esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 2 Related Work
### Our Approach  
To the best of our knowledge, there are no existing studies that simultaneously perform both inpainting and colorization in a fully integrated manner. Most prior works treat these tasks separately, either focusing solely on inpainting [2; 13; 27; 29] or assuming fully reconstructed grayscale images for colorization [3; 36; 37; 38].  
Our method bridges this gap by combining MAT [2] for large-hole inpainting and DDColor [3] for high-fidelity colorization, enabling the robust restoration of grayscale images with arbitrary masks. While these two models are not trained jointly, our framework effectively integrates their strengths, ensuring both structural coherence and natural colorization even under complex masking conditions.  
Unlike prior methods that require fully reconstructed grayscale images for colorization or predefined masks for inpainting, our approach generalizes across diverse input conditions, handling arbitrary missing areas without compromising semantic consistency. As summarized in Table 1, this unified strategy enables our framework to achieve high-quality inpainting and colorization, setting a new standard for holistic image restoration.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Inpainting** & **Colorization** & **Strengths** & **Weaknesses** \\ \hline PatchMatch [13] & ✓ & — & Fast, memory-efficient & Struggles with high resolutions, lacks structural consistency \\ EdgeConnect [27] & ✓ & — & Strong edge recovery, supports high resolutions & Sensitive to mask quality, complex model structure \\ CoModGAN [29] & ✓ & — & Handles large-mask inpainting, GAN-based realism & Requires large datasets, struggles with fine details \\ MAT [2] & ✓ & — & Transformer-based, effective long-range modeling & High computational cost, inefficient at high resolutions \\ \hline CoITran [36] & — & ✓ & Probabilistic color modeling, diverse outputs & Struggles with local consistency, slow inference speed \\ CT2 [37] & — & ✓ & Transformer-based, strong semantic awareness & Expensive, sensitive to initialization \\ ColorFormer [38] & — & ✓ & Memory-assisted color retrieval, hybrid attention & Requires extensive pre-training, performance drop on unseen datasets \\ DDColor [3] & — & ✓ & Dual decoders for better structure and color, reduces color bleeding & High computational cost, limited user control, struggles with transparent objects \\ \hline Ours & ✓ & ✓ & Unified framework, robust inpainting and colorization & Increased computational cost \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of state-of-the-art methods in image inpainting and colorization.
# A Study on Improving M2M Network Security through Abnormal Traffic Control
## 4 Experiment Results
In the case of DDoS attack to the external system, the resource status of the damage system before and after the security filter was verified. Using Ostinato, the TCP Syn packet was maintained at 100 pps for about 5 s as shown in Figure 7. If the attacker was actually a large number of M2M intelligent network devices, it proved that the network security filters can prevent situations that can cause fatal damage to third parties. Figure 5: Iptables in internet environment. Finally, we measured the response speed of M2M intelligent network devices before and after the network security filter was applied by sending packets from the external network to the M2M intelligent network device. Similar results to the communication session of the M2M intelligent network device were found. When testing the Syn and Rst Flag packets that do not fit the TCP Flag combination with the same IP as the session that had been communicated with the management system by the M2M intelligent network device, it lasted for about 10 s at 100 pps. The orange line in Figure 8 shows the Syn and Rst Flag generated that stops after 10 s.  
Figure 6: Experiments on M2M intelligent network device packet and operation (**a**) Air Cleaner, (**b**) 220v Plugin. In the experimental results of the packet sent to the M2M intelligent network devices (a) and (b), when the network security filter was not applied, the response time was (a) 4000 milliseconds and (b) 6000 milliseconds, as shown in Figure 8. After applying the network security filter, it was confirmed that the time required for the M2M intelligent network device (a) was 1000 milliseconds and (b) was 3000 milliseconds. Experimental results show that the response speed is improved due to the abnormal traffic control in the network environment used by the internet devices after applying the network security filter. The comparison of the delay time when the security filter is applied when there is no abnormal traffic is shown in Table 3. When the usual security filter is applied, a response delay time of about 10% occurs, which shows an improvement in the response speed of about 50% or more compared with the application of the security filter in the case of abnormal packet generation as shown in Table 4. By controlling abnormal packets, the security and response of M2M intelligent network devices can be improved and the third victim of DDoS can also be prevented. Figure 8: Packet transmission that does not match TCP Flag combination. Figure 3:0 Packet with modulated source from M2M intelligent network device.
