esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Overall Architecture  
Inspired by the encoder-decoder framework, we propose a novel Differentiable Edge-guided Feature Pyramid Aggregation Network (DEPANet) to aggregate multi-scale features from different stages, as shown in Figure 1.  
The encoder consists of three key components: a backbone network, a Differentiable Edge Feature Pyramid Network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). We selected ResNet34 [11] as the backbone to extract global characteristics from the steel surface due to its strong feature representation and efficient computational efficiency. Let \(x\in R^{W_{i}H\times 3}\) be the input image, ResNet34 generates five level outputs, i.e., \(f_{i}^{d}\), where \(i=1,2,3,4,5\) represents the \(i\)-th layer. The DEFP network consists of five AFSFs, denoted \(\text{AFSF}_{i}\) for \(i=1,2,3,4,5\). Each AFSF applies a learnable filtering operation to generate features \(f_{i}^{bran}\) and \(f_{i}^{d}\). Formally,  
\[f_{i}^{l},f_{i}^{bran}=\text{AFSF}\Big{(}f_{i-1}^{l}\Big{)} \tag{1}\]  
while \(f_{0}^{l}=x\). The EFAMs integrate the overall features \(f_{i}^{d}\) obtained from the backbone network with the edge information \(f_{i}^{l}\) from the DEFP to obtain edge-enhanced feature \(f_{i}^{e}\), where \(i=1,2,3,4,5\). Formally,  
\[f_{i}^{e}=\text{EFAM}\Big{(}f_{i}^{d},f_{i}^{l}\Big{)} \tag{2}\]  
Figure 1: The architecture of DEPANet.  
In the decoder, we employ the Feature Pyramid Network (FPN) [22] to progressively upsample and fuse edge enhanced multiscale features \(f_{i}^{e}\), ultimately restoring high-resolution segmentation results for precise localization of steel surface defects of various sizes and shapes. Formally,  
\[f_{i}^{\prime}=\text{Fuse}\big{(}\text{Upsample}\big{(}f_{i+1}^{e}\big{)},f_{i}^ {e}\big{)},i=1,2,3,4 \tag{3}\]  
where \(\text{Upsample}(\cdot)\) denotes the upsampling operation, and \(\text{Fuse}(\cdot)\) represents the feature fusion operation.  
Our method was proposed within the framework of an end-to-end encoder-decoder architecture for steel surface defect segmentation. The major contributions of this paper include the overall architecture, named the DEFP network, and the proposed EFAM modules. More details about DEFP and EFAM are presented in Sections 3.2 and 3.3, respectively.
## 3 Service Development Methodology
### Skill Extraction from Job Posts  
After the job post acquisition phase, the job posts were available as free text. However, free text cannot facilitate decision support services without further processing. For this reason, a Knowledge Extraction service was developed that receives as input a job post's text and extracts the skills that can be distilled from the text.  
To achieve this, the first step is a text preprocessing phase. More specifically, the text is split into paragraphs and, later during the process, into tokens and bigrams. A token is an individual word, punctuation symbol or whitespace found in a text, while a bigram is a sequence of two consecutive tokens in a text. The frequency of tokens and diagrams in a text is commonly used for the statistical analysis of text.  
As a next step, several part-of-speech (POS) tagging and named entity recognition (NER) techniques are applied in the identified entities as well as text matching to identify skills that are included in a list of predefined skills as listed by the ESCO classification [23]. Afterwards, duplicate entities are removed, and the result is a list of skills for each job post. The results of this service are stored in a relational database (PostgreSQL).  
Of course, this is a good starting point for providing valuable decision support. However, the ESCO list of skills is very thorough, varying from highly generic skills such as programming to very rare ones, was proved to be problematic. This is because software-related jobs' skills, such as \"Programming\", were either explicitly declared a requirement or implied. Thus, a mechanism that would discern which skills are too generic was strongly required.  
For this reason, another functionality was implemented. This classifies each identified skill as \"Product\", \"Tool\", or \"Topic\". As topic skills are too generic in most cases, they were not used neither by the Skill and Course Recommender service nor by the Curriculum Designer. Moreover, the frequency of each skill is counted in the entire dataset, and skills that exist in very few documents are rejected as too specific.
# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 1 Introduction
However, existing models are often trained on limited datasets and can be confused with images that have similar appearances to smoke, such as clouds, fog, and dust. On the other hand, although standard Yolo models used in many studies are satisfactory in terms of accuracy and speed, they cannot show sufficient performance in detecting early-stage fires with sensitivity. In the existing literature, original backbone structures are generally used, which limits the flexibility and customizability of the model. This study provides significant contributions to the field of the early detection of forest fires. A new deep learning-based approach has been developed that goes beyond existing methods and enables the determination of fire signatures from smoke data with high accuracy. In particular, the proposed model's ability to detect early-stage fires with high precision using advanced feature extraction techniques provides a time-critical advantage in controlling forest fires. In this context, a smoke detection system, which forms the basis of the DL-based forest monitoring system, was developed to prevent damage caused by forest fires by early detection. The main contributions of this study are as follows:  
* An improved forest fire smoke image dataset consisting of smoke and smoke-like images was created to evaluate the performance of the deep learning model. * Smoke and normal image data were processed and classified. * Comparative results of a total of 30 object detection models, including 20 models with modified backbone structures and 10 original Yolo models, were shared. * An optimized model for the early detection of forest fire was proposed. With the proposed model, pre-fire smoke images were detected more accurately and quickly, and the accuracy of smoke detection increased. * Compared to the original Yolo model, the number of parameters of the proposed model was reduced by 19.7%, and the number of GFLOPs was reduced by 26.8%. * The processing power was reduced by decreasing the parameter and GFLOP values, and accordingly, the processing time was reduced by 22.7%, and the model size by 19.7%. * Model backbone development techniques were used for the network architecture, detection accuracy, and processing speed. * We ensured that the proposed model had higher accuracy than different Yolo models. The remainder of this paper is organized as follows: In Section 2, studies in the literature on fire detection with DL are mentioned. In Section 3, information is provided about DL, object detection, the dataset used, Yolo algorithms, the proposed model, and how the created and original models are run. In addition, a comparison between the performance of the existing models and the proposed model is presented in Section 4. Section 5 includes a discussion of studies conducted using the same dataset. Finally, the contributions of this study and future research directions are discussed.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 4 Challenges and Discussion
### Energy Consumption and Computing Space  
It can be seen that DL networks have brought impressive results for building Visual SLAM and VOE. However, whether DL is used at one step in the Visual SLAM and VOE system-building model or end-to-end, DL is usually computed on the GPU. To equip GPUs, the cost is more expensive than CPUs, and other devices, especially GPUs, consume a much larger amount of power than CPUs. Visual SLAM and VOE systems are typically installed on CPU-only computers or edge devices. These computers can be mounted on moving robots, industrial autonomous vehicles, self-driving cars, etc. Therefore, the power supply for these computer devices is relatively limited. Although recently there have been some studies on building Visual SLAM and VOE systems by computing on edge devices/split computing on several devices, such as [217; 218; 219; 220]. Figure 3 shows a model for building an environment mapping system that is calculated and executed on an edge server device. However, these studies were still only tested in the laboratory. Another issue of computational space is that when constructing Visual SLAM and VOE in a large space, the computational space will increase according to the number of frames obtained from the environment if the amount of data obtained gradually reduces the calculation speed and reaches a threshold that will overflow the computer's memory, especially in the case of building environment maps and 3D scene reconstruction.  
Figure 3: AdapSLAM’s design model performs distributed computing on edge devices [221].
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
###### Abstract  
In distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time. Representative collective communication libraries such as MPI, GLOO, and NCCL exhibit varying performance based on server environment and communication architecture. In this study, we investigate three key aspects to evaluate the performance of the collective communication libraries in a distributed deep learning setting in an intra-node environment. First, we conduct a comparison and analysis of collective communication library performance within common distributed deep learning architectures, such as parameter servers and ring all-reduce methods. Second, we evaluate the performance of these libraries in different environments, including various container platforms and bare metal setups, considering the scalability and flexibility advantages offered by cloud virtualization. Last, to ensure practicality, we assess the libraries' performance in a Linux shell and within the PyTorch framework. In the cross-docker virtualization environment, NCCL shows up to 213% higher latency compared to single dROC, while GLOO exhibits 36% lower latency in single dROC than in cross docker, and NCCL achieves up to 345% lower execution time in all-reduce operations compared to other libraries (MPI and GLOO). These findings will inform the selection of an appropriate collective communication library for designing effective distributed deep learning environments.  
