LAI after the TGD operation.  
Our preliminary investigation, situated at the intersection of hydrology, geomorphology, biology, and remote sensing, was effectively executed in the MYR to examine river bar and vegetation dynamics in response to the construction of the TGD. This approach furnishes an accessible methodology (ML-based LAI inversion model) and conceptual framework for causal analysis when addressing the biogeomorphological impacts of large dams. Our findings carry significant implications for flood management and ecological restoration in dammed river systems.  
Conceptualization, Y.H., D.L., J.D. and Y.L.; methodology, Y.H. and J.Z.; validation, Y.H. and J.Z.; formal analysis, Y.H.; investigation, Y.H. and C.Y.; data curation, Y.H. and J.Z.; writing--original draft preparation, Y.H.; writing--review and editing, D.L., J.Z., J.D., C.Y. and Y.L.; visualization, Y.H. and J.Z.; supervision, J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant No. 51779185), and the National Key R&D Program of China (Grant No. 2018 YFC0407201).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Comparison of different LAI products in 2020: (**a**,**b**) are LAI products at a spatial resolution of 30 m for bars 10 and 16; (**c**,**d**) are LAI products at a spatial resolution of 500 m for bars 10 and 16.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 4 Bearing Capacity under Eccentric Load
### Parameter Analysis of Bearing Capacity
#### 4.2.3 Influence of Steel Wedge Width  
According to load-displacement curves of DC joints under different steel wedge widths, as listed in Figure 12(a), it can be found that wedge width affects the bearing capacity significantly: bearing capacity increases with steel wedge width. Figure 12(b) shows the effect of the width on initial compression stiffness and yield load. The influence of steel wedge width on initial compression stiffness and yield load is initially weak. When the width increases from 25 mm to 30 mm, the initial compression stiffness and yield load will increase by 104.8 kN/mm and 500 kN, respectively. When the width increases from 40 mm to 45 mm, the initial compression stiffness and yield load increase by 68 kN/mm and 290 kN, respectively, which is only about 60% when the width increases from 25 mm to 30 mm. The increase rates of initial compression stiffness are both about 18.9%, 14.4%, 12.1% and 10.9% as the steel wedge width increases from 25 mm to 45 mm.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 1 Introduction
However, these methods can produce unexpected results and are computationally expensive to train. In this paper, we propose an effective image preprocessing technique, which outperforms existing image enhancement algorithms through thick scattering media in various ways. This enhancement technique is termed Homogeneous Maximum Histogram Equalization (HMHE). To separate useful signals from background illumination, we introduce SDIF to evaluate the residual information in the image, thereby automatically selecting the appropriate filtering kernel to remove inhomogeneous illumination. Simultaneously, the grayscale banding effects caused by filtering are eliminated using a visual optimization algorithm based on the image gradient. Additionally, the filtered image is enhanced through MHE, which scales the desired components to their maximum contrast. Finally, the low and high frequencies of the image are linearly combined to complete the image information. Experiments showed that the proposed method can fully utilize ballistic signals with high fidelity through scattering media, achieving fine visual perception of the desired targets. Due to the simplicity of the principle of our method, the output results are easily understood with confidence. Furthermore, the proposed method has a satisfying computational complexity, making it efficient and practical for real-world applications. The experimental results demonstrate that this method can serve as an effective image preprocessing technique for further neural network applications, such as image enhancement or target recognition. To summarize, the key highlights of our paper are as follows:  
It produces high fidelity and contrast of the ballistic signals. The straightforward principles underlying our method ensure that the results are easily interpretable, fostering confidence in its applications. Our approach maintains a manageable computational complexity, making it suitable for integration into various image preprocessing pipelines for advanced neural network applications. This paper is arranged as follows: In Section 2, we briefly introduce the physical models in atmospheric imaging and the fundamental principles of HMHE. In Section 3, the results in various scenes of HMHE are compared with existing similar methods, with qualitative results and discussion of the differences. The conclusions will be given in Section 4.
## 3 Proposed Method
### Simple and Efficient Data Augmentation
#### 3.1.3. Lemmatizing Based Data Augmentation  
The second method proposed to construct wrong answer options is lemmatizing based data augmentation. Grammar problems exist in TOEIC Part 5, which is characterized by a list of options with similar meanings but different forms. Considering this characteristic, this method used lemmatizing to classify all word segments in the entire data based on their lemma. Subsequently, three wrong answer options were extracted from among the word segments with the same lemma as the correct answer option.  