## 3 Results and Discussion
### Sensorial Evaluation
Furthermore, for OH-treated samples, the attribute juicy had two noteworthy appearances, i.e., two peaks as shown in Figure 4. Notably, both attributes are rated at a level of chance. A faster dominance and a double appearance of the attribute juicy could explain the results obtained during QDA, where juiciness was the only attribute that had a higher value for OH-treated samples compared to COV. The attribute greasy did appear earlier and decreased towards the end of the chewing process for OH-treated samples. In turn, the perception of greasiness increased and reached its maximum at the end of the chewing process for COV-treated samples. As the greasiness is strongly related to the appearance of fat droplets [59], this indicates a difference in fat distribution within the gel network between samples produced by the two heating methods, which is addressed in detail in the following section. Additionally, a lower hardness of OH-treated samples could have enhanced the faster release of fat droplets when similar chewing forces were applied, leading to a faster greasier sensation. Figure 4: (**a**) Results from a quantitative descriptive analysis of Bologna-style sausages heated via ohmic and conventional heating (OH and COV, respectively), (**b**) and (**c**) results from temporal dominance of sensation evaluations of COV-treated and OH-treated samples, respectively. * indicates statistically significant differences (\(p<0.05\)) between OH- and COV-treated samples for the respective attribute.
## 5 Numerical and Experimental Results
### Wind Attack Angles at Main Deck Level
The statistical values of numerical and experimental results of the wind attack angles at main deck level under different cases are shown in Figure 16. It can be seen from the Figure 16 that though  
Figure 15: Wind attack angle at main deck level of wind tunnel tests and CFD simulation. (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.  
there are some discrepancies between the wind tunnel tests and numerical simulation, the majority of the deviation is located within relative errors of 30%.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 2 Literature Review
### Discussion  
This paper focuses on image-based classification of Arabian camel breeds, which is a unique and challenging issue compared to the above reviewed related works. To the best of our knowledge, this is the first study that addresses the problem of image-based Arabian camel breed classification. In this work, a new image dataset of six different Arabian camel breeds is introduced and various pre-trained CNN architectures are fine-tuned on the new dataset to fit the specific classification problem. Furthermore, the proposed model is integrated into a mobile application to validate its performance in real-world scenarios. This poses an additional computational challenge due to the limited resources of mobile devices in terms of memory and processing power.