distributed deep learning; data parallelism; collective communication; cloud virtualization +
Footnote †: journal: Medical Computing  
**Citation:** [PERSON]; [PERSON] Collective Communication Performance Evaluation for Distributed Deep Learning Training. _Appl. Sci._**2024**, _14_, 5100. [[https://doi.org/10.3390/app1425100](https://doi.org/10.3390/app1425100)]([https://doi.org/10.3390/app1425100](https://doi.org/10.3390/app1425100))
## 3 Results and Discussion
### The Grating Slant \(\Phi\)
#### 3.2.3 Impact of the Material's Viscosity  
A variation of the material's viscosity affects the optical shrinkage, as became clear in view of Figure 4. Here, the influence of the viscosity on the grating slant difference is evident by means of scaling of the abscissa. In case of low viscosity the \(\Delta\Phi\)(\(t_{\text{exp}}\))-minimum is allocated around 30 s. In contrast to this, high viscosity results in a minimum around \(t_{\text{exp}}=5\) s. Such a shift is in line with the expectations, since a high viscous material is less flexible, whereas in case of low viscosity a higher amount of curing is required with the result of longer exposure duration necessary to achieve the same result with respect to the optical shrinkage.  
It must be stressed that the viscosity, in the case of this study aligned by means of prebake, is also effected by pre-exposure as well as dark storage of the samples. However, the purpose of the present study is a qualitative analysis of optical shrinkage effects. Further investigations are required to elucidate the relationship and influence of external factors for an estimation in terms of quantity.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
###### Abstract  
Due to the similar characteristics of chaotic systems and cryptography, public key encryption algorithms based on chaotic systems are worth in-depth research and have high value for the future. Chebyshev polynomials have good properties and are often used in the design of public key algorithms. This paper improves the Bose Multi-Chaotic Public Key Cryptographic Algorithm (BMPKC) by applying Chebyshev polynomials. The proposed algorithm (CMPKC-\(k_{i}\)) introduces the selective coefficient \(k_{i}\) based on the properties of Chebyshev polynomials, allowing the special functions that need to be negotiated in the original system to be freely and randomly chosen as Chebyshev polynomials, and can also be expanded to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes shortcomings of the Bose algorithm by iteratively iterating the selected Chebyshev polynomials based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and software testing results indicate that this algorithm has strong robustness against brute force attacks, achieving a higher attack time for breaking the private key compared to the CEPKC, BMPKC, and CMPKC algorithms. Compared to the CMPKC algorithm, our proposal algorithm achieves better performance in the encryption and decryption phases. Furthermore, we combine this Multi-Chaotic System Key Exchange Protocol with the Advanced Encryption Standard (AES) algorithm, while providing a demonstration, offering more possibilities for practical applications of this system.  
multi-chaotic systems; public key algorithm; Chebyshev polynomials; alternative multiplication coefficients; semi-group property +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Data Acquisition and Preprocessing in MRI  
Functional magnetic resonance images were collected using a 3.0 T Signa Excite HD scanner manufactured by General Electric (GE) Healthcare. For precise anatomical localization, a T1-weighted volume was acquired with the following parameters: repetition time (TR) of 6 ms, echo time (TE) of 1 ms, flip angle of 12\({}^{\circ}\), matrix size of 256 \(\times\) 256 pixels, in-plane resolution of 0.98 \(\times\) 0.98 mm, spacing between slices of 1 mm, slice thickness of 1 mm, and no interslice gap. The acquired anatomical slices encompassed the entire brain and were obtained parallel to the anterior-posterior commissure. During the motor paradigm, a series of 385 T2-weighted echo-planar imaging (EPI) volumes were obtained. The EPI sequence parameters included 36 axial slices covering the entire head, with a field of view of 25.6 mm, slice thickness of 4 mm, interslice gap of 1 mm, matrix size of 64 \(\times\) 64, flip angle of 90\({}^{\circ}\), TR of 2 s, and TE of 22.1 ms.  