After splitting the English sentence data into word segments and determining the lemma for each word segment through lemmatizing of NLTK, the word segments of the same lemma were classified by lemma such that they were included in the same set. At this time, for lemmatizing of the NLTK, the more accurate lemma can be found by specifying the parts-of-speech. However, specifying the exact parts-of-speech for all word segments is challenging because each word segment has different parts-of-speech. Thus, in this method, considering all cases, the lemma was determined by applying all the parts-of-speech that could be specified. There are five parts-of-speech that can be specified: nouns, verbs, adjectives, adverbs, and satellite adjectives. For each word segment, all the lemmas of the parts-of-speech were determined, and all the corresponding word segments for each lemma were stored. If this process is conducted for the entire English sentence data, all the word segments existing in the data can be organized based on the lemma. Because the options of TOEIC Part 5 comprise a total of four options, it is possible to configure the options only when there are more than four word segments with the same lemma.  
When generating data based on lemmatizing, as in Section 3.1.2, the entire English sentence data was split into word segments with one of them being replaced with a blank to generate TOEIC data. At this time, by extracting three wrong answer options from among the word segments with the same lemma as the actual correct answer option replaced with a blank, the data can be augmented similar to the grammar related problems wherein options with different forms but similar meanings are listed. Examples of actual data generated through this methodology are shown in the Lemmatizing Based Data column in Table 3.  
As evident from the Lemmatizing Based Data column in Table 3, the data was augmented by reflecting the characteristics of the grammar related problem, comprising options as various forms of words with similar meanings. When data was augmented in this manner, the model could better focus on grammatical relationships within the sentence. Therefore, when solving the actual TOEIC problems, the model can better infer the correct answer to the intention of the problem through comparisons of the options focusing on grammatical relationships of the blank.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.6 Smart Property  
This feature allows the linking digital or physical properties to the blockchain in a permanent, trustful and tamper-proof way, so that they cannot be owned by anyone else until they are transferred. The owner of the asset is the only one who has control over it, and it is not possible to double-spend or double-own it.  
In the educational field, according to this review, the most frequent \"property\" managed are certificates and grades that are issued or revoked by an institution, which, in turn, are \"assigned\" to learners, alumni or professionals, as discussed in Section 4.1. Related to this type of information, there are different approaches in the surveyed works. For example, data can be tamper-proof stored [13], shared with recruiters [14], used to acknowledge the assistance to an event [24], analyzed to award badges on certain achievements [16; 23], or applied to register competences [30], among others. Academic institutions may also be unbiasedly ranked according to educational criteria [28; 30].  
Other smart properties are related to educational resources, so they can be verified for authentication [18] or access [22], or used to manage the digital rights of multimedia content [21].  
The smart properties assigned to learners, professionals or institutions depend on the actual application scenario. However, a relevant difference with other usage fields is that academic records are normally the property of their owners. They may be shared, but they cannot be transferred among final recipients, since they reflect personal skills, acquired knowledge or reputation.  
Nevertheless, there are exceptions where tokens may be explicitly transferred, as they are used as cryptocurrencies [15; 28; 36].
## Materials and Methods  
### Overview of Burst Tests  
Burst test results and material properties of API X70 pipe samples with dent-crack defects were obtained from literature [2] and used to calibrate and validate finite element models for prediction of burst pressure. The pipes had outside diameter (OD) of 762 mm, wall thickness (t) of 8.5 mm, and length (L) of 2.5 m. Fatigue load actuators were used to create 0.3 mm deep fatigue cracks at the tip of 4 mm deep, crack-like v-shaped notches cut at the 12 O'clock position of the pipe using an electro-discharging machine (EDM). Thus, the total crack-like defect was 4.3 mm deep, approximately 50% of wall thickness and the four specimens used in this study had cracks with lengths (2c) \(\times\) depth (a) of 20 \(\times\) 4.3 mm, 60 \(\times\) 4.3 mm, 100 \(\times\) 4.3 mm, and 200 \(\times\) 4.3 mm.  
A rigid rectangular steel block attached to a universal loading actuator was used to create 50 mm wide by 100 mm long dents over the cracks, to obtain the desired dent-crack defects. Dent depth and plastic deformation during the denting process were monitored using linear variable differential transducers (LVDTs). The measured dent depth that remained upon releasing the indenter was 28 mm, approximately 4% OD of the pipe. A pressure of 3.8 MPa, equivalent to 30% of the yield pressure (P\({}_{y}\)) of the pipe, was maintained in the pipe as denting pressure during dent formation. The pressure was administered using a hydrostatic pump and monitored both manually and electronically through transducers. Strains along the pipe length were measured by strain gauges arranged on the pipe surface. Figure 1 shows the set-up of the burst tests.  