## 2 Problem Statement and Active Damping Strategy Analysis
According to Equations (1), (3), (4), (6) and (7), the transfer function of SMEHS is derived as follows:  
\[x_{L}=\frac{\frac{K_{v1}}{A_{1}}x_{v1}-\frac{1}{A_{1}^{2}}(K_{c1}+\frac{V_{L1 }}{\beta_{e}}s)(A_{2}p_{2}+f_{L})}{s\cdot(\frac{\beta^{2}}{\omega_{h}^{2}}+ \frac{2\omega_{h}s}{\omega_{h}}+1)} \tag{8}\]  
where the natural frequency is given in Equation (9) and damping ratio is given in Equation (10). \[\omega_{h}=A_{1}\sqrt{\frac{\beta_{e}}{V_{L1}m}} \tag{9}\]  
\[\zeta_{h}=\frac{K_{c}}{2A_{1}}\sqrt{\frac{\beta_{e}m}{V_{L1}}} \tag{10}\]  
Therefore, the system block diagram is presented in Figure 3:0  
It can be seen from Figure 4 that the spool displacement \(x_{v1}\) and the meter-out pressure \(p_{2}\) affect the piston displacement \(x_{L}\). In other words, this separating-metering system has two degrees of freedom. The ADS establish the correlation between \(p_{2}\) and \(x_{L}\), which is,  
Figure 3:1  
Figure 5 illustrates the block diagram of the separating-metering system with ADS. Figure 3:2 Block diagram of the separating-metering system. Substituting Equation (11) to Equation (8), the transfer function is presented:  
Figure 3:3  
where the damping ratio \(\zeta_{h3}\) and natural frequency \(\omega_{h3}\) are given by  
Figure 3:4  
Figure 3:5  
Divided by Equation (9), Equation (13) is represented as follows. Figure 3:6  
Divided by Equation (10), Equation (14) is represented as follows. Figure 3:7  
Comparing Equations (10) and (14), it is clear that the ADS can improve the damping ratio in the system if \(K_{a}>0\). It is more apparent from (16) that the larger the value of \(K_{a}\), the higher the damping ratio the system will have. However, comparing Equations (9) and (13), the eigenfrequency may become decreasing and the larger the value of \(K_{a}\), the higher eigenfrequency the system will decrease.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 2 Experimental Overview
### In Situ Spraying Mortar Material Performance Experiment
#### 2.1.2 Compressive Strength  
1. Test block preparation  
The test block size was 70.7 \(\times\) 70.7 \(\times\) 70.7 mm. A total of six groups of test blocks were made, with three blocks in each group. The three groups of test blocks were cured for 7 days, 14 days and 28 days under standard curing conditions of 20 \(\pm\) 2 \({}^{\circ}\)C and humidity above 90%. The other three groups of test blocks were cured for 7 days, 14 days and 28 days in the same environment as the strengthened structure.  
2. Testing method  
Compressive strength was tested by the method recommended in JGJ/T 70, 2009 [16]. The test block was placed on the lower pressure plate of the testing machine. The bearing surface of the test block should be perpendicular to the top surface during molding, and the center of the test block should be aligned with the center of the lower pressure plate of the testing machine. The test machine and loading were started. The pressure test should be loaded continuously and evenly, and the loading speed should be 0.25\(\sim\)1.5 kN/s. When the test block was close to failure and began to deform rapidly, adjustment of the throttle of the test machine was ceased until the test block was destroyed, and the failure load was recorded. The calculation formula of compressive strength is as follows:  
\[f_{m,cu}=K\frac{N_{u}}{A} \tag{1}\]  
where \(f_{m,cu}\) is compressive strength (MPa), \(N_{u}\) is failure load (N), \(A\) is pressure area of test block (mm\({}^{2}\)), \(K\) is reduction coefficient (\(K\) = 1.35).
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis
### Test of Orbit Determination for BDS-3 Satellites and LEO Satellites
#### 3.2.3. Test Scenarios of Orbit Determination with Real Data  
Based on real observation data from16 BDS-3 MEO satellites and five LEO satellites from 20 January 2019, the accuracy of orbit determination was evaluated, using the post-orbit precision products released by the MGEX analysis center of Wuhan University as a reference. The designed test scenarios are listed in Table 6.  
It could be seen that:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Scenarios** & **Observation Links** & **Satellites** \\ \hline
1 & Satellite–ground & 16 MEO \\
2 & Satellite–ground + & 16 MEO + 5 LEO \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test scenarios of orbit determination with real data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scenario**} & \multicolumn{6}{c}{**Day**} & \multicolumn{6}{c}{**Average**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline \multirow{4}{*}{\(1\)} & R & 0.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Route Measurement and Scoring  
The choice of routing metric should follow the following criteria:  
1. Routing metrics are independent of each other. There is no direct dependency between routing metrics to ensure that the information contained in the routing metrics is not redundant.
2. The selected routing metrics should accurately reflect the basic state of the routing nodes and links. Values obtained in this way can more accurately reflect the real situation of the network.