The preprocessing of fMRI volumes was conducted using Statistical Parametric Mapping (SPM12) software developed by The Wellcome Trust Centre for Neuroimaging at University College London. The following steps were applied: realignment to correct for motion artifacts, slice timing correction, registration with the T1-weighted structural image, and transformation into the standard anatomical space of the Montreal Neurological Institute (MNI). To suppress noise and account for residual differences in functional and gyri anatomy, the EPI images were subjected to isotropic smoothing with an 8 mm full-width half-maximum kernel. Additionally, a high-pass filter with a cutoff period of 64 s was used to eliminate low-frequency noise associated with breathing and pulse signals [8].
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 4 Cloud Pattern API-Process Mining Method
### Dataset Restructuring Step  
Restructuring for the event log is performed in such a way that process mining algorithms can be applied depending on the understanding or analysis questions that should be answered. These different techniques, some of which implement several known event abstraction approaches [24], can be applied during this step.  
* Enriching the event log dataset: Event logs can be enriched with additional attributes, e.g., by adding external additional data collected or by combining two different datasets. This is, however, often done before the generation of the original CSV file (e.g., by using ETL). Another way is by deriving or computing additional or derived events and data attributes based on the existing attributes in the data, resulting in new columns calculated from other columns. Such additions depend on the required analysis, e.g., for calculating the time difference between the occurrence of two events to reason about durations.
* Aggregating events: Aggregating events into coarser-grained or more abstract events (e.g., along \"is-a\" or \"part-of\" relationships between events) can help reduce complexity and improve the structure of clickstream datasets, leading to better and simpler processes.
* Aggregating rows: This technique includes aggregating several rows into one row, to reduce complexity and (unnecessary) repetition of the same event. For example, let us consider that event \(A\) is repeated multiple times in a given case, but needs to be considered only once. Options here include keeping only the first occurrence of event \(A\), keeping only its last occurrence, or merging all the \(A\) rows into one by applying maximum, minimum, average, and other such functions on the columns' attributes for the repeated event \(A\). This will be illustrated in more detail in the API functions and their implementations.
## 3 Experiments
### Datasets and Preprocessing
#### 3.1.4 Usc-Sipi  
To assess the reconstruction performance of the model on large-scale remote sensing images, this study selected images from the \"aerials\" category in the dataset for testing. The database is divided into volumes based on the basic character of the pictures. Images in each volume are of various sizes such as 256 \(\times\) 256 pixels, 512 \(\times\) 512 pixels, or 1024 \(\times\) 1024 pixels.
## 4 Phase Error Analysis  
In this Ref., the phase error of the sensor is mainly derived from the thermal noise of the phase-locked loop and the phase jitter noise of the onboard atomic clock. The estimation formula for the thermal noise mean square error, \(\sigma_{t}\), is [22; 23]  
\[\sigma_{t}=\frac{T_{0}}{2\pi}\sqrt{\frac{B_{L}}{C/N_{0}}\left(1+\frac{1}{2T_{ Integral}\cdot C/N_{0}}\right)}, \tag{14}\]  
where \(B_{L}\) is the loop noise bandwidth:  
\[B_{L}=\frac{\omega_{n}}{2}\left(\xi+\frac{1}{4\xi}\right). \tag{15}\]  
\(C/N_{0}\) is the carrier-to-noise ratio of the input signal, \(T_{0}\) is the input carrier period, and \(T_{Integral}\) is the loop coherent integration time. The phase jitter noise, \(\sigma_{A}\), of the onboard clock is proportional to the Allan Deviation mean square error, \(\sigma_{A}(\tau)\) and the coherent integration time, \(T_{Integral}\)  
\[\sigma_{A}=T_{Integral}\sigma_{A}(\tau). \tag{16}\]  
Based on the above two partial error sources, the total phase jitter mean square error is  
\[\sigma_{total}=\sqrt{{\sigma_{t}}^{2}+{\sigma_{A}}^{2}}. \tag{17}\]  
Figure 6: Entire process flow of the phase compensation.  