Monotonically increasing internal pressure was supplied to the flawed pipe specimens by a water pump to cause leakage or burst, upon which the test was discontinued. The pressure was monitored mechanically using pressure gauge and electronically using transducers. Table 1 shows the experimental and predicted burst pressures of the different pipe specimens used in the burst test. The pipes in the test had a 28 mm deep dent formed under a pressure of 3.8 MPa, equivalent to 30% of the yield pressure (P\({}_{y}\) = 12.67 MPa) of the pipe.
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 8 Conclusions and Future Work  
In conclusion, we have proposed to solve an NP-Hard problem: NFV-RA, using deep Q-learning (DQL) models. An extensive investigation of DQL's performance under diverse conditions of network density, nodal and core capacity, nodal outage, and VNF-FG complexity is performed. The DQL agent discovered the characteristics of the topologies under specifying conditions and provided near-optimal solutions. To improve performance, we have introduced a heuristic model for better exploration of the action space. In our results, with the increase in network density or nodal capacity or VNF-FG's complexity, the model took extremely high computation time to execute the desirable results. Moreover, with the rise in complexity of the VNF-FG, the resources decline much faster. In terms of the nodal outage, for specific topologies and particular combination of resources, we have achieved between 70-90% SAR even with a 50% nodal outage but at the cost of high computation time.  
This performance can be improved further by introducing a model-free, actor-critic algorithm known as the Deep Deterministic Policy Gradient (DDPG), which deals with larger action spaces. In our future work, we will present a thorough study of the DQL model using DDPG under the specified complex circumstances.  
Funding acquisition, A.N.; Supervision, A.N., H.A.; Modelling and Simulation, S.B.C.; writing--original draft preparation, S.B.C.; writing--review and editing, A.N., S.S., H.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by UCD School of Electrical and Electronic Engineering (Project Code R20813; Cost Center 4077).  
All data used in this work were obtained from open-source, publicly-available repositories.  
The authors declare no conflict of interest.
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 2 Methods
### Convolutional Neural Network (CNN) Backbone  
To minimize overfitting and improve performance, the model's CNN backbone should employ a concise structure with fewer parameters, such as the VGG model [12] or EfficientNetV2. The CNN backbone proposed in this paper is inspired by EfficientNetV2. Subsequently, the CNN branch of the model was designed based on EfficientNetV2, yielding promising results.  
EfficientNetV2, acting as the CNN branch of Eff-Swin, primarily focuses on extracting shallow features from input images and iteratively refining them to capture local characteristics. To further enhance the accuracy of tomato disease image recognition, this study introduces the channel attention (CA) mechanism to further enhance EfficientNetV2. This enhancement strengthens the learning of crucial positional information within tomato leaf disease images.  
EfficientNetV2 provides three initial models: S, M, and L. To minimize overfitting and achieve optimal performance, we chose the lightest initial model, EfficientNetV2-S, as the basis for our improvements. Although EfficientNetV2-S is the lightest among the initial models provided by the original authors, it still encounters challenges during training, including high computational demands, lengthy training times, and significant model space occupation. To address these issues, we made enhancements to the original network structure layers.  
Given the smaller expansion ratio of MBConv versus Fused-MBConv, which results in less memory access overhead, we employ Fused-MBConv in only the initial two stages. In contrast, MBConv modules are utilized in the latter four stages to enhance the model's lightweight characteristics. Furthermore, we replace the SE module in both the MBConv and Fused-MBConv modules with a CA module, as illustrated in Figures 2 and 3, respectively. This modification allows the CA module to grasp long-term dependencies across network channels, thereby enabling the network to concentrate on target-relevant regions without sacrificing precise positional details. Subsequently, we adopt 5 \(\times\) 5 convolutional kernels over the original 3 \(\times\) 3 kernels within MBConv to improve the accuracy and reduce the number of convolutional layers. By modifying the quantity of MBConv modules and substituting certain convolutional layers with larger 5 \(\times\) 5 kernels, we expand this branch's receptive field. Consequently, this leads to a reduced need for convolutional layers to sustain model accuracy, decreasing the network layers in stages 1 through 6 to 1, 2, 2, 3, 4, and 4 layers, respectively. By employing fewer convolutional layers than EfficientNetV2,  
Figure 1: Network architecture of the Eff-Swin model.  
this branch architecture notably enhances the feature extraction capability. In the final stage, we normalize the output from the sixth stage, apply global average pooling, flatten it, and subsequently integrate it with the Swin Transformer branch.