3. The number of routing metrics should be reasonable and appropriate. Although having a larger number of routing metrics can allow better judgment of the network situation, having more routing metrics will directly lead to an exponential increase in the computational cost.  
Based on the above three routing selection criteria, this study used energy, congestion, and routing hops as the routing metrics, and the scoring criteria were selected in accordance with the characteristics of AHP.
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 6 Discussion
### Predictability of Evaluation Metrics to Future Designs  
The proposed metrics cannot only be used to assess the effectiveness of existing designs, but they may also influence changes in future designs and in the design approach overall. In particular, outliers in the design effort evaluation data as shown in Figure 2 can provide valuable insights for developers who aim to improve an existing reference architecture or construct a new one. _Outliers are cases in datasets that behave differently from the majority of data_[57]. There are several ways to identify outliers. This study detects outliers by creating an upper and lower boundary of \(\pm 2\sigma\) for each metric (i.e., for each row in the heat map).  
An outlier in the evaluation data represents a significant difference in the required design effort for a specific low-cost digital design scenario when comparing the three candidate reference architectures. It reveals strengths and shortcomings in the design process imposed by a reference architecture and can therefore be used to improve the overall design approach. Specifically, to increase the effectiveness of reference architecture based designs, the architectural features that are connected to such outliers need to be modified or their negative effect on the design effort needs to be compensated by adding or modifying a different feature. Developers can predict the effectiveness (i.e. required design effort) of future designs by analysing these evaluation outliers:  
* Compared to WoT and PROSA/Erlang, Shoestring adds further complexity to a system design by wrapping features, such as data analysis, into a separate architecture element. However, this overhead could facilitate the design process of new digital systems since Shoestring allows for more architecture elements to be reused. Developers have to consider this trade-off when designing digital systems.
* The share-nothing policy of Erlang/OTP leads to fault-tolerant systems but may also increase their complexity. For example, when replacing the web-based dashboard with a GUI, an additional component is required that serves as an intermediate data repository for PROSA/Erlang. This additional component cannot be avoided without major modifications of the system design. Therefore, developers have to consider that the required design effort varies for different types of resources and components, and it may be useful to spend more time on selecting components to reduce the overall number of design steps.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 2 Research Methodology and EWMA-KAN-Transformer Model
### Structure of KAN-Transformer Prediction Model Based on EWMA Data Processing
#### 2.2.3 Embedding Layer  
The Embedding layer primarily consists of convolutional neural network multi-channel (CNN-MC) Embedding and positional encoding (PE) Embedding. To enhance the capability of extracting local features, the CNN-MC Embedding uses one-dimensional convolution for dimensional expansion. The CNN-MC Embedding module is illustrated in Figure 7.  
In Figure 7, dark green represents the original data, pink represents the padding data, and light green represents the convolutional data, for example, a matrix _Xen_ with dimensions (30, 14) is input, where 30 represents the number of data points, and 14 represents the number of features. Padding (_P_) is applied to _Xen_, resulting in a matrix \(B\) with dimensions (32, 14). The matrix \(B\) is then transposed (T), yielding a matrix \(C\) with dimensions (14, 32). Matrix \(C\) is fed into the CNN module, which uses a convolutional kernel \(D\) with dimensions (14, 3). Convolution is performed on \(C\) with a stride of 1 from left to right, resulting in an output matrix \(E\) with dimensions (1, 30). Matrix \(E\) is then transposed (T) to produce a matrix \(F\) with dimensions (30, 1), thereby incorporating local information into each data point of _Xen_. It is important to note the following two points: First, the process described involves using a single convolutional kernel. To expand the dimension of the Embedding layer to 512, 512 convolutional kernels need to be set. Consequently, the final output of the CNN-MC Embedding layer should be (30, 512). Second, Formula (2) should be used to ensure that the number of data points in matrix F matches the number of data points in matrix _Xen_.  
\[L=H+P-K_{n}+1 \tag{2}\]  
where \(H\) is the number of rows in the original input matrix, \(P\) is the padding dimension, and _K\({}_{n}\)_ is the width of the convolutional kernel.  