Under normal circumstances, the onboard clock of the navigation satellite's \(C/N_{0}\) is better than 120 dB\(\cdot\)Hz (10\({}^{12}\) Hz) and \(\sigma_{A}(\tau)\) is 10\({}^{-12}\). Consider \(B_{L}\) as 20 Hz (we set the classic parameters \(\omega_{n}=37.7\) and \(\xi=0.7\)) and \(T_{Integral}\) is 1 ms. According to Equations (14), (16), and (17), \(\sigma_{total}\) is approximately equal to 0.0696 ps. Set \(n\) equal to 4; as per Equation (12), the error of \(p_{compensate}\) is 4\(\sigma_{total}\), namely, 0.278 ps, that is considerably lesser than the accuracy of the 0.5 ns pseudo-code ranging [24] and a 10.23 MHz clock cycle.
## 4 Results and Discussion
### Influence of Ventilation Duct Position on Dust Dispersion
It is observed that at positions 3 m and 6 m from the face, \(h\) = 3.0 m yields the lowest average concentration, while the differences in concentration at other positions are not significantly large. Consider Figure 22. At the position \(y\) = 1.5 m and \(h\) = 3.0 m, it takes 513 s for the dust concentration to decrease below 10 mg/m\({}^{3}\). For the remaining models with a total time of 600 s, the maximum dust concentration still exceeds 10 mg/m\({}^{3}\). This will adversely affect the health of the workers. The average concentration at \(h\) = 1.1 m was 1320 mg/m\({}^{3}\); at \(h\) = 1.4 m, the average concentration was 1930 mg/m\({}^{3}\); at \(h\) = 1.7 m, the average concentration was 1900 mg/m\({}^{3}\); at \(h\) = 2.0 m, the average concentration was 1330 mg/m\({}^{3}\); at  
Figure 19: The maximum dust concentration at a position 1.0 m from the face corresponds to the location of the air duct. Figure 20: The maximum dust concentration at a position 3.0 m from the face corresponds to the location of the air duct. \(h=2.3\) m, the average concentration was 928 mg/m\({}^{3}\); at \(h=2.7\) m, the average concentration was 422 mg/m\({}^{3}\); and at \(h=3.0\) m, the average concentration was 199 mg/m\({}^{3}\). We can see that the differences in dust concentration are quite large between the positions, with the highest concentration at \(h=1.4\) m, followed by \(h=1.7\) m, \(h=1.1\) m, \(h=2.0\) m, \(h=2.3\) m, lower at \(h=2.7\) m, and the lowest at \(h=3.0\) m  
Therefore, the results indicate that there is no significant difference in average dust concentration at positions 1.0 m, 3.0 m, and 6.0 m from the face, with heights ranging from \(h=1.1\) m to \(h=2.7\) m. However, the position of \(h=3.0\) m for the ventilation duct proves to be more effective in reducing dust concentration within the work area for laborers, as evident from the results. At a height of 1.5 m from the ground, there is a very large difference in dust concentration. The dust reduction model is effective with heights of \(h=3.0\) m.
## 3 Proposed Method
### 8-Bit Brightness Extraction  
In this paper, we need to compare between eight-bit brightness and temperature feature information. The eight-bit brightness is generally acquired by the raw 14-bit DC values.  
\[f(x)=\begin{cases}0&x<u-k_{1}\sigma\\ 255\frac{x-(u-k_{1}\sigma)}{(u+k_{2}\sigma)-(u-k_{1}\sigma)}&u-k_{1}\sigma\leq x \leq u+k_{2}\sigma\\ 255&x\geq u+k_{2}\sigma.\end{cases} \tag{1}\]  
Equation (1) presents the grayscale mapping method using a histogram where \(x\) is the 14-bit DC data for mapping to the eight-bit gray-level and min(\(x\)) is the minimum value of \(x\) and max(\(x\)) is maximum value of \(x\). The parameter \(u\) is the mean value of \(x\), \(\sigma\) is the standard deviation of \(x\), and \(k_{1}\) and \(k_{2}\) are the contrast-streched degree parameters [13]. This mapping method has more stable background variation and image contrast than min-max grayscale mapping method since it includes mean and standard deviation values.