PE Embedding creates a zero matrix with the same dimensions as the output of CNN-MC Embedding. The odd columns of the zero matrix are assigned positional information using the cosine function from Formula (3), while the even columns use the sine function from Formula (3) to assign positional information. Finally, the CNN-MC Embedding and PE Embedding are added together to obtain the final output of the Embedding layer, with the last dimension remaining at 512.  
\[\left\{\begin{array}{l}PE_{(pos,2i)}=\sin(pos/10000^{2i/d_{\text{model}}})\\ PE_{(pos,2i+1)}=\cos(pos/10000^{2i/d_{\text{model}}})\end{array}\right., \tag{3}\]  
Figure 6: Partitioning method of the Informer model.  
Figure 7: CNN-MC Embedding model.  
where _pos_ represents the position, \(i\) represents the dimension, and \(d_{\text{model}}\) represents the expanded dimension.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 3 IPFS (Inter-Planetary File System)
### RDF and JSON Linked Data  
RSUs and vehicles use RDF to represent, share, and process semantic data. As mentioned before, RDF is not a syntax but an abstract language that W3C has standardized to support multiple syntax.  
Figure 5: A camera publishes a new image using its peer ID and the user resolves the camera Inter-Planetary Name System (IPNS) address to fetch the latest published image.  
The most common syntax is RDF/XML and IPFS would not have any problem dealing with this format. However, the IPFS developer community is very interested in semantic data and machine-to-machine interactions, and IPFS includes native support for RDF using the alternative JSON-LD (JSON Linked Data) syntax. Supported also by the W3C, JSON has several advantages over XML. For instance, it is easier to store and parse by computer programs. Besides, when a JSON document is shared by IPFS, IPFS does not treat the content as a plain text file but translates it first to the more efficient compressed version CBOR (Concise Binary Object Representation) [RFC 7049]. Compared to XML, the usage of CBOR can reduce network load up to a 65% [36].  
When combined with IPNS, every peer would be able to publish a root JSON document with basic node information, and instead of creating a large root document, simply add links to additional documents with extended information. In IPFS, the links included in JSON documents are the CID of the referenced document. This allows nodes to add references to documents generated by any node in the network without the need of indicating a network address. Thanks to linked data, a semantic agent running in a vehicle or RSU would be able to efficiently navigate through the semantic data and easily find any kind of information published by any node in the network.
## 6 Investigation on the Accuracy of Proposed Formula
In the final analysis, in order to compare the obtained results by using Equations (9) and (10), two five-story models of buildings with different properties were studied: one with a lumped mass, stiffness and damping ratio of each story equal to 6000 kg, \(6.7\times 10^{6}\) N/m and 0.05, respectively; the second one with a lumped mass, stiffness and damping ratio of each story equal to 9700 kg, \(4.1\times 10^{6}\) N/m and 0.05, respectively. Vibration periods of buildings were calculated as equal to 0.66 and 1.073 s, for the first and second models,  
Figure 10: The lateral displacement time histories of two buildings separated by the seismic gap equal to 11.47 cm. Figure 9: The lateral displacement time histories of buildings separated by the seismic gap of 36.14 cm. respectively. Both structures have been exposed to different ground motions; however, the most representative results obtained for the Parkfield earthquake excitation are shown herein. The maximum lateral displacement under this earthquake was determined as equal to 20.18 cm for the first model and 14.73 cm for the second model. The separation distance was calculated using Equations (2) and (9) as equal to 24.75 cm, while the separation distance of 23.85 cm was obtained based on Equations (2) and (10). The top story displacement time histories, separated by these gap sizes, are shown in Figure 11. It can be seen from the figure that the application of Equation (10) resulted in a smaller separation distance; however, it is still large enough to prevent earthquake-induced structural bounding.